def preprocess_image(image,label,is_training):
'''preprocess a single image of layout[h,w,depth]'''
if is_training:
image,label=preprocessing.randm_rescale_image_and_label(
image,label,_MIN_SCALE,_MAX_SCALE)
#randomly crop or pad a [_HEIGHT,_WIDTH] selection of the image and label
image,label=preprocessing.random_crop_or_pad_image_and_label(
image,label,_HEIGHT,_WIDTH,_IGNORE_LABEL)
#Randomly flip the image and label horizontally.
image, label = preprocessing.random_flip_left_right_image_and_label(
image, label)
def preprocess_image(image, sem, disp, is_training):
"""Preprocess a single image of layout [height, width, depth]."""
disp_ori = disp
disp_ori.set_shape([_EVAL_HEIGHT, _EVAL_WIDTH, 1])
if is_training:
# Randomly scale the image and label.
image, sem, disp = preprocessing.random_rescale_image_and_label(
image, sem, disp, _HEIGHT, _WIDTH, _EVAL_WIDTH / _WIDTH,
_MIN_SCALE, _MAX_SCALE)
# Randomly crop or pad a [_HEIGHT, _WIDTH] section of the image and label.
image, sem, disp = preprocessing.random_crop_or_pad_image_and_label(
image, sem, disp, _HEIGHT, _WIDTH, _IGNORE_LABEL)
# Randomly flip the image and label horizontally.
image, sem, disp = preprocessing.random_flip_left_right_image_and_label(
image, sem, disp)
image.set_shape([_HEIGHT, _WIDTH, 3])
sem.set_shape([_HEIGHT, _WIDTH, 1])
disp.set_shape([_HEIGHT, _WIDTH, 1])
else:
image.set_shape([None, None, 3])
image = tf.image.resize_images(image, (_HEIGHT, _WIDTH),
method=tf.image.ResizeMethod.BILINEAR)
sem.set_shape([None, None, 1])
sem = tf.image.resize_images(sem, (_HEIGHT, _WIDTH),
method=tf.image.ResizeMethod.BILINEAR)
sem = tf.to_int32(sem)
disp.set_shape([None, None, 1])
disp = tf.image.resize_images(disp, (_HEIGHT, _WIDTH),
method=tf.image.ResizeMethod.BILINEAR)
disp = tf.to_int32(disp)
image = preprocessing.mean_image_subtraction(image)
disp = preprocessing.normalization(disp, dataset=FLAGS.dataset)
return image, sem, disp
def preprocess_image(image, label, is_training):
"""Preprocess a single image of layout [height, width, depth]."""
if is_training:
# Randomly scale the image and label.
image, label = preprocessing.random_rescale_image_and_label(
image, label, _MIN_SCALE, _MAX_SCALE)
# Randomly crop or pad a [_HEIGHT, _WIDTH] section of the image and label.
image, label = preprocessing.random_crop_or_pad_image_and_label(
image, label, _HEIGHT, _WIDTH, _IGNORE_LABEL)
# Randomly flip the image and label horizontally.
image, label = preprocessing.random_flip_left_right_image_and_label(
image, label)
image.set_shape([_HEIGHT, _WIDTH, 3])
label.set_shape([_HEIGHT, _WIDTH, 1])
image = preprocessing.mean_image_subtraction(image)
return image, label
