def combine_trainval_sets(im_dirs, partition_files, save_dir):
new_im_dir = ospj(save_dir, 'trainval_images')
may_make_dir(new_im_dir)
new_im_names = []
new_start_id = 0
occluded = []
for im_dir, partition_file in zip(im_dirs, partition_files):
partitions = load_pickle(partition_file)
im_paths = [ospj(im_dir, n) for n in partitions['trainval_im_names']]
im_paths.sort()
occluded_ = [n for n in partitions['occluded']]
occluded += occluded_
new_im_names_, id_mapping = move_ims(im_paths, new_im_dir,
parse_im_name, new_im_name_tmpl,
new_start_id, occluded)
new_start_id += len(id_mapping)
new_im_names += new_im_names_
new_ids = range(new_start_id)
partitions = {
'trainval_im_names': new_im_names,
'trainval_ids2labels': dict(zip(new_ids, new_ids)),
'occluded': occluded
}
partition_file = ospj(save_dir, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file saved to {}'.format(partition_file))
def transform(zip_file, save_dir=None):
"""Refactor file directories, rename images and partition the train/val/test
set.
"""
train_test_split_file = osp.join(save_dir, 'train_test_split.pkl')
train_test_split = save_images(zip_file, save_dir, train_test_split_file)
# train_test_split = load_pickle(train_test_split_file)
# partition train/val/test set
trainval_ids = list(
set([
parse_new_im_name(n, 'id')
for n in train_test_split['trainval_im_names']
]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
trainval_ids.sort()
trainval_ids2labels = dict(zip(trainval_ids, range(len(trainval_ids))))
partitions = partition_train_val_set(train_test_split['trainval_im_names'],
parse_new_im_name,
num_val_ids=100)
train_im_names = partitions['train_im_names']
train_ids = list(
set([parse_new_im_name(n, 'id')
for n in partitions['train_im_names']]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
train_ids.sort()
train_ids2labels = dict(zip(train_ids, range(len(train_ids))))
# A mark is used to denote whether the image is from
# query (mark == 0), or
# gallery (mark == 1), or
# multi query (mark == 2) set
val_marks = [0, ] * len(partitions['val_query_im_names']) \
+ [1, ] * len(partitions['val_gallery_im_names'])
val_im_names = list(partitions['val_query_im_names']) \
+ list(partitions['val_gallery_im_names'])
test_im_names = list(train_test_split['q_im_names']) \
+ list(train_test_split['gallery_im_names'])
test_marks = [0, ] * len(train_test_split['q_im_names']) \
+ [1, ] * len(train_test_split['gallery_im_names'])
partitions = {
'trainval_im_names': train_test_split['trainval_im_names'],
'trainval_ids2labels': trainval_ids2labels,
'train_im_names': train_im_names,
'train_ids2labels': train_ids2labels,
'val_im_names': val_im_names,
'val_marks': val_marks,
'test_im_names': test_im_names,
'test_marks': test_marks
}
partition_file = osp.join(save_dir, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file saved to {}'.format(partition_file))
def save_im_name_mapping(raw_dir, ori_to_new_im_name_file):
im_names = []
for dir_name in ['bounding_box_train', 'bounding_box_test', 'query']:
im_names_ = get_im_names(osp.join(raw_dir, dir_name), return_path=False, return_np=False)
im_names_.sort()
