def _rotate_pp(data): # Create labels in the same structure as images! if create_labels: data["label"] = utils.tf_apply_to_image_or_images( lambda _: tf.constant([0, 1, 2, 3]), data["image"], dtype=tf.int32) data["image"] = utils.tf_apply_to_image_or_images(_four_rots, data["image"]) return data
def _crop_pp(data): crop_fn = functools.partial(__crop, is_training=is_training, crop_size=crop_size) data["image"] = utils.tf_apply_to_image_or_images( crop_fn, data["image"]) return data
def _rotation_pp(data): if is_training: data['image'] = utils.tf_apply_to_image_or_images( lambda img: tf.image.rot90( img, k=tf.random_uniform([], dtype=tf.int32, maxval=4)), data['image']) return data
def _crop_patches_pp(data): image = data["image"] image_to_patches_fn = functools.partial(_image_to_patches, is_training=is_training, split_per_side=split_per_side, channel_jitter=channel_jitter) image = utils.tf_apply_to_image_or_images(image_to_patches_fn, image) data["image"] = image return data
def _random_flip_lr_pp(data): if is_training: data["image"] = utils.tf_apply_to_image_or_images( tf.image.random_flip_left_right, data["image"]) return data
def _to_gray_pp(data): data["image"] = utils.tf_apply_to_image_or_images( lambda img: utils.tf_apply_with_probability( grayscale_probability, _to_gray, img), data["image"]) return data
def _standardization_pp(data): # Trick: normalize each patch to avoid low level statistics. data["image"] = utils.tf_apply_to_image_or_images( tf.image.per_image_standardization, data["image"]) return data
def _to_gray_pp(data): data["image"] = utils.tf_apply_to_image_or_images( lambda img: utils.tf_apply_with_probability( # pylint:disable=g-long-lambda grayscale_probability, _to_gray, img), data["image"]) return data
def _inception_crop_patches_pp(data): # The output becomes float32 because of the tf.image.resize. data["image"] = utils.tf_apply_to_image_or_images( _inception_crop_patches, data["image"], dtype=tf.float32) return data
def _hsvnoise_pp(data): data["image"] = utils.tf_apply_to_image_or_images(_hsvnoise, data["image"]) return data