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@blink1073 @hmaarrfk @glemaitre @stefanv @emmanuelle @soupault @noahstier @ahojnnes @olebole
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r"""
General Description
-------------------
These filters compute the local histogram at each pixel, using a sliding window
similar to the method described in [1]_. A histogram is built using a moving
window in order to limit redundant computation. The moving window follows a
snake-like path:
...------------------------\
/--------------------------/
\--------------------------...
The local histogram is updated at each pixel as the structuring element window
moves by, i.e. only those pixels entering and leaving the structuring element
update the local histogram. The histogram size is 8-bit (256 bins) for 8-bit
images and 2- to 16-bit for 16-bit images depending on the maximum value of the
image.
The filter is applied up to the image border, the neighborhood used is
adjusted accordingly. The user may provide a mask image (same size as input
image) where non zero values are the part of the image participating in the
histogram computation. By default the entire image is filtered.
This implementation outperforms grey.dilation for large structuring elements.
Input image can be 8-bit or 16-bit, for 16-bit input images, the number of
histogram bins is determined from the maximum value present in the image.
Result image is 8-/16-bit or double with respect to the input image and the
rank filter operation.
To do
-----
* add simple examples, adapt documentation on existing examples
* add/check existing doc
* adapting tests for each type of filter
References
----------
.. [1] Huang, T. ,Yang, G. ; Tang, G.. "A fast two-dimensional
median filtering algorithm", IEEE Transactions on Acoustics, Speech and
Signal Processing, Feb 1979. Volume: 27 , Issue: 1, Page(s): 13 - 18.
"""
import functools
import numpy as np
from scipy import ndimage as ndi
from ... import img_as_ubyte
from ..._shared.utils import assert_nD, warn
from . import generic_cy
__all__ = ['autolevel', 'bottomhat', 'equalize', 'gradient', 'maximum', 'mean',
'geometric_mean', 'subtract_mean', 'median', 'minimum', 'modal',
'enhance_contrast', 'pop', 'threshold', 'tophat', 'noise_filter',
'entropy', 'otsu']
def _handle_input(image, selem, out, mask, out_dtype=None, pixel_size=1):
assert_nD(image, 2)
if image.dtype not in (np.uint8, np.uint16):
image = img_as_ubyte(image)
selem = np.ascontiguousarray(img_as_ubyte(selem > 0))
image = np.ascontiguousarray(image)
if mask is None:
mask = np.ones(image.shape, dtype=np.uint8)
else:
mask = img_as_ubyte(mask)
mask = np.ascontiguousarray(mask)
if image is out:
raise NotImplementedError("Cannot perform rank operation in place.")
if out is None:
if out_dtype is None:
out_dtype = image.dtype
out = np.empty(image.shape+(pixel_size,), dtype=out_dtype)
else:
if len(out.shape) == 2:
out = out.reshape(out.shape+(pixel_size,))
is_8bit = image.dtype in (np.uint8, np.int8)
if is_8bit:
n_bins = 256
else:
# Convert to a Python int to avoid the potential overflow when we add
# 1 to the maximum of the image.
n_bins = int(max(3, image.max())) + 1
if n_bins > 2**10:
warn("Bad rank filter performance is expected due to a "
"large number of bins ({}), equivalent to an approximate "
"bitdepth of {:.1f}.".format(n_bins, np.log2(n_bins)))
return image, selem, out, mask, n_bins
def _apply_scalar_per_pixel(func, image, selem, out, mask, shift_x, shift_y,
out_dtype=None):
image, selem, out, mask, n_bins = _handle_input(image, selem, out, mask,
out_dtype)
func(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask,
out=out, n_bins=n_bins)
return out.reshape(out.shape[:2])
def _apply_vector_per_pixel(func, image, selem, out, mask, shift_x, shift_y,
out_dtype=None, pixel_size=1):
image, selem, out, mask, n_bins = _handle_input(image, selem, out, mask,
out_dtype,
pixel_size=pixel_size)
func(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask,
out=out, n_bins=n_bins)
return out
def _default_selem(func):
"""Decorator to add a default structuring element to morphology functions.
