opencv
SHA-256OpenCV is an open-source computer vision and machine learning library with over 2500 optimized algorithms for real-time image and video analysis.
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v5.0.0 · 186.2 MB
A free, open-source computer vision library with 2500+ algorithms for real-time image and video AI tasks.
Core Features
- 2500+ image processing and computer vision algorithms
- Object detection, face recognition, video analysis
- Deep learning inference (TensorFlow/PyTorch integration)
- Cross-platform (Windows/Linux/macOS/Android/iOS)
- GPU acceleration (CUDA/OpenCL)
What It Can't Do
- •Ensure Python version compatibility; use latest stable release. 2. Do not install both opencv-python and opencv-contrib-python simultaneously to avoid conflicts. 3. Some advanced features require manually building from source with contrib modules. 4. GPU acceleration requires additional configuration for CUDA and cuDNN.
Use Cases
- Security & surveillance: real-time face recognition and behavior analysis
- Autonomous driving: lane detection, obstacle recognition
- Medical imaging: lesion detection, cell counting
- Industrial inspection: defect detection, dimension measurement
Detailed Introduction
OpenCV (Open Source Computer Vision Library) is the industry-standard library for computer vision and machine learning, featuring over 2500 optimized algorithms for object detection, face recognition, image processing, video analysis, and deep learning inference. It supports C++, Python, Java, and MATLAB, and runs on Windows, Linux, macOS, Android, and iOS. Compared to proprietary solutions like MATLAB's Computer Vision Toolbox, OpenCV is completely free, open-source, and offers broader community support and faster updates. Unlike smaller libraries (e.g., Dlib), OpenCV provides a comprehensive ecosystem including deep learning model zoo, GPU acceleration (CUDA/OpenCL), and integration with TensorFlow/PyTorch. Its cross-platform nature and extensive documentation make it the top choice for both research and production deployments in robotics, automotive, security, and AR/VR.
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Getting Started
Install the software
Double-click the downloaded installer and follow the prompts
Install Python (3.7+ recommended), then open terminal or command prompt
Run command: pip install opencv-python
Verify installation: execute in Python: import cv2; print(cv2.__version__)
- Install Python (3.7+ recommended), then open terminal or command prompt
- Run command: pip install opencv-python
- Verify installation: execute in Python: import cv2; print(cv2.__version__)
SHA-256 checksum verified
Checksum extracted from GitHub official Release page
SHA256 Checksum
9c6c1fcea58acdf06edba13148b2246e00c2658143fa51e61ecd370db8c39f63This checksum is extracted from the GitHub Release page. Verify file integrity after download.
All SHA-256 checksums on this platform are extracted from the project's official GitHub Release page, without any modification. You can independently verify them on the GitHub Releases page.
Open Source Transparency
View GitHub SourceUninstall Info
Run 'pip uninstall opencv-python' in terminal, then remove any residual cache files (for complete cleanup).
No Extra Dependencies
Ready to use after download. No additional runtime required.
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