HoughLines, HoughLinesP, and HoughLinesPointSet document that lines are
returned when votes > threshold. Because both accumulator votes and
threshold are integers, threshold = 0 is the only value that selects
1-vote bins (votes >= 1), as already supported by HoughLinesPointSet,
CPU HoughLines, and the OpenCL paths.
PR #30286 added CV_CheckGT(threshold, 0) at the HoughLines/HoughLinesP
entry points to avoid a divide-by-zero (`nz * numangle / threshold`) in
the IPP path. Relax this check to CV_CheckGE(threshold, 0) and guard the
IPP fast paths with `threshold > 0` instead (IPP also requires
threshold > 0).
dnn(cuda): transformer layer coverage and CUDA Graph replay on the new engine - #30439
Follow-up to #30415 (ResNet layer coverage and cuDNN JIT conv/pooling on the new engine). Adds CUDA support on the new DNN engine
for the layers transformer models need, and brings the CUDA path to parity with ONNX Runtime.
**Companion PR** opencv/opencv_extra#1448
**Changes**
- CUDA execs for Attention, Cast, ConstantOfShape, Expand, Gather, Not, Reduce, Shape, Slice, Softmax, Tile, Transpose, Concat,
Gelu, LayerNormalization, MatMul, Split, Squeeze, Unsqueeze and the element-wise activations.
- A graph with static shapes is recorded as one CUDA Graph on the second `forward()` and replayed from then on.
- The cuDNN JIT convolution reads NCHW buffers in place; NHWC staging is kept only for FP16 grouped convolutions.
- TF32 for cuBLAS GEMMs (`OPENCV_DNN_CUDA_ALLOW_TF32`), FP32 accumulation for FP16 GEMMs
(`OPENCV_DNN_CUDA_FP16_HALF_ACCUMULATE`), and fused MatMul/Gemm post-ops.
- Batch-broadcast MatMul and Shape are safe to capture in a CUDA Graph; the unused pointer-array `cublas::gemmBatched` is
removed.
- Tests: `forward()` runs three times on batch-broadcast MatMul and Shape models; `MatMul_init_bcast` runs on CUDA again;
`AttentionSharedShapeReshape` uses the FP16 default tolerance on CUDA FP16.
- Perf: new CUDA-only `*_CUDA_pinned` tests pin the input shapes.
**Performance** (ms, median of 50, FP32 with TF32, RTX PRO 6000 Blackwell, idle GPU)
| Model | CPU | CUDA (cuDNN JIT) | CUDA (full cuDNN) | ORT 1.31 CUDA EP |
|---|---|---|---|---|
| ResNet-50 | 8.02 | 0.80 | 0.80 | 0.88 |
| ResNet-18 | 3.27 | 0.44 | 0.44 | 0.48 |
| MobileNetV2 | 1.99 | 0.28 | 0.27 | 0.40 |
| DenseNet-121 | 17.42 | 1.48 | 1.58 | 1.58 |
| EfficientNet-Lite4 | 5.03 | 0.53 | 0.54 | 0.73 |
| ViT-B/32 | 11.06 | 1.02 | 1.02 | 0.95 |
| ViT-Base/16 | 24.38 | 1.16 | 1.17 | 1.13 |
| DeiT-Tiny | 4.58 | 0.65 | 0.65 | 0.68 |
| MobileViT-XS | 6.93 | 0.72 | 0.72 | 0.77 |
| YOLOv3 | 47.21 | 2.60 | 2.59 | 2.72 |
| YOLOv4 | 109.38 | 3.58 | 3.61 | 4.18 |
| YOLOv4-tiny | 7.31 | 0.64 | 0.64 | 0.66 |
| BEiT-Base | 25.30 | 1.24 | 1.24 | 1.19 |
| BERT | 10.00 | 0.97 | 0.97 | 0.99 |
| SigLIP | 41.14 | 2.09 | 2.09 | 2.44
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
patchNaNs asserted CV_32F or CV_64F input, so 16-bit float arrays,
which can hold NaN too, could not be patched.
Add patchNaNs_16, the same bit test as the 32-bit kernel on 16-bit
words, with the infinity pattern of each format (0x7c00 for CV_16F,
0x7f80 for CV_16BF). getPatchNanFunc now takes the depth. The OpenCL
kernel only handles float and double, so other depths fall back to
the CPU path.
- The CV_Bool NOT kernel now computes x == 0 instead of x ^ 1, so a
non-0/1 byte (still true, as in convertTo) becomes 0 instead of 254.
The cvtest::logicOp reference does the same.
- bitwise_not no longer tries OpenCL for CV_Bool. It already ended up
on the CPU kernel, but only because memopTypeToStr() asserts on
CV_Bool and CV_OCL_RUN catches the exception.
- Core_Bitwise.not_bool also checks a UMat input and 255 bytes in both
the SIMD loop and the scalar tail.
The NOT kernel was picked by element size only, so CV_Bool ran the
same ~x kernel as CV_8U and turned 0/1 into 255/254. Both are non-zero,
so every element of ~mask counted as true. Pass the depth to
getNotFunc and use x ^ 1 for CV_Bool. Fix the same ~x in the
cvtest::logicOp reference, which made Core_Not expect 254.
Add a Core_Bitwise regression test.
dnn: derive the Resize scales from the given scales, not from the shapes - #30105
### Pull Request Readiness Checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch (5.x)
- [x] There is a reference to the original bug report and related work (no pre-existing report; found by differential testing against ONNX Runtime, see *Problem*)
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable (companion PR: opencv/opencv_extra#1455)
- [x] The feature is well documented and sample code can be built with the project CMake
---
### Problem
`Resize` resamples with a step of `in/out` when the target size comes from the `scales` input,
but ONNX defines the step along an axis as `1/scale`. The two agree only while `in*scale` is an
integer, which is not the common case. A `1x2x4x5` input with `scales=[1,1,0.5,0.5]` produces a
`1x2x2x2` output, so the step along `W` is `2` (as `1/0.5`) and not `2.5` (as `5/2`):
| `Resize(nearest=False, mode=linear, coordinate_transformation_mode=half_pixel)` | ONNX Runtime 1.30 | before |
|---|---|---|
| `1x2x4x5` input, `scales=0.5`, first output row | `0.49208999 -0.11570000` | `0.28683999 -0.48815000` |
The error is not restricted to downscaling. Any non-integral `in*scale` is affected, e.g. a
7-wide input with `scale=1.5` (output 10) was sampled at `0.7` instead of `2/3`. `cubic`,
`pytorch_half_pixel`, `half_pixel_symmetric`, `tf_half_pixel_for_nn` and `asymmetric` are all
affected; the antialias filter support is stretched the same way, producing a `2.5`-pixel-wide
filter where `1/scale` asks for `2`.
### Root cause
A constant `scales` input is folded into the `zoom_factor_x`/`zoom_factor_y` layer parameters by
`ONNXImporter2::parseResize2` (`ninputs` is set to 1). `Resize2LayerImpl::forward` therefore never
saw the tensor and took the step from `updateOutSizeAndScale`, which derives it from the shapes:
```cpp
if (sizes.empty() && !scales.empty() && halfPixelCenters) // never true for folded scales
scaleHeight = 1.f / scales[hIdx];
...
scaleHeight = float(inpShape[2]) / outHeight; // in/out instead of 1/scale
```
The antialias branch had the same problem for its filter support, which it also read from
`scales` (empty in the folded case) or fell back to `outShape/inpShape`.
### Solution
The scales are resolved once, from the `scales` input or from the folded `zoom_factor_*`, and both
the step and the antialias filter use that value:
```cpp
if (sizes.empty() && !alignCorners && onnxScaleH > 0 && onnxScaleW > 0 &&
coordTransModeE != CoordTransMode::TF_CROP_AND_RESIZE)
{
scaleHeight = 1.f / onnxScaleH;
scaleWidth = 1.f / onnxScaleW;
}
```
`align_corners` keeps the step `updateOutSizeAndScale()` derives from the shapes, `(in - 1) / (out - 1)`
with the integer output length, which is what the operator definition and onnxruntime use:
| `Resize(align_corners, mode=linear)` | ONNX Runtime 1.29 | this PR |
|---|---|---|
| `1x1x2x4`, `scales=0.6`, output `1x1x1x2` | `1 4` | `1 4` |
| `1x1x2x4`, `scales=0.6`, output `1x1x3x3`, `mode=cubic` | `1 2.5 4 7 8.5 10 13 14.5 16` | same |
Two stored conformance references still carry the value produced by the ONNX reference
implementation while it divided by the unrounded `input * scale` ([onnx/onnx#8495](https://github.com/onnx/onnx/issues/8495)).
They are regenerated from onnxruntime 1.29 in opencv/opencv_extra#1455, which is what
`test_resize_downsample_scales_{linear,cubic}_align_corners` then compares against.
`tf_crop_and_resize` ignores the step entirely (its grid comes from the roi) and is untouched.
### Tests
Three accuracy tests in `modules/dnn/test/test_layers.cpp`, all on the folded-scale path with an
input whose `in*scale` is not integral, and each fails on the previous code:
| test | covers |
|---|---|
| `Layer_Test_Resize.downscale_uses_given_scales` | step = `1/scale` for `half_pixel`, bilinear |
| `Layer_Test_Resize.antialias_downscale_uses_given_scales` | antialias filter of support `1/scale` |
| `Layer_Test_Resize.align_corners_downscale` | step = `(in - 1) / (out - 1)` and exact corners |
The references are built from the operator definition: `src = (dst + 0.5)/scale - 0.5` for
`half_pixel`, the ONNX antialias triangle filter for the second, and `src = dst * (in - 1)/(out - 1)`
for `align_corners`.
### Validation
- `opencv_test_dnn`: 5204/5204, no regressions.
- `Test_ONNX_conformance.Layer_Test/test_resize_*`: 40/40 with opencv/opencv_extra#1455 applied,
including the antialias and `half_pixel_symmetric` cases that were already green.
- Differential sweep against ONNX Runtime 1.29/1.30 CPU: the failing `Resize` configurations
(`linear`/`cubic`, 6 coordinate transformation modes, `antialias`, `cubic_coeff_a` and
`exclude_outside` combinations) now agree; every other case in the sweep is unchanged.
- Not tested: Windows/MSVC, Clang, ARM/RISC-V, CUDA/OpenVINO/OpenCL targets (no access to those
configurations here). The patch only changes which scale the CPU kernels are given; the
`scaleWidth`/`scaleHeight` members are not consumed by the CUDA/CANN node builders, which keep
using `outHeight`/`outWidth` and the coordinate mode flags.
java: fix Mat get/put JNI for 0D/1D/ND Mats in OpenCV 5 - #29851#29851
## Summary
Fix incorrect behavior of Java `Mat.get()` / `Mat.put()` JNI bindings for
0D (scalar), 1D, and ND `cv::Mat` objects.
The Java JNI layer in `modules/java/generator/src/cpp/Mat.cpp` still assumed
essentially 2D row/col indexing in several get/put code paths. This caused
wrong results or failed access for scalar and 1D Mats, and incorrect bounds
handling for ND Mats accessed via index arrays.
This PR updates the native get/put helpers in `Mat.cpp` and adds regression
tests in `modules/core/misc/java/test/MatTest.java`.
## Problem (before this patch)
### 1. 0D (scalar) Mats
- `Mat::ptr(const int*)` requires `dims >= 1`, so idx-based paths that called
`m->ptr(idx.data())` directly (e.g. `nGetIdx`, `mat_copy_data`, `PUT_ITEM_IDX`)
could not access a true 0D Mat correctly.
- `idx2Offset()` always read `indices[0]` without checking `dims <= 0`.
- Row/col helpers always built a 2-element index `{row, col}` instead of an
empty index vector for 0D Mats.
Although OpenCV 5 supports 0D Mats at the C++ level, the Java wrapper did not
correctly implement idx-based access with an empty index array (`new int[]{}`).
This PR adds explicit JNI support for that case via `mat_elem_ptr()` and
related helpers.
**Example:** For `Mat m = new Mat(new int[]{}, CvType.CV_64F)` (`dims()==0`),
`m.put(new int[]{}, 42.0)` and `m.get(new int[]{}, buf)` could fail or access
the wrong memory.
### 2. 1D Mats (`dims() == 1`)
- Row/col get/put always converted `(row, col)` to `{row, col}` (length 2),
while a 1D Mat requires a single-element index vector.
- For a 1D Mat, OpenCV 5 represents the shape as `dims == rows == 1` and
`cols == total() == N`. The Java row/col access path must therefore interpret
`(row, col)` as the 1D element index represented by `col` (with `row == 0`),
converting internally to `{col}` via `row_col_to_idx()`.
**Example:** For `Mat m = new Mat(new int[]{10}, CvType.CV_32F)`, calling
`m.put(0, 3, values)` could behave incorrectly because native code treated
the access as `{0, 3}` instead of `{3}`.
### 3. ND Mats (`dims() > 2`) via row/col API
- Row/col get/put only checked `rows` and `cols`, without rejecting Mats with
`dims > 2`.
- ND Mats must be accessed via idx-based APIs; using row/col on them could
produce silently wrong results instead of failing early.
### 4. Idx-based API bounds checking
- Old code checked `size[i] <= idx[i]` but did not validate:
- `idx.length == dims`
- negative index values
- `nPutDIdx` computed the number of remaining elements with a per-dimension
product loop (`channels * (size[i] - idx[i]) * ...`). For ND Mats, this did
not correctly account for the current linear index position when determining
how many elements remain from the selected subarray onward. The fix uses
`(total() - idx2Offset(m, idx)) * channels()`, which matches the linear
layout used by `mat_copy_data()`.
## Changes
- Add `mat_elem_ptr()` to support 0D scalar Mats (`Mat::ptr(const int*)`
requires `dims >= 1`).
- Add `check_row_col_access()`, `check_idx_access()`, and `row_col_to_idx()`
to validate access and convert row/col indices according to OpenCV semantics.
- Update `idx2Offset()`, `offset2Idx()`, and `mat_copy_data()` for 0D/1D Mats.
- Route row/col get/put through `row_col_to_idx()` (1D: element index in `col`,
`row == 0`).
- Restrict row/col APIs to Mats with `dims <= 2`; ND Mats must use idx-based
APIs (`nGet*Idx`, `nPut*Idx`, `nGetIdx`, `nPutDIdx`).
- Fix remaining-element calculation in `nPutDIdx` to use `idx2Offset()`.
## Behavior notes
| API | 0D Mat (`dims==0`) | 1D Mat (`dims==1`) | 2D Mat | ND Mat (`dims>2`) |
|-----|-------------------|-------------------|--------|-------------------|
| `get/put(int[] idx, ...)` | supported (`idx.length == 0`; added by this PR) | supported (`idx.length == 1`) | supported | supported |
| `get/put(row, col, ...)` | supported (`row=0`, `col=0`) | supported (index in `col`, `row=0`) | supported | rejected at JNI layer return 0; |
| `get(row, col)` | supported | supported | supported | rejected at JNI layer return 0; |
| `get(int[] idx)` | supported | supported | supported | supported |
## Test plan
Added regression tests in `MatTest.java`:
- `testGetPut0DMat` — idx and row/col get/put on a scalar Mat (`new int[]{}`)
- `testGetPut1DMat` — idx and row/col get/put on `new Mat(new int[]{5}, ...)`
- `testGetPut1DMatDoubleArray` — bulk double put/get via idx on a 1D Mat
- `testGetPut3DMat` — row/col API rejected for 3D Mat; idx API works
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
core(doc): update introduction for OpenCV 5.1 - #30598
- Remove redundant highgui and videoio modules.
- Add geometry and ptcloud modules.
- Document new 8-bit floating-point types (CV_8F_E4M3FN and CV_8F_E4M3FNUZ).
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
dnn(onnx): add support for ONNX Compress operator - #30576
### Description
This PR adds support for the ONNX `Compress` operator (opset 9-24) to the OpenCV DNN dynamic engine:
- Implements `CompressLayer` in `modules/dnn/src/layers/compress_layer.cpp` supporting dynamic output shapes (`dynamicOutputShapes() { return true; }`).
- Implements full ONNX `Compress` semantics:
- Slices along a specified axis when the `axis` attribute is provided (supports negative axis indexing via `normalize_axis`).
- Flattens the input tensor to 1D before compression when the `axis` attribute is omitted.
- Discards elements exceeding condition length when condition length is shorter than target axis dimension (`std::min(dimAxis, condTotal)`).
- Handles all-false condition ($K=0$) cleanly with early return after fitting the valid empty output shape (`[..., 0, ...]`).
- Enforces continuous tensor invariants with `CV_Assert(data.isContinuous())` and validates `condition` as `CV_Bool` per ONNX spec.
- Slicing optimized via OpenCV native `parallel_for_` flattening multi-dimensional iteration domains to 1D (`totalSlices = outer * K`).
- Un-denylists and passes all 4 official ONNX conformance tests in `test_onnx_conformance_layer_parser_denylist.inl.hpp`.
- Marks `Compress` conformance tests with `SKIP;` in `test_onnx_conformance_layer_filter__openvino.inl.hpp` to ensure OpenVINO CI remains green.
- Adds focused Google Test unit tests in `modules/dnn/test/test_layers.cpp` verifying empty output behavior on all-false condition both with and without `axis`.
### Test Results
All 4 ONNX conformance tests + 2 all-false unit tests pass locally:
```text
[==========] Running 6 tests from 2 test cases.
[----------] Global test environment set-up.
[----------] 2 tests from Test_Compress
[ RUN ] Test_Compress.AllFalse_WithAxis
[ OK ] Test_Compress.AllFalse_WithAxis (29 ms)
[ RUN ] Test_Compress.AllFalse_WithoutAxis
[ OK ] Test_Compress.AllFalse_WithoutAxis (0 ms)
[----------] 2 tests from Test_Compress (30 ms total)
[----------] 4 tests from Test_ONNX_conformance
[ RUN ] Test_ONNX_conformance.Layer_Test/test_compress_0_OCV_CPU
[ OK ] Test_ONNX_conformance.Layer_Test/test_compress_0_OCV_CPU (901 ms)
[ RUN ] Test_ONNX_conformance.Layer_Test/test_compress_1_OCV_CPU
[ OK ] Test_ONNX_conformance.Layer_Test/test_compress_1_OCV_CPU (465 ms)
[ RUN ] Test_ONNX_conformance.Layer_Test/test_compress_default_axis_OCV_CPU
[ OK ] Test_ONNX_conformance.Layer_Test/test_compress_default_axis_OCV_CPU (456 ms)
[ RUN ] Test_ONNX_conformance.Layer_Test/test_compress_negative_axis_OCV_CPU
[ OK ] Test_ONNX_conformance.Layer_Test/test_compress_negative_axis_OCV_CPU (559 ms)
[----------] 4 tests from Test_ONNX_conformance (2383 ms total)
[----------] Global test environment tear-down
[==========] 6 tests from 2 test cases ran. (2413 ms total)
[ PASSED ] 6 tests.
```
### Checklist
- [x] I have read the [OpenCV contribution guidelines](https://github.com/opencv/opencv/wiki/How_to_contribute).
- [x] My code follows the OpenCV coding style guidelines.
- [x] I have tested my changes locally and all relevant unit tests pass.
- [x] No unrelated files or whitespace changes are included.
cv::resize samples destination pixel d at source position
(d + 0.5) * s - 0.5. Mapping keypoints back as (xp - pad) * s left them
short by 0.5 * (s - 1) image pixels per axis (about 0.28 px for 1000x901
into 640x640, 0.5 px at 2x downscale). Use the matching inverse, as
AKAZE and BRISK do for their octaves, and update the letterbox test's
expected mapping accordingly.
INT4 and UINT4 tensors travel unpacked in CV_8S/CV_8U, so QuantizeLinear
only saturated them to the 8-bit range: test_quantizelinear_int4 expects 7
for 40/4+1 and got 11. Clamp the result to [-8, 7] / [0, 15] when the ONNX
output dtype is INT4/UINT4, and enable the four int4 conformance cases
(the DequantizeLinear ones and the uint4 case already passed).
flann: reallocate the kmeans tree roots in loadIndex 🤖🤖🤖 - #30559
`KMeansIndex::loadIndex` reads `trees_` from the saved file and then calls `load_tree(stream, root_[i], i)` for every tree, but it never reallocates `root_`. That array was sized by the constructor from the `trees` index parameter. `cv::flann::Index::load` builds the index with only `algorithm` set, so `root_` always has the default single slot. A saved KMeans index whose `trees_` field is 2 or more makes `load_tree` write node pointers past the end of `root_`, and `free_centers()` and the destructor later read the same slots. With ASan this is a heap-buffer-overflow WRITE of size 8 in `load_tree`, 0 bytes after the 8-byte region allocated in the `KMeansIndex` constructor. The same path is reached through the composite and autotuned loaders, which call `KMeansIndex::loadIndex`.
The fix frees `root_` and reallocates it with the loaded tree count, as `HierarchicalClusteringIndex::loadIndex` and `KDTreeIndex::loadIndex` already do. Both arrays are value-initialized and reset to NULL before the reads, so a truncated file that throws partway through only leaves NULL slots for the destructor. Files saved by OpenCV always store the `trees` value they were built with, so they load exactly as before. Added `Flann_KMeansIndex.load_more_trees_than_default` to `opencv_test_flann`, which stores the same tree twice under `trees_ = 2`. It fails under ASan without the fix. With the fix it passes, along with the rest of `opencv_test_flann`.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
DNN: Optimize block layout handling and fold Mul/Add into BatchNorm - #30313
Requires: https://github.com/opencv/opencv_extra/pull/1439
### PR Changes
- AveragePool runs on plain NCHW input without conversion to block layout; with an avgpool HAL hook registered, block layout is kept as before.
- NaryEltwise with a scalar constant operand runs in block layout.
- BatchNorm absorbs following Mul/Add by scalar or per-channel constants into its scale and bias; ChainFuser marks a [C,1,1] / [1,C,1,1] constant per channel once it proves the input is 4D.
- Global average/max pooling reduces block input directly.
- An elementwise op feeding a conversion to plain now runs after it, on C channels instead of C0 lanes.
- Slice2 on the channel axis keeps block layout for C0-aligned or runtime bounds.
### Performance
Measured on i9-14900KS, default threads (32).
| Model | 5.x | After PR | ORT (1.30) | OpenCV before vs after | After PR vs ORT |
|-------------|-----------:|-----------:|-----------:|-----------------------:|----------------:|
| RAFT | 965.67 ms | 691.03 ms | 604.26 ms | 1.40x | 0.87x |
| DenseNet121 | 14.14 ms | 9.84 ms | 12.05 ms | 1.44x | 1.22x |
| XFeat | 5.80 ms | 5.63 ms | 2.95 ms | 1.03x | 0.52x |
| NAFNet | 1304.50 ms | 1195.67 ms | 1407.77 ms | 1.09x | 1.18x |
| RealESRGAN | 422.57 ms | 403.93 ms | 534.29 ms | 1.05x | 1.32x |
| PPOCRv3_det | 14.25 ms | 14.15 ms | 16.32 ms | 1.01x | 1.15x |
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Disable undefined behavior sanitizer in imgwarp - #30307
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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core: support fp8 depths in minMaxLoc and minMaxIdx - #30526Fixes#30525
### Problem
`minMaxIdxTab` (`modules/core/src/minmax.simd.hpp`) has no entries for `CV_8F_E4M3FN` and `CV_8F_E4M3FNUZ`, so `cv::minMaxLoc` and `cv::minMaxIdx` fail with `func != 0` for fp8 input.
### Fix
- `modules/core/include/opencv2/core/private.hpp`: new `CV_TOGGLE_FP8(x)`, the 8-bit version of `CV_TOGGLE_FLT`: it flips the 7 magnitude bits of negative values, so sign-magnitude fp8 bytes compare in the same order as `int8`.
- `modules/core/src/minmax.simd.hpp`: new `minMaxIdx8f`, used for both fp8 depths. It toggles the input in blocks of 1024 bytes and runs the vectorized `minMaxIdx8s` kernel on them.
- `modules/core/src/minmax.dispatch.cpp`: the fp8 result is toggled back and decoded with `fp8_t::decodeLUT()` / `fp8a_t::decodeLUT()`.
- `modules/core/include/opencv2/core.hpp`: removed the outdated `@note` saying that `CV_16F`/`CV_16BF`/`CV_Bool`/`CV_64U`/`CV_64S`/`CV_32U` are not supported for `minMaxLoc` / `minMaxIdx`.
On 1920x1080 input (single thread, Apple M1 Pro), `minMaxLoc` takes 0.14 ms for both fp8 depths (0.11 ms for `CV_8U`), compared with 2.8 ms for the first version of this PR, which converted every element to `float`. NaN values are not handled specially: like any other byte, they are ordered by their toggled bit pattern.
### Tests
`MinMaxSupportedMatDepth` and `MinMaxAccuracyNewTypes` (`accuracy` and `accuracy_with_mask`) now also run on both fp8 depths, including each format's largest value (448 / 240) and masked-out outliers. The 8 new cases fail on current 5.x (`func != 0`) and pass with this patch. I also compared the results with a float reference on 1600 random fp8 inputs of different sizes, with and without a mask: values and locations match.
All `*MinMax*` / `*minMax*` tests in `opencv_test_core` pass locally on Apple M1 Pro (3113 tests), with OpenCL enabled and with `OPENCV_OPENCL_DEVICE=disabled`. The full `opencv_test_core` passes, except 7 tests that need `opencv_extra` test data, which I didn't have locally (FileStorage base64 and globbing).
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KleidiCV update to version 26.09. - #30128
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getNormFunc and getNormDiffFunc had no entries for CV_8F_E4M3FN and
CV_8F_E4M3FNUZ, so cv::norm failed with "func != 0". Add the fp8 kernels
with float accumulators and the same block accumulation as CV_16F and
CV_16BF. Add cv_absdiff overloads for fp8_t and fp8a_t that return
float, so the difference of two fp8 values is not rounded back to fp8.
Add a Core_Norm_FP8 test.
Address review: like the CV_32F and CV_64F paths, compare integer keys
instead of decoding each fp8 value. CV_TOGGLE_FP8 maps the sign-magnitude
bytes to int8 keys in the same order; the key range [ia, ib) of
[minVal, maxVal) is found once from the decode table, so bounds that are
not fp8 values are still exact. NaN's key is checked separately, since
the E4M3FNUZ NaN sits between the negative values and zero.
Add a Core_CheckRange_FP8 test that checks all 256 values of both fp8
depths against minVal <= v < maxVal for several ranges.
Address review: instead of decoding each fp8 value to float, compare
the bytes as int8 after CV_TOGGLE_FP8, which keeps the order of the
sign-magnitude values. reduceMinMaxImpl runs on a one-byte ToggledFp8
type, so no temporary copy is needed.
Add -1 to the ReduceArgNewTypes values, so the test fails if the fp8
bytes are compared without toggling.
dnn: check weights and soft labels shape in SoftmaxCrossEntropyLoss 🤖🤖🤖 - #30530
`SoftmaxCrossEntropyLossImpl::forward` reads the optional `weights` input as a float array of `C` class weights (`weightBase[y]`, and `wData[c]` on the probability-label path) but never checks its length or type. The probability-label path also reads `labels` with the scores layout (n, c, s) without checking that the labels have the scores shape. Both tensors come straight from the ONNX model, so a short weights initializer or a `[1, C]` labels tensor against `[2, C]` scores reads past the buffer.
Before: weights of shape `[1]` with `C = 64` and labels `C - 1` gives an ASan heap-buffer-overflow READ in `reduceSCEPerSample`. Probability labels `[1, 64]` against scores `[2, 64]` give a READ of size 16 in the forward lambda.
After: both cases throw from `forward`. Weights are checked against `C` and converted to `CV_32F`, which `NegativeLogLikelihoodLoss` already does. Valid models are unchanged, and all 104 `test_sce_*` / `test_nllloss_*` conformance cases pass on OCV/CPU.
Tradeoff: one extra `convertTo` of a `C`-element vector per forward call. In exchange, `float16`/`double` weights are now read with their real values instead of being reinterpreted as float.
A regression test was added to `test_layers.cpp`. It fails under ASan without the fix and passes with it.
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dnn: share equal constants and merge duplicate nodes in the ONNX importer - #30276
# Summary
The ONNX importer now gives initializers with the same type, shape and bytes a single constant,
and `parseNode()` skips a node that has the same op, attributes and inputs as an earlier one,
reusing that node's outputs instead. Many exported models repeat identical work: RAFT pads the
same correlation pyramid again in each of its 10 refinement iterations, and each iteration's
`pads` constant has a different name, so the Pads only look identical when constants are
compared by value. With this change RAFT runs 5 Pads instead of 41, as ONNX Runtime does.
Custom-domain ops, random ops, nodes with subgraphs and nodes producing graph outputs are never
merged. Neither are `QuantizeLinear`/`DequantizeLinear`: `fuseQDQ()` only folds a DQ into
MatMul/Gemm/Conv while it has a single consumer, so merging them would switch off int8 fusion.
Outputs match ONNX Runtime as closely as before.
| Model | This PR | OpenCV 5.0.0 | ORT 1.30 | vs 5.0.0 | vs ORT 1.30 |
|---|---:|---:|---:|---:|---:|
| RAFT | 392.4 ms | 544.5 ms | 291.3 ms | 1.39× faster | 0.74× |
| NAFNet | 1815.8 ms | 2157.6 ms | 1532.6 ms | 1.19× faster | 0.84× |
| YOLOX | 28.3 ms | 29.8 ms | 15.1 ms | 1.05× faster | 0.53× |
> Benchmarked on AMD Ryzen 9 9950X
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#30131 made cv::XFeat letterbox with INTER_LINEAR_EXACT. The test's
hand-made letterbox still used INTER_LINEAR, so on current 5.x the two
blobs differed slightly and some keypoints moved by one network pixel
(1.56 px), failing the exact-mapping check. Use INTER_LINEAR_EXACT so the
network sees an identical blob again.
The reduceR_/reduceC_ dispatch is a chain of explicit sdepth/ddepth pairs and has
never carried a CV_8S or CV_32S source row, so cv::reduce rejected both for every
operation and both dim values. 4.x has the same gap.
CV_8S mirrors CV_8U and accumulates in int. CV_32S accumulates in double: OpAdd<int>
evaluates a + b as int before saturate_cast can clamp it, so an int accumulator
overflows. CV_32S -> CV_32S sums are left unsupported for that reason.
Extend Core_ReduceTest to the new combinations, and add cases for the 32-bit
overflow range and the signed 8-bit extremes, which its randu(0, 100) fill cannot
reach. Its support list also omitted 16U/16S REDUCE_MAX/REDUCE_MIN, which the
existing kernels already handle.
Partially addresses #30569.
python: add documentation signatures for hand-wrapped highgui functions - #30549
Port of #30508 to 5.x
`createTrackbar`, `createButton` and `setMouseCallback` are registered by hand in `cv2.cpp`, so `gen2.py` never sees them and they end up with no Python signature in the generated docs.
Argument names come from the keyword lists in `cv2_highgui.cpp`, which is why `setMouseCallback` reads `window_name, on_mouse` rather than the camelCase in its `cv2.cpp` docstring.
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Added flag for bitexact resize in blobFromImage and overloads. - #30131
Current INTER_LINEAR resize allows small deviations up to rounding. It affects inference result for networks that produce coordinates (object detectors, features) and descriptors. The new option allow to get the same result independently on CPU arch and used HALs.
Companion PR for references update: https://github.com/opencv/opencv_extra/pull/1422
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LightGlueMatcher normalizes each axis by its own image dimension, while
the LightGlue reference (cvg/LightGlue normalize_keypoints, kornia)
shifts by half the image size and divides both axes by half the longer
side. The two only agree for square images, so models exported from
the reference forward without normalization (e.g. XFeat's LighterGlue)
see a stretched geometry on non-square images.
Add LightGlueNormalization with LG_NORM_MODEL (default, unchanged
per-axis mapping of the LightGlueType) and LG_NORM_MAX_SIDE (reference
mapping), selected with setKeypointNormalization() independently of the
model type and kept by clone(). Keypoints passed with an empty image
size are still used as given.
Fixes#30556
FFmpeg for Windows update to 9.0.2 - #30515
aom v3.15.0
ffmpeg n9.0.2
vpx v1.17.0
openh264 v2.7.0
Closes https://github.com/opencv/opencv/issues/30343
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Move the letterbox inverse test body into testXFeatLetterboxInverse()
and run it for 1000x901 (vertical pad) and 901x1000 (horizontal pad),
so the width part of the fix is exercised too.
Fixes#30548
XFeat letterboxes through blobFromImageWithParams(DNN_PMODE_LETTERBOX)
but re-derived the resized size with cvRound, while dnn truncates. When
the resized size had a fractional part >= 0.5, the pad offset was one
network pixel off. It also divided by the nominal factor instead of the
real per-axis ratio. Mirror dnn's truncation and map back with the exact
per-axis inverse, and add a regression test with a non-integral
letterbox (1000x901 into 640x640).
Fixes#30548
checkRange had no branch for CV_8F_E4M3FN and CV_8F_E4M3FNUZ, so it
failed with "Unsupported matrix depth in checkRange". Use
checkTypedRange with fp8_t / fp8a_t, like CV_16F / CV_16BF; NaN values
are reported as out of range.
Add both fp8 depths to the Core_CheckRange_Float test, with a NaN as
the bad value since fp8 has no infinity.
core: support fp8 depths in countNonZero and hasNonZero - #30463Fixes#30461
### Problem
`countNonZeroTab` (`count_non_zero.simd.hpp`) and `hasNonZeroTab` (`has_non_zero.simd.hpp`) have `0` in the `CV_8F_E4M3FN` and `CV_8F_E4M3FNUZ` slots, so `cv::countNonZero` and `cv::hasNonZero` fail with `func != 0` for fp8 input. `cv::findNonZero` already supports both depths since #30341.
### Fix
- `CV_8F_E4M3FN`: new `countNonZero8f` / `hasNonZero8f` kernels. Same as the 8u kernels, but the sign bit is ignored so `-0.0` (`0x80`) counts as zero: the vector path reuses the `v_add_wrap(x, x)` compare of the 16f kernels, and the scalar tail uses `CHECK_NZ_FP8(x) = ((x) & 0x7f) != 0`.
- `CV_8F_E4M3FNUZ`: the existing 8u kernels. This format has no `-0`; `0x80` is its NaN and counts as non-zero, the same as in `findNonZero`.
### Tests
- `CountNonZero1D` now also runs on both fp8 depths.
- `HasNonZeroNegZeros` now also covers `CV_8F_E4M3FNC1`.
- New `CountNonZeroFP8.sign_bit`, a `TEST_P` over both fp8 depths and n = 1, 100, 1031 (scalar tail, single-vector loop, unrolled loop). It fills a `CV_32F` Mat with random values in ±[0.5, 4), sets every 5th element and the last one to `-0.0`, `NaN` or `+0.0`, converts it to fp8, and checks `countNonZero` / `hasNonZero` against the number of non-zero values set. `-0.0` becomes `0x80` in E4M3FN and must count as zero; `NaN` becomes `0x80` in E4M3FNUZ and must count as non-zero.
These fail on current 5.x (`func != 0`) and pass with this patch. All `*NonZero*` and `*FP8*` tests in `opencv_test_core` pass locally on Apple M1 (190 tests), with OpenCL enabled and with `OPENCV_OPENCL_DEVICE=disabled`.
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depthDispatch had no case for CV_Bool, CV_8F_E4M3FN and CV_8F_E4M3FNUZ,
so reduceArgMax / reduceArgMin failed with "Unsupported matrix type".
Dispatch them to bool, fp8_t and fp8a_t; the fp8 types compare through
their float conversion.
Add a ReduceArgNewTypes test, since ElemWiseTest does not generate these
depths.
getLUTFunc had no case for CV_8F_E4M3FN and CV_8F_E4M3FNUZ tables, so
cv::LUT failed with "An unexpected type combination was specified". LUT
only copies table entries, so use the same 1-byte copy as CV_Bool for
both 8-bit and 16-bit indices.
Add both fp8 depths to the LUTAccuracyNewDstTypes test.
* A workaround for the issue in ICV package on Windows implemented in PR opencv/opencv#29771 for IPP Warp perspective implementation for HAL has been reverted since the original issue was fixed in the latest version of ICV package (2026.1.0) enabled in OpenCV in PR #30078.
* The corresponding issue: opencv/opencv#29166.
core: support 5.x depths in sort and sortIdx - #30506Fixes#30505
### Problem
The kernel tables of `cv::sort` and `cv::sortIdx` (`modules/core/src/matrix_operations.cpp`) only have entries for `CV_8U` to `CV_64F`, so every depth added in 5.x fails with `func != 0`.
### Fix
Both tables now use the existing `sort_<T>` / `sortIdx_<T>` templates for:
- `CV_16F` → `hfloat`, `CV_16BF` → `bfloat`, `CV_8F_E4M3FN` → `fp8_t`, `CV_8F_E4M3FNUZ` → `fp8a_t` (compared through their `float` conversion);
- `CV_Bool` → `uchar`, the same kernel as `CV_8U`;
- `CV_64U` → `uint64_t`, `CV_64S` → `int64_t`, `CV_32U` → `uint32_t`.
### Tests
- `Core/sortIdx.simple` now also runs on `CV_16U`, `CV_16F`, `CV_16BF`, `CV_Bool`, `CV_64U`, `CV_64S`, `CV_32U` and both fp8 depths (`CV_16U` was already supported but not tested), and also checks `cv::sort` against sorting a `CV_64F` copy of the same data; there was no `cv::sort` test before.
- New `Core_sort.large_integers` checks exact `sort` / `sortIdx` results for `CV_32S`, `CV_32U`, `CV_64S` and `CV_64U` with values that need the full type: `INT_MIN` / `INT_MAX`, the unsigned high bit (`0x80000000`, `UINT_MAX`, `2^63`, `UINT64_MAX`) and `2^53` vs `2^53 + 1`. With signed kernels for `CV_32U` / `CV_64U` it fails, while the random `sortIdx` test still passes.
The new cases fail on current 5.x and pass with this patch. All `sortIdx` / `sort` tests in `opencv_test_core` pass locally on Apple M1 Pro (242 tests), with OpenCL enabled and with `OPENCV_OPENCL_DEVICE=disabled`. The full `opencv_test_core` passes, except 7 tests that need `opencv_extra` test data, which I didn't have locally (FileStorage base64 and globbing).
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imgcodecs(avif): add support for alpha channel quality control #30499
This pull request adds support for independent alpha channel quality control when encoding AVIF images, resolving #30451.
### What was happening
OpenCV previously only controlled color channel compression via `IMWRITE_AVIF_QUALITY`. Alpha channels remained unhandled or were tied to color quality, making it impossible to adjust color and alpha compression independently (for example, keeping crisp lossless alpha edges while compressing background RGB content lossily to save file size).
### What changed
1. In `modules/imgcodecs/include/opencv2/imgcodecs.hpp`, added `IMWRITE_AVIF_ALPHA_QUALITY = 515` (range 0 to 100, default 95), and updated documentation for `IMWRITE_AVIF_QUALITY` to clarify that it controls color planes.
2. In `modules/imgcodecs/src/grfmt_avif.cpp`:
- Registered `IMWRITE_AVIF_ALPHA_QUALITY` in `m_supported_encode_key`.
- Handled `IMWRITE_AVIF_ALPHA_QUALITY` to set `encoder_->qualityAlpha` (and quantizers for fallback paths).
- Removed the automatic fallback that synced alpha quality to color quality, keeping the two parameters decoupled and order-independent.
3. Added dedicated unit test `Imgcodecs_Avif.alpha_quality_control` in `modules/imgcodecs/test/test_avif.cpp` verifying independent lossy/lossless combinations, order independence, and bounds clamping.
### Testing
- Verified with standalone reproducer (`reproduce_opencv_30451.cpp`):
- Quality=100, AlphaQuality=10: 0 color difference (lossless color) with lossy alpha.
- Quality=10, AlphaQuality=100: 0 alpha difference (lossless alpha) with lossy color.
- Parameter order independence confirmed.
- Native test suite: All 413 AVIF tests passed in `opencv_test_imgcodecs`.
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The eight test_qlinearmatmul_{2D,3D}_{int8,uint8}_{float16,float32} cases
and test_quantizelinear_blocked_asymmetric were on the parser denylist
without test data; opencv_extra#1436 (paired with #30289) added the models
and with them all nine pass on current 5.x. The old denylist comment calls
the QLinearMatMul entries float-output variants, but the suffix is the
scale dtype and the outputs are int8/uint8, which the importer and kernels
already handle.
Remove the nine entries from the denylist and add them to the OpenVINO
filter as SKIP, like the other QLinearMatMul/QuantizeLinear cases there.
Verified on macOS arm64 against current opencv_extra: all 18
parameterizations (OCV/CPU, OCV/CPU_FP16) pass and the whole
Test_ONNX_conformance suite stays green.
Check the [1, 1, N, 7] boxes layout in getMemoryShapes so the output size and the box loop agree on every backend. Emit the extrapolation value for samples outside [0, H - 1] x [0, W - 1] and sample the box center for 1-pixel crops, on both the CPU and CUDA paths.
The OpenVINO conformance filter is exhaustive and fails on an unknown test
name. Mark the five newly enabled cases SKIP there, like the other
QuantizeLinear/DequantizeLinear cases.
According to the ONNX protobuf specification, boolean tensors may be stored either in raw_data or packed into repeated int32 int32_data as individual 32-bit integers (not a bitmask). Previously, getMatFromTensor only read raw_data, causing assertion failures when parsing ONNX models that serialize boolean initializers into int32_data.
This patch adds int32_data extraction with conversion to CV_Bool, adds specification documentation, removes unreachable duplicate branches for UINT16/UINT32/UINT64, and adds tests in test_onnx_importer using models from opencv_extra.
Test data in opencv_extra under the same branch name as this patch: https://github.com/opencv/opencv_extra/pull/TBD
Addresses #19366.
dnn: fix GatherND division by zero on empty indices - #30503
GatherND divided by zero in `forward_impl` when the indices tensor is empty, which kills the process with SIGFPE on x86-64 (seen with `LightGlueMatcher` when nothing matches). The layer now returns early when the output is empty.
Fixes#30501
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ONNX opset 21 allows INT16 and UINT16 as the quantized type of
QuantizeLinear and DequantizeLinear. The importer already maps them to
CV_16S/CV_16U, but both layers stopped at a CV_Assert that only listed the
8-bit, int32 and fp8 types, so the four conformance cases for these dtypes
were on the parser denylist.
Accept CV_16U/CV_16S in both layers and add the dispatch branches for the
fp32/fp16 scale and activation combinations, mirroring the existing
CV_8U/CV_8S ones. The shared kernels already handle the wider types via
saturate_cast. Remove the four cases from the denylist.
Verified on macOS arm64: before the change the 8 cases
(test_{de}quantizelinear_{u,}int16 x OCV/CPU, OCV/CPU_FP16) fail on the
assertion; after it the whole Test_ONNX_conformance suite passes
(3584 tests) and the quantization unit tests pass (118 tests).
dnn : int8 optimisation - #30289
### Correctness
- **AVX-VNNI 1x1 conv, leftover pixels:** the last `H*W % 8` output pixels read the VNNI-repacked
weights in the wrong order. Fixes the wrong top-1 of MobileNetV2 and DenseNet121 int8 with VNNI.
- **Padded taps in scalar paths:** the depthwise (AVX2) tail and the NEON kxk scalar path skipped
out-of-image taps instead of using the input zero point. The VNNI kxk kernel now never leaves a
tile under 8 pixels.
### Speed
- **First conv:** quantizing the float image is now parallel and vectorized. A float-input conv
with up to 4 channels runs as a 1x1 conv over each pixel's window. First conv 5-6x faster
(ResNet50: 0.88 -> 0.16 ms). `OPENCV_DNN_INT8_CONV_WINDOW_PACKING=0` disables the window packing.
- **AVX-VNNI depthwise conv:** a new kernel transposes input rows into one row per channel and
adds 4 taps for 8 pixels with one `pshufb` + `vpdpbusd`. Depthwise about 2x faster, output
bit-identical.
- **int8 MatMul at batch 1:** uses all threads instead of one. AlexNet int8: 2.75 -> 1.52 ms.
- **Asymmetric padding:** convs with TF "SAME" padding at stride 2 are now fused to int8 instead of
running in float between Dequantize and Quantize. Float convs in EfficientNet-Lite4 and MP hand
pose: 5 -> 0-1.
- **Broadcast int8 Add/Mul:** copies whole contiguous runs in parallel instead of computing an
index per element.
- **int8 Quantize/Dequantize:** AVX2 fast path for signed int8; per-tensor Q/DQ keep the block
layout.
### Tests
- Companion PR : opencv/opencv_extra#1436 (test data).
### Results (ms, i9-14900KS , default threads)
| Model | 5.x before (ms) | this PR (ms) | ONNX Runtime 1.30 (ms) | Speedup vs 5.x | ORT / this PR |
|---|---|---|---|---|---|
| DenseNet121 int8 | 109.4 | 14.7 | 16.1 | 7.43x | 1.09x |
| PP-OCRv3 det int8 | 65.7 | 12.7 | 33.1 | 5.15x | 2.60x |
| SFace int8 | 8.1 | 3.0 | 19.0 | 2.75x | 6.43x |
| EfficientNet-Lite4 int8 | 4.1 | 2.9 | 1.9 | 1.40x | 0.67x |
| EfficientNet-Lite4 qdq | 4.6 | 3.1 | 2.0 | 1.49x | 0.65x |
| MP hand pose int8 | 2.5 | 0.8 | 2.2 | 3.06x | 2.69x |
| PPHumanSeg int8 | 2.6 | 1.9 | 5.6 | 1.41x | 3.02x |
Co-authored with @Prasadayus
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Add a calcHistRunParallel wrapper that splits the input image across
threads, each accumulating into a private histogram, then sums the
local copies at the end. This avoids atomic contention on shared bins.
The thread count follows the suggestion from PR #28635 review:
- if N < 256K elements, use a single thread
- otherwise nstripes = clamp(cvRound(N*0.001/M), 1, 8),
where M is the total number of histogram bins
- nstripes is further capped by getNumThreads(), so setNumThreads(0)
and setNumThreads(1) keep the single-histogram path
This keeps large histograms (e.g. 2D 256x256) single-threaded to avoid
excessive memory, while small histograms (e.g. 1D 256 bins) on large
images get up to 8x parallelism.
Performance (1920x1080, 8 threads, median of 20 runs):
- 8U 1D: 0.62ms -> 0.17ms (3.7x)
- 16U 1D: 3.81ms -> 0.95ms (4.0x)
- 32F 1D: 3.62ms -> 0.37ms (9.7x)
Adds a parameterized consistency test verifying that results match a
row-by-row single-threaded baseline bit-exactly across 8U/16U/32F,
1D-3D and various image sizes. Both stripe-splitting branches are
covered: a non-contiguous ROI takes the row-split path, a contiguous
clone takes the column-split path. A 1D mask bypasses IPP/HAL.
In modules/core/src/convert.hpp, v_store_as() for 64-bit integer destinations (int64_t and uint64_t) from v_float32 rounded into a 32-bit integer (v_int32) before widening, causing values outside the 32-bit signed range (e.g. >= 2^31 in CV_16BF / CV_16F) to saturate to INT32_MIN/INT32_MAX on the SIMD path.
Use buffer-based saturate_cast<int64_t> / saturate_cast<uint64_t> to preserve the full 64-bit destination range, matching the existing precedent for v_store_pair_as(v_float64) and v_store_pair_as(unsigned, v_float32).
Add Core_ConvertTo64Int regression tests across vector and scalar lengths.
Resolves#29996.
imgcodecs(avif): fix norm calculation and extend 16-bit tests - #30431
- Correct lossy test normalization from dividing by total samples (N) to root-mean-square (RMS) error divided by sqrt(N), and recalibrate thresholds with margin for 8/10/12/16-bit tests.
- Extend 16-bit AVIF tests to cover all supported channel counts (1, 3, and 4 channels) using testing::Combine.
### Pull Request Readiness Checklist
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dnn: skip MatMul's zero-fill when there is no bias - #30297
Without a bias, MatMul pre-zeroed the whole output with a single-threaded memset and then still passed beta = 1 into the gemm, forcing every kernel to read and multiply-add against that zeroed buffer. Passing beta = 0 instead removes the memset and lets MLAS/fastGemmThin write the product directly. Measured 31-50% faster on a small, write-heavy, no-bias shape (512x32x512, 8 threads); `MatMul.matmul/41` and the new `MatMul.matmul_dynamicB/41` cover the constant-B and dynamic-B paths.
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imgproc: accept CV_Bool input in morphological operations - #29799
### Pull Request Readiness Checklist
Fixes#29798
In 5.0 `cv::Mat_<bool>::depth()` returns `CV_Bool` instead of `CV_8U`, so the morphology filter factories in `morph.simd.hpp` no longer matched any branch and threw `Unsupported data type`. This worked in 4.x.
`CV_Bool` is a 1-byte 0/1 type, so it is dispatched through the same `uchar` min/max filters as `CV_8U`, and the default border value follows the `CV_8U` case (1 for erode, 0 for dilate). The OpenCL path bails out for `CV_Bool` and falls back to the CPU implementation, since `ocl::typeToStr` has no mapping for it.
Accepting `CV_Bool` also exposed a latent bug in `FilterEngine::init`: `borderElemSize` was computed from `CV_MAT_DEPTH(srcType) >= CV_32S`, which assumes the depth enum is ordered by element size. `CV_Bool` is 9, so a 1-byte type took the `sizeof(int)` branch and `borderElemSize` became 0, leaving `borderTab` empty and making every non-`BORDER_CONSTANT` erode/dilate read through a null pointer. It now keys off `CV_ELEM_SIZE1` instead. `CV_16F`/`CV_16BF` had the same latent miscomputation.
Added a test comparing `Mat_<bool>` against the equivalent `CV_8UC1` mask for erode, dilate, open and close over the three structuring element shapes and four border types, plus in-place and multi-iteration calls.
`MORPH_GRADIENT`/`TOPHAT`/`BLACKHAT` remain unsupported for `CV_Bool`: they subtract two masks, which has no meaning for a boolean type, and they throw rather than returning a wrong result.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Do not call Lapack for small operations. - #29909
This reduces the cache for GEMM in calibration: the transposed Jacobian is 6 x 2Number points while the result is 6 x 1.
The previous code was only looking at the input number of rows.
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dnn: avoid unused quadratic workspace for MLAS attention - #30405
`AttentionLayerImpl` allocates a dense `[batch * heads, sequence, sequence]` probability buffer before calling MLAS FlashAttention, even though that path never uses it. With 12 heads and 8,192 tokens, this is an unused 3 GiB allocation that can make inference fail before the tiled kernel runs.
This patch shares the existing fast-path eligibility check between memory planning and execution. It keeps one-element placeholders for the unused probability and output workspaces, preserving the internal buffer indices used by RoPE. If MLAS declines the call, the generic path allocates the dense buffers it needs. Masked attention and builds without MLAS keep their existing workspace plans. The numerical kernels are unchanged.
I reproduced the allocation with a generated FP32 ONNX Attention model loaded through `readNetFromONNX`, using the 5.x graph engine. For batch 1, 12 heads, head width 16 and 8,192 tokens:
| Measurement | Before | After |
| --- | ---: | ---: |
| Largest `cv::Mat` allocation | 3 GiB | 18 MiB |
| Peak live `cv::Mat` storage | 3,279,180,288 bytes | 51,663,368 bytes |
| Forward with a 512 MiB address-space limit | Allocation failure | Completes |
| Peak RSS without the limit | 65,664 KiB | 65,616 KiB |
The allocation trace goes through `allocateLayerOutputs` and `Mat::fit` before the layer's `forward`. The same failure is resolved at 4,096 and 16,384 tokens; at 16,384 tokens, the largest allocation drops from 12 GiB to 36 MiB. Outputs from the constrained runs match the unrestricted runs byte for byte.
The limit above is Linux `RLIMIT_AS`, which limits virtual address space. RSS barely changes because the unused buffer is not touched. The allocation measurements cover `cv::Mat` storage only.
Validation on Linux x86-64:
- 40 targeted tests pass with MLAS in Release, and the same 40 pass under ASan/UBSan with MLAS and SIMD disabled. The workspace regression test fails against the baseline library.
- 56 ONNX baseline/patch comparisons are byte-identical, covering sequence lengths, 1/6 threads, static and dynamic weights, masks, and RoPE. Four of these force `mlasFlashAttention` to return false after dispatch to exercise fallback from a compact workspace plan.
- Reusing a graph across changing sequence lengths passes 40 comparisons against fresh graphs on each version. A separate layer-level matrix adds 144 byte-identical comparisons.
- The existing Attention perf fixture passes all five cases at 1 and 6 threads with `--perf_verify_sanity=true` and no regression data files. This includes new constant-weight cases at 257, 1,024, and 4,096 tokens. Separate longer pinned timing runs measured approximately +0.5% at 257 tokens and -1.1% at 1,024 tokens; timings vary on this shared host, so this PR makes no speedup claim.
The half-input audit passed six configurations that the graph promotes to FP32. Static, unmasked FP16 constants exposed a separate existing weight-packing issue: the baseline already produces invalid outputs, and ASan reports an out-of-bounds read in unchanged packing code. Those cases are excluded from passing validation. ARM and other platforms still need CI.
This follows the MLAS integration in #29126. #30126 addresses a different source of memory use (weight ownership), but touches the same class. `AttentionOnnxAiLayerImpl` is outside this change.
### Pull Request Readiness Checklist
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- [x] The PR targets the proper branch (`5.x`).
- [x] Related work is referenced above; the reproducer and measurements describe the bug.
- [x] Accuracy and performance tests are included. They generate their inputs and require no new `opencv_extra` data.
- [x] No public API changes or new samples requiring documentation.
core/dnn: resolve GCC 16 -Wmaybe-uninitialized and -Warray-bounds warnings - #30308
- core(nd_copy): ensure copyBatch only executes runPlans when np > 0.
- dnn(test): add reserve(3) to input vector to quiet compiler bounds checks.
### Pull Request Readiness Checklist
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When compiling with GCC 16.2.0, I encountered the following error:
modules/core/include/opencv2/core/hal/intrin_forward.hpp:204:49: error: 'INT_MAX' was not declared in this scope
204 | SV32 sat = v_xor(v_shr<31>(hi), SETALL_S32(INT_MAX)); \
core: accept both float->int saturation results in OCL ConvertToSaturate test - #30448
Fixes the `macOS-x64 / BuildAndTest` failure from #30386, which makes 5.x macOS-x64 CI red for every PR (e.g. #30302):
```
[ RUN ] OCL_MatrixOperation/ConvertToSaturate.Accuracy/16, where GetParam() = (CV_32F, CV_32S)
test_matrix_operation.cpp:247: Failure
cv::countNonZero(diff)
Which is: 2
```
INT_MAX isn't representable in float, so a saturated `CV_32F -> CV_32S` result can legitimately be either value:
- x86 CPU (`v_round` / `cvRound`) clamps to `CV__FLT2INT_MAX_F` = 2147483520 before converting (since #30007).
- OpenCL `convert_int_sat_rte` and NEON `vcvtnq_s32_f32` give INT_MAX = 2147483647.
So on an x86 host the out-of-range inputs `3e9` and `1e20` differ by 127 between `Mat` and `UMat`. On Apple M1 both sides give INT_MAX, which is why it only shows up on macOS-x64.
For this pair the test now clamps both results to 2147483520 before the exact comparison, i.e. it accepts any value in [2147483520, INT_MAX] as saturated. In-range values and all other depth pairs are still compared exactly.
**Testing** (Apple M1 Pro, OpenCL on; native arm64 build and an x86_64 build run under Rosetta 2)
`Mat::convertTo` vs `UMat::convertTo` for `CV_32F -> CV_32S`:
| input | arm64 CPU | x86_64 CPU | OpenCL |
|---|---|---|---|
| 3e9 | 2147483647 | 2147483520 | 2147483647 |
| 1e20 | 2147483647 | 2147483520 | 2147483647 |
The x86_64 build reproduces the CI mismatch. With this change, on both builds:
- `opencv_test_core --gtest_filter=*ConvertToSaturate*`: 135 passed.
- `opencv_test_core --gtest_filter=OCL_*:*ConvertTo*:*convertTo*:*Core_ConvertScale*`: 7549 passed.
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dnn: enable vendored MLAS on Windows ARM64 with MSVC - #30332
Close https://github.com/opencv/opencv/issues/30330
After https://github.com/opencv/opencv/pull/30331
### Summary
Enable OpenCV DNN’s vendored MLAS implementation on Windows ARM64 using Microsoft’s C++ compiler (`cl.exe`).
This change builds on the separate MLAS target architecture detection fix.
MLAS is currently disabled on all Windows targets. Enabling ARM64 requires more than removing that restriction: the GNU-style `.S` sources cannot be used by the MSVC toolchain, and the vendored Windows sources lack the SGEMV kernel used by the single-row fast path.
This change:
- Adds the Windows ARM64 SGEMV kernel from the same ONNX Runtime revision as the existing vendored sources.
- Preprocesses the native SGEMM and SGEMV `.asm` files with `cl.exe`, then assembles them with `armasm64`.
- Includes the generated assembly objects in `opencv_dnn`, with configuration-specific output paths.
- Enables MLAS only for Microsoft `cl.exe` targeting ARM64. Other Windows toolchains and targets remain disabled.
- Adds GEMM and SDPA accuracy coverage and documents the Windows build requirements.
The implementation uses the existing upstream `MlasHGemmSupported` probe. No additional HGEMM or FP16 workaround is introduced.
### Validation
On Windows ARM64, based on upstream commit `3074f64`:
- Release and Debug DNN builds linked successfully.
- MLAS and both NEON assembly kernels were enabled.
- All **116 selected tests passed in each configuration**, covering GEMM, single-row GEMV through FlashAttention, transposition, odd-sized tails, and accumulation across FlashAttention blocks.
- CMake guard checks confirmed unsupported Windows compiler/target combinations remain disabled.
A complete Python wheel has not been rebuilt, and performance benchmarks have not been run.
### Related work
- [[opencv/opencv#29343](https://github.com/opencv/opencv/pull/29343)](https://github.com/opencv/opencv/pull/29343): temporary blanket Windows disable; this PR enables only the supported MSVC ARM64 configuration.
- [[opencv/opencv#29885](https://github.com/opencv/opencv/issues/29885)](https://github.com/opencv/opencv/issues/29885): related Windows x86/x64 and clang-cl problems, outside this PR’s scope.
- [[opencv/opencv#29362](https://github.com/opencv/opencv/pull/29362)](https://github.com/opencv/opencv/pull/29362): existing HGEMM capability-probe fix, retained unchanged.
### Pull Request Readiness Checklist
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Remove Tensorflow Importer - #29474
[PR OpenCV Extra](https://github.com/opencv/opencv_extra/pull/1390)
[PR OpenCV Contrib](https://github.com/opencv/opencv_contrib/pull/4171)
Removed tensorflow importers
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fix(js): restore deep copy behavior of Mat.clone() - #30433
## Description
Fixes#30408.
With Emscripten 4+, the existing `onRuntimeInitialized` callback can be registered after runtime initialization has already completed. As a result, the override of Emscripten's shallow `ClassHandle.clone()` is never applied, causing `cv.Mat.clone()` to perform a shallow copy instead of the expected deep copy.
This PR applies the `mat_clone` override immediately when `cv.Mat` is available, while retaining the initialization callback as a fallback.
## Changes
- Restore deep-copy behavior for `cv.Mat.clone()`.
- Preserve the existing `onRuntimeInitialized` callback.
- Add regression tests for matrix data independence and cloning non-contiguous ROIs.
## Testing
Environment:
- OpenCV 5.x
- Emscripten 6.0.12
- Node.js 22.23.3
Results:
- Confirmed the original shallow-copy behavior before the fix.
- Verified deep-copy behavior after the fix.
- Verified that cloning a non-contiguous ROI produces independent, continuous data.
- `CoreMat.test_mat_creation`: 91/91 assertions passed.
- Full OpenCV.js test suite: 414 passed, 8 failed.
The remaining eight failures involve heap-memory views and unavailable JavaScript bindings. These failures have not yet been verified against an unmodified baseline.
Move v_pack_trunc down after v_shr is declared. Otherwise, the error is:
error: use of undeclared identifier 'v_shr'
474 | OPENCV_HAL_IMPL_SATURATING_PACK_64(v_int64x2, v_uint64x2, v_int32x4, v_uint32x4, v_setall_s32, v_pack_trunc, v_pack_trunc)
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
OpenCV/core/hal/intrin_forward.hpp:202:28: note: expanded from macro 'OPENCV_HAL_IMPL_SATURATING_PACK_64'
202 | SV32 hi = PACK_TRUNC_S(v_shr<32>(a), v_shr<32>(b)); \
core: interleave SIMD pow math and combine masks 🤖🤖🤖 - #30325
Partially addresses #30324.
Interleave the two independent SIMD log/exp chains used by fractional `pow` and combine their nonpositive-mask reductions. Arithmetic, scalar fallback, load widths, and alias-tail guards are preserved.
On Apple M4 Max / AppleClang 21 / one thread, the RGB float32 reproduction takes **3.308 → 2.901 ms** for `p=1.5` and **3.324 → 2.956 ms** for `p=1.4` (before → after). This recovers part of the 5.x regression; parity with 5.0.0 is not established.
Validation: 404 accuracy tests and 36 public performance cases pass; 84 native before/after cases are bitwise identical. Half performance outputs are converted before sanity checking. Linux performance remains unmeasured.
### Pull Request Readiness Checklist
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5622958189 replaced pow() with std::pow() in three OpenCL C kernel
files. OpenCL C is a C99-derived language and has no std namespace, so
these kernels fail to compile at runtime on the GPU (e.g.
CL_BUILD_PROGRAM_FAILURE: use of undeclared identifier 'std') and dnn
silently falls back to CPU for the affected activations, LRN and fused
conv layers. Revert to the plain built-in pow() in the kernels.
isScalarLikeMat(sc, cn) previously returned true whenever rows == 1 and
cols == 1 (as long as nval <= 4), without checking whether nval matches
1 or the other operand's channel count cn.
As a result, a 1x1 multi-channel Mat (e.g. 1x1 CV_32FC3, nval == 3) was
mistakenly treated as a scalar for a different channel count (e.g.
cn == 1 when multiplied by a cv::Scalar converted to a 4x1 CV_64FC1 Mat
in Mat::mul). In arithm_op, this caused s1 and s2 to both be true,
demoting the actual scalar operand s2 to false and broadcasting the
operands into a 4x1 CV_32FC3 result instead of a 1x1 CV_32FC3 result.
Require nval == 1 || nval == cn in isScalarLikeMat so that a 1x1 Mat is
only treated as scalar-like when its total element count is compatible
with the target channel count.
dnn: faster ConvTranspose2 on CPU - #30396
Merge pull request #30395 from uwezkhan:onnx-qconv-scale-bias-bound
- With MLAS, every fp32 ConvTranspose2 runs as a packed GEMM per group, followed by a col2im that adds each kernel tap into the output. Threads own bands of output rows, so col2im needs no locking. When taps overlap, output channels are processed in chunks so the column buffer stays in cache. When kernel == stride, each output pixel gets one tap and value + bias is written directly.
- conv2_deconv is now CPU-dispatched (AVX, AVX2, NEON) and uses four accumulators, so the FMAs no longer wait on each other. It is still used when MLAS is not available or the input is not fp32.
- Attention accepts [..., S, C] and treats the leading dims as batch; the SAM3 decoder feeds it a 4D input and failed before.
opencv_extra: https://github.com/opencv/opencv_extra/pull/1442 (test models for the single-tap path, channel chunks, and groups that start inside a channel block)
Measured on an i9-14900KS, times in ms.
**Whole decoder**
| Model | Threads | 5.x | This PR | ORT 1.30 |
|---|---:|---:|---:|---:|
| SAM2 decoder | 1 | 68.5 | **36.8** | 36.5 |
| SAM2 decoder | 8 | 14.0 | **9.7** | 8.2 |
| SAM2 decoder | 32 | 12.8 | **10.5** | 9.8 |
| SAM3 decoder | 1 | 87.9* | **51.1** | 47.1 |
| SAM3 decoder | 8 | 19.0* | **14.1** | 12.6 |
| SAM3 decoder | 32 | 16.4* | **14.4** | 13.0 |
\* SAM3 does not run on 5.x; this column includes the Attention change from this PR.
Best of several interleaved rounds, measured on the first commit. The col2im commit leaves the SAM ConvTranspose layers within a few percent.
**ConvTranspose layer**
| Layer | Threads | 5.x | This PR | ORT 1.30 |
|---|---:|---:|---:|---:|
| SAM2 256→64 ch, 2×2 s2, 64×64 | 1 | 24.0 | **3.35** | 3.54 |
| | 8 | 3.30 | **0.59** | 0.92 |
| | 32 | 1.53 | **0.40** | 0.95 |
| SAM2 64→32 ch, 2×2 s2, 128×128 | 1 | 13.2 | **2.10** | 2.52 |
| | 8 | 1.76 | **0.31** | 1.15 |
| | 32 | 0.85 | **0.24** | 1.88 |
| SAM3 256→64 ch, 2×2 s2, 72×72 | 1 | 27.4 | **4.25** | 4.50 |
| | 8 | 3.85 | **0.59** | 1.16 |
| | 32 | 1.88 | **0.45** | 1.22 |
| SAM3 64→32 ch, 2×2 s2, 144×144 | 1 | 15.5 | **2.72** | 3.41 |
| | 8 | 2.08 | **0.41** | 1.44 |
| | 32 | 1.00 | **0.32** | 1.62 |
| U-Net 256→128 ch, 4×4 s2 p1, 32×32 | 1 | 46.6 | **7.63** | 6.70 |
| | 8 | 6.49 | **1.19** | 1.45 |
| | 32 | 4.50 | **0.97** | 1.68 |
| GAN 128→64 ch, 4×4 s2 p1, 64×64 | 1 | 50.1 | **8.57** | 7.51 |
| | 8 | 7.01 | **1.37** | 2.22 |
| | 32 | 4.28 | **1.16** | 2.53 |
| 128→64 ch, 3×3 s2 p1 op1, 64×64 | 1 | 28.3 | **4.70** | 4.24 |
| | 8 | 3.95 | **0.76** | 1.31 |
| | 32 | 1.79 | **0.66** | 1.75 |
| 64→64 ch, 3×3 s1 p1, 64×64 | 1 | 13.4 | **2.52** | 2.35 |
| | 8 | 1.91 | **0.45** | 0.97 |
| | 32 | 0.80 | **0.35** | 0.98 |
| 128→128 ch, 4 groups, 4×4 s2 p1, 32×32 | 1 | 6.73 | **1.65** | 1.30 |
| | 8 | 0.97 | **0.26** | 0.73 |
| | 32 | 0.44 | **0.21** | 1.00 |
| 64→64 ch, depthwise, 4×4 s2 p1, 64×64 | 1 | 30.1 | **7.54** | 1.54 |
| | 8 | 4.21 | **2.51** | 1.89 |
| | 32 | 1.92 | **1.96** | 2.77 |
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
dnn: bound per-channel w_scale to bias length in onnx parseQConv 🤖🤖🤖 - #30395
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1445
`QLinearConv` import folds a constant int32 bias to float in `parseQConv` (`modules/dnn/src/onnx/onnx_importer2.cpp`): each bias element is multiplied by its per-channel weight scale and written into `bias_float`, which is sized from the bias tensor. The fold loop runs over the length of `w_scale`, but `w_scale` and the bias are independent inputs of the model, so a 1-D `w_scale` longer than the bias writes past `bias_float`. It is reached from `readNetFromONNX` on any `.onnx` carrying such a `QLinearConv`, and the scalar `x_scale` read is likewise unguarded against an empty tensor.
ASan on the default engine loading a model whose bias has one element and whose per-channel `w_scale` has 64:
```
==ERROR: AddressSanitizer: heap-buffer-overflow ... READ of size 4
#0 ONNXImporter2::parseQConv(...)
#1 ONNXImporter2::parseNode(...)
#2 ONNXImporter2::parseGraph(...)
#5 readNetFromONNX2(...)
#6 readNetFromONNX(...)
0x... is located 0 bytes after 4-byte region allocated by Mat::convertTo in parseQConv
```
Require the per-channel `w_scale` to hold one entry per bias element before the loop, the per-axis quantization invariant that pairs one scale and one bias with each output channel, and check the scalar `x_scale` is non-empty before the read. A well-formed model is unaffected; a mismatched one now throws from the importer and the net loads empty instead of overrunning the buffer.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
js: bind the mcc classes under their real 5.0 names (#30409) - #30428Fixes#30409.
The JS whitelist still names the pre-5.0 mcc classes (`mcc_CheckerDetector`, `mcc_DetectorParameters`, `mcc_Checker`), but the classes now live in `modules/objdetect` as `mcc::CCheckerDetector`, `mcc::DetectorParametersMCC` and `mcc::CChecker` (`mcc_checker_detector.hpp`). The generator skips unmatched entries silently, so a default opencv.js build exports only the `mcc_ColorChart` enum.
Changes, following the working entry set from the issue:
- `platforms/js/opencv_js.config.py` and `modules/objdetect/misc/js/gen_dict.json` (both whitelist sources, kept identical): rename the three entries to the real class names and add `processWithROI` (the `CV_WRAP_AS` alias) and `CChecker::create`.
- Per the issue's findings, `getDetectionParams()` is left out (its `const DetectorParametersMCC&` return breaks the generated `select_overload` signature) and `getListColorChecker()`/`draw()` are left out (`std::vector<Ptr<CChecker>>` has no Embind registration, so they fail when called).
- `modules/js/src/core_bindings.cpp`: `using namespace cv::mcc;` in the objdetect block (next to `using namespace cv::aruco;`) and `typedef dnn::Net dnn_Net;` in the dnn block (for the `create(const dnn::Net&)` overload), mirroring the existing `aruco_DetectorParameters` / `QRCodeDetectorAruco_Params` typedef pattern.
Verification: every whitelisted method name was checked against `mcc_checker_detector.hpp` (all found; `processWithROI` is the `CV_WRAP_AS` alias of `process` at line 207). `gen_dict.json` parses and `opencv_js.config.py` compiles. A full `build_js.py --build_wasm` run needs Emscripten and is left to CI; the entry set itself is the one validated end-to-end against a downstream build in the issue (synthetic MCC24 chart detected, `getChartsRGB()` exact).
getAbsdiffFunc_() documents that a signed depth has two outputs: R == T, the saturating |a-b| kept
in the signed range, which is the depth cv::absdiff returns; and R == the unsigned type of the same
width. CV_8S, CV_16S and CV_32S each have both. CV_64S only had the unsigned one, so the signed
form fell through to a path that wraps:
absdiff(INT64_MIN, 0) -> -9223372036854775808
absdiff(INT64_MIN, INT64_MAX) -> -1
at every length, scalar included, in a CV_64S destination. The narrower depths all saturate at
their maximum: absdiff(INT_MIN, INT_MAX) on CV_32S is INT_MAX.
The missing row now uses EwAbsdiffS, whose scalar step gains a 64-bit overload. The generic one
widens into the work type, which the narrower depths have (short, int, int64_t) and 64-bit does not,
and |INT64_MIN - b| does not fit in int64_t anyway. The difference is therefore taken in uint64,
which is exact for every pair, and clamped to INT64_MAX. The narrower depths reach that overload
only through their int64 work type, where |a - b| is at most 2^32 and the clamp is a no-op.
Core_AbsdiffSigned checks both properties - never negative, and saturating at the depth's maximum -
across CV_8S, CV_16S, CV_32S and CV_64S.
The tracker normalizes the images it feeds to the network with Image2BlobParams, whose
per-channel factor has to be 1/(255*std). It was computed as `(1.0 / stdvalue) * (1 /
255.0)`, but `double / Scalar` is Scalar's quaternion inverse, not an elementwise division,
and `Scalar / Scalar` is defined through that same inverse. The last three elements were
therefore conjugated and the whole result divided by their sum of squares, so with the
default std = (0.229, 0.224, 0.225) the green and red planes reached the network negated and
all three were about a third of the intended magnitude.
Build the factor channel by channel in both create() overloads, and cover it with a test that
compares the response of the network the tracker feeds against an identical network fed the
documented normalization of the same template crop.
vectorized and parallel DFT/DCT + matchTemplate - #30061
(Made with a help from Claude Code Fable 5.1)
## Summary
This PR replaces the scalar 1D DFT/DCT kernels in `modules/core/src/dxt.cpp` with a new engine
built on universal intrinsics and compiled per ISA through `ocv_add_dispatched_file`, and makes
the 2D transforms, the `DFT_ROWS`/`DCT_ROWS` batches and `matchTemplate` (`crossCorr`) multi-threaded.
The public API, the output formats (CCS packing of real spectra, `DFT_COMPLEX_OUTPUT` layout,
DCT ordering and normalization) and the IPP/OpenCL paths are unchanged. Only floating-point
rounding may differ (the new twiddle tables are more accurate than the old recurrence-generated
ones, so the error goes down, not up).
Measured on Apple M4 Max (16 cores, NEON), macOS 26.5.x, Xcode 26.6, Release build
(`-O3`, no IPP). All numbers are the minimum over repeated runs, `5.x` vs this PR.
## Changes
### New 1D engine (`dxt.hpp`, `dxt.simd.hpp`, `dxt.cpp`)
- **Split re/im layout.** The input pass gathers the data into separate `re[]`/`im[]` workspace
arrays; all middle stages operate on the split arrays, so a complex multiplication is four
independent vector FMAs with no lane shuffling. The output pass interleaves back (or packs the
CCS / DCT layout) with the scaling fused in.
- **Mixed-radix decimation-in-time with a fused first stage.** The plan chooses the first stage
(radix 1/2/4/8, twiddle-free, fused with the gather and the digit-reversal permutation) from
the power of two in the length, then radix-4 stages, then radix-3, radix-5 and a generic odd
radix. There are no radix-2 middle stages.
- **Vectorized butterflies with universal intrinsics** (`v_float32`/`v_float64`, VLA-safe): one
flat loop per stage with a single clamped counter for both the data and the twiddle offsets
(the back-off clamp handles spans that are not multiples of the vector width; such stages run
out of place, ping-ponging between two workspace pairs, the others run in place).
- **Vectorized first stage.** The radix-2/4/8 first stage reads the input columns contiguously
(`v_load_deinterleave`), runs the small DFT in registers and scatters the outputs of every
lane with one 4-channel interleaved store. Only odd lengths use a scalar table-driven gather.
- **Vectorized edge passes**: interleave/deinterleave, the real-FFT untangling in both directions
(with `v_reverse` for the mirrored half), DCT-II post-twiddle and DCT-III pre-twiddle, the
Makhoul permutations.
- **Inverse = conjugation trick**: the middle stages are direction-free and use forward twiddles
only; the inverse conjugates on the gather and negates the imaginary scale on the output.
- **Immutable plan + per-executor workspace, zero allocations per transform.** The plan
(radix sequence, permutation table, per-stage split twiddle tables) is built once in `init()`
and shared by all rows of a batch; every executor owns a workspace. Twiddles are generated in
double precision from a two-level exact table (a few ulps, much cheaper to build than the old
N-point recurrence, which matters because `cv::dft()` builds a plan on every call).
- **Per-ISA dispatch**: `ocv_add_dispatched_file(dxt SSE2 AVX2 AVX512_ICL RVV)`; the kernels are
reached through a function-pointer table resolved once per depth, following the pattern of
`arithm.simd.hpp` / `warp_kernels.simd.hpp`. The plan is built with the vector width of the
dispatched kernels (never with the baseline `VTraits`).
- The legacy scalar engine, `DFTInit`, the SSE3 `DFT_VecR*` butterflies and the `noPermute`/
`factors[0] >>= 1` machinery are removed (−1400 lines). The legacy `cv_hal_dft` hook (which
took the old tables) is no longer called from the 1D path; the in-tree RISC-V HAL
implementation of it will be updated in a separate PR.
### Parallelism
- `crossCorr` (`matchTemplate`): the correlation tiles are independent and are now distributed
with `parallel_for_`; every worker creates its DFT buffers and `DFT2D` contexts once per range.
(The tile loop had been sequential since the C++ rewrite in 2010, which dropped the OpenMP
pragma of `icvCrossCorr`.)
- 2D DFT/DCT and `DFT_ROWS`/`DCT_ROWS` batches: the row pass, the column pass and the DCT line
loop distribute their lines over threads; the 1D plan is shared, each worker allocates its
workspace once per range. The number of stripes is derived from the N log N cost, so a 256x256
transform stays on the calling thread and 1024x1024 gets ~20 stripes. Nested calls (the 2D DFTs
inside the parallel `crossCorr` tiles) are serialized by `parallel_for_` itself.
- The column pass copies columns in batches (`CopyFromColumns`/`CopyToColumns`, 4..16 adjacent
columns copied row by row into contiguous buffers sized to stay within 128 KB) instead of two
at a time: with power-of-two row steps the old strided copies aliased in the cache and took
60-70% of the 2D time.
### Tests
- `Core_DFT.sweep_1d`, `Core_DCT.sweep_1d`: every 5-smooth length up to 2048 plus odd, prime and
"awkward" lengths, both depths, C2C forward/inverse, R2C (CCS and complex output), C2R (CCS and
complex input), DCT/IDCT, in-place, column vectors, round trips; compared against the naive
O(N^2) references of `test_dxt.cpp` with a tolerance of `32*eps*(log2(N) + 2 + maxfactor/16)`.
- `Core_DFT.sweep_rows`: `DFT_ROWS` batches sharing one plan.
- `Core_DFT.nonzero_rows`: 2D transforms with `nonzero_rows` (previously uncovered).
- `Core_DFT.parallel_large`: large 2D / rows / DCT transforms, multi-threaded result vs the
single-threaded one.
- The naive DCT reference reduces the angle exactly before `cos()` (its accuracy was limiting
the comparison for n > 100 in double precision).
- Perf: `perf_dft.cpp` gains 1D row/column cases for `dft` and `dct`.
- Existing `opencv_test_core` (incl. `Core_DFT*`, `Core_DCT*`, `Core_MulSpectrums`,
`Core_DivSpectrums`) and `opencv_test_imgproc` (phaseCorrelate, matchTemplate, filter2D) pass;
the x86-64 build (SSE3 baseline + SSE4.1/AVX2/AVX512_ICL dispatch) passes the DFT/DCT tests
under Rosetta 2 (AVX2/AVX-512 paths compile-checked only on this machine).
## Performance
### 1D transforms (single row, one thread), microseconds
`C2C` = complex-to-complex, `R2C` = real-to-CCS, `C2R` = CCS-to-real (inverse, scaled),
`32`/`64` = float/double.
| n | C2C32 | C2C64 | R2C32 | C2R32 | R2C64 | DCT32 | IDCT32 | DCT64 |
|---|---|---|---|---|---|---|---|---|
| 256 | 0.82 → 0.55 | 0.86 → 0.74 | 0.67 → 0.53 | 0.63 → 0.52 | 0.64 → 0.56 | 0.88 → 0.56 | 0.88 → 0.56 | 0.90 → 0.66 |
| 1000 | 3.84 → 1.70 | 4.08 → 2.58 | 2.86 → 1.36 | 2.88 → 1.38 | 2.77 → 1.74 | 3.95 → 1.69 | 4.02 → 1.72 | 3.97 → 2.15 |
| 1024 | 3.25 → 1.65 | 3.44 → 2.40 | 2.46 → 1.23 | 2.35 → 1.27 | 2.42 → 1.59 | 3.57 → 1.57 | 3.51 → 1.59 | 3.45 → 2.02 |
| 4096 | 14.9 → 6.4 | 16.4 → 11.5 | 10.7 → 4.3 | 10.2 → 4.5 | 11.0 → 6.9 | 15.4 → 6.0 | 15.3 → 6.0 | 17.0 → 9.5 |
| 4725 (3³·5²·7) | 27.5 → 17.2 | 29.4 → 24.1 | 28.2 → 16.4 | 29.8 → 18.3 | 30.3 → 24.7 | – | – | – |
| 65536 | 350 → 273 | 411 → 494 | 223 → 136 | 274 → 139 | 251 → 249 | 318 → 167 | 299 → 159 | 372 → 320 |
Each `cv::dft()` call builds its plan anew; at n = 4096 the plan takes ~1.9 µs of the 6.4 µs
(the old code spent ~4 µs of 14.9 on its tables). For n = 65536 both versions are bound by the
memory traffic of the out-of-cache data, and C2C64 gets slightly slower (the split layout needs
one more pass over 1 MB of data; a plan cache or larger radices could recover this later).
### 2D transforms, single thread, microseconds
| size | C2C32 | R2C32 | C2R32 | R2C64 | DCT32 | IDCT32 | R2C32 rows |
|---|---|---|---|---|---|---|---|
| 256×256 | 297 → 140 | 162 → 75 | 165 → 77 | 178 → 126 | 259 → 166 | 290 → 174 | 72 → 29 |
| 512×512 | 1660 → 569 | 765 → 306 | 723 → 317 | 896 → 551 | 1381 → 946 | 1377 → 957 | 303 → 125 |
| 480×640 | 1555 → 715 | 895 → 377 | 918 → 392 | 978 → 677 | 1248 → 707 | 1262 → 695 | 389 → 147 |
| 1024×1024 | 7508 → 2625 | 3781 → 1387 | 3883 → 1398 | 4964 → 3201 | 6438 → 4556 | 6805 → 4583 | 1341 → 516 |
| 1080×1920 | 13141 → 5543 | 7143 → 2880 | 7269 → 2970 | 7770 → 5348 | 9735 → 5190 | 9477 → 5381 | 3116 → 1192 |
| 2048×2048 | 38151 → 16822 | 18381 → 6973 | 18784 → 7294 | 25146 → 17476 | 30291 → 21631 | 30540 → 22003 | 5809 → 2242 |
### 2D transforms, default thread count (16), microseconds
| size | C2C32 | R2C32 | C2R32 | R2C64 | DCT32 | IDCT32 | R2C32 rows |
|---|---|---|---|---|---|---|---|
| 256×256 | 309 → 138 | 157 → 75 | 161 → 77 | 174 → 126 | 265 → 167 | 293 → 170 | 71 → 29 |
| 512×512 | 1660 → 162 | 785 → 155 | 788 → 160 | 866 → 256 | 1410 → 235 | 1366 → 228 | 303 → 41 |
| 480×640 | 1531 → 178 | 895 → 142 | 916 → 141 | 957 → 222 | 1241 → 172 | 1248 → 173 | 393 → 46 |
| 1024×1024 | 7495 → 381 | 3755 → 274 | 3767 → 269 | 4875 → 478 | 6323 → 650 | 6583 → 630 | 1343 → 94 |
| 1080×1920 | 12905 → 681 | 7131 → 403 | 7240 → 382 | 7760 → 657 | 9673 → 605 | 9513 → 607 | 3124 → 143 |
| 2048×2048 | 38155 → 1481 | 18324 → 826 | 18559 → 857 | 24861 → 1776 | 30120 → 2592 | 30741 → 2734 | 5890 → 274 |
(5.x is single-threaded for all of these; 256×256 stays single-threaded in the new code by design.)
### matchTemplate (`opencv_perf_imgproc`, `*matchTemplate*`), milliseconds
Whole suite (156 cases): 921 → 284 ms. Selected cases (8-bit image, 16×16 template unless noted):
| image, template | SQDIFF | SQDIFF_NORMED | CCORR | CCORR_NORMED | CCOEFF | CCOEFF_NORMED |
|---|---|---|---|---|---|---|
| 128×128 | 0.15 → 0.12 | 0.14 → 0.12 | 0.09 → 0.05 | 0.13 → 0.09 | 0.11 → 0.08 | 0.13 → 0.10 |
| 320×240 | 0.99 → 0.52 | 1.09 → 0.58 | 0.79 → 0.23 | 0.96 → 0.42 | 0.93 → 0.38 | 1.02 → 0.53 |
| 640×480 | 3.19 → 1.17 | 3.35 → 1.22 | 2.27 → 0.29 | 3.12 → 1.04 | 2.94 → 0.90 | 3.43 → 1.38 |
| 800×600 | 5.86 → 1.64 | 5.61 → 1.77 | 4.11 → 0.34 | 5.48 → 1.53 | 5.19 → 1.28 | 5.82 → 2.04 |
| 1024×768 | 9.73 → 2.72 | 9.59 → 2.90 | 6.88 → 0.48 | 9.11 → 2.45 | 8.44 → 2.05 | 9.84 → 3.44 |
| 1280×1024 | 14.92 → 4.33 | 15.01 → 4.58 | 10.70 → 0.64 | 14.31 → 3.97 | 13.40 → 3.33 | 15.37 → 5.58 |
| 1280×1024, 1260×1000 | 13.06 → 7.90 | 13.08 → 7.74 | 10.75 → 3.58 | 12.23 → 5.03 | 11.11 → 3.94 | 12.03 → 5.05 |
`TM_CCORR` is now dominated by the (still single-threaded) `integral()` and normalization passes
in the other methods; those are candidates for a follow-up.
## Notes for reviewers
- `modules/core/src/dxt.hpp` documents the engine layout; the plan builder (`DftPlan::build`)
and the driver (`runDft`) are in `dxt.cpp`, the kernels in `dxt.simd.hpp`.
- Stages whose span is smaller than the vector width run scalar (on NEON this only affects
lengths of the form 2·odd; on AVX2/AVX-512 also the first radix-4 stage of 4^k lengths);
handling them with narrower fixed-width vectors is a possible follow-up.
- A radix-8 middle stage was implemented and measured slower on NEON than two radix-4 stages
(register pressure), so it is not included.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
video(TrackerNano): fix target scale and penalty calculation - #30353
This PR fixes an algorithmic bug and two precision issues in `cv::TrackerNanoImpl::update`:
Resolves#27190
### 1. Fix scale penalty calculation (`targetPos` -> `targetSz`)
In the Siamese tracking scale penalty calculation:
```cpp
// BEFORE (Bug):
Mat sc = sizeCal(predX2 - predX1, predY2 - predY1)/sizeCal(targetPos[0], targetPos[1]);
// AFTER (Fix):
Mat sc = sizeCal(predX2 - predX1, predY2 - predY1)/sizeCal(targetSz[0], targetSz[1]);
```
`sizeCal` takes `(width, height)`, but `targetPos` holds the target's center pixel coordinates `(x, y)` in the full frame. Passing `targetPos` normalizes candidate scale by screen position rather than object size, breaking translation invariance and heavily dampening candidate scores as the target translates across the frame.
Furthermore, `targetSz` is explicitly multiplied by `scale_z` before this calculation (line 252) and divided back afterwards (line 323). The only reason `targetSz` was brought into the search patch coordinate frame was for this exact line. Passing `targetPos` was apparently a typo.
### 2. Fix integer division in search crop scaling
```cpp
// BEFORE: instanceSize (255) and exemplarSize (127) are const int
float sx = sz * (instanceSize / exemplarSize); // Evaluates to sz * 2 instead of 2.007874f
// AFTER:
float sx = sz * (float(instanceSize) / exemplarSize);
```
### 3. Preserve subpixel floating-point precision in `targetSzSum`
Removed the `(int)` truncation from `targetSzSum` in `update()`:
```cpp
// BEFORE:
int targetSzSum = (int)(targetSz[0] + targetSz[1]);
// AFTER:
float targetSzSum = targetSz[0] + targetSz[1];
```
---
### Verification and Test Results
Tested on a sequence where an object expands from 60x60 to 138x138 across 40 frames while translating across the frame:
* **Before Fix (`targetPos`):**
* Final Tracked Size: `133x131`
* Final Area Ratio (Tracked / Ground Truth): **0.915** (persistent size lag)
core: saturate OpenCL conversions to CV_32U, CV_64U and CV_64S - #30386Fixes#30385
### Problem
`ocl::convertTypeStr()` uses `ddepth >= CV_32F` / `sdepth >= CV_32F` to mean "is a float type". The 5.x integer depths are numbered above `CV_32F`, so conversions to `CV_32U`, `CV_64U` and `CV_64S` are emitted without `_sat` (and without `_rte` from float sources). `UMat::convertTo` and other OpenCL kernels using `convertTypeStr()` then wrap around instead of saturating, e.g. `-5` → `CV_32U` gives `4294967291` instead of `0`.
### Fix
In `convertTypeStr()`:
- use `CV_IS_FLOAT_TYPE(ddepth)` / `CV_IS_FLOAT_TYPE(sdepth)` instead of comparing with `CV_32F`;
- for float → integer, always use `_sat_rte`, matching the CPU's saturating `cvRound` since #30007 (was: `_sat` only below `CV_32S`).
This also fixes `CV_64S`/`CV_64U`/`CV_32U` → `CV_32S` in `UMat::convertTo`, which converts through that float → `CV_32S` path. Conversions between the 4.x depths are otherwise unchanged.
### Tests
New `OCL_MatrixOperation/ConvertToSaturate.Accuracy` compares `UMat::convertTo` with `Mat::convertTo` in the destination type for `CV_8S`/`CV_16S`/`CV_32S`/`CV_64S`/`CV_32F` → `CV_32S`/`CV_32U`/`CV_64U`/`CV_64S`, with values that need clamping or rounding. On current 5.x it fails for 10 of the pairs; with this patch it passes. All `OCL_*` and `convertTo` tests in `opencv_test_core` pass locally (7522 tests).
`CV_64U` sources are left out for now: the CPU path for `CV_64U` → `CV_64S` is a bit copy until #30206 lands.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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Port of #30403 to 5.x. PoseSolver::rot2vec reads the rotation axis
from R - R^T and divides by sin(theta). Both vanish when the rotation
is close to 180 degrees, which is the pose of a square tag seen
exactly head-on, so SOLVEPNP_IPPE_SQUARE returned a pose flipped by
180 degrees. Fall back to Rodrigues in that case and clamp the acos
argument. The fast path for all other rotations is unchanged.
objdetect: default CCheckerDetector chart type to MCC24 - #30412Fixes#30410.
`CCheckerDetectorImpl::m_chartType` had no initializer and the constructor is empty, so a detector from `CCheckerDetector::create()` read an indeterminate chart type unless `setColorChartType()` was called first. It now defaults to `MCC24`, the first `ColorChart` value. With `operator new` filling memory with 0xFF, 5.x reports chart type -1 and `process()` fails on `mcc/MCC24.png`; with the fix it reports `MCC24` and finds the chart. Added `CV_mccRunCCheckerDetectorBasic.default_chart_type`, which checks the default and runs detection without setting the type.
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video: clip TrackerVit box to the image - #30394
Port of #30358 to 5.x, as requested there. Fixes#30350.
`TrackerVitImpl::update()` never bounds the predicted box to the frame, and that box sets the size of the next search crop, so it can keep growing past the image. The box is now clipped to the image after each update, the same way OSTrack does it (`clip_box(..., H, W, margin=10)`). The 5.x code is the same as 4.x, so the change is identical. Added `vittrack.bbox_inside_image`, which fails on 5.x without the fix (box `[498 x 358 from (-9, -6)]` on a 480x352 frame); `accuracy_vittrack` still passes.
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core: fall back to CPU for CV_16BF in OpenCL arithmetic - #30383Fixes#30382
### Problem
`arithm_op()` calls the OpenCL path directly and relies on it returning `false` when it can't run. `ocl_arithm_op()` and `ocl_binary_op()` don't check the depth and pass it to `ocl::typeToStr()`, which asserts for `CV_16BF`. So `add`, `subtract`, `multiply`, `divide`, `absdiff`, `min`, `max`, `addWeighted` and add-with-scalar on a `CV_16BF` `UMat` throw, while the same calls on a `Mat` work.
### Fix
In `modules/core/src/arithm.cpp`, add `noOclType()` for the depths that have no OpenCL type (`CV_16BF`, `CV_Bool`, `CV_8F_E4M3FN`, `CV_8F_E4M3FNUZ`) and return `false` for them:
- `ocl_arithm_op()`: if any of the source, destination or working depths has no OpenCL type.
- `ocl_binary_op()`: if the source depth has no OpenCL type and the op is not bitwise (bitwise ops use `memopTypeToStr()`, not `typeToStr()`).
The CPU path then handles them, as it already does for `Mat`. Same approach as #30252 for `threshold`.
### Tests
New `OCL_Arithm.bf16_falls_back_to_cpu` runs the 9 ops on `CV_16BF` `Mat` and `UMat` inputs and checks the results are identical. It fails on current 5.x with the `typeToStr` assertion and passes with this patch. All `OCL_Arithm*`, `Core_Arithm*`, the CPU add/sub/mul/div/min/max/absdiff/addWeighted tests and the `UMat` tests pass locally (7933 tests).
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dnn(cuda): move the CUDA backend to UMat device buffers - #29807
This PR is in continuation with #29658 and #30259
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dnn: speed up LSTM2 (gate math, input projection, recurrence) - #30309
Speeds up the ONNX LSTM layer (`LSTM2`) in `recurrent2_layers.cpp`:
- Default sigmoid/tanh gates are computed on raw rows with the dispatched activation kernels. Peephole, clip and custom activations keep the old path.
- The input projection runs before the per-direction loop, so the GEMM uses all threads. It goes through MLAS when available.
- Batch 1 (hidden size >= 128): hidden units are split across threads, with one `parallel_for_` per timestep.
- Batch > 1: one packed GEMM per step replaces one GEMV per row.
Ryzen 9 9950X, default threads, 5.x vs this PR:
| Model | 5.x, ms | PR, ms | x-factor |
|---|:-:|:-:|:-:|
| `DNNTestNetwork.CRNN` | 2.693 | 1.562 | 1.72 |
| CRNN EN 2021sep | 2.498 | 1.472 | 1.70 |
| crnn.onnx | 2.650 | 1.434 | 1.85 |
| LSTM2 bidirectional, batch 1, in 512, hidden 256, 24 steps (CRNN layer 1) | 0.710 | 0.212 | 3.36
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Fix code according to fuzzer findings - #30286
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Mat(const std::vector<_Tp>&, bool) in 5.x creates a 1D matrix
(dims == 1, rows == 1, cols == vec.size()), not a single column.
Update the doc to match the implementation and the Mat_ overload wording.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
CV_64U is the only unsigned depth whose range does not fit in the signed type of the same width, so
reading its bit pattern as signed goes unnoticed until the top bit is set. Three places did.
1. CV_64U -> CV_64S dispatched to cvt64s, a raw byte copy, so nothing saturated: 2^63 became
INT64_MIN and UINT64_MAX became -1, at every length. CV_32U -> CV_32S saturates, so this is now
a saturating conversion like the rest.
2. vx_load_pair_as(const uint64_t*, v_float64&, v_float64&) and
vx_load_as(const uint64_t*, v_float32&) both converted with v_cvt_f64(), which takes signed
lanes, so CV_64U to CV_64F, CV_32F, CV_16F and CV_16BF returned negative values above INT64_MAX.
Vector body only, so the same value was right at length 1 and wrong at length 16.
The unsigned conversion splits into two halves below 2^32: each converts exactly, scaling the high
half by 2^32 is exact, and the single add is the only rounding, so the result matches (double)x.
Converting the bit pattern as signed and adding 2^64 back would round twice - for 2^63 + 1025 that
gives 2^63 where (double)x is 2^63 + 2048.
Core_ConvertFrom64U covers the integer and floating-point destinations, with 2^63 + 1025 among the
values since it is the case that distinguishes one rounding from two.
x2 is computed from the disparity value and was used as an index into
disp2cost and disp2buf without a range check, in both the CV_16S and the
CV_32S cost branches. A disparity outside the range given by minDisparity and
numberOfDisparities made x2 negative or not less than cols.
Skip such pixels in the reverse mapping pass. The final consistency pass
already bounds-checks the mapped columns.
Ports #30316 to 5.x. Relates to #25367.
core: explicit UMat transfers for non OpenCL builds - #30259
## Summary
Core groundwork for moving the DNN CUDA backend from mapped `getMat()` to explicit host/device copies (related: #29807):
-`fitSameSize` keeps tensor layout, the missing `_OutputArray::fit*` overloads are defined, `UMat::create` reports a failing bound allocator, and cross-allocator `copyTo` no longer maps a device destination.
- OpenCL paths (`Kernel::set`, `isCLBuffer`, clFFT, `arithm_op`) now decline CUDA UMats instead of passing a CUDA pointer as `cl_mem`, which segfaulted with NVIDIA OpenCL.
- Adds a private `HostDeviceBuffer` (CUDA → OpenCL → CPU fallback) for the follow-up DNN PR, with tests for all of the above.
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elf_aux_info() returns non-zero on failure and leaves the output
buffer untouched. The ppc64 CPU feature detection reused one variable
for both AT_HWCAP and AT_HWCAP2, so if the AT_HWCAP2 lookup failed the
AT_HWCAP value was tested against the PPC_FEATURE2_* bits. Use a
separate variable for AT_HWCAP2 and treat a failed lookup as no
features available.
Normalize processor names and prefer the MSVC compiler target while preserving macOS architecture detection and the Windows MLAS disable.
Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
Adds bias (packed Q/K/V projection bias, split at import time when
constant, at runtime otherwise) and key_padding_mask (dense (batch,
kv_seq) form, cast to the query's runtime dtype, summed with
attention_bias when both are present) support to the importer.
AttentionOnnxAi::getMemoryShapes gains a shared attn_mask broadcast
shape check, used by ONNX Attention/GroupQueryAttention/
MultiHeadAttention alike.
core: fix out-of-bounds funcs[] lookup in ocl::kernelToStr for 5.x depths - #30266Fixes#30265
### Problem
`ocl::kernelToStr()` picks a formatting function from `funcs[]`, an 8-entry table indexed by depth. On 5.x a kernel of depth `CV_16BF`, `CV_Bool`, `CV_64U`, `CV_64S` or `CV_32U` indexes past the end of the table. The value read there is normally not null, so the following `CV_Assert(func != 0)` does not catch it and the call through `func` crashes.
### Fix
Declare the table as `funcs[CV_DEPTH_MAX]` and make `kernelToStr()` work for the 5.x depths:
- `CV_Bool`, `CV_64U`, `CV_64S` and `CV_32U` get `kerToStr` entries and are printed by its default branch.
- `CV_16BF` and the fp8 depths (`CV_8F_E4M3FN`, `CV_8F_E4M3FNUZ`) have no OpenCL literal form, so the kernel is converted to `CV_32F` and printed as a regular float.
Entries that are not listed stay zero-initialized, so the existing `CV_Assert(func != 0)` still guards any depth added later. Output for `CV_8U` to `CV_16F` is unchanged.
`predictOptimalVectorWidth()` in the same file already sizes its depth-indexed table this way.
### Tests
`OCL_KernelToStr_Depth.new_depths` is parameterized over `CV_16BF`, `CV_Bool`, `CV_64U`, `CV_64S`, `CV_32U`, `CV_8F_E4M3FN` and `CV_8F_E4M3FNUZ`, and checks the string returned by `kernelToStr()`.
