dnn: fix packed-B offsets for broadcast batched MatMul
When the constant B of a MatMul has more than one batch slice but fewer than the output batch (B broadcasts over A's leading dims), updatePackedBOffsets derived the packed slice stride from the output batch, so every slice after the first read the wrong packed data. matmul_layer.cpp now passes fastGemmPackBSize(N, K, opt), the size of one packed slice, as the stride.
* imgproc: fix stackBlur results on full-range 16-bit images
The 16-bit SIMD paths overflowed 32 bits in (sum * mul) >> shr and saturated when adding two 16-bit samples before the kernel multiply. Add the samples after widening, and divide as the scalar loops do, so SIMD and scalar agree and the result no longer depends on the thread count.
* imgproc: scale the stackBlur thread test with the machine's thread count
Restore the count with a guard so a throw can't leak it. Use an odd image width: with a width of 160, stripes at some thread counts, including 12, leave nothing to the scalar loop and the test passes on the unfixed code.
* imgproc: use 64-bit sums for large stackBlur kernels on 16-bit images
The largest sum is the largest absolute value times (radius + 1)^2, which no longer fits an int from radius 181 for CV_16U and radius 255 for CV_16S. Those radii now take the scalar loops with int64_t sums. Radii that fit keep the 32-bit SIMD paths.
* imgproc: use 64-bit sums for large stackBlur kernels on 8-bit images
core: fix in-place cartToPolar when only the magnitude overwrites an input - #30646Fixes#30645
### Problem
`cv::cartToPolar` gives wrong results on `CV_32F` when only the magnitude output is the same `Mat` as `x` or `y`. The SIMD loop handles the leftover elements by repeating the last full block. In-place calls must skip this, but `cartToPolar32f_` only checked the angle, so the repeated block read magnitudes as inputs.
### Fix
Added `mag == X || mag == Y` to the check, so these calls handle the leftover elements with the scalar code. `polarToCart` and `magnitude` already check all their outputs.
### Tests
New `Core_CartToPolar_inplace_mag.accuracy` for `CV_32F` with lengths 1 to 39: the magnitude overwrites `x`, then `y`, and the results are compared with a call using separate outputs. The existing `Core_CartToPolar_inplace` test overwrites both inputs, so it never reached this case. `CV_64F` is not tested, because it works on separate float buffers and can't hit this bug.
On Apple M1 Pro, 28 of the 39 cases fail without the fix. All pass with the fix, along with the related `cartToPolar`, `polarToCart`, `magnitude` and `phase` tests, with OpenCL on and off.
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
core: throw an error instead of crashing in inRange on CV_16F #30668Fixes#30667
### Problem
`cv::inRange` crashed with a segmentation fault on `CV_16F` input. `inRangeTab` has `0` for `CV_16F`, and the code called the returned function without checking it.
### Fix
If no function is found, `cv::inRange` now raises `CV_Error` with `StsNotImplemented` and a message saying that `CV_16F` is not supported, like 5.x does for its unsupported depths.
### Tests
New `Core_InRange.unsupported_depth_throws` checks scalar and array bounds on `CV_16F`. Without the fix, the test program crashes. With the fix, it passes, and all `opencv_test_core` tests pass on Apple M1 Pro.
`inRange` supports `CV_16F` on 5.x, so this test does not throw there. It needs to be removed or changed when 4.x is merged into 5.x.
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
`u->mapcount` was incremented before `clEnqueueMapBuffer()`.
If mapping failed, the counter remained incremented even though no
mapping was established, leaving `UMatData` in an inconsistent state
and potentially triggering later assertions such as:
(-215:Assertion failed) u->mapcount == 0 in function 'deallocate'
Update the logic to increment `mapcount` only after a successful
`clEnqueueMapBuffer()` call.
The issue was observed while running:
OCL_PyrDownFixture_PyrDown.PyrDown/6 from opencv_perf_imgproc.
Assisted-by: LLM
Signed-off-by: jiajia Qian <jiajia.qian@nxp.com>
Each key block must have the form "{names | default | help}". The
constructor read the second and third fields without checking that they
exist, so a block such as "{help h}" or "{help h|}" read past the end
of a vector and crashed. Throw a parse error instead, as is already done
for other malformed key strings.
Laplacian picked the kernel type as max(CV_32F, ddepth, sdepth), which
is CV_16F for a CV_16F image because CV_16F > CV_64F. The 3x3 kernel
was then allocated as CV_16F (18 bytes) and filled with 9 doubles. Use
CV_64F when the source or destination is CV_64F and CV_32F otherwise,
so filter2D rejects the unsupported depth without the overflow.
Add an Imgproc_Laplacian regression test.
Validate the one-based Padding dimension before converting it to an
array index, and reject the minimum integer pad before negating it.
Add a serialized Torch7 regression test for valid and invalid bounds.
Signed-off-by: Nathan Keys <professor.moody@pm.me>
Same change as the 5.x port, to keep the two branches in sync.
The wrapper parses window_name, on_mouse and param, but the cv2.cpp help
text and the typing stub in api_refinement.py both say windowName and
onMouse, so the stub promises keywords the runtime rejects:
>>> cv2.setMouseCallback(windowName="w", onMouse=cb)
TypeError: function missing required argument 'window_name' (pos 1)
createTrackbar is left alone: it is registered METH_VARARGS, so its
arguments cannot be passed by keyword at all.
- Reading and writing 16-bit AVIF images requires libavif v1.4.0+
- Note that if the alpha plane contains only fully opaque pixels, the destination saves only color channels due to a libavif limitation.
Port from https://github.com/opencv/opencv/pull/30519
- 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.
Port from https://github.com/opencv/opencv/pull/30431
imgproc: don’t use ippiCopySubpixIntersect for windows crossing the image border - #30363Fixes#30301
### Problem
`ippiCopySubpixIntersect` (used by the IPP HAL for `getRectSubPix` with 8UC1→8U, 8UC1→32F and 32FC1→32F) gets two kinds of border windows wrong:
1. **The window crosses the top edge and reaches the last column** (reported in #30301): the top rows get `src(0, cols - 2)` instead of `src(0, cols - 1)`. For 1-column images this reads the element before the start of the image.
2. **8u→8u window entirely to the left of the image** (not in the original report): the fixed-point interpolation weights overflow and the result is wrong, e.g. a 1x1 image filled with 176 gives 112. This case doesn't read outside the image.
### Fix
Fall back to the generic implementation only for these two cases. The window position is computed the same way IPP does it (`(int)(x + w) - w`, which differs from `cvFloor()` for some inputs), as suggested by @andreyfe1. All other windows, including about 69% of the windows that cross the border, still go through IPP.
### Testing
- Added `Imgproc_GetRectSubPix.regression_30301`: 3 cases (top-right crossing, 1-column image, window left of the image) × 3 type combinations, with the images as ROIs of a 250-filled buffer. It fails on 7 of 9 checks without the patch.
- Compared IPP on vs off with IPPICV 2026.1.0: a grid sweep over 26M windows, plus on both the AVX2 and SSE4.2 paths random images up to 64x64 with windows up to 48x48 and far-away centers (16M), centers within a few ulps of every integer window position (35M), and images placed next to a PROT_NONE page so any out-of-image read crashes (31M). No differences and no crashes with the patch; without it the guard-page run crashes on case 1.
- `opencv_test_imgproc`: no new failures.
FileStorage::Impl::~Impl() calls release(), and release() can throw: for
example workaround() reports an empty key of a delayed Base64 sequence.
WriteStructContext::~WriteStructContext() calls endWriteStruct(), which
throws when the structure was already closed by hand or never opened. A
destructor is noexcept, so either exception ended the process.
Catch it in both destructors and log a warning instead, as other
destructors in OpenCV do. When release() throws in ~Impl(), call it a
second time, as FileStorage::open() does when opening fails: the first
call has already dropped the delayed structure that made it throw, so
the second one writes the rest of the file. ~Impl() then closes the
file in case both calls threw.
imgproc: avoid padding divisible dimensions in CLAHE - #30304
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
`CLAHE::apply` pads both dimensions whenever either dimension is not divisible by the tile grid. For an image with 80 rows and 81 columns and an 8x8 grid, the height already fits, but the implementation adds eight reflected rows anyway. The resulting 11-pixel tile height changes the histograms and interpolation across the image; only seven columns are needed to obtain tiles 11 pixels wide and 10 pixels high.
Compute the minimum reflected padding independently for each dimension in both the CPU and OpenCL paths. Images with both dimensions divisible, or neither dimension divisible, retain their existing tile geometry. No public interface changes.
Reproduction (applies to both `uint8` and `uint16`):
```python
import cv2
import numpy as np
src = np.random.default_rng(42).integers(0, 65536, (80, 81), dtype=np.uint16)
clahe = cv2.createCLAHE(2.0, (8, 8))
actual = clahe.apply(src)
padded = cv2.copyMakeBorder(src, 0, 0, 0, 7, cv2.BORDER_REFLECT_101)
expected = clahe.apply(padded)[:80, :81]
assert np.array_equal(actual, expected) # fails before this fix
```
Testing on `b254d3455a`, Linux x86-64, GCC 15.2, Release, IPP disabled, OpenCL enabled with PoCL 6 CPU device (`OPENCV_OPENCL_DEVICE=:CPU:`, `POCL_MAX_PTHREAD_COUNT=4`, `OPENCV_FOR_THREADS_NUM=2`):
- On base `9d80321677` with only the new tests: 88 failed, 64 passed. All 24 new OpenCL cases fail; the remaining failures are the CPU mixed-divisibility cases.
- With the fix, `opencv_test_imgproc --gtest_filter="*CLAHE_Padding*:*CLAHETest.ReflectedBorder*"`: 152 passed.
- `OPENCV_TEST_DATA_PATH=~/ocv-testdata opencv_test_imgproc --gtest_filter="*Hist*:*BackProject*:*CLAHE*"`: 232 passed, including the existing OpenCL CLAHE accuracy tests. The initial run lacked `cv/optflow/RubberWhale1.png`; rerunning with the official `opencv_extra` image passed.
- `opencv_perf_imgproc --gtest_filter="*CLAHE*" --perf_min_samples=10 --perf_force_samples=10`: 12 existing CLAHE performance cases passed, plus four framework checks.
- `git diff --check`: passed.
- CI revision `801fea96e3`: renamed the OpenCL test RNG to avoid shadowing its fixture member. Rebuilt `opencv_test_imgproc` with no compiler warnings and reran the selected accuracy suite: 232 passed.
The regression matrix covers 8-/16-bit inputs, square/rectangular/single-axis grids, all divisibility combinations, clipping, strided inputs, and in-place use. OpenCL cases cover both axes and UMat offsets. No additional test data is needed. Full imgproc suite and physical GPU execution were not run.
The reproduction above is the original report; no earlier issue or competing fix was found.
imgcodecs: honor PFM grayscale and color read modes 🤖🤖🤖 - #30371Fixes#30370.
Loading a grayscale PFM with the default color mode, or a color PFM in grayscale mode, fails through `imread()` and returns the wrong channel count through `imdecode()`. `PFMDecoder::readData()` relies on `Mat::convertTo()`, which changes depth while preserving source channels; this reallocates the output instead of honoring the requested type.
Convert between one and three channels in the floating-point buffer before the final depth conversion. Existing byte-order handling, scaling, unchanged-mode decoding, and RGB/BGR selection remain in place. No public API changes. This targets 4.x because the same bug exists there; 5.x contains the same affected implementation.
### Regression coverage
32 generated cases cover one/three-channel inputs, file/memory reads, grayscale/BGR/RGB requests, float depth preservation, unchanged/any-color controls, and reuse of a preallocated memory destination. No new external test data is required.
### Validation
Windows x64, GCC 16.2.0, Release, current 4.x base `9b6287ec500ef7b327ac74c4f6c9cd84a2cbc9cd`:
- New regression matrix before the fix: **12 failed, 20 passed**.
- After the fix: **32/32 passed**; existing PFM round-trip test also passed (**33/33 PFM tests**).
