def __init__(self, root='data', split_id=0, verbose=True, use_lmdb=False, **kwargs):
super(GRID, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip'
self.probe_path = osp.join(self.dataset_dir, 'underground_reid', 'probe')
self.gallery_path = osp.join(self.dataset_dir, 'underground_reid', 'gallery')
self.split_mat_path = osp.join(self.dataset_dir, 'underground_reid', 'features_and_partitions.mat')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
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 = split['train']
query = split['query']
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_gallery_pids
num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
if verbose:
print("=> GRID loaded")
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
if use_lmdb:
self.generate_lmdb()
def __init__(self, root='', split_id=0, **kwargs):
super(PRID, self).__init__()
# self.root = osp.abspath(osp.expanduser(root))
self.dataset_dir = osp.join(root, self.dataset_dir)
# self.download_dataset(self.dataset_dir, self.dataset_url)
self.cam_a_dir = osp.join(self.dataset_dir, 'single_shot', 'cam_a')
self.cam_b_dir = osp.join(self.dataset_dir, 'single_shot', 'cam_b')
self.split_path = osp.join(self.dataset_dir, 'splits_single_shot.json')
required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_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]
# self.show_summary()
train, query, gallery = self.process_split(split)
self.train = train
self.query = query
self.gallery = gallery
print("=> PRID loaded")
self.print_dataset_statistics(self.train, self.query, self.gallery)
self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(
self.train)
self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(
self.query)
self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(
self.gallery)
def __init__(self, root='data', split_id=0, verbose=True, use_lmdb=False, **kwargs):
super(VIPeR, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
self.cam_a_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
self.cam_b_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
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 = split['train']
query = split['query'] # query and gallery share the same images
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> VIPeR loaded")
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
if use_lmdb:
self.generate_lmdb()
def __init__(self, root='data', split_id=0, min_seq_len=0, verbose=True, use_lmdb=False, **kwargs):
super(PRID450S, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'https://files.icg.tugraz.at/f/8c709245bb/?raw=1'
self.split_path = osp.join(self.dataset_dir, 'splits.json')
self.cam_a_path = osp.join(self.dataset_dir, 'cam_a')
self.cam_b_path = osp.join(self.dataset_dir, 'cam_b')
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 = split['train']
query = split['query']
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> PRID450S loaded")
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
if use_lmdb:
self.generate_lmdb()
def __init__(self, root='data', split_id=0, verbose=True, use_lmdb=False, **kwargs):
super(CUHK01, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.zip_path = osp.join(self.dataset_dir, 'CUHK01.zip')
self.campus_dir = osp.join(self.dataset_dir, 'campus')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
self._extract_file()
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 = split['train']
query = split['query']
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> CUHK01 loaded")
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
if use_lmdb:
self.generate_lmdb()
def __init__(self, root='data', split_id=0, verbose=True, **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
if verbose:
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, verbose=True, **kwargs):
super(GRID, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip'
self.probe_path = osp.join(self.dataset_dir, 'underground_reid', 'probe')
self.gallery_path = osp.join(self.dataset_dir, 'underground_reid', 'gallery')
self.split_mat_path = osp.join(self.dataset_dir, 'underground_reid', 'features_and_partitions.mat')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
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 = split['train']
query = split['query']
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_gallery_pids
num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
if verbose:
print("=> GRID loaded")
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
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='datasets',
split_id=0,
cuhk03_labeled=False,
cuhk03_classic_split=False,
**kwargs):
# self.root = osp.abspath(osp.expanduser(root))
self.root = root
self.dataset_dir = osp.join(self.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')
required_files = [
self.dataset_dir, self.data_dir, self.raw_mat_path,
self.split_new_det_mat_path, self.split_new_lab_mat_path
]
self.check_before_run(required_files)
self.preprocess_split()
if cuhk03_labeled:
split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
else:
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]
train = split['train']
query = split['query']
gallery = split['gallery']
super(CUHK03, self).__init__(train, query, gallery, **kwargs)
def __init__(self, root='data', split_id=0, verbose=True, use_lmdb=False, **kwargs):
super(iLIDS, self).__init__()
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/images/cam1') # differ from video
self.cam_2_path = osp.join(self.dataset_dir, 'i-LIDS-VID/images/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_imgs, num_train_pids = self._process_data(train_dirs, cam1=True, cam2=True)
query, num_query_imgs, num_query_pids = self._process_data(test_dirs, cam1=True, cam2=False)
gallery, num_gallery_imgs, num_gallery_pids = self._process_data(test_dirs, cam1=False, cam2=True)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> iLIDS (single-shot) loaded")
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
if use_lmdb:
self.generate_lmdb()
def __init__(self, root='', split_id=0, cuhk03_labeled=True,
cuhk03_classic_split=False, verbose=True):
super(CUHK03, self).__init__()
self.dataset_dir = root
# osp.join(root, self.dataset_dir)
self.data_dir = self.dataset_dir
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']
if verbose:
print("=> CUHK03 ({}) loaded".format(image_type))
self.print_dataset_statistics(train, query, gallery)
