tf.image.adjust_saturation
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Adjust saturation of RGB images.
tf.image.adjust_saturation(
image, saturation_factor, name=None
)
This is a convenience method that converts RGB images to float
representation, converts them to HSV, add 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.
Args |
|---|
image
|
RGB image or images. 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.
|
Usage Example:
>> import tensorflow as tf
>> x = tf.random.normal(shape=(256, 256, 3))
>> tf.image.adjust_saturation(x, 0.5)
Raises |
|---|
InvalidArgumentError
|
input must have 3 channels
|
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.image.adjust_saturation\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 2 version](/api_docs/python/tf/image/adjust_saturation) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/ops/image_ops_impl.py#L2083-L2126) |\n\nAdjust saturation of RGB images.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.image.adjust_saturation`](/api_docs/python/tf/image/adjust_saturation), \\`tf.compat.v2.image.adjust_saturation\\`\n\n\u003cbr /\u003e\n\n tf.image.adjust_saturation(\n image, saturation_factor, name=None\n )\n\nThis is a convenience method that converts RGB images to float\nrepresentation, converts them to HSV, add an offset to the saturation channel,\nconverts back to RGB and then back to the original data type. If several\nadjustments are chained it is advisable to minimize the number of redundant\nconversions.\n\n`image` is an RGB image or images. The image saturation is adjusted by\nconverting the images to HSV and multiplying the saturation (S) channel by\n`saturation_factor` and clipping. The images are then converted back to RGB.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------------|------------------------------------------------------------|\n| `image` | RGB image or images. Size of the last dimension must be 3. |\n| `saturation_factor` | float. Factor to multiply the saturation by. |\n| `name` | A name for this operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Adjusted image(s), same shape and DType as `image`. ||\n\n\u003cbr /\u003e\n\n#### Usage Example:\n\n \u003e\u003e import tensorflow as tf\n \u003e\u003e x = tf.random.normal(shape=(256, 256, 3))\n \u003e\u003e tf.image.adjust_saturation(x, 0.5)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|------------------------|----------------------------|\n| `InvalidArgumentError` | input must have 3 channels |\n\n\u003cbr /\u003e"]]
TensorFlow 2 version
View source on GitHub