- Configured `opencv_test_imgcodecs` suite: **601 tests reported, zero failures**, with three skips reported by the OpenCV runner (memory/very-long/other conditions). JPEG/PNG and built-in codecs enabled; IPP/OpenCL disabled.
- `git diff --check` passed; independent read-only code review found no actionable defects.
- The issue reproducer also runs against the official OpenCV 5.0.0 Python wheel. Upstream CI results are separate from these local results.
AI assistance disclosure: investigation, implementation, tests, and this description were prepared with OpenAI Codex; the reproduction and source-build test results were executed in this workspace.
### Pull Request Readiness Checklist
See 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 code under GPL or another license 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 test and test data, if applicable. Tests generate their own data; no opencv_extra change or performance test is needed for this correctness fix.
- [x] The existing documented read-mode behavior is preserved and the code builds with the project CMake configuration. No new API or sample is introduced.
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.
In VideoCapture::open(), PR #22700 added checks to log warnings when a requested
backend cannot perform a capture mode (e.g. capturing by index with CAP_FFMPEG).
However, the condition only tested if (isBackendBuiltIn(apiPreference)), which
evaluates to true for all built-in backends regardless of whether they support the mode.
As a result, whenever a camera or file failed to open (e.g., camera not connected,
invalid camera index, missing or unreadable file), OpenCV emitted false-positive
warnings:
- VIDEOIO(V4L2): backend is generally available but can't be used to capture by index
- VIDEOIO(FFMPEG): backend is generally available but can't be used to capture by name
even though V4L2 and FFMPEG natively support those respective modes.
Track whether apiPreference matches an available backend for the requested mode in
getAvailableBackends_CaptureByIndex, getAvailableBackends_CaptureByFilename, and
getAvailableBackends_CaptureByStream. Only log the warning when the backend is
genuinely absent from the supported list for that mode while being available on the system.
Resolves#26519
Signed-off-by: paneendrakumar0 <paneendra100@gmail.com>
dnn: validate TFLite tensor buffer bounds 🤖🤖🤖 - #30261
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1430
### What this changes
`TFLiteImporter::parseTensor` now checks the buffer index and required tensor bytes before constructing a `Mat` over model data. It rejects negative or overflowing shapes and unsupported sparse tensors. Absent or empty data remains a runtime tensor.
### Why
A TFLite model controls tensor shape, type, and buffer index. On 4.x at `802cf95aa3d42816995695bf68a86b3532f12760`, a tensor can claim more bytes than its selected buffer contains. The `tfliteShortBuffer` model included in this PR caused a stock Release SIGSEGV in three observed runs. The new check rejects it before the `Mat` wraps short data.
Associated upstream issue: none. Related DNN parser work includes #29371 and the closed, unmerged #29336; neither fixes this shape-to-buffer check.
### Tests and reproduction
This PR includes 15 small inline control and malformed TFLite models, importer tests, and `modules/dnn/test/generate_tflite_tensor_bounds_models.py`. The generator uses OpenCV's in-tree TFLite schema and `flatc` 25.12.19. The focused tests need no `opencv_extra` download.
To reproduce with the bytes in this PR, copy `modules/dnn/test/test_tflite_importer.cpp` and `modules/dnn/test/tflite_tensor_bounds_models.inc` from this PR into an otherwise unmodified 4.x checkout at the commit above. Build `opencv_test_dnn`, then run `./build/bin/opencv_test_dnn --gtest_filter=Test_TFLite_Importer.ShortInt32Buffer`. Apply the full PR and run `./build/bin/opencv_test_dnn --gtest_filter='Test_TFLite_Importer.*'`.
On Linux, an earlier tree that combined this patch with a separate ONNX importer fix passed all 15 focused TFLite tests and the 245-test `*Test_TFLite.*:*Test_ONNX_layers.*` filter in Release and GCC ASan builds. The compiled source and test files in this PR are byte-identical to that tree; only a generator comment changed afterwards, and rerunning the generator reproduced the include byte-for-byte. This branch has not been built on its own. Clang, MSVC, MinGW, and 5.x runtime tests have not run.
I used an AI coding tool to help prepare this change and will answer reviewer questions and address requested fixes within a day, following OpenCV's AI usage guideline. A separate reproducer archive is available on request; no files are attached to this PR.
### 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
The test models are inline in this PR; no opencv_extra change is proposed.
- [ ] The feature is well documented and sample code can be built with the project CMake
fix(python): handle non-native NumPy byte order 🤖🤖🤖 - #30369
Python bindings can read non-native-endian NumPy inputs as native bytes, producing incorrect values or NaN. Mark swapped arrays for the existing cast path before constructing `Mat`; this also rejects incompatible output/in-place destinations before writes.
Closes#30368. Targets 4.x so the common bridge fix can be merged into 5.x.
Validation on macOS ARM64, Python 3.11.11, NumPy 2.4.6:
- Built current 4.x and 5.x. Three regression tests cover 20 dtype/layout/output cases; 18 fail before and all pass after. Read-only inputs and rejected destinations remain unchanged.
- 21 native-input timing cells on 4.x, 101 alternating pairs: fixed/baseline median time 0.957–1.005×, identical results. The 5.x check also stayed within 0.971–1.004×.
- Draft: Linux/hosted CI validation remains pending.
### Pull Request Readiness Checklist
- [x] I agree to contribute to the project under Apache 2 License.
- [x] The patch is not based on GPL or other incompatible code.
- [x] The PR targets the appropriate branch.
- [x] The original bug report is linked.
- [x] Regression tests and native-input performance checks are included; no external test data is needed.
- [x] No public API, feature documentation or sample changes are needed.
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.
video: clip TrackerVit box to the image - #30358Fixes#30350
`TrackerVitImpl::update()` never bounds the predicted box to the frame, and that box sets the size of the next search crop. Once it grows past the frame, the crop is mostly padding, the network keeps predicting a large box and it can grow until the crop allocation fails. The box is now clipped to the image after each update, the same way OSTrack does it (`clip_box(..., H, W, margin=10)`); NanoTrack and DaSiamRPN already clip their results too.
With the repro from the issue on the 4.14.0 normalization (before #30168), the box now stays at most the frame size instead of reaching ~5000 px and failing to allocate. On current 4.x the growth is slower, but the box still leaves the frame (for example 1106x882 on a 720x528 frame). Added `vittrack.bbox_inside_image`, which inits with the whole frame on the `tracking/bag` sequence and fails without the fix. `accuracy_vittrack` still passes.
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
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.
Impl::release() skipped the delayed struct (a sequence still waiting for
its first write in Base64 mode) when only the root was on the stack, so
its key leaked and the sequence was missing from the output. Resolve it
first. Reset the public FileStorage::state in release(),
releaseAndGetString() and a failed open(). An unbalanced endWriteStruct()
or closing bracket now throws and keeps the root on the stack; popping
the root made the next flush() read past the empty stack.
startNextStream() now resets the root in place instead of popping it.
A keyless writeRaw() of data into a map now throws in Base64 mode, as
it does without Base64. At the top level it opened a Base64 writer that
was never freed, and in a nested map after a closed structure it wrote
output that could not be read back.
Fixes#30338
Newer LLVM/libc++ versions (e.g. LLVM 23.1.3, llvm-mingw-20261006-ucrt) no longer transitively include <iterator> and <atomic>. This causes build failures in: modules/calib3d/src/stereo_geom.cpp, modules/gapi/include/opencv2/gapi/infer/ie.hpp (std::back_inserter), and modules/gapi/include/opencv2/gapi/gasync_context.hpp (std::atomic). Fixes build with modern LLVM/MinGW toolchains without CMake workarounds.
core: fix wasm SIMD float rounding - #30213
Summary
- Fix rounding in the WebAssembly SIMD implementation of `v_round(const v_float32x4&)`.
- Add regression tests for positive and negative halfway values.
Motivation
The previous implementation added `0.5` before truncating, which caused incorrect results for some negative values and halfway cases.
This change uses `wasm_f32x4_nearest` before converting the result to integers, providing round-to-nearest, ties-to-even behavior while preserving the existing saturating conversion.
Testing
- git diff --check passed.
- WebAssembly SIMD tests have not been run yet because Emscripten is not available in my current environment.
Pull Request Readiness 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.
- [x] The PR is proposed to the proper branch (`4.x`).
- [ ] Reference to the original bug report and related work.
- [ ] Accuracy/performance tests and test data in `opencv_extra`, if applicable.
- [ ] Documentation and sample code, if applicable.
Related work: OpenCV 5.x rounding fix in [[PR #30007](https://github.com/opencv/opencv/pull/30007)](https://github.com/opencv/opencv/pull/30007).
videoio(ffmpeg): fix black frames for interlaced video 🤖🤖🤖 - #30300
Merge with https://github.com/opencv/opencv_extra/pull/1435
### Summary
With FFmpeg 8+, swscale rejects converting a frame flagged as interlaced into the progressive destination frame. The error returned by `sws_scale_frame()` was ignored, so `VideoCapture::read()` returned true with black frames.
The fix copies the interlaced/top-field-first flags from the source frame onto the destination frame (like FFmpeg's scale filter, which keeps the source field flags), and makes `retrieveFrame()` fail if `sws_scale_frame()` still returns an error.
Fixes#30299
### Testing
- macOS arm64, FFmpeg 9.0.2 / swscale 10.1.102.
- New test `videoio_ffmpeg.interlaced_frame_conversion_30299` (embedded fixture) fails before the change and passes after.
- Full `opencv_test_videoio` with opencv_extra testdata: 561/561 pass.
- The reporter's 5-frame tff clip now reads identical content to the progressive clip.
### Notes / limitations
- The error branch has no direct test, because the field-flag fix removes the only deterministic trigger.
- `VideoCapture::read()` still returns true with a reused non-empty Mat when retrieve fails. This is pre-existing behaviour for all backends and is not changed here.
- Older FFmpeg without `AV_FRAME_FLAG_INTERLACED` is unaffected.
Prepared with AI assistance (Claude Code); reviewed and tested by me.
### 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.
(Accuracy test included with an embedded fixture; no opencv_extra patch needed.)
- [ ] The feature is well documented and sample code can be built with the project CMake
(N/A: bug fix, no new API.)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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.
Fixes#25367
dnn: fix blobRectsToImageRects when the image is not resized - #30243
### 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. No existing report covers this case; reproduction is below. Related: #26085, #30042.
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable. Accuracy test added; no test data needed.
- [ ] The feature is well documented and sample code can be built with the project CMake. Not applicable to this bug fix.
### Problem
`Image2BlobParams::blobRectToImageRect()` and `blobRectsToImageRects()` return `Rect(0, 0, 0, 0)` for every rect when `Image2BlobParams::size` equals the image size, in all three padding modes. With `size` left empty (the default, which `blobFromImageWithParams()` treats as "keep the image size"), the mapping divides by zero and returns `INT_MIN` coordinates. Running a 640x640 detector on 640x640 frames is enough to hit the first case.
```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)
```
Observed with the `opencv-python` 5.0.0 wheel and with a 4.x build at `802cf95aa3`. The function is identical on 4.x and has been since it was added in #24539 (4.9.0).
### Cause
`blobFromImagesWithParams()` replaces an empty `size` with the image size and applies no resize, crop or padding when the two sizes match. `blobRectsToImageRects()` only handles the `size != imgSize` branch: when the sizes match, the output keeps the zero rects created by `rImg.resize()`, and an empty `size` goes into the resize formulas as a zero divisor.
### Change
`blobRectsToImageRects()` now follows the same two rules as `blobFromImagesWithParams()`: an empty `size` means the image size, and when the sizes match, blob and image coordinates are the same, so the input rects are returned. The resize, crop-center and letterbox mappings are unchanged.
The new test covers both cases for all three padding modes. It also checks that the blob built with the same parameters is pixel-identical to the image, which is what makes returning the input rects correct here.
#30042 (5.x feature PR) contains an equivalent pass-through for the equal-size case as part of a larger Model API change; this is the standalone fix for 4.x, including the empty-size case.
### Testing
Built `opencv_test_dnn` and the Python bindings from this branch (head `d90164465c`, base 4.x `802cf95aa3`) on Ubuntu 26.04 (WSL2), GCC 15.2, Ninja, `BUILD_LIST=dnn,python3,ts`.
