skimage.filters.rank.autolevel (image, selem)  Autolevel image using local histogram. 
skimage.filters.rank.autolevel_percentile (…)  Return greyscale local autolevel of an image. 
skimage.filters.rank.bottomhat (image, selem)  Local bottomhat of an image. 
skimage.filters.rank.equalize (image, selem)  Equalize image using local histogram. 
skimage.filters.rank.gradient (image, selem)  Return local gradient of an image (i.e. 
skimage.filters.rank.gradient_percentile (…)  Return local gradient of an image (i.e. 
skimage.filters.rank.maximum (image, selem[, …])  Return local maximum of an image. 
skimage.filters.rank.mean (image, selem[, …])  Return local mean of an image. 
skimage.filters.rank.geometric_mean (image, selem)  Return local geometric mean of an image. 
skimage.filters.rank.mean_percentile (image, …)  Return local mean of an image. 
skimage.filters.rank.mean_bilateral (image, selem)  Apply a flat kernel bilateral filter. 
skimage.filters.rank.subtract_mean (image, selem)  Return image subtracted from its local mean. 
skimage.filters.rank.subtract_mean_percentile (…)  Return image subtracted from its local mean. 
skimage.filters.rank.median (image[, selem, …])  Return local median of an image. 
skimage.filters.rank.minimum (image, selem[, …])  Return local minimum of an image. 
skimage.filters.rank.modal (image, selem[, …])  Return local mode of an image. 
skimage.filters.rank.enhance_contrast (image, …)  Enhance contrast of an image. 
skimage.filters.rank.enhance_contrast_percentile (…)  Enhance contrast of an image. 
skimage.filters.rank.pop (image, selem[, …])  Return the local number (population) of pixels. 
skimage.filters.rank.pop_percentile (image, selem)  Return the local number (population) of pixels. 
skimage.filters.rank.pop_bilateral (image, selem)  Return the local number (population) of pixels. 
skimage.filters.rank.sum (image, selem[, …])  Return the local sum of pixels. 
skimage.filters.rank.sum_bilateral (image, selem)  Apply a flat kernel bilateral filter. 
skimage.filters.rank.sum_percentile (image, selem)  Return the local sum of pixels. 
skimage.filters.rank.threshold (image, selem)  Local threshold of an image. 
skimage.filters.rank.threshold_percentile (…)  Local threshold of an image. 
skimage.filters.rank.tophat (image, selem[, …])  Local tophat of an image. 
skimage.filters.rank.noise_filter (image, selem)  Noise feature. 
skimage.filters.rank.entropy (image, selem[, …])  Local entropy. 
skimage.filters.rank.otsu (image, selem[, …])  Local Otsu’s threshold value for each pixel. 
skimage.filters.rank.percentile (image, selem)  Return local percentile of an image. 
skimage.filters.rank.windowed_histogram (…)  Normalized sliding window histogram 
skimage.filters.rank.autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Autolevel image using local histogram.
This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import autolevel >>> img = data.camera() >>> auto = autolevel(img, disk(5))
skimage.filters.rank.autolevel_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)
[source]
Return greyscale local autolevel of an image.
This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Parameters: 


Returns: 

skimage.filters.rank.bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Local bottomhat of an image.
This filter computes the morphological closing of the image and then subtracts the result from the original image.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import bottomhat >>> img = data.camera() >>> out = bottomhat(img, disk(5))
skimage.filters.rank.equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Equalize image using local histogram.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import equalize >>> img = data.camera() >>> equ = equalize(img, disk(5))
skimage.filters.rank.gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return local gradient of an image (i.e. local maximum  local minimum).
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import gradient >>> img = data.camera() >>> out = gradient(img, disk(5))
skimage.filters.rank.gradient_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)
[source]
Return local gradient of an image (i.e. local maximum  local minimum).
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Parameters: 


Returns: 

skimage.filters.rank.maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return local maximum of an image.
Parameters: 


Returns: 

See also
The lower algorithm complexity makes skimage.filters.rank.maximum
more efficient for larger images and structuring elements.
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import maximum >>> img = data.camera() >>> out = maximum(img, disk(5))
skimage.filters.rank.mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return local mean of an image.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean >>> img = data.camera() >>> avg = mean(img, disk(5))
skimage.filters.rank.geometric_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return local geometric mean of an image.
Parameters: 


Returns: 

[1]  Gonzalez, R. C. and Wood, R. E. “Digital Image Processing (3rd Edition).” PrenticeHall Inc, 2006. 
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean >>> img = data.camera() >>> avg = geometric_mean(img, disk(5))
skimage.filters.rank.mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)
[source]
Return local mean of an image.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Parameters: 


Returns: 

skimage.filters.rank.mean_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)
[source]
Apply a flat kernel bilateral filter.
This is an edgepreserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity.
Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element.
Radiometric similarity is defined by the greylevel interval [gs0, g+s1] where g is the current pixel greylevel.
Only pixels belonging to the structuring element and having a greylevel inside this interval are averaged.
Parameters: 


Returns: 

See also
skimage.filters.denoise_bilateral
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import mean_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = mean_bilateral(img, disk(20), s0=10,s1=10)
skimage.filters.rank.subtract_mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return image subtracted from its local mean.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import subtract_mean >>> img = data.camera() >>> out = subtract_mean(img, disk(5))
skimage.filters.rank.subtract_mean_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)
[source]
Return image subtracted from its local mean.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Parameters: 


Returns: 

skimage.filters.rank.median(image, selem=None, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return local median of an image.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import median >>> img = data.camera() >>> med = median(img, disk(5))
skimage.filters.rank.minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return local minimum of an image.
Parameters: 


Returns: 

See also
The lower algorithm complexity makes skimage.filters.rank.minimum
more efficient for larger images and structuring elements.
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import minimum >>> img = data.camera() >>> out = minimum(img, disk(5))
skimage.filters.rank.modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return local mode of an image.
The mode is the value that appears most often in the local histogram.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import modal >>> img = data.camera() >>> out = modal(img, disk(5))
skimage.filters.rank.enhance_contrast(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Enhance contrast of an image.
This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import enhance_contrast >>> img = data.camera() >>> out = enhance_contrast(img, disk(5))
skimage.filters.rank.enhance_contrast_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)
[source]
Enhance contrast of an image.
This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Parameters: 


Returns: 

skimage.filters.rank.pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return the local number (population) of pixels.
The number of pixels is defined as the number of pixels which are included in the structuring element and the mask.
Parameters: 


Returns: 

>>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> rank.pop(img, square(3)) array([[4, 6, 6, 6, 4], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [6, 9, 9, 9, 6], [4, 6, 6, 6, 4]], dtype=uint8)
skimage.filters.rank.pop_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)
[source]
Return the local number (population) of pixels.
The number of pixels is defined as the number of pixels which are included in the structuring element and the mask.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Parameters: 


Returns: 

skimage.filters.rank.pop_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)
[source]
Return the local number (population) of pixels.
The number of pixels is defined as the number of pixels which are included in the structuring element and the mask. Additionally pixels must have a greylevel inside the interval [gs0, g+s1] where g is the greyvalue of the center pixel.
Parameters: 


Returns: 

>>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint16) >>> rank.pop_bilateral(img, square(3), s0=10, s1=10) array([[3, 4, 3, 4, 3], [4, 4, 6, 4, 4], [3, 6, 9, 6, 3], [4, 4, 6, 4, 4], [3, 4, 3, 4, 3]], dtype=uint16)
skimage.filters.rank.sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Return the local sum of pixels.
Note that the sum may overflow depending on the data type of the input array.
Parameters: 


Returns: 

