image.py 文件源码

def standardize(images):
    """Linearly scales `image` to have zero mean and unit norm.
    (A mirror to tf.image per_image_standardization)

    This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
    of all values in image, and
    `adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.

    `stddev` is the standard deviation of all values in `image`. It is capped
    away from zero to protect against division by 0 when handling uniform images.

    Args:
        images: 4-D Tensor of shape `[batch, height, width, channels]` or
                3-D Tensor of shape `[height, width, channels]`.

    Returns:
        The standardized image with same shape as `image`.

    Raises:
        ValueError: if the shape of 'image' is incompatible with this function.
    """
    images_shape = get_shape(images)
    if len(images_shape) > 4:
        ValueError("'image' must have either 3 or 4 dimensions, "
                   "received `{}`.".format(images_shape))

    if len(images_shape) == 4:
        return tf.map_fn(tf.image.per_image_standardization, images)
    return tf.image.per_image_standardization(images)