88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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import sys
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import os
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import os.path as osp
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import glob
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import re
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from torchreid.data.datasets import ImageDataset
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class Market1501(ImageDataset):
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"""Market1501
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Reference:
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Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
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URL: http://www.liangzheng.org/Project/project_reid.html
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Dataset statistics:
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identities: 1501 (+1 for background)
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images: 12936 (train) + 3368 (query) + 15913 (gallery)
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"""
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dataset_dir = 'market1501'
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dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'
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def __init__(self, root='', market1501_500k=False, **kwargs):
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self.root = osp.abspath(osp.expanduser(root))
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self.dataset_dir = osp.join(self.root, self.dataset_dir)
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self.download_dataset(self.dataset_dir, self.dataset_url)
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# allow alternative directory structure
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self.data_dir = self.dataset_dir
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data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')
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if osp.isdir(data_dir):
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self.data_dir = data_dir
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self.train_dir = osp.join(self.data_dir, 'bounding_box_train')
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self.query_dir = osp.join(self.data_dir, 'query')
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self.gallery_dir = osp.join(self.data_dir, 'bounding_box_test')
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self.extra_gallery_dir = osp.join(self.data_dir, 'images')
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self.market1501_500k = market1501_500k
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required_files = [
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self.data_dir,
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self.train_dir,
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self.query_dir,
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self.gallery_dir
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]
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if self.market1501_500k:
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required_files.append(self.extra_gallery_dir)
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self.check_before_run(required_files)
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train = self.process_dir(self.train_dir, relabel=True)
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query = self.process_dir(self.query_dir, relabel=False)
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gallery = self.process_dir(self.gallery_dir, relabel=False)
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if self.market1501_500k:
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gallery += self.process_dir(self.extra_gallery_dir, relabel=False)
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super(Market1501, self).__init__(train, query, gallery, **kwargs)
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def process_dir(self, dir_path, relabel=False):
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img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
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pattern = re.compile(r'([-\d]+)_c(\d)')
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pid_container = set()
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for img_path in img_paths:
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pid, _ = map(int, pattern.search(img_path).groups())
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if pid == -1:
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continue # junk images are just ignored
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pid_container.add(pid)
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pid2label = {pid:label for label, pid in enumerate(pid_container)}
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data = []
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for img_path in img_paths:
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pid, camid = map(int, pattern.search(img_path).groups())
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if pid == -1:
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continue # junk images are just ignored
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assert 0 <= pid <= 1501 # pid == 0 means background
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assert 1 <= camid <= 6
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camid -= 1 # index starts from 0
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if relabel:
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pid = pid2label[pid]
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data.append((img_path, pid, camid))
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return data |