from __future__ import division, print_function, absolute_import
import glob
import numpy as np
import os.path as osp
import zipfile
from torchreid.utils import read_json, write_json
from ..dataset import ImageDataset
[docs]class CUHK01(ImageDataset):
"""CUHK01.
Reference:
Li et al. Human Reidentification with Transferred Metric Learning. ACCV 2012.
URL: `<http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html>`_
Dataset statistics:
- identities: 971.
- images: 3884.
- cameras: 4.
Note: CUHK01 and CUHK02 overlap.
"""
dataset_dir = 'cuhk01'
dataset_url = None
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.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()
required_files = [self.dataset_dir, self.campus_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']
gallery = split['gallery']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
super(CUHK01, self).__init__(train, query, gallery, **kwargs)
def extract_file(self):
if not osp.exists(self.campus_dir):
print('Extracting files')
zip_ref = zipfile.ZipFile(self.zip_path, 'r')
zip_ref.extractall(self.dataset_dir)
zip_ref.close()
[docs] def prepare_split(self):
"""
Image name format: 0001001.png, where first four digits represent identity
and last four digits represent cameras. Camera 1&2 are considered the same
view and camera 3&4 are considered the same view.
"""
if not osp.exists(self.split_path):
print('Creating 10 random splits of train ids and test ids')
img_paths = sorted(glob.glob(osp.join(self.campus_dir, '*.png')))
img_list = []
pid_container = set()
for img_path in img_paths:
img_name = osp.basename(img_path)
pid = int(img_name[:4]) - 1
camid = (int(img_name[4:7]) - 1) // 2 # result is either 0 or 1
img_list.append((img_path, pid, camid))
pid_container.add(pid)
num_pids = len(pid_container)
num_train_pids = num_pids // 2
splits = []
for _ in range(10):
order = np.arange(num_pids)
np.random.shuffle(order)
train_idxs = order[:num_train_pids]
train_idxs = np.sort(train_idxs)
idx2label = {
idx: label
for label, idx in enumerate(train_idxs)
}
train, test_a, test_b = [], [], []
for img_path, pid, camid in img_list:
if pid in train_idxs:
train.append((img_path, idx2label[pid], camid))
else:
if camid == 0:
test_a.append((img_path, pid, camid))
else:
test_b.append((img_path, pid, camid))
# use cameraA as query and cameraB as gallery
split = {
'train': train,
'query': test_a,
'gallery': test_b,
'num_train_pids': num_train_pids,
'num_query_pids': num_pids - num_train_pids,
'num_gallery_pids': num_pids - num_train_pids
}
splits.append(split)
# use cameraB as query and cameraA as gallery
split = {
'train': train,
'query': test_b,
'gallery': test_a,
'num_train_pids': num_train_pids,
'num_query_pids': num_pids - num_train_pids,
'num_gallery_pids': num_pids - num_train_pids
}
splits.append(split)
print('Totally {} splits are created'.format(len(splits)))
write_json(splits, self.split_path)
print('Split file saved to {}'.format(self.split_path))