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Latest Version
OpenCV 4.12.0 LATEST
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Windows XP / Vista / Windows 7 / Windows 8 / Windows 10
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Filename
opencv-4.12.0-windows.exe
Originally developed by Intel, OpenCV has grown into one of the most widely used frameworks for image processing, object detection, facial recognition, and deep learning tasks.
It provides a robust and efficient platform for developers, researchers, and engineers working in fields such as robotics, augmented reality, surveillance, and medical imaging.
Key Features
Extensive Computer Vision Capabilities – Offers a vast range of image processing techniques, including edge detection, segmentation, and feature extraction.
Machine Learning Integration – Supports deep learning models via frameworks like TensorFlow, PyTorch, and Caffe.
Multi-Language Support – Works with Python, C++, Java, and MATLAB, making it versatile for developers.
Cross-Platform Compatibility – Runs on Windows, Linux, macOS, iOS, and Android.
Optimized for Performance – Utilizes hardware acceleration via OpenCL, CUDA, and Intel’s MKL.
Video Processing – Features real-time video capture, processing, and motion detection.
3D Reconstruction – Offers tools for depth estimation, stereo vision, and point cloud generation.
Augmented Reality and Facial Recognition – Provides functionalities for AR applications and biometric identification.
User Interface
Since OpenCV is a library, it does not have a traditional graphical user interface (GUI).
Instead, it is used via command-line interfaces and integrated into development environments such as Visual Studio, PyCharm, and Jupyter Notebook.
Various wrapper libraries, such as OpenCV GUI (Qt-based) and OpenCV-Python, enable visualization of image and video processing tasks.
Installation and Setup
Building from Source:
- Install CMake, Visual Studio, and Ninja.
- Configure using CMake, enabling CUDA or OpenCL if necessary.
- Compile and integrate with Python or C++.
Is OpenCV free?
Yes, OpenCV is open-source and licensed under Apache 2.0, meaning it's free for both personal and commercial use.
What programming languages does OpenCV support?
It supports C++, Python, Java, and MATLAB, making it accessible for a wide range of developers.
Can OpenCV be used with deep learning models?
Yes, it integrates with TensorFlow, PyTorch, and Caffe for deep learning applications.
Does OpenCV support GPU acceleration?
Yes, it supports CUDA, OpenCL, and Vulkan for GPU acceleration.
What industries use OpenCV?
OpenCV is used in healthcare, robotics, security, automotive, and entertainment industries for tasks like facial recognition, autonomous driving, and AR applications.
System Requirements
- OS: Windows 10 or Windows 11
- Processor: Intel or AMD 64-bit CPU
- RAM: Minimum 4GB (8GB+ recommended)
- Storage: ~1GB for the base library; more for additional models and dependencies
- GPU: Optional, but NVIDIA CUDA-supported GPUs can improve performance
- Free and open-source with an active community.
- Extensive features for image processing, video analysis, and AI.
- High performance with GPU acceleration.
- Compatible with multiple programming languages.
- Works on various platforms including Windows, Linux, and macOS.
- No built-in GUI, making it challenging for beginners.
- Requires coding knowledge to utilize fully.
- Some features have a steep learning curve.
- GPU acceleration setup can be complex.
- Lacks official technical support (community-driven help only).
OpenCV remains one of the most powerful and versatile tools for computer vision and machine learning applications on Windows. While it may not be the most user-friendly option for beginners, it offers unparalleled flexibility and performance for developers working on AI-driven image processing projects.
With its strong community support, cross-platform functionality, and continuous improvements, OpenCV is an essential tool for anyone involved in computer vision.
Also Available: Download OpenCV for Mac
What's new in this version:
Core Module:
- Added user-defined logger callback
- Added reinterpret() method to cv::Mat
- Fixed empty ND-array construction
- Fixed bug with int64 support for FileStorage
- Fixed cv::meanStdDev overflow for large images
- Useed HAL for cv::mean function too
- Vectorized cv::normalize / cv::norm
- Enabled SIMD_SCALABLE for exp and sqrt
- vectorized normDiff with universal intrinsics
- Refactored normDiff in hal_rvv and extended with support of more data types
- Further vectorized copyTo with mask
- Fix POWN OpenCL implementation
- Restored legacy universal intrinsics operators in a separate header
- Improved solveCubuc accuracy:
- Deprecated copyData Parameter in cv::UMat construction from std::vector and always copy data
Imgproc Module:
- Optimized and reduced memory consumption in cv::findContours
- Fixed integer overflow in cv::HoughLinesProbabilistic with very large input images
- Fixed several potential overflows in image warping
- Improved robustness for ellipse fitting
- Added cv::THRESH_DRYRUN flag to get adaptive threshold values without input thresholding
- Fixed cv::getPerspectiveTransform for singular case
- Optimized bayer2Gray with universal intrinsics
- Added optional mask to cv::threshold
- Accelerated cv::remap calculation with multi-threading, when cv::WARP_INVERSE_MAP is used
- Reworked HSV color conversion tables initialization in OpenCL branch to fix possible data raise
- Fixed bilateral filter's sigma color and sigma space issue
- Impoved performance of medianblur
- Added cv::getClosestEllipsePoints function to get the closest point on an ellipse
- Corrected IPP implementation for cv::distanceTransform results with single thread
- Fixed cv::matchTemplate with mask crash
- Added diamond structuring element to morphology operations
- Improved cv::intersectConvexConvex accuracy in corner cases
Calib3d module:
- Fixed inliers updating in homography estimation
- Added cv::solvePnPRansac implementation for the fisheye camera model
- Fixed different marker ordering with cv::findChessboardCornersSBWithMeta and cv::CALIB_CB_LARGER flag.
