from __future__ import division, print_function, absolute_import
import copy
import glob
import random
import os.path as osp
from collections import defaultdict
from torchreid.utils import read_json, write_json
from ..dataset import ImageDataset
[docs]class iLIDS(ImageDataset):
"""QMUL-iLIDS.
Reference:
Zheng et al. Associating Groups of People. BMVC 2009.
Dataset statistics:
- identities: 119.
- images: 476.
- cameras: 8 (not explicitly provided).
"""
dataset_dir = 'ilids'
dataset_url = 'http://www.eecs.qmul.ac.uk/~jason/data/i-LIDS_Pedestrian.tgz'
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.data_dir = osp.join(self.dataset_dir, 'i-LIDS_Pedestrian/Persons')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
required_files = [self.dataset_dir, self.data_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, query, gallery = self.process_split(split)
super(iLIDS, self).__init__(train, query, gallery, **kwargs)
def prepare_split(self):
if not osp.exists(self.split_path):
print('Creating splits ...')
paths = glob.glob(osp.join(self.data_dir, '*.jpg'))
img_names = [osp.basename(path) for path in paths]
num_imgs = len(img_names)
assert num_imgs == 476, 'There should be 476 images, but ' \
'got {}, please check the data'.format(num_imgs)
# store image names
# image naming format:
# the first four digits denote the person ID
# the last four digits denote the sequence index
pid_dict = defaultdict(list)
for img_name in img_names:
pid = int(img_name[:4])
pid_dict[pid].append(img_name)
pids = list(pid_dict.keys())
num_pids = len(pids)
assert num_pids == 119, 'There should be 119 identities, ' \
'but got {}, please check the data'.format(num_pids)
num_train_pids = int(num_pids * 0.5)
splits = []
for _ in range(10):
# randomly choose num_train_pids train IDs and the rest for test IDs
pids_copy = copy.deepcopy(pids)
random.shuffle(pids_copy)
train_pids = pids_copy[:num_train_pids]
test_pids = pids_copy[num_train_pids:]
train = []
query = []
gallery = []
# for train IDs, all images are used in the train set.
for pid in train_pids:
img_names = pid_dict[pid]
train.extend(img_names)
# for each test ID, randomly choose two images, one for
# query and the other one for gallery.
for pid in test_pids:
img_names = pid_dict[pid]
samples = random.sample(img_names, 2)
query.append(samples[0])
gallery.append(samples[1])
split = {'train': train, 'query': query, 'gallery': gallery}
splits.append(split)
print('Totally {} splits are created'.format(len(splits)))
write_json(splits, self.split_path)
print('Split file is saved to {}'.format(self.split_path))
def get_pid2label(self, img_names):
pid_container = set()
for img_name in img_names:
pid = int(img_name[:4])
pid_container.add(pid)
pid2label = {pid: label for label, pid in enumerate(pid_container)}
return pid2label
def parse_img_names(self, img_names, pid2label=None):
data = []
for img_name in img_names:
pid = int(img_name[:4])
if pid2label is not None:
pid = pid2label[pid]
camid = int(img_name[4:7]) - 1 # 0-based
img_path = osp.join(self.data_dir, img_name)
data.append((img_path, pid, camid))
return data
def process_split(self, split):
train_pid2label = self.get_pid2label(split['train'])
train = self.parse_img_names(split['train'], train_pid2label)
query = self.parse_img_names(split['query'])
gallery = self.parse_img_names(split['gallery'])
return train, query, gallery