# Images in different original directories may have same names,
# so here we use relative paths as original image names.
im_names_ = [osp.join(dir_name, n) for n in im_names_]
im_names += im_names_
new_im_names = map_im_names(im_names, parse_original_im_name, new_im_name_tmpl)
ori_to_new_im_name = dict(zip(im_names, new_im_names))
save_pickle(ori_to_new_im_name, ori_to_new_im_name_file)
print('File saved to {}'.format(ori_to_new_im_name_file))
##################
# Just Some Info #
##################
print('len(im_names)', len(im_names))
print('len(set(im_names))', len(set(im_names)))
print('len(set(new_im_names))', len(set(new_im_names)))
print('len(ori_to_new_im_name)', len(ori_to_new_im_name))
bounding_box_train_im_names = get_im_names(osp.join(raw_dir, 'bounding_box_train'), return_path=False, return_np=False)
bounding_box_test_im_names = get_im_names(osp.join(raw_dir, 'bounding_box_test'), return_path=False, return_np=False)
query_im_names = get_im_names(osp.join(raw_dir, 'query'), return_path=False, return_np=False)
print('set(bounding_box_train_im_names).isdisjoint(set(bounding_box_test_im_names))',
set(bounding_box_train_im_names).isdisjoint(set(bounding_box_test_im_names)))
print('set(bounding_box_train_im_names).isdisjoint(set(query_im_names))',
set(bounding_box_train_im_names).isdisjoint(set(query_im_names)))
print('set(bounding_box_test_im_names).isdisjoint(set(query_im_names))',
set(bounding_box_test_im_names).isdisjoint(set(query_im_names)))
def split_train_test_set(img_dir, save_pkl_dir):
train_im_names = []
test_im_names = []
gallery_im_names = []
query_im_names = []
query_ids = []
file_list = os.listdir(img_dir)
file_list.sort()
test_id = random.sample(range(total_id_num), test_id_num)
test_id.sort()
# get the id of training/testing in the form of 'XXXX'
test_ids = ['{0:08}'.format(i) for i in test_id]
train_ids = ['{0:08}'.format(i) for i in range(total_id_num) if i not in test_id]
cam_ids = ['{0:04}'.format(i) for i in range(cam_num)]
# generate training set and test set
for tr in train_ids:
for f in file_list:
if tr == '{0:08}'.format(parse_im_name(f, 'id')):
train_im_names.append(f)
for te in test_ids:
for f in file_list:
if te == '{0:08}'.format(parse_im_name(f, 'id')):
test_im_names.append(f)
# generate query in test set
item = 0
while item < test_id_num:
i_1, i_2 = random.sample(test_im_names, 2)
if parse_im_name(i_1, 'id') == parse_im_name(i_2, 'id')\
and parse_im_name(i_1, 'id') not in query_ids:
if parse_im_name(i_1, 'cam') != parse_im_name(i_2, 'cam'):
item += 1
query_im_names.append(i_1)
query_im_names.append(i_2)
query_ids.append(parse_im_name(i_1, 'id'))
else:
continue
else:
continue
# generate gallery in test set
gallery_im_names = [f for f in test_im_names if f not in query_im_names]
train_im_names.sort()
gallery_im_names.sort()
query_im_names.sort()
partitions = {'gallery_im_names': np.asarray(gallery_im_names),
'query_im_names': np.asarray(query_im_names),
'train_im_names': np.asarray(train_im_names)}
partitions = {'detected': partitions,
'labeled': partitions}
save_pickle(partitions, save_pkl_dir)
print("Spliting operation has been done!")
def transform(zip_file, save_dir=None):
"""Refactor file directories, rename images and partition the train/val/test
set.
"""
train_test_split_file = osp.join(save_dir, 'train_test_split.pkl')
train_test_split = save_images(zip_file, save_dir, train_test_split_file)
# train_test_split = load_pickle(train_test_split_file)
# partition train/val/test set
trainval_ids = list(set([parse_new_im_name(n, 'id')
for n in train_test_split['trainval_im_names']]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
trainval_ids.sort()
trainval_ids2labels = dict(zip(trainval_ids, range(len(trainval_ids))))
partitions = partition_train_val_set(
train_test_split['trainval_im_names'], parse_new_im_name, num_val_ids=100)
train_im_names = partitions['train_im_names']
train_ids = list(set([parse_new_im_name(n, 'id')
for n in partitions['train_im_names']]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
train_ids.sort()
train_ids2labels = dict(zip(train_ids, range(len(train_ids))))
# A mark is used to denote whether the image is from
# query (mark == 0), or
# gallery (mark == 1), or
# multi query (mark == 2) set
val_marks = [0, ] * len(partitions['val_query_im_names']) \
+ [1, ] * len(partitions['val_gallery_im_names'])
val_im_names = list(partitions['val_query_im_names']) \
+ list(partitions['val_gallery_im_names'])
test_im_names = list(train_test_split['q_im_names']) \
+ list(train_test_split['mq_im_names']) \
+ list(train_test_split['gallery_im_names'])
test_marks = [0, ] * len(train_test_split['q_im_names']) \
+ [2, ] * len(train_test_split['mq_im_names']) \
+ [1, ] * len(train_test_split['gallery_im_names'])
partitions = {'trainval_im_names': train_test_split['trainval_im_names'],
'trainval_ids2labels': trainval_ids2labels,
'train_im_names': train_im_names,
'train_ids2labels': train_ids2labels,
'val_im_names': val_im_names,
'val_marks': val_marks,
'test_im_names': test_im_names,
'test_marks': test_marks}
partition_file = osp.join(save_dir, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file saved to {}'.format(partition_file))
def save_im_name_mapping(raw_dir, ori_to_new_im_name_file):
im_names = []
for dir_name in ['bounding_box_train', 'bounding_box_test', 'query']:
im_names_ = get_im_names(osp.join(raw_dir, dir_name),
return_path=False,
return_np=False)
im_names_.sort()
