def demo_wgan_ab():
noise = tf.constant(np.random.normal(size=(64, 128)).astype('float32'))
model = Net(train=False)
model.output_dim = 2
colorized = model.GAN_G(noise)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, _CKPT_PATH)
ab = sess.run(colorized) # [-1, 1]
ab *= 110.
l = np.full((64, 64, 64, 1), 50)
lab = np.concatenate((l, ab), axis=-1)
rgbs = []
for i in xrange(64):
rgb = color.lab2rgb(lab[i, :, :, :])
rgbs.append(rgb)
rgbs = np.array(rgbs)
save_images(rgbs, '/srv/glusterfs/xieya/image/color/samples_ab.png')
def demo_wgan_rgb():
noise = tf.constant(np.random.normal(size=(64, 128)).astype('float32'))
model = Net(train=False)
model.output_dim = 3
colorized = model.GAN_G(noise)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, _CKPT_PATH)
rgb = sess.run(colorized) # [-1, 1]
rgb = ((rgb + 1.) * (255.99 / 2)).astype('uint8')
save_images(rgb, '/srv/glusterfs/xieya/image/color/samples_rgb.png')
rgb_new = []
for i in xrange(64):
lab = color.rgb2lab(rgb[i, :, :, :])
lab[:, :, 0] = 50. # Remove l.
rgb_new.append(color.lab2rgb(lab))
rgb_new = np.array(rgb_new)
save_images(rgb_new,
'/srv/glusterfs/xieya/image/color/samples_rgb_ab.png')