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)
def __init__(self, root='data', split_id=0, min_seq_len=0, verbose=True, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
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
if verbose:
print("=> PRID2011 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='', split_id=0, **kwargs):
super(GRID, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.probe_path = osp.join(self.dataset_dir, 'underground_reid',
'probe')
self.gallery_path = osp.join(self.dataset_dir, 'underground_reid',
'gallery')
self.split_mat_path = osp.join(self.dataset_dir, 'underground_reid',
'features_and_partitions.mat')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
required_files = [
self.dataset_dir, self.probe_path, self.gallery_path,
self.split_mat_path
]
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 = split['train']
query = split['query']
gallery = split['gallery']
self.train = [tuple(item) for item in train]
self.query = [tuple(item) for item in query]
self.gallery = [tuple(item) for item in gallery]
print("=> GRID loaded")
self.print_dataset_statistics(self.train, self.query, self.gallery)
self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(
self.train)
self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(
self.query)
self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(
self.gallery)
def __init__(self, root='', split_id=0, **kwargs):
super(VIPeR, self).__init__()
# self.root = osp.abspath(osp.expanduser(root))
self.dataset_dir = osp.join(root, self.dataset_dir)
# self.download_dataset(self.dataset_dir, self.dataset_url)
self.cam_a_dir = osp.join(self.dataset_dir, 'cam_a')
self.cam_b_dir = osp.join(self.dataset_dir, 'cam_b')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
required_files = [self.dataset_dir, self.cam_a_dir, self.cam_b_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 = split['train']
query = split['query'] # query and gallery share the same images
gallery = split['gallery']
self.train = [tuple(item) for item in train]
self.query = [tuple(item) for item in query]
self.gallery = [tuple(item) for item in gallery]
print("=> viper loaded")
self.print_dataset_statistics(self.train, self.query, self.gallery)
self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(
self.train)
self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(
self.query)
self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(
self.gallery)
def convert_matlab_partition(
json_filename: str,
pbz2_filename: str) -> List[Dict[str, Any]]:
"""
Loads a JSON partition file generated from Matlab reid benchmark and
saves it as a python pickled bz2 file. Returns the converted partition
as a dict.
To generate a JSON file from Matlab R2016, use JSONlab:
https://github.com/fangq/jsonlab
>> addpath('/path/to/jsonlab');
>> load('TrainTestSplits/Partition_viper.mat');
>> savejson('partition',partition,'partitions_viper.json')
"""
partition_list = []
data = iotools.read_json(json_filename)
for partition in data['partition']:
idx_train = partition['idx_train']
idx_test = partition['idx_test']
idx_probe = partition['idx_probe']
idx_gallery = partition['idx_gallery']
ix_pos_pair = partition['ix_pos_pair']
ix_neg_pair = partition['ix_neg_pair']
partition_dict = {'idx_train': idx_train,
'idx_test': idx_test,
'idx_probe': idx_probe,
'idx_gallery': idx_gallery,
'ix_pos_pair': ix_pos_pair,
'ix_neg_pair': ix_neg_pair}
partition_list.append(partition_dict)
iotools.save_pbz2_file(partition_list, pbz2_filename)
return partition_list
def __init__(self, root='data', split_id=0, verbose=True, **kwargs):
super(VIPeR, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
self.cam_a_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
self.cam_b_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
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 = split['train']
query = split['query'] # query and gallery share the same images
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> VIPeR loaded")
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
def __init__(self, root='data', split_id=0, verbose=True, **kwargs):
super(CUHK01, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.zip_path = osp.join(self.dataset_dir, 'CUHK01.zip')
self.campus_dir = osp.join(self.dataset_dir, 'campus')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
self._extract_file()
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 = split['train']
query = split['query']
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> CUHK01 loaded")
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
def __init__(self,
root='data',
split_id=0,
min_seq_len=0,
verbose=True,
**kwargs):
super(PRID450S, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'https://files.icg.tugraz.at/f/8c709245bb/?raw=1'
self.split_path = osp.join(self.dataset_dir, 'splits.json')
self.cam_a_path = osp.join(self.dataset_dir, 'cam_a')
self.cam_b_path = osp.join(self.dataset_dir, 'cam_b')
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 = split['train']
query = split['query']
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> PRID450S loaded")
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
def __init__(self, root='data', split_id=0, verbose=True, **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
if verbose:
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,
verbose=True,
use_lmdb=False,
**kwargs):
super(iLIDS, self).__init__()
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/images/cam1') # differ from video
self.cam_2_path = osp.join(self.dataset_dir, 'i-LIDS-VID/images/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_imgs, num_train_pids = self._process_data(train_dirs,
cam1=True,
cam2=True)
query, num_query_imgs, num_query_pids = self._process_data(test_dirs,
cam1=True,
cam2=False)
gallery, num_gallery_imgs, num_gallery_pids = self._process_data(
test_dirs, cam1=False, cam2=True)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> iLIDS (single-shot) loaded")
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
if use_lmdb:
self.generate_lmdb()
def __init__(self, root='data', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, verbose=True, use_lmdb=False, **kwargs):
super(CUHK03, self).__init__()
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
if verbose:
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
if use_lmdb:
self.generate_lmdb()
