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Module: filters.rank

skimage.filters.rank.autolevel(image, selem) Auto-level image using local histogram.
skimage.filters.rank.autolevel_percentile(…) Return greyscale local autolevel of an image.
skimage.filters.rank.bottomhat(image, selem) Local bottom-hat 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.entropy(image, selem[, …]) Local entropy.
skimage.filters.rank.equalize(image, selem) Equalize image using local histogram.
skimage.filters.rank.geometric_mean(image, selem) Return local geometric mean of an image.
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.mean_bilateral(image, selem) Apply a flat kernel bilateral filter.
skimage.filters.rank.mean_percentile(image, …) Return local mean of an image.
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.noise_filter(image, selem) Noise feature.
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.pop(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.pop_percentile(image, selem) Return the local number (population) of pixels.
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.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 top-hat of an image.
skimage.filters.rank.windowed_histogram(…) Normalized sliding window histogram
skimage.filters.rank.bilateral Approximate bilateral rank filter for local (custom kernel) mean.
skimage.filters.rank.bilateral_cy
skimage.filters.rank.core_cy
skimage.filters.rank.generic General Description
skimage.filters.rank.generic_cy
skimage.filters.rank.percentile_cy

autolevel

skimage.filters.rank.autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]

Auto-level image using local histogram.

This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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))

autolevel_percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1 : float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

bottomhat

skimage.filters.rank.bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]

Local bottom-hat of an image.

This filter computes the morphological closing of the image and then subtracts the result from the original image.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : 2-D array

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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))

enhance_contrast

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

Returns

Output image.

out : 2-D array (same dtype as input image)

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))

enhance_contrast_percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1 : float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

entropy

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : ndarray (double)

Output image.

References

[R561561]http://en.wikipedia.org/wiki/Entropy_(information_theory)

Examples

>>> from skimage import data
>>> from skimage.filters.rank import entropy
>>> from skimage.morphology import disk
>>> img = data.camera()
>>> ent = entropy(img, disk(5))

equalize

skimage.filters.rank.equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]

Equalize image using local histogram.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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))

geometric_mean

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

Output image.

References

[R563563]Gonzalez, R. C. and Wood, R. E. “Digital Image Processing (3rd Edition).” Prentice-Hall 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))

gradient

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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))

gradient_percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1 : float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

maximum

skimage.filters.rank.maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]

Return local maximum of an image.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

Output image.

See also

skimage.morphology.dilation

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))

mean

skimage.filters.rank.mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]

Return local mean of an image.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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))

mean_bilateral

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 edge-preserving 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 [g-s0, 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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

s0, s1 : int

Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

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)

mean_percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1 : float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

median

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array, optional

The neighborhood expressed as a 2-D array of 1’s and 0’s. If None, a full square of size 3 is used.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

Output image.

Examples

>>> from skimage import data
>>> from skimage.morphology import disk
>>> from skimage.filters.rank import median
>>> img = data.camera()
>>> med = median(img, disk(5))

minimum

skimage.filters.rank.minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]

Return local minimum of an image.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

Output image.

See also

skimage.morphology.erosion

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))

noise_filter

skimage.filters.rank.noise_filter(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]

Noise feature.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

Output image.

References

[R565565]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))

otsu

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:

image : ndarray

Image array (uint8 array).

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : ndarray

If None, a new array will be allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

Output image.

References

[R567567]http://en.wikipedia.org/wiki/Otsu’s_method

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

percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0 : float in [0, …, 1]

Set the percentile value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

pop

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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)

pop_bilateral

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 [g-s0, g+s1] where g is the greyvalue of the center pixel.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

s0, s1 : int

Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

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)

pop_percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1 : float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

subtract_mean

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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))

subtract_mean_percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1 : float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

sum

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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)

sum_bilateral

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 edge-preserving 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 [g-s0, 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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

s0, s1 : int

Define the [s0, s1] interval around the greyvalue of the center pixel to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

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)

sum_percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0, p1 : float in [0, …, 1]

Define the [p0, p1] percentile interval to be considered for computing the value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

threshold

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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)

threshold_percentile

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:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

p0 : float in [0, …, 1]

Set the percentile value.
Returns:

out : 2-D array (same dtype as input image)

Output image.

tophat

skimage.filters.rank.tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False)[source]

Local top-hat of an image.

This filter computes the morphological opening of the image and then subtracts the result from the original image.

Parameters:

image : 2-D array (uint8, uint16)

Input image.

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : 2-D array (same dtype as input)

If None, a new array is allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).
Returns:

out : 2-D array (same dtype as input image)

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))

windowed_histogram

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:

image : ndarray

Image array (uint8 array).

selem : 2-D array

The neighborhood expressed as a 2-D array of 1’s and 0’s.

out : ndarray

If None, a new array will be allocated.

mask : ndarray

Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is used (default).

shift_x, shift_y : int

Offset added to the structuring element center point. Shift is bounded to the structuring element sizes (center must be inside the given structuring element).

n_bins : int or None

The number of histogram bins. Will default to image.max() + 1 if None is passed.
Returns:

out : 3-D array with float dtype of dimensions (H,W,N), where (H,W) are

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 N-D 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))
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