labelme4.5.9.zip

# Instance Segmentation Example ## Annotation ```bash labelme data_annotated --labels labels.txt --nodata --validatelabel exact --config '{shift_auto_shape_color: -2}' labelme data_annotated --labels labels.txt --nodata --labelflags '{.*: [occluded, truncated], person: [male]}' ```  ## Convert to VOC-format Dataset ```bash # It generates: # - data_dataset_voc/JPEGImages # - data_dataset_voc/SegmentationClass # - data_dataset_voc/SegmentationClassVisualization # - data_dataset_voc/SegmentationObject # - data_dataset_voc/SegmentationObjectVisualization ./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt ``` <img src="data_dataset_voc/JPEGImages/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationClassVisualization/2011_000003.jpg" width="33%" /> <img src="data_dataset_voc/SegmentationObjectVisualization/2011_000003.jpg" width="33%" /> Fig 1. JPEG image (left), JPEG class label visualization (center), JPEG instance label visualization (right) Note that the label file contains only very low label values (ex. `0, 4, 14`), and `255` indicates the `__ignore__` label value (`-1` in the npy file). You can see the label PNG file by following. ```bash labelme_draw_label_png data_dataset_voc/SegmentationClassPNG/2011_000003.png # left labelme_draw_label_png data_dataset_voc/SegmentationObjectPNG/2011_000003.png # right ``` <img src=".readme/draw_label_png_class.jpg" width="33%" /> <img src=".readme/draw_label_png_object.jpg" width="33%" /> ## Convert to COCO-format Dataset ```bash # It generates: # - data_dataset_coco/JPEGImages # - data_dataset_coco/annotations.json ./labelme2coco.py data_annotated data_dataset_coco --labels labels.txt ```