def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
# os.makedirs(args.sample_dir +"/A")
# os.makedirs(args.sample_dir +"/B")
### 因為寫在function裡會一直被呼叫到，所以我才拉出來main寫喔！
# os.makedirs(args.sample_dir +"/to_curved/big")
# os.makedirs(args.sample_dir +"/to_curved/big-left-top")
# os.makedirs(args.sample_dir +"/to_curved/small-seen")
# os.makedirs(args.sample_dir +"/to_curved/small-unseen")
# os.makedirs(args.sample_dir +"/to_straight/big")
# os.makedirs(args.sample_dir +"/to_straight/big-left-top")
# os.makedirs(args.sample_dir +"/to_straight/small-seen")
# os.makedirs(args.sample_dir +"/to_straight/small-unseen")
### 因為寫在function裡會一直被呼叫到，所以我才拉出來main寫喔！
os.makedirs(args.sample_dir + "/to_straight/crop-accurate")
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args) ### 記得要進去改 建立模型的地方
# model.train(args) if args.phase == 'train' else model.test(args) ### 有時間待修，有CYCLE的版本
model.train_kong(args) if args.phase == 'train' else model.test(
args) ### 無Cycle，有D，D有concat
def main(_):
if args.phase == 'train':
args.checkpoint_dir = os.path.join(args.checkpoint_dir, args.exp_id)
args.sample_dir = os.path.join(args.sample_dir, args.exp_id)
args.log_dir = os.path.join(args.log_dir, args.exp_id)
if not os.path.exists(args.log_dir):
print(args.log_dir)
os.mkdir(args.log_dir)
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
with open(os.path.join(args.log_dir, 'training_config.txt'), 'w') as f:
dic = vars(args)
pp = pprint.PrettyPrinter(indent=1, width=80, depth=None, stream=f)
pp.pprint(dic)
else:
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
with open(os.path.join(args.test_dir, 'config.txt'), 'w') as f:
dic = vars(args)
pp = pprint.PrettyPrinter(indent=1, width=80, depth=None, stream=f)
pp.pprint(dic)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
model.train(args) if args.phase == 'train' \
else model.test(args)
def load_cg():
global cyclegan_model
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
sess = tf.Session(config=tfconfig)
cyclegan_model = cyclegan(sess, args)
cyclegan_model.init_load(args)
def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
with tf.Session() as sess:
model = cyclegan(sess, args)
if args.phase == 'train':
model.train(args)
else:
model.test(args)
def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
model.train(args) if args.phase == 'train' else model.test(args)
def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
model.train(args) if args.phase == 'train' \
else model.test(args)
def modification():
r = request
# convert string of image data to uint8s
#nparr = np.fromstring(r.data, np.uint8)
# decode image
#f = r.files['file']
file = r.files['upload']
img = Image.open(file)
img.save('./datasets/makeup2/testA/input.jpg')
#f = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
#file.save(f)
#f.save(secure_filename(f.filename))
#img = cv2.imdecode(nparr,cv2.IMREAD_COLOR)
'''
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
if model.load(args.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
init_op = tf.global_variables_initializer()
model.sess.run(init_op)
out_var, in_var = (model.testB, model.test_A)
img = np.expand_dims(img, axis=0)
img = np.array(img).astype(np.float32)
fake_img = model.sess.run(out_var, feed_dict={in_var: img})
save_images(fake_img, [1, 1], "./output.jpg")
## merge images
'''
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
model.test(args)
response = {'message': 'image received. size={}x{}'.format(1, 1)}
print(response)
# encode response using jsonpickle
response_pickled = jsonpickle.encode(response)
return Response(response=response_pickled,
status=200,
mimetype="application/json")
def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
with tf.Session() as sess:
if args.debug == True:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
model = cyclegan(sess, args)
# model.trainVAE(args) # for pretraining VAE
model.train(args) # training full network
def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.8
#saver = tf.train.Saver()
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
model.train(args) if args.phase == 'train' \
else model.test(args)
def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
if args.phase == 'train':
print('Training start')
model.train(args)
elif args.phase == 'test':
model.test(args)
elif args.phase == 'visualize':
model.visualize(args.sample_dir, 0, True, args)
def main(_):
#if(not os.path.exists('/data1/ICE_DATA/{}'.format(args.dataset_dir))):
# download it
# download_dataset(data_set=args.dataset_dir)
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
with tf.device('/device:GPU:1'):
model = cyclegan(sess, args)
model.train(args) if args.phase == 'train' \
else model.test(args)
def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.sample_dir):
os.makedirs(args.sample_dir)
smoothenes_seperated = str(args.smootheness).split('.')
