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How to construct segmentation dataset for PaddleX

After completing image anotation by EISeg，by applying eiseg2paddlex.py in tool file, yoou can quickly convert data to PaddleX format for training. Execute the following command:

python eiseg2paddlex.py -d save_folder_path -o image_folder_path [-l label_folder_path] [-s split_rate]
  • save_folder_path: path to save PaddleX format data.
  • image_folder_path: path of data to be converted.
  • label_folder_path: path of the label, it is not required, if it is not filled, default is "image_folder_path/label".
  • split_rate: The devision ratio of training set and validation set, default is 0.9.

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Semantic labels to instance labels

The semantic segmentation label is converted to the instance segmentation label (the original label is in range [0,255], and the result is a single-channel image that uses a palette to color. Through the semantic2instance.py, the semantic segmentation data marked by EISeg can be converted into instance segmentation data. Use the following method:

python semantic2instance.py -o label_path -d save_path

Parameters:

  • label_path: path to semantic label, required.
  • save_path: path to instance label, required.

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