# 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 os import os.path as osp import sys import numpy as np from PIL import Image def parse_args(): parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument( 'dir_or_file', help='input gray label directory or file list path') parser.add_argument('output_dir', help='output colorful label directory') parser.add_argument('--dataset_dir', help='dataset directory') parser.add_argument('--file_separator', help='file list separator') return parser.parse_args() def get_color_map_list(num_classes): """ Returns the color map for visualizing the segmentation mask, which can support arbitrary number of classes. Args: num_classes: Number of classes Returns: The color map """ color_map = num_classes * [0, 0, 0] for i in range(0, num_classes): j = 0 lab = i while lab: color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j)) color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j)) color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j)) j += 1 lab >>= 3 return color_map def gray2pseudo_color(args): """将灰度标注图片转换为伪彩色图片""" input = args.dir_or_file output_dir = args.output_dir if not osp.exists(output_dir): os.makedirs(output_dir) print('Creating colorful label directory:', output_dir) color_map = get_color_map_list(256) if os.path.isdir(input): for fpath, dirs, fs in os.walk(input): for f in fs: try: grt_path = osp.join(fpath, f) _output_dir = fpath.replace(input, '') _output_dir = _output_dir.lstrip(os.path.sep) im = Image.open(grt_path) lbl = np.asarray(im) lbl_pil = Image.fromarray(lbl.astype(np.uint8), mode='P') lbl_pil.putpalette(color_map) real_dir = osp.join(output_dir, _output_dir) if not osp.exists(real_dir): os.makedirs(real_dir) new_grt_path = osp.join(real_dir, f) lbl_pil.save(new_grt_path) print('New label path:', new_grt_path) except: continue elif os.path.isfile(input): if args.dataset_dir is None or args.file_separator is None: print('No dataset_dir or file_separator input!') sys.exit() with open(input) as f: for line in f: parts = line.strip().split(args.file_separator) grt_name = parts[1] grt_path = os.path.join(args.dataset_dir, grt_name) im = Image.open(grt_path) lbl = np.asarray(im) lbl_pil = Image.fromarray(lbl.astype(np.uint8), mode='P') lbl_pil.putpalette(color_map) grt_dir, _ = osp.split(grt_name) new_dir = osp.join(output_dir, grt_dir) if not osp.exists(new_dir): os.makedirs(new_dir) new_grt_path = osp.join(output_dir, grt_name) lbl_pil.save(new_grt_path) print('New label path:', new_grt_path) else: print('It\'s neither a dir nor a file') if __name__ == '__main__': args = parse_args() gray2pseudo_color(args)