kernel = np.ones((5,5),np.float32)/25
while epoch_num < EPOCHS:
s = time.time()
epoch_num = step/(num_train/BATCH_SIZE)
idx = np.random.choice(np.arange(num_train), BATCH_SIZE, replace=False)
batchA_paths = trainA_paths[idx]
batchB_paths = trainB_paths[idx]
batchA_images = np.empty((BATCH_SIZE, 256, 256, 3), dtype=np.float32)
batchB_images = np.empty((BATCH_SIZE, 256, 256, 3), dtype=np.float32)
i = 0
for a,b in zip(batchA_paths, batchB_paths):
a_img = data_ops.preprocess(misc.imread(a).astype('float32'))
b_img = data_ops.preprocess(misc.imread(b).astype('float32'))
# Data augmentation here - each has 50% chance
if AUGMENT:
r = random.random()
# flip image left right
if r < 0.5:
a_img = np.fliplr(a_img)
b_img = np.fliplr(b_img)
r = random.random()
# flip image up down
if r < 0.5:
a_img = np.flipud(a_img)
b_img = np.flipud(b_img)
print 'num test:', num_test
c = 0
times = []
for img_path in tqdm(test_paths):
img_name = ntpath.basename(img_path)
img_name = img_name.split('.')[0]
batch_images = np.empty((1, 256, 256, 3), dtype=np.float32)
a_img = misc.imread(img_path).astype('float32')
a_img = misc.imresize(a_img, (256, 256, 3))
a_img = data_ops.preprocess(a_img)
batch_images[0, ...] = a_img
s = time.time()
gen_images = np.asarray(
sess.run(gen_image, feed_dict={image_u: batch_images}))
tot = time.time() - s
times.append(tot)
for gen, real in zip(gen_images, batch_images):
#misc.imsave(IMAGES_DIR+str(step)+'_'+str(c)+'_real.png', real)
#misc.imsave(IMAGES_DIR+str(step)+'_'+str(c)+'_gen.png', gen)
misc.imsave(IMAGES_DIR + img_name + '_real.png', real)
misc.imsave(IMAGES_DIR + img_name + '_gen.png', gen)
print "Could not restore model"
pass
step = int(sess.run(global_step))
clean_images = glob.glob('clean/*.*')
distorted_images = glob.glob('distorted/*.*')
for cimg, dimg in zip(clean_images, distorted_images):
batch_cimages = np.empty((1, 256, 256, 3), dtype=np.float32)
batch_dimages = np.empty((1, 256, 256, 3), dtype=np.float32)
img = misc.imread(cimg).astype('float32')
img = misc.imresize(img, (256, 256, 3))
img = data_ops.preprocess(img)
batch_cimages[0, ...] = img
img = misc.imread(dimg).astype('float32')
img = misc.imresize(img, (256, 256, 3))
img = data_ops.preprocess(img)
batch_dimages[0, ...] = img
# send through decoder and get all layers
all_layers = sess.run(layers, feed_dict={image_u:batch_cimages})
print all_layers[0].shape
exit()
c_embedding = sess.run(embedding, feed_dict={image_u:batch_cimages})
