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 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 save_images(original_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(original_file))
if save_dir is None:
save_dir = root
may_make_dir(osp.abspath(save_dir))
# with ZipFile(original_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.abspath(original_file)
print('raw_dir: ', raw_dir)
im_paths = []
nums = []
im_paths_ = get_im_names(osp.join(raw_dir, 'bbox_train'),
pattern='*/*.png',
return_path=True,
return_np=False)
im_paths_.sort()
im_paths += list(im_paths_)
nums.append(len(im_paths_))
print('dir_name: bbox_train')
print('nums: ', nums)
im_paths_ = get_im_names(osp.join(raw_dir, 'bbox_test'),
pattern='*/*.png',
return_path=True,
return_np=False)
im_paths_.sort()
im_paths_ = [
p for p in im_paths_ if not osp.basename(p).startswith('00-1')
]
im_paths += list(im_paths_)
nums.append(len(im_paths_))
print('dir_name: bbox_test')
print('nums: ', nums)
# 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_names = move_ims(im_paths, new_im_dir, parse_original_im_name,
new_im_name_tmpl)
split = dict()
keys = ['trainval_im_names', 'galleryquery_im_names']
inds = [0] + nums
print('inds: ', inds)
inds = np.cumsum(np.array(inds))
print('inds: ', inds)
print('enumerate(keys): ', enumerate(keys))
for i, k in enumerate(keys):
print('i,k: ', i, k)
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(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 save_images(data_dir, 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")
#get the images and origin name of path
new_im_dir = osp.join(save_dir, 'images')
may_make_dir(osp.abspath(new_im_dir))
# define paths of all images and number of files in four folders
im_paths = []
bb_test = []
bb_test_num = 0
bb_train_num = 0
bb_train = []
query = []
query_num = 0
gt_bb_num = 0
gt_bb = []
nums = []
for data in dataset:
raw_dir = osp.join(data_dir, data)
im_paths_ = get_im_names(osp.join(raw_dir, 'bounding_box_train'),
return_path=True,
return_np=False)
im_paths_.sort()
bb_train += list(im_paths_)
bb_train_num += 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')
]
bb_test += list(im_paths_)
bb_test_num += len(im_paths_)
im_paths_ = get_im_names(osp.join(raw_dir, 'query'),
return_path=True,
return_np=False)
im_paths_.sort()
query += list(im_paths_)
query_num += 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
]
gt_bb += list(im_paths_)
gt_bb_num += len(im_paths_)
im_paths = bb_train + bb_test + query + gt_bb
nums = [bb_train_num] + [bb_test_num] + [query_num] + [gt_bb_num]
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