Add small object tracking demo using SAHI #188
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JuanFKurucz
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JuanFKurucz
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|
@fcakyon maybe you have any comments or suggestions here :) |
demos/sahi/src/utils.py
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| default=True, | ||
| ) | ||
| parser.add_argument( | ||
| "--slice-height", |
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To keep things simpler, we can use just one --slice-size argument instead of two --slice-height and --slice-width and set both slice_height and slice_width as slice_size.
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This is an excellent suggestion. Thanks! I tackled it in the new commit
demos/sahi/src/utils.py
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| default=0.2, | ||
| ) | ||
| parser.add_argument( | ||
| "--overlap-width-ratio", |
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To keep things simpler, we can use just one --overlap-ratio argument instead of two --overlap-width-ratio and --overlap-height-ratio and set both overlap_width_ratio and overlap_height_ratio as overlap_ratio.
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I love this suggestion too, I tackled it to make everything simpler!
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Wow @JuanFKurucz, thanks for this awesome demo! @dekked added some comments. In overall, it looks good 💯 |
demos/sahi/requirements.txt
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| @@ -0,0 +1,3 @@ | |||
| opencv-python==4.2.0.32 | |||
| sahi==0.10.5 | |||
| yolov5==6.1.8 | |||
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Lets use the latest yolov5 and sahi versions :)
| yolov5==6.1.8 | |
| yolov5==6.2.1 |
demos/sahi/requirements.txt
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| @@ -0,0 +1,3 @@ | |||
| opencv-python==4.2.0.32 | |||
| sahi==0.10.5 | |||
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Lets use the latest yolov5 and sahi versions :)
| sahi==0.10.5 | |
| sahi==0.10.6 |
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Awesome! I have updated it and recreated the demo video just in case!
demos/sahi/README.md
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| # SAHI: Slicing Aided Hyper Inference Demo | ||
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| An example of how to use Norfair along with [SAHI: Slicing Aided Hyper Inference](https://github.com/obss/sahi) | ||
| to perform detection and tracking on small objects using YOLOv5x as the detector. |
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| to perform detection and tracking on small objects using YOLOv5x as the detector. | |
| to perform detection and tracking on small objects using [YOLOv5x](https://github.com/ultralytics/yolov5) as the detector. |
JuanFKurucz
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This PR implements a demo using SAHI: Slicing Aided Hyper Inference to be able to track small objects in a video.