- `opencv_test_dnn --gtest_filter=blobRectToImageRect*`: 4 passed. On base `802cf95aa3` 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); the blob pixel-identity assertion passes there too.
- `opencv_test_dnn --gtest_filter='blob*:*Image2Blob*:*imagesFromBlob*'`: 11 passed.
- `modules/dnn/misc/python/test/test_dnn.py`: 10 passed, including all `blob*` tests; 6 model tests failed only because their opencv_extra files are not present locally (`dnn/googlenet_0.png`, `dnn/opencv_face_detector.prototxt`, ...).
- The Python reproducer above prints `(10, 20, 30, 40)` for both cases with this branch.
- No compiler warnings in the touched files; `git diff --check` clean.
- Full `opencv_test_dnn` was not run (needs opencv_extra test data).
imgproc: preserve phaseCorrelate inputs when windowing - #30281
`phaseCorrelate()` currently performs a shallow copy of input matrices provided they already match the optimal DFT size. This is unsafe when a window is supplied because the in-place multiplication would overwrite the contents pointed to by the input matrices' data pointers.
This PR makes the `phaseCorrelate()` function use separate storage for applying the window. This keeps the inputs read-only (while keeping the path behavior the same as the padded case). This PR also adds a regression test for DFT-optimal and padded sizes with `CV_32F` and `CV_64F` types. The test cases include input/window preservation, ROIs, same source headers, and equivalent shift/response results.
### 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
imgcodecs: validate animation encoding parameter lists - #30282
`imwriteanimation()` and `imencodeanimation()` forward encoding parameters without the structural checks used by all the ordinary image encoding APIs.
This PR adds the same key/value-pair and maximum-count check before the functions call the animation encoder. It also adds a test that checks odd-length and over-limit parameter lists are rejected while empty and valid parameter lists still encode and decode correctly.
Related work: #22830 and #27769.
### 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
- [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.
- [ ] The feature is well documented and sample code can be built with the project CMake
Merge pull request #30210 from Kumataro:validateAvailableCodecs
imgcodecs(tiff): validate codec availability - #30210
If a specified compression schema is not available, fallback to LZW (or NONE if LZW is also unavailable).
(e.g. currently bundled libtiff is not configured to support LZMA, ZSTD, and WEBP compression.)
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
imgproc: fix out-of-bounds read in OpenCL color kernels for 3-channel input 🤖🤖🤖 - #30180
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.
### 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
Validate the normalized axis against every constant input rank before
folding Concat and QLinearConcat. Assert equal input ranks before the
ConcatLayer padding loop reads each dimension.
Add self-contained importer regressions and an in-tree model generator.
Adding IMWRITE_TIFF_COMPRESSION_LEVEL - #30165
For some compression schemes (DEFLATE, ZSTD), libtiff supports a TIFFTAG_ZIPQUALITY. This PR adds an IMWRITE_TIFF_COMPRESSION_LEVEL param to set it.
- [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
I don't know what kind of automatic tests could be relevant.
I just checked that
1) the param is honoured
2) for some basic mats, level 1 gives lower compression than level 9
3) level -1 (default) gives a compression ratio between levels 1 and 9 (actually seems to be 6, as expected)
But I am not sure that 2) and 3) are generally guaranteed by the compression scheme for any data, so is it relevant to make a test assert on that ?
video: fix TrackerVit input scalefactor computed as Scalar - #30168Fixes#30164
### Problem
`TrackerVitImpl` computes the input normalisation as
i2bp.scalefactor = (1.0 / parameters.stdvalue) * (1 / 255.0);
`operator/(double, Scalar)` is the quaternion inverse (`conj(b) * a / |b|^2`), not an elementwise reciprocal. With the default `stdvalue = (0.229, 0.224, 0.225)` the applied scalefactor is `(0.00586, -0.00573, -0.00576)` instead of `(0.01712, 0.01751, 0.01743)`: channels 1 and 2 are sign-flipped and all three are about 1/3 of the intended magnitude. This was introduced in 4.11 when the elementwise `(dst - mean) / std` was replaced with `Image2BlobParams`.
### Fix
Compute `1 / (255 * std)` per channel in a small helper used by both constructors. A zero std entry (the unused 4th channel by default) maps to 0.
### Tests
`vittrack.accuracy_vittrack` on the `tracking/bag` sequence, measured locally on macOS ARM64 (4.x and 5.x):
| | min IoU | mean IoU |
|---|---|---|
| before | 0.642 | 0.757 |
| after | 0.682 | 0.776 |
On other CI platforms (x64, Windows, no-HAL) the minimum IoU with the fix is 0.658, so the existing 0.64 threshold is kept.
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
RVV: add fused HAL implementation for rotate - #30057
## Pull Request Readiness Checklist
See details at [https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request](https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request)
- [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 that is incompatible with OpenCV.
- [x] The PR is proposed to the proper branch (`4.x`).
- [x] Accuracy tests are included and performance results are provided below.
## Summary
This PR adds an RVV HAL implementation for `cv_hal_rotate90` and fuses the transpose and flip operations used by 90-degree and 270-degree rotations into a single vectorized pass.
The previous path composes transpose and flip operations, which requires an intermediate image and additional memory traffic. The new RVV path loads several source rows, builds RVV vector tuples, and writes directly to the final rotated positions with strided segmented stores.
## Implementation details
- Registers `cv_hal_rotate90` in the RISC-V RVV core HAL.
- Processes 1-, 2-, and 8-byte elements in blocks of eight source rows using `vssseg8e8`, `vssseg8e16`, and `vssseg8e64`.
- Processes 4-byte elements in blocks of four source rows using `vssseg4e32`.
- Uses positive or negative destination strides to implement clockwise and counter-clockwise rotation without a separate flip pass.
- Uses dynamic VL and strided vector stores for remaining rows and non-multiple tails.
- Supports padded/non-contiguous source rows through `src_step` and `dst_step`.
- Returns `CV_HAL_ERROR_NOT_IMPLEMENTED` for aliased input/output and unsupported element sizes, preserving the existing OpenCV fallback behavior.
- Keeps the existing RVV flip path for 180-degree rotation when safe.
- For 1-byte 180-degree rotation with `VLMAX > 256`, uses 16-bit gather indices with `vrgatherei16` to avoid index wraparound in the existing 8-bit gather path.
- Includes compatibility helpers for Clang versions earlier than 18 that do not provide the required vector tuple creation intrinsics.
The directly accelerated total element sizes are 1, 2, 4, and 8 bytes. Other sizes, including common 3- and 6-byte pixel formats, fall back to the existing implementation.
## Test environment
```text
Build type: Release
Compiler: GCC 14.2.0
ISA flags: -march=rv64gcv
RISCV_RVV_SCALABLE: ON
CPU_BASELINE_REQUIRE: RVV
QEMU: 8.2.2, RVV 1.0
```
## Accuracy tests
The new `Core_Rotate.OddSizesAndPaddedRows` test and the existing randomized rotate test passed under QEMU RVV at VLEN 128, 256, 512, and 1024.
## Performance tests
Performance was measured with `opencv_perf_core` under the environment described above. Detailed performance results are provided in the comment below.
These measurements are intended to show the relative difference between identical baseline and optimized builds under QEMU. Absolute performance should be confirmed on RISC-V hardware.
fix(python): set module metadata on generated functions - #30135Fixes#19349.
Generated Python functions such as `cv2.GaussianBlur` expose `__module__ = None`, so `inspect.getmodule()` returns `None`.
Pass the defining module name to `PyCFunction_NewEx` during registration. The existing `PySafeObject` wrapper manages the string reference. Function definitions, call flags, `__self__` and namespace ownership stay unchanged.
The tests cover both expressions from the ticket and module lookup, native-callable classification and pickle identity for the root, `cv2.utils` and `cv2.utils.nested`. The expected ticket output is:
```text
cv2
<built-in function GaussianBlur>
```
CPython represents native extension functions as `BuiltinFunctionType`, including `math.sin`. This patch preserves that representation while supplying the missing module metadata. See the [Python C API documentation](https://docs.python.org/3.13/c-api/structures.html#c.PyCFunction_Type).
### Validation
- Compiled isolated checks using the pristine and patched `createSubmodule` and `init_submodule` code and the unchanged `PySafeObject` wrapper. Root and nested call, constant, native type, representation and pickle controls pass on both. The pristine module assertion fails with `None`; patched module metadata and `inspect.getmodule()` pass. These checks use an echo callback and validate registration, rather than an OpenCV kernel.
- The ticket regression fails with `None != 'cv2'` against the installed OpenCV 4.12.0 binary. Three existing namespace and class-alias tests and four native-function representation controls pass against that binary and Python's standard library.
- `py -3.13 -m py_compile modules/python/test/test_misc.py` and `git diff --check` pass.
I have not built the full checkout locally. The [current-head CI workflow](https://github.com/opencv/opencv/actions/runs/37276602842) is awaiting maintainer approval (`action_required`) with no jobs yet. The updated binding tests have not run in CI. On the previous head, `8600aa69`, the Python suite reported 291 tests with 18 skips and `test_function_module` passed. The new ticket regression was added afterward, so those results do not validate it. An earlier macOS x64 native stitching test aborted with exit 134 outside this patch; its maintainer-triggered rerun was queued before this update.
### Pull Request Readiness Checklist
See 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
The regressions use existing Python functions and require no external test data or performance benchmark.
- The feature-documentation and sample-build item does not apply to this metadata bug fix.
### AI assistance disclosure
AI assistance disclosure: AI-assisted development tools were used during investigation and implementation. The reported tests were run against the final diff.
python: mark documented None output arrays as optional in typing stubs 🤖🤖🤖 - #30139Fixes#30138
The generated Python stubs type several output arrays as required, although OpenCV's own Python samples and tutorials pass `None` for them and the function allocates the output. These arrays are `InputOutputArray`/`OutputArray` parameters without a C++ default, so the stub generator does not add `| None`. As a result, mypy rejects correct code with `No overload variant ... matches [call-overload]`.
This PR adds `make_optional_arg` entries to `NODES_TO_REFINE` in `modules/python/src2/typing_stubs_generation/api_refinement.py`, the same approach as #28470 (`calibrateCamera`) and #27564:
| Function | Argument |
|---|---|
| `calcOpticalFlowPyrLK` | `nextPts` |
| `calcOpticalFlowFarneback` | `flow` |
| `normalize` | `dst` |
| `kmeans` | `bestLabels` |
| `drawKeypoints` | `outImage` |
| `drawMatches` | `outImg` |
| `drawMatchesKnn` | `outImg` |
`None` is only valid at runtime when the corresponding flag is not set (`OPTFLOW_USE_INITIAL_FLOW`, `KMEANS_USE_INITIAL_LABELS`, `DrawMatchesFlags_DRAW_OVER_OUTIMG`). Like `distCoeffs` in #28470, the change only widens the accepted type and does not change bindings or runtime behaviour.
### Verification
- I ran `gen2.py` from this branch and from `4.x` (62587ae) using the CMake-generated `gen_python_config.json`. The only differences in the generated `cv2/__init__.pyi` are 16 signatures (Mat and UMat overloads for each function; `drawMatches` has two C++ overloads). In each one, the listed argument changes from `cv2.typing.MatLike` / `UMat` to `... | None`. With a fixed `PYTHONHASHSEED` no other file differs; without it, the import order in `cv2/typing/__init__.py` varies from run to run because `generation.py` iterates a set, independently of this change.
- mypy 2.4.0 (MYPYPATH pointing at the generated stubs) on the 7 calls from #30138, for example `cv2.calcOpticalFlowPyrLK(prev, curr, pts, None)` and `cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX)`: all 7 fail with `call-overload` on the base stubs, and mypy reports no errors for these calls on the patched stubs (the generated stubs have some pre-existing internal errors, unchanged by this PR). The same calls run correctly with `opencv-python-headless` 4.14.0.94 and 5.0.0.93.
- No C++ or binding code changes. No regression test is added, because the repository has no existing test for the stub generator or `api_refinement.py` to extend.
