rr, cc = polygon(y, x, (WIDTH, HEIGHT))
for i in range(3):
data[0, rr, cc, i] += color
# here's where you draw it. smooth. neat.
xold = event.x
yold = event.y
data = np.array((np.random.random((1, HEIGHT, WIDTH, 3)) - 0.5) * 4, dtype=float)
in_x = tf.placeholder(tf.float32, shape=(1, WIDTH, HEIGHT, 3))
with tf.variable_scope("gen", reuse=None) as scope:
with tf.name_scope("1"):
out_x = superres_model.superres_model(in_x)
with tf.variable_scope("gen", reuse=True) as scope:
for i in range(6):
with tf.name_scope(str(i + 2)):
out_x = superres_model.superres_model(out_x)
restore_list = [x for x in tf.trainable_variables() if "ssskkds" not in x.name]
saver = tf.train.Saver(var_list=restore_list)
sess = tf.Session()
saver.restore(sess, sys.argv[1])
window = Tk()
crops = []
for i in range(BATCH_SIZE):
crops.append(tf.image.random_crop(jpg, [512, 512]))
jpg = tf.pack(crops)
#jpg = tf.image.resize_bilinear(jpg, [128, 128])
mix = tf.random_uniform([1]);
net_in = mix*jpg + (1.0-mix)*tf.truncated_normal(jpg.get_shape(), dtype=tf.float32, stddev=1)
l10 = net_in
all_images = []
with tf.variable_scope("gen", reuse=None) as scope:
with tf.name_scope("1"):
l10 = superres_model.superres_model(l10)
all_images.append(l10)
with tf.variable_scope("gen", reuse=True) as scope:
for i in range(2,8):
with tf.name_scope(str(i)):
l10 = superres_model.superres_model(l10)
all_images.append(l10)
measure_images = tf.concat(0, [image[:,:,:,0:3] for image in [all_images[i] for i in [0, 2, 4, 6]]])
feedback_images = tf.concat(0, [image[:,:,:,0:3] for image in [all_images[i] for i in [6]]])
show_images = tf.concat(0, [image[0:1,:,:,0:3] for image in all_images])
tf.image_summary("gen", measure_images, max_images=1)
tf.image_summary("real", jpg, max_images=1)