def process_func(self, example_line):
"""Process the single example line and return data
Default behavior, assumes each line is the path to a single image.
This is used to train a VAE.
"""
#return imresize(imread(example_line), [256, 256])
return imread(example_line)
def images_paths(self, set):
origin = 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/horse2zebra.zip'
origin_file_name = os.path.basename(origin)
download_folder = os.path.join(DATA_FOLDER, 'horse2zebra')
download_path = os.path.join(download_folder, origin_file_name)
download(origin, download_path)
extract_path = extract_all(download_path)
extract_path = os.path.join(extract_path, set + self.name)
images_paths = glob(os.path.join(extract_path, '*.jpg'))
final_images_paths = []
for image_path in images_paths:
image = imread(image_path)
if image.shape != self.image_shape:
msg = '{} has wrong shape {}. It should be {}. Removing file'
print(msg.format(image_path, image.shape, self.image_shape))
os.remove(image_path)
else:
final_images_paths.append(image_path)
return np.array(final_images_paths)
if ckpt_path is None:
raise Exception('No checkpoint!')
else:
print('Copy variables from % s' % ckpt_path)
#--test--#
b_list = glob('./Datasets/' + dataset + '/bounding_box_train-Market/*.jpg')
a_list = glob('./Datasets/' + dataset + '/bounding_box_train-Duke/*.jpg')
b_save_dir = './test_predictions/' + dataset + '_spgan' + '/bounding_box_train_market2duke/'
a_save_dir = './test_predictions/' + dataset + '_spgan' + '/bounding_box_train_duke2market/'
utils.mkdir([a_save_dir, b_save_dir])
for i in range(len(a_list)):
a_real_ipt = im.imresize(im.imread(a_list[i]), [crop_size, crop_size])
a_real_ipt.shape = 1, crop_size, crop_size, 3
a2b_opt = sess.run(a2b, feed_dict={a_real: a_real_ipt})
a_img_opt = a2b_opt
img_name = os.path.basename(a_list[i])
img_name = 'market_' + img_name # market_style
im.imwrite(im.immerge(a_img_opt, 1, 1), a_save_dir + img_name)
print('Save %s' % (a_save_dir + img_name))
for i in range(len(b_list)):
b_real_ipt = im.imresize(im.imread(b_list[i]), [crop_size, crop_size])
b_real_ipt.shape = 1, crop_size, crop_size, 3
b2a_opt = sess.run(b2a, feed_dict={b_real: b_real_ipt})
b_img_opt = b2a_opt
img_name = os.path.basename(b_list[i])
def transform(self, data):
image = imread(data[0])
return normalize(image)
def process_func(self, example_line):
return imread(example_line)