def grayscale_image_resizer(image, masks=None): """Convert to grayscale before applying image_resizer_fn. Args: image: A 3D tensor of shape [height, width, 3] masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. Returns: Note that the position of the resized_image_shape changes based on whether masks are present. resized_image: A 3D tensor of shape [new_height, new_width, 1], where the image has been resized (with bilinear interpolation) so that min(new_height, new_width) == min_dimension or max(new_height, new_width) == max_dimension. resized_masks: If masks is not None, also outputs masks. A 3D tensor of shape [num_instances, new_height, new_width]. resized_image_shape: A 1D tensor of shape [3] containing shape of the resized image. """ # image_resizer_fn returns [resized_image, resized_image_shape] if # mask==None, otherwise it returns # [resized_image, resized_mask, resized_image_shape]. In either case, we # only deal with first and last element of the returned list. retval = image_resizer_fn(image, masks) resized_image = retval[0] resized_image_shape = retval[-1] retval[0] = preprocessor.rgb_to_gray(resized_image) retval[-1] = tf.concat([resized_image_shape[:-1], [1]], 0) return retval
def grayscale_image_resizer(image, masks=None): """Convert to grayscale before applying image_resizer_fn. Args: image: A 3D tensor of shape [height, width, 3] masks: (optional) rank 3 float32 tensor with shape [num_instances, height, width] containing instance masks. Returns: Note that the position of the resized_image_shape changes based on whether masks are present. resized_image: A 3D tensor of shape [new_height, new_width, 1], where the image has been resized (with bilinear interpolation) so that min(new_height, new_width) == min_dimension or max(new_height, new_width) == max_dimension. resized_masks: If masks is not None, also outputs masks. A 3D tensor of shape [num_instances, new_height, new_width]. resized_image_shape: A 1D tensor of shape [3] containing shape of the resized image. """ # image_resizer_fn returns [resized_image, resized_image_shape] if # mask==None, otherwise it returns # [resized_image, resized_mask, resized_image_shape]. In either case, we # only deal with first and last element of the returned list. retval = image_resizer_fn(image, masks) resized_image = retval[0] resized_image_shape = retval[-1] retval[0] = preprocessor.rgb_to_gray(resized_image) retval[-1] = tf.concat([resized_image_shape[:-1], [1]], 0) return retval
def grayscale_image_resizer(image, masks=None): # image_resizer_fn returns [resized_image, resized_image_shape] if # mask==None, otherwise it returns # [resized_image, resized_mask, resized_image_shape]. In either case, we # only deal with first and last element of the returned list. retval = image_resizer_fn(image, masks) resized_image = retval[0] resized_image_shape = retval[-1] retval[0] = preprocessor.rgb_to_gray(resized_image) retval[-1] = tf.concat([resized_image_shape[:-1], [1]], 0) return retval
def grayscale_image_resizer(image): [resized_image, resized_image_shape] = image_resizer_fn(image) grayscale_image = preprocessor.rgb_to_gray(resized_image) grayscale_image_shape = tf.concat([resized_image_shape[:-1], [1]], 0) return [grayscale_image, grayscale_image_shape]
def grayscale_image_resizer(image): [resized_image, resized_image_shape] = image_resizer_fn(image) grayscale_image = preprocessor.rgb_to_gray(resized_image) grayscale_image_shape = tf.concat([resized_image_shape[:-1], [1]], 0) return [grayscale_image, grayscale_image_shape]