def test_shapes_to_label(): img, data = get_img_and_data() label_name_to_value = {} for shape in data['shapes']: label_name = shape['label'] label_value = len(label_name_to_value) label_name_to_value[label_name] = label_value cls = shape_module.shapes_to_label(img.shape, data['shapes'], label_name_to_value) assert cls.shape == img.shape[:2]
def get_img_and_lbl(): img, data = get_img_and_data() label_name_to_value = {"__background__": 0} for shape in data["shapes"]: label_name = shape["label"] label_value = len(label_name_to_value) label_name_to_value[label_name] = label_value n_labels = max(label_name_to_value.values()) + 1 label_names = [None] * n_labels for label_name, label_value in label_name_to_value.items(): label_names[label_value] = label_name lbl, _ = shape_module.shapes_to_label(img.shape, data["shapes"], label_name_to_value) return img, lbl, label_names
def main(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument('input_dir', help='input annotated directory') parser.add_argument('output_dir', help='output dataset directory') parser.add_argument('--labels', help='labels file', required=True) 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, 'SegmentationClass')) os.makedirs(osp.join(args.output_dir, 'SegmentationClassPNG')) os.makedirs(osp.join(args.output_dir, 'SegmentationClassVisualization')) print('Creating dataset:', args.output_dir) class_names = [] class_name_to_id = {} for i, line in enumerate(open(args.labels).readlines()): class_id = i - 1 # starts with -1 class_name = line.strip() class_name_to_id[class_name] = class_id if class_id == -1: assert class_name == '__ignore__' continue elif class_id == 0: assert class_name == '_background_' class_names.append(class_name) 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) #colormap1 = labelme.utils.label_colormap(255) colormap1 = np.zeros((256, 3), dtype=float) colormap1[0] = [0, 0, 0] colormap1[1] = [255 ,0 ,0] #[127, 127, 127] colormap1[2] = [191, 191, 191] colormap1[3] = [255, 255, 255] colormap1[4] = [64, 64, 64] colormap1 = colormap1.astype(np.float32) / 255 print(colormap1) for label_file in glob.glob(osp.join(args.input_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_lbl_file = osp.join( args.output_dir, 'SegmentationClass', base + '.npy') out_png_file = osp.join( args.output_dir, 'SegmentationClassPNG', base + '.png') out_viz_file = osp.join( args.output_dir, 'SegmentationClassVisualization', base + '.jpg', ) data = json.load(f) img_file = osp.join(osp.dirname(label_file), data['imagePath']) #img_name = str(label_file).split("/")[1].strip(".json")+".jpg" #print(img_name) #img_file = osp.join(osp.dirname(label_file), img_name) #img_file = data['imagePath'] img = np.asarray(PIL.Image.open(img_file)) PIL.Image.fromarray(img).save(out_img_file) lbl = shapes_to_label( img_shape=img.shape, shapes=data['shapes'], label_name_to_value=class_name_to_id, ) lblsave(out_png_file, lbl) np.save(out_lbl_file, lbl) viz = labelme.utils.draw_label( lbl, img, class_names, colormap=colormap1) PIL.Image.fromarray(viz).save(out_viz_file)