Parameters
----------
func : function
A morphology function such as erosion, dilation, opening, closing,
white_tophat, or black_tophat.
Returns
-------
func_out : function
The function, using a default structuring element of same dimension
as the input image with connectivity 1.
"""
@functools.wraps(func)
def func_out(image, selem=None, *args, **kwargs):
if selem is None:
selem = ndi.generate_binary_structure(image.ndim, image.ndim)
return func(image, selem=selem, *args, **kwargs)
return func_out
def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._autolevel, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._bottomhat, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._equalize, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._gradient, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._maximum, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._mean, image, selem, out=out,
mask=mask, shift_x=shift_x, shift_y=shift_y)
def geometric_mean(image, selem, out=None, mask=None,
shift_x=False, shift_y=False):
"""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.
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))
References
----------
.. [1] Gonzalez, R. C. and Wood, R. E. "Digital Image Processing (3rd Edition)."
Prentice-Hall Inc, 2006.
"""
return _apply_scalar_per_pixel(generic_cy._geometric_mean, image, selem, out=out,
mask=mask, shift_x=shift_x, shift_y=shift_y)
def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._subtract_mean, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
@_default_selem
def median(image, selem=None, out=None, mask=None,
shift_x=False, shift_y=False):
"""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.
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))
"""
return _apply_scalar_per_pixel(generic_cy._median, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._minimum, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""Return local mode of an image.
The mode is the value that appears most often in the 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 modal
>>> img = data.camera()
>>> out = modal(img, disk(5))
"""
return _apply_scalar_per_pixel(generic_cy._modal, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
shift_y=False):
"""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
-------
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))
"""
return _apply_scalar_per_pixel(generic_cy._enhance_contrast, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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)
"""
return _apply_scalar_per_pixel(generic_cy._pop, image, selem, out=out,
mask=mask, shift_x=shift_x,
shift_y=shift_y)
def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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)
"""
return _apply_scalar_per_pixel(generic_cy._sum, image, selem, out=out,
mask=mask, shift_x=shift_x,
shift_y=shift_y)
def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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)
"""
return _apply_scalar_per_pixel(generic_cy._threshold, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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))
"""
return _apply_scalar_per_pixel(generic_cy._tophat, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def noise_filter(image, selem, out=None, mask=None, shift_x=False,
shift_y=False):
"""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).
References
----------
.. [1] N. Hashimoto et al. Referenceless image quality evaluation
for whole slide imaging. J Pathol Inform 2012;3:9.
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 noise_filter
>>> img = data.camera()
>>> out = noise_filter(img, disk(5))
"""
# ensure that the central pixel in the structuring element is empty
centre_r = int(selem.shape[0] / 2) + shift_y
centre_c = int(selem.shape[1] / 2) + shift_x
# make a local copy
selem_cpy = selem.copy()
selem_cpy[centre_r, centre_c] = 0
return _apply_scalar_per_pixel(generic_cy._noise_filter, image, selem_cpy,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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
----------
.. [1] https://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))
"""
return _apply_scalar_per_pixel(generic_cy._entropy, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y,
out_dtype=np.double)
def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""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
----------
.. [1] https://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
"""
return _apply_scalar_per_pixel(generic_cy._otsu, image, selem, out=out,
mask=mask, shift_x=shift_x,
shift_y=shift_y)
def windowed_histogram(image, selem, out=None, mask=None,
shift_x=False, shift_y=False, n_bins=None):
"""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))
"""
if n_bins is None:
n_bins = int(image.max()) + 1
return _apply_vector_per_pixel(generic_cy._windowed_hist, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y,
out_dtype=np.double,
pixel_size=n_bins)
def majority(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""Majority filter assign to each pixel the most occuring value within
its neighborhood.
Parameters
----------
image : ndarray
Image array (uint8, uint16 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.
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))
"""
return _apply_scalar_per_pixel(generic_cy._majority, image, selem,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
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