129 lines
5.5 KiB
Python
129 lines
5.5 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import glob
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import re
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import sys
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import urllib
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import tarfile
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import zipfile
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import os.path as osp
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from scipy.io import loadmat
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import numpy as np
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import h5py
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from scipy.misc import imsave
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from .bases import BaseVideoDataset
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class Mars(BaseVideoDataset):
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"""
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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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def __init__(self, root='data', min_seq_len=0, verbose=True, **kwargs):
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self.dataset_dir = osp.join(root, self.dataset_dir)
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self.train_name_path = osp.join(self.dataset_dir, 'info/train_name.txt')
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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(self.dataset_dir, 'info/tracks_train_info.mat')
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self.track_test_info_path = osp.join(self.dataset_dir, 'info/tracks_test_info.mat')
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self.query_IDX_path = osp.join(self.dataset_dir, 'info/query_IDX.mat')
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self._check_before_run()
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# prepare meta data
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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)['track_train_info'] # numpy.ndarray (8298, 4)
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track_test = loadmat(self.track_test_info_path)['track_test_info'] # numpy.ndarray (12180, 4)
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query_IDX = loadmat(self.query_IDX_path)['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 = [i for i in range(track_test.shape[0]) if i not in query_IDX]
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track_gallery = track_test[gallery_IDX,:]
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train = self._process_data(train_names, track_train, home_dir='bbox_train', relabel=True, min_seq_len=min_seq_len)
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query = self._process_data(test_names, track_query, home_dir='bbox_test', relabel=False, min_seq_len=min_seq_len)
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gallery = self._process_data(test_names, track_gallery, home_dir='bbox_test', relabel=False, min_seq_len=min_seq_len)
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if verbose:
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print("=> MARS loaded")
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self.print_dataset_statistics(train, query, gallery)
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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, _, self.num_train_cams = self.get_videodata_info(self.train)
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self.num_query_pids, _, self.num_query_cams = self.get_videodata_info(self.query)
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self.num_gallery_pids, _, self.num_gallery_cams = self.get_videodata_info(self.gallery)
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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_name_path):
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raise RuntimeError("'{}' is not available".format(self.train_name_path))
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if not osp.exists(self.test_name_path):
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raise RuntimeError("'{}' is not available".format(self.test_name_path))
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if not osp.exists(self.track_train_info_path):
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raise RuntimeError("'{}' is not available".format(self.track_train_info_path))
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if not osp.exists(self.track_test_info_path):
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raise RuntimeError("'{}' is not available".format(self.track_test_info_path))
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if not osp.exists(self.query_IDX_path):
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raise RuntimeError("'{}' is not available".format(self.query_IDX_path))
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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(self, names, meta_data, home_dir=None, relabel=False, min_seq_len=0):
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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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num_pids = len(pid_list)
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if relabel: 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: 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(set(pnames)) == 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(set(camnames)) == 1, "Error: images are captured under different cameras!"
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# append image names with directory information
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img_paths = [osp.join(self.dataset_dir, home_dir, img_name[:4], img_name) for img_name in img_names]
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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 |