Comments (5)
The image reading, resizing, padding, cropping, etc. of PIL.Image is very slow. These operations run on CPU. You can try on cv2 for speeding up. The cv2 is much faster, but is less accurate and it acts differently on different machines.
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@huanglianghua Do you have examples of how cv2 is less accurate and acts differently on different machines? I have been using cv2 for years and it works great!
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@yxchng The decoding of OpenCV is based on libjpeg. Here is a discussion on OpenCV with different versions/platforms producing different results.
It might not be a problem for other CV applications. But for tracking, the performance is very vulnerable to small changes, as is also indicated in the summary on zhihu at the end of the article.
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the result on my computer is only 1FPS. And I found my GPU was not working at all.Do you know how to solve this problem? Thx
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@melodyhappy You can check my new commit, where all image operations (except for reading) are implemented in cv2, which is much faster.
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Related Issues (20)
- more details about requirements.txt HOT 1
- Hanning Window
- 安装menpo的OpenCV:failed HOT 1
- tset on vot2018 HOT 1
- 训练问题
- 运行demo.py问题 HOT 1
- got10K训练到一半时报错 HOT 2
- 运行train.py出错
- train
- RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED
- raw results on Lasot benchmark
- siamfc.py中关于box坐标的问题 HOT 2
- can't test on vot
- test vot2018 AttributeError: shape HOT 1
- Please ask what datasets the pre-trained model was built on
- Validation loss doesn't decrease(驗證 loss 不會下降)
- Question about torch version
- Train on my own data set
- so what's the differences between test.py and demo.py
- the performance of got10k
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