mirror of https://github.com/JDAI-CV/fast-reid.git
110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
from __future__ import print_function, absolute_import
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import glob
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import re
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from os import path as osp
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"""Dataset classes"""
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class Market1501(object):
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"""
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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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def __init__(self, root='/home/liaoxingyu/', **kwargs):
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self.dataset_dir = osp.join(root, self.dataset_dir)
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self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train')
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self.query_dir = osp.join(self.dataset_dir, 'query')
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self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test')
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self._check_before_run()
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train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, relabel=True)
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query, num_query_pids, num_query_imgs = self._process_dir(self.query_dir, relabel=False)
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gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.gallery_dir, relabel=False)
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num_total_pids = num_train_pids + num_query_pids
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num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
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print("=> Market1501 loaded")
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print("Dataset statistics:")
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print(" ------------------------------")
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print(" subset | # ids | # images")
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print(" ------------------------------")
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print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
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print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
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print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
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print(" ------------------------------")
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print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
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print(" ------------------------------")
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self.train = train
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self.query = query
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self.gallery = gallery
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self.num_train_pids = num_train_pids
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self.num_query_pids = num_query_pids
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self.num_gallery_pids = num_gallery_pids
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def _check_before_run(self):
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"""Check if all files are available before going deeper"""
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if not osp.exists(self.dataset_dir):
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raise RuntimeError("'{}' is not available".format(self.dataset_dir))
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if not osp.exists(self.train_dir):
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raise RuntimeError("'{}' is not available".format(self.train_dir))
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if not osp.exists(self.query_dir):
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raise RuntimeError("'{}' is not available".format(self.query_dir))
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if not osp.exists(self.gallery_dir):
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raise RuntimeError("'{}' is not available".format(self.gallery_dir))
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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: 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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dataset = []
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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: pid = pid2label[pid]
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dataset.append((img_path, pid, camid))
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num_pids = len(pid_container)
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num_imgs = len(dataset)
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return dataset, num_pids, num_imgs
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"""Create datasets"""
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__factory = {
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'market': Market1501
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}
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def get_names():
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return __factory.keys()
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def init_dataset(name, *args, **kwargs):
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if name not in __factory.keys():
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raise KeyError("Unknown datasets: {}".format(name))
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return __factory[name](*args, **kwargs)
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