def _save_images(self,
sess,
rec_imgs,
inputs,
labels,
is_training,
x,
y,
imgs,
step=None):
"""Save reconstructed images."""
rec_imgs_ = sess.run(rec_imgs,
feed_dict={
inputs: x,
labels: y,
is_training: False
})
utils.save_imgs(real_imgs=imgs,
rec_imgs=rec_imgs_,
img_path=self.test_image_path,
database_name=self.cfg.DATABASE_NAME,
max_img_in_col=self.cfg.MAX_IMAGE_IN_COL,
step=step,
silent=True,
test_flag=True)
def _save_images(self,
sess,
img_path,
x,
y,
imgs,
step,
silent=False,
epoch_i=None,
test_flag=False):
"""Save reconstructed images."""
rec_images_ = sess.run(self.rec_imgs,
feed_dict={
self.inputs: x,
self.labels: y,
self.is_training: False
})
# rec_images_ shape: [128, 28, 28, 1] for mnist
utils.save_imgs(real_imgs=imgs,
rec_imgs=rec_images_,
img_path=img_path,
database_name=self.cfg.DATABASE_NAME,
max_img_in_col=self.cfg.MAX_IMAGE_IN_COL,
step=step,
silent=silent,
epoch_i=epoch_i,
test_flag=test_flag)
def _save_images_mo(self, sess, rec_imgs, inputs, labels, is_training,
preds_binary, preds_vector):
"""Save reconstructed images."""
utils.thin_line()
print('Getting reconstruction images...')
if len(self.y_test) > self.cfg.MAX_IMAGE_IN_COL**2:
n_test_img = self.cfg.MAX_IMAGE_IN_COL**2
test_img_idx = np.random.choice(len(self.y_test), n_test_img)
else:
test_img_idx = list(range(len(self.y_test)))
rec_imgs_ = []
preds_vec_ = []
if self.cfg.LABEL_FOR_TEST == 'pred':
label_for_img = preds_binary
elif self.cfg.LABEL_FOR_TEST == 'real':
label_for_img = self.y_test
else:
raise ValueError('Wrong LABEL_FOR_TEST Name!')
for x, y_hat, pred_ in tqdm(zip(self.x_test[test_img_idx],
label_for_img[test_img_idx],
preds_vector[test_img_idx]),
total=len(test_img_idx),
ncols=100,
unit=' image'):
# Get new x and y_hat list in which each y contain single object
# [0, 1, 0, 1, 0] -> [[0, 1, 0, 0, 0],
# [0, 0, 0, 1, 0]]
x_new = []
y_hat_new = []
preds_vec_new = []
for i, y_i in enumerate(y_hat):
if y_i == 1:
y_hat_new_i = np.zeros_like(y_hat)
y_hat_new_i[i] = 1
assert y_hat_new_i[i] == y_hat[i]
x_new.append(x)
y_hat_new.append(y_hat_new_i)
preds_vec_new.append(pred_[i])
preds_vec_.append(preds_vec_new)
# Filling x and y tensor to batch size for testing
# [[0, 1, 0, 0, 0],
# [0, 0, 0, 1, 0]] -> [[0, 1, 0, 0, 0],
# [0, 0, 0, 1, 0],
# ...
# [0, 0, 0, 0, 0]]
n_y = len(y_hat_new)
assert n_y == int(np.sum(y_hat))
if n_y > self.cfg.TEST_BATCH_SIZE:
raise ValueError(
'TEST_BATCH_SIZE Must Not Less Than {}!'.format(n_y))
if n_y < self.cfg.TEST_BATCH_SIZE:
for i in range(self.cfg.TEST_BATCH_SIZE - n_y):
x_new = np.append(x_new,
np.expand_dims(np.zeros_like(x), axis=0),
axis=0)
y_hat_new.append(np.zeros_like(y_hat))
assert len(x_new) == self.cfg.TEST_BATCH_SIZE
assert len(y_hat_new) == self.cfg.TEST_BATCH_SIZE
# Get remake images which contain different objects
# y_rec_imgs_ shape: [128, 28, 28, 1] for mnist
y_rec_imgs_ = sess.run(rec_imgs,
feed_dict={
inputs: x_new,
labels: y_hat_new,
is_training: False
})
rec_imgs_.append(y_rec_imgs_[:n_y])
# Get colorful overlapped images
real_imgs_ = utils.img_black_to_color(self.imgs_test[test_img_idx],
same=True)
rec_imgs_overlap = []
rec_imgs_no_overlap = []
for idx, imgs in enumerate(rec_imgs_):
imgs_colored = utils.img_black_to_color(imgs)
imgs_overlap = utils.img_add_overlap(
imgs=imgs_colored,
merge=True,
vec=preds_vec_[idx],
# vec=None,
gamma=0)
imgs_no_overlap = utils.img_add_no_overlap(
imgs=imgs_colored,
num_mul_obj=self.cfg.NUM_MULTI_OBJECT,
vec=preds_vec_[idx],
img_mode='RGB',
resize_filter=Image.ANTIALIAS)
rec_imgs_overlap.append(imgs_overlap)
rec_imgs_no_overlap.append(imgs_no_overlap)
rec_imgs_overlap = np.array(rec_imgs_overlap)
rec_imgs_no_overlap = np.array(rec_imgs_no_overlap)
# Save images
utils.save_imgs(real_imgs=real_imgs_,
rec_imgs=rec_imgs_overlap,
img_path=self.test_image_path,
database_name=self.cfg.DATABASE_NAME,
max_img_in_col=self.cfg.MAX_IMAGE_IN_COL,
silent=False,
test_flag=True,
colorful=True,
append_info='_overlap')
utils.save_imgs(real_imgs=real_imgs_,
rec_imgs=rec_imgs_no_overlap,
img_path=self.test_image_path,
database_name=self.cfg.DATABASE_NAME,
max_img_in_col=self.cfg.MAX_IMAGE_IN_COL,
silent=False,
test_flag=True,
colorful=True,
append_info='_no_overlap')