Module: exposure

skimage.exposure.adjust_gamma(image[, ...]) Performs Gamma Correction on the input image.
skimage.exposure.adjust_log(image[, gain, inv]) Performs Logarithmic correction on the input image.
skimage.exposure.adjust_sigmoid(image[, ...]) Performs Sigmoid Correction on the input image.
skimage.exposure.cumulative_distribution(image) Return cumulative distribution function (cdf) for the given image.
skimage.exposure.equalize_adapthist(image, ...) Contrast Limited Adaptive Histogram Equalization (CLAHE).
skimage.exposure.equalize_hist(image[, ...]) Return image after histogram equalization.
skimage.exposure.histogram(image[, nbins]) Return histogram of image.
skimage.exposure.is_low_contrast(image[, ...]) Detemine if an image is low contrast.
skimage.exposure.rescale_intensity(image[, ...]) Return image after stretching or shrinking its intensity levels.

adjust_gamma

skimage.exposure.adjust_gamma(image, gamma=1, gain=1)[source]

Performs Gamma Correction on the input image.

Also known as Power Law Transform. This function transforms the input image pixelwise according to the equation O = I**gamma after scaling each pixel to the range 0 to 1.

Parameters:

image : ndarray

Input image.

gamma : float

Non negative real number. Default value is 1.

gain : float

The constant multiplier. Default value is 1.
Returns:

out : ndarray

Gamma corrected output image.

See also

adjust_log

Notes

For gamma greater than 1, the histogram will shift towards left and the output image will be darker than the input image.

For gamma less than 1, the histogram will shift towards right and the output image will be brighter than the input image.

References

[R82]http://en.wikipedia.org/wiki/Gamma_correction

Examples

>>> from skimage import data, exposure, img_as_float
>>> image = img_as_float(data.moon())
>>> gamma_corrected = exposure.adjust_gamma(image, 2)
>>> # Output is darker for gamma > 1
>>> image.mean() > gamma_corrected.mean()
True

adjust_log

skimage.exposure.adjust_log(image, gain=1, inv=False)[source]

Performs Logarithmic correction on the input image.

This function transforms the input image pixelwise according to the equation O = gain*log(1 + I) after scaling each pixel to the range 0 to 1. For inverse logarithmic correction, the equation is O = gain*(2**I - 1).

Parameters:

image : ndarray

Input image.

gain : float

The constant multiplier. Default value is 1.

inv : float

If True, it performs inverse logarithmic correction, else correction will be logarithmic. Defaults to False.
Returns:

out : ndarray

Logarithm corrected output image.

See also

adjust_gamma

References

[R83]http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf

adjust_sigmoid

skimage.exposure.adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False)[source]

Performs Sigmoid Correction on the input image.

Also known as Contrast Adjustment. This function transforms the input image pixelwise according to the equation O = 1/(1 + exp*(gain*(cutoff - I))) after scaling each pixel to the range 0 to 1.

Parameters:

image : ndarray

Input image.

cutoff : float

Cutoff of the sigmoid function that shifts the characteristic curve in horizontal direction. Default value is 0.5.

gain : float

The constant multiplier in exponential’s power of sigmoid function. Default value is 10.

inv : bool

If True, returns the negative sigmoid correction. Defaults to False.
Returns:

out : ndarray

Sigmoid corrected output image.

See also

adjust_gamma

References

[R84]Gustav J. Braun, “Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions”, http://www.cis.rit.edu/fairchild/PDFs/PAP07.pdf

cumulative_distribution

skimage.exposure.cumulative_distribution(image, nbins=256)[source]

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.

See also

histogram

References

[R85]http://en.wikipedia.org/wiki/Cumulative_distribution_function

Examples

>>> from skimage import data, exposure, img_as_float
>>> image = img_as_float(data.camera())
>>> hi = exposure.histogram(image)
>>> cdf = exposure.cumulative_distribution(image)
>>> np.alltrue(cdf[0] == np.cumsum(hi[0])/float(image.size))
True

equalize_adapthist

skimage.exposure.equalize_adapthist(image, *args, **kwargs)[source]

Contrast Limited Adaptive Histogram Equalization (CLAHE).

An algorithm for local contrast enhancement, that uses histograms computed over different tile regions of the image. Local details can therefore be enhanced even in regions that are darker or lighter than most of the image.

Parameters:

image : array-like

Input image.

kernel_size: integer or 2-tuple

Defines the shape of contextual regions used in the algorithm. If an integer is given, the shape will be a square of sidelength given by this value.

ntiles_x : int, optional (deprecated in favor of kernel_size)

Number of tile regions in the X direction (horizontal).

ntiles_y : int, optional (deprecated in favor of kernel_size)

Number of tile regions in the Y direction (vertical).

clip_limit : float: optional

Clipping limit, normalized between 0 and 1 (higher values give more contrast).

nbins : int, optional

Number of gray bins for histogram (“dynamic range”).
Returns:

out : ndarray

Equalized image.

See also

equalize_hist, rescale_intensity

Notes

  • For color images, the following steps are performed:
    • The image is converted to HSV color space
    • The CLAHE algorithm is run on the V (Value) channel
    • The image is converted back to RGB space and returned

  • For RGBA images, the original alpha channel is removed.

References

[R86]http://tog.acm.org/resources/GraphicsGems/gems.html#gemsvi
[R87]https://en.wikipedia.org/wiki/CLAHE#CLAHE

equalize_hist

skimage.exposure.equalize_hist(image, nbins=256, mask=None)[source]

Return image after histogram equalization.

Parameters:

image : array

Image array.

nbins : int, optional

Number of bins for image histogram. Note: this argument is ignored for integer images, for which each integer is its own bin.

mask: ndarray of bools or 0s and 1s, optional

Array of same shape as image. Only points at which mask == True are used for the equalization, which is applied to the whole image.
Returns:

out : float array

Image array after histogram equalization.

Notes

This function is adapted from [R88] with the author’s permission.

References

[R88](1, 2) http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html
[R89]http://en.wikipedia.org/wiki/Histogram_equalization

histogram

skimage.exposure.histogram(image, nbins=256)[source]

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.

The histogram is computed on the flattened image: for color images, the function should be used separately on each channel to obtain a histogram for each color channel.

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.

See also

cumulative_distribution

Examples

>>> from skimage import data, exposure, img_as_float
>>> image = img_as_float(data.camera())
>>> np.histogram(image, bins=2)
(array([107432, 154712]), array([ 0. ,  0.5,  1. ]))
>>> exposure.histogram(image, nbins=2)
(array([107432, 154712]), array([ 0.25,  0.75]))

is_low_contrast

skimage.exposure.is_low_contrast(image, fraction_threshold=0.05, lower_percentile=1, upper_percentile=99, method='linear')[source]

Detemine if an image is low contrast.

Parameters:

image : array-like

The image under test.

fraction_threshold : float, optional

The low contrast fraction threshold. An image is considered low- contrast when its range of brightness spans less than this fraction of its data type’s full range. [R90]

lower_bound : float, optional

Disregard values below this percentile when computing image contrast.

upper_bound : float, optional

Disregard values above this percentile when computing image contrast.

method : str, optional

The contrast determination method. Right now the only available option is “linear”.
Returns:

out : bool

True when the image is determined to be low contrast.

References

[R90](1, 2) http://scikit-image.org/docs/dev/user_guide/data_types.html

Examples

>>> image = np.linspace(0, 0.04, 100)
>>> is_low_contrast(image)
True
>>> image[-1] = 1
>>> is_low_contrast(image)
True
>>> is_low_contrast(image, upper_percentile=100)
False

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’:

>>> 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)

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