On current 5.x (macOS, Apple M1 Pro) the test binary crashes on the first case. With this patch all seven pass, with OpenCL enabled and with `OPENCV_OPENCL_DEVICE=disabled`. The other `OpenCL.*` and `OCL_*` tests in `opencv_test_core` still pass locally.
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core: fix convertTo() from CV_32U returning negative values for CV_16BF and losing precision for CV_64F - #30199Fixes#30198.
Two entries in the `32u` block of `convert.simd.hpp` pick the wrong type. Both make `convertTo()` from `CV_32U` return something an unsigned source cannot produce, and both are one-line changes.
### 1. `CV_32U -> CV_16BF` declared its source as `int`
```cpp
DEF_CVT_FUNC(32u16bf, cvt1_, int, bfloat, v_float32)
```
Every other entry in the block says `unsigned`, including the neighbouring `CV_32U -> CV_16F`, which is correct. So everything above `INT_MAX` was read as negative - `UINT_MAX` converted to `-1`, `3000000000` to `-1291845600`.
This one is wrong at **every length, scalar path included**, because the type is wrong at the declaration rather than in the vector code.
### 2. `CV_32U -> CV_64F` widened through `float`
```cpp
DEF_CVT_FUNC(32u64f, cvt_, unsigned, double, v_float32)
```
`float` has a 24-bit mantissa and cannot hold a `uint32`, so values above 2^24 were rounded on the way to a destination that represents all of them exactly: `16777217` came back as `16777216`, `UINT_MAX` as `4294967296`. Only the vector body was affected, so the same value was correct at length 8 and wrong at length 16.
`CV_32U` was the only integer depth converting to `CV_64F` through `float` - `8U`, `8S`, `16U`, `16S` and `32S` go via `v_int32`, `64U` and `64S` via `v_float64`. It now uses the exact 64-bit path like the rest.
### One thing that needed care
`vx_load_pair_as(const unsigned*, v_float64&, v_float64&)` backed that exact path with a scalar spill through a buffer, so simply switching to it would have traded precision for speed - which is not a trade worth offering. It now widens to 64 bits and uses the existing signed conversion, exact because every `uint32` is a non-negative `int64`, and stays vectorized:
```cpp
v_uint64 lo, hi;
v_expand(vx_load(ptr), lo, hi);
a = v_cvt_f64(v_reinterpret_as_s64(lo));
b = v_cvt_f64(v_reinterpret_as_s64(hi));
```
No new HAL primitive is needed - `v_cvt_f64(v_int64)` exists on every backend.
### How these were found
A length-independence sweep over the element-wise operations: 2688 combinations of depth, boundary value and length, checking that an element's result never depends on how many elements sit beside it.
Worth recording that the sweep's first output was misleading. It reported `add`, `absdiff` and `multiply` on `CV_32U` as broken, because it compared results via `convertTo(CV_64F)` - which is defect 2. Reading the raw `uint32` lanes instead showed all three are exact, and that the readback was the bug. I would have filed three imaginary bugs by trusting the first run.
### Testing
`Core_ConvertFrom32U` asserts that no conversion from `CV_32U` is ever negative, and that `CV_64F` - the only destination here that can represent every `uint32` - is exact. Lengths straddle the vector width so the body, the tail and the two together are covered.
Checked it fails without the patch rather than assuming: **36 of its 224 cases fail** on current 5.x with only the test applied.
With the patch: 228/228 on `*ConvertTo*` and `*ConvertFrom32U*` at both the default dispatch and `OPENCV_CPU_DISABLE=AVX2,AVX,FP16`. Full `opencv_test_core` 17185 passed, with only the 7 filestorage/globbing failures this machine always has.
### Note on overlap
#30178 also touches `convert.hpp` and `test_arithm.cpp`, but in the other direction (16-bit float sources into wide integer destinations) and in different regions of both files, so there is no functional overlap - only a possible textual conflict in the test file, which I am happy to rebase around.
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Always oversubscribe 4 tiles/thread in MLAS threading instead of detecting
mixed-core CPUs via CPUID, which never covers ARM big.LITTLE or Apple's
P/E cores. Matches the same tradeoff core/src/arithm_expr.cpp already
accepts for heterogeneous cores.
Also add Fusion.AttentionQKVGrouping{SeqMerge,BatchMerge}, which exercise
readQKVGrouping()/evalDims() against a real Shape/Gather/Mul/Concat chain
feeding the QKV Reshape, rather than injecting qkv_batch/qkv_seq directly
as a layer param like the existing AttentionLayer-level tests do.
dnn: fuse attention when a Q/K/V projection has no bias - #30284
The fused Attention layer needs the full QKV bias. Fill a missing projection bias with zeros in all three attention fusion paths, and leave the block unfused if a bias has the wrong length. The CLIP path dropped all three biases when one was missing, and the generic path read K/V bias blobs that may not exist. All three paths now emit a 1-D bias. Fixes the trOCR encoder.
some extra models and inputs added to opencv_extra: https://github.com/opencv/opencv_extra/pull/1433
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dnn: fix blobRectsToImageRects when the image is not resized (5.x) - #30287
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- [x] There is a reference to the original bug report and related work. 5.x port of #30243, as requested there. Related: #30042.
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### Problem
Port of #30243 to 5.x. `Image2BlobParams::blobRectToImageRect()` and `blobRectsToImageRects()` are identical on 5.x and have the same bug:
- When `Image2BlobParams::size` equals the image size, every output rect is `Rect(0, 0, 0, 0)`, in all three padding modes.
- When `size` is empty (the default, which `blobFromImageWithParams()` treats as "keep the image size"), the mapping divides by zero and returns `INT_MIN` coordinates.
```python
import cv2 as cv
p = cv.dnn.Image2BlobParams()
p.size = (640, 640)
p.paddingmode = cv.dnn.DNN_PMODE_LETTERBOX
print(p.blobRectToImageRect((10, 20, 30, 40), (640, 640)))
# (0, 0, 0, 0), expected (10, 20, 30, 40)
p.size = (0, 0)
print(p.blobRectToImageRect((10, 20, 30, 40), (640, 480)))
# (-2147483648, -2147483648, -2147483648, -2147483648), expected (10, 20, 30, 40)
```
### Change
Same change as #30243, cherry-picked without conflicts. `blobRectsToImageRects()` now follows the same two rules as `blobFromImagesWithParams()` on 5.x: an empty `size` means the image size, and when the sizes match the input rects are returned unchanged. The resize, crop-center and letterbox mappings are unchanged. It also includes the Doxygen `@note` on both functions requested in the #30243 review.
The new `blobRectToImageRect.no_resize` test covers both cases for all three padding modes, and checks that the blob built with the same parameters is pixel-identical to the image.
#30042 contains an equivalent pass-through for the equal-size case as part of a larger Model API change. This PR is the standalone fix, including the empty-size case. If #30042 lands first, the conflict should be limited to `blobRectsToImageRects()`.
### Testing
Built `opencv_test_dnn` and the Python bindings from this branch (base 5.x `6dc5ff5c1f`) on Ubuntu 26.04 (WSL2), GCC 15.2, Ninja, `BUILD_LIST=dnn,python3,ts`.
- `opencv_test_dnn --gtest_filter=blobRectToImageRect*`: 4 passed. With only the `dnn_utils.cpp` change reverted, the new `blobRectToImageRect.no_resize` fails in all six mode/size combinations (`[0 x 0 from (0, 0)]` for equal sizes, `INT_MIN` for empty size).
- `opencv_test_dnn --gtest_filter='blob*:*Image2Blob*:*imagesFromBlob*'`: 11 passed.
- `modules/dnn/misc/python/test/test_dnn.py`: 8 passed, 2 skipped; 6 failed only because their opencv_extra files are not present locally (`dnn/googlenet_0.png`, `dnn/street.png`, ...).
- The Python reproducer above prints `(10, 20, 30, 40)` for both cases with this branch (`5.1.0-dev`).
- No compiler warnings in the build; `git diff --check` clean.
- Full `opencv_test_dnn` was not run (needs opencv_extra test data).
dnn: accept TopK with K = 0 and fix broadcast eltwise ops in block layout - #30285
### TopK with K = 0
Detectors exported from TensorFlow (e.g. SSD MobileNet v1) sort the boxes kept by NMS
with TopK, where K is the number of kept boxes. On an image with no detections K = 0
and TopK2 failed with "dynamic K must be > 0". The ONNX spec asks for K > 0, but ONNX
Runtime accepts K = 0 and returns empty outputs; TopK2 now does the same.
### Broadcast eltwise ops in block layout
When every input of an eltwise op is in block layout (NCHWc), the op stays in block
layout even if one input has a single channel, e.g. BiRefNet's attention gating
`[1,1536,32,32] * [1,1,32,32]`. The single channel lives in lane 0 of its block, so 7 of
every 8 output channels were multiplied by padding, and the output shape lost its block
marking. The 1-channel input now has lane 0 copied into every lane before broadcasting,
and the output is marked as block with the full channel count.
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The fusion read only num_heads and head_dim from the QKV Reshape target,
so a graph reshaping Q/K/V to [B, T*S, H, D] fused the same way as one
reshaping to [B*T, S, H, D], and the fused layer computed the second for
both, giving a wrong result for the first.
The fusion now records the first two target entries, the batch and the
sequence length the attention runs over, each as a constant times a
product of input dims, or as inferred. It reads constants and chains of
Shape, Gather, Mul, Unsqueeze, Squeeze and Cast (as in the SAM3 decoder).
If an entry is computed some other way, or Q, K and V disagree, the block
is left unfused. The layer evaluates the record against the input shape
and runs over that grouping, which is a view of the same contiguous
tokens. Without a record, as for the ONNX Attention op, the input must be
3D.
- test_misc.cpp: merge forward_outputs_are_private/_ort into one
TEST_P(Net_ForwardOutputsArePrivate, Accuracy) over EngineType,
guarding the ENGINE_ORT value with #ifdef HAVE_ONNXRUNTIME.
- 3rdparty/mlas/patches/0001-mlasi-opencv-threading.diff: refresh the
stale hunk header. The second hunk's "+" line number had drifted by
one (1677 -> 1678) because an unrelated, already-merged MinGW/GCC
portability fix added a line earlier in mlasi.h; verified by
reconstructing the pristine upstream file at the documented base
commit and confirming the patch now applies with no offset/fuzz.
Pass the lambda directly to parallel_for_ instead of binding it to a
named fn variable first, matching the other parallel_for_ call sites
in this function. Requested by abhishek-gola on #30277.
Backport #30165 to 5.x branch - #30169
Port of https://github.com/opencv/opencv/pull/30165
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imgproc: fix out-of-bounds read in OpenCL color kernels for 3-channel input 🤖🤖🤖 - #30274
YUV2RGB, RGB2YCrCb, YCrCb2RGB and the PIX_PER_WI_X != 2 path of
RGB2YUV_YV12_IYUV load each source pixel with vload4 but only use the
first three components. With SCN == 3 the load of the last pixel reads
one element past the end of the source buffer, which may result in a
GPU page fault.
Use vload3, as RGB2YUV already does, and drop the now unused
DATA_TYPE_4 macro.
The same issue is fixed in RGB2XYZ, XYZ2RGB, BGR2Lab, Lab2BGR, RGB2HSV,
HSV2RGB, RGB2HLS, HLS2RGB and RGB2RGB5x5, which load each source pixel
with vload4 but only use the first three components when SCN == 3.
Add an OpenCL cvtColor test with a contiguous 3-channel source whose
size is a multiple of 1 MiB, so the last pixel ends exactly at the end
of the buffer, covering 8U and 32F conversions and RGB2RGB5x5.
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dnn: fix ONNX conformance failures in layers and the graph engine - #30181
Fixes wrong results found by running the ONNX backend-scoreboard conformance suite (onnx 1.23.2) against the new DNN engine. No new ops are added; these are all ops OpenCV already claims to support but computed incorrectly.
Updated ONNX coverage is: `[scoreboard] passed=1740 failed=21 skipped=272 total=2033 (85.6% passed)`
**Graph engine / importer**
- Reshape+Reshape fusion now only fires when the outer shape is known and has no `0` entries, since a `0` copies a dim from the inner Reshape's output.
- Scan slices rank-1 inputs to scalars, so scalar loop-carried state keeps rank 0.
- Rank-0 tensors read from ONNX keep rank 0 instead of becoming 1x1.
- When a model is refused, `setInput()` now reports the actual import failure (including unknown ops that throw early) instead of a misleading "missing blob" error.
**Attention**
- Softcap is applied before the bias/mask, as the spec does, so masked keys no longer leak probability.
- `qk_matmul_output_mode` 1 and 2 now return the right tensors (checked against onnxruntime; the old denylist entries blaming the reference data were wrong).
- Attention-25 `left_window_size` / `right_window_size` are supported, including together with `attn_mask`.
- A fully masked row now gives all zeros instead of a uniform distribution.
**Layers**
- GRU: `direction="reverse"` is honoured.
- LSTM reverse: `Y_h` / `Y_c` are taken from the correct time step.
- SpaceToDepth: opset-28 `mode` (CRD / DCR) is supported.
- MaxPool: `storage_order` is respected for the indices output.
- OneHot: out-of-range indices produce an all-`off_value` row instead of wrapping.
- Mod with `fmod=0`: no longer truncates through int (wrong for float, lossy for int64).
- ReduceMax / ReduceMin on an empty bool set return the correct identity.
- Swish: `alpha` is respected (CPU and OpenCL).
- Quantize/DequantizeLinear: scale and zero_point may differ in shape when they're broadcast-compatible (e.g. rank-0 scale with `[1]` zero_point).
- BitShift: signed integer types are supported.
**Tests**
- 12 conformance tests removed from the denylists, a few OpenVINO filter entries added, and new 1D layer tests for the fixes.
- Full C++ conformance passes (5376/5376); `opencv_test_dnn` has no new failures compared with 5.x.
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The output has v_hidden values per token, but getMemoryShapes() sized it
from the input width, so a V wider than the input wrote past the output
buffer. Size it from v_hidden for both output ranks, and test V widths
below, equal to and above the input width.
drop ND prefix from the functions - #30207
Merge pull request #30207 from abhishek-gola:core-drop-nd-suffix
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3rdparty(libtiff): expand WebP compression support for bundled libtiff(5.x) - #30264
## Overview
This pull request enables WebP compression support in the bundled `libtiff` build for OpenCV.
Previously, even when both the bundled `libtiff` and libwebp were enabled, TIFF-WebP compression was unavailable because WebP support was not hooked up in `libtiff`.
Port from https://github.com/opencv/opencv/pull/30231
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- Attention Q/K/V projection: pack each (Q/K/V, head) weight slice with
MLAS and run all per-head GEMMs in one multithreaded
mlasSgemmPackedBatch() call; the fastGemm loop stays as the fallback.
- MLAS: split each operation into 4 tiles per thread on hybrid CPUs.
- forward(): hand the private output Mats to the caller instead of
copying them again.
- Attention: accept [..., S, C] input (SAM3 decoder).
On ARM with CV_SIMD_16F, getMulFunc_, getDivFunc_, and getAddWeightedFunc_
used float16 for 8-bit integer SIMD kernels, leading to precision loss
and overflow (> 65504). Switch to float32 to match x86 and ensure precision.
imgproc: fix segfault in connectedComponents on zero-size input - #30225
`cv::connectedComponents` and `cv::connectedComponentsWithStats` crash with SIGSEGV when passed an image with a zero-length dimension (e.g., shape `(0, 400)`), instead of raising a proper error. All other OpenCV functions handle empty input gracefully.
The root cause is that the labeling algorithms dereference pointers based on image dimensions without first checking for empty input, causing a null dereference when `rows == 0` or `cols == 0`.
The fix adds `CV_Assert(!img.empty())` at the entry point of both public API functions, so callers receive a `cv::Exception` / `cv2.error` consistently with the rest of the OpenCV API (`findContours`, `cvtColor`, `inRange`, etc.).
A regression test `Imgproc_ConnectedComponents.empty_input_29854` is included, covering all three zero-dimension shapes (`{0,400}`, `{400,0}`, `{0,0}`) for both `connectedComponents` and `connectedComponentsWithStats`.
Reproducer:
```python
import numpy as np, cv2
mask = np.zeros((0, 400), dtype=np.uint8)
cv2.connectedComponentsWithStats(mask) # was: SIGSEGV; now: cv2.error
```
Fixes#29854
Extends cv::videoio_registry::enumerateDevices() (#29843) to GigE and
USB3 Vision cameras handled by Aravis. The backend already resolves a
capture index to an entry of the Aravis device list, so the same list is
reported, which keeps VideoDeviceInfo::cam_idx valid for
cv::VideoCapture(idx, CAP_ARAVIS).
Device name is composed of vendor, model and the Aravis device ID, so
cameras of the same model remain distinguishable; all three strings are
optional and the registry falls back to "cam: <index>" if none of them
is available.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
videoio: Added camera device enumeration - #29843#29843
**Resolves** [Issue#4269](https://github.com/opencv/opencv/issues/4269)
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Added Gaussian Splat Renderer Support - #29729
Adds 3D Gaussian Splatting support to `ptcloud`: a loader for trained scenes and a `viz3d` renderer that draws them alpha-blended and depth-sorted alongside existing opaque geometry.
### Result
<img width="1842" height="811" alt="splat" src="https://github.com/user-attachments/assets/abfa5c01-6dbb-4367-b426-a618e3748649" />
[.splat source](https://huggingface.co/cakewalk/splat-data/resolve/main/train.splat)
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calib: make Schur complement solver deterministic - #30175
The Schur-complement LM solver's Jacobian accumulation previously summed per-image contributions into shared `U`/`ea` matrices under a mutex, so results depended on thread-scheduling order. This partitions images into a fixed number of parallel "buckets" (nparts, capped at 16), accumulates each bucket independently without locking, then reduces them serially removing the mutex and making results bit-identical regardless of thread count.
### Benchmark: CalibrateCamera
Median of 5 runs, native 5.x vs this branch:
| Threads | 5.x median (ms) | This branch median (ms) | Change |
|---|---|---|---|
| 32 | 838.8 (777.7–855.6) | 578.4 (574.5–583.2) | 1.45× faster |
| 1 | 1233.8 (1225.8–1236.8) | 1214.6 (1213.1–1214.8) | 1.6% faster |
Single-thread time is essentially unchanged, as expected removing the mutex has no effect without contention. The gain comes from splitting the Jacobian accumulation (`U`/`ea`) into lock-free per-bucket partials instead of serializing every image's update behind one mutex, which scales much closer to linear with thread count.
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imgproc: reject unsupported depths in OpenCL threshold - #30252Fixes#30251
### Problem
`ocl_threshold()` does not check the input depth. On 5.x a `UMat` of depth `CV_64U`, `CV_64S` or `CV_32U` gets through, because `ocl::typeToStr()` maps those depths to valid OpenCL types and the kernel compiles. The function then indexes the 8-entry `min_vals[]` table with a depth of 10, 11 or 12, which reads past the end of the array, and skips the `depth <= CV_32S` flooring of `thresh`.
The CPU implementation rejects these depths with `StsUnsupportedFormat`, so the same call throws for a `Mat` and returns an image for a `UMat`, depending on whether an OpenCL device is available.
`CV_16BF` and `CV_Bool` did not reach the table, but failed with `Assertion failed (result) in typeToStr` instead of `StsUnsupportedFormat`.
### Fix
Return `false` from `ocl_threshold()` for every depth above `CV_64F`, before the kernel is built. Those inputs now fall through to the CPU path, which reports `StsUnsupportedFormat` as it does for `Mat` input.
The depths the OpenCL branch accepted in 4.x (`CV_8U` to `CV_64F`) behave as before.
### Tests
`Imgproc_Threshold_Unsupported_Depth.umat` is parameterized over `CV_16F`, `CV_16BF`, `CV_Bool`, `CV_64U`, `CV_64S`, `CV_32U` and `THRESH_BINARY`, `THRESH_TRUNC`. It checks that `threshold()` on `Mat` and `UMat`, and `thresholdWithMask()` on `UMat`, throw.
On current 5.x with an OpenCL device (Apple M1 Pro, OpenCL 1.2) it fails for `threshold()` on `UMat` with `CV_64U`, `CV_64S` and `CV_32U`. With this patch it passes with OpenCL enabled and with `OPENCV_OPENCL_DEVICE=disabled`. The other `threshold()` tests in `opencv_test_imgproc`, including the OpenCL ones, still pass locally (the `Imgproc_AdaptiveThreshold` tests were not run, they need `opencv_extra` data).
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core: reduceND, used by the DNN Reduce2 layer - #30157
Split out of #30082, which now carries only the resize half.
Adds `cv::reduceND`, which reduces an n-dimensional array over a set of axes with any of the `ReduceTypes`, extended with `REDUCE_PROD`, `REDUCE_L1`, `REDUCE_L2`, `REDUCE_LOG_SUM` and `REDUCE_LOG_SUM_EXP`. `cv::reduce` accepts n-dimensional input through it.
`sum`, `mean` and `minMaxIdx` now share one parallel driver (`stat_parallel.hpp`), so they parallelize over large arrays the same way `reduceND` does.
The new graph engine's `Reduce2` layer calls `cv::reduceND` instead of its own kernels, so `reduce2_kernels.simd.hpp` and the hand-written reduction loops in `reduce2_layer.cpp` go away.
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calib document fix - #30208
Documentation changes - three doxygen blocks in `modules/objdetect`.
- **`findChessboardCorners`** (PR#22083): whether the returned order pins down a unique
physical corner depends on the **parity** of the two `patternSize` dimensions, not on
whether they differ. Quads are built from the dark squares only (`calibinit.cpp:755`
`THRESH_BINARY`, `:796` white frame, `:1919` holes-only), so a rotation is admissible
only if it maps dark squares onto dark squares. For `patternSize` of w x h interior
corners the squares form a (w+1) x (h+1) grid, so 180° preserves the colouring exactly
when w and h share parity:
| `patternSize` | distinct origins | ambiguity |
|--------------------------------|------------------|---------------|
| different parity (9x6, 8x5) | 1 | none |
| same parity, w != h (8x6, 7x5) | 2 | 180° |
| w == h (7x7, 8x8) | 4 | 90° |
The square case is not colour: the transpose at `calibinit.cpp:1745` is a no-op when
`width == pattern_size.width`, so the detector cannot tell rows from columns. Verified
over 24 in-plane rotations and 60 perspective views per board, zero detection failures;
inverting the square colouring moves the origin but not the count.
Practical consequence is the reverse of what was documented: the common 9x6 board is
unambiguous, while this docstring's own sample `Size patternsize(8,6)` is not. The note
now recommends a board whose dimensions differ in parity first, then
#findChessboardCornersSB + `CALIB_CB_MARKER`, then ChArUco. Still harmless for
intrinsics (each view absorbs the rotation into its own pose); still breaks hand-eye.
- **`findCirclesGrid`** (PR#7312): the returned points are the keypoint locations reported
by `blobDetector` as-is, without refinement - the parameter is a `FeatureDetector` and
`SimpleBlobDetector` is only the default, so the points are not necessarily moment
centroids. Under perspective the apparent centre of a projected circle can be displaced
from the projection of the physical centre; the displacement vanishes when the board is
parallel to the image plane and is not removed by fitting an ellipse. Points at the
`blobDetector = NULL` path for supplying corrected centres. Describes the bias
qualitatively rather than quoting a fixed estimate.
- **`setLegacyPattern` / `detectBoard`** (PR#23152, PR#29636): the flag is a no-op on
odd-row boards, and a wrong setting fails silently - markers detected, zero corners,
only a `CV_LOG_DEBUG` line; notes that calib.io boards use the legacy layout and aligns
the 4.6.0 version wording across both headers.
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Previously, arithm_op returned an empty output when one operand was an
empty Mat and the other was a Scalar.
The new element-wise engine asserted `src1.empty() == src2.empty()` before
checking `isScalarArg`, causing calls such as `cv::max(empty_mat, 1.0, dst)`
or `cv::add(empty_mat, Scalar(1), dst)` to fail with an assertion error.
Calling resize(size() - 1) in pop_back() pulls in the buffer growth path
for the unreachable sz == 0 case in release builds (where size() - 1
underflows to SIZE_MAX), which both bloats codegen and triggers an LLVM
ScalarEvolution assertion on 32-bit Hexagon Clang. Guard and decrement
sz directly instead.
Rebasing onto 5.x picked up facc3fdc7d, which vectorized the masked norm for the remaining depths,
and Core_NormL1_Signed then failed on CI for masked CV_32S at INT_MIN - 13 jobs, every platform.
The new kernel takes the magnitude before widening:
v_int32 s = v_reinterpret_as_s32(v_and(v_abs(vx_load(src + i)), v_normmask_u32(mask + i)));
acc0 = v_add(acc0, v_cvt_f64(s));
v_abs() does give |INT_MIN| correctly, as an unsigned 2147483648, but reinterpreting that back as
int32 turns it negative again and the widening carries the sign into the accumulator, so the norm
came out as -16 * 2^31 for 16 elements. Mask the value instead, widen to double - exact for every
int32 - and take the magnitude there, which is what the unmasked path already does.
The 8- and 16-bit masked kernels are not affected: v_abs() returns the unsigned type there too, and
they accumulate in unsigned without reinterpreting back to signed.
- The CV_32S instantiation covers values outside the int32 range, which only apply to CV_64S.
Those now throw SkipTestException so they are reported as skipped rather than silently passing.
- Core_CvAbs checks cv_abs() at the lowest value of each signed type, where the magnitude is not
representable in that type. The norm test only reaches cv_abs() indirectly, so a direct one makes
the contract explicit and fails for an obvious reason if it regresses.
- cv_abs(int64) had the same std::abs UB at INT64_MIN that cv_abs(int) had, so
cv::norm(CV_64S, NORM_L1) still returned a negative value - at every length, masked and unmasked.
Verified that before changing it. Same fix: negate in the unsigned type.
- Core_NormL1_32S becomes Core_NormL1_Signed and covers CV_64S alongside CV_32S, so both signed
integer depths are held to the same rule.
- EXPECT_DOUBLE_EQ -> EXPECT_EQ, since the test does claim exact equality: every value used is
below 2^53 in magnitude and so is its total.
- Shortened the comments in the test and in norm.simd.hpp to describe what the code must do rather
than retell the defects.
Two independent defects, both visible from the same observation: an L1 norm is a sum of
magnitudes, so it can never be negative and can never depend on how many elements sit beside a
given one. cv::norm() on CV_32S broke both rules.
1. Negative result at INT_MIN. cv_abs(int) is (unsigned)std::abs(x), and std::abs(INT_MIN) is
undefined - its result is not representable in int. In optimized builds the contribution came
out negative, so
cv::norm(Mat(1, 1, CV_32S, Scalar(INT_MIN)), NORM_L1) == -2147483648
With a mask this is wrong at every length, since the masked path is scalar throughout.
Fixed at the source by negating in unsigned, where INT_MIN's magnitude is an ordinary value.
Behaviour is unchanged for every other input, and the UB is gone for all other callers too.
2. Precision loss above 2^24 in the vector body. NormL1_SIMD<int, double> widened int32 to float
before accumulating into double, and float cannot represent every int32, so values above 2^24
were rounded first:
127 elements of INT_MAX -> 272730423281, exact answer 272730423169
Only the vector body was affected, so the error appeared once the array was long enough to
enter it - the same answer was correct at length 8 and wrong at length 16. Widen straight to
double instead and take the magnitude there; this is exact over the whole int32 range,
INT_MIN included, and is the same conversion the NEON backend already documents as the
correct replacement for an f32 round-trip.
NORM_INF and NORM_L2 on CV_32S were already correct and are untouched: NORM_INF accumulates in
unsigned, and NORM_L2 uses v_dotprod_expand_fast, which stays in integers.
Core_NormL1_32S covers both defects over masked and unmasked input, asserting non-negativity as
well as the exact value, at lengths straddling the vector width.
MatExprInvoker::operator() previously allocated two cv::AutoBuffer<uchar,
16 * 1024 + 64> instances (scratchBuf and tstoreBuf) simultaneously on
the stack, consuming over 32 KB per buffer and pushing the stack frame
over 36 KB. Furthermore, capElems = 16 * 1024 could allocate up to 128 KB
(16K * 8 bytes for CV_64F) before even accounting for multiple scratch
slots, exceeding a typical 32 KB L1 data cache.
calib: accept float64 points in calibrateCamera - #30176
Lets calibrateCamera, calibrateCameraRO, stereoCalibrate, initCameraMatrix2D, and registerCameras accept Point3d/Point2d (double) input in addition to Point3f/Point2f. Adds a new test suite (float64Points) exercising each of those entry points with double-precision input and comparing accuracy against the float32 path.
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Mixed point layouts broadcast residuals to an incompatible matrix.
Reshape both observed and projected points to rows before subtraction so
calibration and uncertainty estimation accept either layout.
When running DIS Optical Flow on OpenCL devices supporting `cl_khr_subgroups` (such as ARM Mali GPUs), `dis_flow.cpp` detects subgroup support and appends `-DCV_USE_SUBGROUPS=1` to OpenCL kernel build options.
However, `modules/video/src/opencl/dis_flow.cl` was missing `#pragma OPENCL EXTENSION cl_khr_subgroups : enable`. As a result, OpenCL kernel compilation failed with implicit function declaration errors:
<source>:189:12: error: implicit declaration of function 'sub_group_reduce_add' is invalid in OpenCL
sum_diff = sub_group_reduce_add(sum_diff);
^
<source>:229:11: error: implicit declaration of function 'get_sub_group_local_id' is invalid in OpenCL
int sid = get_sub_group_local_id();
Fix by adding `#pragma OPENCL EXTENSION cl_khr_subgroups : enable` inside `#ifdef CV_USE_SUBGROUPS` in `dis_flow.cl`.
Fixes#27507
Calib: assersion fix of fisheye calib - #30173
Replaces a CV_Assert in fisheye::InitExtrinsics with a graceful failure path (returns bool, caller now throws a descriptive StsBadArg naming the degenerate view) instead of crashing on a hard assert. Also adds validation that rejects legacy numeric flag values passed to fisheye::calibrate/stereoCalibrate, and fixes CALIB_CHECK_COND to actually gate the condition check in stereo calibration. Includes an hdr_parser.py change to resolve using Base::CONST enum aliases for Python bindings.
### Pull Request Readiness Checklist
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Patch to opencv_extra has the same branch name.
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objdetect: handle unsupported QR modes gracefully - #30123
### Pull Request Readiness Checklist
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### Description
Resolves#30110.
This PR addresses two related defects in `QRCodeDetector` and Python string conversion bindings:
1. **`objdetect`**: In `QRCodeDecoderImpl::decodeSymbols` (`qrcode_encoder.cpp`), encountering unsupported or unhandled QR mode indicators (such as Hanzi mode 13 or FNC1) previously called `CV_Error(Error::StsNotImplemented, format("mode %d", currMode))`, throwing an unhandled `cv2.error` exception out of high-level detector calls like `detectAndDecodeMulti()`. Replaced `CV_Error` with `return false;` to report symbol decode failure gracefully.
2. **`python`**: In `pyopencv_from(const String& value)` (`cv2_convert.cpp`), converting C++ string payloads to Python `str` previously called `PyString_FromString` (`PyUnicode_FromString`), which raises a `UnicodeDecodeError` when decoding non-UTF-8 payload bytes (e.g. GB2312 or Shift-JIS lead bytes). Updated string conversion to use `PyUnicode_DecodeUTF8(..., "surrogateescape")` with fallback to `PyBytes_FromStringAndSize`.
### Testing
- Added `TEST(Objdetect_QRCode, unsupported_mode_graceful)` in `modules/objdetect/test/test_qrcode.cpp`.
- Move the activation clip clamp and holdFinishedRows into
recurrent_activations.hpp and call them from both GRU and RNN; drop the
redundant useClip flag, since clip > 0 is the switch.
- Select the GRU outputs positionally, matching getMemoryShapes(), instead
of guessing Y / Y_h from their shape and keeping the old single-output
may-be-Y_h policy.
- Reject direction=reverse together with two-direction weights instead of
silently running both sweeps backwards, and drop the unreachable assert.
- Collapse the four inlined GRU references into one shared helper and drop
the stale sentence from the SingleOutputIsSequenceY comment.
- Drop the hand-made hPrev allocation: hInternal.copyTo() already sizes and
types it.
- Gate the GRU reference on the input timestep rather than the loop
position, so reverse + sequence_lens skips the padding instead of the
valid steps, and cover that combination with a test.
Document that fisheye::calibrate forces a fixed K1..K4 coefficient to
0 even when CALIB_USE_INTRINSIC_GUESS supplies a guess for it, and
that CALIB_FIX_INTRINSIC overrides CALIB_FIX_K1..K4 in stereoCalibrate.
Note that the default Schur-complement engine in calibrate() sums
per-view contributions in parallel, so repeated calls are not bit
reproducible; CALIB_DISABLE_SCHUR_COMPLEMENT does not have this issue.
Correct the CALIB_USE_INTRINSIC_GUESS docs for fisheye::calibrate and
fisheye::stereoCalibrate: skew and k1..k4 must also be initialized,
and the default focal length is max(width,height)/2, not /pi.
Document that undistortPoints() runs a fixed 5 iterations by default
and never checks its 0.01 epsilon unless TermCriteria::EPS is set in
the passed criteria, and clarify that fisheye::undistortPoints' default
epsilon is an angular threshold, not comparable to undistortPoints'
pixel-space one. Also name the criteria parameter in
undistortImagePoints().
The test runs DISK plus LightGlue over 1024 keypoints and needs more
memory than the 32-bit Windows x86 build can hand out, so it died with
an OutOfMemoryError on a 512 MB allocation. Every other DNN-backed test
in the module already carries CV_TEST_TAG_MEMORY_2GB; this one was
missing it.
Unify element-wise ops with the core engine - #30083
### Pull Request Readiness Checklist
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Rect_::operator&= computed width - (Rx_max.x - Rx_min.x) before knowing whether
the rectangles actually overlap. For unsigned _Tp the subtraction wraps when the
rectangles are disjoint, so the result was a bogus non-empty rectangle instead of
an empty one:
Rect_<unsigned>(0, 0, 1, 1) & Rect_<unsigned>(2, 2, 1, 1) // was [1 x 1 from (2, 2)]
Return an empty rectangle once the coordinate gap exceeds the corresponding
width/height, before the subtraction can underflow.
Review follow-up. v_pack() already clamps anything above UINT32_MAX to UINT32_MAX, so narrowing as
unsigned first and bringing the result down to INT32_MAX with the same 32-bit v_min() the
uint32 -> int32 direction already uses gives the right answer for the whole uint64 range, and the
last local clamp helper in this file goes away.
convert.hpp now has no hand-written 64-bit clamping left at all.
Review request: fix the narrow globally rather than clamping at one call site.
v_pack() saturated at 8, 16 and 32 bits but truncated at 64->32, and v_pack_u() had no 64-bit form
at all. That is why convertTo() had to clamp by hand before packing: an out-of-range value wrapped
first, and the later saturating narrow to 8 or 16 bits then saturated the WRAPPED value, which is
not merely imprecise but the wrong end of the range (4294967295 -> -1 -> 0 for uchar, not 255).
The backends did not even agree with each other: RVV narrowed with vnclip/vnclipu for 8, 16 and 32
bits but with the truncating vnsra/vnsrl at 64, and NEON used vqmovn for the narrow widths and the
plain vmovn at 64.
All of them now saturate, and all of them gain v_pack_u() for int64 -> uint32:
- NEON, MSA, RVV, RVV071: the saturating primitive already existed next to the one in use
(vqmovn/vqmovun, msa_qpack*, vnclip/vnclipu), so these are one-word changes.
- VSX: vec_packs/vec_packsu instead of vec_pack. The 64-bit v_pack_u was already written here and
commented out with "not implemented on other platforms", so it is simply enabled.
- AVX-512: vpmovsqd / vpmovusqd narrow with saturation natively.
- SSE and AVX2: decided on the two 32-bit halves, so no 64-bit compare is needed - a value fits
exactly when the high half equals sext(low), and cmpgt on the high half yields 0 / all-ones,
which doubles as the saturated value for pack_u.
- WASM, LSX, LASX: no saturating narrow, so OPENCV_HAL_IMPL_SATURATING_PACK_64 in
intrin_forward.hpp builds it from the backend's truncating narrow, again from the 32-bit halves.
- intrin_cpp.hpp: saturate_cast instead of static_cast, matching every other width.
A 64-bit ordering compare would have been the obvious way to decide saturation and is the one thing
to avoid here: at the SSE and NEON baselines v_gt on 64-bit lanes is the sign of (b - a), so it
overflows - and answers the wrong way - exactly when the operands sit at opposite ends of the 64-bit
range, which is the case a saturating narrow has to get right.
convertTo() now calls v_pack()/v_pack_u() directly and keeps one local helper, for uint64 -> int32:
no width has a v_pack_s, so that direction has no HAL counterpart to use. Its int64 -> uint64 path
also stops using v_gt(x, 0) to clamp negatives, for the reason above, and tests the sign bit instead.
Tests: test_pack64_saturation and test_pack64_u_saturation check the three narrows either side of
the destination range, wired into the uint32 and int32 suites. The pack_saturate_cast carve-out in
test_intrin_utils.hpp that documented 64-bit as non-saturating is removed, so the existing
test_pack<> now holds every width to the same rule.
v_rshr_pack_u is left without a 64-bit form: it has no callers and the rounding step would need its
own decision about where the rounding happens relative to the clamp.
GlobalAveragePool and GlobalMaxPool accept N x C x D1 x ... x Dn inputs, but
the layer only worked for at most two spatial dimensions: the parameter
vectors derived from the layer params hold one pad/stride entry per recognized
dimension (kernel_d/h/w), so a 5-D input hit
"CV_CheckLE(addedDims, pads_begin.size())" in getMemoryShapes and the model
could not be loaded at all.
The quantized pooling layer shares the same parameters, so it silently read
past the pad/stride vectors and produced garbage output shapes (and read
garbage kernels) in the same situation.
Fix by giving the paddings (0) and the strides (1) as many entries as the
global kernel has spatial dimensions, both in the shape computation and in
finalize. The extra entries are inserted at the front so that the parameters
stay trailing-aligned with the spatial dimensions, and a check rejects inputs
with more spatial dimensions than the layer can describe instead of reading
the per-dimension flags out of bounds.
N-D Mats (e.g. NHWC batches from Python) were silently processed as a
garbage 2D image by remap() and warpPerspective(). Check src dims up
front and raise a descriptive error instead.
Fixes#29589
dnn: speed up deconvolution-heavy decoders via spatial threading and TransformLayout+Add fusion - #30044
## Summary
Two related changes to the new DNN graph engine, both exercised by the SAM2 decoder model (deep ConvTranspose2 stack with skip connections):
1. **Parallelize the deconvolution spatial loop for narrow-output layers**, which were leaving most threads idle.
2. **Fuse a following NaryEltwise Add into TransformLayout's deinterleave pass** when the Add's other operand is a same-shape, no-broadcast CONST or model input — removes one full read-modify-write pass over the tensor per fused site.
**Decoder wall-clock median: 20.97ms → 13.9ms after the deconvolution threading fix (33.7% faster) → 13.37ms after the TransformLayout+Add fusion (~0.6ms faster still).**
Companion data PR: opencv/opencv_extra#1415 (adds the ONNX models/data these tests load — needs to merge alongside or before this one).
## Details
### 1. Deconvolution spatial threading
`deconvBlock32f` parallelised only over `N*K1` (`K1` = blocked output-channel count). A narrow-channel output — a 32-channel output is just 4 tasks — left most threads idle while each task walked the whole spatial plane serially. The kernel gathers rather than scatters (every output position writes only its own slot), so the spatial loop was already safe to split, it just wasn't. Adds `computeSpatChunks()` (shared between the forward-conv and deconv kernels) to split the spatial range into chunks whenever `N*K1` alone is too few tasks to occupy every thread, targeting `getNumThreads()*8` for load balancing; returns 1 (no-op) whenever `N*K1` already saturates the pool.
### 2. TransformLayout + Add fusion
Goes through the `LayerMath`/`AdjacencyGraph`/`ChainFuser` machinery from #29656 rather than a separate one-off pass. `LayerMath` had no way to represent a second value computed live by another layer — `INPUT` is pinned to node 0, and `CONST`/`PER_CHANNEL_CONST` are both baked into a `Mat` once at fuse time, wrong for a residual whose real value only exists at `forward()` time. Adds a new `FusionEltwiseOp::TENSOR` node kind carrying an `Arg` instead of a value, opt-in per sink (`FusionOps::acceptsTensorOperands`) so an unprepared sink is never offered an expression it can't execute. `TransformLayoutLayerImpl` accepts exactly `ADD(INPUT, TENSOR)`; the fused kernel correctly handles a channel count that isn't a multiple of `C0` (partial last block) rather than assuming it can't happen.
### Pull Request Readiness Checklist
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Add squeeze/unsqueeze/flatten/reshape for N-D arrays - #30091
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lanczos4_tab was still sized [2][8] after CV_DEPTH_MAX grew to 32 in 5.x,
so remap() with INTER_LANCZOS4 indexed past the end of a row for depths
>= CV_16BF. Depending on the depth this silently ran the kernel of another
type (e.g. 16BF -> 8U, 64U -> 16U, 64S -> 16S) or read past the end of the
table and crashed (WARP_RELATIVE_MAP).
Size the table with CV_DEPTH_MAX like nn_tab and linear_tab, so the
unsupported depths hit the existing ifunc != 0 assertion.
core: reject CV_Bool input in inRange instead of crashing - #30154Fixes#30153.
`cv::inRange` has no kernel for `CV_Bool` (or the two `CV_8F` depths), so `getInRangeFunc()` returns a null pointer and the call segfaults. Like `addWeighted` in #29883, it now throws `StsNotImplemented` for these depths, and the caller has to convert the input first.
### Pull Request Readiness Checklist
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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch (`5.x`, `CV_Bool` and `CV_8F` don't exist on `4.x`)
- [x] There is a reference to the original bug report and related work (#30153, #29883)
- [x] There is an accuracy test (`Core_Arithm.inRange_unsupported_depth_30153`, which segfaults without the fix); not applicable: performance test and opencv_extra test data
- [x] N/A: this is a bug fix, no new public API or documentation needed
python: mark documented None output arrays as optional in typing stubs (5.x) 🤖🤖🤖 - #30148
Port of #30139 to 5.x, as requested in https://github.com/opencv/opencv/pull/30139#issuecomment-5994064397. Relates to #30138.
The two commits are cherry-picked from #30139 without code changes. The second commit message is reworded for the 5.x module layout.
**Commit 1:** adds `make_optional_arg` entries to `NODES_TO_REFINE` in `modules/python/src2/typing_stubs_generation/api_refinement.py`. These output arrays are documented and used with `None` in the 5.x samples and tutorials, but the stubs type them as required, so mypy rejects correct code:
| Function | Argument |
|---|---|
| `calcOpticalFlowPyrLK` | `nextPts` |
| `calcOpticalFlowFarneback` | `flow` |
| `normalize` | `dst` |
| `kmeans` | `bestLabels` |
| `drawKeypoints` | `outImage` |
| `drawMatches` | `outImg` |
| `drawMatchesKnn` | `outImg` |
**Commit 2:** skips `NODES_TO_REFINE` entries whose function, class or namespace is absent from the build, as the `dnn`/`cuda` refinements already do. On 5.x this also fixes an existing failure: with `-DBUILD_opencv_calib=OFF`, the `calibrateCamera` entry raises `SymbolNotFoundError` and no stubs are generated at all. A wrong argument name in a present function still raises `RuntimeError`.
### Verification
I ran CMake configure only, then `gen2.py` from this branch and from `5.x` (2d88732) on the generated `gen_python_config.json`, with `PYTHONHASHSEED=0`:
- **Full build:** the only differences are 16 signatures in `cv2/__init__.pyi`. Each listed argument changes from `cv2.typing.MatLike` / `UMat` to `... | None`, across the Mat and UMat overloads, and `drawMatches` has two C++ overloads.
- **`-DBUILD_opencv_video=OFF`:** stubs are generated. The two optical-flow entries are skipped and the other refinements still apply.
- **`-DBUILD_opencv_calib=OFF`:** `5.x` generates no stubs (`SymbolNotFoundError` for `calibrateCamera`). This branch generates them.
- **mypy 2.4.0:** I installed the generated stubs as a stub-only package and type-checked calls that pass `None` for each argument, e.g. `cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX)`. With the `5.x` stubs there are 11 `call-overload` errors; with this branch there are none.
Builds without `features` still fail for a separate reason, the `Feature2D` aliases in `predefined_types.py`. I'll address that in a follow-up.
_Prepared with AI assistance (Claude Code); I reviewed the change and the verification results._
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🤖 Generated with [Claude Code](https://claude.com/claude-code)
TEGRA_RESIZE forwarded 1.0/inv_scale (double) into the carotene resize
helpers that take f32, which triggered a C4244 loss-of-data warning and
failed the warnings check on Windows arm64. Cast the scale values to
float at the call sites.
Reducing over more than two axes walks the reduced sub-block with an odometer
over all of them but the innermost one. When an index carried into the next
axis, the step of that axis was assigned instead of being accumulated, so the
steps of every axis above the one that wrapped were lost and the corresponding
elements were read again and again:
axes [0, 2, 3] on a 3x3x3x3 input, shape (3,3,3,3), steps (27, 9, 3, 1)
projected steps: 0 3 6 27 30 33 27 30 33 <- should end in 54 57 60
Only the first output slice happened to be right, because the carry that
introduced the wrong step is the one that moves to the second one.
The same accounting exists for the unprojected axes just below, which
subtracts the step of the wrapped axis and adds the step of the axis it carries
into; the projected loop now does the same. This fixes test_mvn_expanded and
test_mvn_expanded_ver18 (a MeanVarianceNormalization decomposed into
primitives, denied as "Wrong answer" until now) plus any model that reduces
over three or more axes, since the inner projection also feeds the unprojected
axis steps. The OpenVINO backend still answers test_mvn_expanded_ver18
incorrectly, so it is filtered in
test_onnx_conformance_layer_filter__openvino.inl.hpp.
Found by comparing against onnxruntime: 9 of 200 random (rank, shape, axes set,
keepdims, op) combinations were wrong before and none are now; the two
conformance cases above and a new layer test fail without this change.
samples: fix Python odometry sample for 5.x bindings - #30144Fixes#30143
`samples/python/odometry.py` fails on 5.x:
- It uses `cv.DEPTH`, `cv.RGB` and `cv.RGB_DEPTH`, which the Python bindings don't export. The enum values are exposed as `cv.OdometryType_*`, the same names `modules/ptcloud/misc/python/test_odometry.py` uses. Every mode fails with `AttributeError`.
- RGB mode calls `compute(rgb1, rgb2, Rt)`, and that overload always raises "This odometry algorithm requires depth and rgb data simultaneously" (`modules/ptcloud/src/odometry.cpp`). The sample now loads the depth frames in RGB mode too and uses the 5-argument overload.
Tested with opencv-python 5.0.0.93 on Windows 11 and Linux: `--algo DEPTH`, `RGB`, `RGB_DEPTH` and the default (all modes) each run and print `Rt`.
This change was prepared with the help of an AI coding tool; I reviewed it and reproduced the bug and the fix locally.
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In CV_WARP_SCALAR_SHUFFLE_DEF_*, `srcptr` was computed as
`src + srcstep * iy + ix * cn` before checking whether `ix` and `iy`
are within bounds. Since `srcstep` is `size_t` (unsigned) and `ix`, `iy`
are `int` (signed), negative coordinates cause unsigned wrap-around and
trigger UBSan `pointer-overflow` when added to `src`.
Defer computing `srcptr` to `CV_WARP_SCALAR_SHUFFLE_LOAD_*` where `ix`
and `iy` are guaranteed to be non-negative and within bounds, and index
directly from `src` in `CV_WARP_SCALAR_FETCH_PIXEL_*` after checking
that `ix + dx` and `iy + dy` are within bounds.
core: unified N-D data-movement engine - #30081
closes: https://github.com/opencv/opencv/issues/29315
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Added CMake message if IPP is not found - #30113
The change adds the verbosity if IPP is not found. Currently the output says nothing and it's difficult to understand from CMake output if IPP is used. Now it will explicitly say:
```
-- Other third-party libraries:
-- Intel IPP: NO (download/detection failed)
```
Briefly discussed here: https://github.com/opencv/opencv/pull/30060#issuecomment-5834673271
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geometry: handle points at a circle's center - #30116
## Summary
Handle a point at the center of a circle in `getClosestEllipsePoints`.
When the point is the ellipse-frame origin, `solveFast` computes a zero norm and divides by it, producing NaN coordinates for a circle. Return the nearest point on the minor axis for this degenerate input.
Add a regression test checking the returned point is finite and lies on the circle.
## Validation
- `opencv_test_geometry`: 325/325 passed.
- Differential ellipse probe: 9,135 checks passed; the pristine build reproduces the NaN.
Fixes the circle-centre case in #26078-related ellipse-distance handling.
kmeans: handle NaN and huge input values without crashing/OOB - #30117
kmeans: handle NaN and huge input values without crashing/OOB
When input data contains NaN or values large enough for squared L2 distances to overflow float (Inf), distance comparisons fail and can leave bestCenter or farthest_i as -1 (causing out-of-bounds memory access) or leave clusters empty (triggering CV_Assert(counters[k] != 0)).
Bail out early on the first iteration when non-finite distances are detected in generateCentersPP or KMeansDistanceComputer, zero-initialize the output labels and centers, and return -1.0 for compactness.
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Added support for Granite Docling 258M and PaddleOCR-VL - #29618
Adds `com.microsoft::GroupQueryAttention` to the engine-new ONNX importer, required by GraniteDocling-258M's ,end-to-end samples for GraniteDocling-258M and PaddleOCR-VL-1.5. Verified on Linux x86_64 / GCC / CPU
**companion opencv_extra PR** [Vlm ocr support engine new opencv/opencv_extra#1412](https://github.com/opencv/opencv_extra/pull/1412) for test data
upstream/5.x already lowers `GroupQueryAttention` to `AttentionOnnxAi`, but that path rejects `do_rotary=1`:
```cpp
CV_CheckEQ(params.get<int>("do_rotary", 0), 0, "GroupQueryAttention: do_rotary=1 is not supported");
```
GraniteDocling-258M's export sets it. Confirmed two ways: building a Llama checkpoint from its published `text_config` (9 heads / 3 KV heads, `rope_theta=100000`) and running onnxruntime-genai's `model_builder`; and reading the community export at `onnx-community/granite-docling-258M-ONNX`, which carries 30 `GroupQueryAttention` nodes with `do_rotary=1`, `num_heads=9`, `kv_num_heads=3`, `head_dim=64`.
## Changes
**Importer** (`onnx_importer2.cpp`, +49)
Routing as above, plus the input validation the op needs: packed QKV rejected, past_key / past_value required as a pair, `do_rotary=1` requires cos/sin, unsupported trailing inputs rejected, and `seqlens_k` shorter than the buffer recognised as a shared-buffer export rather than silently mis-attended.
**Layer** (`group_query_attention_layer.cpp`, +380)
Attention is built on `fastGemmBatch` and `fused_softmax_softcap_mask`, the same kernels `attention_onnxai_layer.cpp` uses. Grouping falls out of the batched-GEMM offset table (query head → `h / groupSize`), so no broadcast copy is needed, and the second GEMM writes straight into the `[B, S, num_heads*D]` output. Supports rotary (full and partial), sliding window, softcap, growing and preallocated caches, and optional `present_*` outputs.
**Core** (`persistence_json.cpp`, +92)
`\uXXXX` escapes are UTF-16 code units, so anything above the BMP arrives as a surrogate pair. These were encoded as two separate 3-byte sequences CESU-8, which is not valid UTF-8 - for keys and values alike. Now recombined into one 4-byte sequence, with unpaired surrogates rejected.
**Tests / perf / samples**
`test_layer_group_query_attention.cpp` (+93), `test_tokenizer.cpp` (+28), `test_tokenizer.py` (+42), `perf_layer.cpp` (+125: `Layer_GroupQueryAttention` MHA_ShortCache and Grouped_WithCache, plus `Layer_Sign`), and two samples (`granite_docling_inference.py`, `paddleocr_vl_inference.py`).
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
The 64-bit lane forms of v_select() landed with #30071, so the two
hand-written v_blend_s64 / v_blend_u64 helpers this patch carried are no
longer needed. Drop them and call v_select() directly; the argument order
and the all-ones / all-zeros mask convention are identical, so the
generated code is the same where a backend already had a native blend and
better where it now has one.
The shift-and-equality trick stays: the 64-bit ORDERING compares are still
unusable here, since v_gt on v_int64x2 is the sign of (b - a) at the SSE
and NEON baselines.
The values at the ends of the 64-bit range were crossed with the CV_32U
source as well, where they never reach the narrowing this patch changes -
they only exercise the double -> CV_32U conversion that produces the
source array. That conversion is not length-independent on macOS/ARM64,
so those cases failed there for a reason unrelated to this change.
Split the instantiation in two: CV_32U keeps the values that fit its own
range, and the 64-bit sources keep the 64-bit extremes, which is where
they test the clamp. Reverting the clamp still fails the From64 cases at
INT64_MIN and INT64_MAX, so the coverage this adds is unchanged.
The clamp used the 64-bit ordering compares, which was raised in review as
unsafe. It is: at the SSE and NEON baselines v_gt on v_int64x2 is the sign
of (b - a) (intrin_sse.hpp, intrin_neon.hpp), so it overflows - and answers
the wrong way - exactly when the operands sit at opposite ends of the
64-bit range, which is the case a saturating conversion has to get right.
v_gt(INT64_MIN, INT32_MAX) reports true. Only the 256-bit AVX2 path lowers
to a native compare, so the conversion was right when a build dispatched to
AVX2 and wrong at its own baseline:
Mat src(1, 64, CV_64S, Scalar(INT64_MIN));
src.convertTo(dst, CV_8U); // AVX2: 0 SSE/NEON baseline: 255
Use an arithmetic shift and an equality instead. v_eq on 64-bit lanes is
built from 32-bit compares and is exact, and the shift cannot overflow, so
the result no longer varies with the dispatch level. The unsigned-source
helpers already worked this way and are unchanged.
The parameterised test now covers INT64_MIN, INT64_MAX, INT32_MIN and
INT32_MIN-1 as well, so the boundary cases are exercised; NEON has the same
subtraction-based compare, so the ARM CI jobs guard this directly.
Turn the saturation test into a TEST_P over source depth, destination
depth and value, and drop the length sweep in favour of a single 127
element row, which covers both the vector body and an odd tail. Remove a
stray blank line that was not part of the change.
Document why the 64-bit blend is written out by hand: v_select() has no
64-bit integer form on any backend - intrin_sse.hpp carries those two
entries commented out as TBD and the NEON list stops at v_float64x2 - so
the mask is applied with the same v_and/v_gt shape the file already uses.
v_pack() narrows 64-bit lanes by truncation: at the 8/16/32-bit widths
v_pack saturates, but the 64->32 forms are defined with static_cast (see
OPENCV_HAL_IMPL_C_PACK in intrin_cpp.hpp) because no backend offers a
saturating 64->32 pack. The vectorized conversion helpers packed straight
from a 64-bit source, so a value outside the 32-bit range wrapped, and the
saturating narrow that followed then clamped the WRAPPED value - landing at
the wrong end of the destination's range. The same happened one width down,
where a CV_32U source was reinterpreted as signed without clamping.
Only the vector loop was affected, so the answer depended on how long the
array was: below the vector width the scalar path saturated correctly.
Mat src(1, n, CV_64U, Scalar(4294967295));
src.convertTo(dst, CV_8U); // n < 16: 255 n >= 16: 0
src.convertTo(dst, CV_16U); // n < 16: 65535 n >= 16: 0
Clamp into the destination's range before packing. The unsigned-source
helpers test the high bits with a shift instead of an ordering compare,
since unsigned 64-bit comparison is not uniformly available across the SIMD
backends, and v_select() has no 64-bit form so the blend goes through the
comparison mask by hand.
Affects CV_64U/CV_64S -> CV_8U/CV_8S/CV_16U/CV_16S/CV_32S and
CV_32U -> CV_8S/CV_16S.
With hardware decoding, retrieveFrame() downloads the GPU frame into a new
AVFrame with av_hwframe_transfer_data(). That call copies the pixel data
only: colorspace, color_range, color_primaries, color_trc and
chroma_location are left unspecified in the system memory copy.
With swscale >= 8.12.100 the color conversion goes through
sws_scale_frame(), which configures itself from the properties of the source
frame. Given unspecified properties it falls back to BT.601 limited range, so
hardware-decoded frames are converted with the wrong matrix whatever the
stream signals, and full range streams additionally lose contrast. Software
decoding is not affected, the decoder output carries the properties.
fftools/ffmpeg_dec.c handles this by calling av_frame_copy_props() right
after the transfer; do the same. A failure is only logged: it can only be an
allocation failure on side data, and the picture itself is still valid.
With swscale < 8.12.100 the conversion context is initialized without the
frame properties, so this change has no effect on that path.
Measured on Windows x64 with the OpenCV 5.0.0 FFmpeg wrapper built by the
opencv_3rdparty MinGW recipe against FFmpeg n8.1.3 (libswscale 9.5.103),
D3D11VA decoding, on two synthetic BT.709 H.264 samples:
ffmpeg -f lavfi -i testsrc2=size=1280x720:rate=30 -t 2 -c:v libx264 \
-pix_fmt yuv420p -color_range tv -colorspace bt709 \
-color_primaries bt709 -color_trc bt709 bt709_tv.mp4
ffmpeg -f lavfi -i testsrc2=size=1280x720:rate=30 -t 2 -c:v libx264 \
-pix_fmt yuvj420p -color_range pc -colorspace bt709 \
-color_primaries bt709 -color_trc bt709 bt709_pc.mp4
Frame 0 is compared with a reference built from the same static FFmpeg
libraries: software decode, lossless repack to NV12 (the D3D11VA download
format), av_frame_copy_props(), then sws_scale_frame() to BGR24 exactly as
retrieveFrame() does.
sample before (mean / max abs diff) after
bt709_tv.mp4 7.424 / 94 bit-exact
bt709_pc.mp4 3.286 / 101 bit-exact
Before the patch the output is bit-exact with the same reference built
without the frame properties, which confirms the cause. Software decoding
output is unchanged (bit-exact before and after). Two real H.264 recordings
(BT.709, full and limited range) behave the same way.
Hardware and software decoding still do not produce identical pictures after
this patch: swscale converts NV12 and yuv420p differently around sharp chroma
edges. That difference exists with the same properties outside OpenCV too
and is not addressed here.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Extend TFLite importer to support more models on the new engine - #29569
Requires: https://github.com/opencv/opencv_extra/pull/1399
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
videoio: encoder controls for VideoWriter (bitrate, CRF, preset, GOP, pixel format) - #30064
Resolves [Issue#8961](https://github.com/opencv/opencv/issues/8961)
Until now, the only way to control encoder quality from VideoWriter was to write a GStreamer pipeline string by hand. This PR adds open-time properties that work with both the FFmpeg and GStreamer backends:
- VIDEOWRITER_PROP_BITRATE sets the target bitrate in bits per second.
- VIDEOWRITER_PROP_CRF sets the constant rate factor. It overrides bitrate if both are given.
- VIDEOWRITER_PROP_PRESET takes a value from the new VideoWriterPresets enum, ULTRAFAST through PLACEBO, using the x264/x265 preset names.
- VIDEOWRITER_PROP_GOP_SIZE sets the maximum interval between key frames.
- VIDEOWRITER_PROP_COLOR_SPACE used to be GStreamer-only and now works with FFmpeg too. It takes a fourcc, the same encoding as CAP_PROP_CODEC_PIXEL_FORMAT.
Tests are in test_ffmpeg.cpp and test_gstreamer.cpp. They check that CRF and bitrate change the output size, that GOP and preset values come back from get(), that the pixel format matches the capture property, that an unsupported property makes open() fail, and that nothing changes when no properties are set.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
video: add input validation for OPTFLOW_USE_INITIAL_FLOW in calcOpticalFlowFarneback (port to 5.x) - #30100
This is a port of #30086 to the 5.x branch.
Added `CV_Assert` validation for `OPTFLOW_USE_INITIAL_FLOW` in `calcOpticalFlowFarneback` to ensure the initial flow matrix is not empty, matches the input size, and has the correct type (`CV_32FC2`). Also added corresponding unit tests in `test_OF_accuracy.cpp`.
Unified DNN graph fusion behind fuseChains - #30088
Requires: https://github.com/opencv/opencv_extra/pull/1417
### What This PR Does
Follow-up to #29656, which added the backend-agnostic chain mechanism. That PR introduced it and made pointwise fusion its first consumer; this one moves the rest of the fusion onto it.
- `Net::Impl::fuseBasic()` is gone. Its conv+BatchNorm, conv+activation and conv+residual branches now go through the chain mechanism.
- `fuseBN` and the six other passes now run from inside `fuseChains()`, in their existing order.
- `graph_fusion_basic.cpp` keeps `fuseBN()` and `fuseInstanceNormAffine()`.
- New `FusionOps::foldInputScale` folds a scale sitting before a conv directly into its weights, across the dense, depthwise, grouped and MLAS-prepacked layouts.
### Results
Measured on Intel Core i9-11900K
| Model | 5.x | this PR | speedup |
|-----------------------------|-----------|-----------|---------|
| inception_v2 | 15.04 ms | 11.80 ms | 1.27 |
| efficientnet-lite4 | 14.83 ms | 12.28 ms | 1.21 |
| pose_estimation_mediapipe | 5.66 ms | 5.25 ms | 1.08 |
| siglip_base_patch16_224 | 143.04 ms | 135.27 ms | 1.06 |
| densenet121 | 25.99 ms | 24.76 ms | 1.05 |
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
core: saturating float/double->intXY conversions in cvRound/cvFloor/cvCeil and the corresponding univ. intrinsics. - #30007
Merge pull request #30007 from vpisarev:fix_flt2int
Fixes#28557 (supersedes the tail-only fix from #29895).
### The problem
On x86 `cvtss2si`/`cvtsd2si`/`cvtps2dq` return the "integer indefinite" value `0x80000000` (`INT_MIN`) for any out-of-range input, so
```cpp
cvRound(3e9); // INT_MIN
saturate_cast<ushort>(60000.f*60000.f); // 0 instead of 65535 (#28557: the scalar tail of cv::multiply)
v_round(v_float32(1e10f)); // INT_MIN lanes
```
The problem is not x86-only. `cvRound()` on aarch64, riscv64 and loongarch64 went through `(int)lrint()`, which the compilers implement as a 64-bit conversion followed by truncation to 32 bits, i.e. large values *wrap* instead of saturating (`fcvtzs x0` + `mov w0`, `fcvt.l.d` + `sext.w`). NEON `v_round/v_floor/v_ceil/v_trunc(v_float64x2)` narrowed with a wrapping `vmovn`, and `saturate_cast<unsigned/int64/uint64>(float/double)` were plain UB above the target range.
### The fix
**`fast_math.hpp`**
- `cvRound()`, `cvFloor()`, `cvCeil()` saturate on every platform. New `cvTrunc()` (round towards zero, saturating; the scalar counterpart of `v_trunc`) and `cvRound64()` (round-half-to-even to `int64`, saturating).
- x86 (SSE2): the input is clamped from above (`min`) before `cvt*`; the "indefinite" value is already the correct result below `INT_MIN`. `cvFloor` also clamps from below because of the "-1" correction.
- aarch64 (`__GNUC__`/clang): one-instruction inline asm `fcvtns/fcvtms/fcvtps/fcvtzs` (GCC's `arm_neon.h` has no scalar f64→s32 ACLE functions, and inline asm needs no header).
- riscv64: `fcvt.w.{s,d}` / `fcvt.l.{s,d}` with an explicit rounding mode (`rne`, `rdn`, `rup`, `rtz`).
- loongarch64: `ftint*.w.{s,d}` + `movfr2gr.s` (the old `.l.d` + `movfr2gr.d` wrapped); `cvRound` got a branch too.
- MSVC ARM64: the 64-bit results are clamped before narrowing.
- Portable `#else` branch: hand-written clamp before the conversion, no new headers.
- Exact semantics: `double` saturates to `INT_MIN`/`INT_MAX` exactly. `INT_MAX` is not representable as `float`, so `float` inputs `>= 2^31` give **2147483520** (the largest float below 2^31, the clamp value) where the instruction does not saturate by itself (x86, portable) and `INT_MAX` where it does (ARM, RISC-V, LoongArch). Both are accepted by the tests and documented.
- **NaN handling is out of scope in this PR**.
**`saturate.hpp`**
- `float/double → unsigned/int64/uint64` now saturate and use round-half-to-even via `cvRound64()` (no libm `round()` call; OpenCV builds without `-ffast-math`, where `rint()`/`round()` are real calls).
- `int/int64 → schar/short/int` range checks are done in unsigned arithmetic. The old `(unsigned)(v - SHRT_MIN)` overflowed (UB) for `v > INT_MAX - 32768`; such values were unreachable before but are returned by the saturating `cvRound()` now, and GCC 15 actually exploits the UB (derives `v <= INT_MAX-32768` and folds neighbouring comparisons).
**Universal intrinsics**
- SSE, AVX2, AVX-512: clamp before `cvt` in `v_round/v_floor/v_ceil/v_trunc` (f32 and f64, `v_round(a, b)` included). AVX2 keeps `_mm256_floor_ps/_ceil_ps`.
- NEON f64: saturating narrow (`vqmovn_s64`); `v_floor/v_ceil` use `fcvtms/fcvtps` directly; `v_trunc` used `vcvtaq` (round-to-nearest-away) instead of truncation.
- WASM f32: clamp before the `-1/+1` corrections (they wrapped `INT_MIN`); `v_round` was `trunc_sat(a + 0.5)` (wrong for negatives and ties), now `f32x4.nearest`; f64 `v_trunc` via `cvTrunc`.
- MSA f64: clamp before `pckev.w`, which takes the low 32 bits of the saturated int64.
- RVV f16 `v_floor`: rounding mode `2` (RDN) instead of `1` (RTZ).
- VSX, LSX/LASX, RVV f32/f64, RVV 0.7.1: untouched, the conversion instructions saturate by ISA definition.
- `intrin_cpp.hpp`: `v_trunc` via `cvTrunc`; docs mention the saturation.
`arithm.simd.hpp` is not modified: `Core_Arithm.mul_overflow_28557` passes because the scalar tail's `saturate_cast<ushort>(float)` saturates now.
### Tests
- `Core_Arithm.mul_overflow_28557` re-enabled.
- `Core_Arithm.mul_overflow_tail_and_inplace`: 8U/8S/16U/16S, row lengths 1..70 (vector body + scalar tail), out-of-place and in-place `multiply`, `mul`, `pow(x, 2)`.
- `Core_ConvertTo.float_overflow_saturation`: 32F/64F → 8U/8S/16U/16S/32S/32U/64S/64U with `±1e10`, `±1e30`, `±inf`, `2147483647.5`, `x.5` etc.; vector body vs. scalar reference, extreme inputs must hit the type limits.
- `Core_FastMath.SaturatingRoundingOps`: boundary table (`2147483647.5`, `-2147483648.5`, `2147483520f`, `-2147483904f`, `±DBL_MAX`, `±inf`, ...) plus a 200k-value log-uniform random sweep against `nearbyint/floor/ceil/trunc` clamped to `int`.
- `Core_FastMath.Round64`: boundaries around `±2^63`, `.5` cases, random sweep.
- `Core_SaturateCast.FloatToIntSaturation`, `RoundHalfToEven`, `IntToNarrowerIntBoundaries` (regression for the signed-overflow UB).
- `test_intrin_utils.hpp` (`test_float_math`, `test_round_pair_f64`): overflow/boundary lanes for f32, f64 and f16, compared against the scalar functions and checked against explicit `INT_MIN` / `[2147483520, INT_MAX]` / `INT_MAX` limits. Runs on every backend and dispatch level.
### Verification
- x86-64, GCC 15.2, `CPU_BASELINE=SSE4_2 CPU_DISPATCH=AVX2,AVX512_SKX`: full `opencv_test_core` passes (16864 tests), i.e. SSE4.2 baseline, AVX2 dispatch and the CPP emulator paths. AVX-512 is compile-checked only (no AVX-512 hardware here).
- The standalone scalar checks also pass with the portable `#else` branch forced (`-U__SSE2__`) and under `-fsanitize=undefined`.
- aarch64 (`aarch64-linux-gnu-gcc-14`) and riscv64 (`riscv64-linux-gnu-gcc-14 -march=rv64gcv_zvfh`): compile-checked, generated code inspected (`fcvtns w0, d0`, `fcvt.w.d a0, fa0, rne`, `sqxtn`, `vfcvt.x.f.v`, ...). Not run (no hardware/qemu).
- **Not verified at all** (please watch CI): LoongArch (`ftint*.w.d`/`ftintrne.*` asm), WASM (`wasm_f32x4_nearest`), MSA, MSVC ARM64 (`vcvtd_s64_f64`, `vcvts_s32_f32`, `vcvtnd_s64_f64`).
### Behaviour changes worth noting
- `saturate_cast<unsigned/int64/uint64>(x.5)` now rounds half to even (was half away from zero), consistent with all the other integer targets.
- `cvRound(float)` for inputs `>= 2^31` returns **2147483520** on x86 (was `INT_MIN`); `INT_MAX` on ARM/RISC-V. It would be noticeably slower to implement true saturation to `INT_MAX`. Note that around `INT_MAX` float's cannot represent the integer's exactly anyway.
- `saturate_cast<int>(1e10)` is `INT_MAX` (documentation used to say "no clipping is done for 32-bit integers").
### Pull Request Readiness Checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on code under GPL or another license incompatible with OpenCV.
- [x] The PR is proposed to the proper branch (`5.x`).
- [x] There is a reference to the original bug report and related work: #28557, #29895.
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable: accuracy tests added, no test data needed.
- [x] The feature is well documented and sample code can be built with the project CMake: doxygen comments of `cvRound`/`cvFloor`/`cvCeil`/`cvTrunc`/`cvRound64`, `saturate_cast` and the intrinsics conversion group updated.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
https://claude.ai/code/session_01KVnDjvrFtxcyw7RFzSPews
calib: improve fisheye extrinsic refinement - #30070
This is #29940 cherry-pick to 5.x with adaptions to the new `LevMarq` interface.
Additionally included is a fix to a latent bug also present in 4.x but only exposed by the `3cams-hetero` dataset introduced in 5.x. The reason for this bug is #17454 which introduced sentinel values to `fisheye::undistortPoints` that can corrupt initial estimates and thus need to be filtered.
The bug fix should ideally be applied to the 4.x branch as well.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
libopencv_imgproc grew noticeably with the HarfBuzz-based text engine. Measured on
macOS/arm64 (clang, Release, stripped) the text-related part of the library was
~830 KB: HarfBuzz 486 KB, the two Rubik fonts 296 KB, the engine ~25 KB, plus ~2 MB
of WenQuanYi when WITH_UNIFONT=ON (the previous default). With GCC and an unstripped
.so on Linux the HarfBuzz part alone is 700+ KB.
This patch brings it down to ~550 KB by default (HarfBuzz 377 KB + Rubik 146 KB +
engine 30 KB) without dropping functionality:
* 3rdparty/harfbuzz: build with -Os -fno-exceptions -fno-rtti -fno-threadsafe-statics
(like HarfBuzz's own build; shaping speed is irrelevant thanks to the glyph cache)
and HB_NO_VAR_COMPOSITES (VARC table, a 2024 format with no real fonts yet).
Variable-font support (wght axis etc.) is untouched. -22% of HarfBuzz code.
* Only the "sans" font (Rubik) is compiled into imgproc by default. Rubik Italic and
WenQuanYi Micro Hei became optional: WITH_ITALICFONT (new) and WITH_UNIFONT are
both OFF now. The build summary lists the built-in fonts.
* New API cv::FontFace::setBuiltinFont(name, path) fills or replaces any of the
three built-in slots ("sans", "italic", "uni") with an external font file. The
registered font takes part in the automatic fallback chain exactly like a
compiled-in one, so users can get CJK text or a true italic without rebuilding
OpenCV. The registry is process-wide; each thread's engine reloads its slots
lazily (generation counter), and the glyph cache is dropped on reload since
hb_font pointers are part of its key.
* "italic" resolves to "sans" when no italic font is built in or registered, so
FONT_ITALIC / FONT_HERSHEY_SCRIPT_* in the legacy putText keep working (upright).
* Fix: a character not covered by any font used to be drawn as '?' taken from the
last built-in slot, which can be empty now; the '?' is looked up like any other
character (user font first).
* Tests: opencv_test_imgproc registers Rubik-Italic.ttf.gz and
WenQuanYiMicroHei.ttf.gz from opencv_extra (testdata/highgui/drawing) as the
"italic"/"uni" slots, so Drawing.ttf_text reproduces the reference picture 1:1
with the default build and the legacy hershey-font tests are unchanged.
Drawing.builtin_font_slots covers the new API. Tests skip if the fonts are absent.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Softmax and LogSoftmax up to ONNX opset 12 coerce the input to the 2D tensor
[a_0*...*a_{axis-1}, a_axis*...*a_{n-1}] and reduce over its second dimension, so
axis=1 on a 2x3x4 input normalises the 12 values of the flattened tail per sample,
not the 3 values of that axis. The importer only picked the right default axis for
those opsets and the layer always reduced the single axis, which differed from
onnxruntime by up to 0.63 on such models. opset 12 is included as well: Softmax-11
is still the schema in effect there.
The layer now takes the pre-opset-13 semantics through a `coerced_2d` parameter
(the name the int8 Softmax already uses) and normalises the contiguous 2D view of
the blob. The OCL kernels reduce a single axis, so that case stays on the CPU path.
The Exp/ReduceSum/Div subgraph fold produces a Softmax node too, but it matches a
single-axis ReduceSum, which is not the pre-13 meaning, so it now marks the fused
node with coerced_2d=0 to keep the per-axis semantics of the subgraph it replaces.
Tests: a layer test comparing a 2x3x4 input against the definition, and two ONNX
models whose opset_import is 11 (data added to opencv_extra by the companion
change). Both fail on the previous code with normInf 0.63 and 4.38.
Reported-by: differential testing against ONNX Runtime 1.30 CPU.
`clip` bounds the input of the activations to [-clip, clip]. The layer never
read it, so a model asking for it computed the unclipped gates instead - off by
up to 0.56 on the cases checked here, with no warning.
Apply it to the two places the gates are activated: the z/r pair before
sigmoid() and the candidate before tanh(), i.e. after the recurrent bias has
been folded in. It stays off unless the attribute is present and positive, so
models without it keep the same code path.
Checked against ONNX Runtime 1.30 (CPU): GRU with clip agrees to ~1e-7 for
clip = 0.4 and 0.05, with and without linear_before_reset, forward, reverse and
bidirectionally, with an initial h and with sequence_lens. The new
Layer_GRU_Test_Accuracy_.Clip clamps the gate pre-activations in its reference
and fails on Y and Y_h without the patch.
The ONNX conformance GRU cases do not set clip, so nothing covered this.
The layer never read input #4, so a node with sequence_lens ran every batch
entry to the end of the padded sequence: Y was not zero-padded past each entry's
own length, and Y_h was the state after the whole sequence instead of the one
that entry finished with. RNN2 is the only recurrent layer that implemented it.
Take RNN2's semantics, which are the spec's: an entry stops updating once it
reaches its length, keeping the state it finished with, and contributes zeros to
Y from there on. That also holds for a lone reverse direction, where the scan
starts at the last timestep but the valid range still ends at each entry's own
length.
Checked against ONNX Runtime 1.30 (CPU): GRU with sequence_lens - forward,
reverse and bidirectionally, with and without linear_before_reset, with initial
h and with fewer entries than the batch - agrees to 6e-8 - 1.9e-7 after the
change (it was 0.2 - 0.9 off before). The new
Layer_GRU_Test_Accuracy_.SequenceLens covers a sample at full length, a
truncated one and an empty one, and fails on Y and Y_h without the patch.
The ONNX conformance suite does not cover this: its test_gru_seq_length and
test_rnn_seq_length cases are named for a longer test sequence but carry no
sequence_lens input at all.
The layer only ever compared `direction` against "bidirectional", so a node
with direction=reverse ran forwards: Y came out in forward order and Y_h was
the state left after the last timestep instead of the first. Walking the
sequence backwards only happened for the second direction of a bidirectional
node, while LSTM2/RNN2 honour a lone reverse direction.
Parse the attribute into a `reverse` flag and use it wherever the scan order is
picked. Checked against ONNX Runtime 1.30: the same model with
direction=reverse now matches, and Layer_GRU_Test_Accuracy_.ReverseDirection
compares Y and Y_h against the recurrence evaluated from the last timestep
down. The ONNX conformance suite has no reverse-direction GRU case.
Gelu has had an `approximate` attribute since opset 20: 'none' selects
0.5*x*(1+erf(x/sqrt(2))) and 'tanh' the approximation
0.5*x*(1+tanh(sqrt(2/pi)*(x + 0.044715*x^3))). Every Gelu node was routed to
the erf-based layer and the attribute was ignored, so a node asking for the
tanh form silently got the exact one - a different function, off by up to
4.1e-4 around |x| = 3.
The tanh form already has a layer of its own (GeluApproximation, the one the
decomposed-graph fusion produces), so route the attribute there from a
dedicated parseGelu(). Gelu without the attribute is unchanged.
test_gelu_tanh_1 and test_gelu_tanh_2 were the two cases failing on this and
had their tolerances widened instead: 1.1e-4/1.6e-4 and 9e-5/5e-4. With the
right formula both pass at the default 1e-5/1e-4, so those overrides are
dropped. Reduced-precision targets keep the bounds they had.
fuseMatMulConstBToGemm() rewrites MatMul(A, const B) into a Gemm with
flatten_a=false so that A keeps its leading dims. ONNX MatMul accepts a
rank-1 A: it is promoted to (1, K) and the leading 1 is squeezed from the
result again, so Y is (N,). GemmLayerImpl::getMemoryShapes() rejected that
with "Tensor A must be n-dimensional (n >= 2)", so a model like
MatMul(A[3], const B[3,6]) only loaded while B was still a runtime input;
once the fusion fired it threw. finalize() and forward() indexed
shape_A[dims_A - 2] / shape_Y[dims_Y - 2] unconditionally and would have
read out of bounds for the same shapes.
flatten_a=false is only produced by that rewrite, so gate the relaxed rank
check on it: a plain ONNX Gemm (flatten_a defaults to true) still requires a
2-D A, which is what the op specifies. Where the extra dimension is needed,
treat the rank-1 A / (N,) Y as M == 1 and use A.view as (1, K), matching
what MatMul produced before the rewrite.
Add Test_Gemm.Rank1AFromMatMulRewrite covering fp32/fp64 with and without a
bias, plus a check that a plain Gemm still rejects a rank-1 A. It fails
before the fix with the reported assertion.
For the ONNX recurrent ops output #0 is Y, the hidden state of every
timestep, and output #1 is Y_h, the last one. A node that declares a single
output can only mean Y: asking for Y_h alone requires output #0 to be
declared as an empty name, which makes it a second output, so the declared
count never collapses to one.
The GRU nevertheless answered with Y_h in that case, while LSTM2 and RNN2
both answer Y for the same node. Give slot #0 the Y shape unconditionally
when a single output is requested, which makes resolveOutputPolicy() unused.
Add Layer_GRU_Test_Accuracy_.SingleOutputIsSequenceY, written from the ONNX
GRU equations for requiredOutputs == 1; it fails before the fix and passes
after. The existing ONNX conformance GRU cases declare ['', 'Y_h'] and are
unaffected.
Add cv::linearAssignment() rectangular assignment solver - #29963
Adds `cv::linearAssignment()` to `modules/core`, next to `solveLP`: a Jonker-Volgenant solver that pairs the rows of a cost matrix with its columns at the lowest total cost. It pairs existing tracks with new detections in a tracking pipeline.
`costThreshold` is applied during the solve, so a pair that is too expensive is forbidden and a pair is only made when it beats the price of leaving it unmade. Implemented from Crouse 2016, cited in the doxygen.
Accuracy and performance tests are included and need no new test data.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
MatMulHelper::updatePackedBOffsets derived the per-slice stride of the
packed constant weight as packed_B_size / batch, where `batch` is the
number of matrices in the *output*, i.e. after broadcasting the batch
dims. fastGemmPackB stores one packed matrix per batch slice actually
present in B, so when B is broadcast over the output batch dims the two
counts differ and every slice beyond the first was read at the wrong
offset. The output had the correct shape but wrong values: only the
first index along a broadcast dim matched.
Remember the number of slices stored in B (B_batch) in compute() and use
it to derive the packed stride.
Add a regression test using A(2,1,3,4) @ B(1,5,4,6); it fails before the
fix and passes after.
SequenceTypeNode and ClassTypeNode always format as _typing.Sequence[...]
and _typing.Type[...], but ContainerTypeNode only reports the typing
import as required when compatible_to_runtime_usage is set. Stubs for
modules that use these nodes without any other _typing usage (e.g.
cv2/videoio_registry) reference _typing without importing it.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
core: stop cv::sum() wrapping at 32 bits for CV_32U and CV_32S - #30003Fixes#30002
### Problem
`cv::sum()` returns doubles, but for single-channel `CV_32S` and `CV_32U` it accumulates part of
the total in 32 bits, so a sum beyond the 32-bit range wraps. For `CV_32S` that is signed integer
overflow.
```
Mat m(1, 8, CV_32S, Scalar(INT_MAX));
cv::sum(m)[0]; // -8 expected 17179869176
Mat u(1, 8, CV_32U, Scalar(UINT_MAX));
cv::sum(u)[0]; // 8589934584 expected 34359738360
```
`sum_()`'s unrolled single-channel step adds four elements **to each other** in `WT` before the
result reaches the `ST` accumulator, and `WT` defaults to the source type:
```cpp
s0 += (WT)src[0] + (WT)src[cn] + (WT)src[cn*2] + (WT)src[cn*3];
```
For `CV_8U`/`CV_16U`/`CV_16S` that is harmless — integer promotion widens the operands to `int`
first. For the 32-bit depths the additions happen in 32 bits.
The vectorized specialization only runs from `len >= 6 * vector_width`, so shorter arrays take the
scalar route entirely and longer ones still leave a wrapping tail; and `DEFINE_SUM_SIMD_32` is
instantiated only for `int` and `float`, so `CV_32U` has no vectorized path at all. Multi-channel
inputs are unaffected: those branches add each element straight into its own `double` accumulator.
### Fix
Pass an explicit 64-bit integer `WT` for the two entry points. Widening to 64-bit integers rather
than to `double` keeps the step in integer arithmetic, and four 32-bit values cannot come near the
64-bit range. The 16-bit float entry points already pass an explicit `WT` for the same reason.
### Performance
1920x1080, best of 5 runs of 50 iterations:
| depth | before (wrong) | this patch (int64 WT) | double WT, for comparison |
| --- | --- | --- | --- |
| `CV_32U` | 0.430 ms | 0.407 ms | 0.729 ms |
| `CV_32S` | 0.235 ms | 0.258 ms | 0.268 ms |
| `CV_16U` (not touched) | 0.111 ms | 0.095 ms | |
| `CV_32F` (not touched) | 0.244 ms | 0.254 ms | |
Accumulating in `double` would also fix the wrap but costs about 70% on `CV_32U`, which has no
vectorized path and therefore runs this step for every element. The integer widening has no
measurable cost; run-to-run noise on this machine is around 10%.
### Tests
`Core_SumWide.no_32bit_wraparound` is a `TEST_P` over depth, channel count and length (4, 8, 23,
24, 64, 127 — around the vectorized path's 6-vector-width threshold), asserting the exact total.
It fails on unpatched 5.x:
```text
[ FAILED ] Core_SumWide.no_32bit_wraparound/0, where GetParam() = (12, 1, 4)
Which is: 17179869180
Which is: 4294967292
```
and passes with the patch. Full `opencv_test_core`: 16897 of 16904. The 7 failures
(`Core_InputOutput.filestorage_base64_*`, `Core_globbing.accuracy`) reproduce identically on
unmodified 5.x in this environment and are unrelated.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Promote the update of IPP ICV integration (20260922) to 5.x - #30078
Supported IPP version was updated to IPP 2026.1.0 (202600922) both for Windows and Linux together with enabling cases suspended for the previous release.
IPPICV binaries are here https://github.com/opencv/opencv_3rdparty/pull/111.
PR for `4.x`: #30060
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
core: add ARMPL HAL backend for cv::invert(DECOMP_LU) - #30080
## Summary
- Follow-up to #[30035](https://github.com/opencv/opencv/pull/30035) to apply the same `cv::invert` optimization to the 5.x branch.
- Adds an ARM Performance Library (ArmPL) HAL backend for cv::invert under DECOMP_LU (cv_hal_LU32f / cv_hal_LU64f), replacing OpenCV's default LU decomposition path with ArmPL's LAPACKE_sgesv / LAPACKE_dgesv for float and double matrices on AArch64.
## Changes
**hal/armpl/include/armpl_hal_core.hpp:**
- Declare armpl_hal_LU32f / armpl_hal_LU64f and register them as cv_hal_LU32f / cv_hal_LU64f.
**hal/armpl/src/armpl_hal_core.cpp:**
- Add armpl_lu() function (works for both float and double), that does the LU decomposition of the matrix:
- If a right-hand side b is given, then LAPACKE_sgesv/dgesv is called.
- If no right-hand side is given, it only factorizes the matrix using LAPACKE_sgetrf/dgetrf
- Fall back to OpenCV's default (non-HAL) implementation for small matrices (m < 100).
dnn: fold Shape of static-shape inputs in constFold() - #30065
When a graph input has a fully known shape, `constFold()` now replaces `Shape` ops on it with constants. The usual const-folding cascade then also removes the Gather/Concat/Cast/Slice ops that follow.
**What it adds**
- `graph_const_fold.cpp`: tracks known shapes and types through the graph during folding and folds `Shape` layers whose input shape is known.
- `net_impl2.cpp`: once a `Shape` has been folded, `setGraphInput()` rejects an input of a different shape instead of running with stale constants. It also fixes `tryInferGraphShapes()` to report untyped const or empty inputs as `Mat().type()`. The extra folding exposed a Resize type-check failure without this fix.
- `test_fusion.cpp`: two tests, one checking that no `Shape` layer remains after folding, one checking that a different input shape is rejected.
**Effect on graphs** (outputs identical to 5.x)
CRNN 84 → 34 layers, ViT/DeiT 361 → 274, BEiT 373 → 286, MobileViT_XS 352 → 289.
**Performance** (OCV/CPU, 32 threads; median of 5 interleaved runs per side)
| Model | 5.x, ms | PR, ms | Speedup |
|---|---|---|---|
| MobileViT_XS | 6.50 | 6.23 | 1.04x |
| BEiT_Base_Patch16_224 | 30.03 | 29.23 | 1.03x |
| VIT_Base_Patch16_224 | 27.17 | 26.53 | 1.02x |
| DeiT_Tiny_Patch16_224 | 4.90 | 4.81 | 1.02x |
| CRNN | 2.52 | 2.51 | 1.01x |
| FacePaint | 328.1 | 329.5 | 1.00x |
**Dependency**
This depends on the upcoming CUDA integration in DNN and should be merged after it.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
core: enable CV_64F arithmetic SIMD kernels for scalable SIMD - #30069
### Summary
The CV_64F element-wise arithmetic kernels in `modules/core/src/arithm.simd.hpp` are guarded by `CV_SIMD_64F`, which is 0 on scalable-vector (RVV) builds, while the kernels themselves are already written with width-independent universal intrinsics (`v_float64`, `vx_load`, `VTraits<>::vlanes()`). On RVV every CV_64F arithmetic operation therefore falls back to the scalar kernels even though `CV_SIMD_SCALABLE_64F` is 1 and `v_float64` is fully supported.
The same file already uses the dual guard `CV_SIMD_64F || CV_SIMD_SCALABLE_64F` in four other places (see the comment at L166: "scalable (RVV) has v_float64 without CV_SIMD_64F"), so the 12 remaining single-guard sites look like an oversight. This PR widens them.
### What becomes vectorized on RVV (VLEN=256 => 4 double lanes)
- `cv::add` / `cv::subtract` (CV_64F)
- `cv::multiply` (CV_64F, plus the 32U/32S f64-work-type kernels)
- `cv::divide` (DIVW group: 32U/32S/64U/64S/64F)
- `cv::min` / `cv::max` (CV_64F)
- `cv::absdiff` (CV_64F)
- `cv::compare` (CV_64F)
- `cv::addWeighted` (AWD group: 32U/32S/64U/64S/64F)
All affected kernels are element-wise with no cross-lane reductions, so results are identical to the scalar kernels (per-element IEEE operations). The only theoretical difference is that the addWeighted vector body uses fused `v_fma` while the scalar tail does separate mul+add (at most 1 ulp); the same code already passes x86 AVX2/AVX512 CI where FMA is used as well.
On fixed-width backends (x86, AArch64, LoongArch, MIPS MSA, Power VSX) `CV_SIMD_64F` is already 1, so the guard change compiles to exactly the same code as before — no impact on other architectures. ARMv7/AArch32 and WASM SIMD128 have no f64 vector type at all (`CV_SIMD128_64F=0` by design) and keep the scalar path unchanged.
### Scope
Generic `rv64gc + CPU_DISPATCH=RVV` builds do not benefit yet (`arithm` is not a dispatched file); the win applies to builds where the core library itself is compiled with RVV enabled (`CPU_BASELINE=RVV`, the common embedded configuration on SpacemiT K1/K3 boards).
### Build configuration
Cross-compiled for SpacemiT K1 (8x 1.6 GHz, RVV 1.0, VLEN=256), GCC 14.2.1, `-O3`, both sides built with identical flags:
```bash
cmake -GNinja -DCMAKE_TOOLCHAIN_FILE=riscv64-spacemit-k1.toolchain.cmake \
-DCMAKE_BUILD_TYPE=Release -DCPU_BASELINE=RVV -DCPU_DISPATCH= \
-DBUILD_LIST=core,ts,calib,geometry -DBUILD_TESTS=ON -DBUILD_PERF_TESTS=ON \
-DWITH_LAPACK=OFF -DWITH_EIGEN=OFF -DWITH_OPENCL=OFF \
-DWITH_TIFF=OFF -DWITH_OPENJPEG=OFF -DWITH_JASPER=OFF -DWITH_OPENEXR=OFF \
-DWITH_GDAL=OFF -DWITH_GDCM=OFF -DWITH_AVIF=OFF -DWITH_JPEGXL=OFF -DWITH_IMGCODEC_GIF=OFF ..
ninja opencv_core opencv_test_core opencv_perf_core opencv_test_calib opencv_test_geometry
```
### Correctness validation (on board)
```bash
export OPENCV_TEST_DATA_PATH=.../opencv_extra/testdata
export OPENCV_OPENCL_RUNTIME=disabled
./bin/opencv_test_core --test_threads=4
./bin/opencv_test_calib --test_threads=4
./bin/opencv_test_geometry --test_threads=4
```
| suite | baseline | with this PR |
|---|---|---|
| `opencv_test_core` (16757 tests) | 16756 pass, 1 fail: `Samples.findFile` (missing samples data path, environment) | **identical failure set** |
| `opencv_test_calib` (34 tests) | 34/34 pass | 34/34 pass |
| `opencv_test_geometry` (323 tests) | 323/323 pass | 323/323 pass |
Zero new failures; the arithm accuracy suites (`Core_*ElemWiseTest`, `Core_AddWeighted*`, `Core_*Mixed/ArithmMixedTest`, compare) exercise the newly vectorized kernels at all depths and pass unchanged.
### Performance (SpacemiT K1, single-threaded)
This PR adds a CV_64F perf fixture (`F64ArithmTest`: 9 ops x 4 sizes x CV_64FC1 = 36 cases) to `modules/core/perf/perf_arithm.cpp`, since the existing `BinaryOpTest` fixture does not parametrize CV_64F (and has no `compare`/`addWeighted` cases at all). Numbers below are per-case minima over 4 alternating A/B rounds x 50 samples, reported with the standard `modules/ts/misc/summary.py` (`-m min`).
With the default build flags (`-O3`, GCC 14.2.1):
```
Min (ms)
Name of Test base cand cand
min4 min4 min4
vs
base
min4
(x-factor)
absdiff::F64ArithmTest::(127x61, 64FC1) 0.035 0.026 1.33
absdiff::F64ArithmTest::(640x480, 64FC1) 1.580 1.644 0.96
absdiff::F64ArithmTest::(1280x720, 64FC1) 4.533 5.049 0.90
absdiff::F64ArithmTest::(1920x1080, 64FC1) 10.059 10.936 0.92
add::F64ArithmTest::(127x61, 64FC1) 0.027 0.028 0.98
add::F64ArithmTest::(640x480, 64FC1) 1.529 1.561 0.98
add::F64ArithmTest::(1280x720, 64FC1) 4.560 4.450 1.02
add::F64ArithmTest::(1920x1080, 64FC1) 9.544 9.549 1.00
addWeighted::F64ArithmTest::(127x61, 64FC1) 0.033 0.028 1.14
addWeighted::F64ArithmTest::(640x480, 64FC1) 1.582 1.581 1.00
addWeighted::F64ArithmTest::(1280x720, 64FC1) 4.579 4.797 0.95
addWeighted::F64ArithmTest::(1920x1080, 64FC1) 10.255 9.885 1.04
compare::F64ArithmTest::(127x61, 64FC1) 0.099 0.035 2.85
compare::F64ArithmTest::(640x480, 64FC1) 3.834 1.300 2.95
compare::F64ArithmTest::(1280x720, 64FC1) 11.456 3.847 2.98
compare::F64ArithmTest::(1920x1080, 64FC1) 25.670 8.738 2.94
divide::F64ArithmTest::(127x61, 64FC1) 0.064 0.065 0.98
divide::F64ArithmTest::(640x480, 64FC1) 2.493 2.352 1.06
divide::F64ArithmTest::(1280x720, 64FC1) 7.359 7.192 1.02
divide::F64ArithmTest::(1920x1080, 64FC1) 16.306 15.548 1.05
max::F64ArithmTest::(127x61, 64FC1) 0.029 0.026 1.12
max::F64ArithmTest::(640x480, 64FC1) 1.583 1.600 0.99
max::F64ArithmTest::(1280x720, 64FC1) 4.746 4.963 0.96
max::F64ArithmTest::(1920x1080, 64FC1) 9.827 10.143 0.97
min::F64ArithmTest::(127x61, 64FC1) 0.029 0.026 1.10
min::F64ArithmTest::(640x480, 64FC1) 1.625 1.630 1.00
min::F64ArithmTest::(1280x720, 64FC1) 4.821 5.000 0.96
min::F64ArithmTest::(1920x1080, 64FC1) 9.363 11.126 0.84
multiply::F64ArithmTest::(127x61, 64FC1) 0.029 0.027 1.10
multiply::F64ArithmTest::(640x480, 64FC1) 1.680 1.762 0.95
multiply::F64ArithmTest::(1280x720, 64FC1) 4.835 4.929 0.98
multiply::F64ArithmTest::(1920x1080, 64FC1) 10.427 10.564 0.99
subtract::F64ArithmTest::(127x61, 64FC1) 0.027 0.026 1.06
subtract::F64ArithmTest::(640x480, 64FC1) 1.605 1.660 0.97
subtract::F64ArithmTest::(1280x720, 64FC1) 4.758 5.016 0.95
subtract::F64ArithmTest::(1920x1080, 64FC1) 11.156 10.594 1.05
```
x-factor geomean **1.13x**. `compare` is a robust ~2.9x; most other ops land near parity because GCC already auto-vectorizes the simple scalar f64 loops at `-O3` on RVV (exactly the issue #30066 works around with `-fno-tree-vectorize`). Per-case values near parity swing with the known K1 bimodal timing behavior.
With `-fno-tree-vectorize` added to the build flags (the direction proposed in #30066 for RVV), the same A/B shows the kernel-level gains directly:
```
Min (ms)
Name of Test nt nt nt
base cand cand
min4 min4 min4
vs
nt
base
min4
(x-factor)
absdiff::F64ArithmTest::(127x61, 64FC1) 0.087 0.026 3.41
absdiff::F64ArithmTest::(640x480, 64FC1) 3.351 1.423 2.36
absdiff::F64ArithmTest::(1280x720, 64FC1) 10.189 4.595 2.22
absdiff::F64ArithmTest::(1920x1080, 64FC1) 22.476 10.094 2.23
add::F64ArithmTest::(127x61, 64FC1) 0.037 0.027 1.35
add::F64ArithmTest::(640x480, 64FC1) 1.538 1.446 1.06
add::F64ArithmTest::(1280x720, 64FC1) 4.644 4.403 1.05
add::F64ArithmTest::(1920x1080, 64FC1) 10.316 9.629 1.07
addWeighted::F64ArithmTest::(127x61, 64FC1) 0.076 0.027 2.81
addWeighted::F64ArithmTest::(640x480, 64FC1) 2.978 1.363 2.18
addWeighted::F64ArithmTest::(1280x720, 64FC1) 9.141 4.118 2.22
addWeighted::F64ArithmTest::(1920x1080, 64FC1) 19.856 9.075 2.19
compare::F64ArithmTest::(127x61, 64FC1) 0.103 0.031 3.31
compare::F64ArithmTest::(640x480, 64FC1) 3.993 1.225 3.26
compare::F64ArithmTest::(1280x720, 64FC1) 11.801 3.596 3.28
compare::F64ArithmTest::(1920x1080, 64FC1) 26.611 8.125 3.28
divide::F64ArithmTest::(127x61, 64FC1) 0.156 0.065 2.41
divide::F64ArithmTest::(640x480, 64FC1) 6.095 2.330 2.62
divide::F64ArithmTest::(1280x720, 64FC1) 18.191 6.880 2.64
divide::F64ArithmTest::(1920x1080, 64FC1) 40.743 15.331 2.66
max::F64ArithmTest::(127x61, 64FC1) 0.085 0.025 3.36
max::F64ArithmTest::(640x480, 64FC1) 3.272 1.428 2.29
max::F64ArithmTest::(1280x720, 64FC1) 10.009 4.581 2.18
max::F64ArithmTest::(1920x1080, 64FC1) 21.962 10.130 2.17
min::F64ArithmTest::(127x61, 64FC1) 0.086 0.025 3.40
min::F64ArithmTest::(640x480, 64FC1) 3.316 1.417 2.34
min::F64ArithmTest::(1280x720, 64FC1) 10.133 4.583 2.21
min::F64ArithmTest::(1920x1080, 64FC1) 22.334 10.160 2.20
multiply::F64ArithmTest::(127x61, 64FC1) 0.062 0.024 2.61
multiply::F64ArithmTest::(640x480, 64FC1) 2.430 1.362 1.78
multiply::F64ArithmTest::(1280x720, 64FC1) 7.427 4.344 1.71
multiply::F64ArithmTest::(1920x1080, 64FC1) 16.160 9.370 1.72
subtract::F64ArithmTest::(127x61, 64FC1) 0.038 0.025 1.48
subtract::F64ArithmTest::(640x480, 64FC1) 1.696 1.523 1.11
subtract::F64ArithmTest::(1280x720, 64FC1) 4.794 4.805 1.00
subtract::F64ArithmTest::(1920x1080, 64FC1) 12.058 10.820 1.11
```
x-factor geomean **2.09x** (36 cases, 1.00-3.41x): compare ~3.3x, divide ~2.6x, min/max ~2.2x, absdiff ~2.2x, addWeighted ~2.2x, multiply ~1.7-2.6x. `add`/`subtract` stay near parity in both regimes — they appear to be served by a different (already-vectorized) path before the kernel table is consulted.
