labelme2seg.py

# coding: utf8
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import argparse
import glob
import json
import os
import os.path as osp

import numpy as np
import PIL.Image
import labelme

from gray2pseudo_color import get_color_map_list


def parse_args():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('input_dir', help='input annotated directory')
    return parser.parse_args()


def main(args):
    output_dir = osp.join(args.input_dir, 'annotations')
    if not osp.exists(output_dir):
        os.makedirs(output_dir)
        print('Creating annotations directory:', output_dir)

    # get the all class names for the given dataset
    class_names = ['_background_']
    for label_file in glob.glob(osp.join(args.input_dir, '*.json')):
        with open(label_file) as f:
            data = json.load(f)
            for shape in data['shapes']:
                label = shape['label']
                cls_name = label
                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.input_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)

    color_map = get_color_map_list(256)

    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_png_file = osp.join(output_dir, base + '.png')

            data = json.load(f)

            img_file = osp.join(osp.dirname(label_file), data['imagePath'])
            img = np.asarray(PIL.Image.open(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 ranges [0, 255] for uint8,
            if lbl.min() >= 0 and lbl.max() <= 255:
                lbl_pil = PIL.Image.fromarray(lbl.astype(np.uint8), mode='P')
                lbl_pil.putpalette(color_map)
                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__':
    args = parse_args()
    main(args)