parameters: (image, delta, name=None)
docstring: Adjust hue of RGB images.
  
    This is a convenience method that converts an RGB image to float
    representation, converts it to HSV, adds an offset to the
    hue channel, converts back to RGB and then back to the original
    data type. If several adjustments are chained it is advisable to minimize
    the number of redundant conversions.
  
    `image` is an RGB image.  The image hue is adjusted by converting the
    image(s) to HSV and rotating the hue channel (H) by
    `delta`.  The image is then converted back to RGB.
  
    `delta` must be in the interval `[-1, 1]`.
  
    Usage Example:
  
    >>> x = [[[1.0, 2.0, 3.0],
    ...       [4.0, 5.0, 6.0]],
    ...     [[7.0, 8.0, 9.0],
    ...       [10.0, 11.0, 12.0]]]
    >>> tf.image.adjust_hue(x, 0.2)
    <tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
    array([[[ 2.3999996,  1.       ,  3.       ],
            [ 5.3999996,  4.       ,  6.       ]],
          [[ 8.4      ,  7.       ,  9.       ],
            [11.4      , 10.       , 12.       ]]], dtype=float32)>
  
    Args:
      image: RGB image or images. The size of the last dimension must be 3.
      delta: float.  How much to add to the hue channel.
      name: A name for this operation (optional).
  
    Returns:
      Adjusted image(s), same shape and DType as `image`.
  
    Raises:
      InvalidArgumentError: image must have at least 3 dimensions.
      InvalidArgumentError: The size of the last dimension must be 3.
  
    Usage Example:
  
    >>> image = [[[1, 2, 3], [4, 5, 6]],
    ...          [[7, 8, 9], [10, 11, 12]],
    ...          [[13, 14, 15], [16, 17, 18]]]
    >>> image = tf.constant(image)
    >>> tf.image.adjust_hue(image, 0.2)
    <tf.Tensor: shape=(3, 2, 3), dtype=int32, numpy=
    array([[[ 2,  1,  3],
          [ 5,  4,  6]],
         [[ 8,  7,  9],
          [11, 10, 12]],
         [[14, 13, 15],
          [17, 16, 18]]], dtype=int32)>
docstring_summary: Adjust hue of RGB images.
docstring_tokens: Adjust hue of RGB images .
function: def adjust_hue(image, delta, name=None):
    """Adjust hue of RGB images.
  
    This is a convenience method that converts an RGB image to float
    representation, converts it to HSV, adds an offset to the
    hue channel, converts back to RGB and then back to the original
    data type. If several adjustments are chained it is advisable to minimize
    the number of redundant conversions.
  
    `image` is an RGB image.  The image hue is adjusted by converting the
    image(s) to HSV and rotating the hue channel (H) by
    `delta`.  The image is then converted back to RGB.
  
    `delta` must be in the interval `[-1, 1]`.
  
    Usage Example:
  
    >>> x = [[[1.0, 2.0, 3.0],
    ...       [4.0, 5.0, 6.0]],
    ...     [[7.0, 8.0, 9.0],
    ...       [10.0, 11.0, 12.0]]]
    >>> tf.image.adjust_hue(x, 0.2)
    <tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
    array([[[ 2.3999996,  1.       ,  3.       ],
            [ 5.3999996,  4.       ,  6.       ]],
          [[ 8.4      ,  7.       ,  9.       ],
            [11.4      , 10.       , 12.       ]]], dtype=float32)>
  
    Args:
      image: RGB image or images. The size of the last dimension must be 3.
      delta: float.  How much to add to the hue channel.
      name: A name for this operation (optional).
  
    Returns:
      Adjusted image(s), same shape and DType as `image`.
  
    Raises:
      InvalidArgumentError: image must have at least 3 dimensions.
      InvalidArgumentError: The size of the last dimension must be 3.
  
    Usage Example:
  
    >>> image = [[[1, 2, 3], [4, 5, 6]],
    ...          [[7, 8, 9], [10, 11, 12]],
    ...          [[13, 14, 15], [16, 17, 18]]]
    >>> image = tf.constant(image)
    >>> tf.image.adjust_hue(image, 0.2)
    <tf.Tensor: shape=(3, 2, 3), dtype=int32, numpy=
    array([[[ 2,  1,  3],
          [ 5,  4,  6]],
         [[ 8,  7,  9],
          [11, 10, 12]],
         [[14, 13, 15],
          [17, 16, 18]]], dtype=int32)>
    """
    with ops.name_scope(name, 'adjust_hue', [image]) as name:
      image = ops.convert_to_tensor(image, name='image')
      # Remember original dtype to so we can convert back if needed
      orig_dtype = image.dtype
      if orig_dtype in (dtypes.float16, dtypes.float32):
        flt_image = image
      else:
        flt_image = convert_image_dtype(image, dtypes.float32)
  
      rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)
  
      return convert_image_dtype(rgb_altered, orig_dtype)
function_tokens: def adjust_hue ( image , delta , name = None ) : with ops . name_scope ( name , 'adjust_hue' , [ image ] ) as name : image = ops . convert_to_tensor ( image , name = 'image' ) # Remember original dtype to so we can convert back if needed orig_dtype = image . dtype if orig_dtype in ( dtypes . float16 , dtypes . float32 ) : flt_image = image else : flt_image = convert_image_dtype ( image , dtypes . float32 ) rgb_altered = gen_image_ops . adjust_hue ( flt_image , delta ) return convert_image_dtype ( rgb_altered , orig_dtype )