from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import sys
import os
import os.path as osp
import glob
import numpy as np
import copy
import random
from collections import defaultdict
from torchreid.data.datasets import ImageDataset
from torchreid.utils import read_json, write_json
[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)
num_test_pids = num_pids - num_train_pids # supposed to be 60
splits = []
for _ in range(10):
# randomly choose num_train_pids train IDs and num_test_pids 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, query, gallery = [], [], []
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