_Prepared with AI assistance (Claude Code); I reviewed the change and the verification results._
**Update:** Added a second commit: `NODES_TO_REFINE` entries whose function, class or namespace is absent from the build (e.g. `video` or `calib3d` disabled via `BUILD_LIST`) are now skipped instead of aborting stub generation, as the `dnn`/`cuda` refinements already do. This also fixes the pre-existing failure for builds without `calib3d`. A wrong argument name in a present function still raises `RuntimeError`; a misspelled function name is now skipped like an absent one. Builds without `features2d` still fail for a separate reason (the `Feature2D` aliases in `predefined_types.py`), which I'll address in a follow-up.
### 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
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Fix int32 to double precision loss on ARM NEON - #30115
Fixes https://github.com/opencv/opencv/issues/30114
int32 to float64 conversion on **NEON went through a lossy int32 -> float32 -> float64 round trip**. float32's 24-bit mantissa silently corrupts any value above 2^24, causing `BigData_Imgproc_DistanceTransform.large_image_12218` to fail on ARM64 NEON builds.
Added a directed test (`test_cvt64_int32_precision`) covering large int32 magnitudes on both halves of the register, and confirmed the existing `BigData_Imgproc_DistanceTransform.large_image_12218` test now passes.
### 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
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>
video: add validation for OPTFLOW_USE_INITIAL_FLOW in calcOpticalFlowFarneback - #30086
Add explicit parameter check for initial flow in `calcOpticalFlowFarneback`.
When `OPTFLOW_USE_INITIAL_FLOW` flag is passed but the `flow` matrix is empty/missing, this provides a descriptive runtime error instead of non-deterministic behavior or garbage values.
Fixes#7276.
### 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 (`4.x`).
- [x] There is a reference to the original bug report (#7276).
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable.
- [x] The feature is well documented and sample code can be built with the project CMake.
calib3d: support non-contiguous calibration point vectors - #30013
calibrateCamera rejects valid non-contiguous point-vector Mats due to the calibration data collector validating them with checkVector's default continuity requirement and then assuming linear contiguous storage. As a result, an Nx1 Point3f/Point2f ROI fails while an equivalent continuous matrix with same shape/type/values succeeds.
This PR adds a test comparing continuous and ROI-backed calibration inputs, with the test verifying equivalent RMS, camera matrix, and distortion coefficients, and that the caller's ROI backing data and padding are left unchanged. Allow non-contiguous point vectors during validation, and clone only non-contiguous inputs before the existing linear copy path. Continuous inputs are no-copy'd as is.
### 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
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>
add ARMPL HAL backend for cv::invert(DECOMP_LU) - #30035
## Summary
- 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).
## Performance Benchmarks
<img width="1001" height="105" alt="image" src="https://github.com/user-attachments/assets/ba18378c-24da-4d20-9819-3b4398cad6e2" />
<img width="1002" height="105" alt="image" src="https://github.com/user-attachments/assets/ff33d421-b95d-4fca-aad0-ab7bc035d49b" />
<img width="999" height="108" alt="image" src="https://github.com/user-attachments/assets/d4abb57e-6be0-4ea1-9894-d30074c9429c" />
imgcodecs: fix JPEG 2000 encoding of images smaller than 32 pixels 🤖🤖🤖 - #29926
`imwrite()` and `imencode()` cannot write a JPEG 2000 file for any image narrower or shorter than 32 pixels. The call fails with
```
OpenJPEG2000: Number of resolutions is too high in comparison to the size of tiles
imencode(): can't encode data: OpenCV(4.15.0-dev) .../grfmt_jpeg2000_openjpeg.cpp:802:
error: (-213:The function/feature is not implemented) OpenJPEG2000: Can not start
compression in function 'cv::Jpeg2KOpjEncoder::write'
```
and returns `false`. Every other encoder in the module writes an 8x8 image without complaint, so this is a gap specific to the OpenJPEG path.
`setupEncoderParameters()` never touched `opj_cparameters::numresolution`, leaving it at the OpenJPEG default of 6. `opj_j2k_encoding_validation()` (`3rdparty/openjpeg/openjp2/j2k.c:8840-8851`) requires each tile to be at least `1 << (numresolutions - 1)` in both directions, and because OpenCV does not enable tiling the single tile is the whole image. The threshold is therefore exactly `2^5 = 32` px, which matches the observed boundary: a 32x32 image encodes, a 31x31 image does not.
This patch lowers `numresolution` to what the image size allows. It only ever lowers it, so an image that is at least 32x32 keeps the default of 6 and its encoded bytes are unchanged. No public API is changed and no new test data is needed.
### Validation
Built `opencv_test_imgcodecs` on Windows 10.0.26200 x64 with MSVC 19.51.36256 (Visual Studio 18), Release, `-DBUILD_LIST=core,imgproc,imgcodecs,ts -DCPU_DISPATCH=` (baseline SSE3), bundled OpenJPEG 2.5.3, on `4.x` at `0aa49a8e`.
The test is in the first commit and the fix in the second. Against unmodified `0aa49a8e` the new test fails on the very first size:
```
[ RUN ] Imgcodecs_Jpeg2000.encode_small_image
[ERROR:0@0.034] global grfmt_jpeg2000_openjpeg.cpp:299 cv::`anonymous-namespace'::errorLogCallback OpenJPEG2000: Number of resolutions is too high in comparison to the size of tiles
modules\imgcodecs\test\test_read_write.cpp(341): error: Value of: imencode(".jp2", src, buf)
Actual: false
Expected: true
imencode() failed for .jp2
Google Test trace:
modules\imgcodecs\test\test_read_write.cpp(336): 1x1
[ FAILED ] Imgcodecs_Jpeg2000.encode_small_image (3 ms)
```
With the fix applied, all six sizes (1x1, 8x8, 31x31, 64x8, 8x64, 32x32) encode, decode back to the same size and type, and round-trip bit-exactly:
```
[ RUN ] Imgcodecs_Jpeg2000.encode_small_image
[ OK ] Imgcodecs_Jpeg2000.encode_small_image (23 ms)
[ PASSED ] 1 test.
```
The 64x8 and 8x64 cases cover the tile-height and tile-width conditions separately; 32x32 pins the boundary where the default is still used.
No other result in the module changed. `opencv_test_imgcodecs` with no filter:
| | tests run | passed | failed |
|---|---:|---:|---:|
| unmodified `0aa49a8e` | 757 | 231 | 526 |
| with this patch | 758 | 232 | 526 |
The patch adds exactly one test, it passes, and the failed count is identical. Those 526 failures are pre-existing on this machine and unrelated to this change: `opencv_extra` is not checked out here, so every test that calls `findDataFile()` fails. I did not run the suite against `opencv_extra` data, and I have not tested a JasPer or a non-x86 build.
As a direct check that images at or above the threshold are untouched, I encoded the same fixed inputs before and after the patch and compared the output sizes. They are byte-for-byte identical above the threshold, and only the previously-failing cases change:
```
before after
8x8 CV_8UC3 (failed) 250 bytes
8x8 CV_16UC3 (failed) 249 bytes
32x32 CV_8UC3 755 bytes 755 bytes
32x32 CV_16UC3 1542 bytes 1542 bytes
64x64 CV_8UC3 2952 bytes 2952 bytes
129x129 CV_8UC3 11981 bytes 11981 bytes
129x129 CV_16UC3 24922 bytes 24922 bytes
```
`git diff --check` is clean. `5.x` carries the identical code and the identical defect; this is proposed to `4.x` per the branch policy, to be ported by the core team.
I did not find an existing issue for this, so the PR references none.
### 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] 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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- [ ] There is a reference to the original bug report and related work *(no existing report found)*
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imgcodecs: avif: support 16-bit reading/writing with libavif v1.4.0+ - #30062
### Pull Request Readiness Checklist
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carotene: clamp left-edge weight in downsample_bilinear_8uc1 - #30022
For the leftmost pixel the sample coordinate c = 0.5*wr - 0.5 can be negative, so the left-column weight overflows full-scale and interpolation extrapolates beyond the source, corrupting the first output column.
Clamp the weight to full-scale when c < 0, matching the scalar/OpenCL edge handling. Affects both the 16- and 8-wide column loops.
### 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
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Patch to opencv_extra has the same branch name.
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Update IPP ICV integration to IPP 2026.1.0 (20260922) - #30060
## Description
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.
### 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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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.
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
(CodecID 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 still correct, which makes the failure
silent and easy to miss.
Such a container is not otherwise malformed: its Cues index is complete and
correct, and `ffmpeg -ss` seeks it in constant time at any offset.
Measured with OpenCV 4.14.0 on a 15 h H.264 recording (1628443 frames),
Windows x64, stock prebuilt FFmpeg wrapper versus the same wrapper rebuilt
with this patch:
target before after
10 s 0.246 s 0.092 s
60 s 1.494 s 0.039 s
300 s 7.569 s 0.052 s
10000 s ~4 min 0.061 s
54000 s ~22 min 0.051 s
Before the patch the cost grew strictly linearly with the target position, at
roughly 25 ms per second of video, so the two longest targets were
extrapolated rather than waited out. Decoded frames are unchanged for targets
that both paths reach.
The guard mirrors the one already present in dts_to_sec().
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
core: fix memory leak in glob()'s readdir() on WinRT/_WIN32_WCE - #30005
## Problem
Fixes#30004
In `modules/core/src/glob.cpp`, the WinRT/`_WIN32_WCE` implementation of `readdir()` allocates a new buffer for `dir->ent.d_name` on every call and overwrites the previous pointer without freeing it:
```cpp
char* aname = new char[asize+1];
...
dir->ent.d_name = aname;
```
`cv::glob()` calls `readdir()` once per directory entry, so every call except the last leaks its allocation. Additionally, the `DIR` destructor that releases `d_name` was gated by `#ifdef WINRT` only, so `_WIN32_WCE` builds leaked every allocation, including the last one.
## Fix
- Free the previous `dir->ent.d_name` before overwriting it in `readdir()`, matching how `~DIR()` already frees it on WinRT.
- Extend the `DIR` destructor guard from `#ifdef WINRT` to `#if defined(WINRT) || defined(_WIN32_WCE)` so the final buffer is also released under `_WIN32_WCE`.
## 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.
- [x] The PR is proposed to the proper branch (`4.x`).
- [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.
- [x] The feature is well documented and sample code can be built with the project CMake.
Thanks for reviewing.
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.
Extend core performance tests - #29922
Added more cases for performance tests. It's a part of a bigger PR https://github.com/opencv/opencv/pull/29631
execution time increased by <18% per my measurements.
Duplication of https://github.com/opencv/opencv/pull/29900, but from a different fork.
`opencv_extra` PR is https://github.com/opencv/opencv_extra/pull/1410
Following was added:
| File | Test | Added |
|------|------|-------|
| `perf_arithm.cpp` | `BinaryOpTest.*` (add/subtract/multiply/absdiff/min/max/transpose2d) | types `CV_16UC1`, `CV_64FC1` |
| `perf_compare.cpp` | `compareScalar` | types `CV_16UC1`, `CV_16SC1` |
| `perf_dot.cpp` | `dot` | type `CV_64FC1` |
| `perf_flip.cpp` | `flip` (`FLIP_TYPES`) | types `CV_32FC3`, `CV_32FC4` |
| `perf_mat.cpp` | `Mat_CopyToWithMask` | types `CV_32FC3` |
| `perf_mat.cpp` | `Mat_SetToWithMask` | types `CV_8UC3, CV_8UC4, CV_16UC3, CV_16UC4, CV_32FC3, CV_32FC4` |
| `perf_norm.cpp` | `norm` | norm type `NORM_L2SQR` |
| `perf_norm.cpp` | `norm_mask` | types `CV_8UC3`, `CV_16UC3`, `CV_32FC3`; norm type `NORM_L2SQR` |
| `perf_norm.cpp` | `norm2` | norm types `NORM_L2SQR`, `NORM_RELATIVE+NORM_L2SQR` |
| `perf_norm.cpp` | `norm2_mask` | types `CV_8UC3`, `CV_16UC3`, `CV_32FC3`; norm types `NORM_L2SQR`, `NORM_RELATIVE\|NORM_L2SQR` |
| `perf_sort.cpp` | `sort`, `sorIdx` (`TYPICAL_MAT_TYPES_SORT`) | types `CV_16SC1`, `CV_32SC1`, `CV_64FC1` |
| `perf_stat.cpp` | `sum`, `mean` | types widened `{8UC1,8UC4,32FC1}` → `8U/16U/16S/32F × C1/C3/C4` |
| `perf_stat.cpp` | `mean_mask`, `meanStdDev`, `meanStdDev_mask` | types widened `{8UC1,8UC4,32FC1}` → `8U/16U/32F × C1/C3/C4` |
### 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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Patch to opencv_extra has the same branch name.