# Images in different original directories may have same names,
# so here we use relative paths as original image names.
im_names_ = [osp.join(dir_name, n) for n in im_names_]
im_names += im_names_
new_im_names = map_im_names(im_names, parse_original_im_name,
new_im_name_tmpl)
ori_to_new_im_name = dict(zip(im_names, new_im_names))
save_pickle(ori_to_new_im_name, ori_to_new_im_name_file)
print('File saved to {}'.format(ori_to_new_im_name_file))
##################
# Just Some Info #
##################
print('len(im_names)', len(im_names))
print('len(set(im_names))', len(set(im_names)))
print('len(set(new_im_names))', len(set(new_im_names)))
print('len(ori_to_new_im_name)', len(ori_to_new_im_name))
bounding_box_train_im_names = get_im_names(osp.join(
raw_dir, 'bounding_box_train'),
return_path=False,
return_np=False)
bounding_box_test_im_names = get_im_names(osp.join(raw_dir,
'bounding_box_test'),
return_path=False,
return_np=False)
query_im_names = get_im_names(osp.join(raw_dir, 'query'),
return_path=False,
return_np=False)
print(
'set(bounding_box_train_im_names).isdisjoint(set(bounding_box_test_im_names))',
set(bounding_box_train_im_names).isdisjoint(
set(bounding_box_test_im_names)))
print('set(bounding_box_train_im_names).isdisjoint(set(query_im_names))',
set(bounding_box_train_im_names).isdisjoint(set(query_im_names)))
print('set(bounding_box_test_im_names).isdisjoint(set(query_im_names))',
set(bounding_box_test_im_names).isdisjoint(set(query_im_names)))
def save_images(zip_file, save_dir=None, train_test_split_file=None):
"""Rename and move all used images to a directory."""
print("Extracting zip file")
root = osp.dirname(osp.abspath(zip_file))
if save_dir is None:
save_dir = root
may_make_dir(save_dir)
with ZipFile(zip_file) as z:
z.extractall(path=save_dir)
print("Extracting zip file done")
new_im_dir = osp.join(save_dir, 'images')
may_make_dir(new_im_dir)
raw_dir = osp.join(save_dir, osp.basename(zip_file)[:-4])
im_paths = []
nums = []
for dir_name in ['bounding_box_train', 'bounding_box_test', 'query']:
im_paths_ = get_im_names(osp.join(raw_dir, dir_name),
return_path=True,
return_np=False)
im_paths_.sort()
im_paths += list(im_paths_)
nums.append(len(im_paths_))
im_names = move_ims(im_paths, new_im_dir, parse_original_im_name,
new_im_name_tmpl)
split = dict()
keys = ['trainval_im_names', 'gallery_im_names', 'q_im_names']
inds = [0] + nums
inds = np.cumsum(inds)
for i, k in enumerate(keys):
split[k] = im_names[inds[i]:inds[i + 1]]
save_pickle(split, train_test_split_file)
print('Saving images done.')
return split
def save_images(zip_file, save_dir=None, train_test_split_file=None):
"""Rename and move all used images to a directory."""