- Fixed logical mistake in FilterSpecklesImpl
- Optimized undistort points for the fisheye camera model
- Optimized new camera matrix estimation and some related functions
- Added warning if projected axes are out of camera frame in cv::drawAxes function
- Added image dimension check to avoid StereoSGBM non-determinism
- Fixed bugs in SQPNP orthogonalization and simplified column vectors copying
DNN Module:
- Added TFLite StridedSllice (without strides but just Slice) and enable strides for TF importers
- Added fusion handler of conv+eltwise in case of multi-output node (i.e. Split)
- Added new ops import in TFLite parser: SUB, SQRT, DIV, NEG, SQUARED_DIFFERENCE, SUM
- Fxied scalars and 1d tensors support in TFLite parser
- Reduced mumber of NHWC<->NCHW conversions in TFLite parser
- Added checks before getting latest pin in Net::Impl::getLatestLayerPin
- Added OpenVINO NPU support
- Added more operations to CANN backend
Objdetect module:
- Extend ArUcoDetector to run multiple dictionaries in an efficient manner
- Fixed rotated aruco marker board generation
- Fixed incorrect Aruco marker detection near image edge
- Fixed invalid vector access in QR de/encoder
- Fix QR code encoder with auto version
- Aded QRCode ECI encoding support
- Made ChAruco board consistency check optional
Photo Module:
- Added 16-bit support to cv::fastNlMeansDenoising and updated tests
- Local decolor pipeline optimization
- Fixed Triangle Weights used by Debevec algorithm being non zero at extremes
- Fixed NaNs in HDR Triangle Weights and Tonemapping and Update LDR Ground Truth in tutorial
VideoIO Module:
- Fixed invalid custom FFmpeg options handling provided with environment variable
- Fixed default cv::CAP_PROP_ORIENTATION_ATUO behaviour
- Improved camera opening performance when with DShow backed, if custom options are provided
- Added zoom support into Android native camera capture
- Fixed V4L frame size for non-BGR output
- Added Orbbec Gemini 330 camera support
- Fixed cv::VideoWriter failure on writing colorless images
- Fixed Android setCameraIndex issue
Imgcodecs:
- Extended image I/O API with metadata support
- Added in-memory animation encoding and decoding
- Added hidden frame support for Animated PNG
- Added GIF decode and encode for imgcodecs
- Added animated WebP Support
- Added GDAL multi-channel support
- Fixed AVIF linkage on Windows
- Significantly improved PNG and Animated PNG files handing
- Added several fixes for libspng integration
- Fixed memory leak in JpegXL decoder
- Added support for cv::IMREAD_UNCHANGED and other ImreadFlags into JpegXL implementation
- Implemented imdecode() for JpegXL to directly read from memory
- Added lossless compression support for JpegXL
- Fixed cv::imread() implementation for 16-bit png with libspng back-end
Highgui Module:
- displayOverlay doesn't disappear after timeout
- Fix closing of windows when using the Qt backend
- Fixed QT window resize regression
G-API module:
- Workaround for successfull append of OpenVINO Execution Provider: Moved creation of 'Ort::Env' before it
- Adding AddRgbFeature(), and improving robustness in ComputeRgbDistance
- Added MS Media Foundation availability check in G-API too
Video Module:
- Added trackers factory with pre-loaded dnn models
HAL:
- Extracted HAL implementations as dedicated folder
- Implemented new HAL for RISC-V RVV 1.0 platforms, see all changes by query
- Refactored OpenVX implementation as HAL
- Partially refactored Intel IPP integration as HAL
- Fixed HALs dependency from thre cv namespace
- Updated KleidiCV HAL for ARM to version 0.5
- Extended FastCV HAL for Qualcomm platforms
- Added new HAL entries for cv::sum
Python, Java and JavaScript bindings:
- Added conditional inclusion support to header parser
- Updated predefined_types.py
- Added bindings for animations
- Fix wrong python type hints for cv::imread
- Replaced tostring() and fromstring() call in tests and samples for compatibility with modern Python
- Replaced ndarray.ptp() with np.ptp() for NumPy 2.0 Compatibility
- Extended Java bindings generation in DNN
- Fixed std::vector<Point3f> handling in JS wrappers
- Added cv::VideoCapture buffered stream constructor into Java
- Extended bindings for cv::HoughLinesWithAccumulator
- Fixed Kotlin 2.0 internal error for unsafe coercions
Platforms support:
- Fixed OpenBLAS detection on Linux
- Fixed compatibility with CMake 4
- Fixed Android build with Vulkan support
- Fixed VSX intrinsics (Power) implementation
- Added CMake checks for getauxval and elf_aux_info for POWER architecture
- Fixed build for openBSD
- Added initial support for Nvidia Blackwell GPU arch in CUDA
- Fixed cv namespace dependency in Carotene HAL
- Fixed _DEBUG/NDEBUG handling across modules
- Fixed build for QNX 7.0
- Switched to statically linked FastCV
- Forced C++17 Standard for CUDA targets when CUDA Toolkit >=12.8
- Fixed build for Windows ARM64EC
- Fixed filesystem support under Cygwin environment
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