def _process_dir(self, dir_path, json_path, relabel):
if osp.exists(json_path):
print("=> {} generated before, awesome!".format(json_path))
split = read_json(json_path)
return split['tracklets'], split['num_tracklets'], split['num_pids'], split['num_imgs_per_tracklet']
print("=> Automatically generating split (might take a while for the first time, have a coffe)")
pdirs = glob.glob(osp.join(dir_path, '*')) # avoid .DS_Store
print("Processing {} with {} person identities".format(dir_path, len(pdirs)))
pid_container = set()
for pdir in pdirs:
pid = int(osp.basename(pdir))
pid_container.add(pid)
pid2label = {pid:label for label, pid in enumerate(pid_container)}
tracklets = []
num_imgs_per_tracklet = []
for pdir in pdirs:
pid = int(osp.basename(pdir))
if relabel: pid = pid2label[pid]
tdirs = glob.glob(osp.join(pdir, '*'))
for tdir in tdirs:
raw_img_paths = glob.glob(osp.join(tdir, '*.jpg'))
num_imgs = len(raw_img_paths)
if num_imgs < self.min_seq_len:
continue
num_imgs_per_tracklet.append(num_imgs)
img_paths = []
for img_idx in range(num_imgs):
# some tracklet starts from 0002 instead of 0001
img_idx_name = 'F' + str(img_idx+1).zfill(4)
res = glob.glob(osp.join(tdir, '*' + img_idx_name + '*.jpg'))
if len(res) == 0:
print("Warn: index name {} in {} is missing, jump to next".format(img_idx_name, tdir))
continue
img_paths.append(res[0])
img_name = osp.basename(img_paths[0])
camid = int(img_name[5]) - 1 # index-0
img_paths = tuple(img_paths)
tracklets.append((img_paths, pid, camid))
num_pids = len(pid_container)
num_tracklets = len(tracklets)
print("Saving split to {}".format(json_path))
split_dict = {
'tracklets': tracklets,
'num_tracklets': num_tracklets,
'num_pids': num_pids,
'num_imgs_per_tracklet': num_imgs_per_tracklet,
}
write_json(split_dict, json_path)
return tracklets, num_tracklets, num_pids, num_imgs_per_tracklet
def __init__(self, root='data', split_id=0, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'http://www.eecs.qmul.ac.uk/~xiatian/iLIDS-VID/iLIDS-VID.tar'
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')
self._download_data()
self._check_before_run()
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']
print("# train identites: {}, # test identites {}".format(len(train_dirs), len(test_dirs)))
train, num_train_tracklets, num_train_pids, num_imgs_train = \
self._process_data(train_dirs, cam1=True, cam2=True)
query, num_query_tracklets, num_query_pids, num_imgs_query = \
self._process_data(test_dirs, cam1=True, cam2=False)
gallery, num_gallery_tracklets, num_gallery_pids, num_imgs_gallery = \
self._process_data(test_dirs, cam1=False, cam2=True)
num_imgs_per_tracklet = num_imgs_train + num_imgs_query + num_imgs_gallery
min_num = np.min(num_imgs_per_tracklet)
max_num = np.max(num_imgs_per_tracklet)
avg_num = np.mean(num_imgs_per_tracklet)
num_total_pids = num_train_pids + num_query_pids
num_total_tracklets = num_train_tracklets + num_query_tracklets + num_gallery_tracklets
print("=> iLIDS-VID loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # tracklets")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_tracklets))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_tracklets))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_tracklets))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_tracklets))
print(" number of images per tracklet: {} ~ {}, average {:.1f}".format(min_num, max_num, avg_num))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def __init__(self, root='data', split_id=0, min_seq_len=0, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'https://files.icg.tugraz.at/f/6ab7e8ce8f/?raw=1'
self.split_path = osp.join(self.dataset_dir, 'splits_prid2011.json')
self.cam_a_path = osp.join(self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_a')
self.cam_b_path = osp.join(self.dataset_dir, 'prid_2011', 'multi_shot', 'cam_b')
self._check_before_run()
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']
print("# train identites: {}, # test identites {}".format(len(train_dirs), len(test_dirs)))
train, num_train_tracklets, num_train_pids, num_imgs_train = \
self._process_data(train_dirs, cam1=True, cam2=True)
query, num_query_tracklets, num_query_pids, num_imgs_query = \
self._process_data(test_dirs, cam1=True, cam2=False)
gallery, num_gallery_tracklets, num_gallery_pids, num_imgs_gallery = \
self._process_data(test_dirs, cam1=False, cam2=True)
num_imgs_per_tracklet = num_imgs_train + num_imgs_query + num_imgs_gallery
min_num = np.min(num_imgs_per_tracklet)
max_num = np.max(num_imgs_per_tracklet)
avg_num = np.mean(num_imgs_per_tracklet)
num_total_pids = num_train_pids + num_query_pids
num_total_tracklets = num_train_tracklets + num_query_tracklets + num_gallery_tracklets
print("=> PRID-2011 loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # tracklets")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_tracklets))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_tracklets))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_tracklets))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_tracklets))
print(" number of images per tracklet: {} ~ {}, average {:.1f}".format(min_num, max_num, avg_num))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def __init__(self, root='./data/kitti'):
super(Kitti, self).__init__()
self.root = root
self.raw_split = 'velodyne_raw'
self.gt_split = 'groundtruth'
self.train_split = 'train'
self.val_split = 'val'
self.val_selected_split = 'depth_selection/val_selection_cropped'
self.test_dir = 'depth_selection/test_depth_completion_anonymous/velodyne_raw'
self.splits = osp.join(self.root, 'splits.json')
if not osp.isfile(self.splits):
self._split_dataset()
imgset = read_json(self.splits)
self.trainset = {'raw': imgset['train_raw'], 'gt': imgset['train_gt']}
self.valset = {'raw': imgset['val_raw'], 'gt': imgset['val_gt']}
self.valset_select = {
'raw': imgset['val_selected_raw'],
'gt': imgset['val_selected_gt']
}
self.testset = {'raw': imgset['test_raw']}
def __init__(self, root='data', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release')
self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat')
self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected')
self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled')
self.split_classic_det_json_path = osp.join(self.dataset_dir, 'splits_classic_detected.json')
self.split_classic_lab_json_path = osp.join(self.dataset_dir, 'splits_classic_labeled.json')
self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json')
self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json')
self.split_new_det_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat')
self.split_new_lab_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat')
self._check_before_run()
self._preprocess()
if cuhk03_labeled:
image_type = 'labeled'
split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
else:
image_type = 'detected'
split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path
splits = read_json(split_path)
assert split_id < len(splits), "Condition split_id ({}) < len(splits) ({}) is false".format(split_id, len(splits))
split = splits[split_id]
print("Split index = {}".format(split_id))
train = split['train']
query = split['query']
gallery = split['gallery']
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_total_pids = num_train_pids + num_query_pids
num_train_imgs = split['num_train_imgs']
num_query_imgs = split['num_query_imgs']
num_gallery_imgs = split['num_gallery_imgs']
num_total_imgs = num_train_imgs + num_query_imgs
print("=> CUHK03 ({}) loaded".format(image_type))
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids