Module: exposure
¶
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Return histogram of image. |
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Return image after histogram equalization. |
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Contrast Limited Adaptive Histogram Equalization (CLAHE). |
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Return image after stretching or shrinking its intensity levels. |
Return cumulative distribution function (cdf) for the given image. |
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Performs Gamma Correction on the input image. |
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Performs Sigmoid Correction on the input image. |
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Performs Logarithmic correction on the input image. |
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Determine if an image is low contrast. |
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Adjust an image so that its cumulative histogram matches that of another. |
histogram¶
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skimage.exposure.
histogram
(image, nbins=256, source_range='image', normalize=False)[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, optional
Number of bins used to calculate histogram. This value is ignored for integer arrays.
source_range : string, optional
‘image’ (default) determines the range from the input image. ‘dtype’ determines the range from the expected range of the images of that data type.
normalize : bool, optional
If True, normalize the histogram by the sum of its values.
- Returns
hist : array
The values of the histogram.
bin_centers : array
The values at the center of the bins.
See also
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]))
Examples using skimage.exposure.histogram
¶
equalize_hist¶
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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 [R115] with the author’s permission.
References
Examples using skimage.exposure.equalize_hist
¶
equalize_adapthist¶
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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 ofimage
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”).
- Returns
out : (M, N[, C]) ndarray
Equalized image.
See also
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
Examples using skimage.exposure.equalize_adapthist
¶
rescale_intensity¶
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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, optional
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
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)
Examples using skimage.exposure.rescale_intensity
¶
cumulative_distribution¶
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skimage.exposure.
cumulative_distribution
(image, nbins=256)[source]¶ Return cumulative distribution function (cdf) for the given image.
- Parameters
image : array
Image array.
nbins : int, optional
Number of bins for image histogram.
- Returns
img_cdf : array
Values of cumulative distribution function.
bin_centers : array
Centers of bins.
See also
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
Examples using skimage.exposure.cumulative_distribution
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adjust_gamma¶
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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, optional
Non negative real number. Default value is 1.
gain : float, optional
The constant multiplier. Default value is 1.
- Returns
out : ndarray
Gamma corrected output image.
See also
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()) >>> gamma_corrected = exposure.adjust_gamma(image, 2) >>> # Output is darker for gamma > 1 >>> image.mean() > gamma_corrected.mean() True
Examples using skimage.exposure.adjust_gamma
¶
adjust_sigmoid¶
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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, optional
Cutoff of the sigmoid function that shifts the characteristic curve in horizontal direction. Default value is 0.5.
gain : float, optional
The constant multiplier in exponential’s power of sigmoid function. Default value is 10.
inv : bool, optional
If True, returns the negative sigmoid correction. Defaults to False.
- Returns
out : ndarray
Sigmoid corrected output image.
See also
References
- R121
Gustav J. Braun, “Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions”, http://www.cis.rit.edu/fairchild/PDFs/PAP07.pdf
adjust_log¶
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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 isO = gain*(2**I - 1)
.- Parameters
image : ndarray
Input image.
gain : float, optional
The constant multiplier. Default value is 1.
inv : float, optional
If True, it performs inverse logarithmic correction, else correction will be logarithmic. Defaults to False.
- Returns
out : ndarray
Logarithm corrected output image.
See also
References
Examples using skimage.exposure.adjust_log
¶
is_low_contrast¶
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skimage.exposure.
is_low_contrast
(image, fraction_threshold=0.05, lower_percentile=1, upper_percentile=99, method='linear')[source]¶ Determine 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. [R123]
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”.
- Returns
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
match_histograms¶
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skimage.exposure.
match_histograms
(image, reference, *, multichannel=False)[source]¶ Adjust an image so that its cumulative histogram matches that of another.
The adjustment is applied separately for each channel.
- Parameters
image : ndarray
Input image. Can be gray-scale or in color.
reference : ndarray
Image to match histogram of. Must have the same number of channels as image.
multichannel : bool, optional
Apply the matching separately for each channel.
- Returns
matched : ndarray
Transformed input image.
- Raises
ValueError
Thrown when the number of channels in the input image and the reference differ.
References