c36dd6a35e
Fix Python utility compatibility issues - #29755 ### Problem Several repository Python utilities emit invalid escape sequence SyntaxWarnings under Python 3.13. The Java test checker also attempts to parse non-Java assets as UTF-8, causing UnicodeDecodeError, and relies on a global parser instance. The Apple build utility accepts malformed CMake version strings because one version separator is an unescaped regex wildcard. ### Changes - Use raw strings for regular expressions and replacement templates. - Skip non-Java files in the Java test checker. - Use the current JavaParser instance instead of global state. - Require literal dots in parsed CMake versions. ### Verification - Compiled every tracked Python file with SyntaxWarning treated as an error. - Ran the Java checker against modules/java/test successfully. - Verified valid CMake versions are accepted and malformed versions rejected. - Ran git diff --check.
OpenCV deep learning module samples
Model Zoo
Check a wiki for a list of tested models.
If OpenCV is built with Intel's Inference Engine support you can use Intel's pre-trained models.
There are different preprocessing parameters such mean subtraction or scale factors for different models. You may check the most popular models and their parameters at models.yml configuration file. It might be also used for aliasing samples parameters. In example,
python object_detection.py opencv_fd --model /path/to/model.onnx
Check -h option to know which values are used by default:
python object_detection.py opencv_fd -h
Sample models
You can download sample models using download_models.py. For example, the following command will download network weights for OpenCV Face Detector model and store them in FaceDetector folder:
python download_models.py --save_dir FaceDetector opencv_fd
You can use default configuration files adopted for OpenCV from here.
You also can use the script to download necessary files from your code. Assume you have the following code inside your_script.py:
from download_models import downloadFile
filepath1 = downloadFile("https://huggingface.co/onnxmodelzoo/ssd_mobilenet_v1_12/resolve/main/ssd_mobilenet_v1_12.onnx", None, filename="ssd_mobilenet_v1_12.onnx", save_dir="save_dir_1")
filepath2 = downloadFile("https://huggingface.co/onnxmodelzoo/ssd_mobilenet_v1_12/resolve/main/ssd_mobilenet_v1_12.onnx", "83536889adce1eda154175f8e3b156dd20443631", filename="ssd_mobilenet_v1_12.onnx")
print(filepath1)
print(filepath2)
# Your code
By running the following commands, you will get ssd_mobilenet_v1_12.onnx file:
export OPENCV_DOWNLOAD_DATA_PATH=download_folder
python your_script.py
Note that you can provide a directory using save_dir parameter or via OPENCV_SAVE_DIR environment variable.
Face detection
An origin model
with single precision floating point weights has been quantized using TensorFlow framework.
To achieve the best accuracy run the model on BGR images resized to 300x300 applying mean subtraction
of values (104, 177, 123) for each blue, green and red channels correspondingly.
The following are accuracy metrics obtained using COCO object detection evaluation
tool on FDDB dataset
(see script)
applying resize to 300x300 and keeping an origin images' sizes.
AP - Average Precision | FP32/FP16 | UINT8 | FP32/FP16 | UINT8 |
AR - Average Recall | 300x300 | 300x300 | any size | any size |
--------------------------------------------------|-----------|----------------|-----------|----------------|
AP @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.408 | 0.408 | 0.378 | 0.328 (-0.050) |
AP @[ IoU=0.50 | area= all | maxDets=100 ] | 0.849 | 0.849 | 0.797 | 0.790 (-0.007) |
AP @[ IoU=0.75 | area= all | maxDets=100 ] | 0.251 | 0.251 | 0.208 | 0.140 (-0.068) |
AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.050 | 0.051 (+0.001) | 0.107 | 0.070 (-0.037) |
AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.381 | 0.379 (-0.002) | 0.380 | 0.368 (-0.012) |
AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.455 | 0.455 | 0.412 | 0.337 (-0.075) |
AR @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] | 0.299 | 0.299 | 0.279 | 0.246 (-0.033) |
AR @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] | 0.482 | 0.482 | 0.476 | 0.436 (-0.040) |
AR @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.496 | 0.496 | 0.491 | 0.451 (-0.040) |
AR @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.189 | 0.193 (+0.004) | 0.284 | 0.232 (-0.052) |
AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481 | 0.480 (-0.001) | 0.470 | 0.458 (-0.012) |
AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 | 0.520 | 0.462 (-0.058) |