def _download_data(self):
if osp.exists(self.dataset_dir):
print("This dataset has been downloaded.")
return
mkdir_if_missing(self.dataset_dir)
fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url))
print("Downloading iLIDS-VID dataset")
urllib.urlretrieve(self.dataset_url, fpath)
print("Extracting files")
tar = tarfile.open(fpath)
tar.extractall(path=self.dataset_dir)
tar.close()
def _download_data(self):
if osp.exists(self.dataset_dir):
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 DukeMTMC-reID dataset")
urllib.request.urlretrieve(self.dataset_url, fpath)
print("Extracting files")
zip_ref = zipfile.ZipFile(fpath, 'r')
zip_ref.extractall(self.dataset_dir)
zip_ref.close()
def _download_data(self):
if osp.exists(self.dataset_dir):
print("This dataset has been downloaded.")
return
mkdir_if_missing(self.dataset_dir)
fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url))
print("Downloading iLIDS-VID dataset")
urllib.urlretrieve(self.dataset_url, fpath)
print("Extracting files")
tar = tarfile.open(fpath)
tar.extractall(path=self.dataset_dir)
tar.close()
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 _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 download_dataset(self, dataset_dir, dataset_url):
"""Downloads and extracts dataset.
Args:
dataset_dir (str): dataset directory.
dataset_url (str): url to download dataset.
"""
if osp.exists(dataset_dir):
return
if dataset_url is None:
raise RuntimeError(
'{} dataset needs to be manually '
'prepared, please follow the '
'document to prepare this dataset'.format(
self.__class__.__name__
)
)
print('Creating directory "{}"'.format(dataset_dir))
mkdir_if_missing(dataset_dir)
fpath = osp.join(dataset_dir, osp.basename(dataset_url))
print(
'Downloading {} dataset to "{}"'.format(
self.__class__.__name__, dataset_dir
)
)
# download_url(dataset_url, fpath)
print('Extracting "{}"'.format(fpath))
try:
tar = tarfile.open(fpath)
tar.extractall(path=dataset_dir)
tar.close()
except:
zip_ref = zipfile.ZipFile(fpath, 'r')
zip_ref.extractall(dataset_dir)
zip_ref.close()
print('{} dataset is ready'.format(self.__class__.__name__))
def __init__(self, cfg):
"""
All Saver based on two dir: save_dir and load_dir.
for train : save_dir = load_dir and every time This will make a new one.
for test : save_dir = None , load_dir will automatic be stitched with run_id
for uda : every time save_dir will be made. and load_dir will automatic be stitched with run_id and source dataset
"""
self.cfg = cfg
self.save_dir = ''
self.load_dir = ''
dirname_list = os.path.dirname(__file__).split("/")[:-1]
self.up_dir = "/".join(dirname_list)
source_name, source_mid_name, target_name, target_mid_name = self._get_some_dir_name(
cfg)
if cfg.TEST.IF_ON:
self.load_dir = self.get_load_dir(source_mid_name, source_name)
self.save_dir = self.load_dir
else:
self.save_dir = self.get_save_dir(target_mid_name, target_name)
if target_mid_name == 'direct':
self.load_dir = self.save_dir
else:
self.load_dir = self.get_load_dir(source_mid_name, source_name)
print(f"save dir: {self.save_dir}")
print(f"load dir: {self.load_dir}")
self.model_dir = join(self.save_dir, 'model')
self.image_dir = join(self.save_dir, 'image')
self.tensorboard_dir = join(self.save_dir, 'tensorboard')
self.code_dir = join(self.save_dir, 'code')
mkdir_if_missing(self.save_dir)
mkdir_if_missing(self.model_dir)
mkdir_if_missing(self.image_dir)
mkdir_if_missing(self.tensorboard_dir)
if not os.path.exists(self.code_dir):
shutil.copytree(self.up_dir,
self.code_dir,
ignore=shutil.ignore_patterns('run'))
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)
if img.size == 0 or img.ndim < 3: continue
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)
if not osp.isfile(img_path):
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]) - 1 # make it 0-based
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]) - 1 # make it 0-based
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]) - 1 # make it 0-based
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)
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)
def preprocess_split(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)
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
import h5py
from imageio import imwrite
from scipy.io import loadmat
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)
if img.size == 0 or img.ndim < 3:
continue # skip empty cell
# 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)
if not osp.isfile(img_path):
imwrite(img_path, img)
img_paths.append(img_path)
return img_paths
def _extract_img(image_type):
print('Processing {} images ...'.format(image_type))
meta_data = []
imgs_dir = self.imgs_detected_dir if image_type == 'detected' else self.imgs_labeled_dir
for campid, camp_ref in enumerate(mat[image_type][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]) - 1 # make it 0-based
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]) - 1 # make it 0-based
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]) - 1 # make it 0-based
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 split 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)
split = [{
'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(split, self.split_new_det_json_path)
print('Creating new split 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)
split = [{
'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(split, self.split_new_lab_json_path)
