from __future__ import division, print_function, absolute_import
import glob
import numpy as np
import os.path as osp
from torchreid.utils import read_json, write_json
from ..dataset import ImageDataset
[docs]class VIPeR(ImageDataset):
"""VIPeR.
Reference:
Gray et al. Evaluating appearance models for recognition, reacquisition, and tracking. PETS 2007.
URL: `<https://vision.soe.ucsc.edu/node/178>`_
Dataset statistics:
- identities: 632.
- images: 632 x 2 = 1264.
- cameras: 2.
"""
dataset_dir = 'viper'
dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
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.cam_a_dir = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
self.cam_b_dir = osp.join(self.dataset_dir, 'VIPeR', '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']
train = [tuple(item) for item in train]
query = [tuple(item) for item in query]
gallery = [tuple(item) for item in gallery]
super(VIPeR, self).__init__(train, query, gallery, **kwargs)
def prepare_split(self):
if not osp.exists(self.split_path):
print('Creating 10 random splits of train ids and test ids')
cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_dir, '*.bmp')))
cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_dir, '*.bmp')))
assert len(cam_a_imgs) == len(cam_b_imgs)
num_pids = len(cam_a_imgs)
print('Number of identities: {}'.format(num_pids))
num_train_pids = num_pids // 2
"""
In total, there will be 20 splits because each random split creates two
sub-splits, one using cameraA as query and cameraB as gallery
while the other using cameraB as query and cameraA as gallery.
Therefore, results should be averaged over 20 splits (split_id=0~19).
In practice, a model trained on split_id=0 can be applied to split_id=0&1
as split_id=0&1 share the same training data (so on and so forth).
"""
splits = []
for _ in range(10):
order = np.arange(num_pids)
np.random.shuffle(order)
train_idxs = order[:num_train_pids]
test_idxs = order[num_train_pids:]
assert not bool(set(train_idxs) & set(test_idxs)), \
'Error: train and test overlap'
train = []
for pid, idx in enumerate(train_idxs):
cam_a_img = cam_a_imgs[idx]
cam_b_img = cam_b_imgs[idx]
train.append((cam_a_img, pid, 0))
train.append((cam_b_img, pid, 1))
test_a = []
test_b = []
for pid, idx in enumerate(test_idxs):
cam_a_img = cam_a_imgs[idx]
cam_b_img = cam_b_imgs[idx]
test_a.append((cam_a_img, pid, 0))
test_b.append((cam_b_img, pid, 1))
# 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))