model_dir = "%s_%s_%s_%s_%s" % (args.dataset_dir, args.fine_size, args.h_hops, smoothenes_seperated[0], smoothenes_seperated[1])
if not os.path.exists(args.sample_dir+"/"+model_dir):
os.makedirs(args.sample_dir+"/"+model_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
with tf.device('/gpu:{}'.format(args.gpu)):
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
model.train(args) if args.phase == 'train' \
else model.test(args)
def main(_):
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if not os.path.exists(args.test_dir):
os.makedirs(args.test_dir)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args)
#model 불러옴
OPTIONS = namedtuple(
'OPTIONS', 'batch_size image_size \
gf_dim df_dim output_c_dim is_training')
model.options = OPTIONS._make(
(args.batch_size, args.fine_size, args.ngf, args.ndf,
args.output_nc, args.phase == 'train'))
model._build_model()
model.saver = tf.train.Saver()
#for 문
while (1):
print('py ready')
#동호오빠가 넘겨줄 부분 (0 : 파일이름, 1:확장자, 2:표정, 3: more 버튼이 눌렸는지)
lines = input().split(',')
args.new_file = lines[0] + '.' + lines[1]
args.which_expression = lines[2]
args.more_button = lines[3]
args.new_file_name = lines[0] + '_result.' + lines[1]
if lines[0] == 99:
break
if model.load():
print(" [*] Load SUCCESS")
else:
print(" [*] Load Failed...")
model.test(args)
### analysis
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
for i in range(1):
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if gray_img.mean() < 130:
img = adjust_gamma(img, 1.5)
else:
break
return img
if video == 'v':
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
with tf.Session(config=tfconfig) as sess:
model = cyclegan(sess, args2)
model.loadModel()
vc = cv2.VideoCapture('./data/video3.mp4')
length = int(vc.get(cv2.CAP_PROP_FRAME_COUNT))
print('length :', length)
if args["with_draw"] == 'True':
cv2.namedWindow('show', 0)
dir_name = str(now.year) + "_" + str(now.month) + "_" + str(
now.day) + "_" + str(now.minute) + "_" + str(
now.second) #+ "_" + str(idx)
os.makedirs(dir_name)
print(dir_name)
for idx in range(length):
img_bgr = vc.read()[1]
if img_bgr is None:
break
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
score = model.evaluate(x_train, y_train.reshape(-1, ), verbose=0)
print('svhn Train loss:', score[0])
print('svhn Train accuracy:', score[1])
score = model.evaluate(x_test, y_test, verbose=0)
print('svhn Test loss:', score[0])
print('svhn Test accuracy:', score[1])
print("begining cycle gan.....")
with tf.Session(config=tfconfig) as sess:
domain_adapation_model = cyclegan(sess, args)
x_test_c = np.zeros(x_test.shape)
batch_size = 2
batch_num = int(x_test.shape[0] / batch_size)
for idx in range(batch_num):
print(idx)
x_test_c[idx * batch_size:(idx + 1) *
batch_size] = domain_adapation_model.pix2pix_cylce_gan(
args, x_test[idx * batch_size:(idx + 1) * batch_size])
score = model.evaluate(x_test_c, y_test, verbose=0)
print('svhn Test loss after cycle gan:', score[0])
print('svhn Test accuracy after cycle gan:', score[1])
#x_train_c = domain_adapation_model.pix2pix_cylce_gan(args, x_train)
#score = model.evaluate(x_train_c, y_train, verbose=0)
#print('svhn Train loss after cycle gan:', score[0])
#print('svhn Train accuracy after cycle gan:', score[1])
