OpenCV中cv2.VideoWriter_fourcc()函数和cv2.VideoWriter()函数的结合使用
opencv
OpenCV: 开源计算机视觉库
项目地址：https://gitcode.com/gh_mirrors/opencv31/opencv
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一、VideoWriter_fourcc()常见的编码参数
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二、使用案例
import cv2
vid = cv2.VideoCapture('./ship.mp4')
video_frame_cnt = int(vid.get(7))
video_width = int(vid.get(3))
video_height = int(vid.get(4))
video_fps = int(vid.get(5))
f = cv2.VideoWriter_fourcc('M', 'P', '4', 'V')#VideoWriter_fourcc为视频编解码器
videoWriter = cv2.VideoWriter('./result.mp4', f, video_fps, (video_width, video_height))
GitHub 加速计划 / opencv31 / opencv
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OpenCV: 开源计算机视觉库
最近提交(Master分支：2 个月前 )
b1d75bf4
Adding github CodeQL #28078
Related pipeline in CI repo: https://github.com/opencv/ci-gha-workflow/pull/280
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake 1 天前
8efc0fd4
dnn: add SVE optimized fastGEMM1T function and SVE dispatch #28055
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
**Description**
This PR enables fastGemm1t vectorized with SVE for AARCH64 architecture that called by recurrent layers and fully connected layers with SVE dispatching mechanism.
**ARM Compatibility:**
Modified the build scripts, and configuration files to ensure compatibility with ARM processors.
**Checklist**
Code changes have been tested on ARM devices (Graviton3).
**Modifications**
- Implemented FastGemm1T kernel in SVE with Vector length agnostic approach.
- Added Flags and checks to call our ported Kernel in Recurrent Layer and FullyConnected layer.
- Changes made to cmakelist.txt to dispatch our ported kernel for SVE.
- Flag OpenCV Dispatch with SVE optimization is added to support SVE implemented kernel for OpenCV. According to OpenCV build optimization https://github.com/opencv/opencv/wiki/CPU-optimizations-build-options
cmake \
-DCPU_BASELINE=NEON\
-D CPU_DISPATCH=SVE\
**Performance Improvement**
- The suggested optimizations Improves the performance of LSTM layer and fully connected layer.
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Name of Test | dnn_neon | dnn_sve | dnn_sve vs dnn_neon(x-factor)
-- | -- | -- | --
lstm::Layer_LSTM::BATCH=1, IN=64, HIDDEN=192, TS=100 | 2.878 | 2.326 | 1.24
lstm::Layer_LSTM::BATCH=1, IN=192, HIDDEN=192, TS=100 | 4.162 | 3.08 | 1.35
lstm::Layer_LSTM::BATCH=1, IN=192, HIDDEN=512, TS=100 | 18.627 | 16.152 | 1.15
lstm::Layer_LSTM::BATCH=1, IN=1024, HIDDEN=192, TS=100 | 10.98 | 7.976 | 1.38
lstm::Layer_LSTM::BATCH=64, IN=64, HIDDEN=192, TS=2 | 4.41 | 3.459 | 1.27
lstm::Layer_LSTM::BATCH=64, IN=192, HIDDEN=192, TS=2 | 6.567 | 4.807 | 1.37
lstm::Layer_LSTM::BATCH=64, IN=192, HIDDEN=512, TS=2 | 28.471 | 22.909 | 1.24
lstm::Layer_LSTM::BATCH=64, IN=1024, HIDDEN=192, TS=2 | 15.491 | 12.537 | 1.24
lstm::Layer_LSTM::BATCH=128, IN=64, HIDDEN=192, TS=2 | 8.848 | 6.821 | 1.3
lstm::Layer_LSTM::BATCH=128, IN=192, HIDDEN=192, TS=2 | 12.969 | 9.522 | 1.36
lstm::Layer_LSTM::BATCH=128, IN=192, HIDDEN=512, TS=2 | 55.52 | 45.746 | 1.21
lstm::Layer_LSTM::BATCH=128, IN=1024, HIDDEN=192, TS=2 | 31.226 | 26.132 | 1.19
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Name of Test | dnn_neon | dnn_sve | dnn_sve vs dnn_neon(x-factor)
-- | -- | -- | --
fc::Layer_FullyConnected::([5, 16, 512, 128], 256, false, OCV/CPU) | 5.086 | 4.483 | 1.13
fc::Layer_FullyConnected::([5, 16, 512, 128], 256, true, OCV/CPU) | 8.512 | 8.347 | 1.02
fc::Layer_FullyConnected::([5, 16, 512, 128], 512, false, OCV/CPU) | 9.467 | 8.965 | 1.06
fc::Layer_FullyConnected::([5, 16, 512, 128], 512, true, OCV/CPU) | 14.855 | 13.527 | 1.1
fc::Layer_FullyConnected::([5, 16, 512, 128], 1024, false, OCV/CPU) | 18.821 | 18.023 | 1.04
fc::Layer_FullyConnected::([5, 16, 512, 128], 1024, true, OCV/CPU) | 27.558 | 24.966 | 1.1
fc::Layer_FullyConnected::([5, 512, 384, 0], 256, false, OCV/CPU) | 0.924 | 0.804 | 1.15
fc::Layer_FullyConnected::([5, 512, 384, 0], 256, true, OCV/CPU) | 1.259 | 1.126 | 1.12
fc::Layer_FullyConnected::([5, 512, 384, 0], 512, false, OCV/CPU) | 1.957 | 1.655 | 1.18
fc::Layer_FullyConnected::([5, 512, 384, 0], 512, true, OCV/CPU) | 2.831 | 2.775 | 1.02
fc::Layer_FullyConnected::([5, 512, 384, 0], 1024, false, OCV/CPU) | 5.92 | 6.379 | 0.93
fc::Layer_FullyConnected::([5, 512, 384, 0], 1024, true, OCV/CPU) | 8.924 | 8.993 | 0.99
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1 天前
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