def extract_daquar_feature():
image_dir = '/data/lisatmp4/taesup/data/daquar/image'
train_list_filename = '/data/lisatmp4/taesup/data/daquar/train_image_list.txt'
test_list_filename = '/data/lisatmp4/taesup/data/daquar/test_image_list.txt'
train_desc_filename = '/data/lisatmp4/taesup/data/daquar/train_desc.pkl'
test_desc_filename = '/data/lisatmp4/taesup/data/daquar/test_desc.pkl'
with open(train_list_filename, 'r') as fp:
train_images = fp.readlines()
train_features = OrderedDict()
for i, train_image in enumerate(train_images):
image_name = train_image.split()[0]
image_path = image_dir + '/' + image_name + '.png'
image_data = load_resize_image(image_path)[None,]
features = vgg_feature_extractor(image_data.transpose((0, 3, 1, 2)))[0][0]
sparse_features = sparse.csr_matrix(features.reshape((features.shape[0],features.shape[1]*features.shape[2])))
train_features[image_name] = sparse_features
print 'daquar train {}th image done'.format(i)
with open(train_desc_filename,'wb') as fp:
pkl.dump(train_features,fp)
with open(test_list_filename, 'r') as fp:
test_images = fp.readlines()
test_features = OrderedDict()
for i, test_image in enumerate(test_images):
image_name = test_image.split()[0]
image_path = image_dir + '/' + image_name + '.png'
image_data = load_resize_image(image_path)[None,]
features = vgg_feature_extractor(image_data.transpose((0, 3, 1, 2)))[0][0]
sparse_features = sparse.csr_matrix(features.reshape((features.shape[0],features.shape[1]*features.shape[2])))
test_features[image_name] = sparse_features
print 'daquar test {}th image done'.format(i)
with open(test_desc_filename,'wb') as fp:
pkl.dump(test_features,fp)
def extract_coco_feature():
image_dirs = ['/data/lisatmp4/taesup/data/coco/images/train2014',
'/data/lisatmp4/taesup/data/coco/images/val2014',
'/data/lisatmp4/taesup/data/coco/images/test2014',
'/data/lisatmp4/taesup/data/coco/images/test2015']
desc_files = ['/data/lisatmp4/taesup/data/coco/descriptors/train2014_desc.pkl',
'/data/lisatmp4/taesup/data/coco/descriptors/val2014_desc.pkl',
'/data/lisatmp4/taesup/data/coco/descriptors/test2014_desc.pkl',
'/data/lisatmp4/taesup/data/coco/descriptors/test2015_desc.pkl']
for image_dir, desc_file in zip(image_dirs, desc_files):
image_list = glob.glob(image_dir+'/*')
feature_dict = OrderedDict()
for image_path in image_list:
image_data = load_resize_image(image_path)[None,]
features = vgg_feature_extractor(image_data.transpose((0, 3, 1, 2)))[0][0]
sparse_features = sparse.csr_matrix(features.reshape((features.shape[0],features.shape[1]*features.shape[2])))
image_name = image_path.replace(image_dir,'')
image_name = image_name.split('_')[2][:-4]
image_name = int(image_name)
feature_dict[image_name] = sparse_features
with open(desc_file,'wb') as fp:
pkl.dump(feature_dict,fp)