from __future__ import division, print_function, absolute_import
import os.path as osp
from ..dataset import ImageDataset
# Log
# 22.01.2019
# - add v2
# - v1 and v2 differ in dir names
# - note that faces in v2 are blurred
TRAIN_DIR_KEY = 'train_dir'
TEST_DIR_KEY = 'test_dir'
VERSION_DICT = {
'MSMT17_V1': {
TRAIN_DIR_KEY: 'train',
TEST_DIR_KEY: 'test',
},
'MSMT17_V2': {
TRAIN_DIR_KEY: 'mask_train_v2',
TEST_DIR_KEY: 'mask_test_v2',
}
}
[docs]class MSMT17(ImageDataset):
"""MSMT17.
Reference:
Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.
URL: `<http://www.pkuvmc.com/publications/msmt17.html>`_
Dataset statistics:
- identities: 4101.
- images: 32621 (train) + 11659 (query) + 82161 (gallery).
- cameras: 15.
"""
dataset_dir = 'msmt17'
dataset_url = None
def __init__(self, root='', **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)
has_main_dir = False
for main_dir in VERSION_DICT:
if osp.exists(osp.join(self.dataset_dir, main_dir)):
train_dir = VERSION_DICT[main_dir][TRAIN_DIR_KEY]
test_dir = VERSION_DICT[main_dir][TEST_DIR_KEY]
has_main_dir = True
break
assert has_main_dir, 'Dataset folder not found'
self.train_dir = osp.join(self.dataset_dir, main_dir, train_dir)
self.test_dir = osp.join(self.dataset_dir, main_dir, test_dir)
self.list_train_path = osp.join(
self.dataset_dir, main_dir, 'list_train.txt'
)
self.list_val_path = osp.join(
self.dataset_dir, main_dir, 'list_val.txt'
)
self.list_query_path = osp.join(
self.dataset_dir, main_dir, 'list_query.txt'
)
self.list_gallery_path = osp.join(
self.dataset_dir, main_dir, 'list_gallery.txt'
)
required_files = [self.dataset_dir, self.train_dir, self.test_dir]
self.check_before_run(required_files)
train = self.process_dir(self.train_dir, self.list_train_path)
val = self.process_dir(self.train_dir, self.list_val_path)
query = self.process_dir(self.test_dir, self.list_query_path)
gallery = self.process_dir(self.test_dir, self.list_gallery_path)
# Note: to fairly compare with published methods on the conventional ReID setting,
# do not add val images to the training set.
if 'combineall' in kwargs and kwargs['combineall']:
train += val
super(MSMT17, self).__init__(train, query, gallery, **kwargs)
def process_dir(self, dir_path, list_path):
with open(list_path, 'r') as txt:
lines = txt.readlines()
data = []
for img_idx, img_info in enumerate(lines):
img_path, pid = img_info.split(' ')
pid = int(pid) # no need to relabel
camid = int(img_path.split('_')[2]) - 1 # index starts from 0
img_path = osp.join(dir_path, img_path)
data.append((img_path, pid, camid))
return data