rescale_intensity(image, in_range='image', out_range='dtype')

The desired intensity range of the input and output, :None:None:`in_range` and :None:None:`out_range` respectively, are used to stretch or shrink the intensity range of the input image. See examples below.

Notes

Parameters

image : array

Image array.

in_range, out_range : str or 2-tuple, optional

Min and max intensity values of input and output image. The possible values for this parameter are enumerated below.

'image'

Use image min/max as the intensity range.

'dtype'

Use min/max of the image's dtype as the intensity range.

dtype-name

Use intensity range based on desired dtype . Must be valid key in :None:None:`DTYPE_RANGE`.

2-tuple

Use :None:None:`range_values` as explicit min/max intensities.

Returns

out : array

Image array after rescaling its intensity. This image is the same dtype as the input image.

Return image after stretching or shrinking its intensity levels.

See Also

equalize_hist

Examples

By default, the min/max intensities of the input image are stretched to the limits allowed by the image's dtype, since :None:None:`in_range` defaults to 'image' and :None:None:`out_range` defaults to 'dtype':

This example is valid syntax, but we were not able to check execution

>>> image = np.array([51, 102, 153], dtype=np.uint8)
... rescale_intensity(image)
array([  0, 127, 255], dtype=uint8)

It's easy to accidentally convert an image dtype from uint8 to float:

This example is valid syntax, but we were not able to check execution

>>> 1.0 * image
array([ 51., 102., 153.])

Use rescale_intensity to rescale to the proper range for float dtypes:

This example is valid syntax, but we were not able to check execution

>>> image_float = 1.0 * image
... rescale_intensity(image_float)
array([0. , 0.5, 1. ])

To maintain the low contrast of the original, use the :None:None:`in_range` parameter:

This example is valid syntax, but we were not able to check execution

>>> rescale_intensity(image_float, in_range=(0, 255))
array([0.2, 0.4, 0.6])

If the min/max value of :None:None:`in_range` is more/less than the min/max image intensity, then the intensity levels are clipped:

This example is valid syntax, but we were not able to check execution

>>> rescale_intensity(image_float, in_range=(0, 102))
array([0.5, 1. , 1. ])

If you have an image with signed integers but want to rescale the image to just the positive range, use the :None:None:`out_range` parameter. In that case, the output dtype will be float:

This example is valid syntax, but we were not able to check execution

>>> image = np.array([-10, 0, 10], dtype=np.int8)
... rescale_intensity(image, out_range=(0, 127))
array([  0. ,  63.5, 127. ])

To get the desired range with a specific dtype, use .astype() :

This example is valid syntax, but we were not able to check execution

>>> rescale_intensity(image, out_range=(0, 127)).astype(np.int8)
array([  0,  63, 127], dtype=int8)

If the input image is constant, the output will be clipped directly to the output range: >>> image = np.array([130, 130, 130], dtype=np.int32) >>> rescale_intensity(image, out_range=(0, 127)).astype(np.int32) array([127, 127, 127], dtype=int32)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.exposure._adapthist.equalize_adapthist skimage.exposure.exposure.rescale_intensity

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