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[:-7]))
raw_dir = save_dir
im_paths = []
nums = []
for dir_name in ['bounding_box_train', 'bounding_box_test', 'query']:
# import pdb
# pdb.set_trace()
im_paths_ = get_im_names(osp.join(raw_dir, dir_name),
pattern='*.jpg',
return_path=True,
return_np=False)
im_paths_.sort()
im_paths += list(im_paths_)
nums.append(len(im_paths_))
# import pdb
# pdb.set_trace()
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_rap2(zip_file, annotation_file, query_file, save_dir=None, \
train_test_split_file=None):
"""Rename and move all the used images to a diretory."""
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])
# Get fnames
img_fnames_train, img_fnames_test, img_fnames_query = \
_get_im_names_rap2(annotation_file, query_file, 1)
img_fnames_train.sort()
img_fnames_test.sort()
img_fnames_query.sort()
im_paths = list(img_fnames_train) + list(img_fnames_test) + \
list(img_fnames_query)
nums = [img_fnames_train.shape[0], img_fnames_test.shape[0], \
img_fnames_query.shape[0]]
# Move images
org_img_dir = osp.join(root, 'images-pedestrian')
im_names = _move_ims(org_img_dir, 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_im(im, save_path):
"""im: shape [3, H, W]"""
may_make_dir(ospdn(save_path))
im = im.transpose(1, 2, 0)
Image.fromarray(im).save(save_path)
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])
raw_dir = save_dir
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
'~/Dataset/cuhk03/{}/partitions.pkl'.format(cuhk03_im_type)))
parser.add_argument('--duke_im_dir',
type=str,
default=ospeu('~/Dataset/duke/images'))
parser.add_argument('--duke_partition_file',
type=str,
default=ospeu('~/Dataset/duke/partitions.pkl'))
parser.add_argument('--save_dir',
type=str,
default=ospeu('~/Dataset/market1501_cuhk03_duke'))
args = parser.parse_args()
im_dirs = [
ospap(ospeu(args.market1501_im_dir)),
ospap(ospeu(args.cuhk03_im_dir)),
ospap(ospeu(args.duke_im_dir))
]
partition_files = [
ospap(ospeu(args.market1501_partition_file)),
ospap(ospeu(args.cuhk03_partition_file)),
ospap(ospeu(args.duke_partition_file))
]
save_dir = ospap(ospeu(args.save_dir))
may_make_dir(save_dir)
combine_trainval_sets(im_dirs, partition_files, save_dir)