from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import sys
import os
import os.path as osp
import glob
from scipy.io import loadmat
from torchreid.data.datasets import VideoDataset
from torchreid.utils import read_json, write_json
[docs]class iLIDSVID(VideoDataset):
"""iLIDS-VID.
Reference:
Wang et al. Person Re-Identification by Video Ranking. ECCV 2014.
URL: `<http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html>`_
Dataset statistics:
- identities: 300.
- tracklets: 600.
- cameras: 2.
"""
dataset_dir = 'ilids-vid'
dataset_url = 'http://www.eecs.qmul.ac.uk/~xiatian/iLIDS-VID/iLIDS-VID.tar'
def __init__(self, root='', split_id=0, **kwargs):
self.root = osp.abspath(osp.expanduser(root))
self.dataset_dir = osp.join(self.root, self.dataset_dir)
self.download_dataset(self.dataset_dir, self.dataset_url)
self.data_dir = osp.join(self.dataset_dir, 'i-LIDS-VID')
self.split_dir = osp.join(self.dataset_dir, 'train-test people splits')
self.split_mat_path = osp.join(self.split_dir, 'train_test_splits_ilidsvid.mat')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
self.cam_1_path = osp.join(self.dataset_dir, 'i-LIDS-VID/sequences/cam1')
self.cam_2_path = osp.join(self.dataset_dir, 'i-LIDS-VID/sequences/cam2')
required_files = [
self.dataset_dir,
self.data_dir,
self.split_dir
]
self.check_before_run(required_files)
self.prepare_split()
splits = read_json(self.split_path)
if split_id >= len(splits):
raise ValueError('split_id exceeds range, received {}, but expected between 0 and {}'.format(split_id, len(splits)-1))
split = splits[split_id]
train_dirs, test_dirs = split['train'], split['test']
train = self.process_data(train_dirs, cam1=True, cam2=True)
query = self.process_data(test_dirs, cam1=True, cam2=False)
gallery = self.process_data(test_dirs, cam1=False, cam2=True)
super(iLIDSVID, self).__init__(train, query, gallery, **kwargs)
def prepare_split(self):
if not osp.exists(self.split_path):
print('Creating splits ...')
mat_split_data = loadmat(self.split_mat_path)['ls_set']
num_splits = mat_split_data.shape[0]
num_total_ids = mat_split_data.shape[1]
assert num_splits == 10
assert num_total_ids == 300
num_ids_each = num_total_ids // 2
# pids in mat_split_data are indices, so we need to transform them
# to real pids
person_cam1_dirs = sorted(glob.glob(osp.join(self.cam_1_path, '*')))
person_cam2_dirs = sorted(glob.glob(osp.join(self.cam_2_path, '*')))
person_cam1_dirs = [osp.basename(item) for item in person_cam1_dirs]
person_cam2_dirs = [osp.basename(item) for item in person_cam2_dirs]
# make sure persons in one camera view can be found in the other camera view
assert set(person_cam1_dirs) == set(person_cam2_dirs)
splits = []
for i_split in range(num_splits):
# first 50% for testing and the remaining for training, following Wang et al. ECCV'14.
train_idxs = sorted(list(mat_split_data[i_split, num_ids_each:]))
test_idxs = sorted(list(mat_split_data[i_split, :num_ids_each]))
train_idxs = [int(i)-1 for i in train_idxs]
test_idxs = [int(i)-1 for i in test_idxs]
# transform pids to person dir names
train_dirs = [person_cam1_dirs[i] for i in train_idxs]
test_dirs = [person_cam1_dirs[i] for i in test_idxs]
split = {'train': train_dirs, 'test': test_dirs}
splits.append(split)
print('Totally {} splits are created, following Wang et al. ECCV\'14'.format(len(splits)))
print('Split file is saved to {}'.format(self.split_path))
write_json(splits, self.split_path)
def process_data(self, dirnames, cam1=True, cam2=True):
tracklets = []
dirname2pid = {dirname:i for i, dirname in enumerate(dirnames)}
for dirname in dirnames:
if cam1:
person_dir = osp.join(self.cam_1_path, dirname)
img_names = glob.glob(osp.join(person_dir, '*.png'))
assert len(img_names) > 0
img_names = tuple(img_names)
pid = dirname2pid[dirname]
tracklets.append((img_names, pid, 0))
if cam2:
person_dir = osp.join(self.cam_2_path, dirname)
img_names = glob.glob(osp.join(person_dir, '*.png'))
assert len(img_names) > 0
img_names = tuple(img_names)
pid = dirname2pid[dirname]
tracklets.append((img_names, pid, 1))
return tracklets