### Pull Request Readiness Checklist
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Patch to opencv_extra has the same branch name.
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core: RVV HAL transpose2d for 3-channel element sizes - #30075
### Summary
`cv::transpose` for 3-channel types (`8UC3`/`8SC3`, `16UC3`/`16SC3`/`16FC3`, `32SC3`/`32FC3`) falls back to the scalar core path on RISC-V. The RVV HAL `transpose2d` dispatches on *element size* and only implements `esz ∈ {1, 2, 4, 8}`. This PR adds kernels for
`esz = 3, 6, 12`, and an accuracy test that covers the new paths including non-continuous ROI.
### Root cause
- `hal/riscv-rvv/src/core/transpose.cpp`: the `Transpose2dFunc tab[]` table has entries only at indices 1/2/4/8, so `cv_hal_transpose2d` returns `CV_HAL_ERROR_NOT_IMPLEMENTED` for every 3-channel type and the caller runs the generic path.
- On the scalable RVV backend (`intrin_rvv_scalable.hpp`) `CV_SIMD128` is never defined, so the existing `transpose_8/16/32/48bit_simd` fast paths `modules/core/src/matrix_transform.cpp` are compiled out entirely; what runs is the scalar 4x4 element loop. 8UC3 (the default color type) is the most visible case.
### Implementation
The new kernels deinterleave the three channels of several source rows with `vlseg3e{L}` and emit the transposed rows with strided segment stores (`vssseg{6,8}e{L}`). The number of source rows per block was picked experimentally: 8 rows for 8-bit lanes, 2 rows for 16/32-bit lanes. For 16/32-bit lanes, taller blocks e.g. 8 rows will add register pressure and cause regression. A one-row tail covers the remainder; unaligned 16/32-bit inputs fall back to the generic path.
### Performance
SpacemiT K1 / X60, OpenCV 5.x, `--perf_force_samples=20
--perf_min_samples=20`, `BinaryOpTest.transpose2d`:
| type (esz) | 640x480 | 1280x720 | 1920x1080 |
|---|---:|---:|---:|
| `CV_8UC3` (3) | 2.758 -> 1.648 ms (**1.67x**) | 16.772 -> 10.575 ms (**1.59x**) | 42.901 -> 39.552 ms (1.09x) |
| `CV_16SC3` (6) | 8.384 -> 3.556 ms (**2.36x**) | 31.112 -> 19.450 ms (**1.60x**) | 71.921 -> 62.460 ms (1.15x) |
Untouched element sizes (esz 1/2/4/8, 16 cases at 640x480 + 1280x720): geomean **1.005x**,
range 0.977x ... 1.058x.
### Accuracy
New test `Core_Transpose.C3ElementSizesWithRoi` (`modules/core/test/test_mat.cpp`) sweeps
`CV_8UC3, CV_8UC(6), CV_16SC3, CV_16FC3, CV_8UC(12), CV_32FC3` over sizes `1x1, 2x3, 137x5, 133x4` through non-continuous ROI views, byte-compares every element against the source, and asserts the bytes outside the destination ROI are untouched. On K1:
```
[ PASSED ] 4 tests. # Core_Transpose.C3ElementSizesWithRoi
# Core_Transpose/ElemWiseTest.accuracy/0
# Core_Rotate/ElemWiseTest.accuracy/0
```
`BinaryOpTest.transpose2d` already provides the performance coverage; no `opencv_extra` data is needed.
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- [ ] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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Improve fisheye extrinsic refinement for ill-conditioned views and
poorly scaled object points. Replace the Gauss-Newton update with a
Levenberg-Marquardt solver and normalize translation parameters to make
convergence less sensitive to problem scale.
Fisheye undistortion marks points it cannot undistort with a sentinel
coordinate. Poor intermediate intrinsics, such as those following a bad
initial guess, produce such points, which then corrupt the initial pose
estimate. Exclude them from the estimate.
The linear solver rejects singular normal equations only if rounding
yields a sufficiently small pivot. Whether degenerate views update the
intrinsics arbitrarily thus depends on the input and the platform.
Detect the singularity from the per-view blocks and their Schur
complement instead, and skip the update.
Carotene resize left edge clamp 5x - #30076
### Pull Request Readiness Checklist
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- [ ] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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handleMessage() resets the state only when an error message is already on the bus. When the state change fails before the streaming thread posts its error, the pipeline was released in PAUSED with streaming threads still running, which logs 'Trying to dispose element ... but it is in PAUSED instead of the NULL state' and can crash the process.
dnn: reduce coordinate work in CPU Deconvolution - #30001
CPU `Deconvolution` computes coordinates and checks kernel bounds for each output value. This change builds valid tap tables for each axis, reuses outer coordinates per row, and uses SIMD for consecutive inputs at horizontal stride 1 or 2. It computes input addresses once per run and reuses them for each group of four outputs. Other positions use the scalar loop. Each output keeps the same sum order. The 1×1 path adds bias one channel segment at a time.
The table compares `5.x` at `8d126c4a` with this patch at `d0d1825`. It measures layer `forward()`, including GEMM, table setup, output reconstruction, and bias. The host is a Core Ultra 7 255H, with GCC 13.3, Release, one thread, and WSL2. IPP and OpenCL are disabled.
| Input, channels in/out | Kernel, stride | Upstream | PR | Speedup |
| --- | --- | ---: | ---: | ---: |
| 128×128, 16/16 | 1×1, 1 | 3.023 ms | 0.089 ms | 34.2× |
| 128×128, 16/16 | 2×2, 2 | 76.145 ms | 0.489 ms | 155.9× |
| 64×64, 16/16 | 3×3, 1, pad 1 | 10.709 ms | 0.239 ms | 44.8× |
| 16×16×16, 8/8 | 3×3×3, 1 | 21.843 ms | 0.472 ms | 46.3× |
Each time is the median of seven process medians, with two warmup calls and 31 measured calls per process. Version order alternates. Speedup uses the unrounded times.
Each worker reuses its address lists across rows. Their measured buffer capacities are 16 bytes for the 2×2 case and 96 bytes for the 3×3 case, excluding vector objects and allocator overhead. The SIMD run table is shared by the workers and uses one `size_t` per output column.
All 56 accuracy tests (8 new, 48 existing) pass with one and four threads. The eight performance tests pass. The 768 output comparisons match the base version. The extracted helper passes ASan/UBSan checks with SIMD and scalar code. The new accuracy tests use an independent scatter reference; duplicate pointwise and dynamic-weight cases were removed.
[Test code, raw results, and build steps](https://github.com/Hosi121/opencv/tree/bench/dnn-col2im-workspace/col2im-bench).
This changes the legacy CPU `Deconvolution` path. The new ONNX `ConvTranspose2` path is separate. These are layer results, not complete-model results. ARM hardware was not tested.
English is not my first language, so I used AI to help write this post.
Verify assembler can encode AVX-VNNI before enabling it - #29844
Closes: https://github.com/opencv/opencv/issues/29840
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v_select had no 64-bit integer form on any backend except WASM, so code
that needs to blend v_int64/v_uint64 through a comparison mask had to
apply the mask by hand. intrin_sse.hpp carried the two entries commented
out as TBD; the other backends simply stopped at 32-bit and float64.
Every backend already blends bitwise or through a byte/boolean cast, so
the lane width does not change the operation:
SSE _mm_blendv_pd under CV_SSE4_1, the xor/and/xor form otherwise
AVX _mm256_blendv_epi8, as the narrower types already use
NEON vbslq_u64 / vbslq_s64
MSA msa_bslq_u8 over the byte reinterpretation
VSX vec_sel with the same boolean cast v_float64x2 uses
LSX/LASX __lsx_vbitsel_v / __lasx_xvbitsel_v on the raw register
RVV 0.7.1 vmerge_vvm with the b64 mask
RVV __riscv_vmerge, as for the narrower types
WASM already had both and is unchanged, and the generic v_reg
implementation in intrin_cpp.hpp already covers every type.
The 64-bit test chains could not simply call test_mask(), because its
v_signmask() expectations assume more than two lanes. Added test_select(),
which exercises v_select alone, and called it from TheTest<v_uint64>() and
TheTest<v_int64>().
imgproc: speed up getRectSubPix 8U->32F - #30050
### Summary
`getRectSubPix_8u32f`, the 8U-source -> 32F-patch path of `cv::getRectSubPix`, propagated the horizontal interpolation factor across the row:
```cpp
float prev = (1 - a)*(b1*src[0] + b2*src[src_step]);
for (int j = 0; j < win_size.width; j++) {
float t = a12*src[j+1] + a22*src[j+1+src_step];
dst[j] = prev + t;
prev = (float)(t*s); // loop-carried dependency
}
```
That recurrence is a serial dependency chain running the length of every row, so the loop neither pipelines nor vectorizes.
| patch size | 8U→32F | 32F→32F |
|---|---|---|
| 128×128 | 31.1 ms | 13.5 ms |
The recurrence is only a strength reduction. The bilinear weights are identical for every output pixel of a patch, so applying the constant 4-tap kernel directly gives the same result. This patch does that and vectorizes the loop with universal intrinsics (`vx_load_expand_q` → `v_cvt_f32` → 4× `v_mul`/`v_add` → `v_store`), keeping a scalar tail. `v_add`/`v_mul` are used instead of operators because the RVV scalable backend defines no operator overloads for its native vector types.
### Performance
SpacemiT K1 (RVV 1.0 / X60), `opencv_perf_imgproc --gtest_filter=*GetRectSubPix*`, `--perf_force_samples=20 --perf_min_samples=20`, `ALGO_HINT_ACCURATE`:
| patch | before | after | speedup |
|---|---|---|---|
| 16×16 | 0.610 ms | 0.308 ms | 1.98× |
| 32×32 | 2.116 ms | 0.843 ms | 2.51× |
| 64×64 | 8.016 ms | 2.822 ms | 2.84× |
| 128×128 | 31.109 ms | 10.369 ms | 3.00× |
The 8U→8U and 32F→32F paths are unchanged (0.99×–1.01×), and the whole `GetRectSubPix` perf suite improves from 81.66 ms to 53.95 ms (1.51×).
### Accuracy
`opencv_test_imgproc --gtest_filter=*GetRectSubPix*` passes on the same build.
```
Note: Google Test filter = *GetRectSubPix*
[==========] Running 1 test from 1 test case.
[----------] Global test environment set-up.
[----------] 1 test from Imgproc_GetRectSubPix
[ RUN ] Imgproc_GetRectSubPix.accuracy
[ OK ] Imgproc_GetRectSubPix.accuracy (945 ms)
[----------] 1 test from Imgproc_GetRectSubPix (945 ms total)
[----------] Global test environment tear-down
[==========] 1 test from 1 test case ran. (945 ms total)
[ PASSED ] 1 test.
```
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Switches the warning for unknown or unsupported picture formats in
cap_ffmpeg_impl.hpp from CV_LOG_WARNING to CV_LOG_ONCE_WARNING.
This avoids log spam on every decoded frame when reading video
streams with CAP_PROP_CONVERT_RGB=0.
Generalize tokenizer loading to support method-based family dispatch in DNN - #29675
Companion PR: https://github.com/opencv/opencv_extra/pull/1402
### Changes
Added ALBERT and BERT support end-to-end, with `samples/dnn/albert_inference.py `and `samples/dnn/bert_inference.py `as validation samples, plus expanded coverage in modules/dnn/test/test_tokenizer.cpp. Required changes to `cv::dnn::dnn.hpp`, `graph_fusion_attention.cpp`, and `unicode.cpp/unicode.hpp` to support Unigram and WordPiece tokenizers.
To back ALBERT/BERT, generalized `cv::dnn::Tokenizer `from a single BPE implementation into a method-dispatched frontend, adding `core_wordpiece.cpp/hpp` (WordPiece) and `core_unigram.cpp/hpp` (Unigram) as new backends. `tokenizer.cpp` now routes by method across BPE, Gemma,, SentencePiece, Unigram, and WordPiece behind one shared interface.
Tested against the following samples and the output matches to old tokenizer:
```
gpt2_inference.py
qwen_inference.py
gemma3_inference.py
```
GPT2:
```
Preparing GPT-2 model...
Inferencing GPT-2 model...
Hello, I'm a language model, not a programming language. I'm a language model. I'm a language model. I'm a language model. I'm a language model. I'm a
```
Gemma3:
```
Preparing Gemma3 model...
Prompt:
<start_of_turn>user
What is OpenCV?<end_of_turn>
<start_of_turn>model
Inferencing Gemma3 model...
Response:
Okay, let's break down what OpenCV is.
**What is OpenCV?**
OpenCV (Open Source Computer Vision Library) is a powerful and
```
Qwen2.5:
```
Preparing Qwen2.5 model...
Prompt:
<|im_start|>user
What is OpenCV?<|im_end|>
<|im_start|>assistant
Inferencing Qwen2.5 model...
Response:
OpenCV is a set of computer vision libraries in C++ designed to be used for image and video processing. It provides a wide range of tools and functions for
```
### Tokenizer References
Byte-level BPE: [tokenizers/src/pre_tokenizers/byte_level.rs](https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/pre_tokenizers/byte_level.rs)
(This defines the byte-level mapping rules, which is used in conjunction with the [BPE model](https://www.google.com/search?q=https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/models/bpe/mod.rs))
SentencePiece BPE (Metaspace): [tokenizers/src/pre_tokenizers/metaspace.rs](https://www.google.com/search?q=https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/pre_tokenizers/metaspace.rs)
(This defines the rule for replacing whitespace with the U+2581 _ character and handling byte fallback)
Unigram: [tokenizers/src/models/unigram/mod.rs](https://www.google.com/search?q=https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/models/unigram/mod.rs)
(This contains the core logic for the Unigram lattice scoring and probabilistic tokenization rules)
WordPiece: [tokenizers/src/models/wordpiece/mod.rs](https://www.google.com/search?q=https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/models/wordpiece/mod.rs)
(This explicitly cites Schuster & Nakajima in the code comments and implements the greedy longest-match rule with the ## prefix)
### Pull Request Readiness Checklist
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code under GPL or another license incompatible with OpenCV.
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- [x] There is a reference to the original bug report and related work.
- [x] There is accuracy test and test data in `opencv_extra`, same
branch name (`generalized-tokenizer`) — `bert/`, `t5/` fixtures
back the new C++ tests.
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Adds COLOR_BGR2Oklab, COLOR_RGB2Oklab, COLOR_Oklab2BGR, and
COLOR_Oklab2RGB to cvtColor, implementing Bjorn Ottosson's Oklab
perceptual color space (https://bottosson.github.io/posts/oklab/),
requested in #21760.
Scope for this first PR: CV_8U and CV_32F only (matching the existing
Lab/Luv precedent -- neither supports CV_16U either, likely for the
same reason: the cube-root nonlinearity doesn't lend itself to a
fixed-point integer fast path), plain scalar C++ (no SIMD, no OpenCL
kernel -- cvtColor's CV_OCL_RUN/CV_Assert machinery already falls back
to this CPU path cleanly for codes without an OpenCL case), and the
core RGB<->Oklab conversion only (not the Okhsv/Okhsl variants the
issue also mentions, which the issue itself calls secondary to the
"most directly important" Oklab conversion).
Encoding: CV_32F keeps Oklab's native range (L in [0,1], a/b roughly
in [-0.5,0.5] for in-gamut sRGB colors), matching how the reference
formulas and other tools (e.g. CSS Color 4's oklab()) represent it.
CV_8U scales L by 255 and offsets a/b by 128, mirroring CIE Lab's own
8-bit encoding shape.
Testing:
- The forward matrices were verified against an independent NumPy
reimplementation before writing any C++: round-trip identity over
20000 random RGB samples (~1.6e-6 max error), plus cross-checked
pure red/green/blue/white/black/gray against commonly-published
Oklab reference values -- matches to 6+ significant figures.
- New tests in test_color.cpp: reference-value cross-check (same
known values as above, now through the actual cv::cvtColor), a
channel-order check (COLOR_RGB2Oklab vs COLOR_BGR2Oklab on the same
logical pixels), float32 and uint8 round-trips, and alpha-channel
passthrough on the 4-channel inverse. All pass; verified the uint8
round-trip test actually exercises meaningful precision by comparing
its tolerance against CIE Lab's own established uint8 tolerance
(CV_ColorLabTest accepts up to 37 for the analogous case; this uses
32 for a still-meaningful, not looser, bound).
- Confirmed swapBlue() needed COLOR_BGR2Oklab/COLOR_Oklab2BGR added to
its explicit list -- its default (true) is correct for the RGB
variants by accident but wrong for the BGR ones, which would have
silently swapped red and blue. Caught by code review before testing,
then confirmed fixed via the channel-order test.
- Ran the full opencv_test_imgproc suite: no new failures (the same
360 pre-existing "missing opencv_extra test data" failures present
before this change, e.g. StackBlur/HoughCircles/ColorBayer, all in
unrelated files).
- Verified the UMat (OpenCL) path falls back to the CPU implementation
correctly (no OpenCL kernel is added in this PR).
- Added a perf test (cvtColorOklab) covering both directions, CV_8U
and CV_32F, at VGA/720p/1080p.
Natural follow-ups, intentionally left out of this first PR: SIMD/
fixed-point optimization (this is a plain scalar implementation),
Okhsv/Okhsl, and an OpenCL kernel.
Fix function pointer signature mismatches - #30040
Contrib PR: https://github.com/opencv/opencv_contrib/pull/4224
Calling a function through a function pointer with a mismatched signature is undefined behavior in C/C++ and causes Clang Control Flow Integrity to trap with `SIGILL` (`ud1`) at indirect call sites.
This is a follow-up on https://github.com/opencv/opencv/pull/28939
### Pull Request Readiness Checklist
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Patch to opencv_extra has the same branch name.
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readdir() allocates a new buffer for dir->ent.d_name on every call
and overwrites the previous pointer without freeing it. Since
cv::glob() calls readdir() once per directory entry, every call
except the last leaks its allocation. Under _WIN32_WCE, DIR has no
destructor at all, so every allocation leaks, including the last
one.
(cherry picked from commit 20b9529bc3)
CvCapture_FFMPEG::seek() seeds the seek target with the stream start_time
without checking for AV_NOPTS_VALUE, while dts_to_sec() right above it does
perform that check.
Some containers do not let FFmpeg establish a start time (for instance
Matroska files whose H.264 track is declared through the legacy VfW wrapper,
V_MS/VFW/FOURCC with a BITMAPINFOHEADER, instead of V_MPEG4/ISO/AVC with an
avcC record). For those, start_time is AV_NOPTS_VALUE, the computed target
becomes INT64_MIN plus an offset, and av_seek_frame() with AVSEEK_FLAG_BACKWARD
lands at position 0. The refinement loop below then decodes every single frame
up to the requested one, so every seek degrades to a linear scan. The returned
frame is correct, which makes the failure silent.
Measured on a 15 h H.264 recording (1628443 frames) with OpenCV 5.1.0-dev:
target before after
10 s 0.247 s 0.137 s
60 s 1.488 s 0.047 s
300 s 7.559 s 0.068 s
54000 s ~22 min 0.051 s
Seek cost grew strictly linearly with the target position, at roughly 25 ms
per second of video.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
video: fix DISOpticalFlow heap-buffer-overflow with patch_size > border_size - #29715Fixes#20185.
### What
`DISOpticalFlowImpl` pads `I1` of every pyramid level with a **fixed 16 pixel border** (`border_size`), while `PatchInverseSearch` clamps a patch's sample position to:
```cpp
float i_lower_limit = bsz - psz + 1.0f; // bsz = border_size, psz = patch_size
float i_upper_limit = bsz + dis->h - 1.0f;
```
i.e. a patch may be placed up to `patch_size - 1` pixels outside of the image. `computeSSD()`/`computeSSDMeanNorm()` then read `patch_size` (+1 for bilinear interpolation) rows/columns starting at that clamped position, which stays inside the padded buffer only while `patch_size <= border_size`. `patch_size` is user-settable via `setPatchSize()` with no upper bound relative to the hardcoded `border_size` -- so `setPatchSize()` past 16, or a temporal-candidate flow large enough to push the clamped search against its limit, reads past the end of `I1s_ext` (confirmed via AddressSanitizer: heap-buffer-overflow in `computeSSDMeanNorm`).
### Fix
Size the border to whichever is larger, exactly once, at the top of `prepareBuffers()` (where every `I1s_ext[i]` already gets freshly created/padded on every `calc()` call, so this doesn't need any additional cache-invalidation logic):
```cpp
border_size = max(16, patch_size);
```
The needed inequality (`border_size >= patch_size`) is independent of image size, so this is sufficient at every pyramid level uniformly. `ocl_prepareBuffers()` does not need the equivalent change: `calc()`'s `CV_OCL_RUN` gate only takes the OpenCL path when `patch_size == 8` exactly, which is always within the hardcoded border, so that path can never reach this overflow.
### On the prior attempt
A prior attempt at this exact fix (#29600) was closed by @asmorkalov, who ran its own added regression test and got the same ASan heap-buffer-overflow again ("the patch is not efficient"). I want to be upfront about this rather than just re-submitting the same-looking diff: **I could not reproduce that failure.** I reproduced the original overflow on unfixed 5.x (same crash site / allocator stack as both the original issue report and #29600's own ASan trace), then applied the same one-line fix and ran #29600's own added test 20x plus a new 40-trial randomized stress test (`patch_size` 9-48 -- spanning above and below `border_size=16` -- varying image size, `patch_stride`, flow magnitude up to 120px, and a same-instance second `calc()` call to exercise buffer re-use across a patch-size change) under AddressSanitizer, with zero failures. I don't have a confirmed explanation for the discrepancy -- possibly a stale incremental build on the original attempt's end, since I hit and had to work around exactly that kind of false "no work to do" ninja build-cache issue myself while setting up this reproduction. Flagging this openly rather than asserting certainty either way -- happy to dig further if CI or review surfaces a case this doesn't cover.
### Testing
Verified locally with a dedicated ASan-instrumented Debug build (`-fsanitize=address`, separate from the Release build used to verify the rest of today's changes), specifically because an out-of-bounds read like this often just silently returns garbage in a Release build instead of crashing:
- Confirmed the crash reproduces on unfixed 5.x, and is gone with the fix, under both `regression_20185_patch_larger_than_border` (from #29600, included here) and a new `regression_20185_stress` test (40 randomized trials) -- both passing cleanly under ASan.
- Re-verified both tests pass in the standard Release configuration too.
- Full `opencv_test_video` suite (both ASan and Release): no failures attributable to this change; everything else failing needs `opencv_extra` test data (tracking/ECC/optical-flow reference videos/images) not configured in this scoped build.
### PR checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on code under GPL or another incompatible license.
- [x] The PR is proposed to the proper branch (5.x).
- [x] Accuracy tests included (see above).
- [x] No public API/behavior change, so no documentation or sample updates needed.
js: doc: clean up Emscripten installation guide and remove outdated C++17 note - #30009
### Summary
- Updated `js_setup.markdown` for modern Emscripten (emsdk 6.x) usage.
- Replaced deprecated `./emsdk update` with `git pull`.
- Replaced outdated version recommendations (`2.0.10`) with `latest` and explicit version fallback (`6.0.9`).
- Removed obsolete Note regarding `--cmake_option="-DCMAKE_CXX_STANDARD=17"`, as OpenCV 5 now defaults to C++17.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Process masked integer norms chunk by chunk and reuse each loaded mask
and predicate across channels, avoiding repeated mask processing.
Extend norm_mask performance coverage with three-channel 8U, 8S, 16U,
16S, and 32S inputs for INF, L1, and L2 norms.
Co-authored-by: Yang Wang <yangwang@iscas.ac.cn>
Co-authored-by: Yuansheng <yuansheng@isrc.iscas.ac.cn>
Replace separate widening multiplication and FP64 addition with
widening FMA in the unmasked and masked FP32 L2 norm kernels.
Finite FP32 products are exactly representable in FP64, so this
preserves numerical results without changing the accumulation order.
Tail and mask undisturbed policies are retained.
Co-authored-by: Yang Wang <yangwang@iscas.ac.cn>
Co-authored-by: Yuansheng <yuansheng@isrc.iscas.ac.cn>
Add inline fast paths in mat.inl.hpp guarded by CV_IS_COMPILE_TIME_CONSTANT(flags)
When `flags` is known at compile time (e.g. after inlining or under LTO/ThinLTO),
the compiler folds away the kind switch and directly accesses the underlying
cv::Mat / Matx / std::vector / noArray(), falling back to out-of-line `*_` helpers
when `flags` is dynamic so non-LTO builds incur zero binary size bloat.
As an example, I tweaked cv::resize:
- Evaluate `if (condition)` before entering `try { if (cv::ocl::isOpenCLActivated() ...)`
in CV_OCL_RUN_ (ocl_defs.hpp), avoiding OpenCL activation checks and try/catch
overhead when `condition` is false.
- use InputArray functions in cv::resize for constant folding
imgcodecs: avoid copying PNG IDAT data during header parsing 🤖🤖🤖 - #29872
`PngDecoder::readHeader()` copies the first IDAT chunk into a temporary vector, discards it, then rewinds the input so libpng can read it again. A PNG stored in one large IDAT chunk therefore incurs an avoidable allocation and payload copy before decoding.
Skip materializing IDAT data during header discovery. For memory input, validate the remaining length and advance the cursor. For file input, seek to and read the final CRC byte to retain early rejection of truncated chunks. Keep buffered reads for small file chunks and when the seek offset cannot fit in `long`; libpng still validates IDAT CRC while decoding static PNGs. APNG frame decoding retains its existing chunk-reading path.
Validation against `52377dee533dbba170d6437a7cf6d42a749a3f8f`, with only this PNG patch applied:
- macOS 26.6.2 / Apple M5 Pro / Apple Clang 21: 579 image-codec cases, four default skips, zero failures; all four new perf smoke cases pass.
- Linux (Ubuntu 24.04.4) / Intel Core i7-6850K / GCC 13.3: the same 579 cases, four default skips, zero failures; all four new perf smoke cases pass.
- All 19 new regression cases also pass against the unchanged baseline libraries on both hosts.
- Matching Release settings within each host, bundled libpng 1.6.57 and zlib 1.3.2.
The revised, committed `PNGDecode` performance cases measure `imdecode(..., IMREAD_UNCHANGED)` on generated random, uncompressed RGB PNGs in memory. Seven alternating baseline/candidate pairs use 30 samples per case and one OpenCV thread; Linux runs are pinned to one CPU. The same revised performance executable is used with each library set, and actual loaded library paths are verified. These measurements are separate from the file-loading results below.
| Memory-decoding workload | Mac speedup | Linux speedup |
| --- | ---: | ---: |
| 512×512, ordinary chunks | 1.001× (0.999–1.003) | 0.999× (0.998–1.002) |
| 512×512, single IDAT | 1.024× (1.022–1.030) | 1.069× (1.067–1.070) |
| 3840×2160, ordinary chunks | 0.999× (0.995–1.011) | 1.001× (1.000–1.001) |
| 3840×2160, single IDAT | 1.022× (1.000–1.024) | 1.383× (1.356–1.419) |
Values are paired median speedups with descriptive bootstrap 95% intervals. Every process passed all four cases with exact pixel comparisons.
Separate C++ `imread(..., IMREAD_COLOR)` measurements use the earlier structured 3840×2160 RGB PNG fixtures, warm filesystem cache, one OpenCV thread, and seven alternating baseline/candidate pairs (minimum 0.15 seconds per batch). Linux runs are pinned to one CPU. Each host checks 100 image/mode cases; decoded dimensions, types, and pixel hashes match across every run and between hosts. The values below are paired median speedups, with bootstrap 95% intervals.
| Workload | Mac speedup | Linux speedup |
| --- | ---: | ---: |
| Uncompressed stream in one ~24.9 MB IDAT | 1.077× (1.046–1.110) | 1.171× (1.170–1.171) |
| Same uncompressed stream in ordinary IDAT chunks | 1.014× (0.945–1.025) | 1.000× (0.997–1.001) |
| Compressed stream in one IDAT | 1.014× (1.007–1.020) | 1.024× (1.021–1.025) |
The large-IDAT case has median latency 16.08→14.83 ms on the Mac and 37.23→31.90 ms on Linux. A separate 15-pair Mac check puts the ordinary chunked control at 1.002× (0.994–1.015). The gain depends on first-IDAT size and decoding cost; ordinary chunked PNGs are approximately neutral.
The new performance tests use the test framework’s `theRNG()` and time `imdecode(encoded, IMREAD_UNCHANGED)` entirely in memory. They generate identical uncompressed RGB images with ordinary 8 KiB IDAT chunks or a single full-image IDAT, at 512×512 and 3840×2160. Encoding and repacking happen outside the timed loop; the performance cases create no temporary files. Run the included cases with the following filters. For an A/B comparison, copy only the test/performance changes to the baseline tree:
```sh
opencv_test_imgcodecs --gtest_filter='*Png_ReadIDAT*'
opencv_perf_imgcodecs --gtest_filter='PNGDecode_idat_layout.idat_layout/*'
```
The regression tests exercise file and memory input, 8/16-bit grayscale/RGB/RGBA, single/multiple/empty-first IDAT layouts, incomplete headers/payloads/CRCs, and corrupt CRCs. They check `imread`, `imdecode`, and `imcount`. All fixtures are generated; no `opencv_extra` patch is needed. Windows was not tested locally.
### Pull Request Readiness Checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on code under GPL or another license incompatible with OpenCV.
- [x] The PR is proposed to the proper branch (`5.x`, optimization).
- [x] Accuracy and performance tests are included; test data is generated.
- Original bug report: none; self-contained performance improvement.
- Documentation/sample changes: not applicable; no public API change.
doc: document the bit layout of Mat::type() - #29981
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake
### Description
Fixes#24901.
#### Problem
`cv::Mat::type()` returns a packed bit-field, but the encoding was not documented. Users
had to reverse-engineer the layout from the `CV_*` macros to answer questions such as how many
channels fit, how to build a type from a depth and channel count, and which bits are reserved for
the matrix flags.
#### Change
Expanded the Doxygen for `Mat::type()` in `modules/core/include/opencv2/core/mat.hpp` to describe
the layout used by the 5.x branch:
- bits 0-4 (`CV_MAT_DEPTH_MASK`) – element depth (5 bits);
- bits 5-11 (`CV_MAT_CN_MASK`) – number of channels minus one (7 bits), i.e. 1..`CV_CN_MAX` (128);
- together these occupy the lowest 12 bits (`CV_MAT_TYPE_MASK`).
The description also points to `CV_MAT_DEPTH()`, `CV_MAT_CN()` and `CV_MAKETYPE()`, and notes that
the continuity (`CV_MAT_CONT_FLAG`) and submatrix (`CV_SUBMAT_FLAG`) bits of `Mat::flags` are not
part of the returned value.
#### Branch note
This PR targets `5.x` only. The encoding changed between branches: 5.x uses `CV_CN_SHIFT == 5`
(5-bit depth, 7-bit channel count), whereas 4.x uses `CV_CN_SHIFT == 3`. As requested in the issue,
a separate `4.x` PR would be needed for that branch.
#### Verification
Documentation-only change; no code or behavior is modified. The bit ranges and macro names were
checked against `modules/core/include/opencv2/core/hal/interface.h` and
`modules/core/include/opencv2/core/cvdef.h`:
```
CV_CN_MAX 128
CV_CN_SHIFT 5
CV_DEPTH_MAX (1 << CV_CN_SHIFT) // 32
CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1) // 0x1F -> bits 0-4
CV_MAT_CN_MASK ((CV_CN_MAX - 1) << 5) // 0xFE0 -> bits 5-11
CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX-1) // 0xFFF
```
Extend the CV_SIMD guard in lapack.cpp to include CV_SIMD_SCALABLE,
addressing the TODO updated in PR24325.
Co-authored-by: Yang Wang <yangwang@iscas.ac.cn>
Co-authored-by: Yuansheng <yuansheng@isrc.iscas.ac.cn>
core: optimize float REDUCE_SUM2 with RVV - #29930
### Summary
Add an RVV 1.0 kernel for `cv::reduce` with `dim=0` and `CV_32F`
input/output through the CPU dispatch mechanism.
The optimized path targets the floating-point `REDUCE_SUM2` operation.
### Implementation
Process four source rows per vector iteration to reduce intermediate
buffer traffic while preserving source-row accumulation order.
The RVV kernel uses LMUL=4 and dynamic vector lengths for tail handling.
Output writes are deferred until all input rows have been read. This
preserves correct behavior when the source and destination matrices
overlap.
The implementation is VLEN-agnostic.
### Functional Testing
The Reduce tests were run with:
```bash
./bin/opencv_test_core \
--gtest_filter='*Reduce*:*reduce*' \
--test_threads=1
```
### Performance Testing
Performance was measured with:
```bash
./bin/opencv_perf_core \
--gtest_filter='*reduceR*' \
--perf_threads=1
```
Test environment:
- SpacemiT K3
- VLEN = 256
- GCC 14.3.0
- Release build
- Single thread
- 10 samples per case
Results were compared against an unmodified `5.x` baseline.
`CV_32FC1 REDUCE_SUM2` median execution time (ms):
| Size | Baseline | Patched | Speedup |
| --- | ---: | ---: | ---: |
| 640x480 | 0.20 | 0.08 | 2.50x |
| 1280x720 | 0.64 | 0.29 | 2.21x |
| 1920x1080 | 1.33 | 1.05 | 1.27x |
Speedups are approximate and calculated from the rounded benchmark
output.
### Co-authors
- Yang Wang <yangwang@iscas.ac.cn>
- Yuansheng <yuansheng@isrc.iscas.ac.cn>
### Pull Request Readiness Checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
FP8 model support in DNN - #29834
ONNX coverage after this PR: 76.6%
co-authored by: @SavyaSanchi-Sharma
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
core: fold a*alpha + b*beta + gamma into the fused OP_ADDW kernel - #29953
Part of #29443 — "fuse `a*alpha + b*beta + gamma`, where alpha, beta and gamma are scalars, into
some new `OP_ADDW`". Builds on #29937, which is required for correctness (see below).
### Problem
`OP_ADDW` already computes `a*alpha + b*beta + gamma` as a single kernel over two `v_fma`, and
`emitBinary()` already knows how to emit it — but the string front-end never recognized the
pattern, so `cv::texpr()` always took the written-out path: two multiplies, two adds, three temp
buffers and four passes over the data.
```
{0}*2.0 + {1}*3.0 + 1.0 at CV_32F
insns=4 temps=3 buffers=3 insns=1 temps=0 buffers=0
0: mul(1, 4) -> 5 ==> 0: addWeighted(1, 2) -> 11
1: mul(2, 7) -> 8 params=[2, 3, 1]
2: add(5, 8) -> 9
3: add(9, 11) -> 13
```
### Fix
A peephole in `emitBinary()`: `a*alpha + b*beta` folds into one `OP_ADDW`, and a trailing scalar
folds into that instruction's gamma rather than costing another pass.
`emitBinary()` may wrap a multiply in casts — an integer array times a fractional scalar computes
in the float domain and lands back in the array's own type — so the matcher accepts the optional
widening and narrowing casts around it. That is what makes the common 8-bit blend fuse; without
it `{0}*0.7 + {1}*0.3` at `CV_8U` stays at eight instructions and seven temps.
Shapes that are not an addWeighted keep their own meaning: an `a*b` term has no scalar factor, a
per-channel constant cannot ride the params block, and `CV_Bool` has no `OP_ADDW` form.
**Dependency on #29937.** This retires the instructions it folds, exactly as the
`abs(x - y) -> absdiff` peephole does. Without the `pinned` flag added in #29937 it would
reintroduce that bug for `u = {0}*2.0; v = {1}*3.0; u + v`, where the named terms are still live.
### Semantics on integer types
The fused kernel evaluates at its own work precision, so intermediate results no longer saturate
at each step. On integer inputs the result changes — it now agrees with `cv::addWeighted`, which
is what the expression means. This is the same trade the existing `abs(x - y)` peephole documents
in `emitUnary()`: the saturation artifacts of the literal expansion are never the desired result.
### Performance
1920x1080, best of 5 runs of 50 iterations, same build rebuilt both ways on 03ae9eac50:
| expression | depth | before | after |
| --- | --- | --- | --- |
| `a*0.7 + b*0.3` | 8U | 0.646 ms | 0.159 ms |
| `a*2 + b*3 + 1` | 8U | 0.261 ms | 0.150 ms |
| `a*2.5 + b*-1.5 + 7` | 32F | 0.360 ms | 0.214 ms |
| `a*2 + b*3 + 1` | 32F | 0.213 ms | 0.155 ms |
| `a*b + b*2` (control, not fused) | 32F | 0.393 ms | 0.358 ms |
The control row runs identical code in both builds and still moves by ~9%, so run-to-run noise on
this machine is around 10% — treat the 32F rows as indicative and the 8-bit rows as the real
result. Timings include `cv::texpr()` re-parsing and recompiling the expression on every call, so
the kernel-level gain is larger than the totals suggest.
### Tests
28 parameterised cases — 7 depths crossed with 4 weight sets, including a zero gamma and negative
weights — assert the fused result matches `cv::addWeighted`. Two further tests cover the variants
(no gamma, scalar written first, leading gamma) and the shapes the peephole must decline. 1801
tests in the arithmetic and TExpr suites pass.
Also adds the missing `OP_ADDW` case to `opName()`, which this change makes visible in every dump
of such a program.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Remove deprecated CommaInitializer API - #29948
This goes hand in hand with https://github.com/opencv/opencv_contrib/pull/4217
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Added Backend Agnostic fusion in DNN - #29656
# What This Adds
A way for any layer, on any backend, to absorb trailing per-element math — with the pass knowing nothing about either layer and holding no list of op types.
- **Description**: `LayerMath` (one layer's math) → `AdjacencyGraph` (hash-consed chain)
- **Contract**: two `bool` virtuals on `Layer`
- **CPU implementations**: three, behind that contract
Pointwise fusion is the first consumer, not the point.
## The Problem
```cpp
ActivationLayer* activ = dynamic_cast<ActivationLayer*>(layer_ptr);
Conv2Layer* conv = getLayer<Conv2Layer>(newprog, conv_layer_idx);
if (conv) conv->fuseActivation(layer);
```
Guest type, host type, method name all hardcoded in the pass. `Clip` never fused (fails the cast), `Gemm` never fused (no virtual), no 2-op chain fused at all, and no backend could fuse anything without a CPU-specific method.
## Results
| Model | 5.x | this PR | speedup |
|-----------------------|----------|----------|---------|
| MPHand | 2.34 ms | 1.03 ms | 2.27 |
| EfficientNet | 9.82 ms | 5.00 ms | 1.96 |
| MPPose | 5.14 ms | 2.81 ms | 1.83 |
| MobileNet_SSD_v1_ONNX | 12.58 ms | 8.38 ms | 1.50 |
| BlazeFace | 0.95 ms | 0.77 ms | 1.23 |
| DenseNet_121 | 20.56 ms | 19.08 ms | 1.08 |
| MobileNetv2_ONNX | 2.07 ms | 1.99 ms | 1.04 |
| MobileViT_XS | 6.33 ms | 6.11 ms | 1.04 |
| YuNet_320 | 1.33 ms | 1.27 ms | 1.04 |
| PPOCRv3 | 43.05 ms | 42.15 ms | 1.02 |
| BERT | 9.78 ms | 9.68 ms | 1.01 |
| BEiT_Base_Patch16_224 | 27.48 ms | 27.08 ms | 1.01 |
| DeiT_Tiny_Patch16_224 | 4.74 ms | 4.72 ms | 1.01 |
| MPPalm | 1.88 ms | 1.85 ms | 1.01 |
| SSD | 50.19 ms | 49.82 ms | 1.01 |
| YOLOv4_tiny | 7.10 ms | 7.03 ms | 1.01 |
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Add a CV_SIMD_SCALABLE path for the DIS patch processing kernels using
universal intrinsics and runtime vector lane counts.
Keep the existing CV_SIMD128 implementation unchanged to avoid affecting
fixed-width SIMD targets. The scalable path vectorizes patch rows with
v_float32 and handles remaining elements with the existing scalar code.
This enables native RVV vectorization for processPatch(),
processPatchMeanNorm(), computeSSD(), and computeSSDMeanNorm().
Co-authored-by: Yang Wang <yangwang@iscas.ac.cn>
Co-authored-by: Yuansheng <yuansheng@isrc.iscas.ac.cn>
core: fix overflow in borderInterpolate BORDER_WRAP path - #29884
### Description
Fixes an integer overflow/underflow bug in `cv::borderInterpolate()` when using `BORDER_WRAP`. For extreme values of `p` (e.g. `INT_MIN` or `INT_MAX`), the previous implementation performed the modulo operation directly on `int`, which can invoke undefined behavior on overflow and produce a result outside the valid `[0, len)` range.
The fix widens the intermediate calculation to `int64_t` before taking the modulo, then adjusts for negative results and narrows back to `int` only once the value is confirmed to be in range.
### Changes
- `modules/core/src/copy.cpp`: use 64-bit intermediate arithmetic in the `BORDER_WRAP` branch of `borderInterpolate()` to avoid overflow.
- `modules/core/test/test_misc.cpp`: add `Core_BorderInterpolate.wrap_no_overflow_29232`, a regression test that exercises `BORDER_WRAP` with `INT_MIN` and `INT_MAX` and asserts the result stays within `[0, len)`.
Fixes#29232
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test for the patch (added in test_misc.cpp); no performance test needed as this is a bug fix with negligible performance impact.
- [x] The feature is well documented and sample code can be built with the project CMake
Merge pull request #29931 from abhishek-gola:onnx_conformance_remaining_fixes
fix ONNX auto_pad, PRelu broadcasting and LSTM peepholes - #29931
closes: https://github.com/opencv/opencv/issues/21078
Upated ONNX coverage: 77.8%
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
core: rvv: optimize 8U row reduce sum with vwaddu.wv - #29923
### Summary
Optimize the RVV implementation of `reduceRowSum_8u32s` by using the
native widening add instruction `vwaddu.wv`.
The generic scalable-vector path expands each `u8m1` input vector into
two `u16m1` halves and performs two separate load/add/store sequences
for the `u16` accumulation buffer.
On RVV, an `u8m1` vector and an `u16m2` vector have the same number of
elements. This allows the implementation to use an `u16m2` accumulator
directly and combine widening and addition with `vwaddu.wv`:
```text
u16m2 = u16m2 + u8m1
```
This removes the explicit widening and low/high `u16` accumulator split,
reducing the vector operations in the hot accumulation loop.
The existing scalar tail and 256-row `u16`-to-`u32` flush logic remain
unchanged.
The implementation is VLEN-agnostic.
### Functional Testing
The Reduce tests were run with:
```bash
./bin/opencv_test_core \
--gtest_filter='*Reduce*:*reduce*'
```
### Performance Testing
Performance was measured with:
```bash
OPENCV_FOR_THREADS_NUM=1 ./bin/opencv_perf_core \
--gtest_filter='*Reduce*:*reduce*'
```
Test environment:
- SpacemiT K3 / X100
- RVV 1.0
- VLEN = 256
- GCC 14.3.0
- Release build
- Single thread
Results were compared against an unmodified `5.x` baseline.
Median execution time (ms):
| Size / Type | Baseline | RVV `vwaddu.wv` | Speedup |
| --- | ---: | ---: | ---: |
| 640x480 8UC1 | 0.09 | 0.05 | 1.80x |
| 640x480 8UC4 | 0.36 | 0.16 | 2.25x |
| 1280x720 8UC1 | 0.27 | 0.12 | 2.25x |
| 1280x720 8UC4 | 1.08 | 0.56 | 1.93x |
| 1920x1080 8UC1 | 0.62 | 0.29 | 2.14x |
| 1920x1080 8UC4 | 2.43 | 1.11 | 2.19x |
Geometric mean speedup: **~2.09x**.
`REDUCE_MIN`, `REDUCE_MAX`, and `REDUCE_SUM2` performance remains
essentially unchanged, indicating that the speedup is localized to the
optimized 8-bit row SUM path.
### Co-authors
- Yang Wang <yangwang@iscas.ac.cn>
- Yuansheng <yuansheng@isrc.iscas.ac.cn>
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
imgproc: fix NaN in phaseCorrelateIterative for float32 input - #29871
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
### Summary
`cv::phaseCorrelateIterative` could return the image boundary as the detected shift (e.g. `(-32, -32)` for a 64x64 image) for ordinary float32 input, while the same input in float64 and the non-iterative `cv::phaseCorrelate` both return the correct shift. Reported in #29849 with a reproducer.
### Problem
`calculateCrossPowerSpectrum()` divided by the bin magnitude without a zero check. When a frequency bin of the cross power spectrum has exactly zero magnitude, the division produces NaN (0/0). A single NaN bin is enough: after the inverse DFT the whole correlation landscape becomes NaN, `minMaxLoc` degenerates, the peak lands on the wraparound corner, and the function exits through the out-of-bounds fallback returning `L3peak - L3mid = (-N/2, -M/2)`.
This is reachable with perfectly valid input: for a 64x64 image, bins where either frequency component hits Nyquist produce `dft1[bin] * conj(dft2[bin]) = 0` for Gaussian-like windows (the Hanning-windowed images issue #29849 used), because one of the DFT factors is exactly zero there.
### Solution
Two changes in `calculateCrossPowerSpectrum()`, both matching what the non-iterative implementation already does:
1. Guard the zero-magnitude case: a bin with zero magnitude has undefined phase, so write 0 instead of dividing by zero. `cv::divSpectrums()` handles the same situation by adding an epsilon to the denominator; zeroing the bin is equivalent here and keeps the output strictly on the unit circle.
2. Accumulate in `double` before rounding back to the storage type, as `cv::divSpectrums()` does in its CV_32F branch. This also protects large images, where float32 products of DFT bins can overflow to inf.
### Testing
- Reproduced #29849 with the issue's reproducer ported to C++: float32 returned `(-32, -32)` before the fix, `(-2.96, 1.99)` after (float64 reference: `(-2.94, 1.96)` for the true shift (-3, +2)).
- Added regression test `Imgproc_PhaseCorrelationIterative/64x64_float32_accuracy` covering this case. Verified it fails without the fix (returns `(-32, -32)`) and passes with it.
- `opencv_test_imgproc`: all 1635 tests pass with the fix (opencv_extra testdata, 5.x branch). The pre-existing `float32_overflow` and accuracy tests for the non-iterative path also pass.
Port of #29862 (merged into 4.x) to 5.x.
- Type/stride substitution only; arithmetic and evaluation order unchanged.
- RedBlackSOR: the v_extract<3>(prev, next) previous-lane construction is
replaced by an unaligned vx_load(p_next + j - 1); v_extract<3> is only
previous-lane when vlanes == 4, so this is required for wider lanes.
- HorPass keeps a strict bound, j < len - vlanes, while the other three use
j <= len - vlanes, since the vector body applies UPDATE to every lane and
the rightmost element must not receive UPDATE across the right border.
- No SVE claim: there is no SVE backend in-tree; scalable means RVV today.
video:rvv: optimize PyrLK tracker with RVV - #29947
### Summary
Add an RVV path to `LKTrackerInvoker` for patch interpolation and
covariance matrix accumulation.
### Implementation
Vectorize bilinear interpolation of image pixels and derivatives, and
accumulate A11, A12 and A22 using RVV widening operations. Reuse
temporary vectors and keep the main computation at `e16,m1` and
`e32/f32,m2` to limit vector register pressure.
### Functional Testing
```bash
./bin/opencv_test_video \
--gtest_filter='Video_OpticalFlowPyrLK.accuracy' \
--test_threads=1
./bin/opencv_test_video \
--gtest_filter='Video_OpticalFlowPyrLK.submat' \
--test_threads=1
```
Correctness was verified with:
- `Video_OpticalFlowPyrLK.accuracy`
- `Video_OpticalFlowPyrLK.submat`
### Performance Testing
```bash
OPENCV_FOR_THREADS_NUM=1 \
./bin/opencv_perf_video \
--gtest_filter='*OpticalFlowPyrLK_self*' \
--perf_threads=1
```
Performance was measured with `OpticalFlowPyrLK_self` using a single
thread on an RVV 1.0 system with VLEN=256.
All 96 benchmark cases improved with no regression. The sum of
per-case median execution times decreased from 409.89 ms to 332.33 ms,
a reduction of 18.9%. The best case decreased from 8.41 ms to 5.50 ms,
corresponding to a 34.6% reduction in execution time (1.53x speedup).
Median execution time (ms):
| Metric | Baseline | RVV | Time reduction |
|---|---:|---:|---:|
| Sum of per-case medians across all 96 cases | 409.89 | 332.33 | 18.9% |
| Best case | 8.41 | 5.50 | 34.6% |
### Co-authors
- Yang Wang <yangwang@iscas.ac.cn>
- Yuansheng <yuansheng@iscas.ac.cn>
### Pull Request Readiness Checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Use LightGlueType for both model selection and keypoint normalization while retaining the public helper for cached coordinates. Restore the four-argument setPairInfo interface and reject unsupported model types.
Add coverage for normalization, input validation, equivalent pair-info entry points, cached coordinates, and supported in-memory model loading.
Port of #29862 (merged into 4.x) to 5.x.
- Type/stride substitution only; arithmetic and evaluation order unchanged.
- RedBlackSOR: the v_extract<3>(prev, next) previous-lane construction is
replaced by an unaligned vx_load(p_next + j - 1); v_extract<3> is only
previous-lane when vlanes == 4, so this is required for wider lanes.
- HorPass keeps a strict bound, j < len - vlanes, while the other three use
j <= len - vlanes, since the vector body applies UPDATE to every lane and
the rightmost element must not receive UPDATE across the right border.
- No SVE claim: there is no SVE backend in-tree; scalable means RVV today.
FileStorage's XML/YAML/JSON parsers recurse once per nesting level
with no depth limit, allowing a small crafted file to exhaust the
stack and crash the process with an uncatchable SIGSEGV (CWE-674).
Add a shared CV_PERSISTENCE_MAX_DEPTH constant and thread a depth
counter through parseValue (XML/YAML) and parseSeq/parseMap (JSON),
raising a catchable cv::Exception via CV_PARSE_ERROR_CPP once the
limit is exceeded.
Fixes#29939
TExpr::moveToOutput() and the abs(x - y) -> absdiff(x, y) peephole in
TExpr::emitUnary() retire a value's arg slot (reclassify it to NONE) once
its single consumer has been emitted. That holds for an anonymous
intermediate, but a value the parser bound to a name ("t = ...;") may be
referenced again: the parser's name table still points at the retired slot,
so the later reference resolved to the reserved empty operand and
cv::texpr() silently returned a wrong result - for
t = {0} - {1}; abs(t) + t
t = {0} - {1}; (abs(t), t)
t = {0} + {1}; (t, t)
the reused name yielded input {0} instead of its own value, with no
assertion.
Mark a slot as pinned when the parser binds it to a name and skip both
retire manoeuvres for a pinned slot; each then takes the non-destructive
path it already has - moveToOutput() copies into the output via OP_CAST,
and the abs peephole falls through to the plain absdiff(a, 0) form, keeping
the OP_SUB that the name still needs. Anonymous intermediates are
unaffected, so the zero-temp fast path for single-op programs still fires.
core: fix RVV widening load lane count (#29907) - #29921
# core: fix RVV widening load lane count
Partially fixes#29907.
The RVV `v_load_expand` implementation used the source vector lane count for the widening load. For widening loads, the number of loaded elements must match the destination widened vector lane count instead.
This change:
* uses `VTraits<_Tpwvec>::vlanes()` for the RVV widening load and conversion;
* adds regression coverage for `texpr` `select()` with byte masks across multiple data types, channel counts, mask types, strided matrices, and in-place output.
### Pull Request Readiness Checklist
See details at the OpenCV contribution guidelines.
* [x] I agree to contribute to the project under Apache 2 License.
* [x] To the best of my knowledge, the proposed patch is not based on code under GPL or another license that is incompatible with OpenCV.
* [x] The PR is proposed to the proper branch.
* [x] There is a reference to the original bug report and related work.
* [x] There is an accuracy/regression test where applicable.
* [x] The change does not require documentation or sample updates.
core: handle zero-sized broadcast dimensions - #29911
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Port the fix from #29878 to the 5.x branch.
This adds a guard for zero-sized destination matrices in
cv::broadcast() and a regression test covering broadcasting
from {1, 0} to {3, 0}.
The relevant BroadcastTo.* tests pass locally.
Related: #29878
Add an RVV HAL implementation for CV_32F to CV_16S conversion.
The RISC-V convertScale HAL already handles several source/destination
depth combinations, but CV_32F to CV_16S currently returns
CV_HAL_ERROR_NOT_IMPLEMENTED and falls back to the generic core path.
Implement the missing conversion using native RVV intrinsics. The
identity-scale case skips the multiply-add, while scaled conversions
apply alpha and beta before narrowing to signed 16-bit output.
The implementation is VLEN-agnostic and uses vsetvl for tail handling.
Functional test:
```text
./build-rvv/bin/opencv_test_core \
--gtest_filter='*ConvertScale*'
```
Performance was measured with opencv_perf_core on SpacemiT K3
(RVV 1.0, VLEN=256), comparing against an unmodified 5.x baseline.
CV_32F -> CV_16S median time:
```text
1920x1080 C1, alpha=1:
26.16 ms -> 1.46 ms (17.92x)
1920x1080 C1, alpha=1/255:
24.10 ms -> 1.45 ms (16.62x)
1920x1080 C4, alpha=1:
103.76 ms -> 6.06 ms (17.12x)
1920x1080 C4, alpha=1/255:
96.03 ms -> 5.68 ms (16.91x)
```
All four corresponding opencv_perf_core cases pass.
Co-authored-by: Yang Wang [yangwang@iscas.ac.cn](mailto:yangwang@iscas.ac.cn)
Co-authored-by: Yuansheng [yuansheng@iscas.ac.cn](mailto:yuansheng@iscas.ac.cn)
ptcloud , photo test suit cleanup - #29742
## Test suite cleanup
### Given real assertions
- **ptcloud** — `HugeSceneGrowthTest`: zero assertions, including a `// Reset check` comment followed by no check.
- **ptcloud** — `PointCloud.SaveBadExtension`: passed an empty vertex set, so it exited at the empty-input guard and never reached the extension code it is named for.
- **ptcloud** — new `PointCloud.SaveEmptyVertices`: covers the early-return branch the above was hitting by accident.
### Moved
- **photo** — `Photo_Denoising.speed` → `perf/perf_denoising.cpp`: a `getTickCount` + `printf` stopwatch in the accuracy suite, asserting nothing, costing 393 ms per run.
### Library fixes found while doing the above
- **ptcloud** — `findPlanes` now converts 3-channel input instead of reshaping it: `Mat_<Vec4f>::operator=` reshapes when depths match, so a 320×240 `CV_32FC3` input silently became 240×240.
- **ptcloud** — new `RGBD_Plane.regression_3channel_matches_4channel`: nothing covered the documented 3-channel path, since all 40 `RgbdPlaneGenerate` cases feed `CV_32FC4`.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Joining BINARIES_PATHS with an unconditional trailing separator
creates an empty search-path entry when the variable was unset.
On POSIX that empty entry is CWD (ld.so), so children can load
the wrong libraries. Keep prepending extras, but only insert the
separator when an old value exists. Same join on Windows PATH.
The generic odd-radix DFT path created the index vector with
vsetvlmax_e32mf2() and kept it live across a subsequent setvl() call used
for the current processing chunk.
This code pattern can produce incorrect results with GCC 15.2 using the
default optimized VSETVL strategy. Core_DFT.accuracy and
Core_DCT.accuracy fail with the RVV HAL enabled, while disabling VSETVL
global fusion makes the same tests pass.
Generate the index vector after setting the vector length for each
q-loop chunk instead. Keep all vector operations within the same local
qvl and advance q explicitly by that value.
This preserves the existing odd-radix DFT algorithm while avoiding
vector values that span changes of VL.
Tested on SpaceMIT K3 with GCC 15.2.0, RVV 1.0 and VLEN=256:
RISCV_RVV_SCALABLE=ON
WITH_HAL_RVV=ON
CMAKE_BUILD_TYPE=Release
Core_DFT and Core_DCT accuracy tests pass with GCC's default VSETVL
optimization enabled.
Co-authored-by: Yuansheng <yuansheng@iscas.ac.cn>
Co-authored-by: Yang Wang <yangwang@iscas.ac.cn>
core: fix addWeighted null kernel crash for f64 dtype and bool inputs - #29883Fixes#29880.
`cv::addWeighted` segfaults for `CV_8U`, `CV_8S`, `CV_16U`, `CV_16S`, `CV_16F`, `CV_16BF` and `CV_32F` inputs with `dtype=CV_64F`, and for `CV_Bool` inputs with any dtype. When no direct `T -> rdepth` kernel exists, `TExpr::emitBinary()` picks a wide work type and looks the kernel up again, but for those input types only `T -> T` and `T -> f32` kernels are generated, so the second lookup returns a null function pointer too. The `addInsn()` overload that takes an already resolved kernel stores it without checking, and `runInsn()` then calls through the null pointer.
Cast the operands to the work type when there is no kernel for them either, so the f64 (or f32) kernel runs on widened inputs. That is also what 4.x did, it converted the sources to the working type before computing, so an f64 destination keeps full precision instead of going through an f32 intermediate. Added the `CV_Assert` on the resolved kernel that the other emit paths already carry.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch (`5.x`, the element-wise engine this regressed in does not exist on `4.x`)
- [x] There is a reference to the original bug report and related work (#29880, regressed by #29426)
- [x] There is an accuracy test (`Core_Arithm.addWeighted_dtype_29880`, which segfaults without the fix); not applicable: performance test and opencv_extra test data
- [x] N/A: this is a bug fix, no new public API or documentation needed
Fix Windows ARM64 multithreading and ASM detection - #29833
### PR Changes:
This PR lets Windows ARM64 CI run on OpenCV's default PPL backend — no `-DWITH_OPENMP=ON` and no `-DCMAKE_ASM_COMPILER` workaround needed.
**`modules/core/src/parallel.cpp`** — PPL's default `auto_partitioner` silently drops whole contiguous chunks of a `parallel_for` range on ARM64, so that work never runs (open MSVC defect [1027444](https://developercommunity.visualstudio.com/t/1027444)). Pass `static_partitioner` on ARM64, which has no range-stealing path. x86 unchanged.
**`CMakeLists.txt`** — `check_language(ASM)` picks up the runner's MinGW `cc.exe`, which sets `MINGW` and emits GNU link flags that `link.exe` silently drops, breaking the DirectX samples under `BUILD_EXAMPLES=ON`. Only accept an assembler from the MSVC toolchain.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Port of #29838 - 3rdparty/libtiff: upgrade libtiff to 4.7.2 - #29864
3rdparty/libtiff: Upgrade libtiff to 4.7.2 - port of #29838
[Feature Request] Upgrade libtiff to 4.7.2 (https://libtiff.gitlab.io/libtiff/releases/v4.7.2.html)
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
(cherry picked from commit 5b526645b6)
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
imgproc: fix OpenCL matchTemplate TM_CCOEFF_NORMED precision loss - #29856
### Problem
`cv::matchTemplate(..., TM_CCOEFF_NORMED)` on `UMat` (OpenCL) input can return exactly `-1.0`/`1.0` for windows that are not actually near-perfect (anti-)matches, while the CPU path on the same data returns a sensible, correctly-bounded coefficient. See #21788: on the reporter's images, the CPU path gives `-0.8367...` where the OpenCL path gives exactly `-1.0` at a *different* location, so `minMaxLoc` picks the wrong match entirely.
### Root cause
The per-window denominator in `TM_CCOEFF_NORMED` is a variance-like quantity computed as a difference of two comparable-magnitude sums pulled from the image's integral images (`sum(x^2) - mean^2 * N`) -- classic catastrophic-cancellation territory. The CPU implementation (`common_matchTemplate` in `templmatch.cpp`) always accumulates these sums in `double` regardless of the input image's depth, so this is a non-issue there.
The OpenCL kernel (`matchTemplate_CCOEFF_NORMED` in `match_template.cl`), however, is fed integral images hard-coded to `CV_32F` (`integral(_image, image_sums, image_sqsums, CV_32F, CV_32F)`). On a realistic-sized image, the rounding error from that single-precision subtraction can dwarf a genuinely small-but-nonzero window variance. The corrupted (and effectively noise-dominated) ratio then spuriously trips the kernel's own `+-1` safety clamp (`normAcc()`, meant only for genuinely degenerate/near-constant windows) for windows that are not degenerate at all.
I initially assumed the fix was a missing epsilon guard (the CPU path has one: `diff2 <= min(0.5, 10*FLT_EPSILON*wndSum2) -> denominator = 0`, which the kernel lacks entirely). I verified this hypothesis against real integral-image data from an actual build and it's **false** -- adding the same epsilon guard to the float32 kernel path made *zero* difference (identical spurious-clamp count, tested on 480x640 and 1080x1920 synthetic images). The true variance in the failing windows isn't near-zero; it's just small relative to the accumulated sum magnitude, which is exactly what makes the cancellation error dominate without ever being "obviously degenerate" by the guard's own threshold. Precision is the only lever that actually fixes it.
### Fix
- Use `CV_64F` integral images (matching the CPU path exactly) when the OpenCL device supports double precision (`ocl::Device::getDefault().doubleFPConfig() > 0`), gated the same way the rest of the codebase gates double-precision OpenCL kernels (e.g. `sumpixels.dispatch.cpp`'s own `ocl_integral`, `thresh.cpp`). Verified against real integral-image data pulled from this build: residual error drops from up to `1.13` (!) to `~5e-5` (pure float32 output-storage rounding, since the result `Mat` stays `CV_32F` either way), and the spurious `+-1` clamp count drops from thousands to exactly zero, across multiple image sizes.
- On devices without double support, `matchTemplate_CCOEFF_NORMED` now returns `false` instead of silently running an already-known-inaccurate float32 kernel; `matchTemplate()`'s `CV_OCL_RUN` macro then falls through to the CPU path, which is always correct. This is a correctness-over-acceleration trade-off for this specific normalized method on such devices -- verified this fallback is exact (not just close): `cv::norm(cpuResult, gpuResult, NORM_INF) == 0.0` across three image sizes on such a device.
- Added the standard `cl_khr_fp64`/`cl_amd_fp64` extension-pragma block to `match_template.cl`, copied from the existing, already-shipping `integral_sum.cl` (same idiom used everywhere else in the codebase for this).
### Testing
- New regression test (`ccoeff_normed_large_low_contrast_image` in `modules/imgproc/test/ocl/test_match_template.cpp`) using a large (1920x1080), low-contrast synthetic image. The existing parameterized `OCL_ImageProc/MatchTemplate` test only covers small (<=100x100) images of uniformly random full-range noise, which never accumulates enough integral-sum magnitude to trigger this, so it doesn't catch the bug -- confirmed by temporarily reverting the fix and rerunning: the new test fails with `CPU minVal=-0.159..., GPU minVal=-1` (the exact reported symptom), and passes clean with the fix restored.
- Full existing `OCL_ImageProc/MatchTemplate.*` suite (96 tests, all methods/depths/channels/mask combinations) passes unchanged.
- Full existing `*MatchTemplate*` suite in `opencv_test_imgproc` (286 tests total including the new one) passes.
- Ran the full `opencv_test_imgproc` binary; the only failures (360, e.g. `StackBlur`, `HoughCircles`, `ColorBayer`) are pre-existing "can't find required data file" failures from a missing local `opencv_extra` checkout in my environment, unrelated to this change and confirmed to touch none of `templmatch.cpp`/`match_template.cl`.
Fixes#21788.
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dnn: use the full vector width in blocked-layout pointwise kernels - #29836
Four block-layout kernels share one defect: they vectorize *across the channel block*, so they only use the vector unit while it happens to match `C0`. This PR fixes all four behind one shared helper, and along the way fixes an unrelated correctness bug found in the same function.
## 1. FP16 BatchNorm writes no output
`batch_norm2_layer.cpp`, the `CV_16F` arm of the accelerated block-layout path, wraps its whole body in a condition that cannot be true:
```cpp
} else if (type == CV_16F) {
const hfloat* inptr = ...;
if (type == CV_32F) { // false by construction
```
There is no `else`, so every vector loop in that arm is dead and **nothing is written to the output**. A half-precision BatchNorm on a blocked tensor with `C0` equal to 1, 2 or 4 times the vector width returns whatever the destination buffer already held. The `CV_32F` and `CV_16BF` arms are correct; only the redundant wrapper is removed.
Confirmed with a sentinel-prefilled destination: before the fix the sentinel survives the call (`maxerr = 12346`) for all three `C0` values, after it the output is exact.
## 2. The kernels only use min(C0, VEC_SZ) lanes
`C0` is the block-layout channel block, fixed at 8 (`net_impl.hpp`, `DEFAULT_C0`), so these guards decide how much of the register gets used:
| kernel | guard | consequence with C0=8 |
|---|---|---|
| ChannelsPReLU | `C0 == VEC_SZ` | scalar at 4 lanes **and** at 16/32 |
| BatchNorm | `C0 == vlanes*{4,2,1}` | scalar at 16/32 |
| InstanceNorm | `c0 <= validC0 - VEC_SZ` | scalar at 16/32 |
| GroupNorm | `c0 <= c0_hi - VEC_SZ` | scalar at 16/32 |
PReLU is the worst case: an equality can only hold on an 8-lane build, so a default x86 SSE build (4 lanes) runs it scalar too. The other three use chunk loops that work at <= 8 lanes and fail above.
These files are not in the CPU-dispatch list, so `v_float32` is whatever `CPU_BASELINE` gives -- this is not RISC-V-specific.
### Approach
A block-layout plane is contiguous over `(H, W, C0)` and the coefficients repeat with period `C0`, so one register can span `vlanes/C0` pixels: replicate the coefficients across it and walk the plane flat.
All three spanning call sites now go through one helper, `cpu_kernels/blocked_pointwise.hpp::blockedSpanApply()`, parameterised by the per-element operation (`BlockedAffineOp`, `BlockedPReLUOp`). PReLU additionally gains a chunked path for `C0 > VEC_SZ`, which is what restores it on 4-lane targets. BatchNorm keeps its own unrolled loops and gains a whole-vector step plus a remainder loop. Pre-existing paths are untouched, so targets that already vectorized keep the same code.
**GroupNorm also gains a vector reduction.** Its mean/variance pass walked one channel at a time with a stride of `C0`, which no target vectorized at all, so that half speeds up everywhere rather than only on wide vectors. Both new GroupNorm paths are restricted to blocks owned entirely by one group; where a group boundary falls inside a block the old per-channel code still runs, because spanning would cross into channels another `parallel_for_` task is writing.
### Note for reviewers: the helper's `noinline` is load-bearing
`blockedSpanApply()` carries an explicit `noinline`. Inlined, GCC 15.2 on RISC-V speculates its stores into callers whose guard is false, which silently corrupted a neighbouring group's channels in `fastNormGroupBlockF32`. The symptom was exactly half the elements wrong at VLEN=1024 in the cases where a group splits a block; a runtime trace showed the guard evaluating false on every block, and inserting any call before the `if` made it disappear. Please do not remove the attribute.
## Benchmarks
SpacemiT K3, GCC 15.2, `CPU_BASELINE=RVV`, single thread, median of 11, pristine vs patched built back to back in one session. The board exposes two core types with different VLEN, so both columns are the same binary on the same machine.
Speedup, VLEN=256 / VLEN=1024:
| shape (NxC1xHxWxC0) | Ci | InstanceNorm | BatchNorm | GroupNorm |
|---|---|---|---|---|
| 1x32x56x56x8 | 256 | 1.01x / 14.38x | 0.91x / 11.62x | 1.85x / 14.56x |
| 1x16x28x28x8 | 128 | 1.01x / 16.27x | 0.98x / 3.95x | 1.91x / 16.01x |
| 1x8x112x112x8 | 64 | 1.02x / 12.76x | 1.03x / 1.37x | 1.98x / 12.86x |
| 1x16x56x56x4 | 64 | 3.01x / 12.47x | 4.49x / 6.18x | 3.01x / 11.78x |
ChannelsPReLU, measured separately the same way:
| shape | Ci | VLEN=256 | VLEN=1024 |
|---|---|---|---|
| 1x32x56x56x8 | 256 | 1.45x | 8.37x |
| 1x16x28x28x8 | 128 | 1.55x | 4.16x |
| 1x64x14x14x8 | 512 | 1.46x | 4.05x |
| 1x8x112x112x8 | 64 | 1.47x | 3.77x |
| 1x16x56x56x4 | 64 | 2.99x | 8.47x |
| 1x8x56x56x16 | 128 | 1.20x | 2.33x |
The VLEN=256 columns for InstanceNorm and BatchNorm are flat by construction -- `C0 == VEC_SZ` there, so those shapes already vectorized and the code is unchanged; the 0.91-1.03x spread is measurement noise. The rows that move at 256 are `C0=4` (block narrower than the vector), PReLU (broken at every width), and GroupNorm (reduction).
**Caveat on the BatchNorm numbers.** BatchNorm timings on this board are much less reproducible than the other three. The `1x8x112x112x8` case in particular measured anywhere from 1.4x to 6.9x across builds with byte-identical BatchNorm sources, and its pristine baseline moved by 26% between runs. The working set there is 3.06 MB, an exact multiple of 4096, and this hardware is sensitive to how source and destination alias in the cache; the numbers above are one back-to-back pair rather than a stable figure. InstanceNorm, GroupNorm and PReLU reproduced to within ~1% across every build.
## Testing
Verified against a scalar reference over 43 shape / `C0` / `Ci` / group combinations across the four layers, at VLEN 256 and 1024, at 1 and 8 threads, clean under `MALLOC_CHECK_=3`. Cases include partial trailing blocks, odd planes (7x7, 13x11) that exercise the remainder loops, `C0` from 2 to 64, and GroupNorm configurations where a group boundary falls inside a block -- those take the fallback and match bit-exactly, which is what confirms the ownership guard.
`opencv_test_dnn` was **not** run: the build used here is `BUILD_LIST=dnn`, which generates no dnn test target, and the board has no `opencv_extra` checkout. The blocked path's reachability was confirmed by inspection instead -- `ActivationLayer::getLayouts` passes the producer's layout through, and `useBlockLayout()` runs unconditionally in `finalizeGraph`, so these kernels are on the default path in real nets.
## Platform scope
Nothing here is behind a RISC-V `#ifdef`; this is universal-intrinsic code that compiles into every target.
| target (C0=8) | lanes | what changes |
|---|---|---|
| x86 SSE baseline (default) | 4 | PReLU newly vectorized; GroupNorm reduction newly vectorized |
| x86 AVX2 baseline | 8 | GroupNorm reduction; BatchNorm loop rewritten (same iterations) |
| x86 AVX-512 baseline | 16 | + all spanning paths go live |
| ARM64 NEON | 4 | as SSE baseline |
| ARMv7 NEON | 4 | `CV_SIMD_64F`=0, reduction path skipped |
| RVV 256 | 8 | PReLU, GroupNorm reduction |
| RVV >= 512 | 16/32 | everything |
Two changes reach a **default x86 build**: PReLU, which was scalar there because `C0 == VEC_SZ` cannot hold at 4 lanes, and the GroupNorm reduction, which was scalar everywhere. The latter changes GroupNorm's numerical output on those targets, since summation order differs -- measured at ~1e-7 relative here.
Measurements are RISC-V only; no x86 or ARM machine was available. The spanning branches were exercised on RVV at the same `vlanes/C0` ratios an AVX-512-baseline build would hit (2:1 and 4:1), but the x86-reachable changes above have had no x86 validation and are the part most worth checking in CI.
SIMD support for FP8 - #29832
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The XFeat keypoint demo was added to samples/cpp/aliked_lightglue.cpp
upstream. That sample is replaced here by features_lightglue.cpp, which
now covers ALIKED/DISK LightGlue matching and the XFeat demo.
Move the model-variant enum out of the class so it follows the same
namespace-scoped style as dnn::EngineType. Also relax the DISK LightGlue
test tolerance.
ONNX Runtime version differences in floating-point can shift match
indices. Replace exact-match assertions with ±2 tolerance on both
match count and per-match index comparison.
Keep the single LightGlueMatcher public API from PR #28986. Add a Type
enum (ALIKED/DISK) to create() so users can select the model variant.
Internally, LightGlueMatcherImpl base class with two CV_FINAL subclasses
handles the variant-specific matching via pure virtual dispatch.
- Skip downloading and embedding Unifont data when HAVE_HARFBUZZ is OFF
- Note: When HAVE_HARFBUZZ is OFF, Rubik and Unifont binaries will be excluded from the build.
When chtype is double (used by bicubic64fC1 .. bicubic64fC4),
std::is_same_v<double, float> evaluated to false.
As a result, buftype erroneously defaulted to int, and pixbuf was
allocated as int pixbuf[NCHANNELS][4].
That could trigger out of bound integer computations.
Extracted IPP to HAL for getRectSubPix function in 5.x - #29514
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python: fix int32 output arrays on Windows by mapping 32-bit NPY_LONG - #29767
On Windows numpy spells `int32` as C `long`, so such arrays arrive as `NPY_LONG` (typenum 7) instead of `NPY_INT` (5). `numpyTypeToCvDepth()` has no case for it, so `pyopencv_to()` takes the cast-and-copy path — which is rejected for **output** arguments, even though the data is already bit-identical to `CV_32S`:
```python
cv.watershed(img, np.int32(markers))
```
```
cv2.error: (-5:Bad argument) in function 'watershed'
> Overload resolution failed:
> - Layout of the output array markers is incompatible with cv::Mat
```
Three tests fail on Windows for this reason, all on an int32 output argument (`markers`, `detectedIds`):
- `test_watershed`
- `test_aruco_detector_refine`
- `test_charuco_refine`
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Added Bitcast layer & extended MatMul and DFT layers support - #29594
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Rework accuracy test for legacy C-API cvApproxPoly and CV_PerimeterTest - #29547
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**Details:**
This PR restores the deleted C-API `cvApproxPoly` tests (part of #24957).
I used `cv::pointPolygonTest` to make sure every original point is within the allowed `epsilon` distance to the final approximated polygon rather than calculate the distance to the corresponding line.
**Update:**
Restored `CV_PerimeterTest` *TEST(Imgproc_ContourPerimeter, accuracy)*.
Created a parameterized test for open/closed curves with int/float type shapes. I also added a simple 10x10 square test.
Support ONNX Cast/CastLike for FP8/FP4/INT4/UINT4/E8M0 dtypes - #29360
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Refactoring and moving IPP functions to HAL for bilateral_filter in Imgproc - #29409
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1hnH2aGmc3D88HGnvM34xczQbRZUgsKsAHorcB-DpLq4/edit?usp=sharing
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## dnn: extend engine-new layer dtype coverage (control flow, Range, Hardmax, MaxUnpool, CumSum/CumProd, MaxPool, Resize2, normalization, Gemm, MatMul)
ONNX permits these dtypes on these ops, but the engine-new layers refused them at graph-construction time, so :
- valid models either failed to load outright or
- a layer quietly converted to float32 instead (Resize2)
- ran but silently lost precision above float32's 24-bit mantissa.
Companion PR (test data) : [1406](https://github.com/opencv/opencv_extra/pull/1406)
### Support added, per layer
| Layer | Types added | Gate / kernel |
|---|---|---|
| If | Bool, 16U, 16S, 32U, 32S, 64U | Gate only — the condition-reading switch already handled every depth |
| Loop | Bool, 16U, 16S, 32U, 32S, 64U | Gate only — same as If |
| Scan | Bool, 16U, 16S, 32U, 32S, 64U | Gate only — Scan never inspects element values at all |
| Range | 16S | Kernel only — gate was already an unconditional passthrough |
| Hardmax | 64F | Gate only — the `double` kernel has existed since 2024, just unreachable |
| MaxUnpool | 64F | Gate + a genuine `double` instantiation of the value-scatter routine |
| CumSum | 32U, 64U | Gate + two instantiations of the existing running-sum template (wraparound on overflow) |
| CumProd | 32U, 64U | Gate + two instantiations of the existing running-product template |
| MaxPool | 8S, 8U, 64F | Kernel only (gate was already open) — new scalar kernel for the blocked values-only path **and** the separate values+indices path, which had its own float32-only assert |
| Resize2 | 32S (nearest-neighbor only) | Gate + native `int32` gather; bilinear/cubic now reject 32S explicitly instead of silently converting to `float` |
| RMSNorm | 64F | Kernel — `fast_norm.cpp` templated on `T`, genuine `double` accumulator |
| LayerNorm | 64F | Kernel — same shared `fast_norm.cpp` path |
| LayerNorm2 | 64F | Kernel — same shared `fast_norm.cpp` path |
| InstanceNorm | 64F | Kernel — existing SIMD float32 blocked path left untouched, new scalar `double` path added beside it |
| GroupNorm | 64F | Kernel — same treatment as InstanceNorm |
| Gemm | 64F | Kernel — dedicated `cv::gemm` path, bypassing the float-only fastGemm/MLAS kernels |
| MatMul | 64F, 32S, 64S, 32U, 64U | Gate + two new paths: per-batch `cv::gemm` for 64F, and a direct 64-bit-accumulated loop for the four integer types |
Removed `test_maxpool_2d_uint8` from `opencv_all_denylist` : with 8U now supported, the test passes NORMASSERT on all backend/target combinations .
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This PR is about Introducing cuDNN JIT support for the DNN Module
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Added GridSample BiCubic, Dropout support - #29783
Updated ONNX coverage after this PR: 74.8%
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Extend data type support in elementwise, gather and scatter layers - #29786
### PR Changes:
### Support added, per layer
| Layer | Types added | Gate / kernel |
|---|---|---|
| Abs | 8U, 8S, 16U, 16S, 32U, 32S, 64U, 64S | Gate + new templated integer kernel |
| Sign | 8U, 8S, 16U, 16S, 32U, 32S, 64U, 64S | Gate + same templated kernel |
| Neg | 32S | Gate only. The kernel already had a `CV_32S` branch |
| GatherElements | 64F, 16U, 16S, 32U, 64U | Gate + element-width dispatch |
| Scatter | 64F, 16U, 16S, 32U, 64U | Gate + dispatch arms, all five reductions checked |
| ScatterND | 64F, 16U, 16S, 32U, 64U | Gate + dispatch arms, same as Scatter |
| GatherND | 64F, 16U, 16S, 32U, 64U | Gate + element-width dispatch |
| Slice2 | (fix) | Kernel. Wrong-width copy, see below |
`Abs`/`Sign` use one template over all widths with the signed/unsigned split resolved at compile time;
unsigned `abs` short-circuits to `copyTo` and unsigned `sign` reduces to `x != 0`. `GatherElements` and
`GatherND` only move elements, so their per-dtype arms collapsed to four widths. `GatherND` also unified a
target-conditional gate that split `16F`/`32F` by target in a file with no OpenCL path.
`Slice2` had two duplicated depth chains that both fell through to `run_parallel<float>`, a 4-byte copy, so
`64F`/`64U` truncated and `16U`/`16S` read and wrote past the element. Reachable only when the innermost axis
has `step != 1`, which is why the float32 slice tests passed. Now dispatches on `elemSize()`, matching
`pad2_layer.cpp`.
**Two further fixes:** signed-overflow UB in the int64 `Power`/`Neg` path (`sp[i] * scale` is undefined at
`INT64_MIN`, now multiplied through `uint64_t`), and `CV_OCL_RUN` now skips integer depths, since the OCL
activation kernels are float math and `CV_32S` would have gone through a 24-bit mantissa.
**New accuracy tests in `test_int.cpp`**: `Test_Abs_Int`, `Test_Sign_Int`, `Test_Neg_Int`, `Test_Scatter_Int`,
`Test_GatherND_Int`, with `Test_GatherElements_Int` and `Test_ScatterND_Int` widened to nine depths.
Removed `test_slice_start_out_of_bounds` from the parser denylist.
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feat: support boolean (CV_Bool) images in imwrite - #29407
## Summary
`imwrite` failed on `CV_Bool` (boolean) images because no encoder handles that depth. This normalizes a boolean image to `CV_8U` (mapping true to 255) before encoding, so boolean masks can be written directly instead of requiring a manual `convertTo` at every call site.
Adds round-trip tests covering boolean-image encoding.
Fixes#29365
AI was used for assistance.
Imgproc test cleanup - #29740
co-authored by: @Prasadayus
### Re-enabled as-is (stale disable reasons)
- `FillPolyFully.fillpoly_fully` (`test_drawing.cpp:1142`)
- `Resize_Bitexact` (`test_resize_bitexact.cpp:188`, 4 instantiations): `INTER_NEAREST` and
`INTER_NEAREST_EXACT` agree exactly at integer upscale factors; measured 0.0 diff on all 4.
### Add assertions
- **imgproc** — new `Imgproc_Watershed.regression`: `cv::watershed` had no working coverage at all.
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Objdetect test cleanup - #29741
co-authored by: @Prasadayus
### Given real assertions
| change | reason |
|---|---|
| new `Objdetect_CheckChessboard.accuracy` (`test_checkchessboard.cpp`) | `cv::checkChessboard`'s only validation lived inside a test named `timing`, which aborted at the first of 19 images. The new test iterates all 19 from `chessboard_timing_list.dat` with `SCOPED_TRACE` and `EXPECT_EQ`, so every image is reported instead of stopping at the first mismatch |
| deleted `test_chesscorners_timing.cpp` | what remained after extracting the assertions was a stopwatch — it measured `findChessboardCorners` and asserted nothing about the result |
### Re-enabled - the reason for disabling no longer holds
| test | reason |
|---|---|
| `Charuco.testSeveralBoardsWithCustomIds` (`test_charucodetection.cpp:922`) | disabled by #24338 because 5.x returns charuco corners as `32FC2` rather than `2×32FC1` (#23473). The test never compares shapes: it asserts `expected_corners.total() == c_corners.total() * c_corners.channels()` and then compares `expected_corners.reshape(1, 1)` against `c_corners.reshape(1, 1)` — both flattened to one row, so the channel layout cannot affect the result. The comparison was already made shape-agnostic; only the `DISABLED_` prefix was never removed |
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Linear and Flex attention layers support - #29624
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Add Image Decoder ONNX Layer - #29785
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features: re-enable & triage DISABLED tests - #29752
Requires: https://github.com/opencv/opencv_extra/pull/1404
### PR Changes:
### features test cleanup
### Re-enabled
- `Features2d_Detector_MSER.regression` (`test_detectors_regression.cpp:47`). Two things were needed:
- `MSER_Impl` overrides `detect()` and had dropped the empty-image guard its base class carries, so the
harness's 0x0 probe reached `detectRegions`, which rejects anything under 3x3. Added the guard; FAST and GFTT
already carry the identical block.
- The stored baseline held 215 keypoints against the 210 MSER finds today. The drift comes from
`minDiversity` entering region rejection in `fa73b91e39` and the later `fitEllipse` rework.
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Enabled WarpAffine and WarpPerspective for IPP HAL - #29771
PR enables Warp* functions that were switched off before. The thing is that I added clear 1 for places that agrees with OpenCV due to the changes in OpenCV. Also restrict the transparent border since OpenCV and IPP differently process transparent border with a workaround for current ICV package since it fails on Windows as it was mentioned in https://github.com/opencv/opencv/pull/29669#issuecomment-5339331722.
Locally the `Imgproc_WarpPerspective_Test.accuracy` test passed with ICV that is automatically downloaded by CMake.
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It was warning:
Implicit conversion turns floating-point number into integer: 'float' to 'unsigned int'
when assigning to `worst_score`.
Looks to me like `worst_score` is actually being given the wrong type, and that it should have the same type as `hamming_distance`, which is `DistanceType`.
It order to test, fixed a few adjacent -Wfloat-conversion warnings in translation units that use lsh_index.h.
Fix Python utility compatibility issues - #29755
### Problem
Several repository Python utilities emit invalid escape sequence
SyntaxWarnings under Python 3.13.
The Java test checker also attempts to parse non-Java assets as UTF-8,
causing UnicodeDecodeError, and relies on a global parser instance.
The Apple build utility accepts malformed CMake version strings because
one version separator is an unescaped regex wildcard.
### Changes
- Use raw strings for regular expressions and replacement templates.
- Skip non-Java files in the Java test checker.
- Use the current JavaParser instance instead of global state.
- Require literal dots in parsed CMake versions.
### Verification
- Compiled every tracked Python file with SyntaxWarning treated as an error.
- Ran the Java checker against modules/java/test successfully.
- Verified valid CMake versions are accepted and malformed versions rejected.
- Ran git diff --check.
The system-HarfBuzz probe referenced only hb_raster_draw_create_or_fail()
and hb_raster_draw_render(), while modules/imgproc/src/drawing_text.cpp
calls ten hb_raster_* entry points. A HarfBuzz that declares the whole
hb-raster API in its header but exports only part of it passes the probe,
so OpenCV links against it instead of falling back to the bundled copy,
and the build then fails with
undefined reference to `hb_raster_draw_destroy'
Reference all ten symbols in the probe. The array is volatile because the
check compiles at -O3: a plain function address is never null, so the
compiler folds the test away and emits no relocations, which lets a
partial library pass.
Closes#29458
dnn: vectorize fast_norm for scalable-vector (RVV) targets - #29598
### Problem
The normalization CPU kernels in `fast_norm.cpp` run **scalar** on RISC-V RVV
scalable-vector builds. Their vector code is gated by `#if CV_SIMD`, and on scalable
targets `intrin.hpp` sets `CV_SIMD 0` / `CV_SIMD_SCALABLE 1`, so the blocks are dropped by
the preprocessor. LayerNorm / RMSNorm / InstanceNorm / GroupNorm / MVN are therefore scalar
there. Independently, the `#if CV_SIMD` blocks only covered the block-layout path — the NCHW
paths these layers actually use had no explicit SIMD, and the compiler does not
auto-vectorize them (the per-element `j/step` channel-index division in the GroupNorm apply
and the float→double widening reduction defeat it).
These kernels are the last scalar piece of the new-engine (`ENGINE_NEW`) transformer norm
path; they are shared by the classic engine as well.
### Changes
- Guards → `#if (CV_SIMD || CV_SIMD_SCALABLE)` (6 sites; f64 →
`#if CV_SIMD_64F || CV_SIMD_SCALABLE_64F`), no fixed-width `::nlanes` — same idiom already
used across `modules/dnn/src`.
- Vectorized the NCHW mean/variance reduction (new `normAccumSumSqSum` /
`normAccumSumSqSum64f`, float/double accumulators matching the scalar reference) and the
affine-apply loops.
- `fastNormGroup` apply hoists the per-channel scale/bias out of the inner loop so the
`j/step` division no longer blocks vectorization.
### Testing — SpacemiT K1 (rv64gcv, VLEN=256, 8×1.6 GHz, governor=performance), 5.x
Built `-DCPU_BASELINE=RVV -DRISCV_RVV_SCALABLE=ON`.
**Correctness — zero new failures.** Default `ENGINE_AUTO`:
| Filter | baseline | patch |
|---|---|---|
| `*LayerNorm*:*InstanceNorm*:*MVN*:*Norm*:*GroupNorm*` | 27/27 pass | 27/27 pass |
| `*Test_ONNX_layers*` | 263 pass / 1 fail (`Tile`, pre-existing) | 263 pass / 1 fail (`Tile`) |
Re-run with `OPENCV_FORCE_DNN_ENGINE=2` : the supported subset
(LayerNorm/InstanceNorm/GroupNorm) passes on both baseline and patch (MVN is not implemented
in the new engine and falls back to classic under AUTO).
**Performance** — `opencv_perf_dnn`, geomean of 3 rounds:
| Test | shape | base ms (1thread / 8threads) | patch ms (1t / 8t) | speedup (1t / 8t) |
|---|---|---|---|---|
| GroupNorm::Layer | {2,64,180,240}, g=16 | 104.6 / 14.36 | 12.93 / 10.75 | **8.1×** / 1.34× |
| InstanceNorm::Layer | {2,64,180,240} | 52.96 / 11.76 | 13.63 / 10.72 | **3.9×** / 1.10× |
| LayerNorm::Layer | {1,50,768} | 0.215 / 0.072 | 0.104 / 0.068 | **2.1×** / ~1.0× |
New engine confirmed directly via `readNetFromONNX(layernorm.onnx, ENGINE_NEW)`: 1×512×768
single-thread 2.74 → 1.62 ms (**1.69×**); a base-vs-patch delta under `ENGINE_NEW` proves the
new engine executes the changed kernel.
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ptcloud: fix NaN bypass in TSDF volume integration bounds check - #29766Fixes#29763.
A singular or near-singular `cameraPose` makes `Matx44f::inv()` return NaN. That NaN reaches the SIMD bounds check in `integrateTsdfVolumeUnit()`, which rejects out-of-range pixels by ORing `v_lt(pt, 0)` with `v_ge(pt, upLimits)`. Under IEEE 754 both comparisons return false for NaN, so the check lets it through silently. `v_floor(NaN)` then produces an indefinite integer value (`INT_MIN` on x86), and the resulting row/column index reads out of bounds in `v_load_low()`.
Added `v_not_nan()` to the mask so a NaN lane is always treated as out of range, matching the NaN-guard idiom already used elsewhere in OpenCV (e.g. `bilateral_filter.simd.hpp`). The scalar fallback of the same function goes through `bilinearDepth()` in `tsdf_functions.hpp`, which had the identical gap (`pt.x < 0 || pt.x >= ...` also can't catch NaN), so that's covered too. Added a regression test using the singular pose from the issue's reproducer; note it can only confirm no voxel gets corrupted, not the crash itself, since that needs ASan on x86 to reproduce reliably.
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch (`5.x`, `ptcloud` doesn't exist on `4.x`)
- [x] There is a reference to the original bug report and related work (#29763)
- [x] There is an accuracy test (`Volume.issue_29763`), self-contained (no `opencv_extra` test data needed); not applicable: performance test (not a perf-sensitive change)
- [x] N/A: this is a bug fix, not a new feature -- no new public API or documentation needed
Dynamic KV-cache support - #29642
The core idea is: reserveKVCache() API to pre-allocate memory for attention caches upfront, which eliminates allocation overhead during token decoding. For LLM inference, simply call reserveKVCache(prompt_len + max_new_tokens) before the prefill stage so the decode loop runs without page allocations, significantly reducing per-token latency for models like Gemma3 and Qwen.
Speedups after this PR on AMD Ryzen 9 9950X 16-Core Processor device:
Qwen2.5-0.5B-Instruct, fp32, CPU, tok/s:
```
Tokens Before After Speedup
64 12.49 23.72 1.90×
128 10.37 23.14 2.23×
256 7.20 22.40 3.11×
512 4.25 21.03 4.95×
```
Gemma 3 1B-it, fp32, CPU, 512 tokens :
```
Tokens Before After Speedup
64 6.99 11.84 1.69×
128 5.84 11.72 2.01×
256 4.17 11.50 2.76×
512 2.47 11.15 4.51×
```
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calib: re-enable & triage DISABLED_ tests - #29743
**co-authored by: @varun-jaiswal17**
### PR Changes:
### calib test cleanup
### Removed
- `cameraCalibrationTiltTest.projectPoints` / `.undistortPoints` (`test_cameracalibration_tilt.cpp`, 217 lines
removed): byte-identical duplicates of the copies in `geometry/test/test_cameras.cpp`, left behind when
`99fc573913` moved them out of calib. The geometry copies remain.
- `test_modelest.cpp` (231 lines, removed entirely): includes `_modelest.h` and subclasses `CvModelEstimator2`,
both of which are gone from the tree, so it cannot compile. A byte-identical copy under
`modules/geometry/test/` is removed in a companion PR.
### Re-enabled
- `Calib3d_CalibrateCamera_CPP.accuracy_on_artificial_data` (`test_cameracalibration_artificial.cpp:430`):
restored `CALIB_FIX_K3`, commented out since at least 2011 with no reason given. With `k1`, `k2` and `k3` all
free, radial distortion imitates focal length and LM settles in a local minimum on exact synthetic data,
putting `fx` about 20% off; with the flag it converges correctly.
Three further fixes in the same test:
- `reprojectErrorWithoutIntrinsics` indexed `uv_exp[i]` inside a loop over `j`, so it summed one corner's
error repeatedly instead of averaging over all of them.
- The `rep_error` check was unreachable: `calibrateCamera` already returns a per-point RMS, and the test
divided it by the point count again before comparing, so a 1.57 px RMS passed silently. The extra division is
removed.
- The board-generation retry loop was an unbounded `for(;;)`; it now fails the test after 100 attempts
instead of looping forever.
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Geometry:Test cleanup - #29753
**co-authored by: @varun-jaiswal17**
### geometry test cleanup
### Removed (dead or unbuildable)
- `test_modelest.cpp` (231 lines, removed entirely): includes `_modelest.h` and subclasses `CvModelEstimator2`,
both of which are gone from the tree, so it cannot compile.
- `test_cameras.cpp:11-207` (197 lines removed): a `#if 0` block, never compiled, holding an older
`cvtest::ArrayTest`-based `CV_ProjectPointsTest` built on the removed C API (`CvMat`, `cvProjectPoints2`). A
live class of the same name derived from `cvtest::BaseTest` follows it and covers strictly more: `Rodrigues`
plus five jacobians against the dead block's two.
### Given real assertions
- `Calib3d_FindFundamentalMat.correctMatches` (`test_fundam.cpp:410`): reverting the solvePoly fix gives -nan /
1.79e+308 and it still passed.
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The Python tutorial sample thresholds the orientation with a single cv.threshold call, passing HighThr as the maxval argument rather than as an upper bound:
```_, imgOrientationBin = cv.threshold(imgOrientation, LowThr, HighThr, cv.THRESH_BINARY)```
That computes imgOrientation > LowThr ? HighThr : 0, so HighThr never restricts the angle. The C++ counterpart uses inRange(imgOrientation, Scalar(LowThr), Scalar(HighThr), imgOrientationBin), and the tutorial text states "LowThr and HighThr define orientation range", so the C++ behaviour is the intended one.
geometry: fix strict-aliasing UB in convexHull - #29714Fixes#26952.
### What
`convexHull()` allocated a single `AutoBuffer<Point*>` and obtained a `Point2f**` view of the *same storage* via `reinterpret_cast` to share the sort/`Sklansky_` code between the `CV_32S` and `CV_32F` cases, and unconditionally read the input via `points.ptr<Point>()` regardless of the Mat's actual depth. Both are strict-aliasing violations -- the exact mechanism the issue title describes ("bogus C-cast and illegal assumptions of object layout") -- and are undefined behavior independent of `Point` and `Point2f` happening to be the same size.
### Why this approach
Two prior attempts (#26975, #27020, both by @kallaballa, both open for months before stalling) tried to fix this by rewriting input/output handling around `InputArray::copyTo` and `std::vector`. That cascaded into unrelated scope -- undocumented-input-format questions, new overloaded signatures that broke Java bindings, uncertainty about what the "authoritative spec" for accepted inputs even is -- and neither landed. @asmorkalov's own diagnosis in that thread was narrower: *"the same data is casted as `Point*` and `Point2f*` and used as integer or floating point"* -- i.e. the casting itself, not the input/output handling, is the bug.
This PR makes only that minimal change. `convexHull`'s core is now a function template on the point type (`convexHull_<_Tp,_DotTp>`), so `pointer`/`data0` are always genuinely `Point_<_Tp>*`-typed for whichever branch is active -- no cast is ever needed, because the two instantiations never share storage. `Sklansky_` and `CHullCmpPoints` needed no changes at all; they were already correctly templated -- the UB was only in how the non-template `convexHull()` constructed and reinterpreted the buffers it passed to them. The public signature, input parsing, and output writing are otherwise unchanged, and no other function needed touching (`convexityDefects`/`isContourConvex` already dispatch on depth correctly).
### Testing
- All 55 existing tests covering `convexHull`, the self-intersection index-monotonicity fixup (`!returnPoints`), `minAreaRect`, `minEnclosingTriangle`, and `convexityDefects` pass unchanged -- these call `convexHull` internally, which is exactly what broke under the prior attempts' more invasive rewrites.
- **Performance** (the other thing that sank a prior attempt): my first design (switching the internal representation from a pointer array to an int index array) measured a real, consistent ~10-13% slowdown at n=10000/100000 vs pristine 5.x (A/B via `git stash`, 7-trial medians) -- traced to the extra `index * stride` address computation an index array requires on every dereference that a direct pointer dereference doesn't. Redesigned to keep the *pointer*-array representation (just correctly typed per template instantiation instead of cast), which recovered performance matching baseline within trial-to-trial noise at every size tested. Added `modules/geometry/perf/perf_convhull.cpp` as a permanent regression guard (100/1k/10k/100k points, `CV_32S`/`CV_32F`) -- no such perf test existed before, despite this being exactly the kind of change that regressed performance in prior attempts.
- Full `opencv_test_geometry` suite: no failures attributable to this change (the 3 present are pre-existing, needing `opencv_extra` test data not configured in this scoped build).
### PR checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on code under GPL or another incompatible license.
- [x] The PR is proposed to the proper branch (5.x).
- [x] Accuracy and performance tests included/added (see above).
- [x] No public API/behavior change, so no documentation or sample updates needed.
Add MatMulNBits layer and extend onnx coverage - #29666
Requires:https://github.com/opencv/opencv_extra/pull/1401
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dnn: add HAL hook for general convolution and an RVV kernel - #29689
### Pull Request Readiness Checklist
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### Summary
Adds cv_hal_dnn_conv32f and a RISC-V RVV implementation.
The hook uses the same flat C ABI as the existing DNN hooks and is behaviour-neutral without a backend. The RVV kernel runs e32m2 at vl = K0 = 8 with 10 output positions in flight, and on wide registers packs P = VLMAX/K0 output channel blocks into one vector. It declines to the built-in when an output channel block is only partially filled: K % 8, K/ngroups % 8, or grouped C/ngroups % 8.
