help='The filename of mask, value 255 indicates mask.')
parser.add_argument('--output',
default='output.png',
type=str,
help='Where to write output.')
parser.add_argument('--checkpoint_dir',
default='',
type=str,
help='The directory of tensorflow checkpoint.')
if __name__ == "__main__":
print("In main")
ng.get_gpus(1)
args = parser.parse_args()
model = InpaintCAModel()
image = cv2.imread(args.image)
mask = cv2.imread(args.mask)
assert image.shape == mask.shape
h, w, _ = image.shape
grid = 8
image = image[:h // grid * grid, :w // grid * grid, :]
mask = mask[:h // grid * grid, :w // grid * grid, :]
print('Shape of image: {}'.format(image.shape))
image = np.expand_dims(image, 0)
mask = np.expand_dims(mask, 0)
input_image = np.concatenate([image, mask], axis=2)
if __name__ == "__main__":
#TODO: 실행 전에 터지지 않는 적절한 값으로 딱 한번 초기화 할 것.
core.seg_limit = 4000000 #// 10 # 보통 이게 더 큼
core.compl_limit = 1000000 #// 10 #
segnet_yml = 'segnet/seg48_4[553].yml' # segnet configuration
segnet_model_path = 'segnet/seg48_4[553].h5' # saved segnet model
complnet_ckpt_dir = 'v2_180923' # saved complnets directory
#--------------------------------------------
# for segnet
with open(segnet_yml, 'r') as f:
config = yaml.load(f)
# for complnet
complnet = InpaintCAModel('v2')
ng.get_gpus(1, False) #TODO: 단 한 번만 호출할 것.
#--------------------------------------------
dilate_kernel = core.rect5
if not os.path.isdir('./cleaned'):
os.mkdir('./cleaned')
app = QtWidgets.QApplication(sys.argv)
screen = app.primaryScreen()
print('Screen: %s' % screen.name())
size = screen.size()
print('Size: %d x %d' % (size.width(), size.height()))
rect = screen.availableGeometry()
print('Available: %d x %d' % (rect.width(), rect.height()))
MainWindow = MyMainScreen()
def iter_generate_image(image,
bbox,
id,
checkpoint_dir='model_logs/release_imagenet_256',
iterate=False):
# ng.get_gpus(1)
model = InpaintCAModel()
mask_obj = Mask_obj(top=bbox['y'],
left=bbox['x'],
width=bbox['w'],
height=bbox['h'])
mask = mask_obj.image
assert image.shape == mask.shape
print('Shape of image: {}'.format(image.shape))
iter1_input = preprocess_image(image, mask)
input_shape = iter1_input.shape
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
# input_image = tf.constant(input_image, dtype=tf.float32)
input_image = tf.placeholder(tf.float32, shape=input_shape)
output = model.build_server_graph(input_image)
output = (output + 1.) * 127.5
output = tf.reverse(output, [-1])
output = tf.saturate_cast(output, tf.uint8)
# load pretrained model
vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
assign_ops = []
for var in vars_list:
vname = var.name
from_name = vname
var_value = tf.contrib.framework.load_variable(
checkpoint_dir, from_name)
assign_ops.append(tf.assign(var, var_value))
sess.run(assign_ops)
print('Model loaded.')
output_images = []
# iter1
result = sess.run(output, feed_dict={input_image: iter1_input})
iter1_image = result[0][:, :, ::-1]
output_images.append(iter1_image)
if iterate:
# iter2
iter2_submask_objs = generate_submask_objs([mask_obj])
temp_image = iter1_image
for s in iter2_submask_objs:
submask = s.image
temp_input = preprocess_image(temp_image, submask)
result = sess.run(output, feed_dict={input_image: temp_input})
temp_image = result[0][:, :, ::-1]
iter2_image = temp_image
output_images.append(iter2_image)
# iter3
# iter3_submask_objs = generate_submask_objs(iter2_submask_objs)
# temp_image = iter2_image
# for s in iter3_submask_objs:
# submask = s.image
# temp_input = preprocess_image(temp_image, submask)
# result = sess.run(output, feed_dict={input_image: temp_input})
# temp_image = result[0][:, :, ::-1]
# iter3_image = temp_image
# output_images.append(iter3_image)
print('hi')
return output_images[-1]
def complete(image_file):
print("In complete")
#ng.get_gpus(1,verbose=False)
tf.reset_default_graph()
model = InpaintCAModel()
image = cv2.imread(os.path.join(args.image_dir, image_file))
mask = cv2.imread(os.path.join(args.mask_dir, image_file))
assert image.shape == mask.shape
h, w, _ = image.shape
image_small = image
mask_small = mask
make_small_flag = False
if h > 1700 or w > 1700:
print("girdimm")
image_small = image_small[:, 90:-90, :]
mask_small = mask_small[:, 90:-90, :]
h, w, _ = image_small.shape
make_small_flag = True
print("image shape:", image.shape)
print("image small shape:", image_small.shape)
grid = 8
image_rs = image_small[:h // grid * grid, :w // grid * grid, :]
mask_rs = mask_small[:h // grid * grid, :w // grid * grid, :]
print('image rs shape: {}'.format(image_rs.shape))
image_rs = np.expand_dims(image_rs, 0)
mask_rs = np.expand_dims(mask_rs, 0)
input_image = np.concatenate([image_rs, mask_rs], axis=2)
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
input_image = tf.constant(input_image, dtype=tf.float32)
output = model.build_server_graph(input_image)
output = (output + 1.) * 127.5
output = tf.reverse(output, [-1])
output = tf.saturate_cast(output, tf.uint8)
# load pretrained model
vars_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
assign_ops = []
for var in vars_list:
vname = var.name
from_name = vname
var_value = tf.contrib.framework.load_variable(
args.checkpoint_dir, from_name)
assign_ops.append(tf.assign(var, var_value))
sess.run(assign_ops)
result = sess.run(output)
image_small[:h // grid * grid, :w // grid *
grid, :] = result[0][:, :, ::-1]
if make_small_flag == True:
image[:, 90:-90, :] = image_small
save_value = cv2.imwrite(os.path.join(args.output_dir, image_file),
image)
print("Image small saved:", save_value)
else:
save_value = cv2.imwrite(os.path.join(args.output_dir, image_file),
image_small)
print("Image saved:", save_value)
sess.close()
