Adjust saturation of RGB images.
tf.image.adjust_saturation(
image, saturation_factor, name=None
)
Used in the notebooks
This is a convenience method that converts RGB images to float
representation, converts them to HSV, adds an offset to the
saturation 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 or images. The image saturation is adjusted by
converting the images to HSV and multiplying the saturation (S) channel by
saturation_factor and clipping. The images are then converted back to RGB.
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_saturation(x, 0.5)
<tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
array([[[ 2. , 2.5, 3. ],
[ 5. , 5.5, 6. ]],
[[ 8. , 8.5, 9. ],
[11. , 11.5, 12. ]]], dtype=float32)>
Args |
|---|
image
|
RGB image or images. The size of the last dimension must be 3.
|
saturation_factor
|
float. Factor to multiply the saturation by.
|
name
|
A name for this operation (optional).
|
Returns |
|---|
Adjusted image(s), same shape and DType as image.
|
Raises |
|---|
InvalidArgumentError
|
input must have 3 channels
|
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TensorFlow 1 version
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