rescale-intensity

rescale_intensity

skimage.exposure.rescale_intensity(image, in_range='image', out_range='dtype') [source]

Return image after stretching or shrinking its intensity levels.

The desired intensity range of the input and output, in_range and out_range respectively, are used to stretch or shrink the intensity range of the input image. See examples below.

Parameters:

image : array Image array. in_range, out_range : str or 2-tuple 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 DTYPE_RANGE. 2-tuple Use 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.

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 in_range defaults to ‘image’ and out_range defaults to ‘dtype’:

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>>> 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:

1 2	>>> 1.0 * image array([  51.,  102.,  153.])

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

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

To maintain the low contrast of the original, use the in_range parameter:

1 2	>>> rescale_intensity(image_float, in_range=(0, 255)) array([ 0.2,  0.4,  0.6])

If the min/max value of in_range is more/less than the min/max image intensity, then the intensity levels are clipped:

1 2	>>> 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 out_range parameter:

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>>> image = np.array([-10, 0, 10], dtype=np.int8) >>> rescale_intensity(image, out_range=(0, 127)) array([  0,  63, 127], dtype=int8)