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Computer vision

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Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding of digital images and videos.

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opencv / opencv

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undocumented *_FULL in cv::ColorConversionCodes

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vrabaud
vrabaud commented Nov 17, 2020
System information (version)
  • OpenCV => 4.5
Detailed description

The documentation for color conversions does not explain what _FULL does, e.g. https://docs.opencv.org/4.5.0/d8/d01/group__imgproc__color__conversions.html#gga4e0972be5de079fed4e3a10e24ef5ef0ae0070d7e97f1e565cc0992d038e5498e
I believe this should be explained for each color conversion as there are different inter

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category: documentation category: imgproc good first issue

google / mediapipe

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[Customization]Mediapipe hand pose inference with other tflite models

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momo1986
momo1986 commented Mar 26, 2020

Hello, dear Mediapipe guys.

I want to inference the hand pose with Mediapipe model and my own model.

I have my own tf-lite models, it can work on the RGB bitmap.

I try to query the RGB bitmap from input frame with data packet.

My code is

private static final String INPUT_VIDEO_STREAM_NAME = "input_video";
processor.addPacketCallback(INPUT_VIDEO_STREAM_NAME, (packet)->{
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android good first issue

kmario23 / deep-learning-drizzle

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Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

machine-learning natural-language-processing deep-neural-networks reinforcement-learning computer-vision deep-learning optimization deep-reinforcement-learning artificial-neural-networks pattern-recognition probabilistic-graphical-models bayesian-statistics artificial-intelligence-algorithms visual-recognition
  • Updated Nov 8, 2020

pytorch / vision

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Clean up the documentation of transforms

datumbox
datumbox commented Dec 1, 2020

📚 Documentation

The methods in functional_pil.py and functional_tensor.py are private but contain a lot of duplicate documentation that is not currently used. It's worth cleaning this up and updating the documentation on functional.py to highlight potential differences or limitations between the two backends.

Here is an example of how we typically highlight backend differences in To

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enhancement good first issue module: documentation
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deep-learning gan machine-learning opencv vision
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