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docs/annotation/jingling2seg.py

+#!/usr/bin/env python
+
+from __future__ import print_function
+
+import argparse
+import glob
+import json
+import os
+import os.path as osp
+import sys
+
+import numpy as np
+import PIL.Image
+
+import labelme
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument('label_dir', help='input annotated directory')
+    parser.add_argument('output_dir', help='output dataset directory')
+    args = parser.parse_args()
+
+    if osp.exists(args.output_dir):
+        print('Output directory already exists:', args.output_dir)
+        sys.exit(1)
+
+    os.makedirs(args.output_dir)
+    os.makedirs(osp.join(args.output_dir, 'JPEGImages'))
+    os.makedirs(osp.join(args.output_dir, 'SegmentationClassPNG'))
+    print('Creating dataset:', args.output_dir)
+
+    # get the all class names for the given dataset
+    class_names = ['_background_']
+    for label_file in glob.glob(osp.join(args.label_dir, '*.json')):
+        with open(label_file) as f:
+            data = json.load(f)
+            if data['outputs']:
+                for output in data['outputs']['object']:
+                    name = output['name']
+                    cls_name = name
+                    if not cls_name in class_names:
+                        class_names.append(cls_name)
+
+    class_name_to_id = {}
+    for i, class_name in enumerate(class_names):
+        class_id = i  # starts with 0
+        class_name_to_id[class_name] = class_id
+        if class_id == 0:
+            assert class_name == '_background_'
+    class_names = tuple(class_names)
+    print('class_names:', class_names)
+
+    out_class_names_file = osp.join(args.output_dir, 'class_names.txt')
+    with open(out_class_names_file, 'w') as f:
+        f.writelines('\n'.join(class_names))
+    print('Saved class_names:', out_class_names_file)
+
+    for label_file in glob.glob(osp.join(args.label_dir, '*.json')):
+        print('Generating dataset from:', label_file)
+        with open(label_file) as f:
+            base = osp.splitext(osp.basename(label_file))[0]
+            out_img_file = osp.join(
+                args.output_dir, 'JPEGImages', base + '.jpg')
+            out_png_file = osp.join(
+                args.output_dir, 'SegmentationClassPNG', base + '.png')
+
+            data = json.load(f)
+
+            data_shapes = []
+            if data['outputs']:
+                for output in data['outputs']['object']:
+                    if 'polygon' in output.keys():
+                        polygon = output['polygon']
+                        name = output['name']
+
+                        # convert jingling format to labelme format
+                        points = []
+                        for i in range(1, int(len(polygon) / 2) + 1):
+                            points.append([polygon['x' + str(i)], polygon['y' + str(i)]])
+                        shape = {'label': name, 'points': points, 'shape_type': 'polygon'}
+                        data_shapes.append(shape)
+
+            img_file = osp.join(osp.dirname(label_file), data['path'])
+            img = np.asarray(PIL.Image.open(img_file))
+            PIL.Image.fromarray(img).save(out_img_file)
+
+            lbl = labelme.utils.shapes_to_label(
+                img_shape=img.shape,
+                shapes=data_shapes,
+                label_name_to_value=class_name_to_id,
+            )
+
+            if osp.splitext(out_png_file)[1] != '.png':
+                out_png_file += '.png'
+            # Assume label ranses [0, 255] for uint8,
+            if lbl.min() >= 0 and lbl.max() <= 255:
+                lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='L')
+                lbl_pil.save(out_png_file)
+            else:
+                raise ValueError(
+                    '[%s] Cannot save the pixel-wise class label as PNG. '
+                    'Please consider using the .npy format.' % out_png_file
+                )
+
+
+if __name__ == '__main__':
+    main()