### Verification — K3 board, VLEN 256 and 1024, GCC 15.2 + Clang 22
- Standalone harness, 22 configurations (kernel sizes, strides, dilation, asymmetric pads, 1D/2D/3D, groups, residual, all five activations): pass at both VLENs, bit-identical to a scalar reference
- opencv_test_dnn under OPENCV_FORCE_DNN_ENGINE=2: 960 passed / 29 failed, failure set identical with the hook on and off and at both VLENs; the 29 are pre-existing
- Fault injection flips exactly 19 tests, confirming the hook is on the execution path
### Performance – speedup over the built-in
| Network | 8 threads, VLEN 256 | 1 thread, VLEN 256 | 1 thread, VLEN 1024 |
| :--- | :--- | :--- | :--- |
| **SqueezeNet_v1_1** | 2.91× | 7.8× | 19.4× |
| **Inception_v1** | 2.00× | 7.0× | 13.7× |
| **Squeezenet** | 2.17× | 6.9× | 14.5× |
| **TinyYolov2** | 2.14× | 6.8× | 14.2× |
| **ResNet_50** | 2.56× | 5.7× | 9.7× |
| **LResNet100E_IR** | 1.94× | 5.5× | 11.2× |
Enabled WarpAffine and WarpPerspective for IPP HAL - #29669
PR enables Warp* functions that were switched off before. The thing is that I added clear `1` for places that agrees with OpenCV due to the changes in OpenCV. Also restrict the transparent border since OpenCV and IPP differently process transparent border.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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Re-enable & triage DISABLED tests for DNN module - #29702
Requires: https://github.com/opencv/opencv_extra/pull/1403
**co-authored by: @varun-jaiswal17**
### PR Changes:
## dnn test cleanup: re-enable stale-disabled tests, fix defect-blind tests, remove redundant coverage
### Removed (dead or unbuildable)
- `test_int8_layers.cpp` (1118 lines, removed entirely): cannot compile — `Net::quantize()`,
`getInputDetails`/`getOutputDetails` are all gone from `dnn.hpp`/`dnn/src`. Disabled in that same PR (#24980)
because on-the-fly quantization was removed — every test in this file called `net.quantize()` to calibrate and
run its own int8 conversion. Its own header comment said restore "when test models are quantized outside
OpenCV". Pre-quantized ONNX/TFLite test models already do that.
### Removed (redundant or assertion-free)
- `Tokenizer_BPE.Tokenizer_GPT2_Model`: line-for-line subset of `Tokenizer_GPT2` — same config, same input,
same roundtrip assertion.
- `Test_TensorFlow.read_inception`: printed `out.dims` and asserted nothing about the result;
`inception_accuracy` loads the same `.pb` and checks it against a reference.
- `Test_Caffe_nets` fixture + `INSTANTIATE`: registered **zero** `TEST_P` cases — dead scaffolding for Faster
R-CNN tests removed earlier.
- `Test_ONNX_nets.Squeezenet`: kernels {1×1, 3×3} and every op type already covered by dedicated layer tests.
- `Test_ONNX_nets.VGG16_bn`: single conv kernel (3×3), fully covered by dedicated layer tests; skipped by
default anyway under `mem_6gb`.
- `Test_ONNX_nets.CaffeNet`: identical op multiset, node count (24) and conv signatures to retained `Alexnet`.
- `Test_ONNX_nets.RCNN_ILSVRC13`: `Alexnet` minus `Softmax` (23 vs 24 nodes), identical conv signatures.
- `Test_ONNX_nets.Inception_v1`: same op set as retained `Googlenet` (+1 `Reshape`) — Inception v1 *is*
GoogLeNet.
### Given real assertions instead of stale expectations
- `Test_ONNX_layers.Elementwise_Sqrt`: moved `testONNXModels("sqrt")` below `#endif` — its only work line sat
inside `INF_ENGINE_VER_MAJOR_LT(2021040000)`, so without OpenVINO the body compiled to nothing and reported `[
OK ]` on all 3 backends.
- `Layer_Test_01D.Clip`: now calls `ClipLayer::create` with `"min"`/`"max"` — it set `lp.type = "Clip"` but
constructed `ReLU6Layer::create`, and `runLayer` never reads `layer->type`, so it just re-ran `ReLU6`.
- `Layer_Arg_Test`: removed the "disabled" comment, corrected the `convertTo` comment — the comment said the
test was disabled while it runs 8 cases, and the second said "convert to float" where the code converts to
`CV_64S`.
### Re-enabled as-is (stale disable reasons)
- `Test_ONNX_layers.LSTM`/`LSTM_bidirectional` (`test_onnx_importer.cpp:1551,1558`): disabled by #21522 (2022)
for poor 1-D-mat handling in the importer of that era; no longer reproduces.
- `Test_ONNX_layers.Split_sizes_0d` (`:1373`): disabled by #22652 for a Mul/0-d-tensor shape ambiguity (A×1 vs
1×A); dnn now supports real 1-D Mats, so the output matches the reference exactly.
- `DNNTestNetwork.YOLOv8n`
### Library fixes found while re-enabling
- `Test_ONNX_layers.LSTM_layout_seq`/`LSTM_layout_batch` (`test_onnx_importer.cpp:1721,1728`): `LSTM2` never
transposed `X` for ONNX `layout=1` (batch-first); fixed via `transposeND` gated on `layout==BATCH_SEQ_HID`
(`recurrent2_layers.cpp:172`). Fixture also had a leaked loop variable that made the reference a copy of the
input; rebuilt by hand since ORT itself refuses to run `layout=1`.
- `Test_Graph_Simplifier.ResizeSubgraph` (`test_graph_simplifier.cpp:61`): disabled by the block-layout PR
#28585; expectations updated for the `TransformLayout` pass that PR introduced. The test now covers 4 subgraphs rather than 6, because `GatherCastSubgraph` and `MulCastSubgraph` were removed by `0e36cafcf4` and `7669897910` (`Gather`/`Mul` -> `Cast` is no longer fused, since folding it away silently dropped the `Cast`'s dtype semantics). The dynamic-scale `Shape`/`Gather`/`Cast`/`Floor`/`Concat`/`Unsqueeze`/`Slice` chain these models use to compute Resize's scale factor therefore no longer collapses, and the `Mul` survives as `NaryEltwise`, which is why the expected layer lists grew
### Deliberately kept
- `ZFNet`: its **7×7** conv appears in no dedicated layer test, and its kernel set {7×7, 5×5, 3×3} differs from
`Alexnet`'s {11×11, 5×5, 3×3}.
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Xfeat feature - #29361
## PR Description
### Summary
Integrate XFeat into OpenCV's `features` module as a native `Feature2D` implementation, enabling lightweight neural feature detection and descriptor extraction through OpenCV's standard feature extraction API.
---
### What's included
#### New class
- **`cv::XFeat`** extends `Feature2D`
- CNN-based keypoint detection
- 64-D descriptor extraction via ONNX/DNN
- Score-map based keypoint selection
- Descriptor sampling from the dense feature map
---
### Files added
| File | Description |
|------|-------------|
| `src/feature2d_xfeat.cpp` | XFeat `Feature2D` implementation |
| `test/test_xfeat.cpp` | XFeat unit and regression tests |
---
### Files modified
- `features.hpp`
- Add `cv::XFeat` declaration and public factory APIs
---
### Usage
```cpp
#include <opencv2/features.hpp>
using namespace cv;
// Feature extraction
Ptr<XFeat> xfeat =
XFeat::create("xfeat.onnx", 2000, 0.5f, 640);
std::vector<KeyPoint> keypoints;
Mat descriptors;
xfeat->detectAndCompute(image, noArray(), keypoints, descriptors);
```
---
### Test dependency
Depends on the opencv_extra changes adding the XFeat ONNX model and reference outputs.
Required test data:
https://github.com/opencv/opencv_extra/pull/1383
- `xfeat.onnx`
- `xfeat_lena_640_kpts.npy`
- `xfeat_lena_640_desc.npy`
These files are required for the `Features2d_XFeat` tests in the main OpenCV repository to validate XFeat feature extraction and descriptor generation.
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features: accept CV_Bool masks in SIFT detect (#25895) - #29679
### Problem
`cv::Mat_<bool>::depth()` returns `CV_Bool` in 5.0, where it returned `CV_8U` in 4.x. `SIFT_Impl::detectAndCompute` gates the mask at `sift.dispatch.cpp:980`, so a boolean detection mask now fails with:
```
(-5:Bad argument) mask has incorrect type (!=CV_8UC1) in function 'cv::SIFT_Impl::detectAndCompute'
```
### Fix
Allow `CV_BoolC1` in that check, and update the message accordingly.
No conversion is needed. The mask is consumed by `KeyPointsFilter::runByPixelsMask`, whose `MaskPredicate` reads it with `Mat::at<uchar>`, and `Mat::at` only asserts that the element size matches (`CV_ELEM_SIZE1(traits::Depth<_Tp>::value) == elemSize1()`). `CV_Bool` is one byte like `CV_8U`, so the existing read is already correct for a boolean mask.
That is also why `FastFeatureDetector` and `SimpleBlobDetector`, which have no explicit mask type check and route through the same predicate, already accepted boolean masks. The only thing standing in the way was SIFT's own check.
This follows the same approach `goodFeaturesToTrack` already uses in this module (`featureselect.cpp:301` accepts `CV_8UC1 || CV_BoolC1`).
### Test
`Features2d_Detector_Keypoints_BoolMask.matches_uchar_mask` builds a synthetic image with circles and rectangles, runs FAST, SIFT and SimpleBlobDetector with a `Mat_<bool>` mask and with the equivalent `CV_8UC1` mask, and requires the same keypoints from each.
Verified locally on 5.x: the test fails without the change (SIFT throws the error above) and passes with it. FAST and SimpleBlobDetector are included to pin their already-working behaviour against future regressions. No test data needed, the test is synthetic.
Part of #25895.
### Pull Request Readiness Checklist
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samples: fix HoughLines/HoughLinesP Python sample for 5.0 shape change (fixes#29637) - #29663
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Fixes#29637.
OpenCV 5.0 changed vector-backed Mat/OutputArray to true 1D arrays (see
migration guide: 1D and 0D array semantics). This changes HoughLines/
HoughLinesP Python return shape from (N,1,X) to (N,X), breaking the old
indexing pattern used in the tutorial sample.
Tested locally against opencv-python 5.0 — script runs without error,
lines drawn correctly on samples/data/sudoku.png.
core: gate GEMM SIMD off on 32-bit x86 and macOS-x64 (5.x) #29496
- this is port of https://github.com/opencv/opencv/pull/29242 to 5.x branch
- GEMM SIMD is turned off on 32-bit x86 and macOS-x64 to fix calibration
### Pull Request Readiness Checklist
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imgproc: accept CV_Bool masks in matchTemplate (#25895) - #29677
### Problem
`cv::Mat_<bool>::depth()` returns `CV_Bool` in 5.0, where it returned `CV_8U` in 4.x. `matchTemplateMask` gates the mask at `templmatch.cpp:737`, so passing a boolean mask now fails with:
```
(-215:Assertion failed) _mask.depth() == CV_8U || _mask.depth() == CV_32F in function 'cv::matchTemplateMask'
```
A binary mask is a normal input for masked template matching, so this is a regression against 4.x.
### Fix
Allow `CV_Bool` in the assertion and widen the mask to `CV_8U` before the existing binarization step.
The widening is required rather than passing `CV_Bool` straight through, because `cv::threshold()` does not accept `CV_Bool`. Once widened, the existing `THRESH_BINARY` path treats any non-zero entry as selected, which is exactly the documented `CV_8U` mask semantics. `CV_8U` and `CV_32F` masks take an unchanged path.
The masked path is the only one affected: `cv::matchTemplate` routes every non-empty mask through `matchTemplateMask`, and there is no OpenCL mask variant.
### Test
`Imgproc_MatchTemplateBoolMask.matches_uchar_mask` compares a `Mat_<bool>` mask against the equivalent `CV_8UC1` mask and requires the results to agree, across all six match methods (`TM_SQDIFF`, `TM_SQDIFF_NORMED`, `TM_CCORR`, `TM_CCORR_NORMED`, `TM_CCOEFF`, `TM_CCOEFF_NORMED`) for `CV_8UC1`, `CV_8UC3` and `CV_32FC1` images. 18 parameter combinations.
Verified locally on 5.x: all 18 fail without the source change (with the assertion above) and pass with it. The full `*MatchTemplate*` set, 165 tests, passes. No test data needed, the test is synthetic.
Part of #25895, and follows the same approach as #29580, #29597 and #29622.
### Pull Request Readiness Checklist
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video: accept CV_Bool masks in findTransformECC (#25895) #29681
### Problem
`cv::Mat_<bool>::depth()` returns `CV_Bool` in 5.0, where it returned `CV_8U` in 4.x.
`findTransformECCWithMask` does not type check `inputMask` at all. It passes it straight to `cv::threshold()` at `ecc.cpp:483`, and `cv::threshold()` dispatches only on `CV_8U`, `CV_16S`, `CV_16U`, `CV_32F` and `CV_64F`, erroring otherwise. So a boolean mask fails with an error pointing at imgproc rather than at the mask:
```
modules/imgproc/src/thresh.cpp:1607: error: (-210:Unsupported format or combination of formats)
in function 'cv::threshold'
```
### Fix
Widen a `CV_Bool` mask to `CV_8U` before the `threshold` call. The existing `THRESH_BINARY` step then treats any non-zero entry as selected, which matches the `CV_8U` mask semantics.
`inputMask` is used nowhere else in the function (only at `ecc.cpp:476` for the `empty()` check and at `:483`), so this is the single point that needed handling. `CV_8U` masks take an unchanged path.
Note that `cv::computeECC` already accepts a boolean mask, because it only forwards the mask to `countNonZero`, `meanStdDev` and `subtract`, which all handle `CV_Bool`. This change makes `findTransformECC` consistent with it.
### Test
`Video_ECC_BoolMask.matches_uchar_mask` builds a blurred checkerboard, warps it by a known translation, then runs `findTransformECC` with `MOTION_TRANSLATION` using a `Mat_<bool>` mask and the equivalent `CV_8UC1` mask, and requires the same warp matrix from both.
Verified locally on 5.x: the test fails without the change (with the error above) and passes with it. The full `*ECC*` set, 15 tests, passes with `OPENCV_TEST_DATA_PATH` set. No test data needed for the new test itself, it is synthetic.
Part of #25895, and follows the same approach as #29580, #29597 and #29622.
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cv::Mat_<bool>::depth() returns CV_Bool in 5.0, where it was CV_8U in 4.x, so
code that passed a boolean mask to inpaint() stopped working on the 5.x branch.
icvInpaint() rejected anything that was not CV_8UC1, even though CV_Bool is a
single byte like CV_8U and the uchar reads in the fast marching code are already
correct for it. Relaxing the type gate is therefore sufficient, no conversion of
the mask is needed.
The regression test checks a boolean mask against the equivalent CV_8U mask and
requires both the output type and every pixel to match, for both inpainting
algorithms and for 1 and 3 channel input.
Verified locally: the test fails against unmodified 5.x with "The mask must be
8-bit 1-channel image" and passes with the change. The rest of opencv_test_photo
is unaffected.
Detect three-dimensional NumPy arrays as multichannel inputs before applying the channel limit so OpenCV reports an explicit conversion error instead of treating the channel axis as a matrix dimension. Add regression coverage for a 129-channel remap input.
Assisted-by: OpenAI Codex
is_SetHardwareGain's master-gain parameter is documented as 0-100;
out-of-range values are not guaranteed to be handled predictably by
the SDK, so clamp before passing through.
extractChannel() stopped supporting in-place operation (dst aliasing
src) after commit 416bf3253 (PR #23473), which replaced
Mat src = _src.getMat();
_dst.create(src.dims, &src.size[0], depth);
with a single up-front
_dst.createSameSize(_src, depth);
...
Mat src = _src.getMat();
When _src and _dst reference the same array, the up-front reallocation
reshapes the shared buffer to a single channel before the multi-channel
source header is fetched. mixChannels() then sees a 1-channel source and
throws for any coi >= 1.
Move createSameSize() back to after the source Mat/UMat is obtained, so
the source header keeps the original multi-channel data alive across the
destination reallocation. This preserves the 0D/1D handling introduced
by createSameSize while restoring the pre-existing in-place contract.
Adds regression test Core_Mat.extractChannel_inplace_29568.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
cv::stylization always drives Domain_Filter::filter() with NORMCONV_FILTER,
which calls compute_NCfilter on the working matrix and its transpose each
iteration. When either has a single column, the summed-area-table index
math can produce a negative offset that reads past the box_filter buffer.
A single column has no neighbor to convolve across, so the correct result
is an unchanged output -- exactly what compute_Rfilter already does for
w==1 via its no-op loop bounds. Return early instead of running the
box-filter math in that case.
Fixing compute_NCfilter directly (rather than guarding cv::stylization's
input, as suggested in the issue) also covers cv::edgePreservingFilter
when called with NORMCONV_FILTER, and covers single-row inputs, which hit
the identical bug through the internal transposed pass.
Closes#29614
The 16-bit icvCvt_* helpers in utils.cpp advance the row pointer with
step/sizeof(x[0]) - size.width*N at the end of each row. sizeof is size_t
(unsigned), so the whole subtraction is done in unsigned math and wraps to
a huge offset when the caller passes step == 0.
The TIFF tile decoder does exactly that: it walks rows itself and calls
these helpers with step = 0 and Size(width, 1), so decoding a 16-bit
4-channel TIFF forms an out of range pointer. UBSan reports it as an
unsigned offset overflow (issue #29615).
Cast sizeof(...) to int so the step math is signed. The result is the same
for valid multi-row calls and no longer overflows when step is 0.
Added Imgcodecs_Tiff.regression_29615_16UC4 which round-trips a CV_16UC4
TIFF in memory.
Close the cv::viz3d namespaces outside the trailing #ifdef HAVE_OPENGL
block. Previously the closing braces were inside the guard, so a build
with OpenGL disabled dropped them and failed with
'expected }' at end of input'.
Core, Highgui: OpenGL wrappers & window free-callback API #29485
This PR is a continuation of the work started in [20371](https://github.com/opencv/opencv/pull/20371)
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is_SetHWGainFactor() does not actually change the applied gain on
uEye cameras; use is_SetHardwareGain() instead, which the vendor SDK
documents as the correct call for master gain and matches behavior
confirmed by the issue reporter.
Fixes#22259
* dnn:rvv: restore the fixed block size C0 == 8
#29304 made the net-wide block size follow the RVV vector width
(defaultC0 = max(8, vlanes())), so the blocked memory layout of every tensor
depended on the host's VLEN and on the LMUL of the universal intrinsics in core.
The same model gets a different layout on a 128-, 256- or 1024-bit machine.
Restore the fixed default of 8, which is the mainstream, well-tested setting on
every platform. C0/K0 become compile-time constants again on RVV as they already
were elsewhere, so the runtime block-size plumbing and the vlanes()-based scratch
sizing are dropped, and getConvFunc_ no longer advertises block sizes the kernels
cannot handle.
The blocked kernels currently require C0 == vlanes(), which this alone does not
satisfy; the next commit removes that requirement.
* dnn:rvv: pin the vector length to the channel block
The blocked kernels assumed one channel block is exactly one vector
(C0 == vlanes()). That is not a property of the layout, it is a property of the
hardware, so the kernels only worked where the two happened to coincide: with
C0 fixed at 8 they break wherever vlanes() != 8 -- under-filling the block when
the vector is narrower (silently leaving output channels unwritten) and
overrunning it when the vector is wider (CV_Assert).
The universal intrinsics cannot express this: v_float32 is always vlanes() wide
and there is no way to make it span exactly C0 elements. So the RVV paths drop to
native intrinsics and set the vector length explicitly:
const size_t _vl = __riscv_vsetvl_e32m2(C0); // == 8 on every VLEN >= 128
LMUL=2 is required, not incidental: vlmax(e32,m2) = VLEN/32*2 >= 8 even at
VLEN=128, the narrowest RVV configuration, so _vl == C0 on every RVV 1.0 core.
One vector then spans precisely one channel block regardless of VLEN, and the
C0-vs-vlanes asserts and VLEN-dependent branches are removed rather than extended.
The existing int8 kernels already choose LMUL=2 for the same reason
(conv2_int8_kernels.simd.hpp).
This is a native per-kernel choice and is independent of the LMUL the universal
intrinsics are built with; dnn no longer reads the vector width of
intrin_rvv_scalable.hpp at all.
conv, deconv, maxpool, avgpool and depthwise use native intrinsics. batch_norm
stays portable and instead replicates the C0-wide coefficient pattern across the
vector, which keeps full lane utilisation where a fixed vl would idle most of it.
The x86/ARM paths are restored to their pre-#29304 form and are unchanged.
Tested on SpaceMIT K3 (X100 VLEN=256, A100 VLEN=1024), opencv_test_dnn: no
regressions against upstream on either core.
* core:rvv: switch scalable universal intrinsics from LMUL=2 to LMUL=1
Move the RVV universal-intrinsic types (v_uint8..v_float64) from m2 (paired
registers, 16 usable) to m1 (individual registers, 32 usable) per #29454, to
relieve register pressure in the complex kernels that rely on universal
intrinsics. Simple kernels that regress can take native RVV branches; the complex
ones cannot, so the default should suit them.
Only intrin_rvv_scalable.hpp changes. The widening/narrowing cascades and
max_nlanes are adjusted, and helpers that spell an LMUL into the intrinsic name
shift down one step (m2->m1, m1->mf2): v_popcount, v_lut (vector and f64 paths),
the f64 dot-product reduction (#27003), v_matmul, and the byte-indexed v_lut
added by #29250, whose __riscv_vluxei8_v_u8m2 no longer matches the m1 base type.
VLEN=1024 needs no change: under m1, max_nlanes = CV_RVV_MAX_VLEN/SZ equals
vlanes() at 1024, and CV_RVV_MAX_VLEN already defaults to 1024. Verified on a
1024-bit core.
Tested on SpaceMIT K3, which exposes both a 256-bit (X100) and a 1024-bit (A100)
core. opencv_test_core: 5605/5612, the 7 failures (filestorage_base64, globbing)
non-SIMD and failing identically on the m2 baseline. Perf (x-factor = m2/m1,
>1 = m1 faster), median >= 300us: core 0.966 -> 0.996 and imgproc 0.950 -> 1.038
as VLEN goes 256 -> 1024, i.e. m2's width advantage disappears once both are wide
while m1's register headroom persists. m1 wins resize (1.50/1.41), Exp (1.28),
gaussianBlur (1.23), norm (1.22), scharr (1.20); m2 still wins the light
memory-streaming kernels addWeighted (0.76), compare (0.82) and dot (0.87), which
are the natural candidates for native RVV branches.
* Revert "dnn:rvv: pin the vector length to the channel block"
This reverts commit 40587c6dd9.
* dnn:rvv: disable CV_SIMD_SCALABLE in the new-engine conv kernel
Under the m1 switch the RVV conv kernel breaks at VLEN=128: it assumes
K0 == vlanes(), but m1 makes vlanes()=4 while the block stays C0=K0=8, so it
computes only half of each output block. Temporarily disable the RVV
(CV_SIMD_SCALABLE) branches so conv runs scalar on RVV, exactly as #29180 did for
the other new-engine layers. The vectorized path will be restored in a follow-up.
Only conv is affected: maxpool/avgpool/depthwise/deconv/batch_norm already handle
C0 == 2*vlanes (the SSE/NEON case) and stay on their universal-intrinsic paths.
* dnn:rvv: document the supported VLEN range and the v_setvlmax follow-up
With the fixed C0=8 the RVV blocked kernels cover VLEN=128 and 256 (C0 >= vlanes);
VLEN>=512 (C0 < vlanes) is not yet handled. Add TODO notes recording this and
pointing at the planned fix: a portable v_setvlmax<VT>(n) universal intrinsic that
caps the vector to the block (no-op on fixed-width ISAs, sets the vl/mask on
RVV/SVE), with an optimized multi-pixel variant as a later step, possibly an RVV
HAL for the new dnn engine. See the #29493 discussion.
* dnn:rvv: disable CV_SIMD_SCALABLE in max/avg pooling and depthwise
These three blocked kernels assert (C0 == nlanes || ...) at VLEN>=512, where the
fixed C0=8 is narrower than the vector register — breaking whole networks
(AlexNet/ResNet/YOLO) on wide RVV cores, though CI (VLEN=128) is unaffected.
Disable their RVV (CV_SIMD_SCALABLE) paths so they run scalar on RVV at every
VLEN, matching #29180's scope. Verified on a SpaceMIT K3: this removes the 92
VLEN=1024 assert failures, with no change at VLEN=128/256.
deconv and batch_norm are left enabled: they degrade to scalar at VLEN>=512
(deconv's C0 % vlanes guard, batch_norm's vector-loop bound) without asserting,
so they stay correct and keep their 128/256 vectorization. All of these
re-vectorize uniformly via the v_setvlmax<VT>(n) follow-up.
Add Scan layer to the new engine #29577
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- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Added Cumprod and Causalconv layers in new dnn engine #29579
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
cv::Mat_<bool>::depth() returns CV_Bool in 5.0 where it returned CV_8U in
4.x, so distanceTransform started rejecting boolean masks. CV_Bool is a
1-byte type whose values are only zero-tested here, so accept it wherever
a CV_8UC1 mask was previously accepted, matching the 4.x behaviour and the
4->5 migration guide.
Relaxes the type checks on all four reachable paths: the public wrapper,
distanceTransform_L1_8U, trueDistTrans (DIST_MASK_PRECISE) and
distanceATS_L1_8u (CV_8U output).
Closes#29596
CI (Ubuntu ARM64 x2) showed the Python test suite aborting the whole
process with exit code 134: on a Qt-enabled build, namedWindow() tried
the "xcb" platform plugin, found no display (the Python test step,
unlike the C++ gtest step, does not run under xvfb-run), and Qt
aborted the process instead of raising a catchable exception -- so the
existing try/except around namedWindow never got a chance to run.
Fix: set QT_QPA_PLATFORM=offscreen (only if not already set) before
any GUI call. "offscreen" is the platform plugin Qt ships specifically
for headless/CI use, and is listed as available in the failing CI
job's own log output.
Verified locally: built opencv_python3 with WITH_QT=ON (real Qt6, not
the WITH_QT=OFF build used in the initial verification), and confirmed
that in a clean environment (no DISPLAY, no QT_QPA_PLATFORM preset),
namedWindow + saveWindowParameters + loadWindowParameters all succeed
with this fix, where they would previously depend on a native display
backend being reachable.
Both functions persist/restore a window's size, position, flags,
trackbar values, and zoom/pan state (Qt highgui backend only), take
only a plain String argument, and were already fully documented -- but
were declared CV_EXPORTS instead of CV_EXPORTS_W, so they were
unreachable from Python.
Sibling Qt-only functions in the same header (displayOverlay,
displayStatusBar) already follow the CV_EXPORTS_W + runtime
"not implemented without Qt" convention this change follows.
Adds a Python test that exercises namedWindow + save/loadWindowParameters
and gracefully skips when no GUI backend or no Qt support is present,
consistent with how other environment-dependent tests in this suite
(e.g. CUDA) skip rather than fail.
Verified locally: built opencv_python3 from this branch (WITH_QT=OFF)
and confirmed cv2.saveWindowParameters / cv2.loadWindowParameters are
present on the built module and correctly raise the expected
"compiled without QT support" error, proving the binding reaches the
C++ layer end-to-end.
Both functions are fully implemented and documented but were tagged
CV_EXPORTS instead of CV_EXPORTS_W, so they were unreachable from
Python (and Java/JS) bindings. They use only bindable types
(InputArrayOfArrays, OutputArray, plain enums), so this is a pure
binding-exposure fix, not a behavior change.
Adds Python regression tests for both functions using synthetic
hand-eye data that satisfies the documented AX=XB (calibrateHandEye)
and AX=ZB (calibrateRobotWorldHandEye) relations, verifying the
recovered transform matches the synthetic ground truth.
Verified locally: built opencv_python3 bindings from this branch and
confirmed cv2.calibrateHandEye / cv2.calibrateRobotWorldHandEye are
now present and both new tests pass against synthetic ground-truth
poses.
In 5.0 cv::Mat_<bool>::depth() returns CV_Bool instead of CV_8U, so
boundingRect routed boolean masks into pointSetBoundingRect, which expects
a CV_32S/CV_32F point set and threw. CV_Bool is a 1-byte 0/1 mask type, so
handle it like CV_8U/CV_8S via the byte-wise maskBoundingRect path.
The race is benign functionally because threads only modify
FOREGROUND (255) to VISITED_OUTER_RIGHT (100) or VISITED_ (200),
all of which are NON-ZERO. findStartContourPoint only cares if a
pixel is zero or non-zero, so its termination conditions are
unchanged. However, concurrent read/write of different values is
legally UB in C++ and triggers TSAN.
Updated ONNX conformance list #29560
updated ONNX conformance list with onnx version = 1.22.0
Current ONNX coverage in OpenCV ENGINE_NEW is now **72.1%**
Merge with: https://github.com/opencv/opencv_extra/pull/1398
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Add ColorHashTSDFVolume implementation #27823
# Add ColorHashTSDFVolume implementation [[#25155](https://github.com/opencv/opencv/issues/25155)]
## Description
Added a new ColorHashTSDFVolume implementation that combines the benefits of HashTSDFVolume's efficient spatial hashing with color support. This provides memory-efficient RGB-D fusion with better performance compared to regular ColorTSDFVolume.
### Key Features
- Hash-based spatial data structure for efficient storage
- Color integration during volume updates
- Raycast with color interpolation
- Compatible with existing TSDF interfaces
- CPU implementation with parallel processing support
### Implementation Details
- Added new ColorHashTSDFVolume class with create() factory method
- ColorVoxel structure combining TSDF and RGB data
- Spatial hashing for efficient voxel lookup
- Weighted running average for color updates
- Trilinear interpolation during raycasting
- Unit tests for basic operations and edge cases
### Files Modified/Added
- modules/3d/src/rgbd/color_hash_volume.hpp - New header defining ColorHashTSDFVolume interface
- modules/3d/src/rgbd/color_hash_volume.cpp - Implementation of ColorHashTSDFVolume
- modules/3d/test/test_color_hash_volume.cpp - Unit tests
### Performance
The implementation uses spatial hashing to only store voxels near surfaces, significantly reducing memory usage compared to regular ColorTSDFVolume while maintaining similar processing speed.
### Testing
Added unit tests that verify:
- Basic integration and raycasting operations
- Empty volume handling
- Memory usage patterns
### Future Work
- GPU/OpenCL implementation
- Additional color interpolation methods
- Extended comparison tests with other volume types
The new graph engine has no FP16 execution path yet and runs FP32 on CPU
regardless of the requested target. When an OpenCL FP16 target is requested,
callers may pass CV_16F input types to Net::getFLOPS(), which then flow through
tryInferShapes() -> Layer::getTypes() and get rejected (CV_16F is only allowed
when preferableTarget == DNN_TARGET_OPENCL_FP16, which the new engine never sets).
Normalize FP16 input types to FP32 in the mainGraph path of getFLOPS via a small
filterFP16InputTypes() helper. This fixes the OCL_FP16 parameterization of the
DNNTestNetwork perf tests (e.g. YuNet_320/640/1280) which crashed in getFLOPS.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* experimental new arithmetics; work-in-progress
* continue working on new-gen arithmetic expressions
* * improved performance of the new add on small arrays
* added sub
* extended tests
* fixed potential bug when adding multi-channel array and a single-channel scalar
* improved const handling
* * added copyMask
* added mul/dev (without scale so far)
* * accelerated mul
* addedd scale to mul and div
* * done substantial refactoring; however a few more rounds of refactoring are ahead.
* added min, max, absdiff, addweighted.
* improved performance of the new arithmetic functions, but some of them are still slow, e.g. operations with mask have some bugs (that affect speed, not accuracy).
* * further (significantly) accelerated several functions, especially on small arrays: mul, binary ops with mask
* further polished the new arithmetic engine
* started integration of the new element-wise arithmetic engine into core
* big step forward. We now use the new engine inside cv::add, subtract, multiply, divide, absdiff, min and max.
* big progress:
* added bitwise operations
* fixed and accelerated compare
* ported regression tests to test new broadcasting behaviour of arithmetic functions
* lot's of improvements in compare, divide, addWeighted!
* lot's of small and big performance improvements in the new arithmetics
* * some more optimizations; parsing texpr-expressions is now faster as well
* port new_arithm to Linux/x86: dispatch guards, scalar-Mat compat fallback, dnn shape-contract fixes
Core:
- arithm.simd.hpp: CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY guards (the file is included
once per dispatched mode on x86), vx_load_expand instead of the 128-bit v_load_expand,
VTraits::vlanes() instead of ::nlanes
- arithm.cpp/precomp.hpp: compat fallback for scalar-like Mat operands (1x1, 1xcn/cnx1,
4x1 CV_64F - java/python tuples, operator-(Mat, Matx)): treated as a per-channel scalar
ONLY when the shapes are not broadcast-compatible, so every valid numpy-style broadcast
keeps its meaning and calls that would otherwise throw get the 4.x semantics
DNN (fallout of the stricter shape semantics, found by the new engine):
- dict.hpp: DictValue relied on fresh AutoBuffer having size()==fixed_size; allocate explicitly
- batch_norm: weights_/bias_ are 1-D [n] now; 0/1-D forward runs on exact-shape 1-D views
- net_impl2: extend the post-forward sanity check to non-temp outputs - a layer that
reallocates its preallocated output tensor now fails loudly instead of silently
detaching the result from the graph
- LSTM/LSTM2 batchwise (layout=1): getMemoryShapes now matches what forward() writes
(ONNX: Y=(batch,seq,dirs,hid), Yh/Yc=(batch,dirs,hid)); forward assembles seq-major
results in a local buffer and transposes INTO the preallocated outputs in place
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* u8/s8 multiply: exact integer SIMD path on non-FP16 builds (3-10x vs 5.x)
The unit-scale branch of vecBinaryKernel already supported a separate work-vector
type Wvec1 (used by the ARM f16 build and by u16/s16 everywhere), but on x86 the
u8/s8 same-type multiply still went through the f32 hub. Route it through
v_uint16/v_int16: products of 8-bit values fit exactly (255^2 < 2^16), the
saturating pack on store gives bit-exact results at half the vector traffic.
Also fix a latent kernel bug this exposed: the unit-scale branch stepped by
Wvec's lane count while loading/storing Wvec1 vectors. All previous Wvec1
instantiations had equal lane counts, but u8's v_uint16 has 2x the lanes of
v_float32 - the pairs overlapped (50% redundant work) and the tail backoff
could write VECSZ bytes past the row end. The branch now derives its step,
offsets and tail condition from Wvec1 itself.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* vecBinaryKernel: constexpr Op::useScalar instead of a runtime-only scale check
Every binary op functor now declares whether it consumes the scale scalar
(params[0]): true only for mul and the two div variants. Ops that ignore it
(add/sub/min/max/absdiff) take the fast 2-arg branch unconditionally - the
'scalar == 1' check used to fail for them (their params[0] is 0), sending them
through the preproc branch, and the condition now folds at compile time.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* restore cv::hal::mul8u as a wrapper over the element-wise engine
The symbol is still declared in core/hal/hal.hpp and called directly by external
code (the G-API fluid backend in opencv_contrib), but its implementation went
away with the old arithm kernels. Forward it to getMulFunc(CV_8U, CV_8U) - with
scale==1 it lands on the new exact integer SIMD path.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* silence every new-arithm warning reported by CI (ARM64/Mac) and gcc 15
- arithm.cpp: bitwise_op_ocl and the actualScalarDepth/coerceTypes helpers are
consumed only by the OpenCL paths - guard them with HAVE_OPENCL; haveScalar in
cv::compare is read only inside CV_OCL_RUN - CV_UNUSED for OpenCL-less builds
- arithm_expr.hpp: declare getBitwiseFunc/getNotFunc/getAddWeightedFunc next to
the other per-op entry points (-Wmissing-prototypes in arithm.dispatch.cpp)
- arithm.simd.hpp: define CV_SIMD_16F to 0 when FP16 SIMD is absent (-Wundef);
{}-init the expandScalar staging buffers (-Wmaybe-uninitialized: they are
fully written before use, but the compiler cannot prove it with runtime
vector widths); rename the compare kernel's lambda parameter (-Wshadow)
- arithm_expr.cpp: rename the exec tile-lambda's hot-field locals that shadowed
TExpr members and outer locals (-Wshadow)
- test_new_arithm_extensive.cpp: rename the name-generator lambdas' parameter
shadowing the INSTANTIATE macro's own (-Wshadow), drop an unused variable
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* restore 4.x scalar semantics for the bindings' 4x1 CV_64F Scalar columns
The python/java bindings materialize numbers and tuples as a 2-D 4x1 CV_64F
Mat - or UMat, when the call carries UMat arguments. Three CI-reported python
failures came from those pseudo-scalars reaching the engine as arrays:
- absdiff(int_arr, 0): the (4,1) column is broadcast-COMPATIBLE with a 1-D
array, so numpy semantics silently won - an outer-product f64 result instead
of the int per-channel-scalar one;
- subtract(u8 4x8x4, (40,)): same, by the rows==4 coincidence;
- multiply(UMat, 2., dst=UMat): the scalar arrives as a UMAT, which the
scalar detection did not recognize at all.
isScalarArg now treats the exact bindings shape - 2-D 4x1 CV_64F single-channel
Mat/UMat against a <=4-channel array - as a scalar UNCONDITIONALLY (a 1-D [4]
array has dims==1 and still broadcasts). One exception, decided in arithm_op:
when the partner is itself a tiny scalar-shaped array, both are honest data and
ride the broadcast (compare(Mat 4x1, Mat 1x1) - issue #8999 - stays elementwise).
Small-array discipline, this all runs per engine call: the probes read Mat/UMat
fields directly (rows == 4 alone rejects almost everything, no _InputArray
getter dispatch), and a UMAT scalar's 32 bytes are copied into a caller-stack
buffer - no heap, no getMat mapping. Measured: no latency change on 4x4/16x16
element-wise calls.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* cv::texpr: std::string_view -> const std::string& in the public API
string_view in an exported signature breaks some CUDA toolchain builds, and for
short expression strings the difference is immaterial (SSO, parsed once). The
parser internals keep string_view - the argument converts implicitly. Also drop
the now-unused <string_view> include from cvstd.hpp, so the header does not
reach every nvcc TU.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* compare boundary-rewrite: fixed 4-slot kind/bound arrays instead of AutoBuffers
A CONST operand is capped at 4 channels (addConst), so the per-channel
kind/bound staging needs no dynamic buffers - plain int[4]/double[4], with a
CV_Assert on the contract. This is also what gcc's -Wmaybe-uninitialized was
flagging (it could not see the AutoBuffer's inline storage get filled).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* element-wise engine: unary math kernels (sqrt/exp/log/sin/cos/tanh/erf/relu) + select
New dispatched pair math.simd.hpp / math.dispatch.cpp - the unary/ternary sibling
of arithm.simd.hpp:
- vecUnaryKernel: T -> T over f16/bf16/f32/f64 on top of the intrin_math
primitives (v_exp/v_log/v_sin/v_cos/v_sqrt/v_erf/v_max). f32/f64 compute
natively, f16/bf16 ride the f32 hub inside the kernel (vx_load_pair_as /
v_store_pair_as) - no materialized casts. Continuity collapse + the halide
right-edge backoff, suppressed in-place (it would re-apply Op to
already-written values). tanh = (e^2x-1)/(e^2x+1) with the input clamped to
+/-10 (f32) / +/-20 (f64) - unclamped saturation hits inf/inf = NaN. erf has
no f64 SIMD primitive: std::erf per lane.
- selectKernel(mask, x, y): 1-byte mask expanded to lane width and tested
against zero in the INTEGER domain (immune to DAZ/FTZ), branches of any
depth by element size, broadcast branches supported.
- emitUnary: math over a float input is T -> T now (f16 in -> f16 out, native
kernel when input and result depths match); integer inputs still compute in
the float domain and land in f32.
- emitTernary/select: literal branches are typed via typedConstFrom (an
OP_CAST of a depth-less flex const crashed); a non-1-byte mask is normalized
by an explicit "mask != 0" compare, never a value cast.
texpr already parsed the function names - they now execute. Tests: per-depth
accuracy of all 8 ops against the double std:: reference, integer input,
in-place, select over 4 depths / const branch / float mask.
Perf vs the classic kernels (1920x1080 f32, 16 threads, AVX2): exp 3.7x,
log 2.6x, sqrt 5.7x faster; polarToCart expressed as (r*cos(a), r*sin(a)) 4.0x.
Accuracy improves too (max rel err vs f64 reference): exp 8.1e-8 vs 2.1e-7,
log 8.0e-8 vs 1.5e-7.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* engine: OP_COPY_MASK folded into OP_SELECT; selectKernel moved to arithm.simd.hpp
copyMask(dst, mask, src) is select(mask, src, dst) - one masking primitive
instead of two. The compiler emits the masked-op tail as
addInsn(OP_SELECT, mask, r, out, out): the output slot rides as both arg2 and
the result, so unmasked elements are preserved by reading them back through
the b-branch. OP_COPY_MASK, copyMaskKernel and getCopyMaskFunc are gone.
selectKernel (moved from math.simd.hpp to arithm.simd.hpp) inherits every
copyMaskKernel optimization:
- the interleaved multichannel fast path (2..4 channels under a per-pixel
mask: expand the mask once per VECSZ rows, v_store_interleave across lanes);
- the per-row scalar path with the row-skip when the selected source row IS
dst (the "leave the output untouched" half of copyMask);
- plus the select-specific ones: branch broadcasts (stepx == 0) and the
right-edge tail backoff under dst-aliases-a-branch - safe because re-running
select over already-blended elements is idempotent; only dst == mask keeps
the backoff off (the store would rewrite mask bytes before the re-read).
Masked-add perf is on par with the old copyMask (1280x720, 1 thread: 8UC3
204 -> 198 us, 32FC3 1395 -> 1344, 8UC1/32FC1 within noise). Full core suite
24117 green.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* engine: dedicated vectorized pow kernel (moved from arithm to math.simd.hpp)
pow was the last scalar-only binary op (scalarBinaryKernel + std::pow, f32/f64
only). The new powKernel keeps exact std::pow semantics and is T x T -> T over
all four float depths (f16/bf16 via the f32 hub):
- scalar exponent (the dominant call shape - texpr literals ride as 0-dim
broadcast consts) is dispatched PER ROW to the special cases:
y==2 -> x*x, y==3 -> x*x*x, y==0.5 -> v_sqrt, y==1 -> copy, y==0 -> fill 1;
- everything else - including a per-element exponent array - runs the general
vectorized exp(y * log(x)) path, valid for x > 0; a vector pair containing
any x <= 0 lane falls back to scalar std::pow for that pair (v_check_any),
which preserves every std::pow subtlety: signed results for integer y on
negative bases, NaN for fractional y, the x == 0 family;
- no right-edge tail backoff: pow is not idempotent, in-place calls finish
rows in the scalar tail.
Perf vs the classic cv::pow (1920x1080 f32, 16 threads, AVX2): p=2 1.4x
(classic special-cases it too), p=3 6.1x, p=0.5 6.3x, fractional p 4.5x with
slightly better accuracy (6.1e-7 vs 7.4e-7 max rel err). Tests: exponent
sweep 2/3/0.5/1/0/2.5/-1.5 vs the double std::pow reference on f32/f64,
negative bases (exact signed cubes, NaN for fractional), array exponent.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* powKernel: halide right-edge tail backoff in every SIMD loop
Same shape as vecBinaryKernel: the final partial vector re-processes
[width - VECSZ*2, width) instead of finishing scalar, suppressed when dst
aliases an input (pow is not idempotent - the overlap region must be
recomputed from an untouched source, which the no-alias case guarantees).
Modest measured win (~2% on ROI rows for the special-cased exponents; the
general path tail was already cheap - modern libm powf is fast), no
regressions; mainly aligns the kernel with the house style, where every
SIMD loop ends vector-wide.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix MSVC 2019 C2975: function-local constexpr as a template argument inside a lambda
MSVC 2019 loses the constexpr-ness of function-local constants (LOCAL_OPS,
MAX_DIMS, ...) when they are used as template arguments inside a lambda body
(AutoBuffer<Slice, LOCAL_OPS> / std::array<int, MAX_DIMS> in the parallel
bodies of BroadcastOp::run and TExpr::exec). Hoist them to namespace scope -
no behavior change.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* ocl_arithm_op: route 16U multiply to the CPU engine on Apple OpenCL
The Apple OpenCL driver miscompiles the 16U multiply kernel: products near
the top of the u16 range come back wrapped instead of saturated (CPU vs GPU
NORM_INF up to 65535 in OCL_Arithm/Mul.Mat CV_16U cases). The same arithm.cl
kernel is correct on Intel NEO and NVIDIA drivers - verified not to reproduce
on Linux/Intel iGPU - so gate the decline to __APPLE__ only; the CPU engine
computes 16u multiply exactly (integer SIMD path).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* engine: neg/abs as compositions, clamp kernel, ** and ?: operators, abs(a-b) peephole
- OP_NEG and OP_ABS need no kernels: neg = sub(0, a), abs = absdiff(a, 0) -
including the engine absdiff auto-type rule (signed |a| lands in the
UNSIGNED type of the same width: |SHRT_MIN| fits u16 exactly instead of
saturating; NB the public cv::absdiff auto depth keeps the source type for
4.x compatibility - values agree, the depth rule is the engine own).
- peephole: abs(x - y) rewrites to absdiff(x, y) ALWAYS. On integers the
literal semantics differ (the subtract saturates first: u8 gives
max(x-y, 0)), but whoever writes abs(a - b) means absdiff - we deliberately
hand out the useful semantics instead of the saturation artifact. The just-
emitted OP_SUB is retired via the moveToOutput manoeuvre, so the program
shrinks to the single absdiff instruction. abs(x), abs(x - 0) and
absdiff(x, 0) all give one result.
- OP_CLAMP kernel (arithm.simd.hpp): v_min(v_max(x, lo), hi) over
u8/s8/u16/s16/u32/s32/f32 (+f64 with 64-bit SIMD), scalar f16/bf16/64-bit
ints; lo/hi may broadcast (the common clamp(img, a, b) shape) or be full
arrays; the tail backoff stays on under dst-aliases-x (clamp is idempotent).
emitTernary types literal bounds via typedConstFrom (same flex-const crash
select had) and keeps the auto result type pinned to x.
- parser: "a ** b" == pow(a, b), precedence above * /, RIGHT-associative
(a ** 2 ** 3 == a ** 8); "cond ? a : b" == select(cond, a, b), precedence
below everything, right-associative chains (f1 ? a : f2 ? b : c) work
without parentheses. parseTernary() is the expression entry point now.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* cv::exp/log/sqrt on the engine via math_op; hal functions wrapped as engine kernels
math_op is the master function of the unary math family (the arithm_op
analogue): same-shape same-type output over f16/bf16/f32/f64 (classic
exp/log accepted f32/f64 only - the half floats are new), two tiers:
- small (<= 100000 elements) and continuous: call the kernel DIRECTLY over
the flattened data - no TExpr, no broadcastOp, no parallel_for setup;
- everything else: the usual single-instruction program via compile()/exec()
(parallelism for large arrays, real steps for ROIs).
getMathFunc routes OP_EXP/OP_LOG at f32/f64 through the full cv::hal stack -
an external vendor HAL (CALL_HAL), IPP, or the built-in table kernels,
whichever is installed - by wrapping hal::exp32f/exp64f/log32f/log64f as
engine kernels with the function pointer in TKernel::userdata, the same
mechanism castKernel uses for core BinaryFuncs. The engine adds tiling and
parallelism on top, so every tier gets the best available scalar-span
implementation. v_exp/v_log remain for f16/bf16 (the f32 hub) and the ops
hal has no entry points for.
v_log_default_32f: the degree-8 polynomial is evaluated by Estrin pairing
(4 dependent levels) instead of an 8-FMA Horner chain (~5% on the f16 hub
path). An exp64 Taylor-without-division rewrite was tried and benched SLOWER
than the Cephes Pade scheme (the evaluation is FMA-throughput-bound, and
vdivpd pipelines well enough) - reverted; a table-based reduction is the only
way further there.
cv::exp f32 (16 threads, AVX2+IPP build), old -> new: 640 elements
0.16 -> 0.15 us, 16k 2.79 -> 2.49, 640x480 47 -> 20, 1920x1080 452 -> 60 us
(the old CPU loops were single-threaded); f64 exp 1080p 1295 -> 161 us.
No size regresses; small arrays now run at installed-HAL speed.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* getMathFunc: IPP tier + raw-HAL probing; the exp/log table kernels are deleted
The raw cv_hal_* entry points return int for a reason: without an installed
HAL they are stubs returning CV_HAL_ERROR_NOT_IMPLEMENTED. getMathFunc now
selects the exp/log implementation in three tiers:
1. HAVE_IPP && ipp::useIPP(): ippsExp/ippsLn through thin int adapters (IPP
is not routed through the cv_hal_ hooks, so it needs its own tier);
2. the raw cv_hal_exp32f/... hook, PROBED once with a 1-element call on the
safe input 1.0 (cached in magic statics): implemented -> wrapped as an
engine kernel with the function pointer in TKernel::userdata;
3. the engine own v_exp/v_log kernels.
Whichever wins, the engine adds tiling and parallelism on top.
The EXPTAB/LOGTAB table kernels and their tables (~790 lines in
mathfuncs_core.simd.hpp + mathfuncs.cpp) are DELETED: they benched within
~15% of v_exp/v_log, not worth a second implementation. The public
cv::hal::exp32f/exp64f/log32f/log64f keep their contract - CALL_HAL, then
IPP, then the built-in implementation - but the built-in is now the engine
vector kernel via ew::mathSpanEngine (one contiguous span, exported from
math.dispatch.cpp).
All unary math kernels (vec/scalar/hal wrappers, pow) also handle the
vertical-broadcast tile (s0y == 0, a row expanded into a matrix): the first
row is computed, the rest are memcpy of it - transcendentals cost far more
than a row copy.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* texpr: hypot(x, y) binary op (alias: mag)
hypot = sqrt(x^2 + y^2), NAIVE like cv::magnitude (not the overflow-safe
std::hypot), computed in the float work type; kernels for the four float
depths only (T x T -> T; integer inputs ride the usual f32-compute + cast).
A 10-line EwHypot functor on top of vecBinaryKernel in arithm.simd.hpp -
broadcast branches, continuity collapse and the tail backoff come for free.
Registered in the parser as both "hypot" (the C/numpy name) and "mag" (the
cv::magnitude-flavored alias). A building block for the future cartToPolar.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* texpr: atan2(y, x) binary op - radians, standard C range
v_atan2 (arithm.simd.hpp, generic over the universal-intrinsic float vector):
the fastAtan2 minimax polynomial from mathfuncs_core v_atan_f32 reworked to
plain radians - the 180/pi factor dropped from the coefficients and the C
quadrant logic instead of the [0, 360) wrap, so the result matches std::atan2
over (-pi, pi]. Measured absolute accuracy ~1.6e-4 rad. (v_atan_f32 itself is
untouched - cv::phase/fastAtan2 keep their degree semantics.)
EwAtan2 rides vecBinaryKernel: f16/bf16/f32 through v_atan2 (the f32 hub),
f64 through exact scalar std::atan2. arg0 = y, arg1 = x, like std::atan2;
float depths only, same emitBinary policy as pow/hypot. Parser name "atan2".
Together with hypot this completes the cartToPolar building blocks.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix the RISC-V RVV build and two ARM64 warnings
The new f64 kernel registrations (hypot, pow, the unary math family) gate on
CV_SIMD_64F || CV_SIMD_SCALABLE_64F, but the vx_setall_as(const double*,
v_float64&) helper family in arithm.simd.hpp was still CV_SIMD_64F-only -
scalable platforms (RVV) have v_float64 with CV_SIMD_64F == 0, so
vecBinaryKernel<double, ...> failed to instantiate there. Widen the helper
gate to match (verified with a riscv64 rv64gcv cross-build of opencv_core -
the engine f64 paths now vectorize on RVV instead of not compiling).
cv::exp/cv::log: the depth local is consumed by CV_OCL_RUN only - CV_UNUSED
for OpenCL-less builds (ARM64 -Wunused-variable).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* silence the remaining ARM64 gcc warnings
- compare boundary-rewrite: {}-init the fixed kind/bound arrays (filled for
every channel used below, but gcc cannot prove it across the cn <= 4 loop);
- cv::exp/log: [[maybe_unused]] on the depth local (consumed by CV_OCL_RUN
only), instead of the CV_UNUSED idiom;
- AutoBuffer::reserve: a targeted -Wmaybe-uninitialized suppression around
the live-element copy loop - only [0, sz) is read, all written before, but
gcc inlining a grow-from-inline-storage chain cannot see that. An
annotation for the analyzer, no behavior change.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* saturating 32-bit add/sub kernels; cv::texpr python binding; two CI warnings
- v_add_sat/v_sub_sat for v_int32/v_uint32, local to arithm.simd.hpp for now
(the plan is to grow them into proper universal intrinsics later): NEON
single-instruction vqadd/vqsub, elsewhere the Hacker Delight bit tricks
over universal intrinsics (u32 add is 2 ops: or with the wrapped-compare
mask). EwAdd/EwSub overload vec() for the 32-bit lanes and getAddSubFunc
routes 32S/32U T->T through vecBinaryKernel instead of the former pure
scalar kernel. Semantics unchanged - the scalar int64 tail already
saturated; directed boundary tests added (both rails, 0 - INT_MIN, u32
cases, a full-range random block vs an exact int64 reference).
640x480 32S add: 0.48x of 5.x -> parity (memory-bound); 1080p: 6-8x.
- cv::texpr becomes CV_EXPORTS_W: python gets cv.texpr(expr, [inputs]) ->
tuple of ndarrays, so `res, = cv.texpr(...)` and `mag, ang = cv.texpr(...)`
unpacking both work. modules/python/test/test_expr.py covers arithmetic,
the fused abs(a-b), casts, broadcasting, ?: and ** operators, math
functions vs numpy, clamp, named temporaries, tuple outputs, the one-line
cartToPolar and the int32 saturation cases.
- warnings: {}-init the parser args array (gcc -Wmaybe-uninitialized on
Ubuntu 20/22); the compare short-row block gates sizeof(T) <= 4 as
constexpr so the f64 instantiation does not leave set-but-unused locals
(gcc 9 -Wunused-but-set-variable).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* arithm_op: direct-kernel fast path for small continuous arrays
Building and compiling the 1-instruction program plus the BroadcastOp setup
costs ~40-250ns per call - negligible on big images, dominant at 127x61-class
sizes where the classic 5.x functions were 1.3-2x faster. Mirror math_op two
tiers in arithm_op: two same-type same-shape continuous arrays, no mask, no
scalar, result depth == input depth, <= 100k elements -> call the T x T -> T
kernel directly over the flattened elements (checks ordered cheapest-first).
Applies to add/subtract/min/max/absdiff/multiply/addWeighted/and/or/xor;
compare and divide lower to more than a single kernel (boundary rewrites, int
guards) and keep the ordinary path. addWeighted falls through automatically
for the 32/64-bit int types whose lowering is wide-compute + cast
(getElemwiseFunc returns no direct kernel there).
127x61 vs 5.x, was -> now: add/subtract 8UC1 0.76x -> 1.3x, min/max u8
0.6x -> ~1x, addWeighted 1.0x -> 1.1-1.4x (32SC1 stays 4.5x); the one
remaining laggard is add/sub 32SC1 (0.74-0.82x) - the price of the new
SATURATING semantics (7-instruction AVX2 emulation vs the wrapping single
add of 5.x; single-instruction on NEON).
The 127x61 size is ADDED PERMANENTLY to the arithmetic/addWeighted/compare
perf grids: per-call overhead regressions in these base functions must be
caught by CI, not discovered by users.
dst creation goes through createSameSize (whole-shape transfer including
layout and future metadata, not piecemeal dims+sizes) here and in math_op.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* cv::pow rebuilt on the engine; integer-exponent and 1/sqrt(x) kernel branches
Routing: p = 0/1/2 keep their early special cases (fill/copy/multiply); an
INTEGER array with an INTEGER power keeps the classic iPow multiply chain -
bit-exact compatibility, including its wrap-around quirks (iPow squares in
int, so e.g. pow(255,4) on u8 wraps negative and saturates to 0 - somebody
may rely on that). Everything else goes through the engine with the math_op
two-tier scheme: small continuous arrays call powKernel directly (the
exponent rides as a broadcast T scalar), the rest run the tiled parallel
program. Integer arrays with fractional powers compute in the float domain
and saturate back; the 32U/64-bit depths (classic iPow asserted on them) and
f16/bf16 (the classic float path misread them) now just work.
powKernel gets two new per-row exponent branches:
- p == -0.5: 1/v_sqrt(x) (the classic path used IPP ippsInvSqrt_A21, a
21-bit approximation; ours is exact - slightly slower on small arrays,
4.5x faster at 1080p via parallelism);
- any other INTEGER p (|p| <= 65536): LSB-first binary exponentiation, the
same multiply chain and order as iPow, fully vectorized - a few ulp
accurate vs ~2e-7 of exp(p*log x), and exact on non-positive bases (the
sign falls out of the multiplies, 0^negative divides to inf) - no scalar
patching.
cv::pow f32 vs 5.x: p=0.5 365 -> 61 us at 1080p (6x), p=3 7.7x, p=5 7x AND
faster at every size (the old scalar chain: 0.81 -> 0.47 us at 127x61),
p=2.5 5.3x. The s16^5 iPow path is untouched (118.7 == 118.5 us).
pow_exponents accuracy tests extended to 11 exponents x f32/f64 against the
double std::pow reference.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* perf: SANITY_CHECK_NOTHING for the tests whose grids got the 127x61 size
The 127x61 entry added to guard per-call overhead has no regression data in
opencv_extra, so the legacy SANITY_CHECK in addWeighted/compare failed on CI
(locally it passes silently without the test-data path). Accuracy of both
functions is covered by the accuracy suite; the perf tests should measure
time. PatchNaNs/finiteMask keep their SANITY_CHECK - their grids are
untouched.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix a temp-buffer double release in the liveness pass ("d*d" crash)
When the same temp is passed as SEVERAL arguments of its last-use
instruction (e.g. the named-intermediate expression "d = {0} - {1}; d*d",
where the MUL consumes slot d twice), the buffer-reuse scan pushed the
temp's physical buffer onto the free list once per argument. That
overflows the ntemps-sized freeBufs array (caught by the AutoBuffer range
check in Debug: python test_expr.py::test_named_temporary) and, in larger
programs, would hand the same physical buffer to two live temps.
Release the buffer once by retiring lastUse[t] after the first hit.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* executor: recalibrate opCost to vectorized cycles + clamp the stripe count
The per-element op costs fed into the parallel_for_ stripe hint were
scalar-era estimates (~20x above the vectorized reality: the atan2/exp
polynomials run at ~1.5 cycles/element, not 30). The hint therefore split
transcendental/divide work into hundreds of ~1 us jobs, which the
macOS/GCD backend dispatches poorly under sustained load, on top of the
P/E-core equal-share straggler effect. Measured on M4 Max (12P+4E),
sustained medians @1920x1080 f32: texpr atan2 320 -> 133 us, cv::exp
280 -> 141 us, cv::log 301 -> ~200 us, cv::pow(x,2.5) 388 -> 376 us;
the PR tables' math rows improved ~1.5-2x across the board.
- opCost is now in units of ~1/4 cycle/element of the SIMD kernels:
cheap ops 1 (unchanged), div/sqrt/hypot/convert_scale 10 -> 3,
transcendentals 30 -> 6.
- the stripe hint is clamped by min(4*nthreads, max(32, 3*nthreads)):
~4 stripes/thread is plenty of granularity for element-wise work, and
the ceiling is 32 pieces except on machines with many (heterogeneous)
cores, where anything coarser than ~3 pieces/worker turns the slow
cores into equal-share bottlenecks (measured: 32 stripes on 16 threads
is the worst point of the curve - 193 us vs 137 us at 48 for atan2).
getNumThreads() is clamped from below (WINRT/plugin backends may
report 0).
Not addressed here (needs cross-machine data, M2/M3 Ultra): streaming
memory-bound ops saturate the M4 Max fabric at ~8 fat stripes and E-core
participation only adds contention - a cost model cannot express that;
candidate follow-up is a bytes-aware clamp or a GCD-backend-level fix.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* multiply: v_mul_sat integer kernels (full product clamped to the type)
v_mul_sat(V, V) -> V for u8/s8/u16/s16/u32/s32 - the full-precision
product clamped to the lane type, which is exactly cv::multiply's integer
semantics at scale == 1. Local to arithm.simd.hpp for now, next to
v_add_sat/v_sub_sat, to be promoted into proper universal intrinsics
later. NEON: widening vmull + saturating narrow (vqmovn); other backends:
the portable v_mul_expand + saturating v_pack composition for 8/16-bit
lanes. 32-bit lanes have no universal widening multiply (no v_mul_expand
for s32), so the 32-bit integer fast path is NEON-only for now and the
other backends keep the previous f64 work-vector kernels (which measure
well on x86 with IPP-free AVX2).
EwMul::vec() now routes the integer lane types through v_mul_sat, and
getMulFunc_ passes the NATIVE lane vector as the scale==1 fast-path type:
whole-register loads/stores, the widening happens inside the multiply.
Replaces both the half-register widening loads (u8/s8/u16/s16) and the
scalar-equivalent f64 path for 32S/32U on NEON.
M4 Max, 640x480 (the sizes where the old kernels lost to carotene):
8U 22.0 -> 13.6 us, 8S 15.0 -> 11.1, 16S 21.2 -> 17.4, 32S 84.8 -> 30.5
(parity with the classic path everywhere, 32S was 0.38x). 1920x1080:
8S 1.30x -> 1.78x, 16S 2.57x -> 2.98x, 32S 2.20x -> 4.02x vs 5.x.
Correctness: exhaustive 8-bit (all 65536 pairs per sign), directed
saturation corners for 16/32-bit (46341^2, INT_MIN*-1, 65536*65536, all
sign combinations) against an exact int64 reference.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* NEON: make v_cvt_f64(v_int32) exact (was via f32, losing bits > 2^24)
The NEON implementations of v_cvt_f64/v_cvt_f64_high for v_int32 did
s32 -> f32 -> f64 (vcvt_f32_s32 + vcvt_f64_f32), silently rounding any
|x| > 2^24. Every vectorized f64 work path with int32 inputs on AArch64
was affected: the engine's addWeighted/divide 32S kernels, convertTo
32S -> 64F, etc. Found via addWeighted 32SC1 on values ~1e9: max error
was 32 vs the exact double reference (the classic carotene path is worse
still - it computes in f32 end-to-end with f32-truncated weights, max
error 96 on the same data).
The exact sequence sxtl + scvtf (vmovl_s32 + vcvtq_f64_s64) is the same
2 instructions, so there is no cost. addWeighted 32SC1 on the engine now
matches the exact-double reference bit-for-bit and stays at parity/1.4x
vs the classic path (640x480/1920x1080).
Pre-existing upstream bug (same code in 4.x) - worth a standalone
backport with directed large-value tests.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* math_op: drop the temporary direct-IPP tier for exp/log
Upstream moved the IPP math wrappers into the hal/ipp HAL module (cv_hal_exp32f/
log32f & co now resolve to ipp_hal_* which honor cv::ipp::useIPP via
CV_HAL_CHECK_USE_IPP). The engine's single probeHalUnary(cv_hal_*) probe already
picks that up uniformly, so the stopgap #ifdef HAVE_IPP ippExp/ippLog tier and its
ipp::useIPP() branch in getMathFunc are now redundant - removed.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* arithm: restore cv::hal::and8u/or8u/xor8u/not8u as engine wrappers
These public CV_EXPORTS entry points (core/hal/hal.hpp) lost their definitions when
the bitwise ops moved to the element-wise engine, but they are still declared and called
by other modules (opencv_objdetect's aruco) and external code - the link broke with
undefined references to cv::hal::and8u/xor8u. Restore them as thin forwarders over the
engine's byte-wise bitwise kernels (getBitwiseFunc / getNotFunc), mirroring the existing
mul8u wrapper. CV_Assert guards the kernel lookup.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* blobdetector: guard empty contour when computing blob radius
The new AutoBuffer leaves its tail uninitialized for trivial types, which surfaced a
-Wmaybe-uninitialized in findBlobs where the median of per-point distances is read. Use a
std::vector, default the radius to 0, and compute the median only for a non-empty contour -
no unproven size invariant.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
core: fix RISC-V GCC build break in cvtabs_32f (#29369 follow-up) #29541
### Summary
`modules/core/src/convert_scale.simd.hpp` does not compile on RISC-V with GCC on current 5.x:
```
convert_scale.simd.hpp:44:13: error: 'useSIMD' was not declared in this scope
convert_scale.simd.hpp:45:37: error: 'VECSZ' was not declared in this scope
convert_scale.simd.hpp:55:28: error: 'va' was not declared in this scope
convert_scale.simd.hpp:55:32: error: 'vb' was not declared in this scope
```
This breaks every RISC-V + GCC build of the core module.
### Root cause
In #29369 `cvtabs_32f` ended up with a declaration guard and a usage guard that disagree:
```c
#if (CV_SIMD || (CV_SIMD_SCALABLE && !(defined(__GNUC__) && !defined(__clang__)))) // decl
v_float32 va = ..., vb = ...; const int VECSZ = ...; const bool useSIMD = ...;
#endif
...
#if (CV_SIMD || CV_SIMD_SCALABLE) // usage
if( useSIMD ) for( ; j < size.width; j += VECSZ ) { ... v_fma(v0, va, vb) ... }
```
On RISC-V `CV_SIMD == 0` and `CV_SIMD_SCALABLE == 1`, so under GCC the first is false and the second is true: the declarations are dropped while the loop using them is still compiled.
### Second commit (independent, droppable)
`useSIMD` asks "is this a 128-bit vector machine?" but tests `vlanes() != 8`. `vlanes()` is `VLEN/32 * LMUL`, so the literal `8` encodes both "VLEN == 128" **and** "LMUL == 2" — correct only while `v_float32` stays at LMUL=2. Under #29493 (LMUL=1) the same expression silently becomes "VLEN != 256": disabling SIMD on 256-bit machines and enabling it at VLEN=128, the exact case the workaround exists to avoid. CI cannot catch that regression, since the block is GCC-only.
VLEN is not recoverable from the universal intrinsics (every type scales with LMUL, so the factor never cancels), so this asks the hardware: `__riscv_vlenb()*8 != 128`. Behaviour is unchanged today — with LMUL=2, `vlanes() == 8` iff `VLEN == 128`.
### Testing
SpaceMIT K3, GCC 15.2, **unmodified LMUL=2 backend** (no #29493 changes involved):
| configuration | result |
| --- | --- |
| 5.x as-is | `opencv_core` fails to build (errors above) |
| + commit 1 | builds clean, 0 errors |
| + commits 1&2 | builds clean, 0 errors |
| `Core_ConvertScale*`, X100 @ VLEN=256 | 4/4 pass (incl. `Core_ConvertScaleAbs/ElemWiseTest.accuracy`) |
| `Core_ConvertScale*`, A100 @ VLEN=1024 | 4/4 pass |
| `useSIMD == false` path (temporary probe forcing it at VLEN=256) | passes |
Refactoring and extracting IPP to HAL for threshold function in 5.x #29492
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
FP8 support in core module #29369
Core part of https://github.com/opencv/opencv/issues/29313
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Extracting IPP to HAL for matchTemplate function in 5.x #29466
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Feat: add OpenCL SIFT detector and descriptor #29450
Add OpenCL implementation of SIFT detector and
descriptor extractor, triggered via T-API when
the caller passes a UMat. Targets the features
module (5.x directory layout).
Implementation:
- 4 OpenCL kernels in sift.cl: gaussian_blur_h, gaussian_blur_v, detect_and_orient, and compute_descriptor.
- Host-side dispatch in sift.dispatch.cpp using CV_OCL_RUN_ macro, pure additions to the existing CPU path.
- T-API entry: detectAndCompute, detect, compute.
Gating and fallback:
- 64-bit only. 32-bit falls back to CPU via sizeof(void*) > 4 guard.
- OpenCL 1.1 or later required. Devices below 1.1 fall back to CPU.
- No-OpenCL builds fall back to CPU. Verified with WITH_OPENCL=OFF: 79 tests pass, same 3 pre-existing failures (DISK/AFFINE_FEATURE missing .npy data files).
Correctness:
- nOctaveLayers > 3 OOB fix: gk_coeffs and gk_radius resized to nOctaveLayers+2 via std::vector (were fixed size 5).
- Regression test for nOctaveLayers > 3.
- 10 OCL SIFT tests pass, 60 OCL tests pass, 139 features tests pass (3 pre-existing failures unrelated to SIFT).
Performance optimizations:
- native_exp, native_sqrt, native_recip for hardware approximations (OpenCL 1.1 builtins).
- Eliminate intermediate rawDst[128] buffer; normalize in-place from hist[].
- Non-blocking kernel launches with single ocl::finish() before host reads keypoint count.
- Custom separable Gaussian blur for init image bypassing T-API GaussianBlur dispatch overhead.
- exp2() replaces pow(2.0f, x); scl_octv reused.
- Hoist UMat tmp allocation out of pyramid loop.
- Interleaved keypoint output buffer (6 arrays to 1) for coalesced writes and fewer copies.
- Consolidated descriptor keypoint copies (2N to 2 host-to-device transfers).
- std::map replaced with flat vector indexed by pyramid level (O(1) vs O(log n)).
- DoG computed on-the-fly in detect kernel via READ_DOG macro, eliminating 45 subtract() calls and dog_pack allocation.
- Shared gauss_packs between detect and descriptor, eliminating duplicate copyTo.
- Cross-level packing per octave: one descriptor kernel launch per octave instead of per level. Keypoint buffer expanded to 5 floats (added layer index). Levels packed into single UMat. Reduces kernel launch overhead and improves GPU utilization for octaves with few keypoints.
- __local memory tiling for Gaussian blur kernels with 16x16 workgroups and cooperative halo loading. Reduces global memory traffic.
- Direct uchar descriptor output for CV_8U: kernel writes convert_uchar_sat_rte directly instead of float buffer + host convertTo pass. Eliminates temp allocation and extra kernel launch.
Performance result (stitching/s2.jpg resized):
- DetectAndCompute: OCL wins at >=960x540 (1.32x at 960x540, 1.80x at 1280x720, 1.87x at 1920x1080).
- Compute-only: OCL wins at >=1280x720 (1.60x at 1280x720, 1.78x at 1920x1080).
Tests:
- modules/features/test/ocl/test_feature2d.cpp with OCL SIFT correctness tests on real images (leuven img1.png, a3.png, s2.jpg).
- DescriptorType, Regression_26139, and Batch tests mirror CPU coverage (CV_8U descriptor type, single-keypoint edge case, 6-image detect+compute).
- modules/features/perf/opencl/perf_sift.cpp with real image and 5-size sweep.
- CPU SIFT_Scaled fixture added to perf_feature2d.cpp for aligned OCL vs CPU comparison.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
imgproc: extend boxFilter / gaussianBlurBinomial coverage in RISC-V RVV HAL #29440
### Summary
This PR extends RVV HAL coverage of the two most frequently used smoothing filters. Previously, `cv_hal_boxFilter` was only implemented for single-channel types (plus 32FC3) at 3×3/5×5, and `cv_hal_gaussianBlurBinomial` for 8UC1/16UC1/8UC4. The most common cases in practice — interleaved **8UC3** images and **7×7** box kernels — fell back to the core `FilterEngine` path, which runs single-threaded, costing 6–19× on multi-core RISC-V hardware.
New coverage:
- `boxFilter`: **8UC3 / 8UC4** (3×3, 5×5, 7×7); **7×7** for 8UC1, 16SC1, 32FC1, 32FC3 and the 8U→16U variant
- `gaussianBlurBinomial`: **8UC3** (3×3, 5×5)
Combinations already covered upstream keep their existing kernels byte-for-byte; net diff is +274/−162 across two files.
### Implementation
1. **Channel-blind "flat" kernels** (`boxFilterFlat8U<ksize, cn>`, `gaussianBlurFlat8U<ksize, cn>`). An interleaved row is processed as `width*cn` flat u8 elements whose horizontal taps sit at strides of `cn` elements: element *i* sums exactly its own channel's taps at `i, i+cn, …, i+(ksize−1)·cn`. One template therefore serves all channel counts, and:
- the horizontal pass becomes `ksize` independent unaligned `vle8` loads combined with widening adds — no `vlseg`/`vsseg` and no serial `vslide1down` chains, which are microcoded/serialized on current RVV hardware;
- the vertical pass is a single contiguous u16 stream processed at LMUL=8;
- the output is a plain `vse8` after saturating narrowing.
2. **Incremental vertical sliding window** for box with `ksize > 3`: the column-sum ring buffer keeps `ksize+1` rows so the window slides in O(1) per output row (add the entering row, subtract the leaving row) instead of re-summing `ksize` rows. u16 modular arithmetic stays exact because window sums never exceed 49·255 = 12495.
3. **Division-free normalization**: normalized box output uses multiply-high + shift (`m = ceil(2^18 / k²)`, Granlund–Montgomery; exact for divisors 9/25/49 over the full value range, verified per divisor) instead of long-latency vector integer division. Rounding is bit-identical to the previous `(sum + k²/2) / k²`.
4. **Existing kernels are extended, not rewritten**: `boxFilterC1` and the float `boxFilterC3` gain a 7×7 step in their existing unrolled style, so the 3×3/5×5 instantiations compile to the same code as before. The previous per-channel gaussian 8UC4 segment kernel is replaced by the flat kernel at measured parity, which is why the diff removes more gaussian lines than it adds.
5. Constraints respected: no hard-coded VLEN, RVV 1.0 intrinsics only, no scalable vector types in arrays.
### Performance
SpacemiT K1 (X60, rv64gcv, VLEN=256, 8×1.6 GHz), 1280×720, against a clean build of current 5.x HEAD.
**Measurement protocol:** each case is timed for **30 iterations** (after 2 warm-up runs) and the minimum is taken; the whole benchmark is executed in **2 interleaved rounds per library** (baseline/candidate alternated to cancel frequency/thermal drift, measured at up to 2× per run on this board), reporting the per-case minimum across rounds.
| Case | Generic | HAL | Speedup |
|------|--------:|----:|--------:|
| boxFilter 8UC1 7×7 | 6.539 ms | 0.330 ms | 19.8× |
| boxFilter 8UC3 3×3 | 7.325 ms | 0.994 ms | 7.4× |
| boxFilter 8UC3 5×5 | 8.118 ms | 1.142 ms | 7.1× |
| boxFilter 8UC3 7×7 | 15.930 ms | 1.328 ms | 12.0× |
| boxFilter 8UC4 3×3 | 9.552 ms | 1.466 ms | 6.5× |
| boxFilter 8UC4 5×5 | 10.775 ms | 1.841 ms | 5.9× |
| boxFilter 8UC4 7×7 | 14.268 ms | 2.112 ms | 6.8× |
| gaussianBlur 8UC3 3×3 | 2.382 ms | 1.023 ms | 2.3× |
| gaussianBlur 8UC3 5×5 | 2.746 ms | 1.684 ms | 1.6× |
**Geometric mean: ~6.1×** across the newly covered cases. Most of the box gain is the HAL's row-parallel execution (core `FilterEngine` has no internal parallelism) multiplied by the flat-layout kernels; the incremental window gives 7×7 a further ~1.9× over the plain flat version.
Regression check: the full `opencv_perf_imgproc` sweep (5610 timed cases, `--perf_min_samples=10`, one round per library) shows a geomean of 1.015× with no regressions beyond run-to-run noise — every case initially below 0.90× was re-verified with 2×2 interleaved rounds and per-case minima, and none persisted. The blur fixtures independently confirm 4.7–8.6× on 8UC4 across sizes and border types.
### Accuracy
- **Bit-exact against the core fallback**: for every newly covered combination (all 15 type/ksize pairs exercised), the HAL output was checksummed against the generic implementation on identical random inputs — all checksums match exactly. This includes the multiply-high normalization (bit-identical rounding to the previous division) and the incremental vertical window (u16 modular add/subtract cancels exactly).
- **Full accuracy suites**: `opencv_test_imgproc` filter/blur/smooth tests (`*BoxFilter*:*GaussianBlur*:*Blur*:*blur*:*Smooth*:*smooth*`) were run on K1 against a clean upstream HEAD build: **819 tests pass on both libraries, and the failure sets (pre-existing upstream failures, unrelated to this PR) are line-for-line identical**. These suites cover random anchors, all border modes, ROI offsets, and in-place operation.
- Combinations already covered upstream are unchanged by construction (same kernels, same codegen), so their accuracy behavior is inherited.
Note for reviewers: the large baseline gap also reflects that core `FilterEngine` runs single-threaded; that affects all non-HAL targets and may deserve a separate issue.
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3rdparty(mlas): add missing power (ppc64le) kernel headers #29516
Closes: https://github.com/opencv/opencv/issues/29465
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isMaskSupported now returns false when the matcher is
created with crossCheck enabled, because the mask
path is unsupported in that mode. knnMatchImpl also
drops the mask so it cannot reach batchDistance and
trigger its mask.empty() assertion.
https://github.com/opencv/opencv/issues/22093
core: SIMD optimizations for norm, distance and Hamming APIs (5.x) #29479
- Cache normHamming SIMD impl via a function pointer to cut per-call dispatch cost (improves ORB & BRISK)
- Add masked norm and norm-diff SIMD kernels (incl. cn==4 paths) and AVX-512 dispatch for norm/stat
- Add brute-force matcher perf test
- Alternative port of PR #29335 adapted to the 5.x norm.simd.hpp structure
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Distransform function IPP migration to HAL in 4.x #29463
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Fix calibration tutorial docs, decomposeProjectionMatrix, and convertMaps performance claims #29487Fixes#25655, #26791, #27277.
Three doc fixes:
1. Calibration tutorial: rows/cols swapped, fixed np.mgrid consistency
2. decomposeProjectionMatrix: clarified transVect is camera center in homogeneous coordinates
3. convertMaps: replaced overstated 2x speed claim
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- [x] I agree to contribute under Apache 2 License
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Gapi doc update #29149
This PR improves G-API documentation by clarifying the introduction and kernel API pages
- fixing some spelling errors
- operator|() can now link to the overloaded version that is actually called.
- supplemented and revised the doc based on the author's annotations.
The change is documentation-only and does not affect runtime behavior. I tested the doc reconstruction of this module.
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Extracting IPP integaration as HAL for Canny #29434
Backport of : https://github.com/opencv/opencv/pull/29433
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1RMvUZP1tSQtdjuNQY9JasYmlx1L5iN9XWGhXtG5u1uI/edit?usp=sharing
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Extracting IPP to HAL for matchTemplate function in 4.x #29464
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Replace "static inline" by "inline" in headers #29453
This fixes#29436
I also replaced CV_INLINE which can be removed in a later PR.
I can make a similar PR for 4.x if you want.
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Enable ipp calls for sort, sortIdx#29477
Follow up on https://github.com/opencv/opencv/pull/29184
Also introduced parallelization to sort similar to #29192
Performance is up to ~17x faster per our measurements
+ ~127KB to binary size.
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Documentation fixes, Added How to use pre-built opencv doc #29288
closes: https://github.com/opencv/opencv/issues/29263
co-authored by: @kirtijindal14 @Akansha-977
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dnn: add DynamicQuantizeLinear ONNX layer support #29018
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### Description
Implements the ONNX `DynamicQuantizeLinear` operator (opset 11) for the OpenCV DNN module.
**What it does:**
- Adds `QuantizeDynamicLayer` and `DequantizeDynamicLayer` layer classes
- Registers layers and adds ONNX importer dispatch for `DynamicQuantizeLinear`
- Computes scale and zero-point at runtime from activation min/max
- Quantizes FP32 input to int8 (stored as uint8 - 128, matching OpenCV convention)
**Framework limitation & workaround:**
Due to the single-dtype-per-layer constraint in `LayerData::dtype` (see #29017), the float32 scale output cannot be passed through the CV_8S blob pipeline directly. As a workaround, the scale is encoded as 4 raw bytes in a CV_8S `{1,4}` blob using `memcpy`, and decoded by the downstream `DequantizeDynamic` layer.
**Testing:**
- Custom accuracy tests reproduce all 3 ONNX conformance test cases: `test_dynamicquantizelinear`, `test_dynamicquantizelinear_max_adjusted`, `test_dynamicquantizelinear_min_adjusted`
- Each test verifies: quantized values, scale, zero point, and round-trip dequantize accuracy
- All existing quantization regression tests pass (42/42)
**Conformance tests:**
The 6 conformance tests for `DynamicQuantizeLinear` remain in the parser denylist (they were already denylisted before this PR) because the framework cannot produce mixed-type outputs. The custom tests provide equivalent coverage.
### Files changed
- `modules/dnn/include/opencv2/dnn/all_layers.hpp` — layer class declarations
- `modules/dnn/src/init.cpp` — layer registration
- `modules/dnn/src/onnx/onnx_importer.cpp` — ONNX import dispatch
- `modules/dnn/src/int8layers/quantization_utils.cpp` — layer implementations
- `modules/dnn/test/test_onnx_importer.cpp` — custom accuracy tests
- Count zero lanes via v_signmask()+popcount in AVX-512 dispatch units
- Alternative port of PR #29390 adapted to the 5.x macro-based kernels, including a SIMD path for 64F.
The previous commit bdf348c13a introduced
MLAS support including the s390x backend, but accidentally omitted the
required internal header files. This causes a compilation failure on
s390x:
fatal error: SgemmKernelZVECTOR.h: No such file or directory
This PR restores the build by adding the following missing headers from
upstream (microsoft/onnxruntime):
- FgemmKernelZVECTOR.h
- SgemmKernelZVECTOR.h
This is the same class of bug as #29422 (loongarch64), which was fixed
by PR #29423 for that architecture only.
Fix#29452: Remove <complex.h> to prevent _Complex macro conflicts #29455
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---
**Description:**
Resolves https://github.com/opencv/opencv/issues/29452
**Reason for the issue:**
The C99 `<complex.h>` header defines the macro `complex` on some platforms (like NetBSD with GCC 14). Because it was included before C++ `<complex>`, this caused conflicts where `std::complex<T>` was being expanded into `std::_Complex<T>`, resulting in the reported syntax errors.
**Changes made:**
- Removed the unnecessary C-style `#include <complex.h>`.
- Added a safety guard to `#undef complex` in case any transitive lapack headers attempt to define it, ensuring `std::complex` works cleanly without C-preprocessor interference.
Filter2D IPP extraction to HAL for 5.x #29428
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dnn: silence false-positive CPU target warning for New graph engine #29457
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No existing OpenCV issue or pull request directly covers this warning. This PR is the original report and fix.
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Not applicable. This patch changes only diagnostic behavior for an existing CPU no-op path.
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Not applicable. This patch adds no feature or public API.
## Summary
Suppress a misleading warning emitted when OpenCV 5 New DNN graph engine receives its default CPU target request.
`FaceDetectorYN` and `FaceRecognizerSF` call:
```cpp
net.setPreferableTarget(DNN_TARGET_CPU);
```
after loading their networks.
When New graph engine is active, target switching is not supported. OpenCV therefore currently prints:
```text
Targets are not supported by the new graph engine for now
```
However, New graph engine already executes on CPU. The CPU request does not require a target change, is ignored, and inference continues through New graph engine.
The warning therefore suggests a failed configuration or fallback to Classic DNN even though neither occurs.
## Change
Treat `DNN_TARGET_CPU` as a silent no-op when generic New graph engine is active.
```text
New graph engine + CPU target
→ no target change needed
→ return unchanged
→ no warning
New graph engine + non-CPU target
→ target remains unsupported
→ preserve existing warning
→ return unchanged
```
## Behavior before
```text
New graph engine active
→ caller requests CPU
→ request is ignored
→ warning emitted
→ inference continues on New graph engine
```
## Behavior after
```text
New graph engine active
→ caller requests CPU
→ request is ignored
→ no warning
→ inference continues on New graph engine
```
## Unchanged behavior
* New graph engine selection is unchanged.
* Inference execution is unchanged.
* CPU remains the effective target for generic New graph engine.
* Classic DNN behavior is unchanged.
* ONNX Runtime handling is unchanged.
* Non-CPU targets continue to emit the existing warning.
## Motivation
The current diagnostic is a false positive for the default CPU request. It reports unsupported target selection even though CPU is already the active execution target and inference succeeds through New graph engine.
## Testing
* Built OpenCV locally.
* Ran relevant DNN tests.
* Verified New graph engine still loads and executes affected models.
* Verified CPU target requests no longer emit the misleading warning.
* Verified non-CPU target requests retain the existing unsupported-target warning.
The Caffe importer removal (#28678) left behind the protoc-generated
opencv-caffe.pb.{cc,h} and caffe_io.{cpp,hpp}. The generated files can
only be compiled with an old libprotobuf and can no longer be
regenerated since opencv-caffe.proto was deleted, so any build with
PROTOBUF_UPDATE_FILES=ON or an external protobuf failed (#29291, #29419).
- Move the generic ReadProto* helpers, the only remaining consumers of
caffe_io (used by the TensorFlow importer), to src/protobuf_io.{hpp,cpp};
delete src/caffe and misc/caffe, and move glog_emulator.hpp to src/.
- Guard the protobuf version check so it also works with newer protobuf
where GOOGLE_PROTOBUF_VERSION is not defined.
- Drop the unconditional opencv-onnx.pb.h include from cast2_layer.cpp,
using the spec-fixed ONNX data type values instead, and update stale
readNetFromONNX/readNetFromTensorflow stubs to the current signatures
so WITH_PROTOBUF=OFF builds link again.
core: fix use-after-scope when Mat::mul() is given a scalar #29447
### Summary
`cv::Mat::mul()` called with a scalar returns a `MatExpr` that reads a dead stack slot when it is evaluated. In a normal (non-instrumented) build this produces silently wrong values as soon as the slot is reused:
```cpp
static cv::MatExpr makeExpr(const cv::Mat& m)
{
return m.mul(7); // 7.0 is a temporary double in THIS frame
}
cv::Mat matrix(2, 3, CV_32FC1, cv::Scalar(3.0f));
cv::MatExpr expr = makeExpr(matrix);
// ... any further calls reuse the dead frame ...
cv::Mat result = expr; // observed: all 0, expected: all 21
```
Under AddressSanitizer this is the `stack-use-after-scope` reported in #23577, with the same stack trace (`cvt64s` -> `convertAndUnrollScalar` -> `arithm_op` -> `multiply` -> `MatOp_Bin::assign`).
Storing the expression is the documented lazy-evaluation usage of `MatExpr`; the argument is ordinary supported API usage (`mat.hpp` itself shows `Mat C = A.mul(5/B);`).
### Root cause
A scalar argument binds to `_InputArray(const double& val)`, which records the **address** of the temporary with kind `MATX`:
```cpp
inline _InputArray::_InputArray(const double& val)
{ init(FIXED_TYPE + FIXED_SIZE + MATX + CV_64F + ACCESS_READ, &val, Size(1,1)); }
```
`Mat::mul()` then parks `m.getMat()` inside the returned `MatExpr`. For `MATX` kind, `getMat_()` returns a non-owning, non-refcounted header over that stack memory (`return Mat(sz, flags, obj);`). The temporary dies at the end of the full expression, but the `MatExpr` keeps the header, and `MatOp_Bin::assign()` later feeds it to `cv::multiply()`. `Matx`/`Vec` arguments take the same path.
`Mat::mul()` is the only `MatExpr` factory in `matrix_expressions.cpp` that takes an `InputArray`; every other scalar operand there is stored by value in the `Scalar` member (`e.s`), so no other expression path can capture a stack pointer this way.
### Fix
Snapshot the operand with `clone()` unless it is a `Mat`/`UMat`, which keep the current zero-copy behaviour: their headers are refcounted and already safe to defer. Any other `InputArray` kind (a scalar, `Matx`, `Vec`, `std::vector`, an evaluated expression) is a potentially non-owning view, so it is copied once at expression construction, off any hot path.
### Test
Adds `Core_MatExpr.mul_scalar_use_after_scope_23577` to `modules/core/test/test_operations.cpp`. It builds the expression in a helper frame and overwrites the stack before evaluating; the helpers are called through volatile function pointers so they cannot be inlined, which makes the stale read deterministic. The test fails before the fix (result is all 0 instead of all 21) and passes after. It is self-contained: no opencv_extra data is needed.
Verified locally on macOS/AArch64 (Apple clang 17, Release): full `opencv_test_core` passes, and the AddressSanitizer reproducer from the issue is clean after the fix.
Fixes#23577.
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Self-contained accuracy regression test in `modules/core/test/test_operations.cpp`; no opencv_extra data required. No performance test: no existing perf test covers `Mat::mul` expression construction, and the copy happens once at expression construction, only for non-`Mat`/`UMat` operands.
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N/A - bug fix, no new API.
Filter2D IPP migration to HAL for 4.x #29427
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Merge pull request #29414 from Prasadayus:box_filter_refactor
Moving IPP functions to HAL for box_filter in Imgproc #29414
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1puWmOSTtAFwWjPu8J1SpuccPQvxUJU8NlFQs7mAZwL8/edit?usp=sharing
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Refactoring and optimizing cvtcolor in Imgproc #29389
**Key Changes:**
1. Unified three divergent SIMD swap implementations into a single shared `v_swap` helper.
2. Consolidated the repeated `CV_8U/CV_16U/CV_32F` depth-dispatch chains into a single `CvtColorLoopDepth` template.
3. Extracted the duplicated coefficient-selection loops in the YCrCb/YUV constructors into `selectYuvCoeffs`.
4. Collapsed the shared YUV420 store logic duplicated across both decode invokers into `storeYUV420block`.
5. Generalized the inline blue-channel coefficient swaps in `color_lab.cpp` into `swapBlueCoeffsCols`/`swapBlueCoeffsRows`.
6. Added a `v_dotprod`-based SIMD path (`v_RGB2Y`/`v_RGB2UV`) for the previously scalar `RGB8toYUV422Invoker`.
7. Migrated all inline `CV_IPP_CHECK` cvtColor paths out of the dispatch files and into the IPP HAL plugin (`hal/ipp/src/color_ipp.cpp`), replacing each call site with `CALL_HAL`.
**Performance Numbers on Intel(R) Core(TM) i9-11900K**: https://docs.google.com/spreadsheets/d/1pz0aHlTeG4Ao8RT7LipgdNSjhCJz92QfZG3GmNa63hU/edit?usp=sharing
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Extract IPP integration as HAL function for box_filter #29420
Backport of https://github.com/opencv/opencv/pull/29414
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1kMKiZWh--pH30hqsQo6j1suNw1lfQiKzSankMCMa2FI/edit?usp=sharing
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core: Fix mul32f and addWeighted32f to use native f32 SIMD paths #29413
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1388
- add 32FC1 coverage to addWeighted benchmark
- avoid intermediate double for f32 variants of scaled multiply and addWeighted.
- Relax AddWeighted 32F test tolerance to match f32 FMA semantics.
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core: SIMD optimizations for norm, distance and Hamming APIs. (Improves ORB & BRISK) #29335
Generic universal-intrinsic kernels (benefit all SIMD backends: NEON, AVX2, AVX-512, etc.), plus enabling wider dispatch for the norm module.
- hal::normHamming: cached-pointer dispatch (resolve once, no per-call dispatch chain or trace region) + vector popcount path. cv::norm(NORM_HAMMING) and binary-descriptor matching (BFMatcher ORB/BRISK/FREAK via cv::batchDistance).
- hal::normL2Sqr_ / normL1_: direct inlinable kernels with single-vector tail (cv::batchDistance / BFMatcher float L2/L1, cv::kmeans).
- cv::norm masked NORM_INF: deinterleave SIMD for multichannel + back-step tail.
- cv::norm(src1, src2, type, mask): SIMD masked norm-diff; INF is one templated kernel for all element types, plus uchar L1/L2 and int L1 kernels.
- Enable AVX512_SKX/AVX512_ICL dispatch for the norm module.
- features2d: add BFMatcher knnMatch perf tests (float L2/L1, binary Hamming).
- ORB and BRISK performance improved
### Pull Request Readiness Checklist
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Extended Attention layer support #29333
Implemented present/past KV support.
Merge with: https://github.com/opencv/opencv_extra/pull/1381
Co-authored by: @Akansha-977
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dnn: add Winograd F(6,3) RVV implementation for RISC-V (VLEN≥256) #29411
### Summary
Adds a RISC-V Vector (RVV) backend for the Winograd F(6,3) convolution path
in the DNN module, targeting VLEN≥256 processors (tested on SpacemiT K1,
rv64gcv).
### Background
The Winograd F(6,3) path was already gated on `useSIMD128 || useAVX ||
useAVX2 || useNEON` at runtime, and protected by the same set of compile-time
macros. RVV was simply missing from both guards, so it always fell through to
generic convolution even when the hardware supports it.
A second issue: `conv_winograd_f63` was registered with
`ocv_add_dispatched_file`, which skips ISA variants not present in the host
toolchain during cross-compilation. Changing to `ocv_add_dispatched_file_force_all`
forces the `.rvv.cpp` translation unit to be generated unconditionally, matching
how every other RVV-enabled kernel in the DNN module is registered.
### Implementation notes
**Atom width.** `vsetvlmax_e32m1()` returns 8 on VLEN=256, so `winoAtomF32=8`
is chosen, matching the AVX2 atom width. The `impl_accum_F32` and transform
functions are structured identically to the AVX2 path (4 output channels ×
6 input tiles per atom).
**Input/output transform.** AVX2 uses `_mm256_unpacklo/hi_ps` + `permute2f128`
for an in-register 8×8 transpose. RVV has no equivalent cross-lane shuffle at
this width without `vrgather`, which adds index-vector overhead for a
non-bottleneck step. Instead the 8×8 intermediate matrix is transposed
scalar-in-memory between the two `wino_bt8x8_rvv` / `wino_at8x6_rvv` passes.
This keeps the transform code simple and correct; the GEMM in `impl_accum_F32`
dominates runtime.
**VLEN guard.** `getWinofunc_F32` checks `vsetvlmax_e32m1() >= 8` at runtime
and returns an empty functor on narrower implementations, so the code is safe
on VLEN=128 targets without a separate code path.
### Testing
**Correctness:** `ConvolutionWinograd.Accuracy` passes on the K1 board (9 ms).
**Performance:** `opencv_perf_dnn`, SpacemiT K1 (rv64gcv, VLEN=256),
GCC 13, `-O2 -march=rv64gcv`. Reported times are per-iteration medians from
`[ PERFSTAT ]` output (`--perf_min_samples=5`). "Generic" is the same build
with Winograd disabled via `OPENCV_DNN_DISABLE_WINOGRAD=1`.
| Input → Output C | Winograd (ms) | Generic (ms) | Speedup |
|--------------------------|--------------|-------------|---------|
| {1,128,52,52} → 256 | 35.97 | 62.86 | 1.75× |
| {1,512,13,13} → 1024 | 65.18 | 112.50 | 1.73× |
| {1,256,75,75} → 256 | 159.75 | 271.65 | 1.70× |
| {1,64,300,300} → 64 | 174.21 | 295.32 | 1.69× |
| {1,1152,16,16} → 1152 | 176.12 | 245.33 | 1.39× |
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Optimized gemm implementation with Universal SIMD #29242
- vectorized GEMMSingleMul and GEMMBlockMul
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dnn: implement LpPool ONNX operator #29217
## Summary
Implements the `LpPool` ONNX operator (opset 1–18), which was previously unregistered and caused a parse failure. `LpPool` computes the Lp-norm pooling: `(sum(|x|^p))^(1/p)` over a sliding window.
## Changes
- New `LpPoolLayer` in `modules/dnn/src/layers/lppool_layer.cpp`
- Supports `kernel_shape`, `strides`, `dilations`, `pads`, `auto_pad` (NOTSET/SAME_UPPER), `ceil_mode`, and `p` (default 2)
- SIMD fast paths for p=1 (abs + accumulate) and p=2 (square + accumulate + sqrt); scalar fallback for other values of p
- Registered `LpPool` dispatch entry in both `onnx_importer.cpp` (classic engine) and `onnx_importer2.cpp` (new graph engine)
- Added `LpPoolLayer` declaration to `modules/dnn/include/opencv2/dnn/all_layers.hpp`
- Registered layer class in `modules/dnn/src/init.cpp`
- Re-enabled 8 lppool conformance tests in `test_onnx_conformance.cpp` (previously in parser denylist)
- `test_lppool_2d_same_lower` added to the global conformance denylist — same known SAME_LOWER padding bug that affects `averagepool` and `maxpool`
## Testing
All applicable ONNX conformance tests pass:
| Test | Result |
|------|--------|
| test_lppool_1d_default | PASSED |
| test_lppool_2d_default | PASSED |
| test_lppool_2d_dilations | PASSED |
| test_lppool_2d_pads | PASSED |
| test_lppool_2d_same_lower | SKIPPED (known SAME_LOWER padding bug, consistent with avgpool/maxpool) |
| test_lppool_2d_same_upper | PASSED |
| test_lppool_2d_strides | PASSED |
| test_lppool_3d_default | PASSED |
Tested on: macOS (x86_64/SSE4, Rosetta 2) and Linux x86_64 (AVX2/AVX-512, GCC 13.3.0), Release build
OpenCV version: 5.0.0-pre
## Related Issues
None
---
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videoio(avfoundation): fix SIGSEGV crash when releasing VideoWriter on iOS #29359
Replace deprecated synchronous finishWriting with
finishWritingWithCompletionHandler + dispatch_semaphore
to properly wait for the async completion handler.
The synchronous finishWriting method is deprecated since iOS 6
and, despite blocking until writing status completes, does NOT
wait for internal NSOperation KVO observer blocks that are
dispatched asynchronously to GCD worker threads. When the
destructor returns and drains the autorelease pool, these
blocks may access already-released objects, causing SIGSEGV.
This fix uses a semaphore to block until the
finishWritingWithCompletionHandler callback has fully completed,
eliminating the race condition between the async block and
autorelease pool drain.
Fixes#28165
cmake: fix#29028 macOS dynamic framework RPATH for relocatable install #29396
### Problem
When building OpenCV as a dynamic framework on macOS (`-DAPPLE_FRAMEWORK=ON -DBUILD_SHARED_LIBS=ON`), installed binaries contain an absolute `LC_RPATH` pointing to the build tree, making the install non-relocatable. Moving the install directory or deploying to another Mac causes binaries to crash on launch.
### Root cause
`cmake/OpenCVInstallLayout.cmake` unconditionally sets `CMAKE_INSTALL_RPATH` to `${CMAKE_INSTALL_PREFIX}/${OPENCV_LIB_INSTALL_PATH}` for all platforms. Additionally, `cmake/platforms/OpenCV-Darwin.cmake` was empty and missing the `@executable_path`/`@loader_path` linker flags that the iOS toolchain already sets for the same `APPLE_FRAMEWORK AND BUILD_SHARED_LIBS` case.