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imgproc: document integer filter2D kernels 🤖🤖🤖 - #29913
### Summary
`filter2D` accepts integer kernels, but its parameter documentation currently restricts kernels to floating-point matrices. Document the existing integer support and add parameterized accuracy coverage against `cvtest::filter2D`, the reference implementation.
The 40 cases cover five integer kernel depths (`CV_8U`, `CV_8S`, `CV_16U`, `CV_16S`, `CV_32S`), 3x3 and 13x13 kernels, and four source formats (`CV_8UC1`, `CV_32FC1`, `CV_32FC3`, `CV_64FC1`). Signed kernels include negative coefficients. Kernel depth, size, and source format are separate Google Test parameters.
Fixes#22243. Related work: #29607 was closed without merging. This follow-up accounts for its review requests about parameterized tests and avoids claiming a specific implementation path from a test name. No filtering implementation changes are included.
### Validation
Built `opencv_test_imgproc` from current 4.x (`bb9e3eff13`) on Linux with GCC 13.3.0, Release/SSE3. IPP, OpenCL, and additional CPU dispatch variants were disabled in this local build.
```
./bin/opencv_test_imgproc --gtest_filter='*Imgproc_Filter2D*:*Imgproc_FilterSupportedFormats*'
```
All 58 selected tests passed: 40 new cases and 18 existing filter accuracy/format/regression cases. `git diff --check` passed. This is targeted CPU validation, not a full imgproc suite or a GPU/backend validation claim. The tests use synthetic data and need no opencv_extra assets.
### 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: 4.x for a documentation correction applicable to both maintained branches.
- [x] There is a reference to the original bug report and related work.
- [x] Accuracy tests are included; no external test data or performance change is involved.
- [x] The parameter documentation is updated and the affected test target builds.
### AI assistance
OpenAI Codex assisted with the source/review audit, patch preparation, and local validation. The commit includes an Assisted-by trailer.
imgcodecs: fix Sun Raster encoding checks 🤖🤖🤖 - #29894
SunRasterDecoder saves the header's ras_type field in m_encoding but several validation and decoding checks compared RAS_BYTE_ENCODED and RAS_FORMAT_RGB against m_type. This caused valid byte-encoded and RGB-format Sun Raster images to be rejected before their existing decoding paths could be used.
This patch uses the file encoding to make these decisions and limits byte encoding to 8-bit input and RGB-format input to the supported 24- and 32-bit paths. It also selects channel conversion from the file and requested output orders and avoids indexed-palette conversion when decoding truecolor inputs as grayscale.
The regression tests use Sun Raster files written by Netpbm's `pnmtorast -rle` and ImageMagick's SUN encoder. The matching `opencv_extra` PR (opencv/opencv_extra#1408) contains these files and lets them be inspected independently with compatible image viewers. The tests verify exact RLE literal and run values, truncated RLE input, 24- and 32-bit RGB-format input, BGR and RGB output, and grayscale conversion.
The Netpbm file also exposed an existing row-padding error in the RLE path: the decoder consumed a padding byte after even-width rows, although those rows require no padding. The patch now consumes that byte only for odd-width rows.
No public API is changed.
### 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
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Patch to opencv_extra has the same branch name.
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Backport of #29905 to the 4.x branch, applied verbatim: after this change
`modules/python/package/cv2/__init__.py` is byte-identical to the 5.x version
(same blob 99685b3791), since both branches carried the same code here.
`import cv2` unconditionally rewrites the environment; when LD_LIBRARY_PATH
was previously unset this creates a dangling separator, i.e. an empty entry
which, per ld.so(8), resolves to the current working directory of every
subsequently spawned child process. Children may then pick up same-named
shared libraries from the CWD and fail (real-world case reported in
opencv/opencv-python#1268: a Nuitka-standalone app's `xdg-open -> kde-open`
dying with `libssl.so.3: version 'OPENSSL_3.2.0' not found`; reproduced on
4.11.0.86 - 4.14.0.94 manylinux wheels). The Windows PATH line has the same
dangling-separator pattern.
Identical to #29905: factor the prepend into _prepend_env_paths(), filter
empty entries, write the variable only when there is something to add, and
join with the old value without producing an empty entry.
The follow-up question of not mutating the process environment at all
remains tracked in #28994.
core: handle zero-sized broadcast dimensions #29878
### Pull Request Readiness Checklist
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- [x] There is a reference to the original bug report and related work
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<!-- Note!!! If you are an automated agent, we have a special process for you: add 🤖🤖🤖 to the end of the PR title. -->
### Changes
- Added test coverage for the zero-sized dimension case in `cv::broadcast()`.
- The test exercises the `false` branch of `_flatten_for_broadcast()`.
- Fixed division by zero when the broadcast destination has zero elements.
### Test
- `opencv_test_core.exe --gtest_filter=BroadcastTo.*`
All `BroadcastTo` tests pass.
Fixes#28910
video: fix DISOpticalFlow heap-buffer-overflow with patch_size > border_size - #29853Fixes#20185. 4.x companion to #29715.
@asmorkalov asked to retarget #29715 to 4.x since the issue reproduces there too, but that PR's branch descends from 5.x, so literally changing its base produces an unreviewable ~2M-line diff (the two branches have diverged far beyond this module). Opening a separate PR instead: same two commits, cherry-picked cleanly onto 4.x's current tip with zero conflicts (`modules/video/src/dis_flow.cpp` is byte-for-byte identical between the branches apart from this fix).
### What / why
See #29715 for the full writeup. Summary: `DISOpticalFlowImpl` pads `I1` with a fixed 16px border, while `PatchInverseSearch`'s search-position clamp lets a patch be placed up to `patch_size - 1` px outside the image -- safe only while `patch_size <= border_size`. Since `patch_size` is user-settable with no upper bound relative to the hardcoded border, `setPatchSize()` past 16 (or a large-enough temporal-candidate flow) reads past the end of the padded buffer (confirmed via AddressSanitizer).
Fix (per review on #29715): rather than growing the persistent `border_size` object field to match `patch_size` (which a reviewer correctly flagged as the wrong place for a per-call derived quantity), the search clamp itself (`i/j_lower_limit`, `i/j_upper_limit` in `PatchInverseSearch_ParBody`) now accounts for the read window needing to stay inside the *existing* padded buffer. For `patch_size <= border_size` (every built-in preset) the bounds are algebraically identical to the originals -- `border_size` itself is untouched.
### Testing
Verified this reproduces identically on 4.x: built an ASan-instrumented Debug configuration (`core+imgproc+imgcodecs+features2d+flann+video+ts`), confirmed the crash reproduces on pristine 4.x (same crash site as the original report), confirmed it's gone with this fix, and ran the new `regression_20185_patch_larger_than_border` / `regression_20185_stress` tests (5 repeated runs) plus the full `opencv_test_video` suite -- no failures attributable to this change (everything else failing needs `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 (4.x, per maintainer request on #29715).
- [x] Accuracy tests included (see above).
- [x] No public API/behavior change, so no documentation or sample updates needed.
pointSetBoundingRect() passed CV_32F coordinates to cvFloor() without
honouring its documented INT_MIN..INT_MAX precondition, so a point set
outside the int range collapsed to a 1x1 rect at INT_MIN, while +inf and
-inf gave INT_MIN and INT_MAX - an inverted rectangle. The extrema are
now searched for in floats, as the vectorized path already did, and
floored and saturated to the closest representable bound once, after the
reduction.
The sides are computed in int64 and clamped to INT_MAX: xmax - xmin + 1
overflows int once the extrema saturate, and did so already for a CV_32S
point set spanning the whole int range.
Fixes#29837
- 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.
- RedBlackSOR: drop the pW_next_vec load, unused since the lane-shift now
reads pW_next directly.
imgproc: Optimized Moments & perf test added - #29393
- Added dispatch for moments (CV_8U, CV_16U, CV_32F and CV_64F)
- CV_16S is kept in scalar due to regresssions observed.
- Perf test added for meanShift, CamShift and matchShapes.
- Extend Moments1 perf coverage to CV_8U alongside 16/32/64-bit depths.
### 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
3rdparty/libtiff: Upgrade libtiff to 4.7.2 - #29838
[Feature Request] Upgrade libtiff to 4.7.2 (https://libtiff.gitlab.io/libtiff/releases/v4.7.2.html)
### 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
dnn: fix eltwise layer memory traversal pattern causing Windows-ARM64 slowdown - #29831
## Problem
- `EltwiseInvoker::operator()` (backs the `Eltwise` layer's SUM/PROD/DIV ops) ran slower on Windows-ARM64 than x64.
## Root cause
- The loop tiled the output into fixed-size blocks and looped over the **channel axis inside each block**.
- In NCHW layout, consecutive channels of the same sample sit far apart in memory (`planeSize * 4` bytes apart).
- So each block jumped between ~256 memory locations per tensor — roughly **768 concurrent strided streams** across the three tensors involved.
- Hardware prefetchers can only track a small, fixed number of streams and Windows-ARM64's prefetcher falls over on this pattern.
## Fix
- Walk the output buffer contiguously and track the current (sample, channel) plane from the offset and clamp each block so it never crosses into the next channel.
- This removes the inner channel loop entirely: one channel finishes before the next starts, so every tensor is read/written sequentially instead of jumping around.
- No behavior change, only the traversal order is different.
## Performance Benchmarks:
<img width="1479" height="208" alt="image" src="https://github.com/user-attachments/assets/0f878757-d988-4564-b166-96d80834c77e" />
core: vectorize masked norm/normDiff for remaining depths - #29815
### Summary
- The masked `cv::norm()` / `cv::norm(a, b)` kernels in `norm.simd.hpp` had SIMD specializations only for `uchar`, `ushort` and `float`.
- `schar`, `short`, `int`, `double` and `uchar` L2 paths uses the scalar implementation.
### Changes
- Added new vectorized implementations of MaskedNorm{Inf,L1,L2}_SIMD for schar, short, int, and double.
- Added new vectorized implementation of MaskedNormL2_SIMD<uchar, int>.
- Added new MaskedNormDiff{Inf,L1,L2}_SIMD implementations for double.
- Added cn == 4 v_load_deinterleave paths to the uchar L1/L2 kernels.
- Replaced the single f64 accumulator in MaskedNormL1_SIMD<float,double> with four independent ones, so the widening adds can overlap instead of each waiting on the previous.
### Performance Benchmarks
<img width="715" height="709" alt="image" src="https://github.com/user-attachments/assets/46afea06-df98-4dd4-a346-dc8cd4a065c9" />
imgproc: optimize PyrDownVecH<uchar,int,3> using v_load_deinterleave - #29811
**Summary:**
- buildPyramid on Windows-ARM64 (1920×1080, 8UC3) was ~7× slower than x64.
- Root cause: PyrDownVecH<uchar,int,3> used vx_lut_quads to gather pixels, which on NEON compiles to 80 scalar byte reads per SIMD iteration filling a stack buffer one byte at a time before loading it as a vector. The SIMD unit was effectively idle.
**Root Cause:**
- The 3-channel horizontal pass needs 5 non-contiguous source pixels per output pixel. The existing code precomputes byte-offset index arrays and calls vx_lut_quads(src, idx) five times per loop body. On NEON, vx_lut_quads is implemented as 16 individual scalar reads → vld1q_s8, so 5 calls = 80 scalar loads producing only 12 int32 outputs.