print("Extracting zip file")
root = osp.dirname(osp.abspath(zip_file))
if save_dir is None:
save_dir = root
may_make_dir(save_dir)
with ZipFile(zip_file) as z:
z.extractall(path=save_dir)
print("Extracting zip file done")
new_im_dir = osp.join(save_dir, 'images')
may_make_dir(new_im_dir)
raw_dir = osp.join(save_dir, osp.basename(zip_file)[:-4])
im_paths = []
nums = []
for dir_name in ['bounding_box_train', 'bounding_box_test', 'query']:
im_paths_ = get_im_names(osp.join(raw_dir, dir_name),
return_path=True, return_np=False)
im_paths_.sort()
im_paths += list(im_paths_)
nums.append(len(im_paths_))
im_names = move_ims(
im_paths, new_im_dir, parse_original_im_name, new_im_name_tmpl)
split = dict()
keys = ['trainval_im_names', 'gallery_im_names', 'q_im_names']
inds = [0] + nums
inds = np.cumsum(inds)
for i, k in enumerate(keys):
split[k] = im_names[inds[i]:inds[i + 1]]
save_pickle(split, train_test_split_file)
print('Saving images done.')
return split
def combine_trainval_sets(
im_dirs,
partition_files,
save_dir):
new_im_dir = ospj(save_dir, 'trainval_images')
may_make_dir(new_im_dir)
new_im_names = []
new_start_id = 0
for im_dir, partition_file in zip(im_dirs, partition_files):
partitions = load_pickle(partition_file)
im_paths = [ospj(im_dir, n) for n in partitions['trainval_im_names']]
im_paths.sort()
new_im_names_, id_mapping = move_ims(
im_paths, new_im_dir, parse_im_name, new_im_name_tmpl, new_start_id)
new_start_id += len(id_mapping)
new_im_names += new_im_names_
new_ids = range(new_start_id)
partitions = {'trainval_im_names': new_im_names,
'trainval_ids2labels': dict(zip(new_ids, new_ids)),
}
partition_file = ospj(save_dir, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file saved to {}'.format(partition_file))
def save_images(zip_file, save_dir=None, train_test_split_file=None):
"""Rename and move all used images to a directory."""
print("Extracting zip file")
root = osp.dirname(osp.abspath(zip_file))
if save_dir is None:
save_dir = root
may_make_dir(osp.abspath(save_dir))
with ZipFile(zip_file) as z:
z.extractall(path=save_dir)
print("Extracting zip file done")
new_im_dir = osp.join(save_dir, 'images')
may_make_dir(osp.abspath(new_im_dir))
raw_dir = osp.join(save_dir, osp.basename(zip_file)[:-4])
im_paths = []
nums = []
im_paths_ = get_im_names(osp.join(raw_dir, 'bounding_box_train'),
return_path=True,
return_np=False)
im_paths_.sort()
im_paths += list(im_paths_)
nums.append(len(im_paths_))
im_paths_ = get_im_names(osp.join(raw_dir, 'bounding_box_test'),
return_path=True,
return_np=False)
im_paths_.sort()
im_paths_ = [p for p in im_paths_ if not osp.basename(p).startswith('-1')]
im_paths += list(im_paths_)
nums.append(len(im_paths_))
im_paths_ = get_im_names(osp.join(raw_dir, 'query'),
return_path=True,
return_np=False)
im_paths_.sort()
im_paths += list(im_paths_)
nums.append(len(im_paths_))
q_ids_cams = set([(parse_original_im_name(osp.basename(p), 'id'),
parse_original_im_name(osp.basename(p), 'cam'))
for p in im_paths_])
im_paths_ = get_im_names(osp.join(raw_dir, 'gt_bbox'),
return_path=True,
return_np=False)
im_paths_.sort()
# Only gather images for those ids and cams used in testing.
im_paths_ = [
p for p in im_paths_
if (parse_original_im_name(osp.basename(p), 'id'),
parse_original_im_name(osp.basename(p), 'cam')) in q_ids_cams
]
im_paths += list(im_paths_)
nums.append(len(im_paths_))
im_names = move_ims(im_paths, new_im_dir, parse_original_im_name,
new_im_name_tmpl)
split = dict()
keys = [
'trainval_im_names', 'gallery_im_names', 'q_im_names', 'mq_im_names'
]
inds = [0] + nums
inds = np.cumsum(np.array(inds))
for i, k in enumerate(keys):
split[k] = im_names[inds[i]:inds[i + 1]]
save_pickle(split, train_test_split_file)
print('Saving images done.')
return split
def transform(zip_file, train_test_partition_file, save_dir=None):
"""Save images and partition the train/val/test set.
