133 lines
4.8 KiB
Python
133 lines
4.8 KiB
Python
from __future__ import division, print_function, absolute_import
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import os.path as osp
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import warnings
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from scipy.io import loadmat
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from ..dataset import VideoDataset
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class Mars(VideoDataset):
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"""MARS.
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Reference:
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Zheng et al. MARS: A Video Benchmark for Large-Scale Person Re-identification. ECCV 2016.
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URL: `<http://www.liangzheng.com.cn/Project/project_mars.html>`_
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Dataset statistics:
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- identities: 1261.
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- tracklets: 8298 (train) + 1980 (query) + 9330 (gallery).
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- cameras: 6.
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"""
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dataset_dir = 'mars'
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dataset_url = None
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def __init__(self, root='', **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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self.train_name_path = osp.join(
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self.dataset_dir, 'info/train_name.txt'
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)
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self.test_name_path = osp.join(self.dataset_dir, 'info/test_name.txt')
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self.track_train_info_path = osp.join(
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self.dataset_dir, 'info/tracks_train_info.mat'
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)
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self.track_test_info_path = osp.join(
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self.dataset_dir, 'info/tracks_test_info.mat'
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)
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self.query_IDX_path = osp.join(self.dataset_dir, 'info/query_IDX.mat')
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required_files = [
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self.dataset_dir, self.train_name_path, self.test_name_path,
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self.track_train_info_path, self.track_test_info_path,
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self.query_IDX_path
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]
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self.check_before_run(required_files)
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train_names = self.get_names(self.train_name_path)
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test_names = self.get_names(self.test_name_path)
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track_train = loadmat(self.track_train_info_path
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)['track_train_info'] # numpy.ndarray (8298, 4)
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track_test = loadmat(self.track_test_info_path
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)['track_test_info'] # numpy.ndarray (12180, 4)
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query_IDX = loadmat(self.query_IDX_path
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)['query_IDX'].squeeze() # numpy.ndarray (1980,)
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query_IDX -= 1 # index from 0
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track_query = track_test[query_IDX, :]
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gallery_IDX = [
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i for i in range(track_test.shape[0]) if i not in query_IDX
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]
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track_gallery = track_test[gallery_IDX, :]
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train = self.process_data(
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train_names, track_train, home_dir='bbox_train', relabel=True
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)
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query = self.process_data(
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test_names, track_query, home_dir='bbox_test', relabel=False
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)
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gallery = self.process_data(
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test_names, track_gallery, home_dir='bbox_test', relabel=False
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)
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super(Mars, self).__init__(train, query, gallery, **kwargs)
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def get_names(self, fpath):
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names = []
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with open(fpath, 'r') as f:
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for line in f:
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new_line = line.rstrip()
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names.append(new_line)
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return names
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def process_data(
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self, names, meta_data, home_dir=None, relabel=False, min_seq_len=0
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):
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assert home_dir in ['bbox_train', 'bbox_test']
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num_tracklets = meta_data.shape[0]
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pid_list = list(set(meta_data[:, 2].tolist()))
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if relabel:
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pid2label = {pid: label for label, pid in enumerate(pid_list)}
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tracklets = []
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for tracklet_idx in range(num_tracklets):
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data = meta_data[tracklet_idx, ...]
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start_index, end_index, pid, camid = data
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if pid == -1:
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continue # junk images are just ignored
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assert 1 <= camid <= 6
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if relabel: pid = pid2label[pid]
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camid -= 1 # index starts from 0
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img_names = names[start_index - 1:end_index]
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# make sure image names correspond to the same person
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pnames = [img_name[:4] for img_name in img_names]
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assert len(
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set(pnames)
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) == 1, 'Error: a single tracklet contains different person images'
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# make sure all images are captured under the same camera
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camnames = [img_name[5] for img_name in img_names]
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assert len(
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set(camnames)
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) == 1, 'Error: images are captured under different cameras!'
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# append image names with directory information
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img_paths = [
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osp.join(self.dataset_dir, home_dir, img_name[:4], img_name)
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for img_name in img_names
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]
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if len(img_paths) >= min_seq_len:
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img_paths = tuple(img_paths)
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tracklets.append((img_paths, pid, camid))
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return tracklets
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def combine_all(self):
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warnings.warn(
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'Some query IDs do not appear in gallery. Therefore, combineall '
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'does not make any difference to Mars'
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)
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