opencv/opencv
Apr 2026 – Jun 2026
3 Months active
Languages Used
C++
Technical Skills
C++ developmentcross-platform compatibilityvideo processingC++Computer VisionPerformance Optimization
Teddy-Yangjiale
PROFILE
Teddy-yangjiale
Over a three-month period, contributed to the opencv/opencv repository by developing and optimizing core components in C++ for computer vision and deep learning workloads. Addressed cross-platform build issues by refactoring VideoIO to ensure compatibility with older FFmpeg versions, improving CI stability and portability. Enhanced error handling in the DirectShow backend to prevent application crashes, and accelerated neural network inference by implementing a vectorized Sigmoid function using universal intrinsics. Delivered RISC-V RVV-specific performance improvements, including optimized depth conversions and a new LMUL=1 convolution kernel for the DNN module, enabling efficient vectorized processing on RV64GCV hardware for real-time imaging applications.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
7Total
Bugs
2
Commits
7
Features
3
Lines of code
363
Activity Months3
Your Network
234 peopleSame Organization
@mail.sustech.edu.cn17
Shared Repositories
217
Work History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026: OpenCV gained a new RVV-accelerated convolution kernel to boost DNN performance on RV64GCV hardware. Delivered a dedicated LMUL=1 kernel for convBlock_F32 (ConvolutionLayer) within the DNN module, enabling vectorized processing for selected matrix sizes and integrating with the existing ConvolutionBlock path. Associated commit: c831290769362d1a63d8a86923f8d8f70b31b237. Business value: faster inference and improved energy efficiency for edge deployments, directly supporting performance goals for real-time imaging and computer vision workloads.
1 Commits • 1 Features
Jun 1, 2026June 2026: OpenCV gained a new RVV-accelerated convolution kernel to boost DNN performance on RV64GCV hardware. Delivered a dedicated LMUL=1 kernel for convBlock_F32 (ConvolutionLayer) within the DNN module, enabling vectorized processing for selected matrix sizes and integrating with the existing ConvolutionBlock path. Associated commit: c831290769362d1a63d8a86923f8d8f70b31b237. Business value: faster inference and improved energy efficiency for edge deployments, directly supporting performance goals for real-time imaging and computer vision workloads.
June 2026
May 2026
5 Commits • 2 Features
May 1, 2026May 2026 OpenCV developer monthly summary focusing on stability, performance, and portability across Windows video capture, DNN workloads, and RVV-enabled hardware. Key outcomes include stabilizing the DirectShow backend to avoid crashes, accelerating neural-network workloads with a vectorized Sigmoid, and delivering RVV HAL optimizations that improve throughput and numerical correctness across depth conversions and norm calculations.
May 2026
5 Commits • 2 Features
May 1, 2026May 2026 OpenCV developer monthly summary focusing on stability, performance, and portability across Windows video capture, DNN workloads, and RVV-enabled hardware. Key outcomes include stabilizing the DirectShow backend to avoid crashes, accelerating neural-network workloads with a vectorized Sigmoid, and delivering RVV HAL optimizations that improve throughput and numerical correctness across depth conversions and norm calculations.
April 2026
1 Commits
Apr 1, 2026April 2026: Stabilized VideoIO build compatibility with older FFmpeg versions in opencv/opencv. Implemented a patch that replaces the non-standard AV_TIME_BASE_Q macro with the standard av_make_q(1, AV_TIME_BASE), addressing cross-compiler issues under MSVC /std:c++17 and ensuring compatibility with FFmpeg 4.4.x and older. Leveraged existing cap_ffmpeg_impl.hpp polyfill to keep changes zero-overhead and safe for very old FFmpeg versions. Patch corresponds to a PR merged into the repo (PR #28894).
1 Commits
Apr 1, 2026April 2026: Stabilized VideoIO build compatibility with older FFmpeg versions in opencv/opencv. Implemented a patch that replaces the non-standard AV_TIME_BASE_Q macro with the standard av_make_q(1, AV_TIME_BASE), addressing cross-compiler issues under MSVC /std:c++17 and ensuring compatibility with FFmpeg 4.4.x and older. Leveraged existing cap_ffmpeg_impl.hpp polyfill to keep changes zero-overhead and safe for very old FFmpeg versions. Patch corresponds to a PR merged into the repo (PR #28894).
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability82.8%
Architecture94.2%
Performance94.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++C++ developmentComputer VisionPerformance OptimizationRISC-V architecturecross-platform compatibilitydeep learningerror handlingimage processinglow-level programmingperformance optimizationvectorizationvideo processing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline