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data_manager.py
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from __future__ import print_function, absolute_import
import os
import glob
import re
import sys
import urllib
import tarfile
import zipfile
import os.path as osp
from scipy.io import loadmat
import numpy as np
import h5py
from scipy.misc import imsave
from utils import mkdir_if_missing, write_json, read_json
"""Image ReID"""
class Market1501(object):
"""
Market1501
Reference:
Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
URL: http://www.liangzheng.org/Project/project_reid.html
Dataset statistics:
# identities: 1501 (+1 for background)
# images: 12936 (train) + 3368 (query) + 15913 (gallery)
"""
dataset_dir = 'market1501'
def __init__(self, root='data', **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train')
self.query_dir = osp.join(self.dataset_dir, 'query')
self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test')
self._check_before_run()
train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, relabel=True)
query, num_query_pids, num_query_imgs = self._process_dir(self.query_dir, relabel=False)
gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.gallery_dir, relabel=False)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
print("=> Market1501 loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _check_before_run(self):
"""Check if all files are available before going deeper"""
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.train_dir):
raise RuntimeError("'{}' is not available".format(self.train_dir))
if not osp.exists(self.query_dir):
raise RuntimeError("'{}' is not available".format(self.query_dir))
if not osp.exists(self.gallery_dir):
raise RuntimeError("'{}' is not available".format(self.gallery_dir))
def _process_dir(self, dir_path, relabel=False):
img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
pattern = re.compile(r'([-\d]+)_c(\d)')
pid_container = set()
for img_path in img_paths:
pid, _ = map(int, pattern.search(img_path).groups())
if pid == -1: continue # junk images are just ignored
pid_container.add(pid)
pid2label = {pid:label for label, pid in enumerate(pid_container)}
dataset = []
for img_path in img_paths:
pid, camid = map(int, pattern.search(img_path).groups())
if pid == -1: continue # junk images are just ignored
assert 0 <= pid <= 1501 # pid == 0 means background
assert 1 <= camid <= 6
camid -= 1 # index starts from 0
if relabel: pid = pid2label[pid]
dataset.append((img_path, pid, camid))
num_pids = len(pid_container)
num_imgs = len(dataset)
return dataset, num_pids, num_imgs
class CUHK03(object):
"""
CUHK03
Reference:
Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014.
URL: http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html#!
Dataset statistics:
# identities: 1360
# images: 13164
# cameras: 6
# splits: 20 (classic)
Args:
split_id (int): split index (default: 0)
cuhk03_labeled (bool): whether to load labeled images; if false, detected images are loaded (default: False)
"""
dataset_dir = 'cuhk03'
def __init__(self, root='data', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release')
self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat')
self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected')
self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled')
self.split_classic_det_json_path = osp.join(self.dataset_dir, 'splits_classic_detected.json')
self.split_classic_lab_json_path = osp.join(self.dataset_dir, 'splits_classic_labeled.json')
self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json')
self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json')
self.split_new_det_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat')
self.split_new_lab_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat')
self._check_before_run()
self._preprocess()
if cuhk03_labeled:
image_type = 'labeled'
split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
else:
image_type = 'detected'
split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path
splits = read_json(split_path)
assert split_id < len(splits), "Condition split_id ({}) < len(splits) ({}) is false".format(split_id, len(splits))
split = splits[split_id]
print("Split index = {}".format(split_id))
train = split['train']
query = split['query']
gallery = split['gallery']
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_total_pids = num_train_pids + num_query_pids
num_train_imgs = split['num_train_imgs']
num_query_imgs = split['num_query_imgs']
num_gallery_imgs = split['num_gallery_imgs']
num_total_imgs = num_train_imgs + num_query_imgs
print("=> CUHK03 ({}) loaded".format(image_type))
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _check_before_run(self):
"""Check if all files are available before going deeper"""
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.data_dir):
raise RuntimeError("'{}' is not available".format(self.data_dir))
if not osp.exists(self.raw_mat_path):
raise RuntimeError("'{}' is not available".format(self.raw_mat_path))
if not osp.exists(self.split_new_det_mat_path):
raise RuntimeError("'{}' is not available".format(self.split_new_det_mat_path))
if not osp.exists(self.split_new_lab_mat_path):
raise RuntimeError("'{}' is not available".format(self.split_new_lab_mat_path))
def _preprocess(self):
"""
This function is a bit complex and ugly, what it does is
1. Extract data from cuhk-03.mat and save as png images.
2. Create 20 classic splits. (Li et al. CVPR'14)
3. Create new split. (Zhong et al. CVPR'17)
"""
print("Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)")
if osp.exists(self.imgs_labeled_dir) and \
osp.exists(self.imgs_detected_dir) and \
osp.exists(self.split_classic_det_json_path) and \
osp.exists(self.split_classic_lab_json_path) and \
osp.exists(self.split_new_det_json_path) and \
osp.exists(self.split_new_lab_json_path):
return
mkdir_if_missing(self.imgs_detected_dir)
mkdir_if_missing(self.imgs_labeled_dir)
print("Extract image data from {} and save as png".format(self.raw_mat_path))
mat = h5py.File(self.raw_mat_path, 'r')
def _deref(ref):
return mat[ref][:].T
def _process_images(img_refs, campid, pid, save_dir):
img_paths = [] # Note: some persons only have images for one view
for imgid, img_ref in enumerate(img_refs):
img = _deref(img_ref)
# skip empty cell
if img.size == 0 or img.ndim < 3: continue
# images are saved with the following format, index-1 (ensure uniqueness)
# campid: index of camera pair (1-5)
# pid: index of person in 'campid'-th camera pair
# viewid: index of view, {1, 2}
# imgid: index of image, (1-10)
viewid = 1 if imgid < 5 else 2
img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid+1, pid+1, viewid, imgid+1)
img_path = osp.join(save_dir, img_name)
imsave(img_path, img)
img_paths.append(img_path)
return img_paths
def _extract_img(name):
print("Processing {} images (extract and save) ...".format(name))
meta_data = []
imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir
for campid, camp_ref in enumerate(mat[name][0]):
camp = _deref(camp_ref)
num_pids = camp.shape[0]
for pid in range(num_pids):
img_paths = _process_images(camp[pid,:], campid, pid, imgs_dir)
assert len(img_paths) > 0, "campid{}-pid{} has no images".format(campid, pid)
meta_data.append((campid+1, pid+1, img_paths))
print("done camera pair {} with {} identities".format(campid+1, num_pids))
return meta_data
meta_detected = _extract_img('detected')
meta_labeled = _extract_img('labeled')
def _extract_classic_split(meta_data, test_split):
train, test = [], []
num_train_pids, num_test_pids = 0, 0
num_train_imgs, num_test_imgs = 0, 0
for i, (campid, pid, img_paths) in enumerate(meta_data):
if [campid, pid] in test_split:
for img_path in img_paths:
camid = int(osp.basename(img_path).split('_')[2])
test.append((img_path, num_test_pids, camid))
num_test_pids += 1
num_test_imgs += len(img_paths)
else:
for img_path in img_paths:
camid = int(osp.basename(img_path).split('_')[2])
train.append((img_path, num_train_pids, camid))
num_train_pids += 1
num_train_imgs += len(img_paths)
return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs
print("Creating classic splits (# = 20) ...")
splits_classic_det, splits_classic_lab = [], []
for split_ref in mat['testsets'][0]:
test_split = _deref(split_ref).tolist()
# create split for detected images
train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
_extract_classic_split(meta_detected, test_split)
splits_classic_det.append({
'train': train, 'query': test, 'gallery': test,
'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,
'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,
'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,
})
# create split for labeled images
train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
_extract_classic_split(meta_labeled, test_split)
splits_classic_lab.append({
'train': train, 'query': test, 'gallery': test,
'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,
'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,
'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,
})
write_json(splits_classic_det, self.split_classic_det_json_path)
write_json(splits_classic_lab, self.split_classic_lab_json_path)
def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):
tmp_set = []
unique_pids = set()
for idx in idxs:
img_name = filelist[idx][0]
camid = int(img_name.split('_')[2])
pid = pids[idx]
if relabel: pid = pid2label[pid]
img_path = osp.join(img_dir, img_name)
tmp_set.append((img_path, int(pid), camid))
unique_pids.add(pid)
return tmp_set, len(unique_pids), len(idxs)
def _extract_new_split(split_dict, img_dir):
train_idxs = split_dict['train_idx'].flatten() - 1 # index-0
pids = split_dict['labels'].flatten()
train_pids = set(pids[train_idxs])
pid2label = {pid: label for label, pid in enumerate(train_pids)}
query_idxs = split_dict['query_idx'].flatten() - 1
gallery_idxs = split_dict['gallery_idx'].flatten() - 1
filelist = split_dict['filelist'].flatten()
train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)
query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)
gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)
return train_info, query_info, gallery_info
print("Creating new splits for detected images (767/700) ...")
train_info, query_info, gallery_info = _extract_new_split(
loadmat(self.split_new_det_mat_path),
self.imgs_detected_dir,
)
splits = [{
'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],
'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],
'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],
'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],
}]
write_json(splits, self.split_new_det_json_path)
print("Creating new splits for labeled images (767/700) ...")
train_info, query_info, gallery_info = _extract_new_split(
loadmat(self.split_new_lab_mat_path),
self.imgs_labeled_dir,
)
splits = [{
'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],
'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],
'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],
'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],
}]
write_json(splits, self.split_new_lab_json_path)
class DukeMTMCreID(object):
"""
DukeMTMC-reID
Reference:
1. Ristani et al. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. ECCVW 2016.
2. Zheng et al. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro. ICCV 2017.
URL: https://github.com/layumi/DukeMTMC-reID_evaluation
Dataset statistics:
# identities: 1404 (train + query)
# images:16522 (train) + 2228 (query) + 17661 (gallery)
# cameras: 8
"""
dataset_dir = 'dukemtmc-reid'
def __init__(self, root='data', **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_train')
self.query_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/query')
self.gallery_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_test')
self._check_before_run()
train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, relabel=True)
query, num_query_pids, num_query_imgs = self._process_dir(self.query_dir, relabel=False)
gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.gallery_dir, relabel=False)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
print("=> DukeMTMC-reID loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _check_before_run(self):
"""Check if all files are available before going deeper"""
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.train_dir):
raise RuntimeError("'{}' is not available".format(self.train_dir))
if not osp.exists(self.query_dir):
raise RuntimeError("'{}' is not available".format(self.query_dir))
if not osp.exists(self.gallery_dir):
raise RuntimeError("'{}' is not available".format(self.gallery_dir))
def _process_dir(self, dir_path, relabel=False):
img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
pattern = re.compile(r'([-\d]+)_c(\d)_f([\d]+)')
pid_container = set()
for img_path in img_paths:
pid, _, _ = map(int, pattern.search(img_path).groups())
pid_container.add(pid)
pid2label = {pid:label for label, pid in enumerate(pid_container)}
dataset = []
for img_path in img_paths:
pid, camid, fid = map(int, pattern.search(img_path).groups())
assert 1 <= camid <= 8
camid -= 1 # index starts from 0
if relabel: pid = pid2label[pid]
dataset.append((img_path, pid, camid, fid))
num_pids = len(pid_container)
num_imgs = len(dataset)
return dataset, num_pids, num_imgs
class MSMT17(object):
"""
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'
def __init__(self, root='data', **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.train_dir = osp.join(self.dataset_dir, 'MSMT17_V1/train')
self.test_dir = osp.join(self.dataset_dir, 'MSMT17_V1/test')
self.list_train_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_train.txt')
self.list_val_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_val.txt')
self.list_query_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_query.txt')
self.list_gallery_path = osp.join(self.dataset_dir, 'MSMT17_V1/list_gallery.txt')
self._check_before_run()
train, num_train_pids, num_train_imgs = self._process_dir(self.train_dir, self.list_train_path)
#val, num_val_pids, num_val_imgs = self._process_dir(self.train_dir, self.list_val_path)
query, num_query_pids, num_query_imgs = self._process_dir(self.test_dir, self.list_query_path)
gallery, num_gallery_pids, num_gallery_imgs = self._process_dir(self.test_dir, self.list_gallery_path)
#train += val
#num_train_imgs += num_val_imgs
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
print("=> MSMT17 loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _check_before_run(self):
"""Check if all files are available before going deeper"""
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.train_dir):
raise RuntimeError("'{}' is not available".format(self.train_dir))
if not osp.exists(self.test_dir):
raise RuntimeError("'{}' is not available".format(self.test_dir))
def _process_dir(self, dir_path, list_path):
with open(list_path, 'r') as txt:
lines = txt.readlines()
dataset = []
pid_container = set()
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])
img_path = osp.join(dir_path, img_path)
dataset.append((img_path, pid, camid))
pid_container.add(pid)
num_imgs = len(dataset)
num_pids = len(pid_container)
# check if pid starts from 0 and increments with 1
for idx, pid in enumerate(pid_container):
assert idx == pid, "See code comment for explanation"
return dataset, num_pids, num_imgs
class VIPeR(object):
"""
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'
def __init__(self, root='data', split_id=0, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
self.cam_a_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
self.cam_b_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
self._download_data()
self._check_before_run()
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]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
print("=> VIPeR loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _download_data(self):
if osp.exists(self.dataset_dir):
print("This dataset has been downloaded.")
return
print("Creating directory {}".format(self.dataset_dir))
mkdir_if_missing(self.dataset_dir)
fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url))
print("Downloading VIPeR dataset")
urllib.urlretrieve(self.dataset_url, fpath)
print("Extracting files")
zip_ref = zipfile.ZipFile(fpath, 'r')
zip_ref.extractall(self.dataset_dir)
zip_ref.close()
def _check_before_run(self):
"""Check if all files are available before going deeper"""
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.cam_a_path):
raise RuntimeError("'{}' is not available".format(self.cam_a_path))
if not osp.exists(self.cam_b_path):
raise RuntimeError("'{}' is not available".format(self.cam_b_path))
def _prepare_split(self):
if not osp.exists(self.split_path):
print("Creating 10 random splits")
cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_path, '*.bmp')))
cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_path, '*.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
splits = []
for _ in range(1):
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 = []
for pid, idx in enumerate(test_idxs):
cam_a_img = cam_a_imgs[idx]
cam_b_img = cam_b_imgs[idx]
test.append((cam_a_img, pid, 0))
test.append((cam_b_img, pid, 1))
split = {'train': train, 'query': test, 'gallery': test,
'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))
print("Splits created")
class GRID(object):
"""
GRID
Reference:
Loy et al. Multi-camera activity correlation analysis. CVPR 2009.
URL: http://personal.ie.cuhk.edu.hk/~ccloy/downloads_qmul_underground_reid.html
Dataset statistics:
# identities: 250
# images: 1275
# cameras: 8
"""
dataset_dir = 'grid'
def __init__(self, root='data', split_id=0, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'http://personal.ie.cuhk.edu.hk/~ccloy/files/datasets/underground_reid.zip'
self.probe_path = osp.join(self.dataset_dir, 'underground_reid', 'probe')
self.gallery_path = osp.join(self.dataset_dir, 'underground_reid', 'gallery')
self.split_mat_path = osp.join(self.dataset_dir, 'underground_reid', 'features_and_partitions.mat')
self.split_path = osp.join(self.dataset_dir, 'splits.json')
self._download_data()
self._check_before_run()
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]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_gallery_pids
num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs
print("=> GRID loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _check_before_run(self):
"""Check if all files are available before going deeper"""
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.probe_path):
raise RuntimeError("'{}' is not available".format(self.probe_path))
if not osp.exists(self.gallery_path):
raise RuntimeError("'{}' is not available".format(self.gallery_path))
if not osp.exists(self.split_mat_path):
raise RuntimeError("'{}' is not available".format(self.split_mat_path))
def _download_data(self):
if osp.exists(self.dataset_dir):
print("This dataset has been downloaded.")
return
print("Creating directory {}".format(self.dataset_dir))
mkdir_if_missing(self.dataset_dir)
fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url))
print("Downloading GRID dataset")
urllib.urlretrieve(self.dataset_url, fpath)
print("Extracting files")
zip_ref = zipfile.ZipFile(fpath, 'r')
zip_ref.extractall(self.dataset_dir)
zip_ref.close()
def _prepare_split(self):
if not osp.exists(self.split_path):
print("Creating 10 random splits")
split_mat = loadmat(self.split_mat_path)
trainIdxAll = split_mat['trainIdxAll'][0] # length = 10
probe_img_paths = sorted(glob.glob(osp.join(self.probe_path, '*.jpeg')))
gallery_img_paths = sorted(glob.glob(osp.join(self.gallery_path, '*.jpeg')))
splits = []
for split_idx in range(10):
train_idxs = trainIdxAll[split_idx][0][0][2][0].tolist()
assert len(train_idxs) == 125
idx2label = {idx: label for label, idx in enumerate(train_idxs)}
train, query, gallery = [], [], []
# processing probe folder
for img_path in probe_img_paths:
img_name = osp.basename(img_path)
img_idx = int(img_name.split('_')[0])
camid = int(img_name.split('_')[1])
if img_idx in train_idxs:
# add to train data
train.append((img_path, idx2label[img_idx], camid))
else:
# add to query data
query.append((img_path, img_idx, camid))
# process gallery folder
for img_path in gallery_img_paths:
img_name = osp.basename(img_path)
img_idx = int(img_name.split('_')[0])
camid = int(img_name.split('_')[1])
if img_idx in train_idxs:
# add to train data
train.append((img_path, idx2label[img_idx], camid))
else:
# add to gallery data
gallery.append((img_path, img_idx, camid))
split = {'train': train, 'query': query, 'gallery': gallery,
'num_train_pids': 125,
'num_query_pids': 125,
'num_gallery_pids': 900,
}
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))
print("Splits created")
class CUHK01(object):
"""
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
"""
dataset_dir = 'cuhk01'
def __init__(self, root='data', split_id=0, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
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()
self._check_before_run()
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]
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_train_imgs = len(train)
num_query_imgs = len(query)
num_gallery_imgs = len(gallery)
num_total_pids = num_train_pids + num_query_pids
num_total_imgs = num_train_imgs + num_query_imgs
print("=> CUHK01 loaded")
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
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()
print("Files extracted")
def _check_before_run(self):
"""Check if all files are available before going deeper"""
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.campus_dir):
raise RuntimeError("'{}' is not available".format(self.campus_dir))
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")
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
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 = [], []
for img_path, pid, camid in img_list:
if pid in train_idxs:
train.append((img_path, idx2label[pid], camid))
else:
test.append((img_path, pid, camid))
split = {'train': train, 'query': test, 'gallery': test,
'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))
print("Splits created")
class PRID450S(object):
"""
PRID450S
Reference:
Roth et al. Mahalanobis Distance Learning for Person Re-Identification. PR 2014.
URL: https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/prid450s/
Dataset statistics:
# identities: 450
# images: 900
# cameras: 2
"""
dataset_dir = 'prid450s'
def __init__(self, root='data', split_id=0, min_seq_len=0, **kwargs):
self.dataset_dir = osp.join(root, self.dataset_dir)
self.dataset_url = 'https://files.icg.tugraz.at/f/8c709245bb/?raw=1'
self.split_path = osp.join(self.dataset_dir, 'splits.json')
self.cam_a_path = osp.join(self.dataset_dir, 'cam_a')
self.cam_b_path = osp.join(self.dataset_dir, 'cam_b')
self._download_data()
self._check_before_run()
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]