| # Automatic Annotations | |
| We provide gradio examples to obtain annotations that are aligned to our pretrained production-ready models. | |
| Just run | |
| python gradio_annotator.py | |
| Since everyone has different habit to organize their datasets, we do not hard code any scripts for batch processing. But "gradio_annotator.py" is written in a super readable way, and modifying it to annotate your images should be easy. | |
| In the gradio UI of "gradio_annotator.py" we have the following interfaces: | |
| ### Canny Edge | |
| Be careful about "black edge and white background" or "white edge and black background". | |
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| ### HED Edge | |
| Be careful about "black edge and white background" or "white edge and black background". | |
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| ### MLSD Edge | |
| Be careful about "black edge and white background" or "white edge and black background". | |
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| ### MIDAS Depth and Normal | |
| Be careful about RGB or BGR in normal maps. | |
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| ### Openpose | |
| Be careful about RGB or BGR in pose maps. | |
| For our production-ready model, the hand pose option is turned off. | |
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| ### Uniformer Segmentation | |
| Be careful about RGB or BGR in segmentation maps. | |
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