### Fix
- Guard `CMAKE_INSTALL_RPATH` in `OpenCVInstallLayout.cmake` to use relocatable `@executable_path` tokens for Apple framework builds
- Add equivalent linker flags to `OpenCV-Darwin.cmake`, mirroring `platforms/ios/cmake/Modules/Platform/iOS.cmake`
Fixes#29028
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Use validBitIdThreshold for Aruco refineDetectedMarkers #29252
The goal of this PR is to solve the issue raised by @vrabaud in https://github.com/opencv/opencv/pull/28289 (comment: https://github.com/opencv/opencv/pull/28289#discussion_r3355812646).
**Issue:**
`refineDetectedMarkers()` converted the extracted cell ratios with `convertTo(CV_8UC1)` (an implicit 0.5 threshold) before computing the code distance, ignoring `detectorParams.validBitIdThreshold`.
**Solution:**
Make the refine path consistent with the main detection path `Dictionary::identify`.
**Changes:**
- Add a `Dictionary::getDistanceToId()` overload that takes the float cell pixel ratio matrix and `validBitIdThreshold` (similar to how it's done for the `identify()` overload.
- Move the per cell distance computation into a private `getDistanceToIdImpl` helper used by both `identify()` and the new overload of `getDistanceToId()` to avoid repetitions.
- `refineDetectedMarkers()` now calls the new overload.
**Tests:**
- `CV_ArucoRefine.validBitIdThreshold`: a marker with one degraded cell is recovered at threshold 0.7 but not at 0.49.
- `CV_ArucoDictionary.getDistanceToIdCellPixelRatio`: unit-tests both `getDistanceToId` overloads.
All passed
SVM::predict is dominated by the per-feature kernel reduction over the
support vectors. Vectorize the four reduction kernels in svm.cpp with
universal intrinsics (two independent accumulators + scalar tail):
calc_non_rbf_base (dot product), calc_rbf (squared distance),
calc_intersec (min-sum) and calc_chi2. Same approach as the KNN
findNearest reduction in #29380. Adds modules/ml/perf/perf_svm.cpp
covering the RBF/POLY/INTER/CHI2 kernels.
core: fix Unicode temp path handling on Windows (fix#21960) #28935
## Summary
Fixes getCacheDirectoryForDownloads and related temp file functions failing when the user home directory contains Unicode characters like C:\\Users\\テスト\\AppData\\Local\\Temp.
## Root Cause
On Windows, OpenCV was using ANSI versions of GetTempPath and GetTempFileName which fail with Unicode paths.
## Fix
Replace with wide-character GetTempPathW plus UTF-8 conversion:
- modules/core/src/utils/filesystem.cpp - getCacheDirectory for cache paths
- modules/core/src/system.cpp - temporary file creation
- modules/ts/src/ts_gtest.cpp - test stream capture
## Testing
The fix has been verified compiles. Manual testing on Windows with Unicode username required.
## Risk
Low: Uses same output format, just different encoding path. Existing ASCII functionality preserved.
---
Fixesopencv/opencv#21960
bound alphanumeric values in qr decodeAlpha before map lookup #29378
decodeAlpha in the no-quirc QR backend reads alphanumeric symbols from the post-ECC bitstream and indexes a fixed 45-entry map[] with the raw values. The 11-bit pair from next(11) goes up to 2047, so tuple/45 lands on 45 once the pair passes 2024, and the 6-bit trailing char from next(6) goes up to 63, so map[value] runs out to map[63]. A QR whose data codewords carry an alphanumeric segment with one of those out-of-range values reads past the static array, and the stray byte lands in the string returned by detectAndDecode. WITH_QUIRC defaults off, so this is the decoder a stock build runs.
Before, the only guard was on the encode side, which never emits those values, so the decoder trusted the stream and indexed map[] directly. New version checks value range and return empty string for malformed qr codes.
### Pull Request Readiness Checklist
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Port of #29377 to 5.x. Wires Log, Erf, Exp, Sin, Cos, Sinh, Cosh, Tan,
Softplus, BNLL, Asinh, Acosh, Atanh into the dispatched activation_kernels
registry, plus a Layer_Activation perf test. 1.4-12.3x across
M4/A76/Threadripper/Xeon, correct to <=5e-7 vs scalar.
bound tile axis and repeats length in onnx parseTile #29345
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1385
parseTile sizes repeats_vec from the input-0 rank, then fills it from fields of the model that are never checked against that size. In the tile-1 path the axis taken from the third input indexes repeats_vec directly, and in the tile>1 path the loop writes one entry per element of the repeats tensor. A crafted ONNX with an out-of-range axis, or a repeats tensor longer than the input rank, writes past repeats_vec while loading the model through readNetFromONNX.
The fix runs axis through normalize_axis, the same helper the squeeze and concat paths in this file already use, so a negative or oversized axis is rejected before the write, and it checks the repeats length equals the input rank before the loop. Keeping both bounds in the parser puts the check next to the write instead of trusting the model to be well formed. Before, a repeats tensor shorter than the rank was silently accepted; after, it is rejected, which matches the ONNX rule that repeats carries one entry per input dimension.
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dnn: SIMD for transcendental activation layers (15 functors) 🧑💻🤖#2937
# dnn: SIMD for transcendental activation layers (15 functors) 🧑💻🤖
### Summary
Many `BaseDefaultFunctor`-based activation functors fall back to the scalar `BaseDefaultFunctor::apply`
— one `libm` call per element. This PR adds vectorized `apply()` overrides (universal intrinsics,
matching the existing `MishFunctor`/`GeluFunctor`/`SigmoidFunctor` pattern, each with a scalar tail) to
the **15 transcendental activations** that have a clean SIMD path:
| functor | uses | | functor | uses |
|---|---|---|---|---|
| TanH | `v_exp` (1−2/(e²ˣ+1)) | | Asinh | `v_log`,`v_sqrt` (sign·log(\|x\|+√(x²+1))) |
| Log | `v_log` | | Acosh | `v_log`,`v_sqrt` |
| Erf | `v_erf` | | Atanh | `v_log` |
| Exp | `v_exp` | | Softplus | `v_exp`,`v_log` |
| Sin | `v_sin` | | BNLL | `v_exp`,`v_log` (max(x,0)+log1p(e^−\|x\|)) |
| Cos | `v_cos` | | GeluApproximation | `v_exp` (tanh) |
| Tan | `v_sin`/`v_cos` | | Sinh / Cosh | `v_exp` |
Functors compile at the baseline SIMD width (SSE on x86, NEON on ARM — same as the existing SIMD
functors). `Sqrt`/`Floor`/`Ceil`/`Round`/`Abs`/etc. are intentionally **not** included — those are
single cheap ops the compiler already auto-vectorizes (measured `Sqrt` at 0.95×). `Asin`/`Acos`/`Atan`
are omitted (no `v_atan`/`v_asin`/`v_acos` intrinsic).
### Performance
Real `opencv_perf_dnn` `Layer_Activation` (added here), 8×256×128×100 = 26.2 M-elem CV_32F blob, A/B
vs the scalar fallback, median speedup:
| functor | M4 clang/NEON | A76 gcc/NEON | Threadripper gcc | Xeon W-2235 gcc | functor | M4 | A76 | TR | Xeon |
|---|---|---|---|---|---|---|---|---|---|
| TanH | 9.1× | 2.3× | 7.3× | 7.3× | Sinh | 3.3× | 3.2× | 5.2× | 5.0× |
| Cos | 8.8× | 3.0× | 3.3× | 3.0× | Acosh | 3.2× | 2.9× | 2.7× | 2.6× |
| Log | 7.8× | 3.0× | 2.5× | 2.4× | Cosh | 2.8× | 3.2× | 2.6× | 2.5× |
| Sin | 7.8× | 3.0× | 3.4× | 2.9× | Exp | 2.4× | 2.5× | 1.4× | 1.5× |
| GeluApprox | 7.6× | 2.4× | 6.6× | 5.8× | BNLL | 4.2× | 2.0× | 1.6× | 1.4× |
| Atanh | 7.0× | 3.6× | 6.0× | 5.2× | Softplus | 2.0× | 2.0× | 3.3× | 2.7× |
| Tan | 6.8× | 3.6× | 6.6× | 6.0× | Erf | 4.2× | 2.4× | 3.9× | 3.5× |
| Asinh | 3.9× | 2.4× | 5.8× | 5.7× | | | | | |
Every functor is a win on every tested out-of-order core (worst case 1.4×). On the **in-order
Cortex-A55** (a55-tuned build) all 15 measure 0.985–0.99× — neutral, within noise: the SIMD
polynomials are dependency-chain-bound, which an in-order pipeline can neither accelerate nor (here)
slow down. So: meaningful wins on out-of-order ARM + x86, no regression on in-order ARM.
### Accuracy
Uses the `v_exp`/`v_log`/`v_erf`/`v_sin`/`v_cos` polynomials OpenCV already ships and relies on (Gelu
via `v_erf`, Mish/Sigmoid via `v_exp`). Verified vs scalar `libm` on 320 K random elements per functor
with domain-correct inputs (Acosh x≥1, Atanh |x|<1, …): **max abs error ≤ 3e-5** (most ≤ 2e-6), zero
elements exceeding rel>1e-3 & abs>1e-4. ONNX conformance and the layer accuracy tests exercise these ops.
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch (4.x)
- [ ] There is a reference to the original bug report and related work — N/A (perf improvement)
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable — perf test added (`Layer_Activation`, `SANITY_CHECK_NOTHING`); accuracy covered by existing ONNX conformance/layer tests
- [ ] The feature is well documented and sample code can be built with the project CMake — N/A (internal optimization, no new API)
Fix js ximgproc edge drawing #29387
## Summary
This PR fixes OpenCV.js binding generation for factory functions that return `cv::Ptr<T>` where `T` is a namespaced class exported with a module-prefixed JavaScript binding name.
The failure is reproduced with OpenCV.js plus `opencv_contrib/modules/ximgproc`. The generated binding for `cv::ximgproc::EdgeDrawing` currently contains:
```cpp
.constructor(select_overload<Ptr<EdgeDrawing>()>(&cv::ximgproc::createEdgeDrawing))
```
but `EdgeDrawing` is not available in the generated C++ scope as an unqualified type. The generated constructor should use:
```cpp
.constructor(select_overload<Ptr<cv::ximgproc::EdgeDrawing>()>(&cv::ximgproc::createEdgeDrawing))
```
## Related issues
Fixesopencv/opencv_contrib#4161.
Related to #27963, #28130, and #28143.
#28143 added namespace qualification for factory `Ptr<...>` return types when the inner `Ptr` type matches the generator class key. The remaining `ximgproc::EdgeDrawing` case is different:
```text
inner Ptr type: EdgeDrawing
generator class key: ximgproc_EdgeDrawing
C++ class name: cv::ximgproc::EdgeDrawing
```
Because `EdgeDrawing` does not match `ximgproc_EdgeDrawing`, the previous condition does not handle this case.
## Fix approach
The JS generator now centralizes factory `Ptr<...>` return-type qualification in a helper used by both generator paths:
* `gen_function_binding_with_wrapper`
* `gen_function_binding`
The helper keeps the existing behavior for already qualified types and for the existing class-key match. It additionally handles the case where the inner `Ptr` type matches the basename of the C++ class name:
```text
cv::ximgproc::EdgeDrawing -> EdgeDrawing
```
This allows the generator to emit `Ptr<cv::ximgproc::EdgeDrawing>` for `createEdgeDrawing()` without changing generated files directly.
## Necessity as an opencv code change
The failing API is exposed by `opencv_contrib/modules/ximgproc`, but the invalid C++ line is emitted by OpenCV core's JavaScript binding generator in `modules/js/generator/embindgen.py`.
A contrib-only workaround would have to change the `ximgproc` public declaration or special-case the JS export list for `createEdgeDrawing`. That would only work around one symbol and would not fix the generator's handling of namespaced factory `Ptr` return types. The generator already has the class metadata needed to produce the correct fully qualified C++ type, so the fix belongs in OpenCV core.
## Verification
Tested with:
* OpenCV core `4.x`
* opencv_contrib `4.x`
* Emscripten `6.0.1`
* CMake generator: Ninja
* Build list: `core,imgproc,imgcodecs,video,calib3d,ximgproc,js`
Generator target:
```bash
emcmake cmake \
-S /path/to/opencv \
-B /path/to/build \
-G Ninja \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_CXX_STANDARD=17 \
-DOPENCV_EXTRA_MODULES_PATH=/path/to/opencv_contrib/modules \
-DBUILD_LIST=core,imgproc,imgcodecs,video,calib3d,ximgproc,js \
-DBUILD_SHARED_LIBS=OFF \
-DBUILD_opencv_js=ON \
-DBUILD_TESTS=OFF \
-DBUILD_PERF_TESTS=OFF \
-DBUILD_EXAMPLES=OFF \
-DBUILD_DOCS=OFF
ninja -C /path/to/build gen_opencv_js_source
```
This generated the expected binding:
```cpp
.constructor(select_overload<Ptr<cv::ximgproc::EdgeDrawing>()>(&cv::ximgproc::createEdgeDrawing))
.smart_ptr<Ptr<cv::ximgproc::EdgeDrawing>>("Ptr<ximgproc_EdgeDrawing>")
```
The same patch was also verified with the full OpenCV.js target:
```bash
ninja -C /path/to/build opencv.js
```
Validate onnx tensor payload size in getMatFromTensor (5.x) #29370
5.x port of #29314. The DNN ONNX importer diverged here, so the change is ported manually.
`getMatFromTensor()` sizes the output blob from `TensorProto.dims` and then reads that many elements out of the tensor payload, but the payload is sized independently in the model and never checked against the shape. In 5.x the payload reaches the read through three sources: a typed `*_data` field, `raw_data`, or external-file data routed through `getTensorRAWData()`. An initializer whose dims claim more elements than the payload holds makes the `Mat` copy/convert read past the buffer.
### Before
A FLOAT tensor declaring `[1000000]` backed by a 4-byte `raw_data` reads about 4 MB out of bounds (ASan flags a heap over-read in `cv::Mat::copyTo`). The same mismatch is present in every datatype branch and for both the typed-field and `rawdata` sources, reachable from `readNetFromONNX`/`readNetFromONNXBuffer` for every initializer.
### After
Compute the element count the shape implies once, then check each payload source holds at least that many elements before the read. `getTensorRAWData()` now reports the byte count it returns (covering both `raw_data` and external-file data), and `getMatFromTensor()` validates the typed fields and the raw byte count per datatype. A short payload throws a clear `cv::Exception` instead of over-reading.
### Tradeoffs
The check lives in `getMatFromTensor` because that is the one place the shape and the payload meet, so every initializer and attribute tensor is covered without each caller repeating it. The added cost is one multiply-accumulate over the dims plus a comparison per tensor at load time; well-formed models, where the payload already matches the shape, are unaffected. The element-count accumulation saturates on overflow so an oversized shape can't wrap to a small total and slip past the check.
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fix MSVC warning for minMaxIdx_simd #29379
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fixed Dynamic quantized linear layer error #29386
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Use signmask+popcount only in AVX-512 dispatch units; keep the legacy
batched SIMD kernels for AVX2, NEON, and LASX to avoid regressions on
narrow SIMD widths.
Perf test for gemm #28706
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1336
- Added perf test to verify gemm performance.
- small sizes, square & rectangular matrix shapes are added.
- special case of n=1 and m=1 are added.
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The first-class-language OpenCV CMake config (OpenCVConfig-CUDALanguage.cmake.in)
pinned the consumer's CUDA Toolkit to the exact patch version OpenCV was built
with (e.g. 12.3.107), via find_package(CUDAToolkit ... EXACT) and a full
VERSION_EQUAL check. Patch versions change frequently while the CUDA runtime API
is stable within a minor release, so this forced needless rebuilds and could
FATAL_ERROR even for a matching toolkit.
Per the core team's direction on #26966, the change lives in the installed
config template rather than the build scripts, and is controlled by a
consumer-set variable:
- Default: match major.minor only (fixes the reported patch-pinning bug).
- OPENCV_STRONG_CUDA_VERSION_CHECK: restore the exact full-version match.
The build-time detection (OpenCVDetectCUDALanguage.cmake) is left untouched, so
OpenCV_CUDA_VERSION still records the full version for diagnostics.
Fixes#26965
Vectorize the per-sample squared-Euclidean distance in
BruteForceImpl::findNearestCore with universal intrinsics (two
independent v_fma accumulators + scalar tail). The scalar reduction
accumulates in float in strict order, which the compiler cannot
auto-vectorize without -ffast-math; independent SIMD accumulators
break that serial-accumulation dependency.
Accumulates in float exactly as before (only summation order changes,
~1e-7, below stored float precision); selected neighbors and distances
matched scalar on all test data, ML_KNearest tests pass. Real
findNearest A/B: 3.76x M4, 3.47x Threadripper, 3.73x Xeon, 4.10x A76,
2.82x A55 (in-order).
Adds modules/ml/perf with a findNearest perf test.
Migrate the ushort specialization from CV_SIMD128 to (CV_SIMD ||
CV_SIMD_SCALABLE), enabling AVX2/AVX512/RVV widths instead of 128-bit only.
uchar specialization left at CV_SIMD128 — initial migration regressed on
RVV (Muse Pi v3.0, 0.77~0.85x) when vlanes16 == TILE_SIZE collapses the
SIMD loop to one iteration and per-tile setup overhead dominates.
- replace v_int32x4/v_uint32x4/v_uint64x2 with scalable v_int32/v_uint32/v_uint64
- iota vector in static const sized by VTraits::max_nlanes (initialized once at program load)
- drop buf64; use v_reduce_sum(v_uint64) directly (defined on all backends)
- vx_cleanup() at operator() tail for RVV vsetvl hygiene
Fast-path transposeND for identity and 2D transpose orders #29172
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This PR adds fast paths to cv::transposeND() for two common cases:
- identity permutation: dispatch to copyTo()
- 2D permutation {1, 0}: dispatch to transpose()
All other permutations continue to use the existing generic ND implementation.
Why:
transposeND() currently falls back to the generic memcpy/index-update loop even for the common 2D transpose case, while OpenCV already has an optimized transpose() path. Reusing that path avoids unnecessary index arithmetic and improves performance for 2D inputs passed through transposeND().
Performance:
[BinaryOpTest.transposeND/21 (1920x1080, 8UC3): 28.21 ms -> 0.66 ms (-97.7%)]
[BinaryOpTest.transposeND/22 (1920x1080, 8UC4): 37.61 ms -> 0.88 ms (-97.7%)]
[BinaryOpTest.transposeND/29 (1920x1080, 32FC1): 10.26 ms -> 0.75 ms (-92.7%)]
Full transposeND perf subset: 6760 ms -> 592 ms (-91.2%)
guard the parsePadding NHWC swap on paddings.total()==8 and require the parseResize size tensor to hold height and width, matching the existing parseStridedSlice check
core: Vectorize meanStdDev #29339
- Add SIMD SumSqr_SIMD reductions (previously scalar) plus AVX-512 dispatch for the mean TU.
- Uses universal intrinsics, so all SIMD backends benefit.
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dnn: vectorize fp32 convolution on RISC-V RVV #29357
### Summary
Vectorizes the new DNN engine's fp32 convolution on RISC-V **RVV**. Convolution was the last major block-layout operator still running scalar on RVV; this brings it to parity with the already-vectorized depthwise/pool/batchnorm path.
Single file, **purely additive** (`#elif CV_SIMD_SCALABLE` branches only) — x86/AVX2, ARM/NEON and AArch64 codegen are unchanged.
### Background
- **#28585** introduced block-layout conv with fp32 kernels specialized for AVX2 (`C0=8`) and NEON, scalar `#else` for everything else, and explicitly noted *"we could add the respective kernels later."*
- On RVV that `#else` meant conv ran **scalar at every VLEN** — the C0=8 fixed width approach doesn't transfer to a variable-VLEN ISA.
- **#29304** made the block size track the hardware width (`C0 = vlanes()`), fixing the VLEN≥256 assertion but leaving conv scalar.
- **This PR** supplies the deferred RVV conv vectorization, on top of that `C0=vlanes()` foundation.
### Approach
- Re-enable the `SPAT_BLOCK_SIZE=10` **blocked path** on RVV. Because `K0 == vlanes()`, one `v_float32` accumulator covers a full output-channel block, so 10 accumulators process 10 output positions together. A single K0-wide weight `vx_load` is reused across all 10 — amortizing the per-output-point load that bottlenecks the scalar path (this is what enables multi-core scaling; the scalar/per-FMA-load path is bandwidth-bound).
- Vectorize the **scalar tail** (the `<10`-position remainder) the same way.
- **Vector-length-agnostic:** `C0` is runtime, the kernel is written in terms of `v_float32`/`vlanes()`, so the same binary runs at full width on VLEN 128/256/512/1024 with no recompile. No per-VLEN kernels.
- The six `C0=8` specialized kernels stay `#if !CV_SIMD_SCALABLE`-gated (they can't run at `C0=vlanes()`); on RVV all shapes use the generic kernel.
### Performance
Convolution throughput, blocked (this PR) vs scalar, same machine/engine (VLEN=256, 8× rv64 @ 2.4 GHz), GFLOP/s:
| layer | scalar 1T | this 1T | scalar 8T | this 8T | 8T speedup |
|---|---|---|---|---|---|
| 3×3, 256→256, 64² | 1.13 | 13.23 | 8.28 | 75.62 | **9.1×** |
| 1×1, 256→256, 64² | 1.08 | 11.73 | 8.02 | 71.70 | 8.9× |
| 3×3, 128→128, 128² | 1.10 | 13.02 | 8.45 | 89.14 | **10.6×** |
≈ **12× single-thread, 9–11× at 8 threads**. Convolution dominates CNN inference time, so this is a large end-to-end win on RVV hardware.
### Validation
K3-class RVV board, VLEN=256: `Test_ONNX_layers` **264/264**, `Test_ONNX_conformance` **1641/1641**, **0 assertions**. Output is bit-identical to the scalar path (same accumulation order, just vectorized over `K0`).
Vector-length-agnosticism confirmed by running the VLEN=256 binary unchanged at **VLEN=1024**.
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FileNode::operator double() and operator float() read an INT node via readInt()
(32-bit), truncating values above INT_MAX -- e.g. an integer 6662329666 from an
externally-produced json/yaml/xml is read back as -1927604926. The node stores
the value as int64 (operator int64_t() already reads it correctly via readLong),
so use readLong() for the floating-point conversions too. Values that fit in
int32 are unchanged (sign-extended); larger ones are now correct.
Reader side of #29363 (the writer side was #29364).
dnn: optimize blobFromImages using NEON intrinsics on ARM64 #29316
- This PR optimizes the HWC-to-NCHW conversion in blobFromImages() by adding NEON implementation.
- On x64, the MSVC compiler automatically vectorizes the existing scalar implementation, resulting in significantly better performance.
- On Windows ARM64, MSVC does not auto-vectorize this stride-3 access pattern, leading to a noticeable performance gap compared to x64.
- Added CV_NEON macro guard to ensure SIMD optimization is enabled on ARM64 architecture, leaving behavior on other platforms completely unchanged.
**Performance Improvements**
- This optimization significantly improves the performance of HWC-to-NCHW conversion on Windows ARM64.
<img width="823" height="254" alt="image" src="https://github.com/user-attachments/assets/84b22deb-22da-4607-8009-3405c2529b80" />
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Extended support for Resize and Split layers #29323
Merge with: https://github.com/opencv/opencv_extra/pull/1379
Key changes:
Resize layer:
_antialias_ (linear & cubic) :- PIL-style separable resampling with stretched filter support and edge-clamped, renormalized weights.
_axes_ (incl. reversed [3,2]) :- getOutShape, the scale override, and runtime _tf_crop_and_resize_ ROI parsing now map 2-element sizes/scales/roi by the axes order instead of assuming [2,3].
_keep_aspect_ratio_policy_ (not_larger/not_smaller) :- output size from min/max per-axis scale.
_half_pixel_symmetric_ :- new coordinate-transform mode + importer mapping.
_align_corners_ downsampling :- coordinate scale uses the unfloored scaled length (in−1)/(in·x_scale−1).
Split Layer:
_convertTo empty 1-D Mat:_ the empty-Mat branch collapsed a 1-D [0] to 2-D [1,0] via cv::Size(); now uses allowTransposed like the non-empty path.
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predictOptimalVectorWidth() built its vectorWidths table with 8 entries
(depths CV_8U..CV_16F), but checkOptimalVectorWidth() indexes it by depth.
The 5.x depths CV_16BF, CV_Bool, CV_64U, CV_64S and CV_32U therefore read
past the end of the array; the garbage value can slip past the ckercn <= 0
guard, and the divider normalization loop then shifts the divider to zero,
so "offsets[i] % dividers[i]" raises SIGFPE. The failure is allocation
dependent and shows up as a sequence-dependent crash, e.g. in the OpenCL
Norm tests for CV_32U (cv::norm on a UMat reaches this via ocl_sum, which
calls predictOptimalVectorWidth before its own depth guard bails out).
Size the table to CV_DEPTH_MAX so every depth is in bounds and map the new
fixed-size integer depths to their natural OpenCL vector widths; CV_16BF
has no OpenCL vector type and stays scalar. Add a regression test covering
all depths.
MlasThreadedBufAlloc() and its ThreadedBufHolder guarded the
_aligned_malloc / _aligned_free path on _MSC_VER. Non-MSVC Windows
toolchains (MinGW, clang) therefore fell through to posix_memalign(),
which the Windows CRT does not provide, so the build failed with
"'posix_memalign' was not declared in this scope".
Guard the aligned-allocation path on _WIN32 instead, in both the holder
declaration (mlasi.h) and definition (platform.cpp) so the unique_ptr
deleter type stays consistent across translation units, and include
<malloc.h> on Windows since MinGW only declares the _aligned_* functions
there. The non-Windows posix_memalign / aligned_alloc fallback is
unchanged.
Fixes#29350
- Replace the fixed-128-bit per-depth minMaxIdx kernels with a single templated universal-intrinsic core
- added simd dispatch AVX-512 dispatch
- A dual-accumulator inner loop added
Uses universal intrinsics, so all SIMD backends benefit.
Follow-up to the recent "reject under-length raw exif profile" change.
The declared length in a PNG raw exif profile (tEXt/zTXt "Raw profile type
exif" chunk) is fully attacker controlled. The < 6 guard stops the underflow,
but a large positive value still passes it and reaches HexStringToBytes(),
which does raw_data.resize(expected_length) before reading anything. So a few
bytes in a text chunk can force a ~2GB allocation -> easy memory-amplification
DoS while decoding an otherwise small PNG.
The payload is hex encoded (two characters per byte), so a valid length can
never exceed the profile text carrying it. Reject any declared length greater
than profile_len. This bounds the allocation to the input size and doesn't
reject any valid profile; the valid decode path is unchanged.
imgproc: optimized LUT and equalizeHist with SIMD #29250
- optimized lut with SIMD
- support equalizeHist with v_lut
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Improved color convert and AVX512 dispatch added #28992
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Optimization ArUco: get border errors in one pass for both normal and inverted markers #29329
This PR is a follow up to optimization in https://github.com/opencv/opencv/pull/29267.
## Context:
When detecting inverted markers, we call `_getBorderErrors` twice:
- `int borderErrors = _getBorderErrors(cellPixelRatio, ...);`
- `Mat invCellPixelRatio = 1.f - cellPixelRatio;`
- `int invBError = _getBorderErrors(invCellPixelRatio, ...);`
meaning:
1. Scan border cells once.
2. Allocate a new Mat.
3. Invert the entire `cellPixelRatio` matrix, not just the border.
4. Scan border cells again on the inverted matrix.
5. If inverted marker is better, copy the full inverted matrix back
## Solution
only call `_getBorderErrors` once and compute both
- borderErrors: `cellPixelRatio > validBitIdThreshold`
- invBorderErrors: `1 - cellPixelRatio > validBitIdThreshold`
This saves:
1. full invert into temporary Mat: (`Mat invCellPixelRatio = 1.f - cellPixelRatio;`)
2. scan temporary border: the second `_getBorderErrors`
3. copy temporary Mat back into `cellPixelRatio`: `invCellPixelRatio.copyTo(cellPixelRatio);`
Note that the solution does not implement:
```
if(params.detectInvertedMarker) {
_getBorderErrorsBoth(...);
} else {
borderErrors = _getBorderErrors(...);
}
```
because the extra cost to computing invBorderErrors: `if(1.f - ratio > validBitIdThreshold) invBorderErrors++;` is negligible. this also avoids having to maintain two functions: `_getBorderErrorsBoth` and `_getBorderErrors`
## Tests:
No visible results when testing the full marker detection pipeline because this step is not expensive. There is a roughly 8% noise when re-running the marker detections making it hard to spot small speed diffs.
When testing only this specific step with a float matrix of size: `(markerSize + 2*border)^2` on a AMD Ryzen 7 (8 cores / 16 threads):
- The new code is 30-45× faster at this stage depending on the marker size and whether the candidate was actually an inverted one, saving ~530-580 ns per candidate, mainly because we removed `Mat invCellPixelRatio = 1.f - cellPixelRatio`
- The new code is equivalent when detecting non-inverted markers +/- 3 ns
---
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Restricting the condition with _M_IX86/_M_X64 so it only applies to x86/x64 MSVC builds. MSVC ARM64 now falls through to the existing `#else` branch, which already has a portable CV_SIMD-based exp32f/exp64f implementation
**Performance Benchmarks:**
<img width="976" height="486" alt="image" src="https://github.com/user-attachments/assets/62daf2c3-34ac-4fc7-92d0-268073f746f3" />
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Fix ONNXRuntime dll path mismatch #29309
**1. C2664 build error in `net_impl_backend.cpp`**
`EnableProfiling()` expects `const wchar_t*` on Windows (`ORTCHAR_T`), but was passed `const char*`.
Fixed by converting to `std::wstring`, as suggested in #29278
---
**2. Wrong ORT DLL loaded at runtime (`modules/dnn/CMakeLists.txt`)**
With `DOWNLOAD_ONNXRUNTIME=ON`, the DLL glob only searched `bin/` but the downloaded package places DLLs in `lib/`. This left the build tree with no ORT DLL, causing Windows to fall back to the stale `System32\onnxruntime.dll` (1.17.1), crashing against the ORT 1.25.1 API.
Fixed by adding `lib/` as fallback and staging DLLs into the build bin directory at configure time.
Closes : #29278
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More use of AutoBuffer #28907
When possible, AutoBuffer should be faster than std::vector<>, and should not be worse if it requires a heap allocation rather than a stack allocation.
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dnn: fix new-engine block layout for RVV when VLEN > 128 #29304
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#### Problem
Fixes#28852. The new DNN engine packs activations in a blocked NCHWc layout with a fixed channel-block size `C0 = 8`. The blocked-layout kernels (MaxPool, AveragePool, BatchNorm, depthwise Conv, ConvTranspose, generic Conv) load one block into a SIMD vector and assert: `C0 == nlanes || C0 == nlanes*2 || C0 % (nlanes*4) == 0`
On RISC-V the universal intrinsics are **scalable with LMUL=2**, so `nlanes = (VLEN/32)*2` — **16 at VLEN=256, 64 at VLEN=1024** — which exceeds the fixed `C0=8`. The assertion fails with `-215` and the ONNX pooling/conv conformance tests abort. #29180 worked around it by disabling the RVV SIMD path in those kernels.
#### Fix
Make the block size track the hardware vector width on RVV instead of assuming 8:
- `net_impl.cpp`: `defaultC0 = max(8, VTraits<v_float32>::vlanes())` (16/64 on RVV).
- The fp32 blocked kernels read `C0` at runtime and size scratch buffers by `max_nlanes` (bounded by the compile-time `CV_RVV_MAX_VLEN`, default 1024).
- All changes are guarded by `CV_SIMD_SCALABLE`, so **x86/NEON are unchanged**.
- The int8 quantized kernels are hardwired to `C0=8` (VNNI/NEON packing + per-channel quant) and are scalar on RVV, so quantized graphs are pinned to `C0=8` in `prepareForInference()` (detected via a `CV_8S` arg); only fp32 graphs use the wider block.
#### Validation (native RISC-V)
I have already conducted tests on the Spacemit K3, covering both the X100 (VLEN=256) and the A100 (VLEN=1024).
`opencv_test_dnn`, ENGINE_AUTO (new engine), no assertions:
| VLEN | Test_ONNX_conformance | Test_ONNX_layers (conv/pool/depthwise/quantized/MobileNet_v4) |
|------|-----------------------|--------------------------------------------------------------|
| 256 | 1641 / 1641 | all pass (incl. `Quantized_Convolution`) |
| 1024 | 1641 / 1641 | all pass (incl. `Quantized_Convolution`) |
Reject initializers whose declared shape claims more elements than the tensor payload (raw_data or a typed *_data field) actually holds, before the Mat copy/convert reads them.
Fix remap nearest rounding #29145
Thanks, I checked the Windows x64 test logs. The imgproc failures are from the OpenCL `Remap_INTER_NEAREST` tests with `(16SC2, 16UC1)` maps. I missed the OpenCL fixed-point path in the first patch.
I updated `modules/imgproc/src/opencl/remap.cl` to use the same nearest rounding condition as the CPU path. This should address the OCL remap failures.
The `opencv_test_video` failure seems unrelated to this PR (`ECCfixtures/Video_ECC.accuracy/6` invalid memory access), since this PR only touches remap rounding in imgproc.
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Added harfbuzz; use it instead of STB to render text #29300
Merge with https://github.com/opencv/opencv_extra/pull/1378.
This is the next big step to improve font rendering in OpenCV 5.x. See #18760, #26301. See also OpenCV 5.0 release notes, where it was pointed out that complex scripts are not rendered correctly, and HarfBuzz integration is needed to fix it. So, here it is — HarfBuzz integration:
* Removed tweaked STB engine. STB truetype rendering engine was included into OpenCV 5.0-pre/5.0 and it was extended to instantiate and render variable fonts, but the support was incomplete and immature.
* Instead, we now use [HarfBuzz](https://github.com/harfbuzz/harfbuzz) — famous font shaping library and now also font rendering library, used by many big companies and organizations.
* As a result, we now correctly render complex scripts, such as arabic or devanagari, correctly handle font ligatures (ff, fi, ft etc.)
<img width="1300" height="600" alt="text_test" src="https://github.com/user-attachments/assets/a7d2c754-0fcf-40f8-8104-578f3a14850b" />
* Potentially, we could also support color emoji, but that would be a next step.
* As with STB-based engine, glyphs are cached (the same glyph from the same font is not rendered twice), so the performance should be on par with the previous engine.
* When OpenCV is built with `-DWITH_HARFBUZZ=ON`, which is set by default, it tries to detect HarfBuzz in the system and use it. If it's not detected, OpenCV builds our own small subset of HarfBuzz.
The following table compares size of libopencv_imgproc.dylib.5.0.0 (Release mode) in different configurations:
| Configuration | libopencv_imgproc (stripped) | delta vs 5.0 |
|----------------------------------------|------------------------------|----------|
| imgproc without text rendering (`-DWITH_HARFBUZZ=OFF`) | 4.27 MB | −2.35 MB |
| imgproc with stb-based engine (5.0) | 6.62 MB | 0.0 Mb |
| imgproc with HarfBuzz-based engine (this PR) | 7.07 MB | +0.45 MB |
The biggest source of the size increase when OpenCV is built with text rendering support (STB- or Harfbuzz-based) is that imgproc in this case includes .ttf fonts (Rubik and 'Wen Quan Yi Micro Hei'), which are gzip-compressed, but still have noticeable size, especially 'Wen Quan Yi' (~2Mb). HarfBuzz itself, compared to STB engine, adds just 0.45Mb, or ~7% to imgproc size. On other platforms (Linux x64, Windows), where IPP or other acceleration libraries are linked into libopencv_imgproc, the harfbuzz footprint is even less noticeable.
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imgcodecs(pam): fix out-of-bounds write when reading 2-channel PAM #29296
While poking at the PAM decoder I noticed basic_conversion() writes past the
end of the destination buffer when a GRAYSCALE_ALPHA (2 channel) image is read
with IMREAD_GRAYSCALE.
The 1-channel branch was written as if the destination had 3 channels:
for( ; s < end; d += 3, s += src_sampe_size )
d[0] = d[1] = d[2] = s[layout->graychan];
So for every source pixel it writes 3 bytes and advances d by 3, even though
the output row only has m_width bytes (1 channel). With m_channels == 2 that
ends up writing ~1.5 * m_width bytes per row, which runs off the row and, on
the last row, off the end of the Mat. m_width is taken straight from the file
header (up to 2^20), so the overflow size and contents are attacker controlled.
It's reachable with a plain imread(file, IMREAD_GRAYSCALE) on a crafted file.
While looking at it I also realised the loop bound was off for any multi
channel source: end was set to src + src_width, but the source has
src_width * channels samples, so it only ever processed m_width/channels pixels
instead of all of them. That's why GRAYSCALE_ALPHA / RGB_ALPHA come out only
partially filled.
Fix both at once:
- end now covers the whole row (src_width * src_sampe_size)
- the 1-channel branch writes one byte and advances d by 1
For the common grayscale->color case (channels == 1) the new end is identical
to the old one (m_width * 1 == m_width), so that path is byte for byte the same
and the existing PAM read_write test is unaffected. The only outputs that
change are the GRAYSCALE_ALPHA/RGB_ALPHA conversions, which were already broken.
Added a regression test (Imgcodecs_Pam.decode_graya_as_gray) that builds a small
2-channel PAM with an odd width, decodes it as grayscale and checks the result
matches the gray channel. It overflows/crashes on the old code and passes now.
fix: include ptcloud header and dependency for opengl_testdata_generator sample #29293
## Summary
Fixes the build failure in samples/opengl/opengl_testdata_generator.cpp on the 5.x
branch when building with BUILD_EXAMPLES=ON.
## Root cause
The sample uses `loadMesh`, `TriangleShadingType`, and `TriangleCullingMode`, all
declared in `modules/ptcloud/include/opencv2/ptcloud.hpp`. The sample did not
include this header, and `samples/opengl/CMakeLists.txt` did not list
`opencv_ptcloud` as a required dependency, so the ptcloud module headers/libs
were not available when compiling this sample.
## Changes
- Added `#include "opencv2/ptcloud.hpp"` to opengl_testdata_generator.cpp
- Added `opencv_ptcloud` to `OPENCV_OPENGL_SAMPLES_REQUIRED_DEPS` in
samples/opengl/CMakeLists.txt
## Acceptance criteria
- [x] opengl_testdata_generator.cpp compiles with BUILD_EXAMPLES=ON
- [x] ptcloud module dependency declared so build system links it correctly
Closes#29292
Fix typos in tutorial documentation #29299
This PR fixes spelling typos across several tutorial documentation files (dnn, calib3d, objdetect, app, js_tutorials), including `export=dowload` -> `export=download` in the DNN text-spotting tutorial's Google Drive URLs.
Documentation-only change; no functional code is affected.
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[x] Documentation-only change; no accuracy/performance tests or opencv_extra patch are applicable.
objc: Fix VolumeType enum name clash on MacOS #29262
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**Problem**
When the ObjC wrapper is generated, the global enum `cv::VolumeType` from the ptcloud module is emitted as `typedef NS_ENUM(int, VolumeType)`. macOS defines `typedef OSType VolumeType` in CoreServices, which leads to a typedef-redefinition error during the framework build.
**Fix**
`add_enum()` in `gen_objc.py` now handles namespace-global enums by falling back to a module-level lookup (`self.Module`) when the enum has no enclosing class. A new `gen_dict.json` entry maps `VolumeType` -> `PtcloudVolumeType`, and the import generation automatically uses the renamed enum.
Fixes#29260
doc: remove duplicated bib #29298
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When computing TM_SQDIFF for 8-bit images, crossCorr stores intermediate
results in CV_32F which loses precision for large patch sums (e.g. 25x25
patch of value 255 gives sum=40603125, exceeding float32's ~7 significant
digits). This causes the final SQDIFF result to have incorrect non-zero
values even when image and template are identical.
Fix: use a CV_64F intermediate buffer for crossCorr and common_matchTemplate
when depth==CV_8U and method is TM_SQDIFF or TM_SQDIFF_NORMED, then convert
back to CV_32F for the final output.
Fixes#21786
Improving Parallelism and Memory Access Efficiency for Hough Transform Accumulator #29059
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# [Optimization] Improving Parallelism and Memory Access Efficiency for Hough Transform Accumulator
## Overview / Motivation
The original implementation mainly rely on single‑threaded nested loops, which do not fully utilize the computational power of modern multi‑core CPUs. There is also room for improvement in memory access patterns and branch prediction. **Without altering any core mathematical logic or ensuring result consistency**, this work improves the execution efficiency of these two processes by introducing `cv::parallel_for_` and refactoring data access patterns.
---
## Detailed Changes
* **Original Code Analysis**: The original code uses a double loop – the outer loop scans the image for edge points (`if image[...] != 0`), and the inner loop iterates over all angles, computes the radius, and writes to the global `accum` array. This pattern is hard to parallelize directly because different threads writing to the same accumulator would cause **data races**. Moreover, the sparse conditional branch inside the loop is extremely unfriendly to CPU branch prediction.
* **Optimization Strategy (Sparse Extraction → Parallel over Angles)**:
1. **Sparse Feature Extraction**: First, pre‑scan the image in a single thread, extract the coordinates (`x`, `y`) and values of all non‑zero pixels (edge points) into a contiguous `std::vector` (pre‑allocate 10% of the image size to reduce dynamic reallocation overhead).
2. **Switch the Parallel Dimension**: Instead of splitting the image region, change the parallelization dimension to **angle (`numangle`)**. Use `cv::parallel_for_` to assign different angle ranges to different threads.
3. **Conflict‑free Memory Writes**: Because the `accum` array is strictly partitioned by angle `n` in memory, threads assigned to different angles write to completely non‑overlapping memory blocks in the global `accum`.
---
## Correctness Testing
Correctness tests are based on the provided `opencv_test`. For the Hough tests, no failing test cases were observed.
---
## Performance Benchmarks
> The following tests were run on Intel(R) Core(TM) i9‑14900HX @ 2.2GHz / Windows 11 (WSL: Ubuntu) with GCC 13.3.0.
Using `opencv_perf` tests that include Hough, the results are as follows:
| Test Case | 1st mean | 5th mean | Diff |
|-----------|----------|----------|------|
| `PerfHoughCircles.Basic` | 4.75 | 4.91 | +3.4% |
| `PerfHoughCircles2.ManySmallCircles` | 56.76 | 59.37 | +4.6% |
| `PerfHoughCircles4f.Basic` | 4.14 | 4.03 | -2.7% |
#### II. `OCL_HoughLines` (Standard Hough Line, OCL wrapper, actually executed on CPU)
| Parameters (size, rho, theta) | 1st mean | 5th mean | Diff |
|-------------------------------|----------|----------|------|
| (640×480, 0.1, 0.01745) | 2.57 | 3.21 | +24.9% |
| (640×480, 1, 0.1) | 0.18 | 0.21 | +16.7% |
| (1920×1080, 0.1, 0.01745)|12.27 |15.55 | +26.7% |
| (1920×1080, 1, 0.1) | 0.69 | 0.99 | +43.5% |
| (3840×2160, 0.1, 0.01745)|28.43 |34.78 | +22.3% |
| (3840×2160, 1, 0.1) | 2.15 | 3.51 | +63.3% |
#### III. `OCL_HoughLinesP` (Probabilistic Hough Line)
| Image and parameters (rho, theta) | 1st mean | 5th mean | Diff |
|------------------------------------|----------|----------|------|
| pic5.png, 0.1, 0.01745 | 1.20 | 1.22 | +1.7% |
| pic5.png, 1, 0.01745 | 1.03 | 0.92 | -10.7% |
| a1.png, 0.1, 0.01745 | 8.71 | 9.32 | +7.0% |
| a1.png, 1, 0.01745 | 6.29 | 6.68 | +6.2% |
#### IV. Standard Hough Line (CPU implementation, non‑OCL) `Image_RhoStep_ThetaStep_Threshold_HoughLines`
| Parameters (image, rho, theta, thresh) | 1st mean | 5th mean | Diff |
|-----------------------------------------|----------|----------|------|
| pic5.png, 1, 0.01, 0.5 | 2.19 | 0.82 | **-62.6%** |
| pic5.png, 10, 0.01, 0.5 | 2.08 | 0.53 | **-74.5%** |
| pic5.png, 1, 0.1, 0.5 | 0.24 | 0.15 | -37.5% |
| a1.png, 1, 0.01, 0.5 | 16.06 | 2.65 | **-83.5%** |
| a1.png, 10, 0.01, 0.5 | 17.05 | 1.92 | **-88.7%** |
| a1.png, 1, 0.1, 0.5 | 1.82 | 1.00 | -45.1% |
#### V. Floating‑Point Hough Line (`...HoughLines3f`)
| Parameters (image, rho, theta, thresh) | 1st mean | 5th mean | Diff |
|-----------------------------------------|----------|----------|------|
| pic5.png, 1, 0.01, 0.5 | 1.97 | 0.86 | **-56.3%** |
| pic5.png, 10, 0.01, 0.5 | 1.66 | 0.54 | **-67.5%** |
| a1.png, 1, 0.01, 0.5 | 18.52 | 2.59 | **-86.0%** |
| a1.png, 10, 0.01, 0.5 | 16.30 | 2.05 | **-87.4%** |
| a1.png, 1, 0.1, 0.5 | 1.72 | 0.97 | -43.6% |
### Conclusion
- **Circle detection, probabilistic Hough line**: Differences between the two runs are very small (within ±11%), indicating stable performance.
- **`OCL_HoughLines`**: The parallel test is generally 10%~63% slower than the serial one, but still within a reasonable fluctuation range. Special handling was attempted but did not bring significant improvement and slightly reduced the gains in the subsequent two groups, so it was left unchanged after comprehensive consideration.
- **CPU standard Hough line (non‑OCL)**: The parallel test is significantly faster than the serial one (up to 88% faster), demonstrating that parallel optimization can bring obvious improvements.
By the way at last, I am extremely grateful to the maintainers for helping me test the last PR. However, since I was unable to obtain the development board, I was unable to continue modifying the previous RVV optimization.
Fix speed regression in Aruco identify #29267
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samples: add color-coded bounding boxes for multi QR code detection #29281
Improves the existing qrcode.py sample by adding color-coded bounding boxes when multiple QR codes are detected simultaneously.
Previously all QR codes were drawn with the same green color, making it hard to distinguish between them visually.
This change adds a QR_COLORS palette so each detected QR code gets a unique color, improving visual clarity for multi-QR detection.
Co-authored-by: Ayush Gupta <gupta.ayushg@gmail.com>
core(opengl): fix memory leak in OpenCL extensions gathering #28981
## Problem
Fixes#28980
In `modules/core/src/opengl.cpp`, inside `initializeContextFromGL()`, a `char[]` buffer is allocated to query OpenCL device extensions:
```cpp
extensions = new char[extensionSize];
status = clGetDeviceInfo(..., extensions, &extensionSize);
if (status != CL_SUCCESS)
continue; // leaks `extensions`
```
When `clGetDeviceInfo()` fails, `continue` skips the corresponding `delete[]` on the success path, causing the allocated buffer to leak.
Additionally, the `catch (...)` block also bypasses cleanup, so any thrown exception leaks the buffer as well.
## Fix
Replace the raw `char*` allocation with `std::unique_ptr<char[]>`.
This ensures the buffer is automatically released on all exit paths, including:
- normal execution
- early `continue`
- exception handling paths
## Checklist
- [x] I agree to contribute to the project under the Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on code under GPL or another license incompatible with OpenCV.
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- [x] Accuracy/performance tests and test data in `opencv_extra` are included, if applicable.
- [x] The feature/change is documented and sample code can be built with the project CMake configuration, if applicable.
imgproc: optimize EMD (Earth Mover's Distance) solver performance #29194
### imgproc: optimize EMD solver performance using O(V) spanning tree traversal
---
This PR significantly optimizes the performance of the Earth Mover's Distance (`cv::EMD`) solver in `modules/imgproc/src/emd_new.cpp`.
Specifically, it refactors the dual-variable calculation in `EMDSolver::findBasicVars()` from a naive $O((N+M)^2)$ linked-list scanning approach to an optimized $O(N+M)$ BFS tree traversal utilizing existing adjacency lists, leading to a massive speedup.
---
#### Technical Details & Core Bottleneck Fixed
1. **Algorithmic Complexity Reduction in `findBasicVars()`:**
- **Before**: The original implementation solved the dual variables $u_i$ and $v_j$ by traversing the entire unmarked rows (`u0_head`) or columns (`v0_head`) linked-lists and invoking `getIsX(i, j)` inside nested loops to find connected basic variables. This resulted in an $O((N+M)^2)$ complexity per simplex iteration. For a scale of $2000 \times 2000$, this performed up to $16,000,000$ operations per iteration.
- **After**: Since `EMDSolver` already maintains the adjacency lists of the basic variables tree (`rows_x` and `cols_x`), we can traverse the spanning tree in linear time. This PR implements a dual-queue BFS tree traversal. The complexity per iteration is drastically reduced to $O(N+M)$, performing at most $4000$ operations per iteration.
2. **Cache Locality & Pointer-Chasing Elimination:**
- Replaced pointer-chasing on dynamically-allocated linked lists with contiguous, stack-allocated array queues (`cv::AutoBuffer`), significantly improving CPU L1/L2 cache hit rates and enabling hardware prefetching.
---
#### Performance Benchmarks
Below is the benchmark comparison evaluated on a standard CPU.
#### Test 1: dims = 64
| Scale ($N, M$) | Original EMD (ms) | Optimized EMD (This PR) | Speedup |
| :--- | :--- | :--- | :--- |
| **100** | 5.473 | 3.419 | **1.60x** |
| **500** | 381.871 | 244.355 | **1.56x** |
| **1000** | 1893.053 | 1369.016 | **1.38x** |
| **2000** | 11387.792 | 8331.221 | **1.37x** |
#### Test 2: dims = 3
| Scale ($N, M$) | Original EMD (ms) | Optimized EMD (This PR) | Speedup |
| :--- | :--- | :--- | :--- |
| **100** | 4.433 | 3.042 | **1.46x** |
| **500** | 365.762 | 259.735 | **1.41x** |
| **1000** | 1989.400 | 1421.952 | **1.40x** |
| **2000** | 12731.836 | 7952.210 | **1.60x** |
*(Note: The exact performance figures may vary slightly depending on the compiler and test machine.)*
---
#### Verification
- All existing tests in `opencv_test_imgproc` (including EMD tests) pass successfully. No regressions were introduced.
---
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch
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The relative neighborhood graph (RNG) is a subgraph of the Delaunay
triangulation, so only Delaunay edges are candidates for RNG membership.
The previous implementation tested all N^2 pairs against all N points,
giving O(N^3). The new implementation builds the Delaunay triangulation
with Subdiv2D (O(N log N)), then checks only those ~3N edges for the RNG
lune-emptiness condition, reducing computeRNG to O(N^2) in the worst case
and much better in practice for regular grids.
Added synthetic-grid accuracy tests for both symmetric and asymmetric
patterns across several sizes, and a perf test parameterized by grid size.
core: fix inverted continuity check in cvReshapeMatND() #29132
### Pull Request Readiness Checklist
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- [√] I agree to contribute to the project under Apache 2 License.
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objc: fix conflicting Swift name for LightGlueMatcher.create #29257Fixes#29256
`cv::LightGlueMatcher` (a `DescriptorMatcher` subclass) declares a static
`create(const String& modelPath, ...)` overload. The Objective-C bindings
generator emits it as the selector `+create:`, which is the same selector as
the inherited `DescriptorMatcher::create(MatcherType)` but carries a different
`swift_name`, so clang refuses to build the `opencv2` Objective-C module:
```
LightGlueMatcher.h: error: 'swift_name' and 'swift_name' attributes are not compatible
```
Wrap the file-path overload with `CV_WRAP_AS(createFromFile)` so it gets a
distinct selector, mirroring the sibling `createFromMemory` overload, while
keeping the Swift name `create(modelPath:...)`.
### Pull Request Readiness Checklist
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imgproc: fix missing tipLength validation in arrowedLine #29191
### Pull Request Description
**Issue/Bug:**
Currently, the `cv::arrowedLine` function in `modules/imgproc/src/drawing.cpp` lacks boundary validation for the `tipLength` parameter. Passing an invalid ratio (e.g., negative values or values > 1.0) leads to mathematically distorted arrowhead coordinates being silently passed to the underlying `line()` drawing function, resulting in severely deformed output without any warning.
**Solution:**
Added a `CV_Assert` to enforce the geometric contract of `tipLength`, ensuring it strictly falls within the logical bounds `(0.0, 1.0]`. This adheres to the fail-fast principle and prevents silent geometric failures.
**Testing:**
Successfully built the project locally and added a dedicated regression test (`arrowedLine_tipLength_validation`) using Google Test in `modules/imgproc/test/test_drawing.cpp`. Tested with boundary values (e.g., `tipLength = 1.5` and `-0.5`), and the assertion correctly intercepts the invalid parameters before rasterization.
*(Note: This contribution is part of a university engineering project evaluating open-source workflow and C++ robustness.)*
On 5.x branch, commit c3ca3f4f was accidentally reverted, so the
external KleidiCV will be silently disabled. Revert these changes to
support external KleidiCV builds.
Signed-off-by: Weizhao Ouyang <o451686892@gmail.com>
OpenCV requires C++17, and bundled 3rdparty libraries such as KleidiCV
explicitly request CXX_STANDARD 17. With the CMake Xcode generator,
however, neither the global CMAKE_CXX_STANDARD nor per-target
CXX_STANDARD is emitted as CLANG_CXX_LANGUAGE_STANDARD in the generated
project, so those sources are compiled at the toolchain default
(pre-C++17) and fail, e.g.:
kleidicv/include/kleidicv/traits.h: error: no template named
'is_base_of_v' in namespace 'std'; did you mean 'is_base_of'?
Set CLANG_CXX_LANGUAGE_STANDARD=gnu++17 at the project level via
CMAKE_XCODE_ATTRIBUTE_* in the shared getCMakeArgs(), so every target in
the generated Xcode project (including 3rdparty subprojects) builds with
C++17.
Enable collectContours in detect_blob.cpp and draw the per-blob
contours returned by getBlobContours(), so the sample exercises the
public contour-collection API added in #21942.
Refs #25904
The first conversion to Lab or Luv lazily builds a softfloat lookup table, and that build was doing about 108000 software emulated pow calls plus a fully serial 33x33x33 cube loop. This memoizes applyGamma over the 33 grid values and runs the cube loop with parallel_for_, cutting the first call from around 110 ms to around 25 ms on my machine. The per point computation is unchanged so the tables stay bit exact.
imgproc: fix INTER_NEAREST_EXACT to match Pillow (5.x retarget of #28661) #28873
## Summary
Fixes `INTER_NEAREST_EXACT` producing different results from Pillow for images with dimensions that are multiples of 64 (e.g., 128, 192).
Retargets #28661 to 5.x per core team request.
## Root cause
The old 16-bit fixed-point arithmetic (`ifx`, `ifx0`, `>> 16`) rounds differently from Pillow's pixel-center coordinate mapping. At exact pixel boundaries (e.g., y=7 with 128 to 160: `0.4 + 7*0.8 = 5.999999999999999`), Pillow's truncation drops to 5 while the old opencv formula rounds to 6.
## Changes
- `resize.cpp`: Replace `ifx/ifx0/ify/ify0` 16-bit fixed-point with an int64 arithmetic formula equivalent to `floor((i + 0.5) * src / dst)`, computed as `((int64_t)(i * 2 + 1) * src) / (dst * 2)`. This matches Pillow's pixel-center mapping exactly, is deterministic across platforms, and avoids FP rounding divergence entirely.
- `test_resize_bitexact.cpp`: Add `NearestExact_PillowCompat` covering 4 dimension pairs including the reproducer from #28429.
## Testing
- All 3 `Resize_Bitexact` tests pass (Linear8U, Nearest8U, NearestExact_PillowCompat) on this branch built against 5.x
- Verified bit-exact match to PIL output (Python 3.14, Pillow) on 49 dimension combinations including random pairs
- Built on macOS ARM64 with HAL (carotene/KleidiCV) active
Fixes#28429
[GSOC] feat: Add ALIKED feature extractor and LightGlue matcher with DNN integration #28986
## PR Description
### Summary
Integrate ALIKED and LightGlue into OpenCV's `features` module as native`Feature2D` and `DescriptorMatcher` implementations, enabling end-to-end neural feature matching within OpenCV's ecosystem.
---
### What's included
#### New classes
- **`cv::ALIKED`** extends `Feature2D`
- CNN-based keypoint detection
- 128-D descriptor extraction via ONNX Runtime
- **`cv::LightGlueMatcher`** extends `DescriptorMatcher`
- Deep feature matching with spatial context
- Uses keypoints and image sizes during matching
---
#### API design
- Standard OpenCV patterns:
- `detectAndCompute()`
- `match()`
- `knnMatch()`
- Multiple factory methods:
- ONNX model path
- In-memory model buffer
- Pre-loaded `dnn::Net`
- `Params` structs use `CV_EXPORTS_W_SIMPLE`
for Python/Java bindings support
- Optional DNN dependency:
- `HAVE_OPENCV_DNN` guards
- Stub implementations throw `StsNotImplemented`
---
### Files added
| File | Description |
|------|-------------|
| `src/feature2d_aliked.cpp` | ALIKED implementation |
| `src/matchers_lightglue.cpp` | LightGlueMatcher implementation |
| `src/aliked_context.hpp` | Shared internal context struct |
| `test/test_aliked_lightglue.cpp` | Unit tests (9 test cases) |
| `samples/cpp/example_features_aliked_lightglue.cpp` | Demo application |
---
### Files modified
- `CMakeLists.txt`
- Add `opencv_dnn` as optional dependency
- `features.hpp`
- Add ALIKED and LightGlueMatcher declarations
- `precomp.hpp`
- Add DNN include guard
---
### Usage
```cpp
// Feature extraction
Ptr<ALIKED> aliked =
ALIKED::create("aliked-n16rot-top1k-640.onnx");
vector<KeyPoint> kpts;
Mat descs;
aliked->detectAndCompute(image, Mat(), kpts, descs);
// Feature matching
Ptr<LightGlueMatcher> lg =
LightGlueMatcher::create("aliked_lightglue.onnx");
lg->setPairInfo(
kpts1Mat,
kpts2Mat,
img1.size(),
img2.size()
);
vector<DMatch> matches;
lg->match(descs1, descs2, matches);
````
please refer to samples/cpp/example_features_aliked_lightglue.cpp
---
### Test plan
* Build with `BUILD_LIST=features,dnn`
* Build without DNN:
* Verify stubs compile
* Verify `StsNotImplemented` is thrown
* Run:
* `ctest -R Features2d_ALIKED`
* `ctest -R Features2d_LightGlueMatcher`
* Run sample application with:
* Real images
* Real ONNX models
* Verify Python/Java bindings compile and work
---
### Related
Phase 1 of the
"End-to-End AI Feature Extraction and LightGlue Matching Pipeline"
GSoC project.
Designed to be extensible to:
* XFeat
* SuperPoint
* Other neural feature extractors
### test dependency
Depends on the opencv_extra PR adding ALIKED and LightGlue test models:
- [opencv_extra PR](https://github.com/opencv/opencv_extra/pull/1366)
This PR adds the following ONNX models to `download_models.py`:
- `aliked-n16rot-top1k-640.onnx`
- `aliked_lightglue.onnx`
These models are required for the `features2d` tests in the main OpenCV repository to validate the ALIKED and LightGlue feature extraction and matching pipeline.
### Pull Request Readiness Checklist
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[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220
### Pull Request Readiness Checklist
This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Inverted imgproc-geometry dependency and moved more functions to geometry #29230
Fixes: https://github.com/opencv/opencv/issues/20267
Continues: https://github.com/opencv/opencv/pull/29175
OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4137
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1377
Summary:
- LSD returned back to imgproc
- drawing functions moved to imgproc
- undistort image and related perf-pixel functions moved to imgproc
- moments moved to geometry
- estimateXXXtransform moved to geometry
After the patch the geometry module depends on code and Flann and may be used everywhere without potential circular dependencies
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Update BarcodeDetector super-resolution API to use single-file ONNX #29227
### Pull Request Readiness Checklist
This PR updates the `BarcodeDetector` super-resolution API to support and utilize a single-file ONNX model format.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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little but important fix in bicubic warping 1-channel case #29229
merge together with https://github.com/opencv/opencv_contrib/pull/4136
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Fix MLAS 32-bit x86 build by integrating missing upstream assembly files #29226
### Pull Request Readiness Checklist
This resolves the 32-bit x86 build failure for MLAS. Related to: https://github.com/opencv/opencv/pull/29218
* Added the required `x86` assembly files and headers from upstream.
* Added `__x86_64__` guards around the AMX `syscall` and the FMA3/AVX512F kernel assignments to prevent 32-bit compilation crashes.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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[OpenCV 5] Fix apriltag corners order and update the doc #28823
I have updated documentation for the AprilTag dictionaries.
Corresponding issues:
- https://github.com/opencv/opencv-python/issues/1195
- https://stackoverflow.com/questions/79044142/why-is-the-order-of-the-incoming-corners-different-between-apriltag-and-aruco-ma
This is a breaking change and is targeted only for OpenCV 5.
---
I have updated the ArUco doc with more information about fiducial markers detection.
I have tried to add some recommendations, best practices:
- `DICT_ARUCO_MIP_36h12` should be the recommended family, [see](https://stackoverflow.com/a/51511558)
- link to download pregenerated markers for `MIP_36h12` is [here](https://sourceforge.net/projects/aruco/files/. I have not found some other official links for the other ArUco family, but since `MIP_36h12` should be used, I guess it is fined.
- recommendation to have a white border when printing the marker
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Dedicated pointcloud module #29224
OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4134
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Fixed Out-of-Memory issue and added VLM sample #29221
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Added DISK feature extractor support #29073
closes: https://github.com/opencv/opencv/issues/27083
Merge with: https://github.com/opencv/opencv_extra/pull/1368/
### Pull Request Readiness Checklist
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Caffe importer cleanup #28678
Merge with: https://github.com/opencv/opencv_extra/pull/1324
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Fixed the new chessboard detector by not using OpenCL #29211
This should hopefully fix test failures on macOS x64 builder in the latest 5.x branch.
The code of Chessboard detector (namely, the FastX part) runs tons of different-size box filters on the same image. Optimally, those filters need to be computed all together using once-computed integral image, but instead those multiple box filters, especially given how the current opencl path in box filter is organized, seem to 'overload' our OpenCL cache system and it breaks, at least on macOS. Given that boxfilter is relatively cheap operation, it will unlikely loose much by running on CPU vs GPU. So this is the current solution - switch to CPU path there. Local tests show that even on Apple M4 Max with very fast GPU we get better execution speed on CPU than on GPU.
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Revised bicubic interpolation kernels and updated the corresponding functions #29165
They should work more accurately and faster than the existing implementation. The PR follows the previous work (#26271 etc.) that brought faster bilinear kernels.
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Make cv::VideoCapture::get return cv::CAP_PROP_UNKNOWN (-1) for unsupported properties #21337
The return value indicating an unsupported property is not consistent across backends.
I stumbled on this issue because my code was determining if a property value is valid if it's non-zero (like the documentation says), which worked fine on macOS, but not on Android.
For example, auto-exposure is not supported on macOS, so get() returns 0. But it is supported on Android and 0 means auto-exposure is off.
I think -1 is the better return value to indicate unsupported properties.
I made changes to all the backends. I think I got every case. This breaks API compatibility for some backends, so I based this on branch 3.4.
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Homogeneize some calib/3d behavior #29077
- use CV_Check to validate input sizes (thus throwing for invalid inputs)
- return bool to validate a function result
This fixes#22746
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OpenCV New Documentation #29206
This PR introduces end to end working new Documentation in OpenCV.
Co-authored by: @abhishek-gola @omrope79 @Akansha-977 @Prasadayus @varun-jaiswal17
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Defer the u8→i16 widening (vzext) in the pre-screen phase until after
the quick-reject check passes. This avoids 5 unnecessary widen
operations per pixel-strip when the pre-screen rejects (which happens
~70-80% of the time at the default threshold=20).
The approach uses u8mf2 (same VL as i16m1) for the pre-screen
comparison with vssubu/vsaddu for bounds and vmsgtu for unsigned
comparison. When the pre-screen passes, the already-loaded u8mf2
variables are widened to i16m1 for the score computation, reusing
the same data without redundant memory loads.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
WeChatQR-fix conversion Caffe to ONNX #28746
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The is_simple check used mask.empty() after the default mask
had already been created, making it always false. This caused
the fast path (floodFill_CnIR) to be dead code — every call
went through the slower gradient-based path (floodFillGrad_CnIR).
Fix: save _mask.empty() result before auto-creating the default
mask and use it for the is_simple check.
Reenable ICV and fix#29166#29183
Fixing https://github.com/opencv/opencv/issues/29166 and return back the 2026.0.0 ICV.
Tests are passed locally on Windows machine. Feel free to check if they are passed in bigger test systems.
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Temporarily disabled RVV intrinsics in several layers of the new DNN tested on musebook K1.
Kernels will be re-enabled when we find some good solution for the current 'm2' issue in rvv_scalable intrinsics.
This should fix#28852
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Update default YuNet model to new dynamic inputs #29107
Update the default model in `face_detect.py` and `face_detect.cpp` to
`face_detection_yunet_2026may.onnx`, which has symbolic `height`/`width` input dims.
## Changes
- `samples/dnn/face_detect.py`: update default `--face_detection_model` to `face_detection_yunet_2026may.onnx`
- `samples/dnn/face_detect.cpp`: update default `fd_model` to `face_detection_yunet_2026may.onnx`
Companion PR :
- https://github.com/opencv/opencv_zoo/pull/310
- https://github.com/opencv/opencv_extra/pull/1373
Closes : https://github.com/opencv/opencv/issues/28769
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[FOLLOW UP] Update documentation for Imgproc, 3d, and App modules tutorials #29102
This PR is a follow-up to https://github.com/opencv/opencv/pull/29091 and https://github.com/opencv/opencv/pull/29100, continuing the series of documentation updates for the Imgproc, 3d, and App modules tutorials.
To be merged after: https://github.com/opencv/opencv/pull/29091 and https://github.com/opencv/opencv/pull/29100
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imgcodecs: fix Apple conversions build without AVFoundation #29138Fixes#28553.
This PR fixes Apple imgcodecs conversion builds when AVFoundation is disabled or unavailable on older macOS SDKs.
Changes:
- Guard AVFoundation import with HAVE_AVFOUNDATION.
- Use older macOS-compatible framework imports when ImageIO is unavailable.
- Add __bridge compatibility for older Objective-C++ compilers.
- Use NSMakeSize for older macOS instead of passing CGSize to NSImage API.
Testing:
- Not tested locally: no macOS environment available.
- Patch follows the fix confirmed in #28555 discussion.
Fixing python wrapper around ECCParameters to include itersPerLevel #29148
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Moved geometry transformations from imgproc to 3d, future geometry module #29101
The first step of 2d geometry operations migration to the future geometry module.
I created 2d.hpp to isolate the moved functions for now. I propose to create geometry.hpp when the module is renamed and include all things there.
OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4126
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[FOLLOW UP] Update documentation for core, calib3d, features, and introduction modules #29100
### Pull Request Readiness Checklist
This PR is a follow-up to #29091, continuing the series of documentation updates for the core, calib3d, features, and introductory modules.
To be merged after: #29091
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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New sphinx-documentation build #29091
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### Summary
This PR improves SIMD coverage for masked `cv::accumulate()` on 4-channel images.
The existing masked accumulate SIMD implementation has special paths for `cn == 1` and `cn == 3`, while 4-channel images fall back to the general implementation. This PR adds `cn == 4` SIMD paths using OpenCV Universal Intrinsics.
Compare link:
https://github.com/opencv/opencv/compare/4.x...feitianduowen:opencv:4.x
Modified files:
- `modules/imgproc/src/accum.simd.hpp`
- Add masked `cn == 4` SIMD paths for:
- `CV_32FC4 -> CV_32FC4`
- `CV_8UC4 -> CV_32FC4`
- `modules/video/test/test_accum.cpp`
- Add deterministic correctness regression tests for the new masked 4-channel accumulate paths.
- `modules/imgproc/perf/perf_accumulate.cpp`
- Add performance coverage for masked 4-channel accumulate.
imgproc: add SIMD paths for masked 4-channel accumulate #29094
### Implementation
The new SIMD branches are added in:
- `modules/imgproc/src/accum.simd.hpp`
They handle:
- `src`: `CV_32FC4`, `dst`: `CV_32FC4`, `mask`: `CV_8UC1`
- `src`: `CV_8UC4`, `dst`: `CV_32FC4`, `mask`: `CV_8UC1`
For the `CV_32FC4` path, the implementation uses `v_load_deinterleave`, applies the pixel mask to all 4 channels, accumulates into `dst`, and stores the result with `v_store_interleave`.
For the `CV_8UC4 -> CV_32FC4` path, the implementation loads 4 interleaved uchar channels, applies the pixel mask, widens the values to float, accumulates into `dst`, and stores the results back as interleaved `CV_32FC4`.
The masked SIMD block is guarded with:
```cpp
if (x <= len - cVectorWidth)
{
...
}
```
This check ensures that SIMD setup and vector operations are only executed when at least one full vector iteration can run. For very small inputs or tail-only cases, the function skips SIMD initialization and lets the existing scalar fallback handle the data through `acc_general_(src, dst, mask, len, cn, x)`.
This keeps tail handling unchanged and avoids unnecessary SIMD initialization when the vector loop would be skipped.
### Accuracy tests
Added a deterministic regression test in:
* `modules/video/test/test_accum.cpp`
New test:
```bash
Video_Acc.accuracy_32FC4_masked_cn4
Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
```
Test commands:
```bash
opencv_test_video --gtest_filter=Video_Acc.accuracy_32FC4_masked_cn4
opencv_test_video --gtest_filter=Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
opencv_test_video --gtest_filter="Video_Acc.*:Video_AccSquared.*:Video_AccProduct.*:Video_RunningAvg.*"
```
The filtered accumulate-related tests passed locally.
I added dedicated deterministic tests instead of only extending the existing randomized accumulate tests because this PR only changes the masked `cv::accumulate()` paths for specific 4-channel type combinations. It does not add `cn == 4` SIMD coverage for `accumulateSquare`, `accumulateProduct`, or `accumulateWeighted`.
The existing base accumulate tests are shared by several accumulation functions. Extending the common randomized channel selection to include `cn == 4` would also affect tests for functions that are not optimized by this PR. The new deterministic tests directly cover the modified paths:
* `cv::accumulate(src, dst, mask)`
* `CV_32FC4 -> CV_32FC4`
* `CV_8UC4 -> CV_32FC4`
* `mask`: `CV_8UC1`
The test also covers small sizes and non-vector-multiple sizes, so both the new SIMD path and the existing scalar tail fallback are exercised.
### Performance tests
Added performance coverage in:
* `modules/imgproc/perf/perf_accumulate.cpp`
Perf command:
```bash
opencv_perf_imgproc --gtest_filter="*AccumulateMask32FC4*" --perf_min_samples=1000 --perf_force_samples=1000
opencv_perf_imgproc --gtest_filter="*AccumulateMask8UC4To32FC4*" --perf_min_samples=1000 --perf_force_samples=1000
```
Test environment:
* Release build
* AVX2 enabled
* IPP disabled
* OpenCL disabled
* Windows MinGW build
Performance results:
`CV_32FC4 -> CV_32FC4`
| Size | Before median | After median | Speedup |
| --------- | ------------- | ------------ | ------- |
| 1920x1080 | 3.07 ms | 2.74 ms | 1.12x |
| 1280x720 | 0.84 ms | 0.73 ms | 1.15x |
| 640x480 | 0.17 ms | 0.16 ms | 1.06x |
| 320x240 | 0.04 ms | 0.04 ms | ~1.00x |
`CV_8UC4 -> CV_32FC4`
| Size | Before median | After median | Speedup |
| --------- | ------------- | ------------ | ------- |
| 1920x1080 | 6.09 ms | 1.75 ms | 3.48x |
| 1280x720 | 2.69 ms | 0.82 ms | 3.28x |
| 640x480 | 0.96 ms | 0.28 ms | 3.43x |
| 320x240 | 0.24 ms | 0.06 ms | 4.00x |
| 127x61 | 0.02 ms | 0.01 ms | 2.00x |
### Build and test commands
The following commands were run from Windows `cmd.exe`.
```sh
cmake -S . -B build_release -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DBUILD_PERF_TESTS=OFF -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_release --target opencv_test_video -j 4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.accuracy_32FC4_masked_cn4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.*:Video_AccSquared.*:Video_AccProduct.*:Video_RunningAvg.*
```
Accuracy test commands passed locally.
For the performance comparison, I kept the new perf test in `modules/imgproc/perf/perf_accumulate.cpp` and only reverted `modules/imgproc/src/accum.simd.hpp` to measure the baseline. Then I restored the SIMD patch and measured the optimized version.
Baseline measurement:
```sh
git diff -- modules/imgproc/src/accum.simd.hpp > accum_cn4_simd.patch
git checkout -- modules/imgproc/src/accum.simd.hpp
mkdir perf_logs
cmake -S . -B build_perf_before -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign > perf_logs\before_cmake_config.txt 2>&1
cmake --build build_perf_before --target opencv_perf_imgproc -j 4 > perf_logs\before_build.txt 2>&1
build_perf_before\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\before_perf.txt 2>&1
```
```sh
git diff -- modules/imgproc/src/accum.simd.hpp > accum_cn4_u8_simd.patch
git checkout -- modules/imgproc/src/accum.simd.hpp
cmake -S . -B build_perf_before_u8 -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_perf_before_u8 --target opencv_perf_imgproc -j 4
build_perf_before_u8\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask8UC4To32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\before_perf1.txt 2>&1
```
Optimized measurement:
```sh
git apply accum_cn4_simd.patch
cmake -S . -B build_perf_after -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign > perf_logs\after_cmake_config.txt 2>&1
cmake --build build_perf_after --target opencv_perf_imgproc -j 4 > perf_logs\after_build.txt 2>&1
build_perf_after\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask32FC4* --perf_min_samples=500 --perf_force_samples=500 > perf_logs\after_perf.txt 2>&1
```
```sh
git apply accum_cn4_u8_simd.patch
cmake -S . -B build_perf_after_u8 -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_perf_after_u8 --target opencv_perf_imgproc -j 4
build_perf_after_u8\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask8UC4To32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\after_perf1.txt 2>&1
```
Performance comparison was measured by keeping the new perf tests and only reverting modules/imgproc/src/accum.simd.hpp for the baseline run.
`-mstackrealign` was used for the MinGW Windows build to avoid stack-alignment issues with AVX code generation during local testing.
### Notes
The implementation keeps the existing scalar fallback path unchanged. SIMD is used only for the newly covered masked `cn == 4` vectorizable part, and any remaining tail elements are still handled by `acc_general_()`.
The improvement is most visible on larger images. Small images are dominated by overhead and do not always show meaningful speedup.
Added SDPA layer (Scaled Dot Product Attention) #29104
Merge with: https://github.com/opencv/opencv_extra/pull/1374
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Migrated chessboard and circles grid detectors to objdetect #28804
OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4125
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1375
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fix MSVC error: use name constexp for alignas array size in INT8 kernel #29156
### Problem
Windows CI fails with MSVC error C2131 in `conv2_int8_kernels.simd.hpp`:
error C2131: expression did not evaluate to a constant
failure was caused by a read of a variable outside its lifetime
see usage of 'this'
Two array declarations inside `parallel_for_` lambdas used `constexpr`
variables defined inside the lambda body as array sizes:
alignas(32) int8_t wbuf[128 * K0]; // K0 defined inside lambda
alignas(32) int32_t sumbuf[SPAT_BLOCK_SIZE * K0]; // both defined inside lambda
### Fix
Move the combined size constants to function scope (before the lambda),
where they are true compile-time constants with no `this` involvement:
constexpr int WBUF_SIZE = 128 * 8; // 128 * K0
constexpr int SUMBUF_SIZE = 8 * 8; // SPAT_BLOCK_SIZE * K0
### Related
Fixes Windows CI failure introduced by #29126.
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Attention graph fusion with MLAS FlashAttention #29126
Performance numbers for Owl-v2 model on intel i9:
```
ORT: Average inference time over 10 runs: 1411.55 ms (min 1399.75, max 1438.89)
NEW: Average inference time over 10 runs: 1078 ms (min 1048.04, max 1110.61)
```
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Casting the raw integer fields ras_type and maptype directly to their
respective enum types (SunRasType / SunRasMapType) without a prior range
check is undefined behavior under C++11 §7.2/8 when the stored value falls
outside the enum's valid range.
UBSan reports:
grfmt_sunras.cpp:72: runtime error: load of value 34077, which is not a
valid value for type 'SunRasMapType'
Fix: read both fields into plain ints first, validate them against the
declared enumerator bounds, and return false (reject the image) on any
out-of-range value before performing the enum cast. This is the cheapest
correct approach — two integer comparisons added to a path that was already
doing I/O — and ensures no downstream code ever sees an ill-formed enum
value.
Add test_sunraster.cpp with regression tests covering:
- The exact crash_001 payload (invalid maptype 34077 / 0x851d)
- Invalid ras_type values
- maptype = 2 and UINT_MAX (outside [0,1])
- Truncated header
- A valid 8-bpp grayscale image that must still decode correctly
Signed-off-by: FuzzAnything fuzzanything@gmail.com
Merge pull request #29134 from svigh/gapi_u8_nd_mats_onnx_layout_fix
Gapi u8 N-D mats onnx layout workaround #29134
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core: fix numerical instability and UB in VBLAS SIMD helpers #29140
### Description
**Fixes #28845**
**Root cause identified by:** stubbing out `VBLAS<double>::givens()` to return 0 immediately — both tests pass, confirming the divergence originates in the double-precision Givens SIMD rotation.
**Main fix:** Use FMA form in `VBLAS::{float,double}::givens()` to keep JacobiSVD vector rotation numerically stable on SIMD/FMA builds.
This fixes the fisheye homography initialization divergence (`RMS 36.2553` vs `1`) seen in `RegisterCamerasTest.hetero1/2` under GCC 13.
**Additional fix:** Replace fixed-size temporary storage `sbuf[2]` in `VBLAS<double>::dot()` with `v_reduce_sum()` to avoid out-of-bounds stores on wide SIMD backends (e.g., AVX2 `v_float64` requires 4 lanes, causing UB).
**Verification:**
Passed full `opencv_test_core` and `opencv_test_calib` locally under Ubuntu 24.04 (GCC 13) with AVX2.
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dnn int8 optimization #29079
all_layers.hpp
- Add float_input flag to Conv2Int8Params and Conv2Int8Layer to let the first conv accept raw FP32 input and quantize internally.
graph_fusion_qdq.cpp :
- Fuse DQ → Sigmoid → QL into SigmoidInt8, Similarly for MAxPool.
- Fuse the input QuantizeLinear node into the first Conv2Int8.
conv2_int8_layer.cpp
- Add quantizeInterleaveBlock()
conv2_int8_kernels.simd.hpp
- Add spatial tiling to both convInt8BlockVNNI and convInt8BlockDepthwise: splits output pixels into tiles so total task count is N × ngroups × Kblk × ntiles, fully utilizing all threads even when the channel count is small.
elementwise_layers.cpp
- Widen CV_Assert to accept CV_8U in addition to CV_8S.
eltwise2_int8_layer.cpp
- Add QLinearMul support: new Mul math path for both signed and unsigned int8.
- Add numpy-style broadcast support so QLinearMul / QLinearAdd with scalar
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Add KV cache with paged attention and prefetch #29127
Closes: https://github.com/opencv/opencv/issues/27159
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FP32 KV Cache #28840
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1348
## This PR introduces basic (Paged ) KV-Cache to use on CPU
### Summary:
1. To ensure proper gemm-prepacking,
1.1. The Page Size of Key Cache is currently hardcoded as `FAST_GEMM_F32_NR`(which is 8, 12 or 16 depending on CPU architecture)
1.2. The Page Size of Values Cache is hardcoded as `FAST_GEMM_F32_PACKED_STRIDE_K`
2. there are two phases supported - prefill & generate.
2.1. prefill grows cache by `N` tokens and is allowed **only** for empty cache
2.2. generate grows cache by 1 token.
2.3. **Improtant**: it is currently not allowed to grow non-empty cache by more than one token at a time (thisbehaviour is sufficient for normal LLM querying, but should be extended if we want to implement speculative decoding)
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Dnn pow layer output type fix#29124
### Description
This pull request needs https://github.com/opencv/opencv_extra/pull/1372 to run.
**issue:** when running disk-lightglue `.onnx` model, such error message is thrown:
```bash
Error message OpenCV(5.0.0-pre) /home/tapakya/cpp/opencv_mod/opencv/modules/dnn/src/layers/nary_eltwise_layers.cpp:410: error: (-2:Unspecified error) in function 'virtual void cv::dnn::NaryEltwiseLayerImpl::getTypes(const std::vector<int>&, int, int, std::vector<int>&, std::vector<int>&) const'
> All inputs should have equal types (expected: 'inputs[0] == input'), where
> 'inputs[0]' is 11 (CV_64SC1)
> must be equal to
> 'input' is 5 (CV_32FC1)
```
This is because in `nary_eltwise_layers.cpp`, `getTypes` didn't set the output type of power operation correctly. `int`^`float` should output `float`, whereas `getTypes` set the output type to `int` in this case.
```c++
if (op == OPERATION::POW) {
CV_Assert(inputs.size() == 2);
auto isIntegerType = [](int t) {
return t == CV_8S || t == CV_8U || t == CV_16S || t == CV_16U || t == CV_32S || t == CV_32U || t == CV_64S || t == CV_64U;
};
auto isFloatType = [](int t) {
return t == CV_32F || t == CV_64F || t == CV_16F || t == CV_16BF;
};
int out_type;
const bool baseIsInt = isIntegerType(inputs[0]);
const bool expIsInt = isIntegerType(inputs[1]);
const bool baseIsFloat = isFloatType(inputs[0]);
const bool expIsFloat = isFloatType(inputs[1]);
if ((baseIsInt && expIsInt) || (baseIsFloat && expIsFloat))
{
out_type = (inputs[0] == inputs[1]) ? inputs[0] : CV_32F;
}
else if (baseIsFloat != expIsFloat)
{
out_type = inputs[0];
// if base is int and exp is float, output type should be float
}
```
**fix:** corrected the logic of determining output type
```c++
if ((baseIsInt && expIsInt) || (baseIsFloat && expIsFloat))
{
out_type = (inputs[0] == inputs[1]) ? inputs[0] : CV_32F;
}
else if (baseIsFloat && !expIsFloat)
{
out_type = inputs[0];
}
else if (!baseIsFloat && expIsFloat)
{
out_type = CV_32F;
}
```
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dnn: skip Conv2Int8 fusion for grouped convs with Kg<8 (#28798) #28920
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1360
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---
Fixes#28798.
The YuNet 2023mar int8 model from opencv_zoo doesn't detect anything on 5.x. With `--score_threshold
0.05` the detector still returns no boxes, so it's not just a confidence drop — the network output is
broken.
After dumping intermediate tensors and comparing against onnxruntime, the `obj_*` branches collapse to
~0 (saturated int8 `-128`), and the `cls_*` branches saturate the other way. Final score is `cls * obj`
so nothing ever crosses threshold.
The cause is in `Conv2Int8`'s VNNI kernel. It processes `K0 = 8` output channels per SIMD iteration and
writes them as one `K0`-wide block to `out + (n*K1 + k1)*planesize` with `k1 = k_base / K0`. When
`ngroups > 1` and `Kg = K/ngroups < K0`, consecutive groups share the same `k1` slot and overwrite each
other — only the last group's result survives. YuNet has six `1x1x3x3` depthwise conv heads (`Kg = 1`),
which is exactly this case.
The unfused `DequantizeLinear → Conv2 → QuantizeLinear` path is fine. The minimal fix here is to skip
the rewrite to `Conv2Int8` when `Kg < 8` so we fall back to the float path. A proper depthwise int8
kernel can be added later as a separate optimization.
Verified with `samples/dnn/face_detect.py` on Lena:
Before:
AssertionError: Cannot find a face in samples/data/lena.jpg
After:
Face 0, top-left coordinates: (204, 187), box width: 149, box height 212, score: 0.89
Cross-check vs onnxruntime: `cls_8` mean `0.673` (was `0.93`, ORT `0.673`), `obj_8` max `0.0039` (was
`0`, ORT `0.0039`).
The regression test is a single 16-group depthwise `QLinearConv` with non-zero `x_zp`, added to
`Quantized_Convolution`. Test data is in opencv_extra on the matching branch (~9 KB total).
<img width="1864" height="1060" alt="image" src="https://github.com/user-attachments/assets/a1b15c64-6b84-42b1-86a4-1d55474cb38c" />
Libpng warning fix on Windows #29120
CI warning:
```
Warning: C:\GHA-OCV-3\_work\opencv\opencv\opencv\3rdparty\libpng\pngread.c(4114,21): warning C4146: unary minus operator applied to unsigned type, result still unsigned [C:\GHA-OCV-3\_work\opencv\opencv\build\3rdparty\libpng\libpng.vcxproj]
Warning: C:\GHA-OCV-3\_work\opencv\opencv\opencv\3rdparty\libpng\pngwrite.c(2033,21): warning C4146: unary minus operator applied to unsigned type, result still unsigned [C:\GHA-OCV-3\_work\opencv\opencv\build\3rdparty\libpng\libpng.vcxproj]
```
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Fixed write return status in videoio module#29087
closes: https://github.com/opencv/opencv/issues/24287
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- Replaced scalar fallback and legacy CV_SIMD128 with modern scalable CV_SIMD.
- Added support for multi-channel (2/3/4) and 16-bit (ushort/short) configurations.
- Utilized v_select for branchless execution and vx_load_expand for mixed-bitwidth mask handling.
- Updated accuracy and perf tests.
Fix JPEG chroma configuration & Standardize component settings #28667
### Problem
Incorrectly treated Chroma as a single channel. In $YC_bC_r$, chroma must be set in both separate channels ($C_b$ and $C_r$).
### Fix
- Explicit Setup: Configured all three components ($Y$, $C_b$, $C_r$) individually.
- Standardization: Removed reliance on library defaults to prevent undefined behavior.
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Better Durand-Kerner Initialization #29109
While investigating issue #23644, I have found [this paper](https://link.springer.com/article/10.1007/BF01935059) which presents a good initialization for the Durand-Kerner algorithm. Basically the idea is to put the initial points equidistantly on a circle on the complex plane. The radius of the circle is computed as
<img width="607" height="178" alt="image" src="https://github.com/user-attachments/assets/ea31b002-c924-4b93-9334-3e59597c896b" />
Note that the $a_i$ coefficients in that paper are reversed compared to OpenCV. That's where the `(n - i)` in the code comes from.
I have implemented just the mean of the $u_i$'s for the sake of simplicity. That's already enough to make the algorithm converge in all cases I have tested. I have used this to test for convergence for many polynomials of order 2 and 4 and coefficients of different magnitudes:
```cpp
TEST(Core_SolvePoly, large_test)
{
cv::Mat_<float> coefs3(1,3);
cv::Mat_<float> coefs5(1,5);
cv::Mat r;
double prec;
for (int c0 = -20; c0 <= 20; c0++)
{
coefs3.at<float>(0) = c0;
for (int c1 = -20; c1 <= 20; c1++)
{
coefs3.at<float>(1) = c1;
for (int c2 = -20; c2 <= 20; c2++)
{
coefs3.at<float>(2) = c2;
prec = cv::solvePoly(coefs3, r);
EXPECT_LE(prec, 1e-6);
}
}
}
for (int c0 = -10; c0 <= 10; c0++)
{
coefs5.at<float>(0) = c0;
for (int c1 = -10; c1 <= 10; c1++)
{
coefs5.at<float>(1) = c1;
for (int c2 = -10; c2 <= 10; c2++)
{
coefs5.at<float>(2) = c2;
for (int c3 = -10; c3 <= 10; c3++)
{
coefs5.at<float>(3) = c3;
for (int c4 = -10; c4 <= 10; c4++)
{
coefs5.at<float>(4) = c4;
prec = cv::solvePoly(coefs5, r);
EXPECT_LE(prec, 1e-2);
}
}
}
}
}
for (int i = -10; i < 10; i++)
{
coefs3.at<float>(0) = pow(2, i);
for (int j = -10; j < 10; j++)
{
coefs3.at<float>(1) = pow(2, j);
for (int k = -10; k < 10; k++)
{
coefs3.at<float>(2) = pow(2, k);
prec = cv::solvePoly(coefs3, r);
EXPECT_LE(prec, 1e-6);
}
}
}
}
```
This test passes, but I have not committed it because it runs for a couple of seconds.
This fixes#23644 and replaces #29055. I have checked #29055 and it does not pass the test above. It seems to be optimized to the precise polynomial of #23644.
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videoio(mjpeg): fix heap-buffer-overflow in put_bits off-by-one resize guard #29113
### Summary
Fix a heap-buffer-overflow (WRITE of size 4) in the built-in MJPEG encoder detected by AddressSanitizer during fuzzing.
Fixes#29112
### Root Cause
`mjpeg_buffer::put_bits` in `modules/videoio/src/cap_mjpeg_encoder.cpp` guards buffer resize with:
```cpp
if ((m_pos == (data.size() - 1) && len > bits_free) || m_pos == data.size())
resize(int(2 * data.size()));
```
When `len == bits_free` the guard is **false** (strict `>`), so no resize happens. The subsequent code then:
1. Subtracts `len` from `bits_free`, making it exactly 0.
2. Enters the `bits_free <= 0` branch and executes `++m_pos`.
3. Writes `data[m_pos]` — now equal to `data[data.size()]` — **out of bounds**.
### Fix
Change `len > bits_free` to `len >= bits_free` so the buffer is grown whenever the current slot will be exactly or more than consumed.
```diff
-if ((m_pos == (data.size() - 1) && len > bits_free) || m_pos == data.size())
+if ((m_pos == (data.size() - 1) && len >= bits_free) || m_pos == data.size())
```
### Verification
Reproducer (1×1 grayscale frame, `CAP_OPENCV_MJPEG`):
```cpp
uint8_t pixel = 0xff;
cv::Mat frame(1, 1, CV_8UC1, &pixel);
int fourcc = cv::VideoWriter::fourcc('M', 'J', 'P', 'G');
cv::VideoWriter writer;
writer.open("/tmp/poc.avi", cv::CAP_OPENCV_MJPEG, fourcc, 25.0, cv::Size(1,1), false);
writer.write(frame);
```
Ran under `-fsanitize=address,undefined`; exits cleanly with no error after this fix.
### Regression Test
`TEST(Videoio_MJPEG, put_bits_no_heap_overflow)` added to `modules/videoio/test/test_video_io.cpp` — opens a `CAP_OPENCV_MJPEG` VideoWriter for a 1×1 grayscale file and writes one frame; asserts `EXPECT_NO_THROW`.
Added MLAS third party module and integrated into GeMM path #28934
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fix int32 overflow in shape_utils::total() for large tensors #29093
Fixes https://github.com/opencv/opencv/issues/24914
### Problem
When running inference with a ConvTranspose (deconvolution) layer on large inputs
(e.g. 4864×4864 with 30 channels), the DNN module crashes with:
OpenCV net_impl.cpp: error:
(expected: 'total(ints[i]) > 0'), where
'total(ints[i])' is -1455947776
must be greater than
'0' is 0
The root cause is `shape_utils::total()` which returns `int` (32-bit signed).
`ENGINE_CLASSIC` catches this via `CV_CheckGT` and throws.
`ENGINE_NEW` was silently bypassing the check — the overflow in `total()` itself
was never addressed.
### Changes
**`modules/dnn/include/opencv2/dnn/shape_utils.hpp`** — root fix
- Changed return type of both `total()` overloads from `int` to `size_t`
- Changed accumulator from `int elems = 1` to `size_t elems = 1`
**`modules/dnn/src/net_impl.cpp`**
- Updated `CV_CheckGT(total(...), 0)` to `CV_CheckGT(total(...), (size_t)0)`
to match the new return type
**`modules/dnn/src/net_impl2.cpp`**
- Added the same `CV_CheckGT` shape validation that `ENGINE_CLASSIC` has in
`net_impl.cpp:1333-1337` — `ENGINE_NEW` was missing this check entirely
**`modules/dnn/src/legacy_backend.hpp`**
- Removed the now-incorrect `(int)` cast in `CV_CheckEQ` — both sides are
now `size_t`
### Test
Added `Net.ShapeUtils_total_no_int32_overflow` in `modules/dnn/test/test_misc.cpp`:
- The shape [1920 × 1,478,656] is the exact im2col buffer from the bug report.
EXPECT_EQ verifies total() returns the correct size_t value 2,839,019,520.
EXPECT_LT documents that casting it to int wraps to -1,455,947,776 — the
value that caused the original crash.
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requires (YOLO26n , RetinaFace model download) : https://github.com/opencv/opencv_extra/pull/1357
added performance tests for 14 modern deep learning models to `modules/dnn/perf/perf_net.cpp.`
**Tests added**
- RT_DETR_L
- RF_DETR
- Grounding_DINO
- BlazeFace
- RetinaFace
- YOLO26n
- YOLO26m_Seg
- SegFormer_B2_Clothes
- Depth_Anything_V2
- SigLIP
- OWLv2
- RAFT
- SAM2_Encoder
- SAM2_Decoder
Models are available at: https://drive.google.com/drive/folders/1lpYAWSLMtxcmfDtYjiw0qwzZX0bCH-i4
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Add missing DataLayout constants in generated bindings for 5.x #29074
- Added DATA_LAYOUT_* constants to missing_consts so they
are manually injected into Core.java (same approach used for CV_8U, FILLED etc.)
- Added DataLayout entry to type_dict so the generator correctly maps
DataLayout
### Tests
modules/dnn/misc/java/test/DnnBlobFromImageWithParamsTest.java:
New test added:
- testDataLayoutConstants: verifies all DATA_LAYOUT_* constants are accessible from Core
Pre-existing tests enabled (were commented out earlier):
- testBlobFromImageWithParamsNHWCScalarScale: verifies blobFromImageWithParams
produces correct output with DATA_LAYOUT_NHWC and per-channel scalar scaling
- testBlobFromImageWithParams4chMultiImage: verifies blobFromImagesWithParams
correctly handles a batch of images with DATA_LAYOUT_NHWC layout
Closes : #27264
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[Bug Fix] Slice2 empty-range crash in new DNN engine#29029
Closes: https://github.com/opencv/opencv/issues/29023
Merge with: https://github.com/opencv/opencv_extra/pull/1363
## Summary
ENGINE_NEW asserted `CV_Assert(outsz >= 0)` in `slice2_layer.cpp` when an ONNX Slice op had `start > end` with positive step. Per ONNX spec, such slices are valid and
produce an empty-range output (size 0). The hard assertion crashed SwinIR inference on ENGINE_NEW.
## Fix
- Replaced the fatal assertion with a graceful clamp: when `outsz < 0`, set `outsz = 0` and `end = start`.
- Moved `allEnds[axis] = end` to after the clamp so downstream shape inference receives a consistent value (the previous order propagated un-clamped garbage end values
into `normalize_axis()`, causing SIGSEGV).
## Test
Adds `Reproducibility_SwinIR_ONNX` accuracy test in `test_model.cpp`. Test data registered in opencv_extra PR with matching branch name `fix/dnn-slice2-empty-range`.
- Without fix: 3/3 FAIL with `slice2_layer.cpp:124: (-215:Assertion failed) outsz >= 0`
- With fix: 3/3 PASS (CPU, OCL, OCL_FP16)
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SIMD Kernel speedup for DNN layers #28889
This PR add following speedups for Grounding Dino tiny model.
For Device: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW (Before)` | `ENGINE_NEW (After)` | `ENGINE_ORT` |
| :--- | :--- | :--- | :--- |
| **Grounding Dino Tiny** | 3130 ms | 1872.06 ms| 1800.18 ms|
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core(rvv): fix v_matmul/v_matmuladd scalable semantics and expand lane-group test coverage #29080
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## Platform
SpacemiT X60 (K1), 8-core RISC-V RVV 1.0, VLEN=256, 16GB RAM, OS: Bianbu Linux (kernel 6.6.63), GCC 13.2.0, Build: OpenCV 4.14.0-pre, Release, HAL: YES (RVV HAL 0.0.1)
## Motivation
OpenCV's Universal Intrinsics `v_matmul` and `v_matmuladd` have a semantic bug in the RVV scalable backend (`modules/core/include/opencv2/core/hal/intrin_rvv_scalable.hpp`).
The current implementation uses `v_extract_n(v, 0/1/2/3)` with hardcoded indices, assuming the vector holds exactly 4 float lanes (128-bit fixed). On hardware with VLEN=256 (e.g. SpacemiT K1 / BPI-F3), `v_float32` with LMUL=2 holds 16 lanes. As a result, lanes 4–15 silently reuse the inputs from lanes 0–3, producing wrong results.
OpenCV itself acknowledges this in `modules/core/src/matmul.simd.hpp`:
// v_matmuladd for RVV is 128-bit only but not scalable,
// this will fail the test Core_Transform.accuracy
The RVV scalable `transform_32f` path has been disabled because of this bug. However, the existing `TheTest<R>::test_matmul()` only checked the first 4-lane group (the outer loop was effectively hardcoded to `int i = 0`), so the bug was never caught by CI even on wide-vector backends.
## Modification
**Test fix** (`modules/core/test/test_intrin_utils.hpp`): Expanded `test_matmul()` to iterate over all 4-lane groups:
// Before (only checked lane group i=0)
int i = 0;
for (int j = i; j < i + 4; ++j) { ... }
// After (checks all lane groups)
for (int i = 0; i < VTraits<R>::vlanes(); i += 4)
{
for (int j = i; j < i + 4; ++j) { ... }
}
**Kernel fix** (`modules/core/include/opencv2/core/hal/intrin_rvv_scalable.hpp`): Rewrote `v_matmul` and `v_matmuladd` to process all 4-lane groups correctly. Each group of 4 lanes now independently computes the full matrix multiply using its own `v[i], v[i+1], v[i+2], v[i+3]` inputs. The `transform_32f` RVV path in `matmul.simd.hpp` remains disabled as the autovectorized path shows better performance on current hardware.
## Experiment 1: Bug reproduced on SpacemiT K1 (VLEN=256)
./opencv_test_core --gtest_filter="*intrin*"
Result: `hal_intrin128.float32x4_BASELINE` FAILED with 24 failures, all from lane groups i=4, i=8, i=12 (lanes 4–15).
