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If you convert from Yolo to Supervisely (supervisely_yolo -t y2s) then you need to install the OpenCV python package: pip install opencv-python
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You can specify the location of your source dataset using -p flag
- example 1 => python supervisely_yolo.py -p C:\yolo -t y2s
- example 2 => python supvervisely_yolo.py -p C:\Users\Delilovic\Desktop ([-t s2y] is not required as it is the default flag)
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You can specify if you want to skip copying images from the source to the destination dataset with the -s flag
- WARNING: consider using this flag if you have a lot of images and might run out of space
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When downloading images from Supervisely, images get the extension attached to their names
- e.g. downloading foo.jpg gets renamed as foo.jpg.jpg
- this behaviour is handled by the supervisely_yolo.py script, but it also means you can not currently convert Supervisely data structure created by y2s flag back to Yolo data using the s2y flag (this shouldn't be a use case anyway but is worth mentioning here)
- Please follow this structure strictly
- you can not have two different Supervisely datasets at the moment (if you do, put everything into the dataset folder)
- you can not have Yolo images and labels in one folder (if you do, separate them into labels and images folder)
- data structure is case sensitive (e.g. yolo can not be Yolo)
├── supervisely
├── meta.json
└── dataset1
├── img
│ ├── any_name.jpg or(.jpeg, .png)
│ └── ...
└── ann
├── any_name.json
└── ...
└── dataset2
├── img
│ ├── any_name.jpg or(.jpeg, .png)
│ └── ...
└── ann
├── any_name.json
└── ...
├── yolo
├── data.yaml
├── images
│ ├── any_name.jpg or(.jpeg, .png)
│ └── ...
└── labels
├── any_name.txt
└── ...
- This is the first version and many updates will be required, everybody interested is gladly invited to contribute