skimage.exposure.cumulative_distribution(image) | Return cumulative distribution function (cdf) for the given image. |
skimage.exposure.equalize(image[, nbins]) | Return image after histogram equalization. |
skimage.exposure.histogram(image[, nbins]) | Return histogram of image. |
skimage.exposure.rescale_intensity(image[, ...]) | Return image after stretching or shrinking its intensity levels. |
Return cumulative distribution function (cdf) for the given image.
Parameters : | image : array
nbins : int
|
---|---|
Returns : | img_cdf : array
bin_centers : array
|
References
[R30] | http://en.wikipedia.org/wiki/Cumulative_distribution_function |
Return image after histogram equalization.
Parameters : | image : array
nbins : int
|
---|---|
Returns : | out : float array
|
Notes
This function is adapted from [R31] with the author’s permission.
References
[R31] | (1, 2) http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html |
[R32] | http://en.wikipedia.org/wiki/Histogram_equalization |
Return histogram of image.
Unlike numpy.histogram, this function returns the centers of bins and does not rebin integer arrays. For integer arrays, each integer value has its own bin, which improves speed and intensity-resolution.
Parameters : | image : array
nbins : int
|
---|---|
Returns : | hist : array
bin_centers : array
|
Return image after stretching or shrinking its intensity levels.
The image intensities are uniformly rescaled such that the minimum and maximum values given by in_range match those given by out_range.
Parameters : | image : array
in_range : 2-tuple (float, float)
out_range : 2-tuple (float, float)
|
---|---|
Returns : | out : array
|
Examples
By default, intensities are stretched to the limits allowed by the dtype:
>>> 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:
>>> 1.0 * image
array([ 51., 102., 153.])
Use rescale_intensity to rescale to the proper range for float dtypes:
>>> 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:
>>> 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:
>>> 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:
>>> image = np.array([-10, 0, 10], dtype=np.int8)
>>> rescale_intensity(image, out_range=(0, 127))
array([ 0, 63, 127], dtype=int8)