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Module: exposure¶

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

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. hist : array The values of the histogram. bin_centers : array The values at the center of the bins.

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]))


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. out : float array Image array after histogram equalization.

Notes

This function is adapted from [R187188] with the author’s permission.

References

skimage.exposure.equalize_adapthist(image, kernel_size=None, clip_limit=0.01, nbins=256)[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 : (M, N[, C]) ndarray Input image. kernel_size: integer or list-like, optional Defines the shape of contextual regions used in the algorithm. If iterable is passed, it must have the same number of elements as image.ndim (without color channel). If integer, it is broadcasted to each image dimension. By default, kernel_size is 1/8 of image height by 1/8 of its width. 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 (“data range”). out : (M, N[, C]) ndarray Equalized image.

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

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

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)


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. img_cdf : array Values of cumulative distribution function. bin_centers : array Centers of bins.

References

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


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. out : ndarray Gamma corrected output image.

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

Examples

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


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. out : ndarray Sigmoid corrected output image.

References

 [R199199] Gustav J. Braun, “Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions”, http://www.cis.rit.edu/fairchild/PDFs/PAP07.pdf

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. out : ndarray Logarithm corrected output image.

References

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. [R203203] lower_percentile : float, optional Disregard values below this percentile when computing image contrast. upper_percentile : 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”. out : bool True when the image is determined to be low contrast.

References

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