"""
print("Extracting zip file")
root = osp.dirname(osp.abspath(zip_file))
if save_dir is None:
save_dir = root
may_make_dir(save_dir)
with ZipFile(zip_file) as z:
z.extractall(path=save_dir)
print("Extracting zip file done")
mat_file = osp.join(save_dir, osp.basename(zip_file)[:-4], 'cuhk-03.mat')
save_images(mat_file, save_dir, new_im_name_tmpl)
if osp.exists(train_test_partition_file):
train_test_partition = load_pickle(train_test_partition_file)
else:
raise RuntimeError('Train/test partition file should be provided.')
for im_type in ['detected', 'labeled']:
trainval_im_names = train_test_partition[im_type]['train_im_names']
trainval_ids = list(
set([parse_im_name(n, 'id') for n in trainval_im_names]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
trainval_ids.sort()
trainval_ids2labels = dict(zip(trainval_ids, range(len(trainval_ids))))
train_val_partition = \
partition_train_val_set(trainval_im_names, parse_im_name, num_val_ids=100)
train_im_names = train_val_partition['train_im_names']
train_ids = list(
set([
parse_im_name(n, 'id')
for n in train_val_partition['train_im_names']
]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
train_ids.sort()
train_ids2labels = dict(zip(train_ids, range(len(train_ids))))
# A mark is used to denote whether the image is from
# query (mark == 0), or
# gallery (mark == 1), or
# multi query (mark == 2) set
val_marks = [0, ] * len(train_val_partition['val_query_im_names']) \
+ [1, ] * len(train_val_partition['val_gallery_im_names'])
val_im_names = list(train_val_partition['val_query_im_names']) \
+ list(train_val_partition['val_gallery_im_names'])
test_im_names = list(train_test_partition[im_type]['query_im_names']) \
+ list(train_test_partition[im_type]['gallery_im_names'])
test_marks = [0, ] * len(train_test_partition[im_type]['query_im_names']) \
+ [1, ] * len(
train_test_partition[im_type]['gallery_im_names'])
partitions = {
'trainval_im_names': trainval_im_names,
'trainval_ids2labels': trainval_ids2labels,
'train_im_names': train_im_names,
'train_ids2labels': train_ids2labels,
'val_im_names': val_im_names,
'val_marks': val_marks,
'test_im_names': test_im_names,
'test_marks': test_marks
}
partition_file = osp.join(save_dir, im_type, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file for "{}" saved to {}'.format(
im_type, partition_file))
train_ids2labels = dict(zip(ids, range(len(ids))))
# print(len(ids))
with open(test_path, 'r') as f:
lines = f.readlines()
last_id = -100
cam = 0
for line in lines:
id = int(line.split(',')[0])
if id != last_id:
cam = 0
else:
cam += 1
last_id = id
name = line.split(',')[1]#.split('.')[0] + '.jpg'
ori_name = name.split('.')[0]
marks = int(line.split(',')[2].split()[0])
new_im_name = '{:08d}_{:04d}_{:08d}.jpg'.format(id, cam, int(ori_name))
shutil.copy(osp.join(im_path, name), osp.join(new_im_dir, new_im_name))
test_im_names.append(new_im_name)
test_marks.append(marks)
# print(len(train_im_names),len(train_ids2labels))
partitions = {'train_im_names': train_im_names,
'train_ids2labels': train_ids2labels,
'test_im_names': test_im_names,
'test_marks': test_marks}
partition_file = osp.join(save_path, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file saved to {}'.format(partition_file))
def save_images(zip_file, save_dir=None, train_test_split_file=None):
"""Rename and move all used images to a directory."""