Representative failures from `v_matmul` (line 1526):
i=4 j=4: actual=158 expected=56
i=4 j=5: actual=166.39999 expected=59.200001
i=8 j=8: actual=314.39999 expected=68.800003
i=12 j=12: actual=512.40002 expected=81.599998
Representative failures from `v_matmuladd` (line 1540):
i=4 j=4: actual=147.5 expected=51.5
i=8 j=8: actual=284.70001 expected=60.700001
i=12 j=12: actual=453.89999 expected=69.900002
Lane group i=0 (j=0..3) passed correctly — confirming the bug only affects lanes beyond the first 4, exactly as expected from the hardcoded `v_extract_n(v, 0/1/2/3)` implementation.
## Experiment 2: Both tests pass after fixing the kernel
./opencv_test_core --gtest_filter='hal_intrin128.float32x4_BASELINE'
[ OK ] hal_intrin128.float32x4_BASELINE (1859 ms)
[ PASSED ] 1 test.
./opencv_test_core --gtest_filter='Core_Transform.accuracy'
[ OK ] Core_Transform.accuracy (819 ms)
[ PASSED ] 1 test.
## Experiment 3: RVV transform path remains disabled (performance regression)
After re-enabling the RVV scalable `transform_32f` path experimentally, benchmarks showed a significant regression vs the compiler-autovectorized scalar path (CV_32FC3):
Size RVV path Scalar path Ratio
640x480 7.83 ms 1.48 ms 5.3x slower
1280x720 23.96 ms 5.17 ms 4.6x slower
1920x1080 53.76 ms 10.12 ms 5.3x slower
The compiler-autovectorized path outperforms the hand-written RVV kernel for this workload, consistent with the original comment in `matmul.simd.hpp`. The `transform_32f` RVV path is therefore kept disabled in this PR. The kernel fix to `v_matmul`/`v_matmuladd` remains necessary for correctness on wide-vector hardware, and the expanded test ensures the bug cannot regress silently in future.
Fixed gatherND offset calculation #29084
Bug fix: fixed segmentation fault when running ALIKED onnx model with ENGINE_NEW.
### Description
issue: when running ALIKED onnx model with ENGINE_NEW, segmentation fault happens during the inference.
fix: fixed the offset calculation in gatherND.cpp, so gather does not access out-of-bound memory.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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Patch to opencv_extra has the same branch name.
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KleidiCV update to verison 26.03 #28744
KleidiCV release: https://gitlab.arm.com/kleidi/kleidicv/-/releases/26.03
Tuned DNN test threshold as resize linear produces slightly different result with the new KleidiCV version.
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
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randomnormallike_layer.cpp registered itself via CV_DNN_REGISTER_LAYER_CLASS_STATIC at file scope. Nothing else in the translation unit was referenced from elsewhere, so when OpenCV is built statically (BUILD_SHARED_LIBS=OFF, as in the reporter's MSVC build) the linker drops the object file and the static-init registration never runs. The ONNX importer then sets layerParams.type = "RandomNormalLike" but getLayerInstance has no factory for that type, and parsing fails with "Can't create layer of type RandomNormalLike".
Switch to the standard pattern used by every other layer in the module: declare RandomNormalLikeLayer in all_layers.hpp, expose a static create() factory from the .cpp, and register it explicitly from initializeLayerFactory in init.cpp. Because init.cpp is referenced by the DNN module init path, this pulls in the layer .cpp regardless of build mode and the registration always runs.
The failure was incorrectly attributed to MSVC in the bug report. The bug is build-mode sensitive (static vs shared), not platform sensitive.
Verified locally on linux/gcc-13 by building modules/dnn and
opencv_test_dnn against the patched tree, then running opencv_test_dnn --gtest_filter='Test_ONNX_layers.RandomNormalLike_basic/0:Test_ONNX_layers.RandomNormalLike_complex/0'
Both tests pass; without the patch the same binary reproduces the
"Can't create layer of type RandomNormalLike" error from the report.
Port of the 4.x patch to 5.x. Registers a new
ConsecutiveTransposePairsSubgraph in onnx_graph_simplifier.cpp's
simplifySubgraphs() so that consecutive Transpose nodes whose composed
permutation is identity are eliminated at the ONNX proto level. This
runs before either engine takes over, so both ENGINE_CLASSIC and
ENGINE_NEW benefit.
Equivalent optimizations exist in tf2onnx (TransposeOptimizer._transpose_handler)
and ONNX Runtime (HandleTransposeImpl, 'Permutations cancel' branch).
Update ArUco doc #28824
See https://github.com/opencv/opencv/pull/28823
Update of the ArUco doc but targeted for OpenCV 4.
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch
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core: add YAML 1.2 support for FileStorage #28482Fixes: #26363
CI Update: https://github.com/opencv/ci-gha-workflow/pull/306
Summary:
- Bool true/false literals support
- Header-less YAMLs support for Python YAML module compatibility. Header-less files are parsed as YAMLs by default
- Added FORMAT_YAML_1_0 flag to FileStorage for fallback.
- New YAML1.2 header
Testing:
- Added Core_InputOutput.YAML_1_2_Compatibility test case in test_io.cpp.
- Added YAML interop test in Python
imgproc: add kernel size validation in boxFilter #29040
Check that ksize dimensions are strictly positive in boxFilter(), returning StsBadSize error instead of producing undefined behavior.
Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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- [x] The PR is proposed to the proper branch
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Fixed subgraph name scoping in new DNN engine #28971
closes: https://github.com/opencv/opencv/issues/23663, https://github.com/opencv/opencv/issues/19977
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1362
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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RISC-V: add RVV convertScale paths for selected depth conversions #29058
### Summary
This PR extends the RISC-V RVV HAL `convertScale` implementation with optimized paths for selected `convertTo()` depth conversions:
- `CV_32F -> CV_8U`
- `CV_16U -> CV_32F`
- `CV_16S -> CV_32F`
The change is confined to `hal/riscv-rvv/src/core/convert_scale.cpp`. It does not modify the generic `convertTo()` implementation or affect dispatch for non-RISC-V targets.
### Rationale
`Mat::convertTo()` reaches architecture-specific implementations through the HAL `convertScale` interface. Adding these paths in the RVV HAL keeps the optimization in the existing backend layer and avoids introducing RISC-V-specific branches into common OpenCV code.
The selected conversions were chosen based on measured benefit and practical relevance. `CV_32F -> CV_8U` provides the largest speedup, while `CV_16U -> CV_32F` and `CV_16S -> CV_32F` provide consistent improvements for common image-processing data paths.
### Benchmark
Tested on Banana Pi BPI-F3, an 8-core RISC-V board with RVV 1.0 support.
Build and test configuration:
- OpenCV `4.14.0-pre`
- Release build
- RVV HAL enabled
- Compiler: `riscv64-unknown-linux-gnu-g++ 14.2.1`
- Test binary: `opencv_perf_core`
- Test filter: `*convertTo*`
- Samples: `--perf_force_samples=20 --perf_min_samples=20`
Benchmark command:
```bash
./opencv_perf_core \
--gtest_filter=*convertTo* \
--perf_force_samples=20 \
--perf_min_samples=20
```
Each benchmark case was measured with 20 forced samples. The baseline/after comparison was repeated to check run-to-run stability. The optimized conversion pairs showed consistent improvements.
Second-run comparison for the optimized pairs:
```text
OPTIMIZED_PAIRS: 24 cases, gmean 2.6009x
CV_32F -> CV_8U: 16.0718x
CV_16U -> CV_32F: 1.2334x
CV_16S -> CV_32F: 1.1376x
```
`CV_32F -> CV_8U` showed the largest improvement, with speedups in the `14.5x` to `18.8x` range across the tested image sizes, channel counts, and alpha values.
### Notes on Measurement
The initial benchmark run showed noise in conversion pairs not touched by this PR, likely due to board-level variance such as frequency scaling, thermal state, or background scheduling.
A second run showed untouched pairs close to neutral:
```text
UNTOUCHED_OTHER: gmean 0.9941x
```
The optimized conversion pairs remained consistently faster across both runs, with no observed regression in the added RVV paths.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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Rvv core norm #29057Fixesopencv/opencv#29052
### Problem
`Core_Norm/ElemWiseTest.accuracy/0` failed on RISC-V with RVV enabled when computing norm for `CV_16S` data.
The reported failing case was:
```text
src[0] ~ 16sC4 3-dim (1 x 116 x 40)
```
The expected norm result was a large positive `double`, but the RVV path returned an incorrect value:
```text
expected: 3370900308417
actual: -173296
```
This indicates that the problem was not in the public `cv::norm()` API, but in the RVV HAL implementation used for the `CV_16S` L2/L2SQR accumulation path.
### Root Cause
The RVV HAL has a specialized implementation for `CV_16S` L2 norm:
```cpp
NormL2_RVV<short, double>
```
The implementation widens `int16` values, squares them, converts the widened products to `float64`, accumulates them in an `f64m8` vector, and finally reduces the vector to a scalar `double`:
```cpp
auto s = __riscv_vfmv_v_f_f64m8(0, vlmax);
...
auto v_mul = __riscv_vwmul(v, v, vl);
s = __riscv_vfadd_tu(s, s, __riscv_vfwcvt_f(v_mul, vl), vl);
...
return __riscv_vfmv_f(__riscv_vfredosum(...));
```
The bug was in the scalar initializer passed to `__riscv_vfredosum`.
Before this patch, the code created an `f64m1` scalar vector but used the maximum vector length for `e32m1`:
```cpp
__riscv_vfmv_s_f_f64m1(0, __riscv_vsetvlmax_e32m1())
```
This is inconsistent: the vector type is `f64m1`, so the VL used to initialize it must correspond to `e64m1`, not `e32m1`.
objdetect(aruco): fix maxCorrectionBits in predefined dictionaries #29034
This PR fixes the bugs in the creation of some dictionaries and standarizes its construction.
## Bugs
1. **DICT_ARUCO_MIP_36h12_DATA** was incorrectly assigned `maxCorrectionBits=12`, which is the intermarker distance. This was causing a high rate of false positives during detection, which is a significant problem.
2. **APRILTAG** dictionaries were incorrectly created with `maxCorrectionBits=0`, making it impossible to do error correction.
## Solution
Given a intermarker distance X, the correct `maxCorrectionBits = X/2 - 1`. Thus, standardize the construction of Dictionaries as such.
libpng upgrade to 1.6.57 #29051
### Pull Request Readiness Checklist
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- [ ] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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Python: add context manager compatibility for VideoCapture and VideoWriter #28967
### Description
This PR implements the context manager protocol (`with` statement support) for `cv2.VideoCapture` and `cv2.VideoWriter` objects in Python.
### Problem
Currently, these classes do not support the context manager protocol, leading to a `TypeError: ... object does not support the context manager protocol` when used with a `with` block. As reported in issue #28956, if an exception occurs before an explicit `.release()` call, the video resource or file remains locked in the system[cite: 298, 309].
### Identification & Motivation
By analyzing the issue and the Python binding structure of OpenCV, I identified that while the core logic resides in C++, the Pythonic behavior can be enhanced by injecting `__enter__` and `__exit__` methods during the module initialization. This ensures that resources are automatically and safely released even if the program execution is interrupted by an error[cite: 298, 325].
### Solution
I have modified `modules/python/package/cv2/__init__.py` to dynamically inject the necessary methods:
1. Defined `_video_enter` to return the object itself (standard context manager behavior).
2. Defined `_video_exit` to trigger the `.release()` method automatically.
3. Injected these methods into `VideoCapture` and `VideoWriter` classes immediately after the `bootstrap()` function to ensure the native C++ classes are correctly loaded into the global namespace.
Fixes#28956
## Summary
Replace the existing RVV HAL FAST-16 implementation (`hal/riscv-rvv/src/features2d/fast.cpp`) with a score-first approach, achieving **2-4x speedup** over the previous HAL implementation on RISC-V hardware.
## Approach
The previous HAL implementation used a count-based approach (XOR trick + per-pixel bitmask scanning + separate cornerScore computation). This PR replaces it with a **score-first** approach that processes multiple horizontal pixels in parallel using RVV vectors.
Key optimizations:
1. **Score-first approach**: Directly compute corner scores via min/max reduction over all 9-arc windows, then threshold on score. This eliminates the arc-counting pass and the separate `cornerScore` function.
2. **u8 to i16 zero-extension**: Zero-extend pixel values to int16 for natural signed comparison, eliminating the XOR-delta trick and preventing score overflow.
3. **vcompress batch output**: Extract all corner indices in one vector operation instead of per-lane bitmask scanning.
4. **vlen-adaptive**: Uses `vsetvl_e16m1` for dynamic vector length — works on any RVV 1.0 implementation regardless of VLEN (128, 256, 512, etc.) without code changes.
## Performance
Benchmarked by maintainer on **Muse Pi v3.0** (GCC, SpacemiT toolchain v1.0.4):
| Test | Previous HAL (ms) | This PR (ms) | Speedup |
|------|-------------------|--------------|---------|
| FAST-20 NMS=true | 23.06 | 7.99 | **2.89x** |
| FAST-20 NMS=true (orig) | 7.31 | 1.80 | **4.06x** |
| FAST-30 NMS=false | 20.10 | 7.75 | **2.59x** |
| FAST_DEFAULT s2.jpg | 76.94 | 19.06 | **4.04x** |
| ORB_DEFAULT chess9 | 64.46 | 25.43 | **2.53x** |
Both NMS=true and NMS=false cases are supported.
## Files Changed
- **`hal/riscv-rvv/src/features2d/fast.cpp`**: Replaced count-based implementation with score-first approach
- No changes to `modules/features2d/src/fast.cpp` or any other files
## Test Plan
- [x] Built with RISC-V cross-compilation toolchain (GCC 14.2, RVV 1.0)
- [x] Tested on QEMU (rv64, v=true, vlen=256) — correct results for both NMS modes
- [x] Maintainer tested on Muse Pi v3.0 hardware — 2-4x speedup confirmed
- [ ] CI passes on all platforms
Fix CMake crash when CUDA arch autodetection comes up empty #28954
With CUDA 13 and a system gcc that the toolkit does not support, every NVCC probe inside ocv_filter_available_architecture fails and the result list ends up empty. The next line then calls list(GET ... -1) on that empty list, so CMake dies with the unhelpful message about list GET given empty list and the user has no obvious next step. This change replaces that path with a fatal error that points at the three real fixes, namely setting CUDA_ARCH_BIN, setting CUDA_HOST_COMPILER, or enabling OPENCV_CMAKE_CUDA_DEBUG to see what NVCC actually printed. It also forwards CMAKE_CUDA_HOST_COMPILER into CUDA_HOST_COMPILER since the issue reporter passed the standard CMake variable and it was being silently ignored.
Fixes#28952
Added net profiling support #28752
### Pull Request Readiness Checklist
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imgproc: add findTRUContours - lock-free parallel contour extraction #28773
Integrates the TRUCO algorithm (Threaded Raster Unrestricted Contour Ownership, @cite TRUCO2026) as a transparent fast path inside `cv::findContours`. No API change is required: existing code automatically benefits when `mode=RETR_LIST` and no hierarchy output is requested.
### How it works
When `mode=RETR_LIST` and the caller does not request hierarchy, `findContours` delegates to the TRUCO parallel engine instead of Suzuki-Abe. All ContourApproximationModes are supported; approximation is applied in parallel after extraction. In all other cases the original Suzuki-Abe path is used unchanged.
### Key design ideas
- Row-strip domain decomposition parallelised via `cv::parallel_for_`
- Start-point ownership rule + speculative downward tracing eliminate tile stitching and synchronisation primitives (lock-free)
- 8-bit state space instead of the 32-bit integer labeling required by Suzuki-Abe, giving higher SIMD throughput and lower memory-bandwidth pressure
- Paged contour buffer (`TRUCOPagedContour`) avoids heap reallocation on the hot tracing path
- SIMD-accelerated row scanning via `cv::v_uint8` intrinsics
- Thread count controlled globally via `cv::setNumThreads()`, consistent with OpenCV conventions
### Output correctness
Significant effort has gone into ensuring the output is identical to the original `findContours` in every respect: same contour set, same ordering, and same approximation results for all methods. A dedicated test suite (`test_contours_truco.cpp`) verifies exact match against Suzuki-Abe across all four `ContourApproximationModes` and thread counts from 1 to 39, using noise images, circles, nested rectangles, and mixed scenes.
### Performance vs. Suzuki-Abe (from submitted paper)
- single-thread: ~1.8–1.9× faster (8-bit SIMD advantage)
- 20 threads: ~14–20× faster on i7-13700H (6P+8E, 20T)
- 20 threads: ~10–12× faster on Xeon Silver 4510 (12C/24T)
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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Use Xcode DocC tool for buiding Apple platform docs + add quick-start tutorial #28704
### Pull Request Readiness Checklist
- [x] I agree to contribute to the project under Apache 2 License.
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- [x] There is a reference to the original bug report and related work
This PR resolves https://github.com/opencv/opencv/issues/28702 and https://github.com/opencv/opencv/issues/28703
Additionally it resolves build issues on Apple platforms by making build scripts run with Python 3
Add ARMPL support for DFT Function #28664
- This PR introduces hal/armpl/ with implementation of 1D, 2D DFT and DCT routines using ARM Performance Libraries as a custom HAL replacement for OpenCV's DFT & DCT Function.
- ArmPL MSI package is automatically downloaded and extracted via CMake when building on Windows ARM64, with a WITH_ARMPL option that defaults to ON for that platform.
- Forward and inverse real DFT calls in dxt.cpp are routed through ArmPL when available, with scaling applied only when needed.
- Test error thresholds in test_dxt.cpp are relaxed (from 1e-5 to 2e-4 for float ) to account for numerical differences between ArmPL and OpenCV's reference DFT results.
**Performance Benchmarks :**
<img width="993" height="835" alt="image" src="https://github.com/user-attachments/assets/76def647-6d20-4bce-8bc9-7363e723669f" />
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- Implement inrange_simd() covering uchar/ushort/short, chan=1/2/3/4
- Replace legacy CV_SIMD128 run_inrange3 with modern CV_SIMD API
- Extend perf tests to cover all added type/channel combinations
- Resolves TODO introduced in #12608 (2018)
fix: Guard ndsrvp filter center copy when source span is empty #28915
## Summary
This adds a defensive guard around the centre-region `memcpy` in the ndsrvp HAL filter padding path.
**Description**: Multiple memcpy calls in the ndsrvp HAL filter, bilateral filter, and median blur routines use computed sizes (e.g., cnes * (rborder - j), cn * (rborder - j), src_step * height) without validating that the computed byte count fits within the destination buffer. An attacker supplying a crafted image with manipulated dimensions, channel counts, or border parameters can cause the computed copy size to exceed the allocated destination buffer, resulting in a heap buffer overflow that corrupts adjacent memory.
## Changes
- `hal/ndsrvp/src/filter.cpp`
## Verification
- [x] Build passes
- [x] Scanner re-scan confirms fix
- [x] LLM code review passed
---
*Automated security fix by [OrbisAI Security](https://orbisappsec.com)*
Added custom layer support in new DNN engine #28963
Closes: https://github.com/opencv/opencv/issues/26200
Merge with: https://github.com/opencv/opencv_extra/pull/1358
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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Extend attention fusion for runtime QK scale and add MatMul to Gemm rewriter #28957
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch
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fixes a bug in ENGINE_NEW of incorrect fusion of conv+relu+batchnorm #28722
### Pull Request Readiness Checklist
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1325
Closes Issue https://github.com/opencv/opencv/issues/28689
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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Fixes the issue for Identity functions for 0D #28968
Closes https://github.com/opencv/opencv/issues/25763
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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- [x] The PR is proposed to the proper branch
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fix getUnconnectedOutLayers() in new engine #28982
Solves https://github.com/opencv/opencv/issues/26491
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
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- [x] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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cmake: fix PNG status display when BUILD_PNG=ON #28995
### Description
Fixes#28657.
On macOS (and other Apple platforms), `BUILD_PNG=ON` is the default. The build system correctly clears `PNG_FOUND` in `OpenCVFindLibsGrfmt.cmake` and builds libpng from the bundled source. However, during the subsequent module processing phase, a downstream `find_package(PNG)` call — triggered transitively via `include()` in the **same scope** — overwrites not only `PNG_FOUND`, but also `PNG_INCLUDE_DIR`, `PNG_LIBRARIES`, and `PNG_VERSION_STRING`.
#### Root Cause: FindPNG.cmake Has Asymmetric Guards
Reading CMake 4.3's `FindPNG.cmake` (`/opt/homebrew/share/cmake/Modules/FindPNG.cmake`) reveals:
| Variable | Guard? | Fate |
|---|---|---|
| `PNG_LIBRARY` | `if(NOT PNG_LIBRARY)` at line 138 — **guarded** | Preserved |
| `PNG_PNG_INCLUDE_DIR` | `find_path(...)` at line 115 — **no guard** | **Overwritten** |
| `PNG_INCLUDE_DIR` | Derived from `PNG_PNG_INCLUDE_DIR` | **Chain-overwritten** |
| `PNG_LIBRARIES` | Derived from `PNG_LIBRARY` + `ZLIB_LIBRARY` | **Partly overwritten** |
| `PNG_VERSION_STRING` | Parsed from `PNG_PNG_INCLUDE_DIR/png.h` | **Overwritten** |
`PNG_LIBRARY` survives because FindPNG itself checks `if(NOT PNG_LIBRARY)`. But `find_path(PNG_PNG_INCLUDE_DIR)` runs unconditionally — re-searching and overwriting the bundled path with the system one.
#### Fix: Three Coordinated Changes
**Part A — Status guard** (`CMakeLists.txt`): When `BUILD_PNG=ON`, pass `FALSE` as the condition to always show `"build"` regardless of `PNG_FOUND`.
**Part B — Variable lock** (`cmake/OpenCVFindLibsGrfmt.cmake`): After building from bundled source, lock `PNG_PNG_INCLUDE_DIR` as `CACHE INTERNAL`. CMake's `find_path()` checks the cache first — if the variable exists with a valid path, it skips re-searching. The system path is never found.
**Part C — Cache cleanup** (`cmake/OpenCVFindLibsGrfmt.cmake`): Add `PNG_PNG_INCLUDE_DIR` to the `ocv_clear_internal_cache_vars()` call in the `else` branch. When a user switches `BUILD_PNG=OFF`, the cached variable is cleared, allowing `find_path` to search for the system libpng normally. Prevents stale cache leakage across configuration changes.
#### Verification (macOS 26, CMake 4.3, Homebrew libpng 1.6.58)
```
BEFORE downstream find_package(PNG):
PNG_LIBRARY = libpng
PNG_INCLUDE_DIR = .../3rdparty/libpng
PNG_VERSION_STRING = 1.6.37
AFTER downstream find_package(PNG) [with fix]:
PNG_LIBRARY = libpng ← preserved (FindPNG guard)
PNG_INCLUDE_DIR starts with .../3rdparty/libpng ← locked (Part B)
PNG_VERSION_STRING = 1.6.53 ← from bundled png.h
Status display: build (ver 1.6.53) ← correct (Part A)
```
The full reproduction and analysis are in the attached `bugReview/` directory.
### Checklist
- [x] There is a reference to the original bug report and related work
- [x] The test case is in `bugReview/` directory
- [x] Tested on macOS 26 (arm64) with CMake 4.3 + Homebrew libpng 1.6.58
- [x] Cache hygiene verified for BUILD_PNG ON→OFF→ON transitions
flann: fix Index::radiusSearch output size mismatch #28990
## Problem
`Index::radiusSearch` returns `nn` — the number of neighbors found within the radius — but the output arrays `_indices` and `_dists` are always allocated with `maxResults` columns regardless of how many neighbors were actually found. This creates two inconsistencies:
1. When `nn < maxResults`, the output arrays contain stale entries beyond position `nn`, so `nn != indices.cols` and callers cannot rely on the output size to know how many results are valid.
2. When `nn > maxResults`, the return value exceeds the capacity of the output arrays, which have only `maxResults` columns. The extra neighbors were never stored, so the return value is misleading.
The following snippet reproduces both cases:
```cpp
int testFLANN(){
std::vector<cv::Point2f> corners={{3679.83,1857.22},{3324.43,1850.67},{3278.83,1502.32},{3621.32,1508.69},{3662.26,1837.97},{3336.06,1839.28},{3291.74,1516.03},{3608.94,1518.72},{2980.66,1843.22},{2628.11,1837.14},{2607.73,1491.57},{2948.22,1496.76},{2289.31,1830.19},{1943.99,1824.17},{1945.51,1482.34},{2280.68,1487.39},{1607.47,1819.11},{1260.04,1813.26},{1283.94,1470.34},{1620.03,1475.85},{923.063,1808.08},{576.193,1802.4},{624.713,1459.78},{959.715,1464.93},{3270.25,1494.98},{2952.95,1492.19},{2922.75,1190.02},{3231.05,1194.81},{3284.82,1507.62},{2942.74,1502.59},{2912.32,1178.33},{3242.98,1183.19},{2612.82,1496.37},{2276.44,1491.63},{2267.61,1170.13},{2593.64,1174.43},{1949.73,1486.61},{1614.92,1480.64},{1626.77,1160.34},{1952,1165.51},{1289.38,1476.21},{953.709,1470.21},{986.779,1148.92},{1311.98,1154.89},{3567.28,1195.1},{3237.51,1189.03},{3197.63,884.15},{3518.61,888.76},{2918.31,1183.62},{2588.69,1179.37},{2570.64,875.672},{2889.87,878.902},{2271.97,1174.25},{1947.62,1169.61},{1948.56,868.197},{2263.8,872.533},{1630.99,1164.6},{1306.74,1159.52},{1327.81,858.358},{1642.69,862.765},{992.896,1155.52},{666.107,1149.96},{705.246,845.776},{1023.25,851.582},{3204.48,889.981},{2883.49,885.252},{2859.31,593.752},{3175.43,594.736},{2575.86,880.331},{2259.44,876.647},{2252.33,589.235},{2560.47,592.332},{1954.36,873.708},{1636.7,868.068},{1646.93,580.76},{1956.14,584.797},{1332.02,862.624},{1017.11,856.71},{1045.32,572.178},{1350.95,577.635},{3481.36,605.324},{3169.05,601.083},{3138.84,324.116},{3446.12,325.545},{2866.14,599.61},{2555.38,597.142},{2541.51,320.931},{2845.5,322.419},{2256.79,593.253},{1950.17,590.12},{1951.8,317.445},{2250.88,319.418},{1654.16,587.663},{1344.83,582.787},{1362.8,309.253},{1663.89,313.056},{1050.94,577.867},{741.476,571.648},{776.433,300.444},{1076.97,304.56},{3327.05,1847.65},{2977.74,1840.48},{2945.48,1499.68},{3281.82,1504.97},{2630.85,1834.22},{2287.2,1828.06},{2278.56,1489.51},{2610.64,1494.31},{1946.11,1822.05},{1604.75,1816.18},{1617.11,1478.59},{1947.62,1484.48},{1262.96,1810.53},{920.46,1805.05},{956.712,1467.57},{1286.66,1473.27},{3618.7,1511.71},{3281.82,1504.97},{3240.24,1186.11},{3564.22,1192.53},{2945.48,1499.68},{2610.64,1494.31},{2591.52,1176.55},{2915.32,1180.97},{2278.56,1489.51},{1947.62,1484.48},{1949.81,1167.56},{2269.79,1172.18},{1617.11,1478.59},{1286.66,1473.27},{1308.98,1157.53},{1628.88,1162.47},{956.712,1467.57},{627.315,1462.81},{669.943,1146.75},{989.494,1151.86},{3240.25,1186.11},{2915.32,1180.98},{2887.03,881.727},{3200.69,886.725},{2591.52,1176.55},{2269.79,1172.19},{2261.62,874.587},{2573.63,878.324},{1949.81,1167.55},{1628.88,1162.47},{1640.44,864.751},{1950.74,870.259},{1308.98,1157.53},{989.494,1151.86},{1019.41,854.787},{1329.92,860.49},{3515.88,891.68},{3200.69,886.725},{3171.89,598.263},{3478.26,602.785},{2887.04,881.725},{2573.63,878.324},{2558.28,594.392},{2863.1,597.005},{2261.62,874.587},{1950.74,870.259},{1952.4,588.114},{2254.56,591.241},{1640.44,864.751},{1329.92,860.49},{1348.65,579.559},{1650.55,584.209},{1019.41,854.787},{708.649,849.44},{745.388,568.534},{1047.42,574.313},{3171.89,598.263},{2863.1,597.005},{2842.6,325.169},{3141.93,326.654},{2558.28,594.392},{2254.56,591.241},{2248.65,321.425},{2544.55,323.537},{1952.4,588.113},{1650.55,584.209},{1660.07,316.284},{1954.02,319.457},{1348.65,579.559},{1047.43,574.312},{1073.06,307.674},{1366.42,312.707}};
cv::flann::KDTreeIndexParams indexParams(1);
cv::Mat data = cv::Mat(corners).reshape(1, static_cast<int>(corners.size()));
auto index = std::make_shared<cv::flann::Index>(data, indexParams);
int maxResults = 4;
int nTimesError1 = 0; // nn != indices.size()
int nTimesError2 = 0; // nn > maxResults
for (size_t i = 0; i < corners.size(); i++) {
std::vector<int> indices(4);
std::vector<float> dists(4);
int nn = index->radiusSearch(data.row(static_cast<int>(i)), indices, dists, 100, maxResults);
if (nn != (int)indices.size()) { nTimesError1++; }
if (nn > maxResults) { nTimesError2++; }
}
std::cout << "nTimesError1: " << nTimesError1 << std::endl; // > 0 before fix
std::cout << "nTimesError2: " << nTimesError2 << std::endl; // > 0 before fix
return 1;
}
```
## Fix
- Capture the search result in `rsearch_ret` instead of returning early from each `switch` case (this also required adding the missing `break` statements that would otherwise have caused fall-through between type-incompatible distance specialisations).
- Cap `rsearch_ret` at `maxResults` so the return value never exceeds the capacity of the output arrays.
- When `rsearch_ret < maxResults`, trim `_indices` and `_dists` to `query.rows × rsearch_ret` via `colRange(0, rsearch_ret).copyTo(...)` so the output dimensions always match the returned count.
update default ONNX Runtime version to 1.25.1 #28969
Bump ONNX Runtime default version to latest version: 1.25.1
Includes the following update:
https://github.com/microsoft/onnxruntime/pull/27164 : Adds LpNormalization opset-22 kernel support missing in 1.24.2.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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Extended primitive core operations to support new types #28964
The support was already there, this PR tests them on edge cases and patch the fix.
closes: https://github.com/opencv/opencv/issues/24580
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Fixed dequantizelinear slicing bug #28966
closes: https://github.com/opencv/opencv/issues/25999
### Pull Request Readiness Checklist
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core : add NEON intrinsics support for norm_mask function #28610
- This PR adds NEON intrinsics-based implementations for masked norm operations in norm.simd.hpp for ARM64 architecture.
- The optimized implementation uses ARM NEON intrinsics to accelerate masked norm computations (Infinity norm, L1 norm, and L2 norm) used by the norm function when a mask is provided.
- In the x64 architecture, masked norm operations benefit from IPP-based optimized implementations. However, on ARM64, the execution falls back to scalar implementations, which results in lower performance.
- To achieve performance parity with x64, NEON-based SIMD implementations have been added for ARM64.
- Additionally, scalar loop unrolling optimizations have been added for non-masked norm operations.
- After introducing these changes, masked norm operations showed significant performance improvements on ARM64 platforms, particularly for single-channel (cn=1) operations where NEON intrinsics provide the greatest benefit.
<img width="952" height="822" alt="image" src="https://github.com/user-attachments/assets/12d35f93-a316-4520-9d9c-12ce6b371ddb" />
- [x] I agree to contribute to the project under Apache 2 License.
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- [x] The PR is proposed to the proper branch
Added fully functional convTranspose layer to new DNN engine #27560
Closes https://github.com/opencv/opencv/issues/26307
### Pull Request Readiness Checklist
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The previous OpenCL implementation of UMat::diag() relied on a sequence of
operations involving buffer initialization followed by copy/transpose on
an aliased diag() view. Although these operations were enqueued on an
in-order command queue, this implicitly assumed memory visibility between
kernels operating on aliased regions of the same buffer.
According to the OpenCL specification, in-order command queues only guarantee
command scheduling order, while memory visibility between commands is only
established through explicit command-level synchronization points (e.g.
events, barriers, or clFinish).Under OpenCL’s relaxed memory model, such
assumptions are not guaranteed without an explicit command-level synchronization
point, and can lead to data races and incorrect results, especially for very
small matrices (e.g. 1x1). The issue is more likely to be exposed on Mesa-based drivers
when the GPU is running at lower frequencies (e.g. 500 MHz).
This change introduces a dedicated OpenCL kernel to construct the diagonal
matrix in a single kernel invocation, avoiding intermediate aliasing and
eliminating the need for implicit ordering assumptions. If the OpenCL path
is unavailable or unsupported, the implementation transparently falls back
to the CPU path.
Signed-off-by: jiajia Qian <jiajia.qian@nxp.com>
Extend several operations to support block layout #28839
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1347
After this PR, we observed the following improvements in the listed models:
Model | Before (ENGINE_NEW) | After (ENGINE_NEW) | ENGINE_ORT | % Improvement (Before v/s After)
-- | -- | -- | -- | --
Face_Paint | 514.68ms | 384.40ms | 394.38ms | 25.31%
BlazeFace | 1.03ms | 0.83ms | 0.66ms | 19.42%
**Device details:**
Model name: Intel(R) Core(TM) i9-14900KS, x86_64, 32 Cores, Ubuntu 22.04.5 LTS
Operations covered:
- [x] Pad
- [x] Resize
- [x] Reshape
- [x] Shape
- [x] InstanceNorm
- [x] GroupNorm
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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hal/riscv-rvv: implement laplacian #28887
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1353
An implementation of laplacian for riscv-rvv hal
Added benchmark in perf_laplacian.cpp
The performance is evaluated on K1 by running
./opencv_perf_imgproc --gtest_filter="Perf_Laplacian*"
Results are attached in the figure below.
CV_8U fast path supports ksize=3 with BORDER_CONSTANT/BORDER_REPLICATE; unsupported combinations fall back to default implementation. See performance results bellow.
### Pull Request Readiness Checklist
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Feat: Add OpenCL support for AKAZE features #28879
Implement OpenCL-accelerated AKAZE feature
detection and descriptor extraction
Benchmarked on RTX 5060 Ti: 1.2x to 3.31x faster
for image sizes from 640x480 to 3840x2160
- Added 8 OpenCL kernels for feature detection and descriptor extraction
- Refactored compute_kcontrast to use OpenCV magnitude() for consistency
- Added comprehensive tests with >95% accuracy vs CPU baseline
### Pull Request Readiness Checklist
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The minMaxIdx dispatch table in modules/core/src/minmax.cpp stored
seven differently typed functions through C-style casts to a single
MinMaxIdxFunc pointer type, which is undefined behaviour and trips
UBSan's -fsanitize=function for any CV_32F or CV_64F input. This
change switches the typedef and every depth-specific helper to take
void pointers for src, minval and maxval, with the original typed
pointer recovered at function entry. The dispatch table no longer
needs C-style casts and the int pointer punning at the call site
goes away. Fixes#28928.
Better AutoBuffer API #28909
Mimic std::vector<> to help replacing std::vector<T> by cv::AutoBuffer<T> when possible
### Pull Request Readiness Checklist
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videoio(dshow): fix crash in videoInput::start() when pVih is NULL #28914
### Summary
This PR fixes a critical application crash in the DirectShow backend when encountering certain legacy or virtual cameras (e.g., Microsoft's DirectShow "Ball" Sample).
### The Problem
In `modules/videoio/src/cap_dshow.cpp`, the function `videoInput::start()` calls `pConfig->GetConnectedMediaType(&mt)`.
- In some edge cases, this call returns `S_OK`, indicating success.
- However, the associated format block `mt.pbFormat` (which `pVih` points to) can still be `NULL`.
- The current implementation uses `CV_Assert(pVih)`, which triggers a hard crash of the entire application when this occurs.
### The Fix
I have replaced the `CV_Assert(pVih)` with a null-pointer check. If `pVih` is `NULL`, the function now returns `false`. This allows the high-level `cv::VideoCapture` to handle the failure (e.g., by returning `false` from `.open()`) instead of crashing the process.
### Engineering Rationale
- Execution Path Consistency: For devices providing valid format headers, `pVih` remains non-NULL. In these cases, the conditional check is skipped, and the execution path is identical to the original implementation.
- Process Stability: Replacing `CV_Assert` prevents immediate process termination (SIGABRT) when a DirectShow filter provides an invalid or empty format block.
- Error Propagation: Returning `false` in `videoInput::start()` allows the initialization failure to propagate through the call stack. This ensures that `cv::VideoCapture::open()` returns `false`, enabling the caller to detect the issue via the standard API (e.g., `isOpened()`) instead of the process being terminated.
### Evidence
I have manually reproduced this crash using the Microsoft DirectShow Ball Sample filter on Windows. As shown in the attached debugger screenshot:
- `deviceID` is 2 (the virtual camera).
- `hr` is `S_OK`.
- `pVih` is `NULL`.
<img width="773" height="1413" alt="dshow_null_pvih_debug_evidence" src="https://github.com/user-attachments/assets/5bc454b4-4a0b-4703-825d-0d5fd23cba28" />
Fixes#28904
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- Replace unsafe pointer arithmetic and direct buffer modification with std::string methods.
- Update documentation to clarify that the last digit is used as compression level and truncated from the actual filename.
- Add test cases for .gz and .gz0-9
Added Attention fusion and thin gemm support #28859
Merge with: https://github.com/opencv/opencv_extra/pull/1350
Performance numbers after these optimizations:
For Device: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW (Before)` | `ENGINE_NEW (After)` | `ENGINE_ORT` |
| :--- | :--- | :--- | :--- |
| **BERT** |26.3 ms| 9.15 ms| 9.13 ms|
| **ViT** | 79.65 ms| 63.23 ms| 32.3 ms|
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Fix Clp library detection for vcpkg on Windows #28423
## Problem
Building OpenCV with `WITH_CLP=ON` on Windows (vcpkg) fails:
LINK : fatal error LNK1181: cannot open input file 'libClp.lib'
vcpkg generates `Clp.lib` and `CoinUtils.lib`, but OpenCV expected `libClp.lib`/`libCoinUtils.lib`.
## Solution
Modernize CLP detection with a robust, platform-aware approach:
1. **CMake `find_package(Clp CONFIG)`**: detects vcpkg and uses imported targets (`Coin::Clp`, `Coin::CoinUtils`)
2. **Fallback**: pkg-config on Unix/Linux
3. **Skip** Android/iOS
## Benefits
✅ Fixes Windows build with vcpkg
✅ Uses modern CMake targets
✅ Backward compatible with Unix/Linux
✅ Safe for CI and mobile builds
✅ Graceful fallback if CLP unavailable
## Testing
- **Windows 11 + vcpkg**: build succeeds
- **No CLP installed**: configure/build succeeds
- **Linux/Unix**: existing workflows unaffected
- **Android/iOS**: properly skipped
## Changes
- Update `cmake/OpenCVFindLibsPerf.cmake`
- Replace hardcoded library names with CMake targets
- Add mobile platform guards
- Preserve backward compatibility
Fixes#26592
---
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- [x] There is a reference to the original bug report and related work (#26592)
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- [x] No additional tests required - this is a build system fix that maintains existing functionality
doc: modernize Doxygen comments to support for v1.15.0 #28903
This PR addresses several documentation build failures encountered with modern Doxygen versions, particularly v1.15.0 (shipped with Ubuntu 26.04).
- flann module: Updated license headers from /**** to /*M****. This prevents Doxygen from misinterpreting the license text (specifically the unclosed backticks in ``AS IS'') as documentation blocks, which previously caused "Reached end of file" errors.
- core module: Fixed a typo in operations.hpp where a doubled backtick (``) caused parsing to fail.
- tutorials: Fixed a missing backtick in real_time_pose.markdown (around line 108) and corrected typos in the RobustMatcher class name.
- Links: Resolved explicit link request failures in calib3d.hpp by ensuring proper namespace resolution.
These fixes ensure that the documentation can be generated without errors on the latest toolchains.
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Add Gemma3 tokenizer support for dnn #28837
- Adds Gemma3 tokenizer support
- Implements character-level BPE
- Adds 6 tests covering English, phrase, mixed case, numbers, special tokens, and encode/decode
- add gemma3_inference.py
Merge with:
- **Companion PR** : https://github.com/opencv/opencv_extra/pull/1346
- forward pass bug in gemma3_inference.py : https://github.com/opencv/opencv/pull/28836
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- Emscripten 5.0.1+ changed version macros to uppercase and deprecated lowercase ones.
- Updated CMake to detect the version from both cases.
- Added macro aliases in intrin_wasm.hpp to avoid deprecated warnings and ensure compatibility.
Zlib upgrade 1.3.2 #28779
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Added QLinear layer support #28811
closes: https://github.com/opencv/opencv/issues/26310
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videoio: fix C4576 build error with older ffmpeg versions #28894Fixes#28875
### Description
This PR fixes a build failure (`error C4576`) that occurs when building the `videoio` module with older FFmpeg versions (e.g., 4.4.4) using MSVC under strict C++17 mode.
**Root Cause:**
In older FFmpeg headers, the `AV_TIME_BASE_Q` macro is defined using a C99 compound literal `(AVRational){1, AV_TIME_BASE}`. This syntax is non-standard in C++ and is strictly rejected by MSVC when `/std:c++17` is enabled.
**Solution:**
Replaced the `AV_TIME_BASE_Q` macro with FFmpeg's standard `av_make_q(1, AV_TIME_BASE)` function. This resolves the C4576 compiler error and ensures modern C++ compatibility.
*(Note: `av_make_q` is already smoothly polyfilled in `cap_ffmpeg_impl.hpp` for extremely old FFmpeg versions, making this a highly safe, consistent, and zero-overhead replacement.)*
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Add CAP_PROP_IMAGE_SEQ_START #28844
Resolves https://github.com/opencv/opencv/issues/28843
New CAP_PROP_IMAGE_SEQ_START as an open only property for setting the first frame index when a VideoCap is opening with pattern, squashes test_images TODO.
`CAP_FFMPEG`: forwarded to the image2 demuxers "start_number" option.
`CAP_IMAGES`: Now a WithParams variant so the value reaches open() before probe runs, replaces 0 or 1 auto probe for sequence start.
Property is opt in so default behavior is not affected.
Please note that G_STREAMER is not considered here as I think its out of scope, it already has a "start-index" property and its backend is driven by the pipeline strings rather then these printf patterns.
If an alternative solution is preferred that will not involve a new property or promoting CAP_IMAGES to a "WithParams" variant I don't mind implementing.
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Bump some calib3d files to C++ #28825
This is just copy/pasting files from 5.x (first commit) and applying some light patch for compilation (second commit).
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Add Prior Box Layer to support the WeChat QR model #28855
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1349
### Pull Request Readiness Checklist
This PR is part 1 of the split from the original PR: WeChatQR-fix conversion Caffe to ONNX #28746.
It focuses on adding the PriorBox layer and its related unit tests. The WeChatQR specific changes will be submitted in a separate PR.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Avoid lossy Latin-1 conversions when creating/looking up Qt HighGUI windows and convert C-string entry points to UTF-8 explicitly. This fixes non-ASCII title handling paths that could lead to mismatched window lookup and silent display failures.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
objdetect: clear stale outputs in CharucoDetector::detectDiamonds #28818
## Summary
- clear `diamondCorners`/`diamondIds` outputs at the beginning of `CharucoDetector::detectDiamonds`
- prevent stale detections from previous calls when the current call has fewer than 4 markers or finds no diamonds
- add a regression test that pre-fills outputs, calls `detectDiamonds` with 3 markers, and verifies outputs are empty
Fixes#28783.
## Testing
- built `opencv_test_objdetect` locally
- ran `opencv_test_objdetect --gtest_filter=Charuco.detectDiamondsClearsOutputsWithLessThanFourMarkers`
- result: PASS
Parallelize DNN layers using chunking #28821
At a high level, I replaced tensor-level parallelism with chunk-level parallelism. Previously, parallel_for_ was dispatched over the number of input or output tensors i.e. one thread handled one whole tensor's copy.
The new approach precomputes each tensor's destination offset and per-slice size upfront, then slices the total byte work into fixed 64 KB chunks. The full chunk count is handed to parallel_for_ as a single flat range, and each worker decodes its chunk index back into (tensor, slice, byte_offset) using a prefix-sum table before running a plain memcpy on its piece. A small-size threshold falls back to the sequential path so we don't get threading overhead on small tensors.
Performance numbers after these optimizations:
For Device: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW` | `ENGINE_ORT` |
| :--- | :--- | :--- |
| **YOLOv8n** |10.9 ms| 12.15 ms|
| **YOLOv5n** | 8.36 ms| 9.23 ms|
| **YOLOX-S** | 23.46 ms| 25.16 ms|
For Device: Macbook M1 Air
| Model | `ENGINE_NEW` | `ENGINE_ORT` |
| :--- | :--- | :--- |
| **YOLOv8n** |34.45 ms| 42.52 ms|
| **YOLOv5n** | 31.62 ms| 25.52 ms|
| **YOLOX-S** | 88.9 ms| 116.7 ms|
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Fix dnn NaN bugs in GELU SIMD and softmax for large inputs #28836
### Bug Description
- **GELU SIMD bug:** `exp(2*inner)` overflows to `inf` for large inputs (e.g. `x=10.6` → `inner≈51`), causing `inf/inf = NaN`; fixed by clamping `inner` to [-9, 9] as `tanh` already saturates to ±1.0 beyond this range.
- **Softmax bug:** All `-inf` inputs (masked attention rows) produce `sum=0`, then `1/0 = inf` and `0*inf = NaN`;
fixed by outputting zeros when `sum == 0`.
- Add regression tests for both fixes
Depends on : https://github.com/opencv/opencv/pull/28837
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Multiscale ECC #28802
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1338
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Previously, OpenCL in-place flip kernels (rows/cols/both) could produce
incorrect results compared to the CPU implementation on strict OpenCL
drivers such as Mesa. The kernels relied on implicit load–load–store–store
(LLSS) ordering when src and dst alias, which is not guaranteed by the
OpenCL memory model and may be reordered.
Some vendor drivers happened to preserve the expected ordering, masking
the issue, but Mesa correctly exposes the undefined behavior.
This change introduces dedicated in-place flip kernels that:
- Explicitly detect in-place execution (src == dst)
- Stage data through local memory tiles
- Enforce correct ordering with work-group barriers
- Avoid global memory read/write aliasing hazards
The non in-place path is unchanged.
With this fix, OpenCL in-place flip produces correct and consistent results
across drivers, matches CPU behavior, and complies with the OpenCL memory
model.
Signed-off-by: jiajia Qian <jiajia.qian@nxp.com>
Avfoundation fractional fps seek fix#28833
Accompanied by PR on [opencv_extra](https://github.com/opencv/opencv_extra/pull/1345)
Fixes https://github.com/opencv/opencv/issues/28831
AVFoundation backend seeking slowly drifts for any video which has a non-integer frame rate.
Narrowing of a float `mAssetTrack.nominalFrameRate` to int32_t means fractional rates are treated as integer when seeking by frame number.
Solution implemented in this branch is to use the tracks native rational frame duration when seeking (and available).
I've also included an AVF only test case which uses a new test file in opencv_extra. This test just compares the video caps expected ms vs actual for a known rate file (23.976).
Could consider extending this test to more then just AVF, as from what I can see there are no fractional rate tests / files.
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Fix issues with MultiViewTest.OneLineInitialGuess and MultiViewTest.OneLine #28817
To fix issues caused by https://github.com/opencv/opencv/issues/28789
This probably can be fixed for 4.x, but honestly I don't believe it is worth it to have to deal with users tests failing because it was 0.02% off so we can save them like 2 minutes, so I believe it is best to leave the optimization for 5.x.
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imgcodecs(png): support cICP metadata for imreadWithMetadata() #28615
Close https://github.com/opencv/opencv/issues/28606
Related https://github.com/opencv/opencv/pull/27741 (support for imwriteWithMetadata)
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The existing 2-pass cross-check implementation requires both forward and
reverse distance computations on GPU, followed by CPU-side filtering.
This adds a single-pass kernel using int64 atomics to eliminate the
reverse pass. The BruteForceMatch_CrossCheckMatch kernel performs forward
matching and tracks best train->query mappings in one GPU pass, reducing
memory transfers. Falls back to 2-pass approach on devices without
cl_khr_int64_base_atomics extension.
Tested on NVIDIA RTX 5060 Ti: 1.65x-1.97x faster than 2-pass GPU,
3.49x-7.33x faster than CPU at 640x480 to 1920x1080 resolutions.
build: fix bundled protobuf headers being overridden by system-installed protobuf #28745
On macOS with Apple Clang 17, the compiler implicitly injects `/usr/local/include` as a high-priority user include (`-I`). The `SYSTEM` keyword in `target_include_directories` emits `-isystem` for the bundled protobuf path, which is searched after user `-I` paths, allowing a system-installed protobuf v4+ (which requires C++17 and abseil-cpp) to override the bundled headers.
Removing `SYSTEM` causes CMake to emit `-I` instead of `-isystem`, placing the bundled path before the implicit system include, restoring the intended header resolution order regardless of what protobuf version is installed on the host.
### Background
Apple Clang 17 (shipped with macOS 26 Tahoe Command Line Tools) changed the compiler driver behavior: it now unconditionally adds `-I/usr/local/include` to the search path, which is where Homebrew installs headers. Previous Apple Clang versions either omitted this path or added it as a lower-priority system include.
When Homebrew's `protobuf` is installed (v4+, e.g. `33.4_1`), its headers at `/usr/local/include/google/protobuf/` are resolved before the bundled `3rdparty/protobuf/src/` because `SYSTEM PUBLIC` in `target_include_directories` causes the bundled path to be emitted as `-isystem`, which ranks below `-I` in compiler search order. This was confirmed by inspecting the generated `flags.make`:
```
# before fix
-isystem /path/to/opencv/3rdparty/protobuf/src
# after fix
-I /path/to/opencv/3rdparty/protobuf/src (first in the include list)
```
The system protobuf v4+ headers require C++17 and abseil-cpp. Since OpenCV 4.x compiles with `-std=c++11`, every translation unit in `3rdparty/protobuf/` fails:
```
/usr/local/include/google/protobuf/port_def.inc: error: Protobuf only supports C++17 and newer.
/usr/local/include/absl/base/policy_checks.h: error: C++ versions less than C++17 are not supported.
```
Note that `include_directories(BEFORE ...)` on the line above was intended to prevent exactly this, but it was overridden by `target_include_directories(SYSTEM PUBLIC ...)` deduplicating the path into a single `-isystem` entry. This is essentially the same class of problem tracked in #13328.
### Impact
The change is a one-line diff. Removing `SYSTEM` has no effect on Linux or Windows since those toolchains do not inject `/usr/local/include` implicitly. Warning suppression for protobuf headers in dependent modules is unaffected because OpenCV already applies explicit `-Wno-*` flags for all protobuf-related warnings in `3rdparty/protobuf/CMakeLists.txt`.
### Tested on
- macOS 26.3 (Tahoe), Apple Clang 17.0.0 (`clang-1700.6.4.2`), Homebrew `protobuf 33.4_1`, full build passes with `BUILD_PROTOBUF=ON`.
Fixes#28743, related to #27886.
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Fix flatten onnx axis==rank case #28812
fix ONNX Flatten layer incorrect output when axis equals input rank edge case
Resolves : https://github.com/opencv/opencv/pull/28781#discussion_r3060088247
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1342
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Patch to opencv_extra has the same branch name.
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Add Qwen2.5 tokenizer support for dnn #28781
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1334
Extended the dnn tokenizer support to qwen2.5 tokenization.
- Add QWEN2_5 pre-tokenizer regex pattern to utils.hpp
- Generalised buildTokenizerGPT to buildTokenizerFromJson to handle gpt2/gpt4/ and qwen2.5
- Add qwen2/qwen2.5 model type support with special token handling
- Add Qwen2.5 tests
- Add end-to-end qwen_inference script for Qwen2.5 ONNX model
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Extended fusion for activation functions in new DNN engine #28750
After this fusion, we see following improvements in YOLO models:
| Model | Before (`ENGINE_NEW`) | After (`ENGINE_NEW`) | `ENGINE_ORT` | % Improvement (Before v/s After) |
| :--- | :--- | :--- | :--- | :--- |
| **YOLOv8n** | 18.89 ms| 12.06 ms| 12.15 ms| 36.16% |
| **YOLOv5n** | 17.12 ms| 9.29 ms| 9.23 ms| 45.73% |
| **YOLOX-S** | 38.78 ms| 25.56 ms| 25.16 ms| 34.09% |
Device details:
- Model name: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
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Int8 block layout support #28741
After this patch we got the following speed ups on **resnet50-qdq.onnx** model.
- Inference time now: **_~6.6ms_** (inference time using onnxruntime is ~5.7ms).
- Inference time before: _**~11.5ms**_ [after QDQ PR #28595]
- Speed up: _**~42.6% or 1.74x**_
- Device details:
- Model name: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
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Allow TIFF compression schemes for 32F#28785
Related to [https://github.com/opencv/opencv/issues/28775](https://github.com/opencv/opencv/issues/28775)
Previously, only 32FC3+SGILOG could be specified for TIFF encoding. But when floats use quantization, compression schemes can be efficient even on 32F data. This PR will allow them.
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I just don't know what kind of accuracy/performance tests should be added.
On systems like OpenBSD, CBLAS functions are provided by a standalone
libcblas that is not returned by find_package(LAPACK). This caused link
failures when building libopencv_core because cblas_sgemm and friends
from hal_internal.cpp could not be resolved.
Fixes#28768
Added OnnxRuntime GPU wrapper #28588
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GSoC 2025: Add Tokenizer Support to DNN Module #27534
merge with https://github.com/opencv/opencv_extra/pull/1276
### Summary
This pull request introduces initial support for a tokenizer module under `modules/dnn/src/tokenizer` as part of Google Summer of Code 2025 (Project: Tokenization for OpenCV DNN).
### Status
- [x] Project structure in place
- [x] Initial BPE tokenizer loading
- [x] Regex splitting (in progress)
- [x] Encoding logic for GPT-2 tokenizer (in progress)
- [ ] Documentation (to be improved)
### Goals
The goal is to support Hugging Face-compatible tokenization (e.g., GPT-2) natively in C++ to be integrated with DNN inference pipelines.
The core pipeline lives in `dnn/src/tokenizer/core_bpe.hpp` and `dnn/src/tokenizer/encoding.hpp`. For Unicode handling I’m using `dnn/src/tokenizer/unicode.hpp`, which is adapted from llama.cpp.
### Feedback
Please share early feedback on:
- General design structure
- Integration strategy with `dnn`
- Code organization or naming conventions
### Reference
Project: https://summerofcode.withgoogle.com/programs/2025/projects/79SW6eNK
Fixes redundant code in Cloning::illuminationChange() which performed
unnecessary copyTo operations. This satisfies Issue #22056.
Testing: No functional changes made; logic identical to before.
Resolves#22056
core(opencl): fix inplace transpose race by enforcing LLSS ordering via local barrier #28686
The former inplace transpose implementation allowed a reordering of global-memory operations across work-items. Specifically, the intended LLSS (Load–Load–Store–Store) access pattern could be reordered by the GPU into LSLS (Load–Store–Load–Store), causing partially written tiles to be observed by other work-items and producing incorrect output.
This patch introduces a tiled LDS-based algorithm and adds an explicit:
barrier(CLK_LOCAL_MEM_FENCE);
between the load and store phases.
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python: fix segfault on 0-channel numpy array input #28747
### Pull Request Readiness Checklist
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N/A: a Python unit test is added in `modules/python/test/test_mat.py`. No external test data required.
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N/A: this is a bug fix, not a new feature. No documentation update needed.
### Problem
Passing a numpy array with shape `(H, W, 0)` (0 channels) to any OpenCV function that accepts a `Mat` argument (e.g. `cv2.resize`, `cv2.warpAffine`, `cv2.blur`) causes a **segfault**.
**Reproducer:**
```python
import cv2
import numpy as np
arr = np.zeros((100, 100, 0), np.uint8)
cv2.resize(arr, (200, 200)) # segfault
```
### Root Cause
In `modules/python/src2/cv2_convert.cpp`, the numpy→Mat conversion checks channel validity only against `CV_CN_MAX` (upper bound):
```cpp
if (channels > CV_CN_MAX) // channels=0 passes this check
```
With `channels=0`, `CV_MAKETYPE(0, 0)` produces `type=-8`, which corrupts the Mat's internal type field and causes undefined behavior downstream.
### Fix
Extend the check to also reject `channels < 1`:
```cpp
if (channels < 1 || channels > CV_CN_MAX)
```
**After fix:**
```
cv2.error: src unable to wrap channels, invalid count (0, must be in [1, 512])
```
### Notes
- This affects all functions that accept a `Mat` input, not just `cv2.resize`
- The same bug exists in the `5.x` branch
- A 0-channel array has no valid OpenCV Mat representation; rejecting it with a clear error is the correct behavior and poses no backward-compatibility risk (the previous behavior was a crash)
Nms empty detection fix#28749
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1330
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videoio(gstreamer): fix timestamp drift and color negotiation on Apple #28535
This commit addresses two issues on macOS with Apple M3 hardware:
1. Replaces floating-point timestamp math with gst_util_uint64_scale_int to ensure nanosecond precision.
2. Explicitly forces I420 format in the encoding profile to prevent hardware encoder negotiation failure.
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core: add NEON implementation for rotate function #28609
- This PR adds a NEON intrinsics-based implementation for the rotate function in matrix_transform.cpp for Windows-ARM64.
- The optimized implementation uses ARM NEON intrinsics to accelerate the internal transpose step used by the rotate function.
- In the x64 architecture, the rotate operation benefits from IPP-based optimized implementations. However, on ARM64, the execution falls back to the scalar implementation, which results in lower performance.
- To achieve performance parity with x64, a NEON-based SIMD implementation has been added for ARM64.
- After introducing these changes, the rotate function showed noticeable performance improvements on ARM64 platforms.
<img width="1009" height="817" alt="image" src="https://github.com/user-attachments/assets/8bec0041-b19c-4fc8-9103-532746224515" />
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dnn: fix missing ONNX dispatch entries and RotaryEmbedding bugs #28714
- Wire RMSNormalization and RotaryEmbedding parsers into the ONNX dispatch map. Both parse functions were declared and implemented but never registered, causing the importer to fall through to the generic path and skip constant-folding of cos/sin caches.
- Fix NonMaxSuppression dispatch key typo ("NonMaxSuprression" -> "NonMaxSuppression") so the ONNX op name matches the registered layer class. Also fix the type string set inside the parser.
- Fix tautological self-comparison in RotaryEmbeddingLayer::getMemoryShapes (cos_cache_shape.dims == cos_cache_shape.dims -> sin_cache_shape.dims).
- Fix typo in RotaryEmbedding error message ("cos_cahe" -> "cos_cache").
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dnn: fix Resize initNgraph for two-input case #28724
## Summary
Fixes the issue #28707
When a Resize/Upsample layer has two inputs, the data tensor and a reference tensor whose **shape** defines the output spatial size, the OpenVINO/NGRAPH backend's `initNgraph()` was ignoring `nodes[1]` entirely and relying solely on the `outHeight`/`outWidth` member variables.
These variables are set by `finalize()` from the pre-computed output blob dimensions. However, when the output shape is determined dynamically at runtime from the second input, `finalize()` sets them from the live tensor, but the OpenVINO backend calls `initNgraph()` to build a static compiled graph. If the member variables are 0 at that point, the compiled `Interpolate` node gets hardcoded with `{0, 0}` output dimensions, causing CV_Assert failure: {N,C,0,0} vs {N,C,H2,W2}
BFMatcher::match() with crossCheck=true previously skipped the OCL
dispatch in knnMatchImpl entirely, falling back to CPU even when UMat
inputs and an OpenCL device were available. This adds
ocl_matchWithCrossCheck() for CV_32FC1 descriptors (e.g. SIFT, SURF):
both the forward and reverse nearest-neighbour passes run on the GPU
via the existing ocl_matchSingle() kernel, then the cross-check filter
runs on the CPU. Only two small index arrays (1×N ints) are downloaded
— the O(N²×D) distance work stays on the device.
The OCL dispatch in knnMatchImpl is also refactored to unify the
Mat/UMat train collection selection before branching on crossCheck.
On a NVIDIA RTX 3060 with SIFT descriptors the OCL path is 6–9×
faster than CPU at 2k–10k features per image. Binary descriptors
(ORB, BRIEF — CV_8U) are unaffected; the existing type guard in
ocl_matchSingle keeps them on the CPU path as before.
Also adds a correctness test (Features2d_BFMatcher_CrossCheck) and an
OCL perf test (BruteForceMatcherFixture/MatchCrossCheck).
Fixes#28396 : out-of-bounds read in SIMD type conversion #28397Fixes#28396Fixes#27080
The vx_load_expand function in WASM intrinsics was using
wasm_v128_load which always loads a full 128-bit register
(16 bytes), even when the function only needed 8 elements.
For example, when converting uint8 to float32:
- vx_load_expand needs 8 uint8 elements
- But wasm_v128_load reads 16 bytes from memory
- This causes an 8-byte out-of-bounds read
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3d: fix depth precision inconsistency under ASan/RelWithDebInfo builds #28687
Resolves FMA inconsistency between build types by using std::fma(a, b, c) to get the same precision everywhere ASan, Release, Debug.
Change optimisation :
Without FMA : 96.54 ms
FMA : 32.33 ms
Merged with : opencv/ci-gha-workflow/pull/297
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Added conv 1x1 and conv 3x3 block layout support#28691
After this patch we get the following speed ups on resnet50.onnx:
Inference time after: ~7.6ms (inference time using onnxruntime: ~6.67ms)
Inference time before: ~14ms
Speed up: ~46% or 1.84x
Device details:
Model name: Intel(R) Core(TM) i9-14900KS
Cores: 32
RAM: 128 GB
Architecture: x86
OS: Ubuntu 24.04
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Added Output Tensor Names support in new DNN engine #28637
closes: https://github.com/opencv/opencv/issues/26201
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Optimize calibrateCamera with Schur‑complement LM and parallel Jacobian accumulation #28461
## Summary
- Optimized `calibrateCamera` for faster runtime without changing outputs using Schur‑complement LM, Parallel Jacobian accumulation, alongside other optimizations.
- Reduced time complexity from O(n^3) to O(n)
- Add a perf test that uses a 500-image chessboard dataset for performance testing.
## Performance
<img width="1200" height="800" alt="base_vs_fast_results" src="https://github.com/user-attachments/assets/6dafa19f-f9cb-4f7f-ba40-0940373712e8" />
<img width="1200" height="800" alt="fast_vs_ceres_results" src="https://github.com/user-attachments/assets/7157af27-8a2b-4810-8b53-3cc9972a8493" />
<img width="1200" height="800" alt="base_vs_fast_param_deviation" src="https://github.com/user-attachments/assets/fe4f954c-34f9-4b9a-b1b2-46e4c76ce08c" />
[Testing repo
](https://github.com/Ron12777/OpenCV-benchmarking)
## Testing
- All local tests pass
## Related
- [opencv_extra PR with test images](https://github.com/opencv/opencv_extra/pull/1312)
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Fix kernel shape handling in ONNX importer2 #28646
Requires opencv_extra: https://github.com/opencv/opencv_extra/pull/1323
closes: https://github.com/opencv/opencv/issues/28321
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author vrooomy <vj.bro.833@gmail.com> 1773655621 +0530
committer vrooomy <vj.bro.833@gmail.com> 1774357668 +0530
added SIMD support for 64 bit float and fallback for 64 bit int
removed trailing white-spaces
add SIMD optimization for 32 unsinged int and clean fallback for 64u and 64s
add SIMD support for 32u and fallback for 64u and 64s
changed CalibrateDebevec test threshold to 0.25 (same as ARM) for IPP=NO
relaxed threshold to 0.22
conflict resolved
Convert exportResource() and loadResource() to use try-with-resources
to ensure InputStream, FileOutputStream, and ByteArrayOutputStream are
properly closed even when exceptions occur.
Also remove printStackTrace() in exportResource(), as the exception is
already rethrown as CvException with the original exception details.
Signed-off-by: ffccites <99155080+PDGGK@users.noreply.github.com>
Replace System.exit(-1) with exceptions in HighGui.java (#28696) #28699
## Summary
Library code should never call System.exit() as it kills the entire JVM. Replaced all 3 instances with appropriate exceptions.
Closes#28696
## Changes
- `imshow()` with empty image: `System.exit(-1)` -> `throw new IllegalArgumentException("Image is empty")`
- `waitKey()` with no windows: `System.exit(-1)` -> `throw new IllegalStateException("No windows created. Call imshow() first")`
- `waitKey()` with null window image: `System.exit(-1)` -> `throw new IllegalStateException("No image set for window: ... Call imshow() first")`
- `InterruptedException` catch: `printStackTrace()` -> `Thread.currentThread().interrupt()`
dnn: fix dst_dp assertion in broadcast for size-1 dims causing crash in lightglue.onnx model #28692
The original assertion CV_Assert(dst_dp == 1) does not handle valid cases where the innermost dimension size is 1 like [10, 5, 1], resulting in dst_dp == 0.
This occurs during broadcasting in LightGlue ONNX model and leads to assertion failure.
Allow dst_dp == 0 for size-1 dimensions to handle this edge case correctly.
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Added SIMD support for 64 bit float and fallback for 64 bit int#28663
Merged with : https://github.com/opencv/ci-gha-workflow/pull/299
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When a module is disabled in an incremental build (e.g. switching from
-DBUILD_opencv_gapi=ON to OFF), its typing stub directory persists from
the previous build. The stubs generator only creates directories for
enabled modules but never removes old ones, and the copy step merges
rather than replaces, so stale .pyi files propagate to both the loader
directory and the install prefix.
This causes type-checkers (mypy, pyright) to report errors for stubs
that reference symbols from modules that are no longer available.
Fix both propagation paths:
- generation.py: remove all subdirectories under the stubs output root
before regenerating, so only currently enabled module stubs exist in
the build directory. Top-level files (py.typed) are preserved.
- copy_typings_stubs_on_success.py: remove stale .pyi files and
py.typed markers from the loader directory before copying fresh stubs,
so leftover stubs from a previous copy are cleaned up. Runtime .py
files are not affected.
imgproc: add SIMD support for distance transform function #28636
- This PR adds OpenCV SIMD intrinsics-based optimizations to the distance transform functions for improved performance.
- The optimized implementation uses vectorized operations to accelerate the forward and backward passes of distance computation.
- In x64 architecture, distance transform function benefit from IPP-based optimized implementations. However, on ARM64 platforms, the execution falls back to scalar implementation, which results in lower performance.
- After introducing these changes, the distance transform functions showed noticeable performance improvements on Windows-ARM64.
<img width="800" height="706" alt="image" src="https://github.com/user-attachments/assets/9786606a-9d92-489d-a5ea-d2e57453ef02" />
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Fix two issues that cause `import cv2` to fail when building with
-DBUILD_opencv_gapi=OFF:
1. In `has_all_required_modules()`, the function parameter `type_node`
was ignored in favor of the enclosing scope's loop variable `node`.
This works by accident when called as `has_all_required_modules(node)`
but is incorrect — the parameter should be used directly.
2. In `__load_extra_py_code_for_module()`, only `ImportError` was
caught. When a stale gapi submodule directory exists from a previous
build, gapi/__init__.py raises `AttributeError` (not `ImportError`)
because it tries to access C++ bindings that don't exist. Now catches
both exception types for defense-in-depth.
Refs: #26098
The tutorial code used cv.NORM_L2 (which takes a square root) and then
averaged those values. The correct RMSE formula should use NORM_L2SQR
to get squared errors, average them, and take the square root at the
end. Updated the explanatory text to match.
Fixes https://github.com/opencv/opencv/issues/28651
Added getFLOPS support in new DNN engine #28634
closes: https://github.com/opencv/opencv/issues/26199
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Block layout-based convolution in DNN #28585
merge together with https://github.com/opencv/opencv_extra/pull/1321
Some core parts of the new engine in DNN module have been revised substantially:
1. all tests seem to pass, except for `Test_Graph_Simplifier.ResizeSubgraph`, which has been disabled because it does not take the newly added `TransformLayoutLayer` into account. The test should be reworked perhaps.
1. convolution and related operations (maxpool/avgpool) now use so-called block layout (`DATA_LAYOUT_BLOCK`), where `NxCxHxW` tensors are represented as `NxC1xHxWxC0`, where `C1=(C + C0-1)/C0` and `C0` is a power-of-two (usually 4, 8, 16 or 32).
1. graph is now pre-processed and `TransformLayoutLayer` is inserted to convert data from NCHW or NHWC layout to the block layout or vice versa. The transformations are done in a lazy way only when they are really needed. For example, in the whole Resnet only 2 transformations are performed.
1. transformer-based models and other models that do not use convolutions will run as usual, without going to block layout.
1. there is yet another graph preprocessing stage added that embeds constant weights/scale and bias into convolution and batch norm layers.
1. 'batchnorm', 'activation' and 'adding a residual' are now fused with convolution, just like in the old engine. That brings some noticeable acceleration.
1. optimized convolution kernels have been added.
* depthwise convolution, as well as maxpool and avgpool support C0=4, 8, 16 etc. _as long as_ C0 is divisible by the number of fp32 lanes in a SIMD register of the target platform (e.g. on ARM with NEON there must be `C0 % 4 == 0`, on x64 with AVX2 `C0 % 8 == 0`).
* non-depthwise convolution only supports C0=8 for now. C0=8 seems to be a sweetspot for ARM with NEON, x64 with AVX2 or RISC-V with RVV (with 128- or 256-bit registers). For some platforms with dedicated matrix accelerators C0=16 or even C0=32 might be more efficient, but we could add the respective kernels later.
* only fp32 kernels have been added. fp16/bf16 kernels might be added a little later.
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video: Optimized ScharrDeriv#28632
- Move to CV_SIMD_SCALABLE
- avx2 & avx512 dispatch added for ScharrDeriv
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Optimized flip Horizontal #28614
- Refactor flipHoriz implementation.
- Optimizations for horizontal image flipping added.
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## This tutorial describe how to build opencv from source in windows using VSCode and MSYS2
CPU Kernels for fp32 KV Cache #28524
The kernels are:
- pagedAttnQKGemmKernel
- pagedAttnAVGemmKernel
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core: fix heap-buffer-overflow in YAML parseKey for empty keys #28620
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### Description
Fixes https://github.com/opencv/opencv/issues/28619
Moves the "empty key" check before the backward do-while scan in `YAMLParser::parseKey()`.
**Problem:** When parsing a YAML mapping with an empty key (e.g. `: 10` at column 0), `endptr == ptr` after the forward scan finds `:`. The do-while loop `do c = *--endptr; while(c == ' ')` always executes at least once, so it decrements `endptr` to `ptr-1` and reads one byte before the heap allocation (ASan: heap-buffer-overflow READ of size 1).
**Fix:** Check `endptr == ptr` before entering the backward loop. If the key is empty, raise `CV_PARSE_ERROR_CPP("An empty key")` immediately without the OOB read.
This contribution was developed with AI assistance (Claude Code).
imgproc: fix HoughCircles Python return type to allow None #28621
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### Description
Fixes https://github.com/opencv/opencv/issues/28528
`HoughCircles` returns `None` when no circles are found, but the auto-generated Python type stub declares the return type as `MatLike` without `None`. This causes type checkers (mypy/pyright) to miss potential `None` dereferences.
**Fix:** Add `HoughCircles` to `NODES_TO_REFINE` in `api_refinement.py` using the existing `make_optional_none_return` helper - the same pattern already used for `imread` and `imdecode`.
This contribution was developed with AI assistance (Claude Code).
fitEllipseDirect: replace det(M) threshold with eigenvector quality check
- Add Ts ≈ 0 guard to avoid division by zero in Schur complement
- Move eigenNonSymmetric inside perturbation loop
- Validate eigenvector with 4ac-b² > 1e-6*||v||² to filter garbage from
complex eigenvalues
fitEllipseAMS: scale threshold by 1/n^5 to match det(M) magnitude
Add Imgproc_FitEllipseDirect_NearCircular regression test
Fix gather cast axis #28633
Closes: https://github.com/opencv/opencv/issues/23231
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- Replace execute_process(tar) with file(ARCHIVE_EXTRACT) for native .zip support
and better Windows path handling when CMAKE_VERSION >= 3.18
- Replace execute_process copy with file(COPY) and existence check for reliable
CMakeLists.txt copying to avoid timing/path issues on Windows
- Maintains backward compatibility with older CMake versions and Linux behavior
Fixes issue #28608
imgproc: fix minEnclosingCircle O(n^3) worst case by adding Welzl shuffle #28548
Welzl's algorithm requires random permutation of input points to
achieve expected O(n) time. Without shuffling, sorted inputs such
as those produced by findContours() trigger O(n^3) worst case.
Fix: copy input to std::vector<PT> and apply cv::randShuffle()
before processing. Uses OpenCV's RNG so cv::setRNGSeed() ensures
reproducible behavior.
Original benchmark (5088 contour points, Release, AVX2):
findContours output: 3.93 ms → 0.033 ms (119x speedup)
Random points: 0.051 ms → 0.049 ms (no regression)
Perf test results (this PR, Release, AVX2):
| Input | N | Time |
|-------|---|------|
| Sequential circle points | 10000 | 0.03 ms |
| Sequential circle points | 5000 | 0.01 ms |
| Random points (CV_32F) | 100000 | 1.87 ms |
| Random points (CV_32S) | 100000 | 2.81 ms |
Fixes#28546
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Identify ArUco markers based on threshold to reduce false positives #28289
**Goal:** parametrize the current marker identification process (pixel-based majority count) to reduce the number of false positives while maintaining high recall. Useful in high risk scenarios in which false positives are not acceptable.
**Context:** This PR builds on top of https://github.com/opencv/opencv/pull/23190 in which we've introduced a pixel-based confidence in the marker detection.
**Solution:** Include a new parameter: `validBitIdThreshold` used to identify markers based on the pixel count of each cell. Set the parameter default either to 50% which is equivalent to the current majority count implementation or to 49% which already singnificantly reduces the number of false positives (see details below).
**Test coverage:**
- Unit tests: `CV_ArucoDetectionThreshold`, `CV_InvertedArucoDetectionThreshold`
- The impact of `validBitIdThreshold` on false positives was also tested using the benchmark dataset: `MIRFLICKR-25k` https://www.kaggle.com/datasets/skfrost19/mirflickr25k which contains random images without any markers. Every marker detection is a false positive.
Example of images in the dataset:
**Results:** A threshold of 49% already allows to significantly reduce the number of false positives for the dict `DICT_4X4_1000`:
- `5942` false positives for `validBitIdThreshold = 0.5`
- `629` false positives for `validBitIdThreshold = 0.49` and `0.46`
- number of false positives divided by `9.5` when compared to `validBitIdThreshold = 0.5`
- `139` false positives for `validBitIdThreshold = 0.43` and `0.4`
- number of false positives divided by `42` when compared to `validBitIdThreshold = 0.5`
Dicts with a higher number of cells are not as impacted since it's much harder to obtain false positives. However, the less cells in a marker the further away it can be reliably detected, so the dict `DICT_4X4_1000` is commonly used.
<img width="1280" height="800" alt="false_positive_image_rate" src="https://github.com/user-attachments/assets/1a0ee16a-221d-443e-835b-022ed6dea6b0" />
In the image attached, the values of `validBitIdThreshold` tested are: `0.10f, 0.20f, 0.30f, 0.40f, 0.43f, 0.46f, 0.49f, 0.50f, 0.53f, 0.56f, 0.60f, 0.70f, 0.80f, 0.90f`
Summary of the results: [summary.csv](https://github.com/user-attachments/files/24315662/summary.csv)
Note that we can also analyse the number of false positives per marker `id`. For example, here's the histogram for the dict `DICT_4X4_1000`. (The CSV attached contains all the results)
<img width="1440" height="640" alt="false_positive_ids_DICT_4X4_1000_thr0 50" src="https://github.com/user-attachments/assets/af4f3ff8-9b8f-4682-9d51-a090c2610d8c" />
For example, the marker id 17 is detected 252 times with `validBitIdThreshold = 0.5` and only 34 times with `validBitIdThreshold = 0.49`. Looking at marker 17 (see below), we understand that this simple pattern randomly occurs in images.
<img width="447" height="441" alt="Marker17" src="https://github.com/user-attachments/assets/f5d09227-b39b-4598-94f9-b529f8300703" />
Results for every dict and every `validBitIdThreshold` [per_id.csv](https://github.com/user-attachments/files/24315667/per_id.csv)
**Missing coverage:** there is no labeled dataset with images containing markers to analyse the impact of on the recall (i.e. look at the true positive rate). For my specific use case (drones) any threshold above `0.4` allows to maintain a high recall in all conditions.
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DNN: Fix OpenVINO 2026 build failure due to ov::Tensor::data() const change #28592
Fixes: https://github.com/opencv/opencv/issues/28586
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### PR Description
OpenVINO 2026 changed ov::Tensor::data() to return `const void*`
instead of `void*`. This causes a build error in
modules/dnn/src/op_inf_engine.cpp when constructing a cv::Mat
wrapper over Tensor memory.
Tensor memory for input/output blobs remains mutable, but the API
now enforces const-correct access. This patch casts away const with
an explicit comment to preserve existing zero-copy semantics and
restore compatibility with OpenVINO 2026.
Preserves existing zero-copy semantics of the OpenVINO backend without altering runtime behavior.
Tested by building OpenCV 4.x against OpenVINO 2026.0.0 on Ubuntu 24.04.
In fitEllipseDirect, `double det = fabs(cv::determinant(M))` applies
fabs() unnecessarily since the next line `if (fabs(det) > 1.0e-10)`
already takes the absolute value. Remove the outer fabs() to avoid
the redundant operation.
Fix inRange type signature to accept Scalar values #28536Fixes#28534
The Python type signature for cv2.inRange was too restrictive, requiring MatLike for lowerb and upperb parameters. However, the C++ implementation accepts InputArray which includes Scalar values (tuples, floats, etc.).
This caused type checkers to incorrectly flag valid code from official tutorials as type errors.
Added make_matlike_or_scalar_arg() refinement function to create union types MatLike | Scalar for affected parameters, matching the C++ InputArray behavior.
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* added empty set support
* accept unnamed dynamic dims
* Fix for LSTM test failure
* removed converToND
* openvino failing test fix
* ARM CI issue fix
* ARM issue fix
Added ONNX Runtime as an optional wrapper #28444
This PR adds ONNXRuntime (ORT) as an _optional_ wrapper, which can be enabled by adding **WITH_ONNXRUNTIME** flag in CMake command.
Using ORT wrapper the inference time for _resnet50.onnx model_ has come to _**~7ms**_ from _**~14ms**_.
Also, we are able to run models like `ssd_mobilenet_v1.onnx`.
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imgcodecs(webp): improve IMWRITE_WEBP_LOSSLESS_MODE to support exact lossless compression #28519Close#28503
Add IMWRITE_WEBP_LOSSLESS_MODE enum to control lossless strategy:
- IMWRITE_WEBP_LOSSLESS_OFF: Lossy compression.
- IMWRITE_WEBP_LOSSLESS_ON: Optimize/drop color in transparent pixels.
- IMWRITE_WEBP_LOSSLESS_PRESERVE_COLOR: Preserve color in transparent pixels.
Note: Currently limited to still images; animated WebP support is deferred per YAGNI.
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[BUG FIX] Incorrect shape assert bug fix in 5.x #28575
Closes: https://github.com/opencv/opencv/issues/28563
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Added GRU layer in new DNN engine #28558
Closes: https://github.com/opencv/opencv/issues/26309 and https://github.com/opencv/opencv/issues/21078 [for GRU layer]
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Fix IPP warpAffine #28562
The issue is that we compute the inverse transformation and pass that to the HAL, but tell IPP that we use the actual transformation.
a269a489b9/modules/imgproc/src/imgwarp.cpp (L2399-L2412)
I have added an additional test with `CV_16S` Mats to test the IPP implementation, because right now no other `warpAffine` test enters the IPP branch.
This fixes#28554.
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This commit addresses a compiler warning encountered when building with GCC 13 and C++20.
The compiler triggers a false-positive -Wstringop-overflow warning in `findKSides`.
By adding `sides.reserve(k)`, we explicitly inform the compiler about the required
memory allocation, which suppresses the warning and provides a minor optimization
by avoiding potential reallocations.
DNN: Fix Squeeze to remove all size-1 dims when axes is empty #28425Fixes#28424
OpenCV Extra: [opencv/opencv_extra#1308](https://github.com/opencv/opencv_extra/pull/1308)
This PR fixes the ONNX Squeeze operator to correctly remove all size-1 dimensions when `axes` is not provided, conforming to the ONNX specification.
### Details
Per [ONNX Squeeze specification](https://onnx.ai/onnx/operators/onnx__Squeeze.html):
> 'If axes is not provided, all the single dimensions will be removed from the shape.'
Previously, OpenCV DNN would not remove any dimensions in this case, causing shape mismatch errors with models like LaMa (inpainting).
### Example
```python
# Input: [1, 1, 2, 4]
# Squeeze with no axes attribute
# Before: [1, 1, 2, 4] ✗ (No change)
# After: [2, 4] ✓ (matches ONNX Runtime)
```
### Tests
Added `testONNXModels("squeeze_no_axes")` which validates this behavior with new test data.
opencv_extra_pr=opencv/opencv_extra#1324
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- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
docs(calib3d): clarify coordinate systems in projectPoints, undistort Points, and fisheye::undistortPoints #28333
This PR improves documentation clarity by explicitly describing:
- Input/output coordinate systems (pixel vs normalized coordinates)
- When to use normalized vs pixel coordinates in undistortPoints output
- Differences between fisheye and standard pinhole camera models
- Coordinate system transformations for better understanding
Changes:
- projectPoints: Added explicit note about input (world coords) and output (pixel coords)
- undistortPoints: Clarified that input is pixel coordinates, and output depends on parameter P (normalized vs pixel)
- fisheye::undistortPoints: Added comprehensive documentation including coordinate systems, when to use fisheye vs standard model, and distortion coefficient differences
These improvements help users avoid ambiguity when using these functions for camera calibration and 3D vision pipelines.
Fix absdiff with int arguments #28267
I believe the fix to the undefined behavior described in #27080 is simply casting to unsigned before subtraction, because
1. casting int to unsigned is well-defined; negative values get represented modulo $2^{32}$
2. overflow in unsigned subtraction is well-defined and the results are modulo $2^{32}$
Since we are computing everything modulo $2^{32}$ and the result must always be a non-negative number below $2^{32}$, this computation should be well-defined and correct.
I have verified this on ARM Apple Clang and on x64 Linux gcc with `-O3` and both produce the correct values in the reproducer of #27080. The test I have added fails with the integer overflow on both platforms I have tested. Perhaps @fengyuentau could verify if this also fixes the issue on the platforms he has tested?
I have added a fix and removed the workarounds. I recommended this approach in #28229, but he decided to revert it.
### Pull Request Readiness Checklist
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features2d: add missing KAZE DIFF_CHARBONNIER support #28324
### Description
This PR fixes the missing implementation for `DIFF_CHARBONNIER` diffusivity type in KAZE feature detector.
### Changes
- Added `charbonnier_diffusivity()` call for `DIFF_CHARBONNIER` type in `Create_Nonlinear_Scale_Space()`
- Added proper error handling for unsupported diffusivity types
### Problem
When using KAZE with `DIFF_CHARBONNIER` diffusivity type, keypoint coordinates were not computed at subpixel level, because the code lacked the specific handling for this diffusivity mode.
### Solution
The `charbonnier_diffusivity()` function already exists in `nldiffusion_functions.cpp`, it just wasn't being called. This PR adds the missing `else if` branch to call it, matching the pattern already implemented in `AKAZEFeatures.cpp`.
Fixes#27134
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Added RoiAlign layer support in new DNN engine #28453
Fixes `Unsupported Operation: RoiAlign` issue in https://github.com/opencv/opencv/issues/20258 and https://github.com/opencv/opencv/issues/22099, model parsing is successful now.
Merge with: https://github.com/opencv/opencv_extra/pull/1310
### Pull Request Readiness Checklist
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optimize(dnn): parallelize Slice layer implementation (4.x) #28511
### Summary
Backport of PR #28447 to 4.x branch.
### Description
This PR optimizes the SliceLayer implementation for strided inputs (where step > 1). The original implementation used a recursive element-wise copy (getSliceRecursive) for any strided slice, which was extremely inefficient.
This PR introduces:
- **Parallelization**: Uses `cv::parallel_for_` to parallelize the outermost dimension of the slice operation.
- **Memcpy Optimization**: Automatically detects "pseudo-contiguous" blocks in strided slices (e.g., slicing an outer dimension but keeping inner dimensions intact) and uses `std::memcpy` instead of scalar loops.
- **Refactoring**: Replaces the recursive function with a dedicated `ParallelSlice` loop body.
### Impact
Significant performance improvement for strided slice operations (common in detection heads, strided sampling, etc.).
### Benchmark Results
Tested on CPU with 20 threads.
| Test Case | Baseline (ms) | Optimized (ms) | Speedup |
| :--- | :--- | :--- | :--- |
| Strided Axis 0 [::2, ...] | 1.10 | 0.02 | **~55x** |
| Strided Axis 2 [..., ::2] | 1.15 | 0.11 | **~10.5x** |
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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- Change 'element' to 'kernel' in LaTeX formulas to match actual parameter name
- Fix erode() and dilate() function documentation (lines 2330, 2338, 2362, 2370)
- Fix MorphTypes enum documentation (MORPH_OPEN, MORPH_CLOSE, MORPH_GRADIENT, MORPH_TOPHAT, MORPH_BLACKHAT)
- Fix backtick formatting in dilate() documentation
Resolves#18095
optimize(dnn): optimize Slice layer for strided inputs #28447
## Description
This PR optimizes the `SliceLayer` implementation for strided inputs (where `step > 1`). The original implementation used a recursive element-wise copy (`getSliceRecursive`) for any strided slice, which was extremely inefficient.
This PR introduces:
1. **Parallelization**: Uses `cv::parallel_for_` to parallelize the outermost dimension of the slice operation.
2. **Memcpy Optimization**: Automatically detects "pseudo-contiguous" blocks in strided slices (e.g., slicing an outer dimension but keeping inner dimensions intact) and uses `std::memcpy` instead of scalar loops.
3. **Refactoring**: Replaces the recursive function with a dedicated `ParallelSlice` loop body.
## Impact
Significant performance improvement for strided slice operations (common in detection heads, strided sampling, etc.).
**Benchmark Results:**
| Test Case | Before (ms) | After (ms) | Speedup |
| :--- | :--- | :--- | :--- |
| **Strided Axis 0** `[::2, ...]` | 1.10 | **0.02** | **~55x** |
| **Strided Axis 2** `[..., ::2]` | 1.10 | **0.06** | **~18x** |
| **Contiguous** (Baseline) | 0.10 | 0.10 | 1.0x (Unchanged) |
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
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dnn: improve robustness of MVN layer#28308
This change adds small defensive improvements to the MVN layer implementation:
->Guard against zero-sized OpenCL kernel launches
->Add input validation in finalize()
->Use int64 for FLOPS computation to avoid overflow
No functional or performance changes are intended.
### Pull Request Readiness Checklist
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imgcodecs: fix integer overflow in BMP and SunRaster decoders #28379
### Pull Request Readiness Checklist
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Fixes#28350
### Description
This PR fixes integer overflow vulnerabilities in BMP and SunRaster image decoders that could cause heap buffer over-read when decoding malformed images.
### Root Cause
The `src_pitch` and `width3` calculations in `BmpDecoder::readData()` and `SunRasterDecoder::readData()` used `int` arithmetic:
```cpp
int src_pitch = ((m_width * m_bpp + 7) / 8 + 3) & -4;
int width3 = m_width * nch;
```
When `m_width` or `m_bpp` are large values from a crafted malicious file, the multiplication overflows, resulting in a small or negative value. This leads to insufficient buffer allocation, causing heap buffer over-read during subsequent decoding operations.
### Fix
1. **Use `size_t` arithmetic**: Cast operands to `size_t` before multiplication:
```cpp
const size_t bits_per_row = static_cast<size_t>(m_width) * static_cast<size_t>(m_bpp);
const size_t src_pitch_size = ((bits_per_row + 7) / 8 + 3) & ~static_cast<size_t>(3);
```
2. **Add size validation**: Added `CV_CheckLT` to reject images requiring buffers larger than 256MB:
```cpp
const size_t MAX_SRC_PITCH = static_cast<size_t>(1) << 28;
CV_CheckLT(src_pitch_size, MAX_SRC_PITCH, "BMP: src_pitch exceeds maximum allowed size");
```
3. **Safe conversion**: Use `validateToInt()` for safe conversion back to `int`.
### Files Changed
- `modules/imgcodecs/src/grfmt_bmp.cpp` - BmpDecoder::readData()
- `modules/imgcodecs/src/grfmt_sunras.cpp` - SunRasterDecoder::readData()
### Testing
- Code compiles without warnings
- Basic BMP and SunRaster encode/decode tests pass
- Overflow conditions are now properly rejected with CV_CheckLT
hal/riscv-rvv: Fix test failures on hardware that implements tail-agnostic as all 1s #28375
### Pull Request Readiness Checklist
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### Summary
Fix test failures in `opencv_test_core` and `opencv_test_imgproc` on hardware that implements the tail-agnostic policy by overwriting tail elements with all 1s
### Root Cause
According to the [RISC-V Vector Extension Specification v1.0](https://github.com/riscvarchive/riscv-v-spec/blob/master/v-spec.adoc#343-vector-tail-agnostic-and-vector-mask-agnostic-vta-and-vma):
> "When a set is marked agnostic, the corresponding set of destination elements in any vector destination operand can either retain the value they previously held, or are overwritten with 1s."
Both `dotProd_32f` and `dotProd_32s` functions use accumulator variables(`s`) that persist across loop iterations, but were using vector intrinsics with tail-agnostic policy. The tail elements can be overwritten with 1s, corrupting the accumulator and producing NaN during the final reduction sum.
### Solution
Changed both functions to use `tail-undisturbed`(`tu`) policy to ensure tail elements remain unchanged across loop iterations.
### Reproduce Steps
The failures can be reproduced using [QEMU](https://gitlab.com/qemu-project/qemu) with `rvv_ma_all_1s=true` and `rvv_ta_all_1s=true` options to emulate hardware implementing tail-agnostic policy as all 1s.
#### Reproduce **`opencv_test_core`** failures:
```
qemu-riscv64 -cpu rv64,zba=true,zbb=true,zbc=true,zbs=true,v=true,vlen=512,elen=64,vext_spec=v1.0,rvv_ma_all_1s=true,rvv_ta_all_1s=true -L '/PATH_TO_SYSROOT/sysroot/' ./opencv_test_core --gtest_filter="Core_DotProduct*"
```
Before the patch:
```
[ FAILED ] Core_DotProduct.accuracy (24 ms)
```
After the patch:
```
[ PASSED ] 1 test.
```
#### Reproduce **`opencv_test_imgproc`** failures:
```
qemu-riscv64 -cpu rv64,zba=true,zbb=true,zbc=true,zbs=true,v=true,vlen=512,elen=64,vext_spec=v1.0,rvv_ma_all_1s=true,rvv_ta_all_1s=true -L '/workspace/riscv/sysroot/' ./opencv_test_imgproc --gtest_filter="fitLine_Modes.accuracy/*"
```
Before the patch
```
[ FAILED ] 24 tests, listed below:
[ FAILED ] fitLine_Modes.accuracy/0, where GetParam() = (13, 1)
[ FAILED ] fitLine_Modes.accuracy/1, where GetParam() = (13, 2)
[ FAILED ] fitLine_Modes.accuracy/2, where GetParam() = (13, 4)
[ FAILED ] fitLine_Modes.accuracy/3, where GetParam() = (13, 5)
[ FAILED ] fitLine_Modes.accuracy/4, where GetParam() = (13, 6)
[ FAILED ] fitLine_Modes.accuracy/5, where GetParam() = (13, 7)
[ FAILED ] fitLine_Modes.accuracy/6, where GetParam() = (21, 1)
[ FAILED ] fitLine_Modes.accuracy/7, where GetParam() = (21, 2)
[ FAILED ] fitLine_Modes.accuracy/8, where GetParam() = (21, 4)
[ FAILED ] fitLine_Modes.accuracy/9, where GetParam() = (21, 5)
[ FAILED ] fitLine_Modes.accuracy/10, where GetParam() = (21, 6)
[ FAILED ] fitLine_Modes.accuracy/11, where GetParam() = (21, 7)
[ FAILED ] fitLine_Modes.accuracy/12, where GetParam() = (12, 1)
[ FAILED ] fitLine_Modes.accuracy/13, where GetParam() = (12, 2)
[ FAILED ] fitLine_Modes.accuracy/14, where GetParam() = (12, 4)
[ FAILED ] fitLine_Modes.accuracy/15, where GetParam() = (12, 5)
[ FAILED ] fitLine_Modes.accuracy/16, where GetParam() = (12, 6)
[ FAILED ] fitLine_Modes.accuracy/17, where GetParam() = (12, 7)
[ FAILED ] fitLine_Modes.accuracy/18, where GetParam() = (20, 1)
[ FAILED ] fitLine_Modes.accuracy/19, where GetParam() = (20, 2)
[ FAILED ] fitLine_Modes.accuracy/20, where GetParam() = (20, 4)
[ FAILED ] fitLine_Modes.accuracy/21, where GetParam() = (20, 5)
[ FAILED ] fitLine_Modes.accuracy/22, where GetParam() = (20, 6)
[ FAILED ] fitLine_Modes.accuracy/23, where GetParam() = (20, 7)
```
After the patch
`[ PASSED ] 24 tests.`
optimize(dnn): parallelize Resize layer implementation #28442
### optimize(dnn): parallelize Resize layer implementation
This PR addresses the `TODO` in `modules/dnn/src/layers/resize_layer.cpp` regarding the slow implementation of the `Resize` layer when using `opencv_linear` or `nearest` interpolation with specific configurations.
### Changes
- Replaced the serial nested loop (batch x channels) with `cv::parallel_for_`.
- This allows OpenCV to utilize multi-threading for the resizing operation, which was previously single-threaded for these specific paths.
### Performance Results
Benchmark run on 20-thread CPU (AVX2):
| Test Case | Resolution Change | Before (ms) | After (ms) | Speedup |
| :--- | :--- | :--- | :--- | :--- |
| **Upsample Linear** | 64x64 -> 128x128 | 1.47 ms | **0.18 ms** | **~8.1x** |
| **Downsample Nearest** | 128x128 -> 64x64 | 1.55 ms | **0.45 ms** | **~3.4x** |
**Test Configuration:**
- **Upsample**: `[4, 64, 64, 64]` -> `[4, 64, 128, 128]` (Factor: 2.0, Linear)
- **Downsample**: `[4, 128, 128, 128]` -> `[4, 128, 64, 64]` (Factor: 0.5, Nearest)
### Pull Request Readiness Checklist
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- Added `modules/dnn/perf/perf_resize.cpp` to verify performance gains.
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Fix ~0.5px systematic offset in CharucoDetector subpixel refinement #28380Resolves#25539
### Problem
`CharucoDetector::detectBoard` produces Charuco corner coordinates that are consistently offset by approximately **+0.5 pixels** compared to `findChessboardCorners + cornerSubPix`.
This offset is systematic (mean ≈ −0.5 px when comparing legacy − Charuco) and reproducible across images. Visual inspection also shows that the legacy chessboard detector aligns better with the actual corner locations.
### Root cause
In `charuco_detector.cpp`, the points passed to `cornerSubPix` are manually shifted by `-Point2f(0.5f, 0.5f)` before refinement and then shifted back by `+Point2f(0.5f, 0.5f)` after refinement.
However, `cornerSubPix` refines corners in **absolute image coordinates** and converges to the true saddle point based on image gradients. Shifting the initial guess does not affect the converged result as long as the true corner lies within the refinement window.
The additional `+0.5` shift applied after refinement therefore introduces a constant bias, resulting in:
```
P_out = P_true + 0.5
```
### Solution
Remove the manual `±0.5` coordinate shifts around the `cornerSubPix` call and let the refined result be returned directly.
### Test updates
Updated expected corner values in the following tests:
- `testBoardSubpixelCoords`
- `testSeveralBoardsWithCustomIds`
The previous expected values (e.g. `200`, `250`, `300`) were only correct due to the +0.5px bias introduced by the bug.
`generateImage` creates checkerboard squares of exactly **50 pixels**, which places true corner locations on **pixel boundaries** rather than pixel centers. As a result, the correct subpixel coordinates are:
```
199.5, 249.5, 299.5
```
instead of the previously expected integer values.
After updating the expected values, all **30 Charuco-related tests pass** with the fix applied.
### Verification
I verified the fix using a Python reproducer that compares `CharucoDetector` output against `findChessboardCorners + cornerSubPix`:
- **Before fix:** mean error ≈ −0.501 px
- **After fix:** mean error ≈ −0.001 px
After the change, the Charuco detector output aligns with the legacy chessboard detector both numerically and visually.
### Notes
This change only affects the post-refinement coordinate handling and does not alter detection logic, refinement parameters, or performance.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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- [x] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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Fix integer overflow in medianBlur #28386
The bug in #28385 is that the pointer arithmetic in
d8bc5b94b8/modules/imgproc/src/median_blur.simd.hpp (L666-L668)
overflows if `i*sstep` is larger than `INT_MAX`, because both variables are of type `int`. If one operand is of type `size_t` instead, the other operand gets promoted to `size_t` before multiplication and overflow doesn't happen anymore.
The test allocates 2.3GB for the Mat; I hope that's okay.
This PR fixes#28385
### Pull Request Readiness Checklist
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The TopK layer was incorrectly rejecting K values equal to the input
dimension size. According to ONNX specification, K should be allowed
to equal the dimension size (to retrieve all elements).
Changed validation from 'K < input_shape[axis]' to 'K <= input_shape[axis]'
This fixes the error when loading YOLOv10 ONNX models that use TopK
with K equal to the dimension size.
Fixes#28445
docs: fix spelling errors in documentation and code #28301
- [x] I agree to contribute to the project under Apache 2 License.
