Module: exposure¶
| 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. |
cumulative_distribution¶
- skimage.exposure.cumulative_distribution(image, nbins=256)¶
Return cumulative distribution function (cdf) for the given image.
Parameters : image : array
Image array.
nbins : int
Number of bins for image histogram.
Returns : img_cdf : array
Values of cumulative distribution function.
bin_centers : array
Centers of bins.
References
[R22] http://en.wikipedia.org/wiki/Cumulative_distribution_function
equalize¶
- skimage.exposure.equalize(image, nbins=256)¶
Return image after histogram equalization.
Parameters : image : array
Image array.
nbins : int
Number of bins for image histogram.
Returns : out : float array
Image array after histogram equalization.
Notes
This function is adapted from [R23] with the author’s permission.
References
[R23] (1, 2) http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html [R24] http://en.wikipedia.org/wiki/Histogram_equalization
histogram¶
- skimage.exposure.histogram(image, nbins=256)¶
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
Input image.
nbins : int
Number of bins used to calculate histogram. This value is ignored for integer arrays.
Returns : hist : array
The values of the histogram.
bin_centers : array
The values at the center of the bins.
rescale_intensity¶
- skimage.exposure.rescale_intensity(image, in_range=None, out_range=None)¶
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
Image array.
in_range : 2-tuple (float, float)
Min and max allowed intensity values of input image. If None, the allowed min/max values are set to the actual min/max values in the input image.
out_range : 2-tuple (float, float)
Min and max intensity values of output image. If None, use the min/max intensities of the image data type. See skimage.util.dtype for details.
Returns : out : array
Image array after rescaling its intensity. This image is the same dtype as the input image.
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)