diff --git a/docs/annotation/jingling2seg.py b/docs/annotation/jingling2seg.py new file mode 100644 index 0000000000000000000000000000000000000000..18626157738671afc8415471d108e9d6a04f8495 --- /dev/null +++ b/docs/annotation/jingling2seg.py @@ -0,0 +1,110 @@ +#!/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()