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### Description
Fixed multiple spelling errors across documentation, comments, and code:
- 'colummn' → 'column' (cublas.hpp, 3 occurrences)
- 'points_per_colum' → 'points_per_column' (calib3d.hpp, 3 occurrences)
- 'Asignee' → 'Assignee' (sift files, 2 occurrences)
- 'compability' → 'compatibility' (face.hpp, 2 occurrences)
- 'orignal' → 'original' (aruco_detector.cpp)
- 'refrence' → 'reference' (chessboard.cpp)
- 'indeces' → 'indices' (stitching.hpp)
- 'OutputPrecison' → 'OutputPrecision' (test)
- 'tranform' → 'transform' (slice_layer.cpp, 3 occurrences)
Total: 24 fixes across 14 files. Documentation and comment changes only, no functional impact.
docs(calib3d): fix grammar and clarify radial distortion formula in camera calibration tutorial #28422
This PR improves the camera calibration tutorial documentation by:
- Fixing minor grammatical issues for better readability.
- Clarifying the radial distortion model by explicitly defining r² = x² + y².
These changes do not affect functionality and are intended to make the mathematical explanation clearer for readers and new users.
AttentionOnnxAiLayer #27988
Implements https://onnx.ai/onnx/operators/onnx__Attention.html#attention-23
### Pull Request Readiness Checklist
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Precompute X values in Forney algorithm to eliminate redundant gfPow calls.
Previously, gfPow(2, ...) was computed multiple times for the same error
location across different loop iterations, resulting in O(L²) redundant
Galois field arithmetic operations.
This change:
- Precomputes all X values once before the main loop
- Reduces complexity from O(L²) to O(L) for X value computations
- Removes the TODO comment at line 1642
- No functional changes, only performance improvement
The optimization is most beneficial when there are many error locations
in QR codes (larger L values).
core: add NEON support for cvFloor in fast_math.hpp #28243
- This PR adds NEON intrinsics-based implementation for the cvFloor function in fast_math.hpp for Windows-ARM64.
- Both float and double overloads now use NEON intrinsics for cvFloor Function.
- calchist and calchist1d function uses cvFloor function for its computations.
- After adding these changes both functions showed improvement in performance.
**Performance Benchmarks:**
<img width="956" height="273" alt="image" src="https://github.com/user-attachments/assets/a00c98cd-d245-4d11-a9fd-361a3bd89f59" />
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
imgproc: replace assertion in cornerSubPix with descriptive error #28404
Replace CV_Assert with explicit bounds check for initial corners in cornerSubPix.
This provides a descriptive runtime error instead of abrupt termination,
improving error handling for Python and C++ users.
No algorithmic or behavioral change for valid inputs.
Fixes#25139 (Related to #7276).
### Pull Request Readiness Checklist
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Fixes#28436.
Replaces a nullptr dereference (unguaranteed to cause a SEGFAULT) by `std::terminate` which will call the current `std::terminate_handler` and then SIGARBT.
Properly preserve KAZE/AKAZE license as mandated by BSD-3-Clause #28441
Close https://github.com/opencv/opencv/issues/28440
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doc: update image codec configuration reference (add AVIF, fix JPEG XL) #28393
This PR updates the build configuration documentation to:
- Add AVIF to the supported format list (it was missing).
- Clarify that some codecs (AVIF, JPEG XL) do not support BUILD_* options because OpenCV does not bundle their source code.
- Update the description to include "write" capabilities for these formats.
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Prevent a potential crash in FMEstimatorCallback::runKernel #28361
With solveCubic sometimes failing, n can be -1.
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PR #27972 added _dst.create(size(), type()) in copyTo's empty() block.
In Debug builds, Mat::release() was resetting flags to MAGIC_VAL,
clearing the type information and causing assertion failures when
destination has fixedType().
Preserve type flags in Mat::release() debug mode by using:
flags = (flags & CV_MAT_TYPE_MASK) | MAGIC_VAL
Thanks to @akretz for suggesting this better approach.
- Remove embed_bitcode parameter from Builder class
- Remove bitcode compilation flags for iOS and Catalyst targets
- Remove BITCODE_GENERATION_MODE from Xcode build commands
- Remove bitcode flags from dynamic library linking
- Remove --embed_bitcode command line argument
- Fix OSXBuilder parameter mismatch after bitcode removal
Bitcode has been deprecated by Apple since Xcode 14 and is no longer
required for App Store submissions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
In OpenCV 5.x, C++17 is the minimum requirement.
The hardcoded `-std=c++11` in `modules/js/CMakeLists.txt` is no longer necessary and
actually causes build failures when using **Emscripten 4.0.20+**, as Embind now requires C++17 or newer.
Unlike the fix in #28178 for the 4.x branch,
this PR takes a cleanup approach for the 5.x branch by removing the legacy flag entirely.
- Fixes build failure with Emscripten 4.0.20+ on 5.x branch.
- Aligns with the C++17 requirement of OpenCV 5.x.
Respect QApplication::quitOnLastWindowClosed() before calling qApp->quit().
This prevents OpenCV from unintentionally terminating externally managed
Qt applications when the last HighGUI window is closed.
Fixes#28291
Fixed picture_sw object leak in ffmpeg backend with hardware codecs. #28283
Replacement for https://github.com/opencv/opencv/pull/28221
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docs(imgproc): clarify cv::moments behavior for degenerate contours #28227
relates to https://github.com/opencv/opencv/issues/28222
Clarifies that for degenerate contours (single point or collinear points),
cv::moments() returns m00 == 0 and centroid is undefined.
Documents common workarounds such as boundingRect center or point averaging.
Added Hannwindow, Hammingwindow & Blackmanwindow support #28075
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docs(features2d): document getBlobContours and collectContours in SimpleBlobDetector #28275
Description: This PR adds missing Doxygen documentation for the getBlobContours() method and the collectContours parameter in SimpleBlobDetector. These features were previously undocumented, making the contour collection functionality difficult for users to discover and use correctly.
Changes:
Added a @brief and detailed note for SimpleBlobDetector::Params::collectContours.
Added documentation for SimpleBlobDetector::getBlobContours(), including a @note regarding the required parameter setup.
Testing:
Verified the documentation build locally on macOS using ninja opencv_docs.
Confirmed the generated HTML displays the descriptions and cross-references accurately.
Partially fixes: #25904
Related PR that adds method: https://github.com/opencv/opencv/pull/21942
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modified Input/OutputArray methods to handle 'std::vector<T>' or 'std::vector<std::vector<T>>' properly #28242
This is port of #26408 with some further improvements (all switch-by-vector-type statements are consolidated in a single macro)
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G-API: Add support to set workload type dynamically in both OpenVINO and ONNX OVEP #27460
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Include pixel-based confidence in ArUco marker detection #23190
The aim of this pull request is to compute a **pixel-based confidence** of the marker detection. The confidence [0;1] is defined as the percentage of correctly detected pixels, with 1 describing a pixel perfect detection. Currently it is possible to get the normalized Hamming distance between the detected marker and the dictionary ground truth [Dictionary::getDistanceToId()](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_dictionary.cpp#L114) However, this distance is based on the extracted bits and we lose information in the [majority count step](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_detector.cpp#L487). For example, even if each cell has 49% incorrect pixels, we still obtain a perfect Hamming distance.
**Implementation tests**: Generate 36 synthetic images containing 4 markers each (with different ids) so a total of 144 markers. Invert a given percentage of pixels in each cell of the marker to simulate uncertain detection. Assuming a perfect detection, define the ground truth uncertainty as the percentage of inverted pixels. The test is passed if `abs(computedConfidece - groundTruthConfidence) < 0.05` where `0.05` accounts for minor detection inaccuracies.
- Performed for both regular and inverted markers
- Included perspective-distorted markers
- Markers in all 4 possible rotations [0, 90, 180, 270]
- Different set of detection params:
- `perspectiveRemovePixelPerCell`
- `perspectiveRemoveIgnoredMarginPerCell`
- `markerBorderBits`
The code properly builds locally and `opencv_test_objdetect` and `opencv_test_core` passed. Please let me know if there are any further modifications needed.
Thanks!
I've also pushed minor unrelated improvement (let me know if you want a separate PR) in the [bit extraction method](https://github.com/opencv/opencv/blob/4.x/modules/objdetect/src/aruco/aruco_detector.cpp#L435). `CV_Assert(perspectiveRemoveIgnoredMarginPerCell <=1)` should be `< 0.5`. Since there are margins on both sides of the cell, the margins must be smaller than half of the cell. When setting `perspectiveRemoveIgnoredMarginPerCell >= 0.5`, `opencv_test_objdetect` fails. Note: 0.499 is ok because `int()` will floor the result, thus `cellMarginPixels = int(cellMarginRate * cellSize)` will be smaller than `cellSize / 2`
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Fix the out-of-bounds read in cv::bilateralFilter for 32f images #28259
### Root Cause Analysis
The issue was caused by a discrepancy between the image range used to allocate the color weight look-up table (LUT) and the actual range of pixel values encountered during filtering, especially near the image borders.
- Range Computation: `cv::bilateralFilter` computes the min/max values of the source image and allocates a `LUT (expLUT)` of size `kExpNumBins + 2` based on this range.
- Border Padding: If `cv::BORDER_CONSTANT` is used (defaulting to 0), and 0 is outside the image's original range (e.g., an image with values between 100 and 200), the padded image will contain values (0) that create differences larger than those accounted for in the `LUT`.
- Out-of-Bounds Access: When calculating the color weight, the code computes an index `idx` from the absolute difference. If this difference exceeds the expected range, `idx` can reach or exceed `kExpNumBins + 1`. Since the code performs linear interpolation using `expLUT[idx]` and `expLUT[idx + 1]`, an `idx` of `kExpNumBins + 1` causes an access to `expLUT[kExpNumBins + 2]`, which is out of bounds.
### Fix
I implemented a robust clamping mechanism in both the SIMD (AVX/SSE) and scalar paths of the bilateral filter invoker:
- Signature Update: Updated `bilateralFilterInvoker_32f` to accept `kExpNumBins` (the maximum valid `LUT` index).
- Clamping: Clamped the computed color difference (alpha) to `kExpNumBins` before calculating the `LUT` index. This ensures that any difference exceeding the planned range is safely treated as the maximum difference in the `LUT` (which usually corresponds to a weight of 0), avoiding any out-of-bounds memory access.
### Modified Files
Modified Files:
`modules/imgproc/src/bilateral_filter.simd.hpp`: Updated the invoker class and SIMD/scalar loops to clamp the LUT index.
`modules/imgproc/src/bilateral_filter.dispatch.cpp`: Updated the dispatch call site to pass the correct LUT size.
Closes#28254
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js: restore deep copy behavior for Mat.clone() #28216
### Problem
In OpenCV.js, `cv.Mat.clone()` may resolve to Embind `ClassHandle.clone()` (handle/shallow clone) instead of OpenCV deep copy.
### Changes
- modules/js/src/helpers.js: override `cv.Mat.prototype.clone` -> `mat_clone` after runtime init
- modules/js/test/test_mat.js: update/extend tests to validate deep copy semantics of `clone()`
Fixes #27572
Related: PR #26643 (js_clone_fix), PR #27985 (documentation update)
### Verification
- Built OpenCV.js with Emscripten 2.0.10
- QUnit: bin/tests.html
- CoreMat: test_mat_creation (0 failures)
<img width="745" height="362" alt="clone_fix" src="https://github.com/user-attachments/assets/16399abf-b94c-4591-b4aa-6e0fbd6cf23b" />
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imgproc: fix heap-buffer-overflow in stackBlur #28233#28250
### Summary
Fixes a heap-buffer-overflow in `cv::stackBlur` when the kernel size is larger than the image dimensions
### Changes
* Added input validation to clamp the kernel size to the image dimensions.
* Added a regression test (`regression_28233`) covering 1x1 and small image cases.
Fixes#28233
core: fix solveCubic numerical instability via coefficient normalization (fixes#27748) #28117
Summary
This PR fixes numerical instability in `cv::solveCubic` when the leading coefficient `a` is non-zero but extremely small relative to other coefficients (Issue #27748).
It introduces a **normalization step** that scales all coefficients by their maximum magnitude before solving. This ensures robust detection of when the equation should degenerate to a quadratic solver, without breaking valid cubic equations that happen to have small coefficients (e.g., scaled by 1e-9).
The Problem (Issue #27748)
The previous implementation checked `if (a == 0)` to decide whether to use the cubic or quadratic formula.
- When `a` is extremely small (e.g., 1e-17) but not exactly zero, and other coefficients are normal (e.g., 5.0), the standard cubic formula suffers from catastrophic cancellation and overflow, producing incorrect roots (e.g., 1e14).
The Fix
1. Normalization: The solver now finds `max_coeff = max(|a|, |b|, |c|, |d|)` and scales all coefficients by `1.0 / max_coeff`.
2. Relative Threshold: It then checks `if (abs(a) < epsilon)` on the *normalized* coefficients.
Why this is better than previous attempts
In a previous attempt (PR #28057), a simple absolute check `abs(a) < epsilon` was proposed. That approach was rejected because it failed for scaled equations.
Fixes#27748
Added randomNormalLike layer to 4.x branch #28164
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1297
Backport of https://github.com/opencv/opencv/pull/28110 to 4.x
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Stateless HAL for filters and morphology. #28208
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[video] Add setCoarsestScale to DISOpticalFlow (Fixes#25068) #28217
### Description
This PR addresses issue #25068 regarding the number of scales in `DISOpticalFlow`.
Currently, `DISOpticalFlow` automatically computes the `coarsest_scale` based on the image size. While generally effective, this behavior can cause errors in specific use cases (e.g., mechanics/speckle pattern analysis) where high pyramid levels degrade quality.
This change introduces a manual override:
- Added `setCoarsestScale(int val)` and `getCoarsestScale()` to the public API.
- Updated `DISOpticalFlowImpl::calc` and `ocl_calc` to use the user-defined scale if set.
- Preserved the existing "automatic" behavior as the default (when set to -1).
### Changes
- **`modules/video/include/opencv2/video/tracking.hpp`**: Added virtual method declarations.
- **`modules/video/src/dis_flow.cpp`**: Implemented `set/getCoarsestScale` in `DISOpticalFlowImpl` and updated calculation logic.
- **`modules/video/test/test_OF_accuracy.cpp`**: Added `DenseOpticalFlow_DIS.ManualCoarsestScale` regression test.
Fixes#25068
Chromatic aberration correction #27491
Merge with https://github.com/opencv/opencv_extra/pull/1266
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Parent issue: https://github.com/opencv/opencv/issues/27206
Related PR: [#27490](https://github.com/opencv/opencv/pull/27490)
This PR adds chromatic aberration correction in C++ based on calibration data from the python app.
This code adds a function for chromatic aberration correction based on the calibration file (Mat correctChromaticAberration(InputArray image, const String& calibration_file)), and a class ChromaticAberrationCorrector which can be used to correct images of the same camera under the same settings (so for the same calibration data that is initialized in the beginning).
Also, basic functionality and performance tests are added.
Keep convexHull output indices monotone if possible #28163
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24907 ?
resolves https://github.com/opencv/opencv/issues/4954
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Replaces wasteful temporary object construction with in-place construction using emplace_back. Covers hot paths in objdetect, imgproc, and stitching modules.
dnn(tflite): add support for MAXIMUM layer #28171Fixes#26433
This PR adds support for the `MAXIMUM` layer in the TFLite importer.
It maps the TFLite `MAXIMUM` opcode to the existing OpenCV Element-wise `Max` operation.
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Fix regex escape warning in _coverage.py by using raw string #2819
while i am running '_coverage.py' I noticed there is a syntax warning:"\." It is an invalid escape sequence
The regex was written as a normal string with '\.' which is not valid escape in string literals and give problems and error in a new python versions.
To fix this switch to raw string(r'cv2?\.\w+') which can passes the backslashes directly to the regex engine. This removes the warning and keeps the code and pattern working properly.
This is a small fix but important for future compatibilities.
core: suppress LAPACK deprecation warnings on macOS #28203
### Description
On macOS (Apple Silicon) with newer Xcode/Clang, the CLAPACK interface is deprecated.
Compiling `hal_internal.cpp` triggers multiple warnings like:
`warning: 'sgesv_' is deprecated: first deprecated in macOS 13.3 - The CLAPACK interface is deprecated. [-Wdeprecated-declarations]`
Since migrating to the new Accelerate interface (Apple's recommended fix) requires significant changes to the HAL implementation and breaks the build if headers are mismatched, this PR takes the pragmatic approach. It suppresses the `-Wdeprecated-declarations` warning for this specific file using Clang pragmas to clean up the build output.
### Verification
- [x] Verified build is silent on macOS (M3).
- [x] Verified pragmas are correctly scoped within `HAVE_LAPACK` to prevent scope mismatch on non-LAPACK builds.
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imgcodecs(avif): add safety checks for AVIF decoder and encoder #28200
Add defensive checks to prevent potential null dereference in AVIF decoder and encoder paths when handling malformed input or allocation failures.
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Fixed issues identified by PVS Studio #28185
Partially fixes https://github.com/opencv/opencv/issues/28167
Paper: https://pvs-studio.com/en/blog/posts/cpp/1321/
Closed items: N2, N4, N5, N6, N7, N8, N10, N11, N13, N14.
To be continued...
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Emscripten 3.1.71+ introduced ClassHandle.clone() which performs a shallow copy.
This shadowed the original OpenCV clone() method which was intended for deep copying.
This patch:
1. Hooks into onRuntimeInitialized in helpers.js
2. Overrides cv.Mat.prototype.clone to point to mat_clone (deep copy)
3. Updates JS tests to use clone() to verify the fix
Added RandomNormalLike layer for fixing ViTs parsing issue #28110
closes: https://github.com/opencv/opencv/issues/27603
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Introduce option to generate Java code with finalize() or Cleaners interface #28159
Closes https://github.com/opencv/opencv/issues/22260
Replaces https://github.com/opencv/opencv/pull/23467
The PR introduce configuration option to generate Java code with Cleaner interface for Java 9+ and old-fashion finalize() method for old Java and Android. Mat class and derivatives are manually written. The PR introduce 2 base classes for it depending on the generator configuration.
Pros:
1. No need to implement complex and error prone cleaner on library side.
2. No new CMake templates, easier to modify code in IDE.
Cons:
1. More generator branches and different code for modern desktop and Android.
TODO:
- [x] Add Java version check to cmake
- [x] Use Cleaners for ANDROID API 33+
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Fix frame seeking with negative DTS values in FFMPEG backend #27878
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1289
Fixes https://github.com/opencv/opencv/issues/27819
Fixes https://github.com/opencv/opencv/issues/23472
Accompanied by PR on https://github.com/opencv/opencv_extra/pull/1289
The FFmpeg backend fails to correctly seek in H.264 videos that contain negative DTS values in their initial frames. This is a valid encoding practice used by modern video encoders (such as DaVinci Resolve's current export) where B-frame reordering causes the first few frames to have negative DTS values.
When picture_pts is unavailable (AV_NOPTS_VALUE), the code falls back to using pkt_dts:
`picture_pts = packet_raw.pts != AV_NOPTS_VALUE_ ? packet_raw.pts : packet_raw.dts;`
If this DTS value is negative (which is legal per H.264 spec), it propagates through the frame number calculation:
`frame_number = dts_to_frame_number(picture_pts) - first_frame_number;`
This results in negative frame numbers, messing up seeking operations.
Solution implemented in this branch is a timestamp normalization similar to FFmpegs -avoid_negative_ts_make_zero flag:
- Calculate a global offset once on the first decoded frame by getting the minimum timestamp in either:
- Container start_time
- Stream start_time
- First observed timestamp (PTS, then DTS).
- Apply the offset consistently to all timestamps, shifting negative values to begin at 0 while keeping relative timing.
- Simplify timestamp converters to remove `start_time` subtractions since timestamps are pre-normalized.
This also includes a new test `videoio_ffmpeg.seek_with_negative_dts`
This test verifies that seeking behavior performs as expected on a file which has negative DTS values in the first frames.
A PR on opencv_extra accompanies this one with that testing file: https://github.com/opencv/opencv_extra/pull/1279
```
opencv_extra=ffmpeg-videoio-negative-dts-test-data
```
<cut/>
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Added 16U and 32F support in merge functions in photo module #28168
closes: https://github.com/opencv/opencv/issues/27873
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js: add C++17 requirement check for Emscripten 4.0.20+ #28179
Close https://github.com/opencv/opencv/issues/28178
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rvv_hal: fix flip inplace #28180
Fixes https://github.com/opencv/opencv/issues/28124
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Docs(imgcodecs): clarify animation frame duration units #28176
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Docs-only change. No code or tests affected.
videoio(ios): fix NSInvalidArgumentException in VideoWriter release #28173
Summary
Fixes a crash on iOS when calling `VideoWriter::release()` in OpenCV 4.12.0.
Issue
Fixes#28165
Detailed Description
This PR resolves a regression where an `NSInvalidArgumentException` was thrown with the message ` -[NSAutoreleasePool retain]: Cannot retain an autorelease pool`.
The crash was caused by the manual usage of `NSAutoreleasePool` in the `~CvVideoWriter_AVFoundation` destructor. The local pool variable was being captured by the completion handler block passed to `[mMovieWriter finishWritingWithCompletionHandler:]`. Since `NSAutoreleasePool` instances cannot be retained, capturing them in a block causes a crash.
Changes:
- Replaced manual `NSAutoreleasePool` allocation and draining with modern `@autoreleasepool` blocks in `CvVideoWriter_AVFoundation`.
- applied this modernization to the Constructor, Destructor, and `write` methods.
- Specifically in the destructor, an inner `@autoreleasepool` is now used inside the completion block, ensuring no pool object needs to be captured from the outer scope.
Iterative Phase Correlation #28146
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WebP update to version 1.6.0 #28139
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RMSNorm: reference cpu impl #28104https://onnx.ai/onnx/operators/onnx__RMSNormalization.html
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Enable KleidiCV on Linux and Mac Mx by default #27640
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1296
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Update deprecated ov::element::undefined #28127
### Summary
Fixing OpenCV build error below.
Relates to OpenVINO 2026.0 updates on `ov::element::undefined` (https://github.com/openvinotoolkit/openvino/pull/32573) - replaced by `ov::element::dynamic`.
```
/home/jenkins/agent/workspace/openVINO-builder/opencv_source/opencv-opencv-f627368/modules/gapi/src/backends/ov/govbackend.cpp
[2025-12-03T21:25:54.540Z] /home/jenkins/agent/workspace/openVINO-builder/opencv_source/opencv-opencv-f627368/modules/gapi/src/backends/ov/govbackend.cpp: In function ���ov::element::Type toOV(int)���:
[2025-12-03T21:25:54.540Z] /home/jenkins/agent/workspace/openVINO-builder/opencv_source/opencv-opencv-f627368/modules/gapi/src/backends/ov/govbackend.cpp:114:25: error: ���undefined��� is not a member of ���ov::element���
[2025-12-03T21:25:54.540Z] 114 | return ov::element::undefined;
...
/home/jenkins/agent/workspace/openVINO-builder/opencv_source/opencv-opencv-f627368/modules/gapi/test/infer/gapi_infer_ov_tests.cpp
[2025-12-03T21:26:19.642Z] /home/jenkins/agent/workspace/openVINO-builder/opencv_source/opencv-opencv-f627368/modules/gapi/test/infer/gapi_infer_ov_tests.cpp: In function ���ov::element::Type opencv_test::toOV(int)���:
[2025-12-03T21:26:19.642Z] /home/jenkins/agent/workspace/openVINO-builder/opencv_source/opencv-opencv-f627368/modules/gapi/test/infer/gapi_infer_ov_tests.cpp:832:25: error: ���undefined��� is not a member of ���ov::element���
[2025-12-03T21:26:19.642Z] 832 | return ov::element::undefined;
```
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Remove floating point arithmetic from angle computation in QR codes #28157
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resolves https://github.com/opencv/opencv/issues/24646
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Test: Add regression test for LINE_4 vs LINE_8 connectivity #28120
Add test to verify correct behavior of LINE_4 (4-connected) and LINE_8 (8-connected) line drawing. This test ensures:
- LINE_4 produces staircase pattern (more pixels) for diagonal lines
- LINE_8 produces diagonal steps (fewer pixels)
- LINE_4 pixels have only horizontal/vertical neighbors (no diagonal-only)
Regression test for issue #26413 where LINE_4 and LINE_8 behaviors were swapped.
Fix ORB inconsistency for masks with values 255 and 1. #26366
### Pull Request Readiness Checklist
The PR fixes : [25974](https://github.com/opencv/opencv/issues/25974)
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Fix JS bindings for namespaced Ptr factory return types #28143
This PR fixes an issue in the JS bindings generator for factory functions returning cv::Ptr<T> where T belongs to a namespaced class (for example cv::ximgproc::EdgeDrawing).
The generator previously produced unqualified C++ template arguments such as:
.constructor(select_overload<Ptr<EdgeDrawing>()>(&cv::ximgproc::createEdgeDrawing))
This results in invalid C++ because EdgeDrawing is not found in the global namespace.
Fixes https://github.com/opencv/opencv/issues/28130
In modules/js/generator/embindgen.py, inside both:
gen_function_binding_with_wrapper
gen_function_binding
a check is added:
When factory == True,
And the return type begins with Ptr<...>,
And the inner type is missing a namespace (::),
Ptr<T> → Ptr<class_info.cname>
This ensures the fully-qualified class name (e.g. cv::ximgproc::EdgeDrawing) is used in the generated bindings.
.constructor(select_overload<Ptr<cv::ximgproc::EdgeDrawing>()>(&cv::ximgproc::createEdgeDrawing))
Configured OpenCV with:
cmake .. -DBUILD_opencv_js=ON
Ran:
make -j gen_opencv_js_source
JS generator completed successfully without errors.
This change does not modify generated files directly — it modifies the generator logic so the correct namespace is applied automatically.
OpenJPEG update to 2.5.4 #28140
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Fixes#28095
Problem:
- Stateful kernels dereference null state pointer on line 446 in gcpukernel.hpp
- jinboson confirmed state_ptr is null on x86 Ubuntu (7 hours ago)
- Causes crash with std::shared_ptr assertion on LoongArch64 and strict platforms
Solution (addressing Copilot review feedback from PR #28096):
1. Added null pointer check using CV_Error instead of CV_Assert:
- CV_Assert with && 'message' doesn't display the message correctly
- CV_Error properly reports cv::Error::StsNullPtr with clear message
2. Fixed test kernels to properly initialize state using std::make_shared:
- GOCVStInvalidResize: Initialize state in setup()
- GOCVCountStateSetups: Initialize state before incrementing counter
- Used std::make_shared<int>() for modern C++ best practice
Impact:
- Prevents crashes on platforms with strict null pointer checking
- Provides actionable error message for developers
- Fixes StatefulKernel.StateInitOnceInRegularMode and InvalidReallocatingKernel tests
* Updated libpng to v1.6.51 and added RISC-V optimimizations.
* Force 3rdparty build for CI test.
* Added RISC-V RVV diagnostics for PNG.
* RISC-V RVV diagnostic fix.
* Disabled incorrect in-place flip HAL on RISC-V RVV.
* Disabled risc-v rvv of png_read_filter_row_paeth in libpng.
* Set PNG simd configuration defaults accornding to OpenCV settings.
* Update to libpng 1.6.52 with RISC-V RVV fix.
* Build fix for RISC-V RVV.
* Reverted CI changes.
Dropped OPENCV_FOR_OPENMP_DYNAMIC_DISABLE environment varibale in favor of standard OMP_DYNAMIC #28122
Fixes: https://github.com/opencv/opencv/issues/25717
Replaces: https://github.com/opencv/opencv/pull/28084
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Adding github CodeQL #28078
Related pipeline in CI repo: https://github.com/opencv/ci-gha-workflow/pull/280
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Merge pull request #28112 from asmorkalov:as/jpeg_turbo_3.1.2
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stitching: pass warp params by value to avoid CUDA constant races #28118
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Fixes#26870.
In `modules/stitching/src/cuda/build_warp_maps.cu`, the original implementation copied parameters into global GPU constant symbols:
```cpp
cudaSafeCall(cudaMemcpyToSymbol(build_warp_maps::ck_rinv, k_rinv, 9 * sizeof(float)));
cudaSafeCall(cudaMemcpyToSymbol(build_warp_maps::cr_kinv, r_kinv, 9 * sizeof(float)));
cudaSafeCall(cudaMemcpyToSymbol(build_warp_maps::cscale, &scale, sizeof(float)));
```
As discussed in the issue, this can cause race conditions when multiple warps are built concurrently. This patch removes the use of these global constant symbols and instead passes the required data as kernel parameters (a total of 11 floats encapsulated in `WarpParams`).
One potential concern is increased register pressure due to additional kernel arguments. However, based on experiments using the test case from issue #26870, there is no significant performance regression; in fact, a small speed‑up was observed.
Testing was performed on an NVIDIA GeForce RTX 4090 (single GPU).
Note: ./bin/opencv_perf_stitching did not run successfully on my system even with an unmodified git checkout, so performance was evaluated using the test case from issue #26870 instead.
catch _com_error exceptions to suppress debugger flooding #28073Resolves: #27643
This PR fixes an issue where the Microsoft Media Foundation (MSMF) backend triggers repeated C++ exceptions (_com_error) during video capture. While these exceptions are often non-fatal "first-chance" exceptions, they cause the Visual Studio debugger to break execution repeatedly, making debugging difficulty and flooding the output window.
Changes
cap_msmf.cpp: Added try-catch blocks around critical COM interaction paths.
Updated SourceReaderCB::OnReadSample (Async callback) to catch _com_error.
Updated CvCapture_MSMF::grabFrame (Synchronous grab) to catch _com_error.
Added CV_LOG_WARNING to log the error message from the caught exception, ensuring that actual errors are still visible in the logs without crashing the application or halting the debugger.
Impact
Users debugging OpenCV applications on Windows with Visual Studio will no longer be interrupted by internal MSMF exceptions when using the default backend.
The application flow remains uninterrupted even if MSMF encounters transient internal errors.
fix: set default pixel format for Aravis cameras when unsupported format #28086Fixes#26523
This PR fixes an issue where Aravis VideoCapture returns empty frames when the camera's pixel format is not explicitly set via CAP_PROP_FOURCC.
Problem:
The Aravis backend's retrieveFrame() function only processes four specific pixel formats:
- ARV_PIXEL_FORMAT_MONO_8
- ARV_PIXEL_FORMAT_BAYER_GR_8
- ARV_PIXEL_FORMAT_MONO_12
- ARV_PIXEL_FORMAT_MONO_16
If the camera has a different pixel format configured (or the camera's default format is unsupported), retrieveFrame() returns false on line 333 and all frames appear empty. This happens even though:
- The camera opens successfully (isOpened() returns true)
- The camera is capturing data
- The user has set up autotrigger and auto-exposure correctly
Root Cause:
In open() at line 271, the code retrieves the camera's current pixel format with arv_camera_get_pixel_format(). But if this format doesn't match one of the four supported formats, there's no fallback, causing all subsequent frames to fail retrieval.
Solution:
Added a check after retrieving the camera's pixel format. If the format is not one of the four supported formats, the code now:
1. Sets pixelFormat to a sensible default (MONO_8)
2. Applies this format to the camera via arv_camera_set_pixel_format()
This ensures:
- Cameras work out-of-the-box without requiring explicit CAP_PROP_FOURCC setup
- Users can still override the format with CAP_PROP_FOURCC if needed
- Behavior matches user expectations from other camera backends (V4L2, MSMF, etc.)
The default of MONO_8 was chosen because it's the most universally supported format across USB3 Vision and GigE Vision cameras.
Changes:
modules/videoio/src/cap_aravis.cpp: Added pixel format validation and default setting (10 lines)
Testing:
With this fix:
- Cameras with unsupported default formats will automatically switch to MONO_8
- The sample code from the issue works without uncommenting the CAP_PROP_FOURCC line
- Users can still explicitly set their preferred format via CAP_PROP_FOURCC
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N/A - This requires specialized USB3 Vision / GigE Vision hardware not available in CI
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The fix maintains existing API behavior and makes the backend work as expected
Resolving failing tests of rotary embedding layer on win32 #28080
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Fix tempfile race condition on Windows (issue #19648) #28087
Fix tempfile race condition on Windows
Addresses issue #19648
Problem
The cv::tempfile() function on Windows used GetTempFileNameA() followed by an immediate DeleteFileA() call. This created a race condition where multiple OpenCV processes running simultaneously could receive the same temporary filename, leading to name collisions.
Root Cause
The previous implementation:
Called GetTempFileNameA() to generate a temp filename
Immediately deleted the file to free the name
Returned just the filename string
Between steps 2 and 3, another process could call GetTempFileNameA() and receive the same filename, causing a collision.
Solution
Replaced GetTempFileNameA() with GUID-based filename generation using CoCreateGuid(), following the same approach already used in GetTempFileNameWinRT() and Microsoft's recommendations for scenarios requiring many temp files.
Changes
modules/core/src/system.cpp:
Removed GetTempFileNameA() and DeleteFileA() calls
Added CoCreateGuid() to generate unique GUID-based filenames
Format: "ocv{GUID}" where GUID ensures uniqueness across processes
Benefits
Eliminates race condition in multi-process scenarios
No file I/O overhead from creating and deleting placeholder files
Consistent with WinRT implementation approach
Follows Microsoft best practices
Testing
Standard OpenCV test suite. The change only affects Windows temp file naming and maintains the same String return type and usage pattern.
The pointer offset to move from top-right to bottom-right was incorrect. This change corrects the pointer calculation to use the proper row stride, ensuring it lands on the correct pixel.
Fixed multiband blender memory leak 27333 #28085Fixes#27333
This PR fixes a memory leak in MultiBandBlender where memory from pyramid vectors was not being released when prepare() was called multiple times or when the blender object was reused.
Problem:
MultiBandBlender retains hundreds of MB to several GB of memory even after the blender pointer is released. The issue occurs because:
1. The resize() function on std::vector does not release memory when the new size is less than or equal to the current size
2. It only adjusts the size marker while retaining the capacity and existing data
3. When prepare() is called, the pyramid vectors are resized but old data remains allocated
Example from the bug report: Blending 14 images (1920x1080) retained ~200MB after blender.release(). With larger images, several GB could be retained.
Root Cause:
GPU path (lines 254-256): Correctly calls clear() before operations
Non-GPU path (lines 285-298): Missing clear() calls, causing resize() to retain old data
Solution:
Added .clear() calls before .resize() in the non-GPU path to match the GPU path behavior. This ensures:
- Memory from previous blend operations is released in prepare()
- Reusing a blender object doesn't accumulate memory
- Behavior is consistent between GPU and non-GPU code paths
Changes:
modules/stitching/src/blenders.cpp: Added 2 clear() calls (dst_pyr_laplace_.clear() and dst_band_weights_.clear()) before resize() in the non-GPU path
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N/A - This is a memory management fix that doesn't affect algorithm behavior. Existing stitching tests verify correctness.
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The fix maintains existing API behavior, no documentation changes needed
Changed condition to correctly route LINE_8 to Line() instead of Line2().
The condition 'line_type == 1 || line_type == 4' was causing LINE_8 (value 8)
to be processed by Line2() which implements 4-connectivity, and LINE_4 (value 4)
to be processed by Line() which was implementing 8-connectivity behavior. This
resulted in swapped line connectivity.
Changed to 'line_type == 1 || line_type == 8' so LINE_8 goes to Line() with
8-connectivity and LINE_4 goes to Line2() with 4-connectivity, matching the
documented behavior where LINE_4 should produce 4-connected lines and LINE_8
should produce 8-connected lines.
Fixes#26413
Onnx importer2 dispatch map #28058
I have noticed that PR[28032]( https://github.com/opencv/opencv/pull/28032) was incomplete - it fixed only the new ONNX importer. Also, building the dispatch map based on the parsed opset version hypotetically can cause trouble if the graph simplifier inserts a node with a different opset version which is not included in the dispatch map. So I removed this parameter in `buildDispatchMap_COM_MICROSOFT` and `buildDispatchMap_ONNX_AI` for now. It was marked as `CV_UNUSED` anyway.
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Fix memory leak in pyopencv_to for path-like objects #28047
This PR fixes a memory leak in pyopencv_to when handling path-like objects (e.g., pathlib.Path).
Problem:
PyOS_FSPath() returns a new strong reference, but the code was not calling Py_XDECREF to decrement it, causing a memory leak on every call with path-like arguments.
Solution:
Store the returned reference from PyOS_FSPath() in a separate variable path_obj
Call Py_XDECREF(path_obj) on all function exit paths (both success and error paths)
This ensures proper reference counting without changing the function's behavior
Testing:
The leak can be reproduced using the steps in issue #28046 with Python built with --with-address-sanitizer. This fix ensures the reference is properly released.
Fixes#28046
Pull Request Readiness Checklist
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Correct minAreaRect angle to be in range [-90, 0) #28051
### Pull Request Readiness Checklist
Box angle range over all imgproc tests is in interval `[-90, -0.0581199]`
resolves https://github.com/opencv/opencv/issues/27667
resolves https://github.com/opencv/opencv/issues/19472
resolves https://github.com/opencv/opencv/issues/24436
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Rotary position layer #28031
Implemented https://onnx.ai/onnx/operators/onnx__RotaryEmbedding.html#rotaryembedding-23
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imgcodecs: Fix IMWRITE_AVIF_DEPTH typo and update AVIF details on imwrite() #28063
Close https://github.com/opencv/opencv/issues/28062
- Corrected the documentation for `IMWRITE_AVIF_DEPTH`.
- Added descriptions for the new AVIF encoder parameters in `imwrite()` documentation.
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Fixes issue with libavif >= 1.3.0 where subsampling with identity matrix
coefficients is invalid. Aligns OpenCV AVIF writer with updated libavif
conformance rules.
samples: add compatibility note for Mask R-CNN in OpenCV 5.0 #28053
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### Summary
Updates documentation in `samples/dnn/mask_rcnn.py` to clarify a compatibility issue with OpenCV 5.0.
### Details
As verified in issue #27240, OpenCV 5.0 introduces stricter graph optimization. The default `.pbtxt` used in this sample treats `detection_out_final` as an intermediate layer (it is not listed in `getUnconnectedOutLayersNames`).
While OpenCV 4.x implicitly allows retrieving this layer, OpenCV 5.0 throws an error when requesting it:
> "the number of requested and actual outputs must be the same"
This PR adds:
1. A warning note in the file header explaining the strict output requirement.
2. An inline comment near `net.forward()` to guide users debugging this error.
Relates to issue: #27240
Add optional template mask for findTransformECC #27952
Supersedes #22997
**Summary**
Add optional template mask support to findTransformECC so that only pixels valid in both the template and the image are used in ECC. Backward compatibility is preserved (existing signatures unchanged; one new overload adds templateMask).
**Motivation**
- Real-world frames often contain moving foreground artifacts (e.g., a football over a static field). Masking the object in one frame only is insufficient because its position changes independently of the background. Since we don’t know the warp a priori, we can’t back-project a single mask across frames. The correct approach is to supply both masks and take their intersection.
- Templates may include uninformative/low-texture or noisy regions, or partial overlaps with other objects. Excluding such regions from the alignment improves robustness and convergence.
This PR completes and replaces https://github.com/opencv/opencv/pull/22997
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Handle near-zero convexity in convexHull #28043
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/21482
closes https://github.com/opencv/opencv/issues/14401
Also skip a code that determines orientation inside rotatingCalipers and rely on the order after convexHull
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Undistort points convergence #27993
I have looked into the `undistortPoints()` problem of issue #27916 and have found a solution. The problem is, as @Linhuihang has correctly pointed out, that the fixed-point iterations do not converge. Here are the functions which are optimized for the undistortion problem:
$$
\begin{aligned}
r^2 &= x'^2 + y'^2 \\
f_1(x') &= \frac{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}{1 + k_1 r^2 + k_2 r^4 + k_3 r^6} (x'' - 2p_1 x' y' - p_2(r^2 + 2 x'^2) - s_1 r^2 + s_2 r^4) = x' \\
f_2(y') &= \frac{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}{1 + k_1 r^2 + k_2 r^4 + k_3 r^6} (y'' - p_1 (r^2 + 2 y'^2) - 2 p_2 x' y' - s_3 r^2 - s_4 r^4) = y'
\end{aligned}
$$
where $x', y'$ are the undistorted points we want to compute and and $x'', y''$ are the given distorted points. This problem is solved using fixed-point iterations like
$$
x'_{k+1} = f_1(x'_k),\quad
y'_{k+1} = f_2(y'_k)
$$
I guess the issue here is that the distortion function does not necessarily satisfy the [Banach fixed-point theorem](https://en.wikipedia.org/wiki/Banach_fixed-point_theorem), i.e. the slope of the function can be too large. This can be seen in @Linhuihang's comment https://github.com/opencv/opencv/issues/27916#issuecomment-3417883642 - the point series jumps around and doesn't converge.
A common solution is to instead do damped fixed-point iterations, so that the updates are "more smooth".
$$
x'_{k+1} = (1 - \alpha) x'_k + \alpha f_1(x'_k),\quad
y'_{k+1} = (1 - \alpha) y'_k + \alpha f_2(y'_k)
$$
I have implemented a simple logic which starts with $\alpha = 1$ (so just like it is now) and reduces $\alpha$ whenever the optimization error would increase. This seems reasonable to me: the initial logic is to do normal fixed-point iterations and to gradually become "more damped" when we notice that we don't converge. Perhaps there is a better way to ensure convergence, but this is the most straightforward modification to the current code that I have found.
This problem is not due to the $\tau_x, \tau_y$ parameters; it also occurs when they are zero. In fact, the fixed-point iterations are done when the tilt correction of $\tau_x, \tau_y$ has already been applied. I have added a test to reproduce the problem. This PR fixes#27916.
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make cuda::GpuMatND compatible with InputArray/OutputArray #23913
continuation of [PR#19259](https://github.com/opencv/opencv/pull/19259)
Make cuda::GpuMatND wrappable in InputArray/OutputArray
The goal for now is just wrapping, some functions are not supported (InputArray::size(), InputArray::convertTo(), InputArray::assign()...)
No new feature for cuda::GpuMatND
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Switch to the new Github actions pipeline for Windows in 5.x too #28035
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Onnx importer2 dispatch map #28032
in the new onnx_importer all domains in the dispatch map should be included per default.
See https://github.com/opencv/opencv/pull/27988#issuecomment-3521140872
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core: add copyAt() for ROI operation #27318
Close https://github.com/opencv/opencv/issues/27320
Close https://github.com/opencv/opencv/issues/27298
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Optimize audio buffer duration calculation in MSMF capture. Fixes#27969#28017
### Description
Cache the repeated calculation of `(bit_per_sample/8)*nChannels` in a local variable to avoid redundant computations in `grabFrame()` and `configureAudioFrame()` functions.
### Changes
- Added `bytesPerSample` local variable in both functions
- Replaced 6 repeated calculations with the cached variable
- Improves performance in frequently called audio processing code
Fixes#27969
[3d] Port estimateTranslation2D into 5.x #28023
Merge with opencv_extra PR: opencv/opencv_extra#1290
This PR ports the newly added function `estimateTranslation2D` that was merged into 4.x under this PR: opencv/opencv#27950
The implementation is now adapted to the 5.x branch and integrates cleanly with the updated refinement API.
Specifically, this 5.x version makes use of the improved LevMarq interface to support optional non-linear refinement of the translation parameters.
Functionality, tests, and documentation follow the same design and behavior as the original 4.x PR.
Further details can be found in the linked PR above.
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Added BitShift option to CLAHE #28014
Briefly, this PR is the fix of https://github.com/opencv/opencv/issues/28002
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docs(py): add pip-based install tutorial #27943
- New tutorial: `doc/tutorials/py_tutorials/py_setup/py_pip_install/py_pip_install.markdown` with anchor `{#tutorial_py_pip_install}`.
- Python setup TOC updated to include the new page near the top.
- Windows/Ubuntu/Fedora pages: added **Quick start (pip)** callout
- Expanded troubleshooting in the pip tutorial (env mismatch, headless GUI notes, wheel mismatches, Raspberry Pi/ARM guidance).
This aligns with the issue request to make `pip install opencv-python` the default path for Python newcomers and reduce confusion from legacy content.
Fixes#24360
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Building OpenCV with oneAPI #28003
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resolves https://github.com/opencv/opencv/issues/27580
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imgproc: supports CV_SIMD_SCALABLE in pointSetBoundingRect #27479
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Fixed standard HoughLines output shift for rho. #27992
Closes: https://github.com/opencv/opencv/issues/25038
Replaces: https://github.com/opencv/opencv/pull/25043
Merge with https://github.com/opencv/opencv_extra/pull/1288
The original implementation introduces systematic shift (-rho/2) for odd indexes. Integer division just gives proper rounding.
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Added affine grid layer to new DNN engine #27894
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Added CMake define option to enforce IPP calls in IPP HAL when building with IPP integration #27925
Extra build option needed to simplify custom builds with IPP integration to ensure the IPP calls done even in cases when the results are not bitwise compliant to the reference OpenCV implementation. Option name is ```WITH_IPP_CALLS_ENFORCED```, and it is disabled by default.
Requested by some IPP customers and might be used in general as a way providing calls with better performance for the cases the aligned precision is not as important aspect.
Supposed to be used in HAL only, so added only in IPP HAL CMake, currently it affects only Warp Affine, Warp Perspective and Remap integrations since the results may vary depending on HW and algorithm implementations.
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Added DFT layer to new DNN engine #27941
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Fixed unrichable code with MSVC on x86 32-bit systems. #27991
Suppress the following warnings:
```
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\onevpl\file_data_provider.cpp(131): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\onevpl\source.cpp(77): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\onevpl\source.cpp(82): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\onevpl\source.cpp(103): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\onevpl\source.cpp(90): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\onevpl\source.cpp(94): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\onevpl\source.cpp(86): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\onevpl\source.cpp(99): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\gstreamer\gstreamersource.cpp(366): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\gstreamer\gstreamersource.cpp(360): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
Warning: C:\GHA-OCV-5\_work\opencv\opencv\opencv\modules\gapi\src\streaming\gstreamer\gstreamerpipeline.cpp(77): warning C4702: unreachable code [C:\GHA-OCV-5\_work\opencv\opencv\build\modules\gapi\opencv_gapi.vcxproj]
```
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Fix missing vec_cvfo on IBM POWER9 due to unavailable VSX float64 conversion #27990
Replaces https://github.com/opencv/opencv/pull/27633
Closes https://github.com/opencv/opencv/issues/27635
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docs(js): Fix Mat.clone() documentation to use mat_clone() for deep copy #27985
- Update code example to use ```mat_clone()``` instead of ```clone()```
- Add explanatory note about shallow copy issue due to Emscripten embind
Problem
- OpenCV.js documentation shows ```Mat.clone()``` usage, but this method performs shallow copy instead of deep copy due to Emscripten embind limitations, causing unexpected behavior where modifications to cloned matrices affect the original.
Related Issues and PRs
- Fixes documentation aspect of issue #27572
- Related to PR #26643 (js_clone_fix)
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Force empty output type where it's defined in API #27972
The PR replaces:
- https://github.com/opencv/opencv/pull/27936
- https://github.com/opencv/opencv/pull/21059
Empty matrix has undefined type, so user code should not relay on the output type, if it's empty. The PR introduces some exceptions:
- copyTo documentation defines, that the method re-create output buffer and set it's type.
- convertTo has output type as parameter and output type is defined and expected.
Fix alpha handling for blending in PNG format #27981
### Pull Request Readiness Checklist
closes#27974
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Remove performance bottleneck caused by redundant type conversions in the
initGMMs() function's tight nested loops that iterate over all image pixels.
Changes:
- Changed storage vectors from Vec3f to Vec3b to store pixel data directly
without intermediate conversion
- Modified kmeans preparation to convert Vec3b data to CV_32FC1 only when
creating the Mat for clustering (using convertTo instead of per-pixel cast)
- Explicitly convert Vec3b to Vec3d only when calling addSample() method
Performance Impact:
Previously: Vec3b -> Vec3f (in loop) -> Vec3d (implicit in addSample)
Now: Vec3b (in loop) -> Vec3d (explicit, only in addSample call)
This eliminates one unnecessary type conversion per pixel in the tight loop,
reducing computational overhead especially for large images.
Fixes#27968
Add BUILD_INFO_SKIP_SYSTEM_VERSION option to exclude the host kernel version
from build outputs, enabling reproducible builds across systems with different
kernel versions.
Changes:
- Modified CMakeLists.txt to conditionally include CMAKE_HOST_SYSTEM_VERSION
in the build platform status based on BUILD_INFO_SKIP_SYSTEM_VERSION flag
- Updated 3rdparty/tbb/CMakeLists.txt to set TBB_HOST_VERSION conditionally
- Modified 3rdparty/tbb/version_string.ver.cmakein to use the new variable
When BUILD_INFO_SKIP_SYSTEM_VERSION=ON is set, the host system version is
excluded from both the CMake status output and the TBB version strings,
producing identical binaries on equivalent build systems regardless of
kernel version differences.
Default behavior is unchanged (version included) for backward compatibility.
Fixes#27961
[calib3d] Add estimateTranslation2D() #27950
Merge with opencv_extra PR: opencv/opencv_extra#1286
### **Description**
This PR adds a new API, `cv::estimateTranslation2D()`, to the **calib3d** module.
It computes a **pure 2D translation** between two sets of corresponding points using robust methods (`RANSAC` and `LMedS`).
The function mirrors the interface and behavior of `estimateAffine2D()` and `estimateAffinePartial2D()`, but constrains the transformation to translation only.
This model is particularly useful for cases where the motion between images is purely translational, such as:
- Aerial stitching and planar mosaics.
- Image alignment in fixed-camera systems.
- Lightweight pipelines where affine or homography models are unnecessarily complex.
The implementation introduces a new internal class `Translation2DEstimatorCallback` and integrates seamlessly into OpenCV’s existing robust estimation framework (`PointSetRegistrator`).
---
### **Key Features**
- Implements `cv::estimateTranslation2D()` in the `calib3d` module.
- Supports robust methods **RANSAC** and **LMedS**.
- Adds accuracy and performance tests.
- Provides full **C++ and Python bindings**.
- Includes **Doxygen documentation** consistent with OpenCV’s standards.
- Verified correctness across noise, outlier, and datatype variations.
---
### **Testing & Verification**
**Unit Tests** (`modules/calib3d/`)
- **Minimal sample:**
`test1Point` validates that a single correspondence recovers the correct translation under both **RANSAC** and **LMedS** across 500 randomized trials.
- **Robustness to noise and outliers:**
`testNPoints` generates 100 correspondences, injects noise and outliers (≤40% for RANSAC, ≤50% for LMedS), and verifies that:
- Estimated **T** closely matches ground truth (`cvtest::norm(..., NORM_L2)`).
- Inlier mask consistency and correctness are maintained.
- **Datatype conversion:**
`testConversion` checks mixed input datatypes (integer → float) to ensure correct conversion and consistent results.
- **Input immutability:**
`dont_change_inputs` confirms that input arrays remain unchanged after function execution, mirroring affine behavior.
**Performance Tests** (`modules/calib3d/`)
- `EstimateTranslation2DPerf` benchmarks **RANSAC** and **LMedS** using:
- Point counts: 1000
- Confidence levels: 0.95
- Refinement iterations: 10, 0
These tests confirm **numerical stability**, **performance scaling**, and **consistency** across datatypes and noise levels.
---
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Fixed ptrdiff_t cast on windows 32-bit #27976
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Fixed pre-built ffmpeg diagnostics on Windows for ARM. #27977
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Add macOS support for Orbbec Gemini330 camera #27930
### Description of Changes
Adds macOS support for Orbbec Gemini330 in videoio by integrating OrbbecSDK v2. Completes the non-macOS work in [#27230](https://github.com/opencv/opencv/pull/27230).
#### Motivation
[#27230](https://github.com/opencv/opencv/pull/27230) skipped macOS. On macOS, UVC alone lacks required device controls; OrbbecSDK v2 provides them.
#### Key Change
- macOS: fetch/link OrbbecSDK v2.5.5 (replaces v1.9.4).
- videoio (macOS): switch OrbbecSDK API usage from v1 to v2.
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Try to fix distant points to save time when ThickLine() calls FillConvexPoly() #27366
Proposal for #27365
cv::clipLine() is useful, but one should take care of a margin to preserve line caps.
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Clarified supported types in cv::patchNaNs() documentation #27959
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Updated the docs for cv::patchNaNs() to specify that both CV_32F and CV_64F types are supported. Fixes incomplete information.
In `fisheye::initUndistortRectifyMap` states, that the function 'compensate radial and tangential lens distortion'. But in fact, fisheye camera model in OpenCV does not uses tangential distortion. It uses only radial distortions, with 4 distortion k_1, k_2, k_3, k_4, which all are radial. In the code of the function all of those koeficients indeed are used as radial lens distortion coefficients. Possible reason of that issue is similar documentation of pinhole camera, that as first four coefficients uses 2 radial and 2 tangential lens distortion coefficients - k_1, k_2, p_1, p_2.
Added Onehot layer support to new DNN engine #27902
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Update documentation for Mat_ constructor to clarify vector behavior #27937
This PR updates the documentation comment for the constructor:
`explicit Mat_(const std::vector<_Tp>& vec, bool copyData=false);`
The original docstring stated that this constructor creates a matrix with a single column. However, in OpenCV version 5.x, this constructor now creates a matrix with a single row and the number of columns equal to the size of the vector.
This change clarifies the behavior for users migrating to or working with OpenCV 5.x and reduces confusion due to the discrepancy between documented and actual matrix shape.
This PR addresses GitHub issue #27862.
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tiff: use a per-TIFF error handler #27927
libtiff 4.5 introduced [per-TIFF error handlers](https://libtiff.gitlab.io/libtiff/functions/TIFFOpenOptions.html). This PR removes the global OpenCV error handlers and uses per-handle error handlers. This reduces any risks associated with modifying global state, e.g. if another library also tries to set the global error handlers and OpenCV clobbers them.
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Fix HDR tutorial result mismatch by adding gamma note #27866
This PR fixes#22219 by clarifying the gamma correction value in the HDR tutorial.
The function cv.createTonemap() has a default gamma value of 1.0. To match the tutorial example results, gamma should be explicitly set to 2.2. This note has been added to the Tonemap HDR image section of the tutorial.
Visualization script for one or more cameras calibration with calibration.cpp #27627
* It reads one or more YAML files produced by `calibration.cpp`.
* displays an interactive 3D scene of the calibration setup with camera frustums and board meshes.
* Supports both **camera-centric** and **board-centric** views.
* exports the entire scene as a `.obj/.mtl` for use in meshlab.
* also includes an error bar plot and frustum highlighting for single-camera setups.
calibration results generated by `calibration.cpp`: https://drive.google.com/drive/folders/13NvZHaxTFdAB-bzlNve8kKSMIg9gAu6K
Results Uploaded at: https://drive.google.com/drive/folders/1fkFyoBRGcNY4UCQhGgadO6uKlAkrzjTe
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core: support 16 bit LUT for 5.x #27918
Porting from https://github.com/opencv/opencv/pull/27890
Porting from https://github.com/opencv/opencv/pull/27911
And support new OpenCV5 types for 16 bit LUT.
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Open camera device by index through FFmpeg #27841
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/26812
Example of explicit backend option (similar to `-f v4l2` from command line)
```
export OPENCV_FFMPEG_CAPTURE_OPTIONS="f;v4l2"
```
see https://trac.ffmpeg.org/wiki/Capture/Webcam for available options
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Update Gao P3P with Ding P3P #27736
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---
The current Gao P3P implementation does not cover all the degenerate cases, **see last line** in: 6d889ee74c/modules/calib3d/src/p3p.cpp (L211-L221)
See also:
- https://github.com/opencv/opencv/issues/4854
---
<details>
<summary>OBSOLETE</summary>
To fix this, the USAC P3P from OpenCV 5 is used instead: 7e6da007cd/modules/3d/src/usac/pnp_solver.cpp (L282)
---
## Some results
### Old P3P vs new
In the following video, I have tried to highlight the viewpoints which cause issues:
https://github.com/user-attachments/assets/97bec6a6-4043-4509-b50e-a9856d6423bd
| | Old P3P | New P3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.045701 | 0.024816 |
| Median (ms) | 0.025146 | 0.023193 |
| Std (ms) | 0.028953 | 0.006124 |
### New P3P vs AP3P
https://github.com/user-attachments/assets/eaeb21dc-3ffd-4b6c-9902-4352f824aa45
The AP3 method is superior both in term of accuracy and computation time:
| | New P3P | AP3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.043750 | 0.023442 |
| Median (ms) | 0.023193 | 0.021484 |
| Std (ms) | 0.039920 | 0.005265 |
### New P3P vs AP3P (range test)
https://github.com/user-attachments/assets/572e7b7a-2966-4bed-8e0c-b93d863987dc
The implemented P3P method does not work well when the tag is small, at long range.
| | New P3P | AP3P |
| -------- | ------- | ------- |
| Mean (ms) | 0.031351 | 0.025189 |
| Median (ms) | 0.022217 | 0.020996 |
| Std (ms) | 0.024920 | 0.009633 |
---
- I have tried to simplify the P3P code, hope I did not break the implementation code
- calculations are performed using double type for simplicity.
- code such as the following are redundant and no more needed and should be replaced by `cv::Rodrigues`:
6d889ee74c/modules/calib3d/src/usac/pnp_solver.cpp (L395)
</details>
Use cairosvg for pattern rendering to get rid of double resize. #27906
Depends on https://github.com/opencv/ci-gha-workflow/pull/269
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core: support 16 bit LUT #27890
Close https://github.com/opencv/opencv/issues/26899
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Use `link_libraries` instead of `add_defintions` to link against Qt 6.9
and newer to avoid un-expanded generator expressions from Qt cmake files
being appended to linker flags when building the HighGUI module.
The actual bug is likely in how Qt cmake files end up with these
un-expanded generator expressions in the first place — see discussion in
https://bugreports.qt.io/browse/QTBUG-134774 — but the recommended way
to link against the library is to use `link_libraries` anyway, so this
fix should do the trick.
Fixes issue #27223.
Refactor minEnclosingCircle tests #27900
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Separate input points for tests
Before this, next input points depended on previous ones and it was not obvious which input points specific test checked
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- Changed splitting logic to improved performance for all warp related functions in IPP HAL for multithreaded mode.
- Removed IPP 7.0 preprocessor condition from warp in IPP HAL, since unsupported.
- Added preprocessor condition to enforce IPP warp calls for custom builds.
2025-10-14T05:53:31.5387050Z C:\GHA-OCV-1\_work\ci-gha-workflow\ci-gha-workflow\opencv\modules\imgcodecs\src\bitstrm.cpp(156,57): warning C4244: 'argument': conversion from 'int64_t' to 'ptrdiff_t', possible loss of data [C:\GHA-OCV-1\_work\ci-gha-workflow\ci-gha-workflow\build\modules\imgcodecs\opencv_imgcodecs.vcxproj]
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Optional Known Foreground Mask for Background Subtractors #27810
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### Description
This adds an optional foreground input mask parameter to the MOG2 and KNN background subtractors, in line with issue https://github.com/opencv/opencv/issues/26476
4 tests are added under test_bgfg2.cpp:
2 for each subtractor type (1 with shadow detection and 1 without)
A demo shows the feature with only 3 parameters and with a 4th optional foreground mask for both core subtractor types.
Note: To patch contrib inheritance of the background subtraction class, empty apply method which throws a not implemented error is added to contrib subclasses. This is done to keep the overloaded apply function as pure virtual. Contrib PR to be made and linked shortly.
Contrib Repo Paired Pull Request: https://github.com/opencv/opencv_contrib/pull/4017
Enabled fp16 conversions, but disabled NEON FP16 arithmetics on Windows for ARM for now #27897
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Added center crop pad layer to new DNN engine #27892
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Fix charuco_board_pattern in generate_pattern.py #27876
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Fix#27871
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Added support for SCE and NLL losses #27809
This pull request adds the support for Negative Log-Likelihood loss and Softmax Cross-Entropy loss in new DNN engine.
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Extended Activation layers support #27882
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Extended Reduce layer support in new DNN engine #27816
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Added Bitwise layer to new DNN engine #27845
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Fix QRCodeDetector::detectAndDecode crash #27877
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Fix#27807
The problem is that when we find closest points from hull, we can get same closest point for several different points
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Fix invalid memory access in USAC #27865
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Fix#27863
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3D object pose estimation tutorial update #27874
1. Use cv::loadMesh instead of custom csv-based reader.
2. Use code snippets
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Improved blur #27822
* Perform row and column filter operations in a single pass.
* Temporary storage of intermediate results are avoided.
* Impacts 32F and 64F inputs for ksize <=5.
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Add OPENCV_FFMPEG_SKIP_LOG_CALLBACK to preserve custom FFmpeg logging #27864
OpenCV’s InternalFFMpegRegister overwrites av_log_set_callback, blocking custom FFmpeg log handlers in statically linked apps.
Add `OPENCV_FFMPEG_SKIP_LOG_CALLBACK` to let applications keep their own logging.
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Added cast and castlike layers support in new DNN engine #27698
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Cleaned up parser denylist and removed passing tests. #27857
Merge with: https://github.com/opencv/opencv_extra/pull/1278
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dnn: added neon intrinsics implementation of fastGEMM1T function #27785
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- This PR improves the performance of the LSTM function on ARM64 targets.
- Added a NEON intrinsics implementation of the fastGEMM1T function and enabled its use in fully connected and recurrent layers file.
- As a result, ARM64 now benefits from vectorized matrix–vector multiplications, leading to measurable performance improvements in the LSTM layer.
- This change is limited to ARM64 and does not affect other architectures.
**Performance impact:**
- The optimization significantly improves the performance of lstm functions on ARM64 targets.
<img width="930" height="313" alt="image" src="https://github.com/user-attachments/assets/92e251cd-dc6c-4cda-9586-acc19bf16dfd" />
Moved pattern generator to apps and rewrote tutorial #27833
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WebPMuxAssemble always alloc output buffer and do not reuse provided one.
It overrites provided buffer and it's not freed in the previous version.
WebPDataClear cannot be used with shared pointer as it does not free the object memory itself.
webpFree expects void*, but not const void* that's why const cast is required here.
Updated ONNX conformance tests and parser denylist #27827
Merge with https://github.com/opencv/opencv_extra/pull/1277
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Fixed wrong NaN handling when scaling depth for OdometryFrame #27821
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Fix string property bindings in JS generator #27726Fixes#27712
This PR fixes the binding generation logic in embindgen.py to correctly handle enum and string properties:
Enum properties now use binding_utils::underlying_ptr(&Class::property).
Standard string properties are bound directly with &Class::property.
Other properties continue to use the default template.
Testing:
Verified generated bindings locally to ensure the expected output for enums and strings.
Add 5.x types for DLPack. Keep uint32/int64/uint64 data type for conversion to Numpy. More types support for GpuMat::convertTo #27779
### Pull Request Readiness Checklist
**Merge with contrib**: https://github.com/opencv/opencv_contrib/pull/4000
related: https://github.com/opencv/opencv/pull/27581
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imgproc: add minEnclosingConvexPolygon #27369
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core: ARM64 loop unrolling in kmeans to improve Weighted Filter performance #27596
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- This PR improves the performance of the Weighted Filter function from the ximgproc module on Windows on ARM64.
- The optimization is achieved by unrolling two performance-critical loops in the generateCentersPP function in modules/core/src/kmeans.cpp, which is internally used by the Weighted Filter function.
- The unrolling is enabled only for ARM64 builds using #if defined(_M_ARM64) guards to preserve compatibility and maintain performance on other architectures.
**Performance Improvements:**
- Improves execution time for Weighted Filter performance tests on ARM64 without affecting other platforms.
<img width="772" height="558" alt="image" src="https://github.com/user-attachments/assets/ae28c0af-97d3-460b-ad5a-207d3fc6936f" />
Improved Gaussian Blur #27795
- Horizontal and vertical kernels of 3N131 and 5N14641 are combined for non-border(inner) regions.
- Temporary storage of intermediate results are avoided by combining the kernels.
- Further refinement of other 3N, 5N to be added later.
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imgcodecs: bmp: relax decoding size limit to over 1GiB #27811
Close https://github.com/opencv/opencv/issues/27789
Close https://github.com/opencv/opencv/issues/23233
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libtiff upgrade to version 4.7.1 #27806
close https://github.com/opencv/opencv/issues/27784
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Fixed HashTSDF expand bug #27801
HashTSDF should expand volume units during integration. It didn't happen in fact because of wrong Mat passing.
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stitching: enable loop unrolling in fast.cpp to improve ARM64 performance #27642
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- This PR introduces an ARM64-specific performance optimization in the FAST_t function by applying loop unrolling.
- The optimization is guarded with #if defined(_M_ARM64) to ensure it only affects ARM64 builds.
- This optimizations lead to performance improvements in stitching module functions.
**Performance Improvements:**
- This change significantly improved the performance on Windows ARM64 targets.
<img width="935" height="579" alt="image" src="https://github.com/user-attachments/assets/a03833d1-ac9b-408f-916b-243fd6ae2d53" />
dnn: improve performance of softmax_3d with loop unrolling #27777
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- This PR applies loop unrolling in the softmax function.
- The change does not affect functional correctness.
**Performance Improvements**
- The optimization significantly improves the performance of softmax_3d on Windows ARM64 targets.
<img width="703" height="203" alt="image" src="https://github.com/user-attachments/assets/85997c15-f543-432c-95e5-69099d71fe71" />
Added nonmaxsuppression (NMS) layer to new DNN engine #27674
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Patch to opencv_extra has the same branch name.
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Added fully functional resize layer to new DNN engine #27586
### Pull Request Readiness Checklist
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dnn: Tune CONV_NR_FP32 size for WASM #27773
We can see ~20% inference time reduction on local benchmark.
The local benchmark includes face detection with res10_300x300_ssd_iter_140000_fp16.caffemodel and image classification with squeezenet.onnx .
### Pull Request Readiness Checklist
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Added unique layer to new DNN engine #27676
### Pull Request Readiness Checklist
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Use MatShape instead of MatSize inside cv::Mat/cv::UMat #27757
**Merge together with https://github.com/opencv/opencv_contrib/pull/3996**
---
This PR continues cv::Mat/cv::UMat refactoring. See #26056, where `MatShape` was introduced. Now it's put inside cv::Mat/cv::UMat instead of a weird `MatSize`. MatSize is now an alias for MatShape:
**before:**
```
struct MatShape { ... };
struct MatSize { ... };
struct Mat {
...
int dims;
int rows;
int cols;
...
MatShape shape() const { ... /* constructs MatShape out of MatSize and returns it;
layout is always 'unknown', because we don't store it */ }
MatSize size; // size is not valid without the parent cv::Mat,
// because size.p may point to Mat::rows or to Mat::cols,
// depending on the dimensionality, and dims() returns Mat::dims.
MatStep step; // may allocate memory, depending on the dimensionality.
...
};
```
**after:**
```
struct MatShape { ... };
typedef MatShape MatSize; // they are now synonyms
struct Mat {
...
int dims;
int rows;
int cols;
...
MatShape shape() const { return size; } // just return the embedded shape (including the proper layout information)
MatSize size; // size is self-contained data structure that can be used without the parent cv::Mat.
// size.dims is now a copy of dims; size.p[*] contains copies of Mat::rows and Mat::cols when dims <= 2.
MatStep step; // does not allocate extra memory buffers.
...
};
```
There are several reasons to do that:
1. the main reason is to be able to store data layout (MatShape::layout) inside each cv::Mat/cv::UMat. This is necessary for the proper shape inference in DNN module. In particular, it's necessary for the next step of DNN inference optimization where we introduce block-layout-optimized convolution and other operations. Later on, we can use layout information to support non-interleaved images (e.g. RRR...GGG...BBB...) or even batches of such images in core/imgproc modules.
2. the other reason is to represent 3D/4D/5D etc. tensors as cv::Mat/cv::UMat instances more conveniently, without extra dynamic memory allocation. Before this patch we allocated some memory buffers dynamically to store shape & steps for more than 2D arrays. Now the whole cv::Mat/cv::UMat header can be stored completely on stack/in a container. Creating another copy of Mat/UMat header is now done more efficiently.
3. the third reason is to introduce the new coding pattern: `dst.create(src.size, <dst_type>);`. The pattern is suitable for most of element-wise (including cloning) and filtering operations. This pattern does not only look crisp and self-documenting, it will also automatically copy shape (including layout) from the source tensor into the destination matrix/tensor.
4. in the future we might add `colorspace` member to MatShape that will allow to distinguish RGB from BGR or NV12. `dst.create(src.size, <dst_type>);` will then copy the colorspace information as well.
### Pull Request Readiness Checklist
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features2d: performance optimization of detect function on Windows-ARM64 #27776
### Pull Request Readiness Checklist
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- This PR improves the performance of the detect function on Windows ARM64 targets.
- Added the defined(_M_ARM64) macro in agast.cpp and agast_score.cpp files, aligning ARM64 behavior with how x64 selects internal functions for computation.
- As a result, ARM64 now executes the same internal functions as x64 where applicable, leading to measurable performance improvements in detect function.
- This changes is limited to Windows ARM64 and does not affect other architectures
**Performance impact:**
- Detect function shows improved runtime on ARM64 targets due to reuse of existing efficient computation paths.
<img width="1419" height="408" alt="image" src="https://github.com/user-attachments/assets/feab411a-d256-4bff-bec2-22b2583f63d1" />
Skip ARM assembly file on QNX #27774
This fixes build failures on QNX caused by unsupported ARM NEON assembly in arm/filter_neon.S. The QNX environment does not handle this assembly source correctly, resulting in compilation errors.
Build and test instruction for QNX:
https://github.com/qnx-ports/build-files/blob/main/ports/opencv/README.md
### Pull Request Readiness Checklist
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Added nonzero layer to new DNN engine #27701
### Pull Request Readiness Checklist
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Add strict validation for encoding parameters for APNG, Animation WebP #27769
Extra fix for https://github.com/opencv/opencv/issues/27557
- Fix for build errors with libspng library.
- Add strict validation for APNG.
- Add strict validation for Animation WebP.
### Pull Request Readiness Checklist
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Fix Python Scalar typing issue #27528#27620
- Add ScalarInput and ScalarOutput types for better type safety
- ScalarInput: Union[Sequence[float], float] for function parameters
- ScalarOutput: Sequence[float] for function return values
- Keep original Scalar type for backwards compatibility (deprecated)
- Add refinement functions to apply new types to specific functions
- Functions returning scalars now use ScalarOutput (mean, sumElems, trace)
- Drawing functions now use ScalarInput for color parameters
- Resolves MyPy compatibility issues with scalar return values
- Maintains full backwards compatibility
closes#27528
### Pull Request Readiness Checklist
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Added Pow layer to new DNN engine #27710
### Pull Request Readiness Checklist
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Added gridsample layer to new DNN engine #27700
### Pull Request Readiness Checklist
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Optimize FFmpeg VideoCapture with swscale threads option #27755
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/21969
* Switch to `sws_scale_from` for `libswscale >= 6.4.100` (FFmpeg >= 5.0)
* Use new context init API with threads option (`libswscale >= 8.12.100`: 2a091d4f2e)
* Replicate `sws_getCachedContext` with threads option for `libswscale < 8.12.100`
1 hour mp4 video every frame reading
| HW | sws_scale | sws_scale_frame + 16 threads | sws_scale_frame + 24 threads (#cpus) |
|---|---|---|---|
| Intel Core i9-12900 CPU | 45.1 sec | 25.4 sec (x1.77) | 30 sec (x1.50) |
| NVIDIA GPU 4090 | 232 sec | 89.4 sec (x2.59) | 77 sec (x3.01) |
```
import time
import numpy as np
import os
import cv2 as cv
# os.environ["OPENCV_FFMPEG_CAPTURE_OPTIONS"] = "hwaccel;cuvid|video_codec;h264_cuvid|vsync;0"
start = time.time()
video = "test.mp4"
cap = cv.VideoCapture(video, cv.CAP_FFMPEG)
while True:
has_frame, frame = cap.read()
if not has_frame:
break
print(time.time() - start)
```
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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Patch to opencv_extra has the same branch name.
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imgcodecs: bmp: support to write 32bpp BMP with BI_BITFIELDS #27559
### Pull Request Readiness Checklist
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Set limitation to IPP Bilateral Filter tiles number to avoid too small tiles #27720
### Pull Request Readiness Checklist
This PR fixes the following issue in Bilateral Filter tiling in IPP integration: image ROI can't be closer to the image border than the filter window radius. This issue shows itself during separation of the image to tiles for multithreaded processing. If the tile size small enough, the second tile is closer to the upper image border than the bilateral filter radius, which leads to the incorrect result. To fix this, we need a limitation to the tile size - done in this PR.
_Note: red build status looks like unrelated to the current change_
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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[videoio][VideoWriter] Fix return code from CvVideoWriter_FFMPEG::writeFrame() when encapsulating encoded video #27737
Currently the return code from `CvVideoWriter_FFMPEG::writeFrame()` when `encode_video==true` (encapsulating raw encoded video) is wrong and results in the following warning implying it has been unsuccessful
> [ WARN:0@15.551] global cap_ffmpeg.cpp:198 write FFmpeg: Failed to write frame
### Pull Request Readiness Checklist
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doc: fix doxygen warnings for imgcodecs, flann and objdetect #27730
Close https://github.com/opencv/opencv/issues/27729
### Pull Request Readiness Checklist
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libpng upgrade to 1.6.45 and cICP metadata support for PNG imwrite #27741
### Pull Request Readiness Checklist
resolves#24185
libpng docs: https://www.w3.org/TR/png-3/#cICP-chunk
similar code from ffmpeg: a700f0f72d/libavcodec/pngenc.c (L452-L456)
So issue #24185 can be solved by replacing `cv.imwrite` in user's code to `cv.imwriteWithMetadata`:
```python
cv.imwriteWithMetadata("frame.png", frame, [cv.IMAGE_METADATA_CICP], np.array([[9, 18, 0, 1]], np.uint8))
```
```
$ exiftool /home/d.kurtaev/opencv_build/frames_pr/image_38.png
ExifTool Version Number : 12.76
File Name : image_38.png
Directory : /home/d.kurtaev/opencv_build/frames_pr
File Size : 3.8 MB
File Modification Date/Time : 2025:09:02 20:48:22+03:00
File Access Date/Time : 2025:09:02 20:48:22+03:00
File Inode Change Date/Time : 2025:09:02 20:48:22+03:00
File Permissions : -rw-r--r--
File Type : PNG
File Type Extension : png
MIME Type : image/png
Image Width : 1080
Image Height : 1920
Bit Depth : 8
Color Type : RGB
Compression : Deflate/Inflate
Filter : Adaptive
Interlace : Noninterlaced
Color Primaries : BT.2020, BT.2100
Transfer Characteristics : BT.2100 HLG, ARIB STD-B67
Matrix Coefficients : Identity matrix
Video Full Range Flag : 1
Image Size : 1080x1920
Megapixels : 2.1
```
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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Patch to opencv_extra has the same branch name.
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fix: FFmpeg 8.0 support #27746
### Pull Request Readiness Checklist
related comment: https://github.com/opencv/opencv/pull/27691#discussion_r2322695640
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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e.g.
contrib\modules\cudev\include\opencv2\cudev/ptr2d/warping.hpp(86): warning C4505: 'cv::cudev::affineMap': unreferenced function with internal linkage has been removed
contrib\modules\cudev\include\opencv2\cudev/ptr2d/warping.hpp(134): warning C4505: 'cv::cudev::perspectiveMap': unreferenced function with internal linkage has been removed
Improve fitEllipseDirect tests #27717
### Pull Request Readiness Checklist
Previous `fit_and_check_ellipse` implementation was very weak - it only checks that points center lies inside ellipse.
Current implementation `fit_and_check_ellipse` checks that points RMS (Root Mean Square) algebraic distance is quite small. It means that on average points are near boundary of ellipse. Because for points on ellipse algebraic distance is equal to `0` and for points that are close to boundary of ellipse is quite small
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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Patch to opencv_extra has the same branch name.
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Add canny, scharr and sobel for riscv-rvv hal. #27378
### Pull Request Readiness Checklist
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Fix checking that point lies inside ellipse #27704
### Pull Request Readiness Checklist
Previous `check_pt_in_ellipse` implementation was incorrect. For points on ellipse `cv::norm(to_pt)` should be equal to `el_dist`.
I tested current implementation with following Python script:
```
import cv2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
def check_pt_in_ellipse(pt, el):
center, axes, angle = ellipse
to_pt = pt - center
el_angle = angle * np.pi / 180
to_pt_r_x = to_pt[0] * np.cos(-el_angle) - to_pt[1] * np.sin(-el_angle)
to_pt_r_y = to_pt[0] * np.sin(-el_angle) + to_pt[1] * np.cos(-el_angle)
pt_angle = np.arctan2(to_pt_r_y / axes[1], to_pt_r_x / axes[0])
x_dist = 0.5 * axes[0] * np.cos(pt_angle)
y_dist = 0.5 * axes[1] * np.sin(pt_angle)
el_dist = np.sqrt(x_dist * x_dist + y_dist * y_dist)
assert abs(np.linalg.norm(to_pt) - el_dist) < 1e-10
# TEST(Imgproc_FitEllipse_Issue_4515, accuracy) {
points = np.array([
[327, 317],
[328, 316],
[329, 315],
[330, 314],
[331, 314],
[332, 314],
[333, 315],
[333, 316],
[333, 317],
[333, 318],
[333, 319],
[333, 320],
])
ellipse = cv2.fitEllipseDirect(points)
center, axes, angle = ellipse
angle_rad = np.deg2rad(angle)
points_on_ellipse = []
for point_angle_deg in range(0, 360, 10):
point_angle = np.deg2rad(point_angle_deg)
point = np.array([0., 0.])
point_x = axes[0] * 0.5 * np.cos(point_angle)
point_y = axes[1] * 0.5 * np.sin(point_angle)
point[0] = point_x * np.cos(angle_rad) - point_y * np.sin(angle_rad)
point[1] = point_x * np.sin(angle_rad) + point_y * np.cos(angle_rad)
point[0] += center[0]
point[1] += center[1]
points_on_ellipse.append(point)
points_on_ellipse = np.array(points_on_ellipse)
for point in points_on_ellipse:
check_pt_in_ellipse(point, ellipse)
plt.figure(figsize=(8, 8))
plt.scatter(points[:, 0], points[:, 1], c='red', label='points')
plt.scatter(points_on_ellipse[:, 0], points_on_ellipse[:, 1], c='yellow', label='ellipse')
ellipse = Ellipse(xy=center, width=axes[0], height=axes[1],
angle=angle, facecolor='none', edgecolor='b')
plt.gca().add_patch(ellipse)
plt.gca().set_aspect('equal')
plt.legend()
plt.show()
```
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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- [x] The PR is proposed to the proper branch
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- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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- Added vector_MatShape and vector_vector_MatShape to gen_dict.json
- Implemented MatShape_to_vector_MatShape, vector_MatShape_to_MatShape, MatShape_to_vector_vector_MatShape, and vector_vector_MatShape_to_MatShape conversion functions in dnn_converters.h/cpp and Converters.java
- Added testGetLayersShapes test to verify List<List<MatShape>> conversion
- Added vector_vector_Mat to gen_dict.json
- Implemented Mat_to_vector_vector_Mat and vector_vector_Mat_to_Mat conversion functions in converters.h/cpp and Converters.java
- Added DnnForwardAndRetrieve.java test to verify List<List<Mat>> conversion : Reference: C++ test in modules/dnn/test/test_misc.cpp - TEST(Net, forwardAndRetrieve)
Enable Java wrapper generation for Vec4i #27567
Fixes an issue where Java wrapper generation skips methods using Vec4i.
Related PR in opencv_contrib: https://github.com/opencv/opencv_contrib/pull/3988
The root cause was the absence of Vec4i in gen_java.json, which led to important methods such as aruco.drawCharucoDiamond() and ximgproc.HoughPoint2Line() being omitted from the Java bindings.
This PR includes the following changes:
- Added Vec4i definition to gen_java.json
- Updated gen_java.py to handle jintArray-based types properly
- ~~Also adjusted jn_args and jni_var for Vec2d and Vec3d to ensure correct JNI behavior~~
The modified Java wrapper generator successfully builds and includes the expected methods using Vec4i.
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Added image super-resolution samples using seemoredetails model #27592
Based on "See More Details: Efficient Image Super-Resolution by Experts Mining" (ICML 2024)
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This pull request adds a new sample named seemore_superres under samples/dnn/, implemented in both Python and C++.
The sample demonstrates image super-resolution using the Seemoredetails model with OpenCV’s DNN module.
### Files Added:
- samples/dnn/seemore_superres.cpp
- samples/dnn/seemore_superres.py
- Updated samples/dnn/models.yml
### Functionality:
- Performs image upscaling(4x) using a specified Seemoredetails ONNX model.
- Accepts image path and ONNX model path as command-line arguments.
- Outputs the original and super-resolved images side by side for visual comparison.
### Sample Usage:
*C++*
./seemore_superres --input=path/to/image.jpg
`
*Python*
python seemore_superres.py --input=path/to/image.jpg
`
### Pull Request Readiness Checklist
resolves#16295
```
docker run --gpus 0 -v ~/opencv:/opencv -v ~/opencv_contrib:/opencv_contrib -it nvidia/cuda:12.8.1-cudnn-devel-ubuntu22.04
apt-get update && apt-get install -y cmake python3-dev python3-pip python3-venv &&
python3 -m venv .venv &&
source .venv/bin/activate &&
pip install -U pip &&
pip install -U numpy &&
pip install torch --index-url https://download.pytorch.org/whl/cu128 &&
cmake \
-DWITH_OPENCL=OFF \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_DOCS=OFF \
-DWITH_CUDA=ON \
-DOPENCV_DNN_CUDA=ON \
-DOPENCV_EXTRA_MODULES_PATH=/opencv_contrib/modules \
-DBUILD_LIST=ts,cudev,python3 \
-S /opencv -B /opencv_build &&
cmake --build /opencv_build -j16
export PYTHONPATH=/opencv_build/lib/python3/:$PYTHONPATH
```
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Refactor Blackwell #27537
In CUDA 13:
- 10.0 is b100/b200 same for aarch64 (gb200)
- 10.3 is GB300
- 11.0 is Thor with new OpenRm driver (moves to SBSA)
- 12.0 is RTX/RTX PRO
- 12.1 is Spark GB10
Thor was moved from 10.1 to 11.0 and Spark is 12.1.
Related patch: https://github.com/pytorch/pytorch/pull/156176
libtiff upgrade to version 4.7.0 #27679
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Added bitshift layer to new DNN engine #27666
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Subdiv2d rect2f clean #27641
Closes https://github.com/opencv/opencv/issues/27623
Changes:
- Added Subdiv2D(Rect2f) constructor overload
- Added initDelaunay(Rect2f) method overload
- No changes to the previous implementation to keep it backward compatible
- Added tests for init and testing with edge case of extremely small coordinates
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See original pull request at : https://github.com/opencv/opencv/pull/27631
Added Determinant (Det) layer to new DNN engine #27658
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Added IsInf layer to new DNN engine #27660
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Added IsNan layer to new DNN engine #27661
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This aligns with other virtual method declarations in cap_dshow.hpp
and silences compiler warnings (-Wsuggest-override) while improving
compile-time safety.
Add Alpha matting samples (C++ and Python) #27593
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imgproc: Bilateral filter performance improvement #27433
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Add strict validation for encoding parameters #27621
Close https://github.com/opencv/opencv/issues/27557
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Added Size layer to new DNN engine #27656
Merge with https://github.com/opencv/opencv_extra/pull/1274
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G-API: Implement cfgClampOutputs option to OpenVINO Params #27600
Added the option `cfgClampOutputs` to control where output clamping is performed for OpenVINO models. When enabled, output values are clamped in the PrePostProcessor stage instead of by the device or plugin. This provides a consistent and standardized clamping method across devices, helping to maintain accuracy regardless of device-specific clamping behavior.
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Feat #25150: Pose Graph MST initialization #27423
Implements an MST-based initialisation for pose graphs, as proposed in issue #25150. Both Prim’s and Kruskal’s algorithms were added to the 3D module. These receive a vector of node IDs and a vector of edges (each with source and target IDs and a weight), and return a vector with the resulting edges. These MST implementations treat edges as undirected internally, meaning users only need to provide one direction (A→B or B→A), and duplicates are handled automatically.
Additionally, a new pose graph initialisation method using MST (Prim) was implemented. It constructs the MST over the pose graph, then traverses it to reconstruct node poses. With this, users can call `poseGraph->initializePosesWithMST()` to create an initial solution for the pose graph problem.
A set of test cases validating the implementation was also included.
#### Notes
- The edge weight used in the MST for pose graphs is calculated as:
`weight = || translation || + λ * rotation_angle`,
where λ = 0.485 was determined empirically based on optimiser performance;
- Validated on [Sphere-a](https://lucacarlone.mit.edu/datasets/) pose graph, showing similar convergence behaviour with or without MST initialisation;
- Alternative weight formulas, such as the Mahalanobis distance formula, were also tested, but the current formula yielded better results.
#### Future Work
- Extend testing to more diverse pose graphs (currently limited to [Sphere-a](https://github.com/opencv/opencv_extra/blob/5.x/testdata/cv/rgbd/sphere_bignoise_vertex3.g2o) in opencv_extra)
- Explore adaptive tuning of the λ parameter for broader applicability.
Co-authored-by: @miguel1099
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Cuda 13.0 compatibility #27636
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### Issue
CUDA 13 deprecated some fields, resulting in build failures with CUDA 13. This updates to use the replacement API.
The reference to the deprecated features is here: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#id6
### Testing
This was testing by building on the following configurations:
OS: Ubuntu 24.04
CUDA: 12.9, 13.0
optimize some drawing with stack allocation #27599
Some drawings can try a stack allocation instead of a std::vector
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Performance tests for writing and reading animations #27605
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related : https://github.com/opencv/opencv/pull/27496
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KleidiCV support on Apple devices #27607
Scope:
- Disabled bitcode generation in iPhone framework by default.
- Enabled KleidiCV build for iPhone.
- Added Github Actions log tags to group per-architecture builds and format logs.
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Changed line 69 from "Remember, we together" to "Remember, together we..."
I saw that this page encouraged us to contribute no matter how small the contribution is so I decided to "shoot my shot" as a beginner developer and fix a small grammatical error. Would be nice if I could be allowed to be recognized as a contributor with my contribution albeit pretty small, hopefully in the future, more meaningful contributions will be made! :)
Jpeg Metadata (iccp and xmp) #27583
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to do :
- [x] consider `m_read_options`
- [x] check if ICCP metadata is suitable to write PNG
- [x] the file `testExifOrientation_3.jpg` used in the test has an ICCP data. this data can be saved in jpg format but could not in PNG and WEBP format. Impovements to check this case with PNGEncoder and WEBPEncoder planned.
Take into account overflow for connected components #27582
### Pull Request Readiness Checklist
Fix#27568
The problem was caused by a label type overflow (`debug_example.npy` contains `92103` labels, that doesn't fit in the `CV_16U` (`unsigned short`) type). If pass `CV_32S` instead of `CV_16U` as `ltype` - everything will be calculated successfully
Added overflow detection to throw exception with a clear error message instead of strange segfault/assertion error
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core: support parsing back slash \ in parseKey in FileStorage (JSON) #27587Fixes#27585
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imgcodecs: fix Imgcodecs_Png.write_big test for spng #27589Close#27588
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Added TopK layer with dynamic K support for new DNN engine #27547
This pull request adds TopK layer support to new DNN engine along with dynamic K support.
Initial version inherited from https://github.com/opencv/opencv/pull/26731. Credits to Abduragim.
Closes:
- https://github.com/opencv/opencv/issues/27061
- https://github.com/opencv/opencv/issues/25712
Also closes the topk issue for below issues but having (`Unsupported operations: NonZero` for new dnn engine) which is unrelated to topk.
- https://github.com/opencv/opencv/issues/23663
- https://github.com/opencv/opencv/issues/23297
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core: support parsing null in json parser in FileStorage #27579
Fixes https://github.com/opencv/opencv/issues/27578
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This is a useful property for modularized libraries and applications to be able to use OpenCV
A full module-info.java file for true compatibility with the Java module system would require compiling with Java 9+ and either use a multi-release JAR (which can be tricky to maintain) or compile the entire library with Java 9+ (which would break Android consumers on older SDK versions). In the interest of causing the least disruption, only an automatic module name is set so that modular Java 9+ consumers can use OpenCV but not break anybody else.
Fixed build issue with some old GCC versions #27569
Fixes build for GCC 4.8.5 at least. Looks like the method default signature differs cross GCC versions or newer version has softer checker.
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[GSOC 2025] PNG&WebP Metadata Reading Writing Improvements #27503
Merge with https://github.com/opencv/opencv_extra/pull/1271
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G-API: Implement cfgEnsureNamedTensors option to OpenVINO Params #27549
Added the option cfgEnsureNamedTensors to be applied on OpenVINO models with nameless tensors. If a tensor doesn't have a name, it sets a default one. The default name is created using OpenVINO's standard `make_default_tensor_name` . `make_default_tensor_name` had to be rewritten because it is only available in OpenVINO's dev_api.
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Add enum IMWRITE_PNG_ZLIBBUFFER_SIZE #27551
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This patch enables users to set the internal zlib compression buffer size for PNG encoding
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Fix compilation problems with MSVC+Cuda 12.9 #27522
fix for #27521
Actually, when ENABLE_CUDA_FIRST_CLASS_LANGUAGE is enabled, the fix it not necessary. However, even when ENABLE_CUDA_FIRST_CLASS_LANGUAGE is enabled, I have checked that the fix is harmless So I propose to keep it simple for now and enable the fix whatever the state of ENABLE_CUDA_FIRST_CLASS_LANGUAGE
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Add Raspberry Pi 4 and 5 V4L2 Stateless HEVC Hardware Acceleration with FFmpeg #27453
This PR enables V4L2 stateless HEVC hardware acceleration for Raspberry Pi 5 within OpenCV's videoio module. It leverages FFmpeg's drm acceleration ([FFmpeg API changes](ee1f79b0fa/doc/APIchanges (L1529))), significantly improving HEVC decoding performance on RPi5 for robotics and embedded vision applications.
I have a working proof-of-concept with local benchmarks showing clear gains.
Checklist Status:
Ready: License, branch (4.x), FFmpeg reference, and (linked) related issue (#27452).
Seeking Guidance: Need help with formal C++ performance/accuracy tests, opencv_extra integration, and full documentation/examples.
As a Python developer, I welcome C++ best practice feedback and assistance with testing setup.
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Trilu layer #27527
Trilu layer https://onnx.ai/onnx/operators/onnx__Trilu.html is needed for importing paligemma
Merged with https://github.com/opencv/opencv_extra/pull/1264
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Onnx multifile import #27449
Merge with https://github.com/opencv/opencv_extra/pull/1259
LLMs are larger than 2GB and don't fit into single file onnx. this patch adds support for importing large onnx models with external data
updated `opencv-onnx.proto` to version 1.18.0 [(https://github.com/onnx/onnx/releases/tag/v1.18.0](https://github.com/onnx/onnx/blob/v1.18.0/onnx/onnx.proto)
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Update FastCV lib hash for Linux and Android
Updated libs PR [opencv/opencv_3rdparty#101](https://github.com/opencv/opencv_3rdparty/pull/101)
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IfLayer add to new DNN engine #27508
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eigen: fix to get version from eigen after v3.4.0 #27536
Close https://github.com/opencv/opencv/issues/27530
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imgcodecs: OpenEXR multispectral read/write support #27485
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1262/
Adds capability to read and write multispectral (>4 channels) images in OpenEXR format.
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FIX: CvCapture_FFMPEG::setProperty(CAP_PROP_POS_*) followed by getProperty #27523
Partially fixes#23088 and #23472. This PR fixes `get(CAP_PROP_POS_MSEC)` calls after a `set(CAP_PROP_POS_*)` without calling `read` first.
Since `seek` calls `grabFrame` which already sets `picture_pts`, manually setting `picture_pts` anywhere else in the call stack should not be necessary (except for the special case of seeking to frame 0).
Minimal example from #23088
```cpp
for(int i = 0; i < 3; i++) cap.read(img);
printf("at: %f frames, %f msec\n", cap.get(CAP_PROP_POS_FRAMES), cap.get(CAP_PROP_POS_MSEC));
cap.set(CAP_PROP_POS_FRAMES, 3);
printf("at: %f frames, %f msec\n", cap.get(CAP_PROP_POS_FRAMES), cap.get(CAP_PROP_POS_MSEC));
```
Current
```txt
at: 3.000000 frames, 80.000000 msec
at: 3.000000 frames, 0.234375 msec
```
PR
```txt
at: 3.000000 frames, 80.000000 msec
at: 3.000000 frames, 80.000000 msec
```
It similarly helps with `CAP_PROP_POS_MSEC`:
Current
```txt
at: 3.000000 frames, 80.000000 msec
at: 2.000000 frames, 6.250000 msec
```
PR
```txt
at: 3.000000 frames, 80.000000 msec
at: 2.000000 frames, 40.000000 msec
```
Note the seek operation is still inconsistent between the `CAP_PROP_POS_*` options as mentioned by #23088, and VFR video seeking has issues discussed in #9053. For fixed-frame rate video, we could change 0.5 to 1 in `void CvCapture_FFMPEG::seek(double sec);` to align `CAP_PROP_POS_MSEC` with `CAP_PROP_POS_FRAMES`, but `CAP_POS_AVI_RATIO` and VFR video would still be broken.
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The Kullback-Leibler divergence works with histogram that have integral = 1,
otherwise it can return negative values. The normalization of the histograms
have been changed accordingly, and all the six comparison methods have been
used in the histogram comparison tutorial.
In my configuration with bazel, when building the Java bindings,
it is not like building C++ and including videio/videoio.hpp
triggers:
error this is a compatibility header which should not be used inside the OpenCV library
Extend image I/O API with metadata support #27499
Covered with the PR:
* AVIF encoder can write exif, xmp, icc
* AVIF decoder can read exif
* JPEG encoder can write exif
* JPEG decoder can read exif
* PNG encoder can write exif
* PNG decoder can read exif
This PR is a sort of preamble for #27488. I suggest to merge this one first to OpenCV 4.x, then promote this change to OpenCV 5.x and then provide extra API to read and write metadata in 5.x (or maybe 4.x) in a style similar to #27488. Maybe in that PR exif packing/unpacking should be done using a separate external API. That is, metadata reading and writing can/should be done in 2 steps:
* [1] pack and then [2] embed exif into image at the encoding stage.
* [1] extract and then [2] unpack exif at the decoding stage.
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Issue 26972: Proper treatment of float values in intersectConvexConvex #26974
As outlined in https://github.com/opencv/opencv/issues/26972 the function `intersectConvexConvex()` may not work as expected in the corner case, where two polygons intersect at a corner. A concrete example is given that I added as unit test. The unit test would fail without the proposed bug fix. I recommend porting the fix to all versions.
Now concerning the fix: When digging into the implementation I found, that when the line intersections are computed, openCV currently does not apply floating point comparison syntax, but pretends that line end points are exact. Instead I replaced the formulation using the eps that is already used in another component of the function in line.277: `epx=1e-5`. IMO that is solid enough, definitely better than assuming an exact floating point comparison is possible.
As a follow up I would suggest to use a scalable eps, s.t. also cases with high floating point numbers would be less error prone. However that would need to be done in all relevant sub steps, not just the line intersection code. So for me outside the scope of this fix.
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Fix for 2 channel PNGs #27469closes#26825
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Fixing imread() function 16 bit reading png problem with libspng #27113
The purpose of the PR was to load bit-exact compatible results with libspng and libpng. To test this, `Imgcodecs_Png_PngSuite `was improved. Files containing gamma correction were moved to a separate test called `Imgcodecs_Png_PngSuite_Gamma `because the logic created for the other files did not apply to those with gamma correction. As a result, libspng now works in bit-exact compatibility with libpng. The code can be refactored later.
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cuda: Fix GpuMat::convertTo issues described in 27373 #27379
Fix https://github.com/opencv/opencv/issues/27373.
1. `GpuMat::convertTo` uses `convertToScale` due to incorrect overload.
2. There are no runtime checks to prevent the use of `CV_16U` data types in Release builds.
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Changes about when APNG has a hidden frame #27127
closes : #27074
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imgproc: vectorize cv::createHanningWindow #27368
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imread: GDAL multi-channel support #27458
- tested on 30-channel FITS and 186-channel ENVI files
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fix for the issue #27456#27457closes#27456
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Consider QRCode ECI encoding #24426
### Pull Request Readiness Checklist
related: https://github.com/opencv/opencv/pull/24350#pullrequestreview-1661658421
1. Add `getEncoding` method to obtain ECI number
2. Add `detectAndDecodeBytes`, `decodeBytes`, `decodeBytesMulti`, `detectAndDecodeBytesMulti` methods in Python (return `bytes`) and Java (return `byte[]`)
3. Allow Python bytes to std::string conversion in general and add `encode(byte[] encoded_info, Mat qrcode)` in Java
Python example with Kanji encoding:
```python
img = cv.imread("test.png")
detect = cv.QRCodeDetector()
data, points, straight_qrcode = detect.detectAndDecodeBytes(img)
print(data)
print(detect.getEncoding(), cv.QRCodeEncoder_ECI_SHIFT_JIS)
print(data.decode("shift-jis"))
```
```
b'\x82\xb1\x82\xf1\x82\xc9\x82\xbf\x82\xcd\x90\xa2\x8aE'
20 20
こんにちは世界
```
source: ba4d6c859d/modules/objdetect/test/test_qrcode_encode.cpp (L332)
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Fix Typos in Comments and Documentation #27455
Description:
This pull request corrects minor typos in comments and documentation within the codebase:
- Replaces "representitive" with "representative" in kmeans.cpp.
- Replaces "indices" with the correct spelling in a comment in main.cu.
Fixed bugs in orthogonalization; simplified column vectors copying #27437
This PR mirrors to OpenCV a bug fix addressed by commit [a03d34b](a03d34b641) in SQPnP
It also fixes bugs in the orthogonalization introduced during the porting to OpenCV and simplifies column vectors copying, eliminating double loops.
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Fix Typos and Improve Clarity in Documentation #27444
Description:
This pull request addresses minor typographical errors and improves the clarity of documentation in several markdown files. Specifically:
- Corrected spelling mistakes such as "traslation" to "translation".
- Improved phrasing for better readability and understanding.
- Added references to specific setter methods in the StereoBM documentation for more detailed guidance.
These changes are limited to documentation and do not affect any code functionality.
Implemented:
- A C++ proxy class PythonCustomStreamSource that implements the
IStreamSource interface. This class acts as a bridge between
G-API’s internal streaming engine and user-defined Python
objects. Internally, it stores a reference to a Python object
(PyObject*) and is responsible for: calling the Python object’s
pull() method to retrieve the next frame, calling the descr_of()
method to obtain the frame format description, acquiring and
releasing the Python GIL as needed, converting the returned
numpy.ndarray into cv::Mat, and handling any exceptions or
conversion errors with proper diagnostics.
