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tf.image.adjust_hue

Adjust hue of RGB images.

tf.image.adjust_hue(
    image, delta, name=None
)

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)>

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).

Adjusted image(s), same shape and DType as image.

InvalidArgumentError image must have at least 3 dimensions. InvalidArgumentError The size of the last dimension must be 3. ValueError if delta is not in the interval of [-1, 1].

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)>