**Fix:**
- Replace the gather loop with v_load_deinterleave, which maps to vld3q_u8 on AArch64 — single instruction, loads 48 bytes and hardware-deinterleaves R/G/B in one shot
**Performance Benchmarks:**
<img width="1021" height="536" alt="image" src="https://github.com/user-attachments/assets/6c4b5d71-d096-4dfd-9bc4-4315d4e679af" />
open() used to accept only Mono8/Mono12/Mono16/BayerGR8 and silently forced
the camera to Mono8 for everything else, so color cameras were degraded to
grayscale. retrieveFrame() passed the payload through as is, leaving the
demosaicing and the bit depth of the result to the caller.
open() now enumerates the pixel formats the camera offers and picks the best
one it can decode, in this order:
1. true color - BGR8/RGB8, BGRa8/RGBa8, BGR10/12, RGB10/12,
2. Bayer CFA - BayerRG/BG/GR/GB, 8, 10, 12 and 16 bit,
3. grayscale - Mono8, Mono10, Mono12, Mono14, Mono16.
The least deep format is preferred within a group, as the result is 8 bit
anyway. Bit packed formats are not selected, their payload cannot be mapped
to a Mat without unpacking it first.
retrieveFrame() converts whatever arrives to BGR CV_8UC3: Bayer data is
demosaiced, RGB is swapped to BGR, grayscale is replicated to three
channels and deeper formats are scaled down from their significant bits.
The pixel format description is kept in a single table, which also drives
CAP_PROP_FOURCC. Both directions of the property now cover all the formats
above instead of just four, setProperty() reports failure for a fourcc the
camera cannot provide and refreshes the cached format description, which
the old code did not do.
Note that Aravis names a Bayer pattern after its top left 2x2 tile, while
OpenCV names it after the second row, so the codes are related by an
R <-> B swap: BayerRG8 is demosaiced with COLOR_BayerBG2BGR.
An explicit payload size check is added to retrieveFrame(), the frame is no
longer wrapped in a Mat without knowing the buffer is large enough.
core: fix unsigned Rect intersection for disjoint rectangles (#11988) - #29069Fixes#11988
## Problem
`Rect_<_Tp>::operator&` / `operator&=` returned a non-empty rectangle when two **unsigned** rectangles do not overlap.
### Example:
```cpp
cv::Rect_<unsigned> r1(0, 0, 1, 1);
cv::Rect_<unsigned> r2(2, 2, 1, 1);
auto inter = r1 & r2; // was [1 x 1 from (2, 2)], expected empty
```
Root cause: the previous implementation subtracted edge coordinates before checking overlap. For `unsigned _Tp`, expressions like `width - (x_max - x_min)` can underflow when rectangles are disjoint.
### Solution
Add a check for underflow
### Tests
Added regression test `Core_Rect.test_unsigned_overflow in modules/core/test/test_misc.cpp`.
Speed up imgproc warpAffine and warpPerspective for BORDER_TRANSPARENT - #29779
### 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.
- [x] The feature is well documented and sample code can be built with the project CMake
## Why
I was using `warpPerspective` to draw several source images on a large destination image in mode `BORDER_TRANSPARENT` and ran into `warpPerspective` being surprisingly slow. Upon reading the code, it turns out that `warpPerspective`, as well as `warpAffine`, iterates over all the destination image's pixels even if the source image gets projected to only a small part of the destination image, resulting in considerable overhead for my use case. I believe other users would also benefit from making this case more efficient.
## Changes
d19c343704 calculates the ROI of the source image in the destination image and then limits the destination image walk to that area. Given that the existing tests didn't cover `BORDER_TRANSPARENT`, I extended that in e451f59cda. Next to that, I added a small performance test dedicated to this use case (c4e271c2dc).
## Performance improvement
The following table show the timing difference before and after, generated using the added perf test (source image projects to 64x64, drawn in a 512x512 destination image):
| function | type | interp | base [ms] | opt [ms] | speedup |
| --- | --- | --- | --- | --- | --- |
| warpAffine | 8UC1 | NEAREST | 0.204 | 0.010 | 19.8× |
| warpAffine | 8UC1 | LINEAR | 0.428 | 0.026 | 16.6× |
| warpAffine | 8UC4 | NEAREST | 0.239 | 0.021 | 11.5× |
| warpAffine | 8UC4 | LINEAR | 0.433 | 0.031 | 14.0× |
| warpPerspective | 8UC1 | NEAREST | 0.759 | 0.033 | 23.2× |
| warpPerspective | 8UC1 | LINEAR | 1.125 | 0.067 | 16.8× |
| warpPerspective | 8UC4 | NEAREST | 0.786 | 0.040 | 19.5× |
| warpPerspective | 8UC4 | LINEAR | 1.110 | 0.101 | 11.0× |
In short, a 10 to 20x speedup.
## Notes
- This is my first PR for the OpenCV project, I'm sorry in case I didn't respect all contribution guidelines.
- If relevant, Clause Opus 4.8 was used for exploring the codebase, some code and style suggestions and review.
objdetect: do not modify the input features in FaceRecognizerSF::match - #29804
`FaceRecognizerSF::match()` normalizes its two `InputArray` features in place, writing
through to the caller's buffers, and returns a wrong score when the two overlap.
### Fix
Normalize both features into their own destinations. This yields bit-exact the same
values as the in-place form, checked across a range of shapes and depths including a
non-continuous ROI, so scores for non-overlapping inputs do not move.
### 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 (no issue reports this; #7298 is the related RFC)
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable (accuracy test added in-repo; it reuses the existing model, so there is no opencv_extra patch)
- [ ] The feature is well documented and sample code can be built with the project CMake (n/a — bug fix, no API, sample or documentation change)
Extract IPP integration as HAL function for bilateral_filter - #29410
Backport of https://github.com/opencv/opencv/pull/29409
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1rmNB3X_V8rWttUGBqXRs1FmkxeKR0O93x_ez5tVjutY/edit?usp=sharing
### 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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SunRasterDecoder::readData() only called CvtPaletteToGray() when the
file contained a color palette (m_maptype == RMT_EQUAL_RGB). But
SunRasterEncoder always writes RMT_NONE (no palette), so gray_palette[]
stayed zero-initialized and FillGrayRow8() mapped every pixel to 0.
Fix: call CvtPaletteToGray() whenever reading as grayscale, regardless
of palette type. readHeader() already fills m_palette correctly via
FillGrayPalette() for the RMT_NONE case.
Add regression test Imgcodecs_SunRaster.imread_grayscale_roundtrip
that writes a full 0-255 ramp, reads it back with IMREAD_GRAYSCALE,
and checks NORM_INF == 0. Without the fix, NORM_INF == 255 (all black).
Remove the "broken (black result)" skip for ras in read_write_GRAYSCALE.
Remove Intel-only gates from dedicated 3x3/5x5 GaussianBlur kernels and
single-pass separable filter paths so AMD and other OpenCL devices can
use the same optimized implementations with existing fallbacks.
imgproc: Extended spatialGradient API and applied to different detector algorithms - #29273
imgproc: Add fused Sobel2D gradient API and use it in different detector algorithms
Add a public Sobel2D API computing dx/dy in a single fused pass with 3x3 and 5x5 kernels (SIMD-dispatched). The float (CV_32F) path folds the output scale and float store into the kernel, avoiding a separate convertTo pass.
- Add runtime SIMD dispatch for the Canny edge path.
- Integrate fused Sobel2D into:
- Canny
- cornerEigenValsVecs (cornerHarris, cornerMinEigenVal, cornerEigenValsAndVecs, goodFeaturesToTrack)
- GeneralizedHough
- IntelligentScissors
- HoughCircles
- Add performance and accuracy tests.
### 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
calib3d: fix division by zero in SGBM 3-way mode when uniquenessRatio is 100 - #29762Fixes#29761.
`SGBM3WayMainLoop` derives its uniqueness threshold in the SIMD path as `(100*min_cost)/(100-uniquenessRatio)`, so a `uniquenessRatio` of 100 divides by zero and the process dies with SIGFPE. The scalar fallback right below it, and the other SGBM modes, express the same test as `cost*(100 - uniquenessRatio) < min_cost*100`, which needs no division and copes with the value fine. `MODE_HH4` goes through `CalcHorizontalSums`, which never divides, so only `MODE_SGBM_3WAY` reproduces.
The SIMD shortcut is now skipped once `uniquenessRatio` reaches 100 and the scalar loop decides on its own, which is exactly what a build without SIMD already does. Ratios below 100 keep the fast path and produce identical output. The diff looks long because the existing block is indented one level, `?w=1` shows the real change.
The second commit fixes the neighbouring case: `thresh` grows to `100*min_cost` as the ratio approaches 100, well past `SHRT_MAX`, and `(short)(thresh+1)` wraps. On the reporter's image pair at ratio 99, 3-way marked 48723 pixels valid while `MODE_SGBM`, `MODE_HH` and `MODE_HH4` all landed near 48370; saturating brings it to 48371.
Verified with opencv_extra test data: `Calib3d_StereoSGBM.regression`, `Calib3d_StereoSGBM.deterministic`, `Calib3d_StereoSGBM_HH4.regression` and `Calib3d_StereoBM.regression` still pass. The new `Calib3d_StereoSGBM.regression_29761` aborts on unpatched 4.x under `-fsanitize=integer-divide-by-zero` and passes with the fix.
The same code sits at `modules/stereo/src/stereosgbm.cpp` on 5.x, which is the path the reporter cited. The module move means the merge will not apply cleanly, so tell me if you would rather have a separate 5.x PR.
### 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
Optimize fastGEMM1T NEON: extend 4-wide to 8-wide outer loop unrolling - #29716
**PR Description:**
- This PR extends the NEON fastGEMM1T optimization by adding an 8-wide outer loop before the existing 4-wide loop. The 8-wide block processes 8 output neurons per iteration instead of 4, reducing the total number of outer loop iterations by half and sharing the vector load cost across 8 output accumulator registers instead of 4.
- On x86, the AVX2 path processes 8 floats per instruction (256-bit registers) and AVX-512 processes 16 floats per instruction (512-bit registers). On ARM, NEON is 128-bit, only 4 floats per instruction. Intel's wider registers naturally cover more outputs per inner step. This patch brings ARM NEON closer to Intel parity through wider outer loop unrolling.
**Performance results:**
<img width="1236" height="478" alt="image" src="https://github.com/user-attachments/assets/c933e64c-2200-450e-9887-3fd0cbdd5b8e" />
Notes:
- The existing 4-wide loop is retained to handle remainders when nvecs is not a multiple of 8
- No existing tests modified
- Follows the same pattern as the existing 4-wide NEON path.
- [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
Same as #29757 in 5.x
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.
core: fix JacobiSVD SIMD accumulation to match scalar path - #29727
Replace FMA with mul-add in dotD/givensD to avoid Windows MSVC rounding drift.
- Address the FMA drift introduced in https://github.com/opencv/opencv/pull/29720
### 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.
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* The condition under which the test `AsyncAPICancelation/cancel/0.basic` doesn't compile using GCC 11 is narrowed down to the only GCC versions which are affected: GCC 11.0 and 11.1. The issue was fixed in GCC 11.2 (verified using 11.2.0, 11.3.0, 11.4.0, 11.5.0, 12.1.0 versions of GCC).
* The corresponding issue: opencv/opencv#19678.
CV_Assert permits rvec/tvec as Size(1,3) or Size(3,1), but the
implementation only handled column vectors:
- LM path read rvec/tvec via .at<double>(i,0), OOB for a row vector.
- LM path's convertTo() write-back reallocated a local Mat alias
instead of writing in place whenever the source/dest shapes
mismatched, silently discarding the refined result for row vectors.
- VVS path's "R1 * tvec" requires a column vector; a row vector threw
a cv::Exception from gemm's shape assertion.
Fixed all three with shape-agnostic .at<double>(i) indexing and
reshape() before convertTo()/matrix arithmetic so orientation always
matches. Added Calib3d_SolvePnP.refine_row_vector covering both
solvePnPRefineLM and solvePnPRefineVVS with both orientations.
Fixes#29747
imgproc: avoid NaN in masked TM_CCOEFF_NORMED for constant templates - #29071
### Pull Request Readiness Checklist
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- [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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Patch to opencv_extra has the same branch name.