print("Extracting zip file")
root = osp.dirname(osp.abspath(zip_file))
if save_dir is None:
save_dir = root
may_make_dir(osp.abspath(save_dir))
with ZipFile(zip_file) as z:
z.extractall(path=save_dir)
print("Extracting zip file done")
new_im_dir = osp.join(save_dir, 'images')
may_make_dir(osp.abspath(new_im_dir))
raw_dir = osp.join(save_dir, osp.basename(zip_file)[:-4])
im_paths = []
nums = []
im_paths_ = get_im_names(osp.join(raw_dir, 'bounding_box_train'),
return_path=True, return_np=False)
im_paths_.sort()
im_paths += list(im_paths_)
nums.append(len(im_paths_))
im_paths_ = get_im_names(osp.join(raw_dir, 'bounding_box_test'),
return_path=True, return_np=False)
im_paths_.sort()
im_paths_ = [p for p in im_paths_ if not osp.basename(p).startswith('-1')]
im_paths += list(im_paths_)
nums.append(len(im_paths_))
im_paths_ = get_im_names(osp.join(raw_dir, 'query'),
return_path=True, return_np=False)
im_paths_.sort()
im_paths += list(im_paths_)
nums.append(len(im_paths_))
q_ids_cams = set([(parse_original_im_name(osp.basename(p), 'id'),
parse_original_im_name(osp.basename(p), 'cam'))
for p in im_paths_])
im_paths_ = get_im_names(osp.join(raw_dir, 'gt_bbox'),
return_path=True, return_np=False)
im_paths_.sort()
# Only gather images for those ids and cams used in testing.
im_paths_ = [p for p in im_paths_
if (parse_original_im_name(osp.basename(p), 'id'),
parse_original_im_name(osp.basename(p), 'cam'))
in q_ids_cams]
im_paths += list(im_paths_)
nums.append(len(im_paths_))
im_names = move_ims(
im_paths, new_im_dir, parse_original_im_name, new_im_name_tmpl)
split = dict()
keys = ['trainval_im_names', 'gallery_im_names', 'q_im_names', 'mq_im_names']
inds = [0] + nums
inds = np.cumsum(np.array(inds))
for i, k in enumerate(keys):
split[k] = im_names[inds[i]:inds[i + 1]]
save_pickle(split, train_test_split_file)
print('Saving images done.')
return split
def transform(zip_file, train_test_partition_file, save_dir=None):
"""Save images and partition the train/val/test set.
"""
print("Extracting zip file")
root = osp.dirname(osp.abspath(zip_file))
if save_dir is None:
save_dir = root
may_make_dir(save_dir)
with ZipFile(zip_file) as z:
z.extractall(path=save_dir)
print("Extracting zip file done")
mat_file = osp.join(save_dir, osp.basename(zip_file)[:-4], 'cuhk-03.mat')
save_images(mat_file, save_dir, new_im_name_tmpl)
if osp.exists(train_test_partition_file):
train_test_partition = load_pickle(train_test_partition_file)
else:
raise RuntimeError('Train/test partition file should be provided.')
for im_type in ['detected', 'labeled']:
trainval_im_names = train_test_partition[im_type]['train_im_names']
trainval_ids = list(set([parse_im_name(n, 'id')
for n in trainval_im_names]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
trainval_ids.sort()
trainval_ids2labels = dict(zip(trainval_ids, range(len(trainval_ids))))
train_val_partition = \
partition_train_val_set(trainval_im_names, parse_im_name, num_val_ids=100)
train_im_names = train_val_partition['train_im_names']
train_ids = list(set([parse_im_name(n, 'id')
for n in train_val_partition['train_im_names']]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
train_ids.sort()
train_ids2labels = dict(zip(train_ids, range(len(train_ids))))
# A mark is used to denote whether the image is from
# query (mark == 0), or
# gallery (mark == 1), or
# multi query (mark == 2) set
val_marks = [0, ] * len(train_val_partition['val_query_im_names']) \
+ [1, ] * len(train_val_partition['val_gallery_im_names'])
val_im_names = list(train_val_partition['val_query_im_names']) \
+ list(train_val_partition['val_gallery_im_names'])
test_im_names = list(train_test_partition[im_type]['query_im_names']) \
+ list(train_test_partition[im_type]['gallery_im_names'])
test_marks = [0, ] * len(train_test_partition[im_type]['query_im_names']) \
+ [1, ] * len(
train_test_partition[im_type]['gallery_im_names'])
partitions = {'trainval_im_names': trainval_im_names,
'trainval_ids2labels': trainval_ids2labels,
'train_im_names': train_im_names,
'train_ids2labels': train_ids2labels,
'val_im_names': val_im_names,
'val_marks': val_marks,
'test_im_names': test_im_names,
'test_marks': test_marks}
partition_file = osp.join(save_dir, im_type, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file for "{}" saved to {}'.format(im_type, partition_file))
