filters.rank
¶

Autolevel image using local histogram. 
Return greyscale local autolevel of an image. 


Local bottomhat of an image. 

Equalize image using local histogram. 

Return local gradient of an image (i.e. 
Return local gradient of an image (i.e. 


Return local maximum of an image. 

Return local mean of an image. 

Return local geometric mean of an image. 

Return local mean of an image. 

Apply a flat kernel bilateral filter. 

Return image subtracted from its local mean. 
Return image subtracted from its local mean. 


Return local median of an image. 

Return local minimum of an image. 

Return local mode of an image. 

Enhance contrast of an image. 
Enhance contrast of an image. 


Return the local number (population) of pixels. 

Return the local number (population) of pixels. 

Return the local number (population) of pixels. 

Return the local sum of pixels. 

Apply a flat kernel bilateral filter. 

Return the local sum of pixels. 

Local threshold of an image. 
Local threshold of an image. 


Local tophat of an image. 

Noise feature. 

Local entropy. 

Local Otsu’s threshold value for each pixel. 

Return local percentile of an image. 
Normalized sliding window histogram 


Majority filter assign to each pixel the most occuring value within its neighborhood. 
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”.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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
¶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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [p0, p1] percentile interval to be considered for computing the value.
Output image.
skimage.filters.rank.autolevel_percentile
¶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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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).
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [p0, p1] percentile interval to be considered for computing the value.
Output image.
skimage.filters.rank.
maximum
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶Return local maximum of an image.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
See also
Notes
The lower algorithm complexity makes skimage.filters.rank.maximum more efficient for larger images and structuring elements.
Examples
>>> 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.maximum
¶skimage.filters.rank.
mean
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶Return local mean of an image.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
References
Gonzalez, R. C. and Wood, R. E. “Digital Image Processing (3rd Edition).” PrenticeHall Inc, 2006.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [p0, p1] percentile interval to be considered for computing the value.
Output image.
skimage.filters.rank.mean_percentile
¶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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
Output image.
See also
skimage.filters.denoise_bilateral
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [p0, p1] percentile interval to be considered for computing the value.
Output image.
skimage.filters.rank.
median
(image, selem=None, out=None, mask=None, shift_x=False, shift_y=False)[source]¶Return local median of an image.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s. If None, a full square of size 3 is used.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
See also
skimage.filters.median
Implementation of a median filtering which handles images with floating precision.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
See also
Notes
The lower algorithm complexity makes skimage.filters.rank.minimum more efficient for larger images and structuring elements.
Examples
>>> 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.minimum
¶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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
The result of the local enhance_contrast.
Examples
>>> 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
¶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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [p0, p1] percentile interval to be considered for computing the value.
Output image.
skimage.filters.rank.enhance_contrast_percentile
¶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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [p0, p1] percentile interval to be considered for computing the value.
Output image.
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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
Output image.
See also
skimage.filters.denoise_bilateral
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Define the [p0, p1] percentile interval to be considered for computing the value.
Output image.
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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Set the percentile value.
Output image.
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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
References
N. Hashimoto et al. Referenceless image quality evaluation for whole slide imaging. J Pathol Inform 2012;3:9.
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
References
Examples
>>> 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.
Image array (uint8 array).
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array will be allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
References
Examples
>>> 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.
Input image.
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array is allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Set the percentile value.
Output image.
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
Image array (uint8 array).
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array will be allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
The number of histogram bins. Will default to image.max() + 1
if None is passed.
the dimensions of the input image and N is n_bins or
image.max() + 1
if no value is provided as a parameter.
Effectively, each pixel is a ND feature vector that is the histogram.
The sum of the elements in the feature vector will be 1, unless no
pixels in the window were covered by both selem and mask, in which
case all elements will be 0.
Examples
>>> 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))
skimage.filters.rank.windowed_histogram
¶skimage.filters.rank.
majority
(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]¶Majority filter assign to each pixel the most occuring value within its neighborhood.
Image array (uint8, uint16 array).
The neighborhood expressed as a 2D array of 1’s and 0’s.
If None, a new array will be allocated.
Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).
Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Output image.
Examples
>>> from skimage import data
>>> from skimage.filters.rank import majority
>>> from skimage.morphology import disk
>>> img = data.camera()
>>> maj_img = majority(img, disk(5))