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### Summary
Fixes#23257.
This patch handles a degenerate masked `TM_CCOEFF_NORMED` case in `matchTemplate()`. When the template is constant over the effective mask area, the template norm can become zero or NaN, which may propagate NaN/Inf values into the result. The masked path now returns all ones for this case, matching the existing behavior of the unmasked `TM_CCOEFF_NORMED` implementation for constant templates.
### Tests
- `cmake --build build_project4 --target opencv_test_imgproc -j4`
- `./build_project4/bin/opencv_test_imgproc '--gtest_filter=Imgproc_MatchTemplateWithMask.regression_23257_constant_template'`
- `./build_project4/bin/opencv_test_imgproc '--gtest_filter=*MatchTemplate*'`
The MatchTemplate-related test filter ran 147 tests successfully.
allow cv::resize to support more than 4 channels in AREA path - #29691
Fixes #29651
### 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
- [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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Extracted IPP to HAL for getRectSubPix function in 4.x - #29512
### 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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imgproc: Optimize AVX-512 path for accumulate - #29718
- Add AVX512_SKX/AVX512_ICL to accum dispatch
- Video_RunningAvg.accuracy failure observed in #29394 has been fixed.
### 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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- [ ] There is a reference to the original bug report and related work
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Add VBLAS::dotD and VBLAS::givensD to vectorize JacobiSVDImpl_'s dot-product,
Givens rotation and norm loops with double-precision accumulation matching the
scalar path.
Dispatch lapack for both AVX512_SKX and AVX512_ICL.
gdal: skip raster band mapped to an out-of-range channel in readData - #29439
Repro: read a 3-band GDT_Byte raster (no palette) whose third band is tagged `GCI_AlphaBand`, via `imread(path, IMREAD_LOAD_GDAL)`. ASan reports a heap-buffer-overflow WRITE of size 1 at `grfmt_gdal.cpp:259`.
Cause: an alpha band makes `readData` pass `color = 3`, but the `gdalChannels == 3 && image.channels() == 3` branch of `write_pixel` indexes `Vec3b[channel]` with no bound, so `channel == 3` stores one element past the 3-lane pixel. On the last pixel that lands past the Mat buffer.
Fix (per review): `readData` checks the mapped color index against `img.channels()` in the band loop, before the pixel iteration. An out-of-range band is skipped as a whole with a `CV_LOG_WARNING` naming the band and range, instead of a silent per-pixel guard. Valid RGB rasters decode identically.
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
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imgproc: add simd support for calchist & calchist1d function - #28728
- This PR adds OpenCV SIMD intrinsics-based optimizations to the Calchist and calcHist1d function for improved performance on Windows-ARM64 platforms.
- The optimized implementation uses vectorized operations to accelerate histogram computation.
- In x64 architecture, `calcHist1d` benefits 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 calcHist and calchist1d function showed noticeable performance improvements on Windows-ARM64.
**Performance Benchmarks:**
<img width="1247" height="460" alt="image" src="https://github.com/user-attachments/assets/ed740d87-158e-49d4-889e-9c7dda482a63" />
<img width="552" height="210" alt="image" src="https://github.com/user-attachments/assets/76b9e46e-304e-480c-9a57-af0aba6e937a" />
- [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: Optimized accumulate with AVX512 dispatch and AVX2 kernels improved #29394
- Add AVX512_SKX/AVX512_ICL to accum dispatch
- Fine tune AVX2 kernels for float, cn = 1 and 3 cases.
### 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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Patch to opencv_extra has the same branch name.
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imgproc: support arbitrary channel counts in medianBlur #29638
## Summary
Fixes#29592.
`cv::medianBlur`/`cv2.medianBlur` rejects `CV_8U` images with channel counts other than 1, 3, or 4 whenever the optimized large-kernel path is needed — `ksize >= 7` always, and `ksize == 5` on SIMD-enabled builds (the `ksize == 3`/`ksize == 5`-without-SIMD "sort net" path already handles arbitrary channel counts generically, so this only affects the fast path):
```
src.depth() == CV_8U && (cn == 1 || cn == 3 || cn == 4)
```
Median filtering is channel-independent, so there's no correctness reason for this restriction — it's an implementation detail of the optimized SIMD kernels (which hard-code 1/3/4-channel-interleaved layouts), not a property of the algorithm. Users with 2, 5, 6, 9+ channel images (multispectral, feature maps, stacked masks) currently have to split, filter, and re-concatenate manually outside the library — exactly the workaround already implemented downstream in Albucore, per the issue.
## Fix
In `cv::medianBlur` (`modules/imgproc/src/median_blur.dispatch.cpp`), before dispatching to HAL/OpenCL/the optimized SIMD path: if the channel count isn't 1, 3, or 4, split the image into individual channels, run `medianBlur` on each one independently (a single channel is always supported by every existing path, recursively), and merge the results back together. This touches none of the performance-critical kernels — it's a fallback that only runs for channel counts those kernels don't support, and the existing 1/3/4-channel fast paths are completely unaffected.
## Test plan
- [x] Added `Imgproc_MedianBlur.arbitrary_channel_count_29592` in `modules/imgproc/test/test_filter.cpp`, covering channel counts `{1,2,3,4,5,6,9}` × kernel sizes `{3,5,7,9}`. For each combination it asserts `medianBlur` doesn't throw, preserves size/type, and — critically — produces output bit-identical to filtering each channel independently via `cv::split`/`cv::merge` (the reference/expected behavior).
- [x] Built `opencv_core` + `opencv_imgproc` + `opencv_test_imgproc` locally (MSVC) and ran the new test — passes.
- [x] Ran the full existing `Imgproc_MedianBlur.*`, `Imgproc_Filter2D.*`, `Imgproc_Blur.*`, `Imgproc_GaussianBlur.*` suites — all 14 pass, no regressions.
- Note: `GaussianBlurVsBitexact`, `sepFilter2D_types`, and `StackBlur` tests in the same binary fail/crash in this local environment, but reproduce identically on unmodified `4.x` with no changes at all — confirmed pre-existing and unrelated to this change (not investigated further, out of scope here).
The generated cv_cpu_helper.h sets CV_TRY_<OPT> to 1 both when <OPT> is a
dispatch target and when it is part of the baseline, but an opt_<OPT> namespace
is only emitted for the dispatch case: __ocv_add_dispatched_file() guards it
with CPU_DISPATCH_FINAL, and a baseline optimization is deliberately kept out of
that list. Code that hand-rolls "#if CV_TRY_<OPT> ... opt_<OPT>::fn" therefore
fails to compile as soon as the optimization lands in the baseline, e.g. with
-DCPU_BASELINE=AVX512_ICL or -march=native on an ICL capable CPU:
lut.dispatch.cpp:22:16: error: 'opt_AVX512_ICL' has not been declared
CV_CPU_CALL_<OPT>() already handles the namespace selection for a call. Add the
same thing for code that needs the function pointer instead:
CV_CPU_GET_FN_PTR_<OPT>(fn) per optimization
CV_CPU_GET_FN_PTR_BASELINE(fn) chain terminator
CV_CPU_DISPATCH_FN(fn, modes) full chain, mirrors CV_CPU_DISPATCH()
In the baseline case it resolves to cpu_baseline::fn, which is the <OPT> build
of the kernel, so no implementation is lost and no second copy is emitted. In
the dispatch case it keeps the cv::checkHardwareSupport() test and returns
opt_<OPT>::fn, and it compiles out when the optimization is unavailable.
Use it in the two places that hit this, which also removes the hand-written
preprocessor blocks. Generated code for the dispatch configuration is unchanged
(byte-identical .text with -DCPU_BASELINE=SSE3 -DCPU_DISPATCH=AVX512_ICL).
Fixes#29694
cvtColor's @param src promised 8U, 16U and 32F unconditionally, but the accepted
depths depend on the conversion code: CV_16U is rejected by COLOR_BGR2HSV,
COLOR_BGR2Lab, COLOR_BGR2Luv and the packed 16-bit codes, while COLOR_BGR2GRAY,
COLOR_BGR2XYZ, COLOR_BGR2YUV and RGB<->RGB accept it. That is what #26447
reported.
State it on @param src and point at the [8U]/[16U]/[32F] markers already
documented on ColorConversionCodes. The @note only said the source "must be of
an appropriate type", which is now redundant, so drop it from both functions.
demosaicing has the same problem: the Variable Number of Gradients codes accept
8-bit input only, while the other Bayer codes also accept 16-bit.
Fixes#26447
AVCodec::supported_framerates was deprecated in FFmpeg 7.1 and removed in
FFmpeg 9, so direct field access no longer builds against FFmpeg 9.
Read the supported frame rate list through avcodec_get_supported_config()
when building against libavcodec 61.13.100 or newer. That call returns the
same list plus its entry count, so the loop iterates by count instead of the
old sentinel terminator. Older FFmpeg keeps the previous field access.
Fixed bindings generation for Charuco boards #28517
Fixes https://github.com/opencv/opencv/issues/28512
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features2d: fix heap buffer overflow in AKAZE generateDescriptorSubsample #29623
Prevent out-of-bounds write in `generateDescriptorSubsample()` during AKAZE MLDB descriptor generation when `nchannels < 3` (e.g. `nchannels = 2`).
Fixes#29613
### Summary of Changes
- **Module:** `features2d` (AKAZE)
- **Problem:** When `generateDescriptorSubsample()` is called with `nchannels < 3`, the code hardcoded 3-channel matrix index offsets (`+ 0`, `+ 1`, `+ 2`). Since `comps` allocates rows based on `nchannels * npicks`, writing to index offset `+ 2` when `nchannels = 2` causes a heap buffer overflow past the last row of `comps`.
- **Fix:** Replaced hardcoded 3-channel matrix index assignments with a dynamic loop that iterates over `0` to `nchannels - 1`.
- **Regression Test:** Added `Features2D_AKAZE.Subsample_Channels_Overflow_Fix` in `modules/features2d/test/test_akaze.cpp`.
-
### Acknowledgments
Thanks to @abhishek-gola for reviewing the test and suggesting the correct approach.
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The four memset overloads in cuda4dnn document their first parameter as
src while it is called dest, and the directions are the wrong way round:
the output buffer is marked [in] and the 8-bit fill value is marked [out].
The prose above two of them already says dest, only the tags disagree.
The 16-bit icvCvt_* helpers in utils.cpp move the row pointer with
step/sizeof(x[0]) - size.width*N. sizeof is unsigned, so this is done in
unsigned math. When the caller passes step == 0, it underflows to a huge
value and the pointer goes out of range, which is undefined behavior.
The TIFF decoder hits this: it walks rows itself and calls these helpers
with step = 0, so reading a 16-bit 4-channel TIFF triggers the bug.
Cast sizeof(...) to int so the math is signed. Same result for normal
calls, no overflow when the step is 0.
Added Imgcodecs_Tiff.regression_29615_16UC4 which round-trips a CV_16UC4
TIFF in memory.
This is the 4.x version of the fix, requested in
https://github.com/opencv/opencv/pull/29620 (issue #29615).
Truncation toward zero produces incorrect interpolation indices for
negative border coordinates. Use nearest-even rounding so OpenCL remap
matches the CPU behavior.
Implemented a check to enable instrumentation for IPP HAL #29542
To enable instrumentation for IPP HAL correctly it needed to generalize a part of `private.hpp` and put it to the separate file that can be included in `private.hpp` and `ipp_utils.hpp` both to avoid duplication.
Built with `-DENABLE_INSTRUMENTATION=ON` and ran `opencv_perf_imgproc --perf_instrument=1`:
| Test | IPP weight before | IPP weight after |
|---|---|---|
| `distanceTransform` (8U->32F) | 0.0% | ~76% |
| `ippSobel` | 0.0% | ~98% |
| `scharrViaSobelFilter` | 0.0% | ~90% |
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In HOGCache::init the block gaussian weights are computed into two buffers:
AutoBuffer<float> di(blockSize.height), dj(blockSize.width);
The vectorized (CV_SIMD128) loop that fills dj was bounded by
`blockSize.height` instead of `blockSize.width`. When the block is taller than
it is wide, `v_store(_dj + j, ...)` writes past the end of the width-sized dj
buffer, causing a heap buffer overflow / access violation (e.g. HOGDescriptor
with a 198x281 window, or the 2399x2400 case from the report).
