exposure
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
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
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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
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
References
[R83] http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf
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
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
References
[R84] Gustav J. Braun, “Image Lightness Rescaling Using Sigmoidal Contrast Enhancement Functions”, http://www.cis.rit.edu/fairchild/PDFs/PAP07.pdf
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
Number of bins for image histogram.
Returns: img_cdf : array
Values of cumulative distribution function.
bin_centers : array
Centers of bins.
See also
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
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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
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
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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 [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
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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
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
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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
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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
andout_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 inDTYPE_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’ andout_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)
© 2011 the scikit-image team
Licensed under the BSD 3-clause License.
http://scikit-image.org/docs/0.12.x/api/skimage.exposure.html