Hello, Community! This post is a summary of development on OpenCV 5 in the last week. You can always find the most up-to-date information on the OpenCV 5 Work Board. Many thanks to Jia Wu for her excellent notes!

Latest Developments from the OpenCV Core Team:

• Unified Samples for Edge Detection: Improved and unified samples for edge detection in PR #25515, enhancing the user experience and consistency across different edge detection algorithms. These submissions are awaiting review.
• DNN Image Classification Samples: PR #25519 introduces improved samples for DNN image classification, streamlining the process and providing users with more efficient and informative examples. These submissions are also awaiting review.
• Combined C++ Samples Cleanup: PR #25252 proposes a combined cleanup of C++ samples, addressing issues and enhancing clarity and consistency. This PR is awaiting review, consolidating efforts to improve the quality of sample code.
• Exploring Semantic Segmentation with U-2-Net: For semantic segmentation tasks, we’re considering the usage of U-2-Net, an effective and efficient model for producing high-quality segmentation masks.
• Continued Work on G-API: Our efforts on G-API continue as we strive to enhance its capabilities and performance.
• Advancements in the New Inference Engine: We’re making progress on the new inference engine, with a focus on improving the ONNX parser for seamless integration with OpenCV.
• DNN Support Enhancements: We’re enhancing DNN support with improvements such as 0D/1D support and OpenVINO backend integration. Next, we plan to work on additional features like bool layers and logical layers to further enhance the functionality and flexibility of the DNN module.
• HAL Improvements: We’re making strides in improving the Hardware Abstraction Layer (HAL), optimizing performance and efficiency across different hardware architectures.
• OpenCV Numpy Integration: Integration with OpenCV Numpy is ongoing, providing users with enhanced capabilities for data manipulation and analysis.
• Documentation Enhancements: We’re actively working on improving documentation, ensuring it remains comprehensive, up-to-date, and accessible to users of all levels.
• fp16 Intrinsics PR Merged: A PR for fp16 intrinsics has been merged, enhancing performance and efficiency in certain operations, particularly on hardware that supports half-precision floating-point arithmetic.
• MacOS Building Warning PR Awaiting Review: A PR addressing building warnings on MacOS is awaiting review, ensuring smooth integration and compatibility with MacOS platforms.
• GoTurn Model Deletion: The GoTurn model has been deleted, streamlining the model zoo and focusing resources on more relevant and impactful models.
• Experiments with ann-benchmark Framework: We’ve conducted experiments with the ann-benchmark framework.
• Creation of Segmentation Sample: We’re creating a segmentation sample to showcase advanced segmentation techniques and provide users with practical examples for segmentation tasks.

How to Contribute to OpenCV:

Interested in contributing to OpenCV? Follow these steps:

1. Check out the Contribution Guidelines on the OpenCV Wiki for detailed instructions on how to contribute code, report issues, and participate in discussions.
2. Familiarize yourself with the OpenCV development process, including coding standards and conventions, version control practices, and testing procedures.
3. Join the vibrant OpenCV community on GitHub and start collaborating with developers from around the world. Your contributions, no matter how big or small, play a crucial role in shaping the future of OpenCV.

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Thanks for reading this OpenCV 5 update, we’ll be back with more soon.