Bound the dj loop by `blockSize.width` to match the buffer size and the scalar
tail loop. This keeps the SIMD path (unlike the workaround in the report, which
disabled it).
Added a regression test that computes a HOGDescriptor with a tall block; without
the fix it triggers a heap-buffer-overflow (confirmed with AddressSanitizer at
hog.cpp:731 inside HOGCache::init).
Fixes#23580
hal_rvv core: Cover more data types for split with native RVV intrinsics #29557
Covering 16-bit, 32-bit and 64-bit split.
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SourceReaderCB owns an Event created for asynchronous frame delivery,
but its destructor did not close the handle. Close it when the callback
is destroyed to prevent one Event handle from leaking across repeated
MSMF capture lifecycles.
Fixes#29562
The IPP HAL (hal/ipp/src/warp_ipp.cpp) routed cv::warpAffine with
INTER_NEAREST to iwiWarpAffine for CV_16S{C1,C3,C4}, CV_64F{C1,C3,C4}
and CV_16UC4 via the non-enforced (default) impl[] dispatch table,
whose comment promises results "strictly aligned to OpenCV
implementation". That contract was broken: IPP rounds source
coordinates at the half-pixel boundary differently from the native
fixed-point kernel (warpAffineBlocklineNN, 10-bit AB_BITS), so under
rotation or fractional translation about 0.05-0.07% of destination
pixels resolve to a different source pixel. Because INTER_NEAREST does
no blending, those pixels take entirely different values, so warping
identical data as e.g. CV_16S (IPP path) versus CV_16U (native path)
produced non-identical output, and x86_64 (IPP) silently diverged from
ARM (no IPP). This is the same "Different results" behavior for which
IPP warpAffine was disabled from ~2017 through 4.11.
Zero the INTER_NEAREST column of the non-enforced dispatch table for
the affected types so the dispatch guard falls back to the native
kernel, restoring the "strictly aligned" contract. The LINEAR and
CUBIC columns for these rows were already 0, so IPP was only ever used
for NEAREST here. The IPP_CALLS_ENFORCED table is left untouched so the
opt-in performance-benchmarking build still exercises IPP.
Adds a regression test (Imgproc_Warp.regression_29279) that warps a
gradient image with INTER_NEAREST as each affected type and asserts
bit-exact equality with a native (CV_32F) reference across several
rotation angles and a fractional translation.
Fixes#29279
When a user disables a bundled image-codec dependency (e.g.
BUILD_ZLIB=OFF) to supply an external build but find_package()
cannot locate it, OpenCVFindLibsGrfmt.cmake silently falls back to
compiling the bundled 3rdparty version. As reported in the issue,
this is misleading: the editable ZLIB_LIBRARY_DEBUG/RELEASE cache
entries have no effect after the fallback and nothing points the
user at the real knob (ZLIB_ROOT / ZLIB_DIR).
Emit a message(WARNING) in each fallback branch, guarded by
NOT BUILD_<lib>, so the built-in fallback is no longer silent when
the user explicitly opted out of the bundled build. The warning
names the correct search variables for that library and points to
the CMake find_package / Find<lib> documentation. Applied
consistently to the zlib, libjpeg, libtiff, libpng and libwebp
fallback branches.
The existing fallback behavior is intentionally left unchanged, so
configurations that rely on the built-in fallback continue to build.
Fixes#26746
Disable test test that sporadically fails with OpenVINO on CI #29531
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core: vectorize cv::reduce #27510
- [ ] ~reduceR_~ Dropped due to performance
- [x] reduceC_
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imgproc: Optimized OpticalFlowPyrLK (PyrDownH) #28650
- Optimized Uchar PyrDown Horizontal processing intermediate storage.
- Improved horizontal processing with AVX512 vbmi ISA.
- Todo: further refinement for other archs.
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imgproc: Optimized remap interpolation #29507
- Add a SIMD dispatch file for remap and vectorize the single-channel (C1) in-bounds paths of bilinear, bicubic and lanczos4 interpolation (32F / 16U / 16S) using width-agnostic gather
- Dispatch the bilinear C1 path and drop the per-pixel weight-table gathers
- Widen the fixed-point coordinate map conversion
- 32F lanczos4 is kept on the scalar path: its vectorized 64-tap accumulation deviates beyond the set float accuracy tolerance
- Vectorize the bilinear inlier/outlier run detection so any-channel linear remap skips constant-status runs with SIMD instead of a per-pixel bounds test
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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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- [ ] Accuracy test, performance test, test data: N/A (doc-only)
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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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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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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
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.
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.
### 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
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.
- [ ] The feature is well documented and sample code can be built with the project CMake
N/A - bug fix, no new API.
Filter2D IPP migration to HAL for 4.x #29427
### 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
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
### 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: 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.
### 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: 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
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
Optimized gemm implementation with Universal SIMD #29242
- vectorized GEMMSingleMul and GEMMBlockMul
### 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
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
### 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
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
- [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
- [ ] The feature is well documented and sample code can be built with the project CMake
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.
- [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
- [ ] The feature is well documented and sample code can be built with the project CMake
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
- [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 (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
```
fix MSVC warning for minMaxIdx_simd #29379
### 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
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.
### 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
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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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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- 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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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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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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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 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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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
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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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- [ ] 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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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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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.)*
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.
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>
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.
### Pull Request Readiness Checklist
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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
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch
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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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### 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.
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
### Pull Request Readiness Checklist
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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]
```
### Pull Request Readiness Checklist
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Patch to opencv_extra has the same branch name.
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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.
### 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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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.
### Pull Request Readiness Checklist
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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`.
core(rvv): fix v_matmul/v_matmuladd scalable semantics and expand lane-group test coverage #29080
### 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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Patch to opencv_extra has the same branch name.
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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.
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
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
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.
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
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
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
- [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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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.
- [ ] The feature is well documented and sample code can be built with the project CMake
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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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- [ ] 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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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
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
- [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
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.
- [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
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)*
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.
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.
- [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
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>
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
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
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
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
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
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
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
### 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
- 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
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
---
### 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 (`4.x`)
- [x] There is a reference to the original bug report and related work (#26592)
- [x] The feature is well documented and sample code can be built with the project CMake
- [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.
### 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
- 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
### 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
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.)*
### 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
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.
### 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
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).
### 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
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
Multiscale ECC #28802
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1338
### 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
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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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)
### Pull Request Readiness Checklist
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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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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.
### Pull Request Readiness Checklist
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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
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.
### Pull Request Readiness Checklist
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python: fix segfault on 0-channel numpy array input #28747
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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)
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 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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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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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()`
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
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.
### Pull Request Readiness Checklist
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### Pull Request Readiness Checklist
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## This tutorial describe how to build opencv from source in windows using VSCode and MSYS2
core: fix heap-buffer-overflow in YAML parseKey for empty keys #28620
### Pull Request Readiness Checklist
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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
### Pull Request Readiness Checklist
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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
- 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.
### Pull Request Readiness Checklist
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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
### Pull Request Readiness Checklist
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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.
### Pull Request Readiness Checklist
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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.
### Pull Request Readiness Checklist
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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.
### Pull Request Readiness Checklist
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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
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch (4.x for bug fixes)
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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.
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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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
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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
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.`
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.
- [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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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
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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
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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.
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" />
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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).
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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>
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.
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
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
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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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
Pull Request Readiness Checklist:
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- [x] There is a reference to the original bug report and related work (issue #26523)
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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
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
Pull Request Readiness Checklist:
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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
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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✓ I agree to contribute to the project under Apache 2 License.
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✓ The PR is proposed to the proper branch (4.x)
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Patch to opencv_extra has the same branch name.
✓ The feature is well documented and sample code can be built with the project CMake
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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Patch to opencv_extra has the same branch name.
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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.
### Pull Request Readiness Checklist
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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.
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
### Pull Request Readiness Checklist
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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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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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.
### Pull Request Readiness Checklist
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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
- [X] I agree to contribute to the project under Apache 2 License.
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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
### Pull Request Readiness Checklist
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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
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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### Pull Request Readiness Checklist
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resolves https://github.com/opencv/opencv/issues/27580
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imgproc: supports CV_SIMD_SCALABLE in pointSetBoundingRect #27479
### Pull Request Readiness Checklist
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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.
### Pull Request Readiness Checklist
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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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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)
### Pull Request Readiness Checklist
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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.
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.
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
### Pull Request Readiness Checklist
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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Fix charuco_board_pattern in generate_pattern.py #27876
### Pull Request Readiness Checklist
Fix#27871
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Fix QRCodeDetector::detectAndDecode crash #27877
### Pull Request Readiness Checklist
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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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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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.
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.
imgproc: add minEnclosingConvexPolygon #27369
### Pull Request Readiness Checklist
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Patch to opencv_extra has the same branch name.
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core: ARM64 loop unrolling in kmeans to improve Weighted Filter performance #27596
### Pull Request Readiness Checklist
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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.
### Pull Request Readiness Checklist
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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
### Pull Request Readiness Checklist
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libtiff upgrade to version 4.7.1 #27806
close https://github.com/opencv/opencv/issues/27784
### Pull Request Readiness Checklist
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stitching: enable loop unrolling in fast.cpp to improve ARM64 performance #27642
### Pull Request Readiness Checklist
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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
### Pull Request Readiness Checklist
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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" />
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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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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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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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
- [x] I agree to contribute to the project under Apache 2 License.
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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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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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
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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
- [ ] 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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- 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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### 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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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
This aligns with other virtual method declarations in cap_dshow.hpp
and silences compiler warnings (-Wsuggest-override) while improving
compile-time safety.
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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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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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
### Pull Request Readiness Checklist
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Performance tests for writing and reading animations #27605
### Pull Request Readiness Checklist
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.
### Pull Request Readiness Checklist
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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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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
### Pull Request Readiness Checklist
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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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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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>
Close https://github.com/opencv/opencv/issues/27413
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Correct IPP distanceTransform results with single thread #27432
### Pull Request Readiness Checklist
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.
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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required for https://github.com/opencv/opencv/pull/27370
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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
### Pull Request Readiness Checklist
Fixes#27352
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Improve solveCubic accuracy #27347
### Pull Request Readiness Checklist
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
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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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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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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
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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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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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.
### Pull Request Readiness Checklist
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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
- [x] I agree to contribute to the project under Apache 2 License.
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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
```
### Pull Request Readiness Checklist
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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
```
### Pull Request Readiness Checklist
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Make sure there are enough channels to check for opacity #27040
### Pull Request Readiness Checklist
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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
- [x] I agree to contribute to the project under Apache 2 License.
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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)
### Pull Request Readiness Checklist
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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
- [x] I agree to contribute to the project under Apache 2 License.
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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)
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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" />
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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" />
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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.
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APNG encoding optimization #26849
related #26840
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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)
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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" />
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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>
Documentation to enable FastCV based OpenCV HAL and Extensions #26910
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Fix Logical defect in FilterSpecklesImpl #26996
Fixes : #24963
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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
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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
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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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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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[HAL] split8u RVV 1.0 #26884
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* 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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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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Performance tests for image encoders and decoders and code cleanup #26872
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Issue25250 lens distortion documentation unclear #26600
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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
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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
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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
### Pull Request Readiness Checklist
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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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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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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It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice.
This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License.
8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License.
9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation.
10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally.
NO WARRANTY
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found.
One line to give the program's name and a brief idea of what it does.
Copyright (C) <year> <name of author>
<one line to give the program's name and an idea of what it does.>
Copyright (C) < yyyy> <name of author>
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program.
You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker.
signature of Ty Coon, 1 April 1989
Ty Coon, President of Vice
This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License.
<signature of Ty Coon>, 1 April 1989 Ty Coon, President of Vice
This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License.
; This file should be assembled with NASM (Netwide Assembler) or Yasm.
%include "jcolsamp.inc"
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