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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # 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.
- """
- File: convert_cocostuff.py
- This file is based on https://github.com/nightrome/cocostuff to generate PASCAL-Context Dataset.
- Before running, you should download the COCOSTUFF from https://github.com/nightrome/cocostuff. Then, make the folder
- structure as follow:
- cocostuff
- |
- |--images
- | |--train2017
- | |--val2017
- |
- |--annotations
- | |--train2017
- | |--val2017
- """
-
- import os
- import argparse
-
- import cv2
- import numpy as np
- from PIL import Image
- from tqdm import tqdm
-
-
- def parse_args():
- parser = argparse.ArgumentParser(description='Generate COCOStuff dataset')
- parser.add_argument(
- '--annotation_path',
- default='annotations',
- help='COCOStuff anotation path',
- type=str)
- parser.add_argument(
- '--save_path',
- default='convert_annotations',
- help='COCOStuff anotation path',
- type=str)
-
- return parser.parse_args()
-
-
- class COCOStuffGenerator(object):
- def __init__(self, annotation_path, save_path):
-
- super(COCOStuffGenerator, self).__init__()
-
- self.mapping = [
- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19,
- 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
- 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57,
- 58, 59, 60, 61, 62, 63, 64, 66, 69, 71, 72, 73, 74, 75, 76, 77, 78,
- 79, 80, 81, 83, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97,
- 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
- 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124,
- 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
- 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
- 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
- 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
- 177, 178, 179, 180, 181
- ]
- self.annotation_path = annotation_path
- self.save_path = save_path
-
- def encode_label(self, labelmap):
- ret = np.ones_like(labelmap) * 255
- for idx, label in enumerate(self.mapping):
-
- ret[labelmap == label] = idx
- return ret.astype(np.uint8)
-
- def generate_label(self):
- train_path = os.path.join(self.annotation_path, 'train2017')
- val_path = os.path.join(self.annotation_path, 'val2017')
- save_train_path = os.path.join(self.save_path, 'train2017')
- save_val_path = os.path.join(self.save_path, 'val2017')
-
- if not os.path.exists(save_train_path):
- os.makedirs(save_train_path)
- if not os.path.exists(save_val_path):
- os.makedirs(save_val_path)
-
- for label_id in tqdm(os.listdir(train_path), desc='trainset'):
- label = np.array(
- Image.open(os.path.join(train_path, label_id)).convert('P'))
- label = self.encode_label(label)
- label = Image.fromarray(label)
- label.save(os.path.join(save_train_path, label_id))
-
- for label_id in tqdm(os.listdir(val_path), desc='valset'):
- label = np.array(
- Image.open(os.path.join(val_path, label_id)).convert('P'))
- label = self.encode_label(label)
- label = Image.fromarray(label)
- label.save(os.path.join(save_val_path, label_id))
-
-
- def main():
- args = parse_args()
- generator = COCOStuffGenerator(
- annotation_path=args.annotation_path, save_path=args.save_path)
- generator.generate_label()
-
-
- if __name__ == '__main__':
- main()
- #/mnt/haoyuying/data/cocostuff/convert_annotations/val2017/000000086336.png
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