>>> from skimage.morphology import square >>> import skimage.filters.rank as rank >>> img = np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> rank.sum(img, square(3)) array([[1, 2, 3, 2, 1], [2, 4, 6, 4, 2], [3, 6, 9, 6, 3], [2, 4, 6, 4, 2], [1, 2, 3, 2, 1]], dtype=uint8)
skimage.filters.rank.sum_bilateral(image, selem, out=None, mask=None, shift_x=False, shift_y=False, s0=10, s1=10)
[source]
Apply a flat kernel bilateral filter.
This is an edgepreserving and noise reducing denoising filter. It averages pixels based on their spatial closeness and radiometric similarity.
Spatial closeness is measured by considering only the local pixel neighborhood given by a structuring element (selem).
Radiometric similarity is defined by the greylevel interval [gs0, g+s1] where g is the current pixel greylevel.
Only pixels belonging to the structuring element AND having a greylevel inside this interval are summed.
Note that the sum may overflow depending on the data type of the input array.
Parameters: 


Returns: 

See also
skimage.filters.denoise_bilateral
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import sum_bilateral >>> img = data.camera().astype(np.uint16) >>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10)
skimage.filters.rank.sum_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1)
[source]
Return the local sum of pixels.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Note that the sum may overflow depending on the data type of the input array.
Parameters: 


Returns: 

skimage.filters.rank.threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Local threshold of an image.
The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean.
Parameters: 


Returns: 

>>> from skimage.morphology import square >>> from skimage.filters.rank import threshold >>> img = 255 * np.array([[0, 0, 0, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> threshold(img, square(3)) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]], dtype=uint8)
skimage.filters.rank.threshold_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0)
[source]
Local threshold of an image.
The resulting binary mask is True if the greyvalue of the center pixel is greater than the local mean.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Parameters: 


Returns: 

skimage.filters.rank.tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Local tophat of an image.
This filter computes the morphological opening of the image and then subtracts the result from the original image.
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import tophat >>> img = data.camera() >>> out = tophat(img, disk(5))
skimage.filters.rank.noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Noise feature.
Parameters: 


Returns: 

[1]  N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9. 
>>> from skimage import data >>> from skimage.morphology import disk >>> from skimage.filters.rank import noise_filter >>> img = data.camera() >>> out = noise_filter(img, disk(5))
skimage.filters.rank.entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Local entropy.
The entropy is computed using base 2 logarithm i.e. the filter returns the minimum number of bits needed to encode the local greylevel distribution.
Parameters: 


Returns: 

[1]  http://en.wikipedia.org/wiki/Entropy_(information_theory) 
>>> from skimage import data >>> from skimage.filters.rank import entropy >>> from skimage.morphology import disk >>> img = data.camera() >>> ent = entropy(img, disk(5))
skimage.filters.rank.otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False)
[source]
Local Otsu’s threshold value for each pixel.
Parameters: 


Returns: 

[1]  http://en.wikipedia.org/wiki/Otsu’s_method 
>>> from skimage import data >>> from skimage.filters.rank import otsu >>> from skimage.morphology import disk >>> img = data.camera() >>> local_otsu = otsu(img, disk(5)) >>> thresh_image = img >= local_otsu
skimage.filters.rank.percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0)
[source]
Return local percentile of an image.
Returns the value of the p0 lower percentile of the local greyvalue distribution.
Only greyvalues between percentiles [p0, p1] are considered in the filter.
Parameters: 


Returns: 

skimage.filters.rank.windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None)
[source]
Normalized sliding window histogram
Parameters: 


Returns: 

>>> from skimage import data >>> from skimage.filters.rank import windowed_histogram >>> from skimage.morphology import disk >>> img = data.camera() >>> hist_img = windowed_histogram(img, disk(5))
© 2011 the scikitimage team
Licensed under the BSD 3clause License.
http://scikitimage.org/docs/0.14.x/api/skimage.filters.rank.html