- A Python-facing factory function, cv.gapi.wip.make_py_src(),
which takes a Python object as an argument and wraps it into a
cv::Ptr<IStreamSource>. Internally, this function constructs a
PythonCustomStreamSource instance and passes the Python object to
it. This design allows Python users to define any class that
implements two methods: pull() and descr_of(). No subclassing or
special decorators are required on the Python side. The user
simply needs to implement the expected interface.
Co-authored-by: Leonor Francisco <leonor.francisco@tecnico.ulisboa.pt>
Adoptions of TFLife fixes to new engine #27439
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This PR covers updates from the following PRs:
#27273, #27307 (ported to 5.x by #27370)
resolves#27397
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Adding color correction module to photo module from opencv_contrib #27051
This PR moved color correction module from opencv_contrib to main repo inside photo module.
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Close https://github.com/opencv/opencv/issues/27413
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Correct IPP distanceTransform results with single thread #27432
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resolves#24082
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Fix Typos in Comments and Error Messages Across Multiple Files #27434
Description:
This pull request corrects several typographical errors in comments and error messages in the following files:
- `samples/directx/d3d11_interop.cpp`: Fixed typo in the error message ("betweem" → "between").
- `samples/dnn/yolo_detector.cpp`: Fixed typo in a comment ("elemets" → "elements").
- `samples/winrt/ImageManipulations/MediaExtensions/OcvTransform.cpp`: Fixed typo in a comment ("peferred" → "preferred").
These changes improve code readability and maintain consistency in documentation and error reporting. No functional code was modified.
Added DNN based deblurring samples #27349
Corresponding pull request adding quantized onnx model to opencv_zoo: https://github.com/opencv/opencv_zoo/pull/295
Model size: 88MB
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Document Qualcomm's FastCV DSP based OpenCV Extension APIs and usage instructions #27430
This PR updates the documentation by:
- Adding a new section: Qualcomm's FastCV DSP based OpenCV Extension APIs list, detailing the OpenCV APIs under the cv::fastcv::dsp namespace and their corresponding Qualcomm's FastCV DSP accelerated implementations.
- Providing a step-by-step guide on how to use the Qualcomm's FastCV DSP APIs.
- Update resizeDown and warpAffine mappings
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Fixed Android setCameraIndex issue. #27419
Fixed camera switching issue on Android devices: When a device has more than two cameras, the setCameraIndex method failed to switch to non-default cameras.
Root cause: The original code only considered two default camera IDs when updating camera IDs, ignoring all others. This fix ensures all camera IDs are properly handled.
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Cover all seek directions in VideoCapture Java test #27421
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Deprecate copyData Parameter in UMat Construction from std::vector and Always Copy Data #27408
Overview
This PR simplifies and modernizes the construction of cv::UMat from std::vector by removing the legacy copyData parameter, always copying the data, and ensuring clearer, safer semantics. This brings UMat in line with current best practices and paves the way for the upcoming OpenCV 5.x series.
What Changed?
1. Header Documentation Update
Removed confusing or obsolete documentation about copyData and clarified the behavior:
Old: builds matrix from std::vector with or without copying the data
New: builds matrix from std::vector. The data is always copied. The copyData parameter is deprecated and will be removed in OpenCV 5.0.
2. Implementation Update
In UMat::UMat(const std::vector<_Tp>& vec, bool copyData), the copyData parameter:
Is now ignored and marked as deprecated.
Marked with CV_UNUSED(copyData) for backward compatibility and to avoid warnings.
The constructor always copies the data from the input vector, regardless of the value of copyData.
All branching logic around copyData has been removed. Any code for "not copying" was not implemented and is now dropped.
This guarantees data safety and predictable behavior.
3. Test Added
A new test construct_from_vector in test_umat_from_vector.cpp:
Verifies that UMat copies the vector data, not referencing it.
Modifies the source vector after construction to confirm that the UMat is unaffected (proving copy, not reference).
Checks matrix shape, type, and content to ensure correctness.
Why This Change?
1. Safety and Predictability
Always copying avoids dangling references and hard-to-debug lifetime issues with stack/heap-allocated vectors.
Removes an undocumented, unimplemented branch (copyData=false).
2. Backward Compatibility
The constructor signature remains for now, but the copyData parameter is marked as deprecated and ignored.
Codebases that pass the parameter will still compile and run as before (but always copy).
3. API Clarity and Maintenance
Documentation now matches the real implementation.
No misleading expectations about zero-copy.
Code is cleaner, future-proof, and easier to maintain.
4. Preparation for OpenCV 5.0
The copyData parameter is deprecated and will be removed in OpenCV 5.x.
Prepares users and downstream libraries for the planned change.
How This Helps OpenCV Users and Developers
Guarantees data safety and makes behavior explicit.
Removes legacy/ambiguous code.
Provides a clear path to OpenCV 5.x.
Minimizes future migration pain.
Ensures all users see the same, reliable behavior (copy semantics).
Refer:#27409
fix#26276: cv::VideoWriter fails writing colorless images #27153
When writing grayscale images (`isColor=false`), `cv::VideoWriter` failed as FFmpeg backend requires input frames to be in a grayscale format. Unlike other backends, FFmpeg doesn't perform this conversion internally and expects the input to be pre-converted. To fix this, I inserted a check in the `CvVideoWriter_FFMPEG_proxy::write` to convert input frames to grayscale when the input has more than 1 channel. If this is true, then the input is converted to a gray image using `cv::cvtColor` (with `cv::COLOR_BGR2GRAY`).
Additionally, as suggested in the issue comments, I have correctly propagated the return value of `CvVideoWriter_FFMPEG::writeFrame` back to the `CvVideoWriter_FFMPEG_proxy::write`. This return value wasn't being used, and `writeFrame` was always returning false since the FFmeg's return code `AVERROR(EAGAIN)` was mistakenly being treated as an error. Now it's handled as a signal for additional input (current input was successfully written, and a new frame should be sent. [See FFmpeg documentation for `avcodec_receive_packet`](https://ffmpeg.org/doxygen/6.1/group__lavc__decoding.html)). A warning is displayed if `CvVideoWriter_FFMPEG::writeFrame` returns false. Alternatively, this could be propagated back up to the user, making cv::VideoWriter::write a boolean.
Finally, I added a test case to verify if the grayscale conversion is being done correctly.
Fixes#26276
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FastCV latest libs hash update #27403
Update hash for the fastcv libs for Linux
Updated libs PR: https://github.com/opencv/opencv_3rdparty/pull/97
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Fix NaNs in HDR Triangle Weights and Tonemapping and Update LDR Ground Truth in tutorial #27396
The PR closes#27392
Updated the triangle weights to use a small epsilon value instead of zero to prevent NaN issues in HDR processing.
Also fixed a float-to-double division issue by explicitly casting double values to float, which was previously producing garbage values and leading to NaNs in tonemapping.
The current LDR ground truth image used in the tutorial [ldr.png](https://github.com/opencv/opencv/blob/4.x/doc/tutorials/others/images/ldr.png) was originally generated using TonemapDurand (check this commit 833f8d16fa), which was moved to opencv_contrib a long time ago in this commit: 742f22c09b. However, the current Tonemap implementation in OpenCV main only performs normalization and gamma correction, which produces noticeably different results. This PR updates the LDR grouth truth image in tutorial with the result of TonemapDrago, and tutorials to use TonemapDrago as Tonemap gives a darker image.
Tonemap output:
TonemapDrago output:
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Check MS Media Foundation availability in G-API too #27355
Tries to address https://github.com/opencv/opencv-python/issues/771
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imgcodecs: jpegxl: support lossless compression #27384
Close https://github.com/opencv/opencv/issues/27382
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Add HoughCirclesWithAccumulator binding #27389
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Fix#27377
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Fix#25696: Solved the problem in Subdiv2D, empty delaunay triangulation #27149
Detailed description
Expected behaviour:
Given 4 points, where no three points are collinear, the Delaunay Triangulation Algorithm should return 2 triangles.
Actual:
The algorithm returns zero triangles in this particular case.
Fix:
The radius of the circumcircle tends to infinity when the points are closer to form collinear points, so the problem occurs because the super-triangles are not large enough,
which then results in certain edges are not swapped. The proposed solution just increases the super triangle, duplicating the value of constant for example.
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Feature: Add OpenVINO NPU support #27363
## Why
- OpenVINO now supports inference on integrated NPU devices in intel's Core Ultra series processors.
- Sometimes as fast as GPU, but should use considerably less power.
## How
- The NPU plugin is now available as "NPU" in openvino `ov::Core::get_available_devices()`.
- Removed the guards and checks for NPU in available targets for Inference Engine backend.
## Test example
### Pre-requisites
- Intel [Core Ultra series processor](https://www.intel.com/content/www/us/en/products/details/processors/core-ultra/edge.html#tab-blade-1-0)
- [Intel NPU driver](https://github.com/intel/linux-npu-driver/releases)
- OpenVINO 2023.3.0+ (Tested on 2025.1.0)
### Example
```cpp
#include <opencv2/dnn.hpp>
#include <iostream>
int main(){
cv::dnn::Net net = cv::dnn::readNet("../yolov8s-openvino/yolov8s.xml", "../yolov8s-openvino/yolov8s.bin");
cv::Size net_input_shape = cv::Size(640, 480);
std::cout << "Setting backend to DNN_BACKEND_INFERENCE_ENGINE and target to DNN_TARGET_NPU" << std::endl;
net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(cv::dnn::DNN_TARGET_NPU);
cv::Mat image(net_input_shape, CV_8UC3);
cv::randu(image, cv::Scalar(0, 0, 0), cv::Scalar(255, 255, 255));
cv::Mat blob = cv::dnn::blobFromImage(
image, 1, net_input_shape, cv::Scalar(0, 0, 0), true, false, CV_32F);
net.setInput(blob);
std::cout << "Running forward" << std::endl;
cv::Mat result = net.forward();
std::cout << "Output shape: " << result.size << std::endl; // Output shape: 1 x 84 x 6300
}
```
model files [here](https://limewire.com/d/bPgiA#BhUeSTBnMc)
docker image used to build opencv: [ghcr.io/mro47/opencv-builder](https://github.com/MRo47/opencv-builder/blob/main/Dockerfile)
Closes#26240
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Fix#27352: Add checks before getting latest pin in Net::Impl::getLatestLayerPin() #27353
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Fixes#27352
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Improve solveCubic accuracy #27347
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Fix#27323
```
2e-13 * x^3 + x^2 - 2 * x + 1 = 0 -> x^3 + 5e12 * x^2 - 1e13 * x + 5e12 = 0
```
The problem that coefficients have quite big magnitudes and current calculations are subject to round-off error
```
Q = (a1 * a1 - 3 * a2) * (1./9)
R = (2 * a1 * a1 * a1 - 9 * a1 * a2 + 27 * a3) * (1./54)
Qcubed = Q * Q * Q = a1^6/729 - (a1^4 a2)/81 + (a1^2 a2^2)/27 - a2^3/27
R * R = R^2 = a1^6/729 - (a1^4 a2)/81 + (a1^2 a2^2)/36 + (a1^3 a3)/27 - (a1 a2 a3)/6 + a3^2/4
d = Qcubed - R * R
```
Let `a1`, `a2`, `a3` have quite big same magnitudes, then we see that `Qcubed` and `R * R` have same terms `a1^6/729` and `-(a1^4 a2)/81` (which will be reduced in `d`), but they level out the other terms (these terms have `6`th and `5`th degree and other terms - less or equal than `4`th degree).
So, if these terms will participate in the calculation, this will lead to a huge round-off error.
But if we expand the expression, then round-off error should be less
```
d = Qcubed - R * R = 1/108 (a1^2 a2^2 - 4 a2^3 - 4 a1^3 a3 + 18 a1 a2 a3 - 27 a3^2)
```
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build: fix more warnings from recent gcc versions after #27337#27343
More fixings after https://github.com/opencv/opencv/pull/27337
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Fix typos #27338
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Update hash for the fastcv libs for both Linux and Android #27340
Replaces https://github.com/opencv/opencv/pull/27290
Updated libs PR: https://github.com/opencv/opencv_3rdparty/pull/95
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build: fix warnings from recent gcc versions #27337
This PR addresses the following found warnings:
- [x] -Wmaybe-uninitialized
- [x] -Wunused-variable
- [x] -Wsign-compare
Tested building with GCC 14.2 (RISC-V 64).
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Bug fix unstable crf #27270
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The PR resolves the issue for triangle Weights used by debevec algorithm being non zero at extremes.
It resolves#24966
The fix needs ground truth data to be changed in order to pass existing tests. PR to opencv_extra: https://github.com/opencv/opencv_extra/pull/1253
Add tests for solveCubic #27331
### Pull Request Readiness Checklist
Related to #27323
I found only randomized tests with number of roots always equal to `1` or `3`, `x^3 = 0` and some simple test for Java and Swift.
Obviously, they don't cover all cases (implementation has strong branching and number of roots can be equal to `-1`, `0` and `2` additionally).
So, I think it will be useful to try explicitly cover more cases (and implementation branches correspondingly)
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hal/imgproc: add hal for calcHist and implement in hal/riscv-rvv #27332
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TFLite fixes for Face Blendshapes V2 #27307
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* Scalars support
* Better handling of 1D tensors
* New ops import: SUB, SQRT, DIV, NEG, SQUARED_DIFFERENCE, SUM
* Number of NHWC<->NCHW layouts compatibility improvements
resolves#27211
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1257
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imgproc: medianblur: Performance improvement #27299
* Bottleneck in non-vectorized path reduced.
* AVX512 dispatch added for medianblur.
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Add image dimension check to avoid StereoSGBM non-determinism #27305
Addresses #25828
Users noticed that StereoSGBM would occasionally give non-deterministic results for `.compute(imgL, imgR)`.
I and others traced the cause to out-of-bounds access that was not being caught when the input images were not wide enough for the input block size and number of disparities to StereoSGBM. The specific math and logic can be found in the above issue's discussion.
This PR adds a CV_Check to make sure images are wider than 1/2 of the block size + the max disparity the algorithm will search.
The check was only added to the regular `compute` method for StereoSGBM and not to the other modes, as I did not observe the non-deterministic behavior with the other compute modes like HH.
In addition, this PR adds a test case to Calib3d to make sure the check is being thrown in the problem case and that the results are deterministic in the good case.
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Make sure to not access outside normDiffTabMake sure to not access outside normDiffTab #27321
If the norm is outside the array (e.g. Hamming), memory is read outside of the array, which does not matter because the invalid pointer is not used oustide of the function (e.g. the Hamming path is taken) but it triggers the sanitizer.
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Looks like UWP builds were broken on 5.x:
```
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,33): error C3646: 'size': unknown override specifier (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,37): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,33): error C3646: 'size': unknown override specifier (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\videoio_registry.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,37): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\videoio_registry.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,33): error C3646: 'size': unknown override specifier (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_bridge.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_capture.hpp(65,37): error C4430: missing type specifier - int assumed. Note: C++ does not support default-int (compiling source file C:\GHA-OCV-6\_work\opencv\opencv\opencv\modules\videoio\src\cap_winrt_bridge.cpp) [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
C:\Program Files (x86)\Windows Kits\10\bin\10.0.19041.0\XamlCompiler\Microsoft.Windows.UI.Xaml.Common.targets(486,5): error MSB4181: The "CompileXaml" task returned false but did not log an error. [C:\GHA-OCV-6\_work\opencv\opencv\build\modules\videoio\opencv_videoio.vcxproj]
```
Notes:
- Pipeline passes even though there are errors in the build
- I decided to remove _highgui_ WinRT backend, because it uses C-API which is has been removed in 5.x. I believe nobody uses it anyway.
- Change in anneal library is caused by the issue in WinAPI header - it does not declare two functions for UWP, even though documentation states compatibility. See also https://github.com/openssl/openssl/pull/18311https://learn.microsoft.com/en-us/windows/win32/api/memoryapi/nf-memoryapi-virtuallock
> Minimum supported client Windows XP [desktop apps | UWP apps]
> Minimum supported server Windows Server 2003 [desktop apps | UWP apps]
python3 "/opencv/platforms/android/build_sdk.py" --build_doc --config "/opencv/platforms/android/default.config.py" --sdk_path "$ANDROID_HOME" --ndk_path "$ANDROID_NDK_HOME" /build | tee /build/build-log.txt
python3 "/opencv/platforms/android/build_java_shared_aar.py" --offline --ndk_location="$ANDROID_NDK_HOME" --cmake_location=$(dirname $(dirname $(which cmake))) /build/OpenCV-android-sdk
hal/riscv-rvv: make use of function tab in copyToMasked and CV_ELEM_SIZE1 in place of elem_size_tab #27315
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Java VideoCapture buffered stream constructor #27284
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resolves#26809
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hal/riscv-rvv: refactor the building process #27301
Current hal/riscv-rvv is built with all headers without building an object. This slows down the compilation progress, especially when re-compiling for minor changes in those headers (~170 files need to be re-compiled). This patch solves the problem.
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imgcodecs: png: add log if first chunk is not IHDR #27297
Close https://github.com/opencv/opencv/issues/27295
To optimize for the native pixel format of the iPhone's early PowerVR GPUs, Apple implemented a non-standard PNG format.
Details: https://theapplewiki.com/wiki/PNG_CgBI_Format
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hal_rvv: further optimized flip #27257
Checklist:
- [x] flipX
- [x] flipY
- [x] flipXY
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Extract all HALs from 3rdparty to dedicated folder. #27252
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imgproc: cvtColor: remove to copy edge pixels for COLOR_Bayer*_VNGs. #27226
Close https://github.com/opencv/opencv/issues/27225
Close https://github.com/opencv/opencv/issues/5089
Related https://github.com/opencv/opencv_extra/pull/1249
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FastCV gemm hal #27184
FastCV hal for gemm 32f
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videoio: add Orbbec Gemini 330 camera support #27230
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### Description of Changes
#### motivated：
- Orbbec has launched a new RGB-D camera — the Gemini 330. To fully leverage the capabilities of the Gemini 330, Orbbec simultaneously released version 2 of the open-source OrbbecSDK. This PR adapts the support for the Gemini 330 series cameras to better meet and respond to users’ application requirements.
#### change：
- Add support for the Orbbec Gemini330 camera.
- Fixed an issue with Femto Mega on Windows 10/11; for details, see [issue](https://github.com/opencv/opencv/pull/23237#issuecomment-2242347295).
- When enabling `HAVE_OBSENSOR_ORBBEC_SDK`, the build now fetches version 2 of the OrbbecSDK, and the sample API calls have been updated to the v2 format.
### Testing
| OS | Compiler | Camera | Result |
|:----------:|:---------------------------------------:|:-----------------:|:------:|
| Windows 11 | (VS2022) MSVC runtime library version 14.40 | Gemini 335/336L | Pass |
| Windows 11 | (VS2022) MSVC runtime library version 14.19 | Gemini 335/336L | Pass |
| Ubuntu22.04| GCC 11.4 | Gemini 335/336L | Pass |
| Ubuntu18.04| GCC 7.5 | Gemini 335/336L | Pass |
### Acknowledgements
Thank you to the OpenCV team for the continuous support and for creating such a robust open source project. I appreciate the valuable feedback from the community and reviewers, which has helped improve the quality of this contribution!
HAL: implemented cv_hal_transpose in hal_rvv #27229
Checklists:
- [x] transpose2d_8u
- [x] transpose2d_16u
- [ ] ~transpose2d_8uC3~
- [x] transpose2d_32s
- [ ] ~transpose2d_16uC3~
- [x] transpose2d_32sC2
- [ ] ~transpose_32sC3~
- [ ] ~transpose_32sC4~
- [ ] ~transpose_32sC6~
- [ ] ~transpose_32sC8~
- [ ] ~inplace transpose~
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Fix hard dependency of dnn for mcc module. #27246
Currently building objdetect module without dnn fails due to mcc module. This PR makes the dependency optional, by checking if DNN is available in mcc module.
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Minor changes in calib3d docs for clarity #27221
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Explicitly specify enum type scopes to improve Java wrapper generation #27228
Changed DataLayout and ImagePaddingMode to dnn::DataLayout and dnn::ImagePaddingMode to explicitly specify their scopes. This allows gen_java.py to correctly register disc_type, preventing constructors and methods using these enum types from being skipped during Java wrapper generation.
Similarly updated QRCodeEncoder::CorrectionLevel and QRCodeEncoder::EncodeMode with explicit scope declarations.
Also added a new Java test class `DnnBlobFromImageWithParamsTest` based on: https://github.com/opencv/opencv/blob/4.x/modules/dnn/test/test_misc.cpp#L133-L243
Related issues
#23753
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Fix QR code encoder with autoversion #27244
The autodetected version is not honored in the `QRCodeEncoderImpl::encode*` methods. This fixes#27183
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Android-SDK: check flag IPP package #27239
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HAL: implemented cv_hal_dotProduct in hal_rvv #27201
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Optimize gaussian blur performance in FastCV HAL #27217
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Parallel_for in box Filter and support for 32f box filter in Fastcv hal #27182
Added parallel_for in box filter hal and support for 32f box filter
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Adding latest FastCV static libs
updated libs PR: [opencv/opencv_3rdparty/pull/94](https://github.com/opencv/opencv_3rdparty/pull/94)
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imgproc: disable SIMD for compareHist(INTERSECT) if f64 is unsupported #27220
Close https://github.com/opencv/opencv/issues/24757
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core: further vectorize copyTo with mask #27145
Merge with https://github.com/opencv/opencv_extra/pull/1247.
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HAL: implemented cv_hal_div* and cv_hal_recip* in hal_rvv #27175
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Specify DLS and UPnP mappings to EPnP in all places for solvePnP* tests #27185
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build: Check supported C++ standard features and user setting #27107Close#27105
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Imgcodec: gif: remove unnecessary warning #27169
Close https://github.com/opencv/opencv/issues/27168
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HAL: added copyToMask and implemented in hal_rvv #27162
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Adding AddRgbFeature(), and improving robustness in ComputeRgbDistance().
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Add RISC-V HAL implementation for cv::resize #27160
This patch implements `cv_hal_resize` using native intrinsics, optimizing the performance of `cv::resize` for `CV_INTER_NEAREST/CV_INTER_NEAREST_EXACT/CV_INTER_LINEAR/CV_INTER_LINEAR_EXACT/CV_INTER_AREA` modes.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.1.
```
$ ./opencv_test_imgproc --gtest_filter="*Resize*:*resize*"
$ ./opencv_perf_imgproc --gtest_filter="*Resize*:*resize*" --perf_min_samples=300 --perf_force_samples=300
```
View the full perf table here: [hal_rvv_resize.pdf](https://github.com/user-attachments/files/19480756/hal_rvv_resize.pdf)
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User-defined logger callback, C-style. #27154
This is a competing PR, an alternative to #27140
Both functions accept C-style pointer to static functions. Both functions allow restoring the OpenCV built-in implementation by passing in a nullptr.
- replaceWriteLogMessage
- replaceWriteLogMessageEx
This implementation is not compatible with C++ log handler objects.
This implementation has minimal thread safety, in the sense that the function pointer are stored and read atomically. But otherwise, the user-defined static functions must accept calls at all times, even after having been deregistered, because some log calls may have started before deregistering.
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Notify the main GUI thread upon receiving a close event instead of when
the windows is destroyed. Additionally there was a logic error in in
GuiReceiver::isLastWindow() that is corrected.
Fixes#6479 and #20822
* Added mcc to opencv modules
* Removed color correction module
* Updated parameters return type
* Added python sample for macbeth_chart_detection
* Added models.yml support to samples
* Removed unnecessary headers and classes
* fixed datatype conversion
* fixed datatype conversion
* Cleaned headers and added reference/actual colors to samples
* Added mcc tutorial
* fixed datatype and header
* replaced unsigned with int
* Aligned actual and reference color function, added imread
* Fixed shadow variable
* Updated samples
* Added last frame colors prints
* updated detector class
* Added getter functions and useNet function
* Refactoring
* Fixes in test
* fixed infinite divison issue
Fix heap buffer overflow and use after free in imgcodecs #27138
This fixes:
- https://g-issues.oss-fuzz.com/issues/405243132
- https://g-issues.oss-fuzz.com/issues/405456349
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Add RISC-V HAL implementation for cv::warp series #27119
This patch implements `cv_hal_remap`, `cv_hal_warpAffine` and `cv_hal_warpPerspective` using native intrinsics, optimizing the performance of `cv::remap/cv::warpAffine/cv::warpPerspective` for `CV_HAL_INTER_NEAREST/CV_HAL_INTER_LINEAR/CV_HAL_INTER_CUBIC/CV_HAL_INTER_LANCZOS4` modes.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*Remap*:*Warp*"
$ ./opencv_perf_imgproc --gtest_filter="*Remap*:*remap*:*Warp*" --perf_min_samples=200 --perf_force_samples=200
```
View the full perf table here: [hal_rvv_warp.pdf](https://github.com/user-attachments/files/19403718/hal_rvv_warp.pdf)
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Fix minAreaRect and boxPoints docs (#26799) #27139
As requested from issue #26799 the docs regarding minAreaRect and boxPoints are extended specifying the order of the corners for boxPoints and the way the angle is computed for the rotated rect returned by minAreaRect
core: refactored normDiff in hal_rvv and extended with support of more data types #27115
Merge wtih https://github.com/opencv/opencv_extra/pull/1246.
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Test gemm 3inputs #27102
Merge with test data: https://github.com/opencv/opencv_extra/pull/1245
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5.x merge 4.x: vectorized normDiff #27068
Merge with https://github.com/opencv/opencv_extra/pull/1243
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Move IPP norm and normDiff to HAL #27128
Continues https://github.com/opencv/opencv/pull/26880
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Initial version of IPP-based HAL for x86 and x86_64 platforms #26880
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cuda: Force C++17 Standard for CUDA targets when CUDA Toolkit >=12.8 #27112
Fix https://github.com/opencv/opencv/issues/27095.
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Fix RISC-V HAL solve/SVD and BGRtoLab #27046Closes#27044.
Also suppressed some warnings in other HAL.
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When WARP_INVERSE_MAP is used, accelerate the calculation with multi-threading #27108
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GIF: Make sure to resize lzwExtraTable before each block #27081
This fixes https://g-issues.oss-fuzz.com/issues/403364362
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Add RISC-V HAL implementation for cv::blur series #27097
This patch implements `cv_hal_gaussianBlurBinomial`, `cv_hal_medianBlur`, `cv_hal_boxFilter` and `cv_hal_bilateralFilter` using native intrinsics, optimizing the performance of `cv::GaussianBlur/cv::medianBlur/cv::boxFilter/cv::bilateralFilter` for `3x3/5x5` kernels.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*Filter*:*Blur*"
$ ./opencv_perf_imgproc --gtest_filter="*gauss*:*box*:*Bilateral*:*median*" --perf_min_samples=2000 --perf_force_samples=2000
```
View the full perf table here: [hal_rvv_blur.pdf](https://github.com/user-attachments/files/19335582/hal_rvv_blur.pdf)
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Add RISC-V HAL implementation for cv::moments #27096
This patch implements `cv_hal_imageMoments` using native intrinsics, optimizing the performance of `cv::moments` for data types `CV_16U/CV_16S/CV_32F/CV_64F`.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*Moments*"
$ ./opencv_perf_imgproc --gtest_filter="*Moments*" --perf_min_samples=1000 --perf_force_samples=1000
```
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imgcodecs: gif: support animated gif without loop #26971Close#26970
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determine gemm op mode at runtime #27017
See https://github.com/opencv/opencv/issues/26209
TODOs:
- [x] Determine OP mode on runtime
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core: improve norm of hal rvv #26991
Merge with https://github.com/opencv/opencv_extra/pull/1241
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Add RISC-V HAL implementation for cv::threshold and cv::adaptiveThreshold #27072
This patch implements `cv_hal_threshold_otsu` and `cv_hal_adaptiveThreshold` using native intrinsics, optimizing the performance of `cv::threshold(THRESH_OTSU)` and `cv::adaptiveThreshold`.
Since UI is as fast as HAL `cv_hal_rvv::threshold::threshold` so `cv_hal_threshold` is not redirected, but this part of HAL is keeped because `cv_hal_threshold_otsu` depends on it.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*thresh*:*Thresh*"
$ ./opencv_perf_imgproc --gtest_filter="*otsu*:*adaptiveThreshold*" --perf_min_samples=1000 --perf_force_samples=1000
```
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displayOverlay doesn't disappear after timeout #27082Fixes#26555
### Expected Behaviour
An overlay should be displayed atop an image and then disappear after `delayms` has timed out, but it doesn't. Also, `displayStatusBar` doesn't appear to set any text on the window.
### Actual Behaviour
The overlay appears but doesn't disappear unless a mouse move event happens on the image.
### Changes
- Fixed the issue with `displayOverlay` not disappearing after the timeout.
### Checklist
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[HAL RVV] unify and impl polar_to_cart | add perf test #26999
### Summary
1. Implement through the existing `cv_hal_polarToCart32f` and `cv_hal_polarToCart64f` interfaces.
2. Add `polarToCart` performance tests
3. Make `cv::polarToCart` use CALL_HAL in the same way as `cv::cartToPolar`
4. To achieve the 3rd point, the original implementation was moved, and some modifications were made.
Tested through:
```sh
opencv_test_core --gtest_filter="*PolarToCart*:*Core_CartPolar_reverse*"
opencv_perf_core --gtest_filter="*PolarToCart*" --perf_min_samples=300 --perf_force_samples=300
```
### HAL performance test
***UPDATE***: Current implementation is no more depending on vlen.
**NOTE**: Due to the 4th point in the summary above, the `scalar` and `ui` test is based on the modified code of this PR. The impact of this patch on `scalar` and `ui` is evaluated in the next section, `Effect of Point 4`.
Vlen 256 (Muse Pi):
```
Name of Test scalar ui rvv ui rvv
vs vs
scalar scalar
(x-factor) (x-factor)
PolarToCart::PolarToCartFixture::(127x61, 32FC1) 0.315 0.110 0.034 2.85 9.34
PolarToCart::PolarToCartFixture::(127x61, 64FC1) 0.423 0.163 0.045 2.59 9.34
PolarToCart::PolarToCartFixture::(640x480, 32FC1) 13.695 4.325 1.278 3.17 10.71
PolarToCart::PolarToCartFixture::(640x480, 64FC1) 17.719 7.118 2.105 2.49 8.42
PolarToCart::PolarToCartFixture::(1280x720, 32FC1) 40.678 13.114 3.977 3.10 10.23
PolarToCart::PolarToCartFixture::(1280x720, 64FC1) 53.124 21.298 6.519 2.49 8.15
PolarToCart::PolarToCartFixture::(1920x1080, 32FC1) 95.158 29.465 8.894 3.23 10.70
PolarToCart::PolarToCartFixture::(1920x1080, 64FC1) 119.262 47.743 14.129 2.50 8.44
```
### Effect of Point 4
To make `cv::polarToCart` behave the same as `cv::cartToPolar`, the implementation detail of the former has been moved to the latter's location (from `mathfuncs.cpp` to `mathfuncs_core.simd.hpp`).
#### Reason for Changes:
This function works as follows:
$y = \text{mag} \times \sin(\text{angle})$ and $x = \text{mag} \times \cos(\text{angle})$. The original implementation first calculates the values of $\sin$ and $\cos$, storing the results in the output buffers $x$ and $y$, and then multiplies the result by $\text{mag}$.
However, when the function is used as an in-place operation (one of the output buffers is also an input buffer), the original implementation allocates an extra buffer to store the $\sin$ and $\cos$ values in case the $\text{mag}$ value gets overwritten. This extra buffer allocation prevents `cv::polarToCart` from functioning in the same way as `cv::cartToPolar`.
Therefore, the multiplication is now performed immediately without storing intermediate values. Since the original implementation also had AVX2 optimizations, I have applied the same optimizations to the AVX2 version of this implementation.
***UPDATE***: UI use v_sincos from #25892 now. The original implementation has AVX2 optimizations but is slower much than current UI so it's removed, and AVX2 perf test is below. Scalar implementation isn't changed because it's faster than using UI's method.
#### Test Result
`scalar` and `ui` test is done on Muse PI, and AVX2 test is done on Intel(R) Xeon(R) Gold 6140 CPU @ 2.30GHz.
`scalar` test:
```
Name of Test orig pr pr
vs
orig
(x-factor)
PolarToCart::PolarToCartFixture::(127x61, 32FC1) 0.333 0.294 1.13
PolarToCart::PolarToCartFixture::(127x61, 64FC1) 0.385 0.403 0.96
PolarToCart::PolarToCartFixture::(640x480, 32FC1) 14.749 12.343 1.19
PolarToCart::PolarToCartFixture::(640x480, 64FC1) 19.419 16.743 1.16
PolarToCart::PolarToCartFixture::(1280x720, 32FC1) 44.155 37.822 1.17
PolarToCart::PolarToCartFixture::(1280x720, 64FC1) 62.108 50.358 1.23
PolarToCart::PolarToCartFixture::(1920x1080, 32FC1) 99.011 85.769 1.15
PolarToCart::PolarToCartFixture::(1920x1080, 64FC1) 127.740 112.874 1.13
```
`ui` test:
```
Name of Test orig pr pr
vs
orig
(x-factor)
PolarToCart::PolarToCartFixture::(127x61, 32FC1) 0.306 0.110 2.77
PolarToCart::PolarToCartFixture::(127x61, 64FC1) 0.455 0.163 2.79
PolarToCart::PolarToCartFixture::(640x480, 32FC1) 13.381 4.325 3.09
PolarToCart::PolarToCartFixture::(640x480, 64FC1) 21.851 7.118 3.07
PolarToCart::PolarToCartFixture::(1280x720, 32FC1) 39.975 13.114 3.05
PolarToCart::PolarToCartFixture::(1280x720, 64FC1) 67.006 21.298 3.15
PolarToCart::PolarToCartFixture::(1920x1080, 32FC1) 90.362 29.465 3.07
PolarToCart::PolarToCartFixture::(1920x1080, 64FC1) 129.637 47.743 2.72
```
AVX2 test:
```
Name of Test orig pr pr
vs
orig
(x-factor)
PolarToCart::PolarToCartFixture::(127x61, 32FC1) 0.019 0.009 2.11
PolarToCart::PolarToCartFixture::(127x61, 64FC1) 0.022 0.013 1.74
PolarToCart::PolarToCartFixture::(640x480, 32FC1) 0.788 0.355 2.22
PolarToCart::PolarToCartFixture::(640x480, 64FC1) 1.102 0.618 1.78
PolarToCart::PolarToCartFixture::(1280x720, 32FC1) 2.383 1.042 2.29
PolarToCart::PolarToCartFixture::(1280x720, 64FC1) 3.758 2.316 1.62
PolarToCart::PolarToCartFixture::(1920x1080, 32FC1) 5.577 2.559 2.18
PolarToCart::PolarToCartFixture::(1920x1080, 64FC1) 9.710 6.424 1.51
```
A slight performance loss occurs because the check for whether $mag$ is nullptr is performed with every calculation, instead of being done once per batch. This is to reuse current `SinCos_32f` function.
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Add test for ArucoDetector::detectMarkers #27079
### Pull Request Readiness Checklist
Related to #26968 and #26922
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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[RVV HAL] Add copyright and replace '#pragma once'. #27056
Add copyright and in RVV HAL, since other companies or teams may join the development and add their copyright.
And the '#pragma once' are replaced.
### Pull Request Readiness Checklist
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[HAL RVV] reuse atan | impl cart_to_polar | add perf test #27000
Implement through the existing `cv_hal_cartToPolar32f` and `cv_hal_cartToPolar64f` interfaces.
Add `cartToPolar` performance tests.
cv_hal_rvv::fast_atan is modified to make it more reusable because it's needed in cartToPolar.
**UPDATE**: UI enabled. Since the vec type of RVV can't be stored in struct. UI implementation of `v_atan_f32` is modified. Both `fastAtan` and `cartToPolar` are affected so the test result for `atan` is also appended. I have tested the modified UI on RVV and AVX2 and no regressions appears.
Perf test done on MUSE-PI. AVX2 test done on Intel(R) Xeon(R) Gold 6140 CPU @ 2.30GHz.
```sh
$ opencv_test_core --gtest_filter="*CartToPolar*:*Core_CartPolar_reverse*:*Phase*"
$ opencv_perf_core --gtest_filter="*CartToPolar*:*phase*" --perf_min_samples=300 --perf_force_samples=300
```
Test result between enabled UI and HAL:
```
Name of Test ui rvv rvv
vs
ui
(x-factor)
CartToPolar::CartToPolarFixture::(127x61, 32FC1) 0.106 0.059 1.80
CartToPolar::CartToPolarFixture::(127x61, 64FC1) 0.155 0.070 2.20
CartToPolar::CartToPolarFixture::(640x480, 32FC1) 4.188 2.317 1.81
CartToPolar::CartToPolarFixture::(640x480, 64FC1) 6.593 2.889 2.28
CartToPolar::CartToPolarFixture::(1280x720, 32FC1) 12.600 7.057 1.79
CartToPolar::CartToPolarFixture::(1280x720, 64FC1) 19.860 8.797 2.26
CartToPolar::CartToPolarFixture::(1920x1080, 32FC1) 28.295 15.809 1.79
CartToPolar::CartToPolarFixture::(1920x1080, 64FC1) 44.573 19.398 2.30
phase32f::VectorLength::128 0.002 0.002 1.20
phase32f::VectorLength::1000 0.008 0.006 1.32
phase32f::VectorLength::131072 1.061 0.731 1.45
phase32f::VectorLength::524288 3.997 2.976 1.34
phase32f::VectorLength::1048576 8.001 5.959 1.34
phase64f::VectorLength::128 0.002 0.002 1.33
phase64f::VectorLength::1000 0.012 0.008 1.58
phase64f::VectorLength::131072 1.648 0.931 1.77
phase64f::VectorLength::524288 6.836 3.837 1.78
phase64f::VectorLength::1048576 14.060 7.540 1.86
```
Test result before and after enabling UI on RVV:
```
Name of Test perf perf perf
ui ui ui
orig pr pr
vs
perf
ui
orig
(x-factor)
CartToPolar::CartToPolarFixture::(127x61, 32FC1) 0.141 0.106 1.33
CartToPolar::CartToPolarFixture::(127x61, 64FC1) 0.187 0.155 1.20
CartToPolar::CartToPolarFixture::(640x480, 32FC1) 5.990 4.188 1.43
CartToPolar::CartToPolarFixture::(640x480, 64FC1) 8.370 6.593 1.27
CartToPolar::CartToPolarFixture::(1280x720, 32FC1) 18.214 12.600 1.45
CartToPolar::CartToPolarFixture::(1280x720, 64FC1) 25.365 19.860 1.28
CartToPolar::CartToPolarFixture::(1920x1080, 32FC1) 40.437 28.295 1.43
CartToPolar::CartToPolarFixture::(1920x1080, 64FC1) 56.699 44.573 1.27
phase32f::VectorLength::128 0.003 0.002 1.54
phase32f::VectorLength::1000 0.016 0.008 1.90
phase32f::VectorLength::131072 2.048 1.061 1.93
phase32f::VectorLength::524288 8.219 3.997 2.06
phase32f::VectorLength::1048576 16.426 8.001 2.05
phase64f::VectorLength::128 0.003 0.002 1.44
phase64f::VectorLength::1000 0.020 0.012 1.60
phase64f::VectorLength::131072 2.621 1.648 1.59
phase64f::VectorLength::524288 10.780 6.836 1.58
phase64f::VectorLength::1048576 22.723 14.060 1.62
```
Test result before and after modifying UI on AVX2:
```
Name of Test perf perf perf
avx2 avx2 avx2
orig pr pr
vs
perf
avx2
orig
(x-factor)
CartToPolar::CartToPolarFixture::(127x61, 32FC1) 0.006 0.005 1.14
CartToPolar::CartToPolarFixture::(127x61, 64FC1) 0.010 0.009 1.08
CartToPolar::CartToPolarFixture::(640x480, 32FC1) 0.273 0.264 1.03
CartToPolar::CartToPolarFixture::(640x480, 64FC1) 0.511 0.487 1.05
CartToPolar::CartToPolarFixture::(1280x720, 32FC1) 0.760 0.723 1.05
CartToPolar::CartToPolarFixture::(1280x720, 64FC1) 2.009 1.937 1.04
CartToPolar::CartToPolarFixture::(1920x1080, 32FC1) 1.996 1.923 1.04
CartToPolar::CartToPolarFixture::(1920x1080, 64FC1) 5.721 5.509 1.04
phase32f::VectorLength::128 0.000 0.000 0.98
phase32f::VectorLength::1000 0.001 0.001 0.97
phase32f::VectorLength::131072 0.105 0.111 0.95
phase32f::VectorLength::524288 0.402 0.402 1.00
phase32f::VectorLength::1048576 0.775 0.767 1.01
phase64f::VectorLength::128 0.000 0.000 1.00
phase64f::VectorLength::1000 0.001 0.001 1.01
phase64f::VectorLength::131072 0.163 0.162 1.01
phase64f::VectorLength::524288 0.669 0.653 1.02
phase64f::VectorLength::1048576 1.660 1.634 1.02
```
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[HAL RVV] impl magnitude | add perf test #27002
Implement through the existing `cv_hal_magnitude32f` and `cv_hal_magnitude64f` interfaces.
**UPDATE**: UI is enabled. The only difference between UI and HAL now is HAL use a approximate `sqrt`.
Perf test done on MUSE-PI.
```sh
$ opencv_test_core --gtest_filter="*Magnitude*"
$ opencv_perf_core --gtest_filter="*Magnitude*" --perf_min_samples=300 --perf_force_samples=300
```
Test result between enabled UI and HAL:
```
Name of Test ui rvv rvv
vs
ui
(x-factor)
Magnitude::MagnitudeFixture::(127x61, 32FC1) 0.029 0.016 1.75
Magnitude::MagnitudeFixture::(127x61, 64FC1) 0.057 0.036 1.57
Magnitude::MagnitudeFixture::(640x480, 32FC1) 1.063 0.648 1.64
Magnitude::MagnitudeFixture::(640x480, 64FC1) 2.261 1.530 1.48
Magnitude::MagnitudeFixture::(1280x720, 32FC1) 3.261 2.118 1.54
Magnitude::MagnitudeFixture::(1280x720, 64FC1) 6.802 4.682 1.45
Magnitude::MagnitudeFixture::(1920x1080, 32FC1) 7.287 4.738 1.54
Magnitude::MagnitudeFixture::(1920x1080, 64FC1) 15.226 10.334 1.47
```
Test result before and after enabling UI:
```
Name of Test orig pr pr
vs
orig
(x-factor)
Magnitude::MagnitudeFixture::(127x61, 32FC1) 0.032 0.029 1.11
Magnitude::MagnitudeFixture::(127x61, 64FC1) 0.067 0.057 1.17
Magnitude::MagnitudeFixture::(640x480, 32FC1) 1.228 1.063 1.16
Magnitude::MagnitudeFixture::(640x480, 64FC1) 2.786 2.261 1.23
Magnitude::MagnitudeFixture::(1280x720, 32FC1) 3.762 3.261 1.15
Magnitude::MagnitudeFixture::(1280x720, 64FC1) 8.549 6.802 1.26
Magnitude::MagnitudeFixture::(1920x1080, 32FC1) 8.408 7.287 1.15
Magnitude::MagnitudeFixture::(1920x1080, 64FC1) 18.884 15.226 1.24
```
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Make sure there are enough channels to check for opacity #27040
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Test for in-memory animation encoding and decoding #27013
Tests for https://github.com/opencv/opencv/pull/26964
### Pull Request Readiness Checklist
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Find contours speedup #26834
It is an attempt, as suggested by #26775, to restore lost speed when migrating `findContours()` implementation from C to C++
The patch adds an "Arena" (a pool) of pre-allocated memory so that contours points (and TreeNodes) can be picked from the Arena.
The code of `findContours()` is mostly unchanged, the arena usage being implicit through a utility class Arena::Item that provides C++ overloaded operators and construct/destruct logic.
As mentioned in #26775, the contour points are allocated and released in order, and can be represented by ranges of indices in their arena. No range subset will be released and drill a hole, that's why the internal representation as a range of indices makes sense.
The TreeNodes use another Arena class that does not comply to that range logic.
Currently, there is a significant improvement of the run-time on the test mentioned in #26775, but it is still far from the `findContours_legacy()` performance.
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Added optional mask to cv::threshold #26842
Proposal for #26777
To avoid code duplication, and keep performance when no mask is used, inner implementation always propagate the const cv::Mat& mask, but they use a template<bool useMask> parameter that let the compiler optimize out unnecessary tests when the mask is not to be used.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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core: vectorize normDiff with universal intrinsics #27042
Merge with https://github.com/opencv/opencv_extra/pull/1242.
Performance results on Desktop Intel i7-12700K, Apple M2, Jetson Orin and SpaceMIT K1:
[perf-normDiff.zip](https://github.com/user-attachments/files/19178689/perf-normDiff.zip)
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Fix Aruco marker incorrect detection near image edge #26968
### Pull Request Readiness Checklist
Fix#26922
As I understood the algorithm, at the first stage we search for the contours of the marker several times (adaptive threshold with different windows sizes). Therefore, for the same marker, we get several contours (inner and outer with different sizes due to the different windows sizes). In the second stage, we group the contours for the same marker into one group, from which we take the largest contour as the best candidate (which should best match the border of the marker).
The problem is that using the `minDistanceToBorder` parameter, we discard contours at the first stage. Thus, we discard the best candidates most appropriate to the marker border, and inner contours may remain, representing a significantly smaller marker border (which we observe in the issue).
But if we use the `minDistanceToBorder` parameter to discard the best candidate of the group at the second stage, then there will be no such problems and we will completely discard markers located too close to the border of the image.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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[HAL RVV] impl sqrt and invSqrt #27015
Implement through the existing interfaces `cv_hal_sqrt32f`, `cv_hal_sqrt64f`, `cv_hal_invSqrt32f`, `cv_hal_invSqrt64f`.
Perf test done on MUSE-PI and CanMV K230. Because the performance of scalar is much worse than universal intrinsic, only ui and hal rvv is compared.
In RVV's UI, `invSqrt` is computed using `1 / sqrt()`. This patch first uses `frsqrt` and then applies the Newton-Raphson method to achieve higher precision. For the initial value, I tried using the famous [fast inverse square root algorithm](https://en.wikipedia.org/wiki/Fast_inverse_square_root), which involves one bit shift and one subtraction. However, on both MUSE-PI and CanMV K230, the performance was slightly lower (about 3%), so I chose to use `frsqrt` for the initial value instead.
BTW, I think this patch can directly replace RVV's UI.
**UPDATE**: Due to strange vector registers allocation strategy in clang, for `invSqrt`, clang use LMUL m4 while gcc use LMUL m8, which leads to some performance loss in clang. So the test for clang is appended.
```sh
$ opencv_test_core --gtest_filter="Core_HAL/mathfuncs.*"
$ opencv_perf_core --gtest_filter="SqrtFixture.*" --perf_min_samples=300 --perf_force_samples=300
```
CanMV K230:
```
Name of Test ui rvv rvv
vs
ui
(x-factor)
Sqrt::SqrtFixture::(127x61, 5, false) 0.052 0.027 1.96
Sqrt::SqrtFixture::(127x61, 5, true) 0.101 0.026 3.80
Sqrt::SqrtFixture::(127x61, 6, false) 0.106 0.059 1.79
Sqrt::SqrtFixture::(127x61, 6, true) 0.207 0.058 3.55
Sqrt::SqrtFixture::(640x480, 5, false) 1.988 0.956 2.08
Sqrt::SqrtFixture::(640x480, 5, true) 3.920 0.948 4.13
Sqrt::SqrtFixture::(640x480, 6, false) 4.179 2.342 1.78
Sqrt::SqrtFixture::(640x480, 6, true) 8.220 2.290 3.59
Sqrt::SqrtFixture::(1280x720, 5, false) 5.969 2.881 2.07
Sqrt::SqrtFixture::(1280x720, 5, true) 11.731 2.857 4.11
Sqrt::SqrtFixture::(1280x720, 6, false) 12.533 7.031 1.78
Sqrt::SqrtFixture::(1280x720, 6, true) 24.643 6.917 3.56
Sqrt::SqrtFixture::(1920x1080, 5, false) 13.423 6.483 2.07
Sqrt::SqrtFixture::(1920x1080, 5, true) 26.379 6.436 4.10
Sqrt::SqrtFixture::(1920x1080, 6, false) 28.200 15.833 1.78
Sqrt::SqrtFixture::(1920x1080, 6, true) 55.434 15.565 3.56
```
MUSE-PI:
```
GCC | clang
Name of Test ui rvv rvv | ui rvv rvv
vs | vs
ui | ui
(x-factor) | (x-factor)
Sqrt::SqrtFixture::(127x61, 5, false) 0.027 0.018 1.46 | 0.027 0.016 1.65
Sqrt::SqrtFixture::(127x61, 5, true) 0.050 0.017 2.98 | 0.050 0.017 2.99
Sqrt::SqrtFixture::(127x61, 6, false) 0.053 0.031 1.72 | 0.052 0.032 1.64
Sqrt::SqrtFixture::(127x61, 6, true) 0.100 0.030 3.31 | 0.101 0.035 2.86
Sqrt::SqrtFixture::(640x480, 5, false) 0.955 0.483 1.98 | 0.959 0.499 1.92
Sqrt::SqrtFixture::(640x480, 5, true) 1.873 0.489 3.83 | 1.873 0.520 3.60
Sqrt::SqrtFixture::(640x480, 6, false) 2.027 1.163 1.74 | 2.037 1.218 1.67
Sqrt::SqrtFixture::(640x480, 6, true) 3.961 1.153 3.44 | 3.961 1.341 2.95
Sqrt::SqrtFixture::(1280x720, 5, false) 2.916 1.538 1.90 | 2.912 1.598 1.82
Sqrt::SqrtFixture::(1280x720, 5, true) 5.735 1.534 3.74 | 5.726 1.661 3.45
Sqrt::SqrtFixture::(1280x720, 6, false) 6.121 3.585 1.71 | 6.109 3.725 1.64
Sqrt::SqrtFixture::(1280x720, 6, true) 12.059 3.501 3.44 | 12.053 4.080 2.95
Sqrt::SqrtFixture::(1920x1080, 5, false) 6.540 3.535 1.85 | 6.540 3.643 1.80
Sqrt::SqrtFixture::(1920x1080, 5, true) 12.943 3.445 3.76 | 12.908 3.706 3.48
Sqrt::SqrtFixture::(1920x1080, 6, false) 13.714 8.062 1.70 | 13.711 8.376 1.64
Sqrt::SqrtFixture::(1920x1080, 6, true) 27.011 7.989 3.38 | 27.115 9.245 2.93
```
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Update tutorials #26441
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Threshold otsu doc update #27039
PR for #27038
(I had already done that, but encounters git madness after branch renaming)
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[Refactor](HAL RVV): Consolidate Helpers for Code Reusability #26977
This PR introduces a new helper file with utility types and templates to standardize function interfaces. This refactor allows us to avoid duplicate code when types differ but logic remains the same.
The `flip` and `minmax` implementations have been updated to use the new generic helpers, replacing the previously defined, redundant classes.
Due to the large number of functions, not all interfaces are unified yet. Future development can extend the types as needed. While the usage of function templates is currently limited, this will ease future development.
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Fix assert failure in Sobel test when enable FastCV #27033
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Add RISC-V HAL implementation for cv::cvtColor #27007
This patch implements the following functions in RVV_HAL using native intrinsics, optimizing the performance of `cv::cvtColor` for all possible data types and modes (except for `COLOR_Bayer`, `COLOR_YUV2GRAY_420` and `COLOR_mRGBA`, as these modes have no HAL interface):
```
cv_hal_cvtBGRtoBGR
cv_hal_cvtBGRtoBGR5x5
cv_hal_cvtBGR5x5toBGR
cv_hal_cvtBGRtoGray
cv_hal_cvtGraytoBGR
cv_hal_cvtBGR5x5toGray
cv_hal_cvtGraytoBGR5x5
cv_hal_cvtBGRtoYUV
cv_hal_cvtYUVtoBGR
cv_hal_cvtBGRtoXYZ
cv_hal_cvtXYZtoBGR
cv_hal_cvtBGRtoHSV
cv_hal_cvtHSVtoBGR
cv_hal_cvtBGRtoLab
cv_hal_cvtLabtoBGR
cv_hal_cvtTwoPlaneYUVtoBGR
cv_hal_cvtBGRtoTwoPlaneYUV
cv_hal_cvtThreePlaneYUVtoBGR
cv_hal_cvtBGRtoThreePlaneYUV
cv_hal_cvtOnePlaneYUVtoBGR
cv_hal_cvtOnePlaneBGRtoYUV
```
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*Color*-*Bayer*"
$ ./opencv_perf_imgproc --gtest_filter="*Color*-*Bayer*" --gtest_also_run_disabled_tests --perf_min_samples=100 --perf_force_samples=100
```
View the full perf table here: [hal_rvv_color.pdf](https://github.com/user-attachments/files/19055417/hal_rvv_color.pdf)
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
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Add RISC-V HAL implementation for cv::solve #26892
This patch implements `cv_hal_LU/cv_hal_Cholesky/cv_hal_SVD/cv_hal_QR` function in RVV_HAL using native intrinsics, optimizing the performance for `cv::solve` with method `DECOMP_LU/DECOMP_SVD/DECOMP_CHOLESKY/DECOMP_QR` and data types `32FC1/64FC1`.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_core --gtest_filter="*Solve*:*SVD*:*Cholesky*"
$ ./opencv_perf_core --gtest_filter="*SolveTest*" --perf_min_samples=100 --perf_force_samples=100
```
The tail of the perf table is shown below since the table is too long.
View the full perf table here: [hal_rvv_solve.pdf](https://github.com/user-attachments/files/18725067/hal_rvv_solve.pdf)
<img width="1078" alt="Untitled" src="https://github.com/user-attachments/assets/c01d849c-f000-4bcc-bfe0-a302d6605d9e" />
### Pull Request Readiness Checklist
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Add RISC-V HAL implementation for cv::dft and cv::dct #26865
This patch implements `static cv::DFT` function in RVV_HAL using native intrinsic, optimizing the performance for `cv::dft` and `cv::dct` with data types `32FC1/64FC1/32FC2/64FC2`.
The reason I chose to create a new `cv_hal_dftOcv` interface is that if I were to use the existing interfaces (`cv_hal_dftInit1D` and `cv_hal_dft1D`), it would require handling and parsing the dft flags within HAL, as well as performing preprocessing operations such as handling unit roots. Since these operations are not performance hotspots and do not require optimization, reusing the existing interfaces would result in copying approximately 300 lines of code from `core/src/dxt.cpp` into HAL, which I believe is unnecessary.
Moreover, if I insert the new interface into `static cv::DFT`, both `static cv::RealDFT` and `static cv::DCT` can be optimized as well. The processing performed before and after calling `static cv::DFT` in these functions is also not a performance hotspot.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ opencv_test_core --gtest_filter="*DFT*"
$ opencv_perf_core --gtest_filter="*dft*:*dct*" --perf_min_samples=30 --perf_force_samples=30
```
The head of the perf table is shown below since the table is too long.
View the full perf table here: [hal_rvv_dxt.pdf](https://github.com/user-attachments/files/18622645/hal_rvv_dxt.pdf)
<img width="1017" alt="Untitled" src="https://github.com/user-attachments/assets/609856e7-9c7d-4a95-9923-45c1b77eb3a2" />
### Pull Request Readiness Checklist
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Support bool type for FileStorage Base64 #26937
### Pull Request Readiness Checklist
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1238
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Impl hal_rvv LUT | Add more LUT test #26941
Implement through the existing `cv_hal_lut` interfaces.
Add more LUT accuracy and performance tests:
- **Accuracy test**: Multi-channel table tests are added, and the boundary of `randu` used for generating test data is broadened to make the test more robust.
- **Performance test**: Multi-channel input and multi-channel table tests are added.
Perf test done on
- MUSE-PI (vlen=256)
- Compiler: gcc 14.2 (riscv-collab/riscv-gnu-toolchain Nightly: December 16, 2024)
```sh
$ opencv_test_core --gtest_filter="Core_LUT*"
$ opencv_perf_core --gtest_filter="SizePrm_LUT*" --perf_min_samples=300 --perf_force_samples=300
```
```sh
Geometric mean (ms)
Name of Test scalar ui rvv ui rvv
vs vs
scalar scalar
(x-factor) (x-factor)
LUT::SizePrm::320x240 0.248 0.249 0.052 1.00 4.74
LUT::SizePrm::640x480 0.277 0.275 0.085 1.01 3.28
LUT::SizePrm::1920x1080 0.950 0.947 0.634 1.00 1.50
LUT_multi2::SizePrm::320x240 2.051 2.045 2.049 1.00 1.00
LUT_multi2::SizePrm::640x480 2.128 2.134 2.125 1.00 1.00
LUT_multi2::SizePrm::1920x1080 7.397 7.380 7.390 1.00 1.00
LUT_multi::SizePrm::320x240 0.715 0.747 0.154 0.96 4.64
LUT_multi::SizePrm::640x480 0.741 0.766 0.257 0.97 2.88
LUT_multi::SizePrm::1920x1080 2.766 2.765 1.925 1.00 1.44
```
This optimization is achieved by loading the entire lookup table into vector registers. Due to register size limitations, the optimization is only effective under the following conditions:
- For the U8C1 table type, the optimization works when `vlen >= 256`
- For U16C1, it works when `vlen >= 512`
- For U32C1, it works when `vlen >= 1024`
Since I don’t have real hardware with `vlen > 256`, the corresponding accuracy tests were conducted on QEMU built from the `riscv-collab/riscv-gnu-toolchain`.
This patch does not implement optimizations for multi-channel tables.
Previous attempts:
1. For the U8C1 table type, when `vlen = 128`, it is possible to use four `u8m4` vectors to load the entire table, perform gathering, and merge the results. However, the performance is almost the same as the scalar version.
2. Loading part of the table and repeatedly loading the source data is faster for small sizes. But as the table size grows, the performance quickly degrades compared to the scalar version.
3. Using `vluxei8` as a general solution does not show any performance improvement.
### Pull Request Readiness Checklist
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APNG encoding optimization #26849
related #26840
### Pull Request Readiness Checklist
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Use map to manage unique marker size candidate trees.
Avoid code duplication.
Add a test to show double detection with overlapping dictionaries.
Generalize to marker sizes of not only predefined dictionaries.
Fix issues in RISC-V Vector (RVV) Universal Intrinsic #27006
This PR aims to make `opencv_test_core` pass on RVV, via following two parts:
1. Fix bug in Universal Intrinsic when VLEN >= 512:
- `max_nlanes` should be multiplied by 2, because we use LMUL=2 in RVV Universal Intrinsic since #26318.
- Related tests are also expanded to match longer registers
- Relax the precision threshold of `v_erf` to make the tests pass
2. Temporary fix #26936
- Disable 3 Universal Intrinsic code blocks on GCC
- This is just a temporary fix until we figure out if it's our issue or GCC/something else's
This patch is tested under the following conditions:
- Compier: GCC 14.2, Clang 19.1.7
- Device: Muse-Pi (VLEN=256), QEMU (VLEN=512, 1024)
### Pull Request Readiness Checklist
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Add RISC-V HAL implementation for cv::pyrDown and cv::pyrUp #26958
This patch implements `cv_hal_pyrdown/cv_hal_pyrup` function in RVV_HAL using native intrinsics, optimizing the performance for `cv::pyrDown`, `cv::pyrUp` and `cv::buildPyramids` with data types `{8U,16S,32F} x {C1,C2,C3,C4,Cn}`.
Tested on MUSE-PI (Spacemit X60) for both gcc 14.2 and clang 20.0.
```
$ ./opencv_test_imgproc --gtest_filter="*pyr*:*Pyr*"
$ ./opencv_perf_imgproc --gtest_filter="*pyr*:*Pyr*" --perf_min_samples=300 --perf_force_samples=300
```
<img width="1112" alt="Untitled" src="https://github.com/user-attachments/assets/235a9fba-0d29-434e-8a10-498212bac657" />
### Pull Request Readiness Checklist
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Optimize undistort points #26988
Skips unnecessary rotation with identity matrix if no R or P mats are given.
---------
Co-authored-by: Daniel <daniel@mail.de>
Fix HAL dispatch in cv::minMaxIdx #26946
Closes https://github.com/opencv/opencv/pull/26939#issuecomment-2669617834.
Closes https://github.com/opencv/opencv/issues/26947
### Pull Request Readiness Checklist
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Documentation to enable FastCV based OpenCV HAL and Extensions #26910
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Fix Logical defect in FilterSpecklesImpl #26996
Fixes : #24963
### Pull Request Readiness Checklist
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Fix getPerspectiveTransform for singular case #26926
### Pull Request Readiness Checklist
Fix#26916
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Some minor fixes#26992
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Imgcodecs: gif: support Disposal Method #26930
Close https://github.com/opencv/opencv/issues/26924
### Pull Request Readiness Checklist
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videoio: print test params instead of indexes #26948
_videoio_ test names changed - use string instead of index.
E.g. `videoio_read.threads/0` is now `videoio_read.threads/h264_0_RAW`.
It allows to filter tests independently of the platform.
**Notes:**
- not all tests has been updated - only simpler ones and those which have varying parameters depending on platform
Add a test related IMWRITE_PNG_COMPRESSION parameter #26973
### Pull Request Readiness Checklist
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Impl RISC-V HAL for cv::flip | Add perf test for flip #26943
Implement through the existing `cv_hal_flip` interfaces.
Add perf test for `cv::flip`.
The reason why select these args for testing:
- **size**: copied from perf_lut
- **type**:
- U8C1: basic situation
- U8C3: unaligned element size
- U8C4: large element size
Tested on
- MUSE-PI (vlen=256)
- Compiler: gcc 14.2 (riscv-collab/riscv-gnu-toolchain Nightly: December 16, 2024)
```sh
$ opencv_test_core --gtest_filter="Core_Flip/ElemWiseTest.*"
$ opencv_perf_core --gtest_filter="Size_MatType_FlipCode*" --perf_min_samples=300 --perf_force_samples=300
```
```
Geometric mean (ms)
Name of Test scalar ui rvv ui rvv
vs vs
scalar scalar
(x-factor) (x-factor)
flip::Size_MatType_FlipCode::(320x240, 8UC1, FLIP_X) 0.026 0.033 0.031 0.81 0.84
flip::Size_MatType_FlipCode::(320x240, 8UC1, FLIP_XY) 0.206 0.212 0.091 0.97 2.26
flip::Size_MatType_FlipCode::(320x240, 8UC1, FLIP_Y) 0.185 0.189 0.082 0.98 2.25
flip::Size_MatType_FlipCode::(320x240, 8UC3, FLIP_X) 0.070 0.084 0.084 0.83 0.83
flip::Size_MatType_FlipCode::(320x240, 8UC3, FLIP_XY) 0.616 0.612 0.235 1.01 2.62
flip::Size_MatType_FlipCode::(320x240, 8UC3, FLIP_Y) 0.587 0.603 0.204 0.97 2.88
flip::Size_MatType_FlipCode::(320x240, 8UC4, FLIP_X) 0.263 0.110 0.109 2.40 2.41
flip::Size_MatType_FlipCode::(320x240, 8UC4, FLIP_XY) 0.930 0.831 0.316 1.12 2.95
flip::Size_MatType_FlipCode::(320x240, 8UC4, FLIP_Y) 1.175 1.129 0.313 1.04 3.75
flip::Size_MatType_FlipCode::(640x480, 8UC1, FLIP_X) 0.303 0.118 0.111 2.57 2.73
flip::Size_MatType_FlipCode::(640x480, 8UC1, FLIP_XY) 0.949 0.836 0.405 1.14 2.34
flip::Size_MatType_FlipCode::(640x480, 8UC1, FLIP_Y) 0.784 0.783 0.409 1.00 1.92
flip::Size_MatType_FlipCode::(640x480, 8UC3, FLIP_X) 1.084 0.360 0.355 3.01 3.06
flip::Size_MatType_FlipCode::(640x480, 8UC3, FLIP_XY) 3.768 3.348 1.364 1.13 2.76
flip::Size_MatType_FlipCode::(640x480, 8UC3, FLIP_Y) 4.361 4.473 1.296 0.97 3.37
flip::Size_MatType_FlipCode::(640x480, 8UC4, FLIP_X) 1.252 0.469 0.451 2.67 2.78
flip::Size_MatType_FlipCode::(640x480, 8UC4, FLIP_XY) 5.732 5.220 1.303 1.10 4.40
flip::Size_MatType_FlipCode::(640x480, 8UC4, FLIP_Y) 5.041 5.105 1.203 0.99 4.19
flip::Size_MatType_FlipCode::(1920x1080, 8UC1, FLIP_X) 2.382 0.903 0.903 2.64 2.64
flip::Size_MatType_FlipCode::(1920x1080, 8UC1, FLIP_XY) 8.606 7.508 2.581 1.15 3.33
flip::Size_MatType_FlipCode::(1920x1080, 8UC1, FLIP_Y) 8.421 8.535 2.219 0.99 3.80
flip::Size_MatType_FlipCode::(1920x1080, 8UC3, FLIP_X) 6.312 2.416 2.429 2.61 2.60
flip::Size_MatType_FlipCode::(1920x1080, 8UC3, FLIP_XY) 29.174 26.055 12.761 1.12 2.29
flip::Size_MatType_FlipCode::(1920x1080, 8UC3, FLIP_Y) 25.373 25.500 13.382 1.00 1.90
flip::Size_MatType_FlipCode::(1920x1080, 8UC4, FLIP_X) 7.620 3.204 3.115 2.38 2.45
flip::Size_MatType_FlipCode::(1920x1080, 8UC4, FLIP_XY) 32.876 29.310 12.976 1.12 2.53
flip::Size_MatType_FlipCode::(1920x1080, 8UC4, FLIP_Y) 28.831 29.094 14.919 0.99 1.93
```
The optimization for vlen <= 256 and > 256 are different, but I have no real hardware with vlen > 256. So accuracy tests for that like 512 and 1024 are conducted on QEMU built from the `riscv-collab/riscv-gnu-toolchain`.
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Enable SIMD_SCALABLE for exp and sqrt #26886
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```
CPU - Banana Pi k1, compiler - clang 18.1.4
```
```
Geometric mean (ms)
Name of Test baseline hal ui hal ui
vs vs
baseline baseline
(x-factor) (x-factor)
Exp::ExpFixture::(127x61, 32FC1) 0.358 -- 0.033 -- 10.70
Exp::ExpFixture::(640x480, 32FC1) 14.304 -- 1.167 -- 12.26
Exp::ExpFixture::(1280x720, 32FC1) 42.785 -- 3.538 -- 12.09
Exp::ExpFixture::(1920x1080, 32FC1) 96.206 -- 7.927 -- 12.14
Exp::ExpFixture::(127x61, 64FC1) 0.433 0.050 0.098 8.59 4.40
Exp::ExpFixture::(640x480, 64FC1) 17.315 1.935 3.813 8.95 4.54
Exp::ExpFixture::(1280x720, 64FC1) 52.181 5.877 11.519 8.88 4.53
Exp::ExpFixture::(1920x1080, 64FC1) 117.082 13.157 25.854 8.90 4.53
```
Additionally, this PR brings Sqrt optimization with UI:
```
Geometric mean (ms)
Name of Test baseline ui ui
vs
baseline
(x-factor)
Sqrt::SqrtFixture::(127x61, 5, false) 0.111 0.027 4.11
Sqrt::SqrtFixture::(127x61, 6, false) 0.149 0.053 2.82
Sqrt::SqrtFixture::(640x480, 5, false) 4.374 0.967 4.52
Sqrt::SqrtFixture::(640x480, 6, false) 5.885 2.046 2.88
Sqrt::SqrtFixture::(1280x720, 5, false) 12.960 2.915 4.45
Sqrt::SqrtFixture::(1280x720, 6, false) 17.648 6.107 2.89
Sqrt::SqrtFixture::(1920x1080, 5, false) 29.178 6.524 4.47
Sqrt::SqrtFixture::(1920x1080, 6, false) 39.709 13.670 2.90
```
Reference
Muller, J.-M. Elementary Functions: Algorithms and Implementation. 2nd ed. Boston: Birkhäuser, 2006.
https://www.springer.com/gp/book/9780817643720
core: vectorize cv::normalize / cv::norm #26885
Checklist:
| | normInf | normL1 | normL2 |
| ---- | ------- | ------ | ------ |
| bool | - | - | - |
| 8u | √ | √ | √ |
| 8s | √ | √ | √ |
| 16u | √ | √ | √ |
| 16s | √ | √ | √ |
| 16f | - | - | - |
| 16bf | - | - | - |
| 32u | - | - | - |
| 32s | √ | √ | √ |
| 32f | √ | √ | √ |
| 64u | - | - | - |
| 64s | - | - | - |
| 64f | √ | √ | √ |
*: Vectorization of data type bool, 16f, 16bf, 32u, 64u and 64s needs to be done on 5.x.
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Removal of deprecated functions in imgproc #26914
Fixes : #26410
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Added trackers factory with pre-loaded dnn models #26875
Replaces https://github.com/opencv/opencv/pull/26295
Allows to substitute custom models or initialize tracker from in-memory model.
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Migrate remaning OpenVX integrations to OpenVX HAL (core) #26903
Tested with OpenVX 1.2 & 1.3 sample implementation.
Steps to build and test:
```
git clone git@github.com:KhronosGroup/OpenVX-sample-impl.git
cd OpenVX-sample-impl
python3 Build.py --os=Linux --conf=Release
cd ..
mkdir build
cmake -DWITH_OPENVX=ON -DOPENVX_ROOT=/mnt/Projects/Projects/OpenVX-sample-impl/install/Linux/x64/Release/ ../opencv
make -j8
```
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Extension to PR #26605 ldm inpainting sample #26904
This PR adds and fixes following points in the ldm_inpainting sample on top of original PR #26605 by @Abdurrahheem
DONE:
1. Added functionality to load models from a YAML configuration file, allowing for automatic downloading if models are not found locally.
2. Updated the script usage instructions to reflect the correct command format.
3. Improved user interaction by adding instructions to the image window for inpainting controls.
4. Introduced a new models.yml configuration section for inpainting models weights downloading, including placeholders for model SHA1 checksums.
5. Fixed input types and names of the onnx graph generation.
6. Added links to onnx graphs in models.yml
7. Support added for findModels and standarized the sample usage similar to other dnn samples
8. Fixes issue in download_models.py for downloading models from dl.opencv.org
9. Fixes issue in common.py which used to print duplicated positional arguments in case of samples that use multiple models.
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---------
Co-authored-by: Abdurrahheem <abduragim.shtanchaev@xperience.ai>
[HAL] split8u RVV 1.0 #26884
### Pull Request Readiness Checklist
* Banana Pi BF3 (SpacemiT K1)
* Compiler: Syntacore Clang 18.1.4 (build 2024.12)
```
Geometric mean (ms)
Name of Test baseline hal hal
ui vs
baseline
ui
(x-factor)
split::Size_Depth_Channels::(127x61, 8UC1, 2) 0.012 0.004 3.12
split::Size_Depth_Channels::(127x61, 8UC1, 3) 0.019 0.006 2.91
split::Size_Depth_Channels::(127x61, 8UC1, 4) 0.028 0.011 2.64
split::Size_Depth_Channels::(127x61, 8UC1, 5) 0.067 0.033 2.02
split::Size_Depth_Channels::(127x61, 8UC1, 6) 0.084 0.040 2.11
split::Size_Depth_Channels::(127x61, 8UC1, 7) 0.103 0.055 1.88
split::Size_Depth_Channels::(127x61, 8UC1, 8) 0.113 0.032 3.50
split::Size_Depth_Channels::(640x480, 8UC1, 2) 0.454 0.179 2.54
split::Size_Depth_Channels::(640x480, 8UC1, 3) 0.677 0.298 2.27
split::Size_Depth_Channels::(640x480, 8UC1, 4) 0.901 0.410 2.20
split::Size_Depth_Channels::(640x480, 8UC1, 5) 3.781 3.010 1.26
split::Size_Depth_Channels::(640x480, 8UC1, 6) 4.886 4.009 1.22
split::Size_Depth_Channels::(640x480, 8UC1, 7) 5.777 4.770 1.21
split::Size_Depth_Channels::(640x480, 8UC1, 8) 4.596 1.330 3.46
split::Size_Depth_Channels::(1280x720, 8UC1, 2) 1.377 0.709 1.94
split::Size_Depth_Channels::(1280x720, 8UC1, 3) 2.091 1.034 2.02
split::Size_Depth_Channels::(1280x720, 8UC1, 4) 2.744 1.573 1.74
split::Size_Depth_Channels::(1280x720, 8UC1, 5) 9.542 6.284 1.52
split::Size_Depth_Channels::(1280x720, 8UC1, 6) 11.114 7.850 1.42
split::Size_Depth_Channels::(1280x720, 8UC1, 7) 14.083 11.879 1.19
split::Size_Depth_Channels::(1280x720, 8UC1, 8) 13.524 3.865 3.50
split::Size_Depth_Channels::(1920x1080, 8UC1, 2) 3.108 1.395 2.23
split::Size_Depth_Channels::(1920x1080, 8UC1, 3) 4.659 2.128 2.19
split::Size_Depth_Channels::(1920x1080, 8UC1, 4) 6.127 2.818 2.17
split::Size_Depth_Channels::(1920x1080, 8UC1, 5) 26.733 16.625 1.61
split::Size_Depth_Channels::(1920x1080, 8UC1, 6) 31.242 22.414 1.39
split::Size_Depth_Channels::(1920x1080, 8UC1, 7) 35.968 27.658 1.30
split::Size_Depth_Channels::(1920x1080, 8UC1, 8) 29.997 8.655 3.47
```
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doc: update supporting imgcodec format settings #26889
Close https://github.com/opencv/opencv/issues/26877
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Update window_cocoa.mm #26662
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Add RISC-V HAL implementation for cv::norm and cv::normalize #26804
This patch implements `cv::norm` with norm types `NORM_INF/NORM_L1/NORM_L2/NORM_L2SQR` and `Mat::convertTo` function in RVV_HAL using native intrinsic, optimizing the performance for `cv::norm(src)`, `cv::norm(src1, src2)`, and `cv::normalize(src)` with data types `8UC1/8UC4/32FC1`.
`cv::normalize` also calls `minMaxIdx`, #26789 implements RVV_HAL for this.
Tested on MUSE-PI for both gcc 14.2 and clang 20.0.
```
$ opencv_test_core --gtest_filter="*Norm*"
$ opencv_perf_core --gtest_filter="*norm*" --perf_min_samples=300 --perf_force_samples=300
```
The head of the perf table is shown below since the table is too long.
View the full perf table here: [hal_rvv_norm.pdf](https://github.com/user-attachments/files/18468255/hal_rvv_norm.pdf)
<img width="1304" alt="Untitled" src="https://github.com/user-attachments/assets/3550b671-6d96-4db3-8b5b-d4cb241da650" />
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imgcodecs:gif: support IMREAD_UNCHANGED and IMREAD_GRAYSCALE #26859Close#26858
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Support 64-bit integer types for FileStorage Base64 #26871
### Pull Request Readiness Checklist
Related to https://github.com/opencv/opencv/pull/26846#issuecomment-2618159277
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1232
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Add missing include in gislandmodel.hpp #26879
Add `<exception>`, `<string>`, and `<cstddef>` includes to `gislandmodel.hpp` which are required due to the usage of `std::exception_ptr`, `std::string`, and `size_t` in this header.
Notably one of those causes a build error on recent versions of Xcode: #26780
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Added lama inpainting onnx model sample #26736
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Performance tests for image encoders and decoders and code cleanup #26872
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Issue25250 lens distortion documentation unclear #26600
### Pull Request Readiness Checklist
This pull request addresses the issue in https://github.com/opencv/opencv/issues/25250. Using the method recommended by oleg-alexandrov.
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Fixed AVIF linkage on Windows #26762Closes#26747
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Add RISC-V HAL implementation for minMaxIdx #26789
On the RISC-V platform, `minMaxIdx` cannot benefit from Universal Intrinsics because the UI-optimized `minMaxIdx` only supports `CV_SIMD128` (and does not accept `CV_SIMD_SCALABLE` for RVV).
1d701d1690/modules/core/src/minmax.cpp (L209-L214)
This patch implements `minMaxIdx` function in RVV_HAL using native intrinsic, optimizing the performance for all data types with one channel.
Tested on MUSE-PI for both gcc 14.2 and clang 20.0.
```
$ opencv_test_core --gtest_filter="*MinMaxLoc*"
$ opencv_perf_core --gtest_filter="*minMaxLoc*"
```
<img width="1122" alt="Untitled" src="https://github.com/user-attachments/assets/6a246852-87af-42c5-a50b-c349c2765f3f" />
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Fix oss-fuzz bugs 391934081 and 392318892 #26854
- fix a potential overflow in x0+w0
- use the proper function to deal with background color to deal with all cases of the spec
- use BGR layout for APNG background color
### Pull Request Readiness Checklist
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Added 16-bit support to fastNlMeansDenoising and updated tests #26831
Fixes : #26582
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Fix bug with int64 support for FileStorage #26846
### Pull Request Readiness Checklist
Fix#26829, https://github.com/opencv/opencv-python/issues/1078
In current implementation of `int64` support raw size of recorded integer is variable (`4` or `8` bytes depending on value). But then we iterate over nodes we need to know it exact value
dfad11aae7/modules/core/src/persistence.cpp (L2596-L2609)
Bug is that `rawSize` method still return `4` for any integer. I haven't figured out a way how to get variable raw size for integer in this method. I made raw size for integer is constant and equal to `8`.
Yes, after this patch memory consumption for integers will increase, but I don't know a better way to do it yet. At least this fixes bug and implementation becomes more correct
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Update includes in filter.hpp #26850
Fixes :
```
identifier "Mat" is undefinedC/C++(20)
namespace "std" has no member "vector"C/C++(135)
```
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imgcodecs: jpegxl: imdecode() directly read from memory #26844Close#26843
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`step` and `maskStep` are used to increase/decrease `pImage`.
But it's done on unsigned type, relying on overflow, which is UB.
(step is size_t but seed.y is int and can be negative, the result
is therefore unsigned which can overflow)
Add direct pdf links in the bibliography #26754
Update and add pdf links in the bibliography.
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Initial support Blackwell GPU arch #26820
10.0 blackwell b100/b200
12.0 blackwell rtx50
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Corrections on bKGD chunk writing and reading in PNG #26835
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Zoom functionality for Android native camera capture #26837
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solvePnPRansac implementation for Fisheye camera model #26669
Related: https://github.com/opencv/opencv/pull/25028
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Add cv::THRESH_DRYRUN flag to get adaptive threshold values without thresholding #26836
A first proposal for #26777
Adds a `cv::THRESH_DRYRUN` flag to let cv::threshold() compute the threshold (useful for OTSU/TRIANGLE), but without actually running the thresholding. This flags is a proposal instead of a new function cv::computeThreshold()
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OpenEXR 2.2 or earlier cannot be used with C++17 or later #26678
Close https://github.com/opencv/opencv/issues/26673
Close https://github.com/opencv/opencv/issues/25313
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Android camera refactoring #26646
This patch set does not contain any functional changes. It just cleans up the code structure to improve readability and to prepare for future changes.
* videoio(Android): Use 'unique_ptr' instead of 'shared_ptr'
Using shared pointers for unshared data is considered an antipattern.
* videoio(Android): Make callback functions private static members
Don't leak internal functions into global namespace. Some member
variables are now private as well.
* videoio(Android): Move resolution matching into separate function
Also make internally used member functions private.
* videoio(Android): Move ranges query into separate function
Also remove some unneccessary initialisations from initCapture().
* videoio(Android): Wrap extremly long source code lines
* videoio(Android): Rename members of 'RangeValue'
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Fix potential READ memory access #26782
This fixes https://oss-fuzz.com/testcase-detail/4923671881252864 and https://oss-fuzz.com/testcase-detail/5048650127966208
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Fixed default cap_prop_orientation_auto behaviour #26800
Fixes : #26795
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Replaced sprintf with snprintf #26815
Fixes : #26814
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Improve robustness for fitEllipseAMS #26810
### Pull Request Readiness Checklist
Related to #26694
Added functionality to add noise to points in degenerate cases and try again for `fitEllipseAMS`. `fitEllipseNoDirect` and `fitEllipseDirect` already have this
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jpegxl: support cv::IMREAD_UNCHANGED and other ImreadFlags #26788
Close https://github.com/opencv/opencv/issues/26767
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Added CV_WRAP to Animation struct #26813closes#26808
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Improve robustness for ellipse fitting #26773
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Related to #26694
Current noise addition is not very good because for example it turns degenerate case of one horizontal line into degenerate case of two parallel horizontal lines
Improving noise addition leads to improved robustness of algorithms
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3rdparty:ittnotify: update to v3.25.4 #26802
Close https://github.com/opencv/opencv/issues/26801
See https://github.com/opencv/opencv/pull/26797
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- Updated the documentation of the putText function to clarify the behavior of the fontScale parameter.
- Explained how fontScale affects text rendering: magnifying (>1), minimizing (<1), and mirroring (<0).
Fix rotated aruco marker board generation #26753
### Issue : [25884](https://github.com/opencv/opencv/issues/25884)
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Remove MSVS 2013 related #26651
OpenCV 5.x requires MSVC >= 2017 15.7 and MSVS 2013 is EOF currently.
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Bug fix for #25546 - Updating inliers for homography estimation #26742Fixes#25546
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Fix bugs in GIF decoding #26738
### Pull Request Readiness Checklist
this is related to #25691
i solved two bugs here:
1. the decoding setting:
according to [https://www.w3.org/Graphics/GIF/spec-gif89a.txt](https://www.w3.org/Graphics/GIF/spec-gif89a.txt)
```
DEFERRED CLEAR CODE IN LZW COMPRESSION
There has been confusion about where clear codes can be found in the
data stream. As the specification says, they may appear at anytime. There
is not a requirement to send a clear code when the string table is full.
It is the encoder's decision as to when the table should be cleared. When
the table is full, the encoder can chose to use the table as is, making no
changes to it until the encoder chooses to clear it. The encoder during
this time sends out codes that are of the maximum Code Size.
As we can see from the above, when the decoder's table is full, it must
not change the table until a clear code is received. The Code Size is that
of the maximum Code Size. Processing other than this is done normally.
Because of a large base of decoders that do not handle the decompression in
this manner, we ask developers of GIF encoding software to NOT implement
this feature until at least January 1991 and later if they see that their
particular market is not ready for it. This will give developers of GIF
decoding software time to implement this feature and to get it into the
hands of their clients before the decoders start "breaking" on the new
GIF's. It is not required that encoders change their software to take
advantage of the deferred clear code, but it is for decoders.
```
at first i didn't consider this case, thus leads to a bug discussed in #25691. the changes made in function lzwDecode() is aiming at solving this.
2. the fetch method of loopCount:
in the codes at https://github.com/opencv/opencv/blob/4.x/modules/imgcodecs/src/grfmt_gif.cpp#L410, if the branch is taken, 3 more bytes will be taken, leading to unpredictable behavior.
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Improved dumpVector, cv::Rect operator<< and exceptions #26602
- Applied format for vector element formatting to ensure consistent and clear output representation.
- Moved `operator<<` to the `cv` namespace to align with OpenCV's coding standards and improve maintainability.
- Enhanced error handling by including detailed exception messages using `e.what()` for better debugging.
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Ensure Obj-C header files are generated correctly if under /private/var #26713Fix#26712
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Clean up sse_utils.hpp #26676
Remove unused functions in sse_utils.hpp. If they are still needed, I believe universal intrinsics should be more appropriate.
Related: https://github.com/opencv/opencv/issues/25002
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Add more boundary checks. #26739
Also fix a bug in read_chunk where we could end up with png_get_uint_32(len) + 12 < 4
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Feature: weighted Hough Transform #21407
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Fix for png durations and memory leak #26714
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AndroidMediaNdkVideoWriter pixel format enhancement #26698
* videoio(Android): Add source pixel formats RGBA and GRAY to AndroidMediaNdkVideoWriter
Let AndroidMediaNdkVideoWriter::write() deduce source pixel format from matrix type:
CV_8UC3 -> BGR (as before)
CV_8UC4 -> RGBA (use in conjunction with CvCameraViewFrame)
CV_8UC1 -> GRAY
* samples/android/video-recorder: Send images to VideoWriter in RGBA format
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Fix leaks in cv::PngDecoder #26701
Bug: oss-fuzz:386688709
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Animated PNG Support #25715
Continues https://github.com/opencv/opencv/pull/25608
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AndroidMediaNdkCapture pixel format enhancement #26656
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Open VideoCapture from data stream #25584
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Add VideoCapture option to read a raw binary video data from `std::streambuf`.
There are multiple motivations:
1. Avoid disk file creation in case of video already in memory (received by network or from database).
2. Streaming mode. Frames decoding starts during sequential file transfer by chunks.
Suppoted backends:
* FFmpeg
* MSMF (no streaming mode)
Supporter interfaces:
* C++ (std::streambuf)
* Python (io.BufferedIOBase)
resolves https://github.com/opencv/opencv/issues/24400
- [x] test h264
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Use size_t when calculating size of all_points #26650Closes: #26642
Asan log
```
=================================================================
==41401==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x7fc55a02a3fc at pc 0x7fc58e304131 bp 0x7ffd54787b00 sp 0x7ffd54787af8
WRITE of size 4 at 0x7fc55a02a3fc thread T0
#0 0x7fc58e304130 in cv::QRDetectMulti::checkSets(std::vector<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >, std::allocator<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > > > >&, std::vector<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >, std::allocator<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > > > >&, std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >&) /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:3726
#1 0x7fc58e3054b0 in cv::QRDetectMulti::localization() /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:3829
#2 0x7fc58e308020 in cv::ImplContour::detectMulti(cv::_InputArray const&, cv::_OutputArray const&) const /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:3987
#3 0x7fc58e30b5b1 in cv::ImplContour::detectAndDecodeMulti(cv::_InputArray const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > >&, cv::_OutputArray const&, cv::_OutputArray const&) const /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:4176
#4 0x7fc58e28922f in cv::GraphicalCodeDetector::detectAndDecodeMulti(cv::_InputArray const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > >&, cv::_OutputArray const&, cv::_OutputArray const&) const /home/fanta/source/opencv/modules/objdetect/src/graphical_code_detector.cpp:42
#5 0x5954e8 in Body /home/fanta/source/opencv/modules/objdetect/test/test_qrcode.cpp:48
#6 0x594fc0 in TestBody /home/fanta/source/opencv/modules/objdetect/test/test_qrcode.cpp:42
#7 0x67ee6a in void testing::internal::HandleSehExceptionsInMethodIfSupported<testing::Test, void>(testing::Test*, void (testing::Test::*)(), char const*) /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3919
#8 0x6734a4 in void testing::internal::HandleExceptionsInMethodIfSupported<testing::Test, void>(testing::Test*, void (testing::Test::*)(), char const*) /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3955
#9 0x641fe8 in testing::Test::Run() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3993
#10 0x6431ac in testing::TestInfo::Run() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:4169
#11 0x643d15 in testing::TestCase::Run() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:4287
#12 0x659ff3 in testing::internal::UnitTestImpl::RunAllTests() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:6662
#13 0x681205 in bool testing::internal::HandleSehExceptionsInMethodIfSupported<testing::internal::UnitTestImpl, bool>(testing::internal::UnitTestImpl*, bool (testing::internal::UnitTestImpl::*)(), char const*) /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3919
#14 0x675127 in bool testing::internal::HandleExceptionsInMethodIfSupported<testing::internal::UnitTestImpl, bool>(testing::internal::UnitTestImpl*, bool (testing::internal::UnitTestImpl::*)(), char const*) /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3955
#15 0x65734c in testing::UnitTest::Run() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:6271
#16 0x5907f0 in RUN_ALL_TESTS() /home/fanta/source/opencv/modules/ts/include/opencv2/ts/ts_gtest.h:22240
#17 0x590cdd in main (/home/fanta/source/opencv-build-4.x-clang/bin/opencv_test_objdetect+0x590cdd) (BuildId: a9363fc788d57c48225fc0559ac9199d07d415db)
#18 0x7fc58ab242ad in __libc_start_call_main (/lib64/libc.so.6+0x2a2ad) (BuildId: 03f1631dc9760d3e30311fe62e15cc4baaa89db7)
#19 0x7fc58ab24378 in __libc_start_main@@GLIBC_2.34 (/lib64/libc.so.6+0x2a378) (BuildId: 03f1631dc9760d3e30311fe62e15cc4baaa89db7)
#20 0x417014 in _start ../sysdeps/x86_64/start.S:115
0x7fc55a02a3fc is located 0 bytes after 2938510332-byte region [0x7fc4aadc8800,0x7fc55a02a3fc)
allocated by thread T0 here:
#0 0x7fc58e590298 in operator new(unsigned long) (/lib64/libasan.so.8+0xfd298) (BuildId: da72ee674d801ced58193987786b90646d94ff8d)
#1 0x7fc58e34d010 in std::__new_allocator<cv::Vec<int, 3> >::allocate(unsigned long, void const*) /usr/include/c++/14/bits/new_allocator.h:151
SUMMARY: AddressSanitizer: heap-buffer-overflow /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:3726 in cv::QRDetectMulti::checkSets(std::vector<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >, std::allocator<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > > > >&, std::vector<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >, std::allocator<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > > > >&, std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >&)
Shadow bytes around the buggy address:
0x7fc55a02a100: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x7fc55a02a180: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x7fc55a02a200: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x7fc55a02a280: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x7fc55a02a300: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
=>0x7fc55a02a380: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00[04]
0x7fc55a02a400: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x7fc55a02a480: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x7fc55a02a500: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x7fc55a02a580: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x7fc55a02a600: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
Shadow byte legend (one shadow byte represents 8 application bytes):
Addressable: 00
Partially addressable: 01 02 03 04 05 06 07
Heap left redzone: fa
Freed heap region: fd
Stack left redzone: f1
Stack mid redzone: f2
Stack right redzone: f3
Stack after return: f5
Stack use after scope: f8
Global redzone: f9
Global init order: f6
Poisoned by user: f7
Container overflow: fc
Array cookie: ac
Intra object redzone: bb
ASan internal: fe
Left alloca redzone: ca
Right alloca redzone: cb
==41401==ABORTING
```
`(true_points_group[i].size()` is 1794 and `(true_points_group[i].size() - 2 ) * (true_points_group[i].size() - 1) * true_points_group[i].size())` is 5764222464 which overflows `int`
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Faster implementation of blobFromImages for cpu nchw output #26127
Faster implementation of blobFromImage and blobFromImages for
HWC cv::Mat images -> NCHW cv::Mat
case
Running time on my pc in ms:
**blobFromImage**
```
image size old new speed-up
32x32x3 0.008 0.002 4.0x
64x64x3 0.021 0.009 2.3x
128x128x3 0.164 0.037 4.4x
256x256x3 0.728 0.158 4.6x
512x512x3 3.310 0.628 5.2x
1024x1024x3 14.503 3.124 4.6x
2048x2048x3 61.647 28.049 2.2x
```
**blobFromImages**
```
image size old new speed-up
16x32x32x3 0.122 0.041 3.0x
16x64x64x3 0.790 0.165 4.8x
16x128x128x3 3.313 0.652 5.1x
16x256x256x3 13.495 3.127 4.3x
16x512x512x3 58.795 28.127 2.1x
16x1024x1024x3 251.135 121.955 2.1x
16x2048x2048x3 1023.570 487.188 2.1x
```
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Animated WebP Support #25608
related issues #24855#22569
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V4l default image size #25500
Added ability to set default image width and height for V4L capture. This is required for cameras that does not support 640x480 resolution because otherwise V4L capture cannot be opened and failed with "Pixel format of incoming image is unsupported by OpenCV" and then with "can't open camera by index" message. Because of the videoio architecture it is not possible to insert actions between CvCaptureCAM_V4L::CvCaptureCAM_V4L and CvCaptureCAM_V4L::open so the only way I found is to use environment variables to preselect the resolution.
Related bug report is [#25499](https://github.com/opencv/opencv/issues/25499)
Maybe (but not confirmed) this is also related to [#24551](https://github.com/opencv/opencv/issues/24551)
This fix was made and verified in my local environment: capture board AVMATRIX VC42, Ubuntu 20, NVidia Jetson Orin.
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FastCV-based HAL for OpenCV acceleration 2ndpost-2 #26619
### Detailed description:
- Add support for multiply 8u, 16s and 32f
- Add support for cv_hal_pyrdown 8u
- Add support for cv_hal_cvtBGRtoHSV and cv_hal_cvtBGRtoYUVApprox 8u
Requires binary from [opencv/opencv_3rdparty#90](https://github.com/opencv/opencv_3rdparty/pull/90)
Depends on: [opencv/opencv#26617](https://github.com/opencv/opencv/pull/26617)
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Fix VideoCapture fails to read single image with digits in name #26637
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Fix#26457
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js: fix generation of "const const" in code #26640
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Add RISC-V HAL implementation for meanStdDev #26624
`meanStdDev` benefits from the Universal Intrinsic backend of RVV, but we also found that the performance on the `8UC4` type is worse than the scalar version when there is a mask, and there is no optimization implementation on `32FC1`.
This patch implements `meanStdDev` function in RVV_HAL using native intrinsic, significantly optimizing the performance for `8UC1`, `8UC4` and `32FC1`.
This patch is tested on BPI-F3 for both gcc 14.2 and clang 19.1.
```
$ opencv_test_core --gtest_filter="*MeanStdDev*"
$ opencv_perf_core --gtest_filter="Size_MatType_meanStdDev*
```
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Android camera feature enhancements #26627
Closes https://github.com/opencv/opencv/issues/24687
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FastCV-based HAL for OpenCV acceleration 2ndpost-1 #26617
### Detailed description:
- Add parallel support for cv_hal_sobel
- Add cv_hal_gaussianBlurBinomial and parallel support.
- Add cv_hal_addWeighted8u and parallel support
- Add cv_hal_warpPerspective and parallel support
Requires binary from [opencv/opencv_3rdparty#90](https://github.com/opencv/opencv_3rdparty/pull/90)
Related patch to opencv_contrib: [opencv/opencv_contrib#3844](https://github.com/opencv/opencv_contrib/pull/3844)
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More convenient GpuMatND constructor #26472Closes#26471
For convenience, GpuMatND can now accept a step.size() equal to size.size(), as long as the last step is equal to elemSize()
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According to GCC doc, -Wlong-long: Warn if long long type is used.
This is enabled by either -Wpedantic or -Wtraditional in ISO C90
and C++98 modes. To inhibit the warning messages, use -Wno-long-long.
OpenCV 4.x requires C++11. As result, this option is useless.
Ref: https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html
Switch calibration.cpp to C++ #26490
The CvLevMarq code has to be kept in order to keep the same accuracy (the C++ solver is not as good).
There are two ways to review this PR: by comparing to the old code, or by checking what is different from the 5.x version (which is the first commit).
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Support C++20 standard #26590
Close https://github.com/opencv/opencv/issues/26589
Related https://github.com/opencv/opencv_contrib/pull/3842
Related: https://github.com/opencv/opencv/issues/20269
- do not arithmetic enums and ( different enums or floating numeric)
- remove unused variable
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Update printingin GPT2 sample #26584
This PR update how GPT2 prints its output
**Note**: As the length of the prompt increases while inference, the token generation time slows down. May be its right time to introduce QK cashing to speed up the inference
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Previously, the yoloPostProcessing function assumed that the number of classes (nc) was fixed at 80. This caused incorrect behavior when a different number of classes was specified, leading to mismatched output shapes.
This update modifies the code to use the provided `nc` value dynamically, ensuring that the output shapes are correctly calculated based on the specified number of classes. This prevents issues when `nc` is not equal to 80 and allows for greater flexibility in model configurations.
This branch and commit address an issue in the YOLO example (samples/dnn/yolo_detector.cpp) where the mean and scale parameters only affected the first channel (B) due to single-value input. The modification updates these parameters to accept multi-channel values, ensuring consistent preprocessing across all image channels.
Fix#25812: Add error handling for invalid nu parameter in SVM NU_SVC #26587
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Updated trackbar callback function and improved documentation #26524
This Fixes#26467
Description:
This pull request improve the OpenCV documentation regarding the Trackbar functionality. The current documentation does not provide clear guidance on certain aspects, such as handling the value pointer deprecation and utilizing callback arguments in C. This update addresses those gaps and provides an updated example for better clarity.
Changes:
Updated Documentation:
Clarified the usage of the value pointer and explained how to pass an initial value, since the value pointer is deprecated.
Added more detailed explanations about callback arguments in C, ensuring that users understand how to access and use them in Trackbar callbacks.
Added a note on how to properly handle initial value passing without relying on the deprecated value pointer.
Updated Tutorial Example:
Renamed and used callback function parameters to make them more understandable.
Included a demonstration on how to utilize userdata in the callback function.
Additional Notes:
Removed reliance on the value pointer for updating trackbar values. Users are now encouraged to use other mechanisms as per the current implementation to avoid the runtime warning.
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An upcoming change in Protobuf will change the return types of various
methods like Descriptor::name() and Message::GetTypeName() from const
std::string& or std::string to absl::string_view. This CL fixes users
of those methods to work both before and after the change.
[GSoC] Add GIF decode and encode for imgcodecs #25691
this is related to #24855
we add gif support for `imread`, `imreadmulti`, `imwrite` and `imwritemulti`
opencv_extra: https://github.com/opencv/opencv_extra/pull/1203
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Improvement of macOS installation guide in documentation #26564
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Opencv 5.0.0-alpha release.
The alpha release for the new OpenCV generation. The release is designed as technology preview and not ready for production usage yet.
Missing include directories needed for wayland-util and xkbcommon #26563
See: #26561
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Added Fastcv HAL changes in the 3rdparty folder.
Code Changes includes HAL code , Fastcv libs and Headers
Change-Id: I2f0ddb1f57515c82ae86ba8c2a82965b1a9626ec
Requires binaries from https://github.com/opencv/opencv_3rdparty/pull/86.
Related patch to opencv_contrib: https://github.com/opencv/opencv_contrib/pull/3811
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
[](https://github.com/harfbuzz/harfbuzz/actions/workflows/linux.yml)
[](https://github.com/harfbuzz/harfbuzz/actions/workflows/macos.yml)
[](https://github.com/harfbuzz/harfbuzz/actions/workflows/msvc.yml)
HarfBuzz started as a text shaping engine but has grown into a
full font platform — the `ffmpeg` of text shaping. It primarily
supports [OpenType][1], but also [Apple Advanced Typography][2].
HarfBuzz shapes the majority of text on modern screens.
HarfBuzz is optimized for robustness, correctness, and performance
— in that order. Achieve all.
**[Try it live at harfbuzz-world.cc](https://harfbuzz-world.cc/)** — an interactive playground for shaping, subsetting, rasterization, vector output, and GPU rendering, all running in your browser.
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