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