(image,
                     labels,
                     bboxes,
                     out_shape,
                     data_format,
                     is_training=False,
                     **kwargs)
Pre-process an given image.

    Args:
      image: A `Tensor` representing an image of arbitrary size.
      output_height: The height of the image after preprocessing.
      output_width: The width of the image after preprocessing.
      is_training: `True` if we're preprocessing the image for training and
        `False` otherwise.
      resize_side_min: The lower bound for the smallest side of the image for
        aspect-preserving resizing. If `is_training` is `False`, then this value
        is used for rescaling.
      resize_side_max: The upper bound for the smallest side of the image for
        aspect-preserving resizing. If `is_training` is `False`, this value is
         ignored. Otherwise, the resize side is sampled from
         [resize_size_min, resize_size_max].

    Returns:
      A preprocessed image.
Pre-process an given image.
Pre - process an given image .
def light_head_preprocess_image(image,
                     labels,
                     bboxes,
                     out_shape,
                     data_format,
                     is_training=False,
                     **kwargs):
    """Pre-process an given image.

    Args:
      image: A `Tensor` representing an image of arbitrary size.
      output_height: The height of the image after preprocessing.
      output_width: The width of the image after preprocessing.
      is_training: `True` if we're preprocessing the image for training and
        `False` otherwise.
      resize_side_min: The lower bound for the smallest side of the image for
        aspect-preserving resizing. If `is_training` is `False`, then this value
        is used for rescaling.
      resize_side_max: The upper bound for the smallest side of the image for
        aspect-preserving resizing. If `is_training` is `False`, this value is
         ignored. Otherwise, the resize side is sampled from
         [resize_size_min, resize_size_max].

    Returns:
      A preprocessed image.
    """
    if is_training:
        return light_head_preprocess_for_train(image, labels, bboxes,
                                            out_shape=out_shape,
                                            data_format=data_format)
    else:
        return light_head_preprocess_for_eval(image, labels, bboxes,
                                           out_shape=out_shape,
                                           data_format=data_format,
                                           **kwargs)
def light_head_preprocess_image ( image , labels , bboxes , out_shape , data_format , is_training = False , * * kwargs ) : if is_training : return light_head_preprocess_for_train ( image , labels , bboxes , out_shape = out_shape , data_format = data_format ) else : return light_head_preprocess_for_eval ( image , labels , bboxes , out_shape = out_shape , data_format = data_format , * * kwargs )