def test2(status='X2Y'):
start = time.time()
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
content_img = cv2.resize(getImg(FLAGS.input),
(FLAGS.img_size, FLAGS.img_size))
content = np.expand_dims(encode(content_img), 0)
network = CycleGAN(FLAGS.batch_size,
FLAGS.ngf,
FLAGS.img_size,
is_training=True,
use_E=FLAGS.USE_E,
Norm=FLAGS.Norm)
network.test(content.shape)
sess.run(tf.global_variables_initializer())
var_list = [
var for var in tf.global_variables() if 'generator_net' in var.name
or 'discriminator_net' in var.name or 'edge_net' in var.name
]
saver = tf.train.Saver(var_list=var_list)
saver.restore(sess, FLAGS.checkpoint)
print("restored all")
s = time.time()
if status == 'X2Y':
out = sess.run(network.Ygenerated,
feed_dict={network.testA: content})
else:
out = sess.run(network.Xgenerated,
feed_dict={network.testB: content})
# result = np.clip(out[0], 0, 255).astype(np.uint8)
print("Transform in {} s".format((time.time() - s)))
saveImg(out[0], FLAGS.output)
print("Finished all process in {} s".format(time.time() - start))
def generateBatch(files, batch_shape):
batch = np.zeros(batch_shape, dtype=np.float32)
while True:
try:
choosed = random.sample(files, batch_shape[0])
for i, s in enumerate(choosed):
batch[i] = augmentor(resizeTo(getImg(s), 800, 1800))
batch[i] = encode(batch[i])
yield batch
except:
continue
def generateBatch(files, batch_shape):
batch = np.zeros(batch_shape, dtype=np.float32)
while True:
try:
choosed = random.sample(files, batch_shape[0])
for i, s in enumerate(choosed):
batch[i] = imgRandomCrop(s, 256, 256, FLAGS.img_size)
batch[i] = encode(batch[i])
yield batch
except:
continue
def test1(dir, status='X2Y'):
capture = cv2.VideoCapture(dir)
capture.set(cv2.CAP_PROP_FRAME_WIDTH, FLAGS.img_size)
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, FLAGS.img_size)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
_, content_img = capture.read()
content = np.expand_dims(encode(content_img), 0)
network = CycleGAN(FLAGS.batch_size,
FLAGS.ngf,
FLAGS.img_size,
is_training=False,
Norm=FLAGS.Norm)
network.test(content.shape)
sess.run(tf.global_variables_initializer())
var_list = [
var for var in tf.global_variables() if 'generator_net' in var.name
or 'discriminator_net' in var.name or 'edge_net' in var.name
]
saver = tf.train.Saver(var_list=var_list)
saver.restore(sess, FLAGS.checkpoint)
print("restored all")
while True:
if status == 'X2Y':
out = sess.run(network.Ygenerated,
feed_dict={network.testA: content})
else:
out = sess.run(network.Xgenerated,
feed_dict={network.testB: content})
# result = np.clip(out, 0, 255).astype(np.uint8)
cv2.imshow("test", result)
c = cv2.waitKey(1)
if c == 27:
break
_, content_img = capture.read()
content = np.expand_dims(encode(content_img), 0)
capture.release()
cv2.destroyAllWindows()