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', 'gt_bbox']:
im_names_ = get_im_names(osp.join(raw_dir, dir_name), return_path=False, return_np=False)
im_names_.sort()
# Filter out id -1
if dir_name == 'bounding_box_test':
im_names_ = [n for n in im_names_ if not n.startswith('-1')]
# 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)
gt_bbox_im_names = get_im_names(osp.join(raw_dir, 'gt_bbox'), 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_train_im_names).isdisjoint(set(gt_bbox_im_names))',
set(bounding_box_train_im_names).isdisjoint(set(gt_bbox_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)))
print('set(bounding_box_test_im_names).isdisjoint(set(gt_bbox_im_names))',
set(bounding_box_test_im_names).isdisjoint(set(gt_bbox_im_names)))
print('set(query_im_names).isdisjoint(set(gt_bbox_im_names))',
set(query_im_names).isdisjoint(set(gt_bbox_im_names)))
print('len(query_im_names)', len(query_im_names))
print('len(gt_bbox_im_names)', len(gt_bbox_im_names))
print('len(set(query_im_names) & set(gt_bbox_im_names))', len(set(query_im_names) & set(gt_bbox_im_names)))
print('len(set(query_im_names) | set(gt_bbox_im_names))', len(set(query_im_names) | set(gt_bbox_im_names)))
def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(net=cfg.net, pretrained=False)
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
model = model.base
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
###################
# Extract Feature #
###################
im_dir = osp.expanduser(cfg.image_dir)
im_paths = get_im_names(im_dir,
pattern='*.jpg',
return_path=True,
return_np=False)
all_feat = []
import scipy.io as sci
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
# all_feat = normalize(all_feat, axis=1)
# You can save your im_paths and features, or do anything else ...
sci.savemat(cfg.saved_feature_mat_file, {'features': all_feat})
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 main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride)
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
im_dir = osp.expanduser('~/Dataset/market1501/Market-1501-v15.09.15/query')
im_paths = get_im_names(im_dir,
pattern='*.jpg',
return_path=True,
return_np=False)
all_feat = []
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
all_feat = normalize(all_feat, axis=1)
def main():
cfg = Config()
TVT, TMO = set_devices(cfg.sys_device_ids)
#########
# Model #
#########
model = Model(last_conv_stride=cfg.last_conv_stride)
# Set eval mode. Force all BN layers to use global mean and variance, also disable dropout.
model.eval()
# Transfer Model to Specified Device.
TMO([model])
#####################
# Load Model Weight #
#####################
used_file = cfg.model_weight_file or cfg.ckpt_file
loaded = torch.load(used_file, map_location=(lambda storage, loc: storage))
if cfg.model_weight_file == '':
loaded = loaded['state_dicts'][0]
load_state_dict(model, loaded)
print('Loaded model weights from {}'.format(used_file))
###################
# Extract Feature #
###################
im_dir = osp.expanduser('../../5Labeled Data/labeledNew')
im_paths = get_im_names(im_dir,
pattern='*.png',
return_path=True,
return_np=False)
currentData = {}
imagesKnown = set()
with open('../../7Data Features/dataFeatures.json') as json_file:
currentData = json.load(json_file)
for image in currentData["images"]:
imagesKnown.add(image["imageName"])
all_feat = []
for i, im_path in enumerate(im_paths):
im = np.asarray(Image.open(im_path).convert('RGB'))
im = pre_process_im(im, cfg)
im = Variable(TVT(torch.from_numpy(im).float()), volatile=True)
feat = model(im)
feat = feat.data.cpu().numpy()
all_feat.append(feat)
if (i + 1) % 100 == 0:
print('{}/{} images done'.format(i, len(im_paths)))
all_feat = np.concatenate(all_feat)
print('all_feat.shape:', all_feat.shape)
all_feat = normalize(all_feat, axis=1)
# You can save your im_paths and features, or do anything else ...
for i, im_path in enumerate(im_paths):
if (im_path in imagesKnown):
print("image", im_path, "already exists")
pass
else:
currentData['images'].append({
'imageName': im_path,
'features': all_feat[i].tolist()
})
if (i + 1) % 100 == 0:
print('{}/{} images saved'.format(i, len(im_paths)))
with open('../../7Data Features/dataFeatures.json', 'w') as outfile:
json.dump(currentData, outfile)
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
