mirror of
https://github.com/wassname/scikit-image.git
synced 2026-08-09 12:30:07 +08:00
Improve doc strings of generic rank filters
This commit is contained in:
Johannes Schönberger
+236
-169
@@ -77,16 +77,19 @@ def _apply(func, image, selem, out, mask, shift_x, shift_y, out_dtype=None):
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def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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"""Autolevel image using local histogram.
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"""Auto-level image using local histogram.
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This filter locally stretches the histogram of greyvalues to cover the
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entire range of values from "white" to "black".
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -97,7 +100,7 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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out : ndarray (same dtype as input image)
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out : 2-D array (same dtype as input image)
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Output image.
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Examples
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@@ -105,10 +108,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import autolevel
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>>> # Load test image
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>>> ima = data.camera()
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>>> # Stretch image contrast locally
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>>> auto = autolevel(ima, disk(20))
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>>> img = data.camera()
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>>> auto = autolevel(img, disk(5))
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"""
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@@ -117,17 +118,20 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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"""Returns greyscale local bottomhat of an image.
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"""Local bottom-hat of an image.
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This filter computes the morphological closing of the image and then
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subtracts the result from the original image.
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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mask : ndarray
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : 2-D array
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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shift_x, shift_y : int
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@@ -137,8 +141,16 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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bottomhat : ndarray (same dtype as input image)
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The result of the local bottomhat.
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out : 2-D array (same dtype as input image)
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Output image.
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import bottomhat
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>>> img = data.camera()
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>>> out = bottomhat(img, disk(5))
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"""
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@@ -151,12 +163,12 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -167,7 +179,7 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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out : ndarray (same dtype as input image)
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out : 2-D array (same dtype as input image)
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Output image.
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Examples
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@@ -175,10 +187,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import equalize
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>>> # Load test image
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>>> ima = data.camera()
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>>> # Local equalization
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>>> equ = equalize(ima, disk(20))
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>>> img = data.camera()
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>>> equ = equalize(img, disk(5))
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"""
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@@ -187,18 +197,16 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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"""Return greyscale local gradient of an image (i.e. local maximum - local
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minimum).
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"""Return local gradient of an image (i.e. local maximum - local minimum).
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -209,9 +217,17 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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out : ndarray (same dtype as input image)
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out : 2-D array (same dtype as input image)
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Output image.
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import gradient
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>>> img = data.camera()
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>>> out = gradient(img, disk(5))
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"""
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return _apply(generic_cy._gradient, image, selem,
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@@ -219,17 +235,16 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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"""Return greyscale local maximum of an image.
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"""Return local maximum of an image.
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -240,7 +255,7 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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out : ndarray (same dtype as input image)
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out : 2-D array (same dtype as input image)
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Output image.
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See also
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@@ -249,8 +264,16 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Notes
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-----
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* the lower algorithm complexity makes the rank.maximum() more efficient
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for larger images and structuring elements
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The lower algorithm complexity makes the `skimage.filter.rank.maximum`
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more efficient for larger images and structuring elements.
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import maximum
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>>> img = data.camera()
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>>> out = maximum(img, disk(5))
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"""
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@@ -259,16 +282,16 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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"""Return greyscale local mean of an image.
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"""Return local mean of an image.
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -279,7 +302,7 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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out : ndarray (same dtype as input image)
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out : 2-D array (same dtype as input image)
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Output image.
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Examples
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@@ -287,10 +310,8 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import mean
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>>> # Load test image
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>>> ima = data.camera()
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>>> # Local mean
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>>> avg = mean(ima, disk(20))
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>>> img = data.camera()
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>>> avg = mean(img, disk(5))
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"""
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@@ -304,12 +325,12 @@ def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -320,9 +341,17 @@ def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
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Returns
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-------
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out : ndarray (same dtype as input image)
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out : 2-D array (same dtype as input image)
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Output image.
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import subtract_mean
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>>> img = data.camera()
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>>> out = subtract_mean(img, disk(5))
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"""
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return _apply(generic_cy._subtract_mean, image, selem,
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@@ -330,16 +359,16 @@ def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
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def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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"""Return greyscale local median of an image.
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"""Return local median of an image.
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -350,7 +379,7 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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out : ndarray (same dtype as input image)
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out : 2-D array (same dtype as input image)
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Output image.
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Examples
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@@ -358,10 +387,8 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import median
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>>> # Load test image
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>>> ima = data.camera()
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>>> # Local mean
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>>> avg = median(ima, disk(20))
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>>> img = data.camera()
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>>> med = median(img, disk(5))
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"""
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@@ -370,16 +397,16 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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"""Return greyscale local minimum of an image.
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"""Return local minimum of an image.
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
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Input image.
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selem : 2-D array
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -390,7 +417,7 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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out : ndarray (same dtype as input image)
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out : 2-D array (same dtype as input image)
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Output image.
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See also
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@@ -399,8 +426,16 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Notes
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-----
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* the lower algorithm complexity makes the rank.minimum() more efficient
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for larger images and structuring elements
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The lower algorithm complexity makes the `skimage.filter.rank.minimum` more
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efficient for larger images and structuring elements.
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Examples
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--------
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>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import minimum
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>>> img = data.camera()
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>>> out = minimum(img, disk(5))
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"""
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@@ -409,16 +444,18 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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"""Return greyscale local mode of an image.
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"""Return local mode of an image.
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The mode is the value that appears most often in the histogram.
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Parameters
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----------
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image : ndarray (uint8, uint16)
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Image array.
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selem : ndarray
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image : 2-D array (uint8, uint16)
|
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Input image.
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||||
selem : 2-D array
|
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray (same dtype as input)
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If None, a new array will be allocated.
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out : 2-D array (same dtype as input)
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If None, a new array is allocated.
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mask : ndarray
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Mask array that defines (>0) area of the image included in the local
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neighborhood. If None, the complete image is used (default).
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@@ -429,9 +466,17 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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Returns
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-------
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out : ndarray (same dtype as input image)
|
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out : 2-D array (same dtype as input image)
|
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Output image.
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Examples
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||||
--------
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||||
>>> from skimage import data
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>>> from skimage.morphology import disk
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>>> from skimage.filter.rank import modal
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>>> img = data.camera()
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>>> out = modal(img, disk(5))
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"""
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return _apply(generic_cy._modal, image, selem,
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@@ -440,18 +485,20 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
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def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
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shift_y=False):
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"""Enhance an image replacing each pixel by the local maximum if pixel
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greylevel is closest to maximimum than local minimum OR local minimum
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otherwise.
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"""Enhance contrast of an image.
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||||
This replaces each pixel by the local maximum if the pixel greyvalue is
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closer to the local maximum than the local minimum. Otherwise it is
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replaced by the local minimum.
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||||
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||||
Parameters
|
||||
----------
|
||||
image : ndarray (uint8, uint16)
|
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Image array.
|
||||
selem : ndarray
|
||||
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 : ndarray (same dtype as input)
|
||||
If None, a new array will be allocated.
|
||||
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).
|
||||
@@ -462,7 +509,7 @@ def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
|
||||
|
||||
Returns
|
||||
Output image.
|
||||
out : ndarray (same dtype as input image)
|
||||
out : 2-D array (same dtype as input image)
|
||||
The result of the local enhance_contrast.
|
||||
|
||||
Examples
|
||||
@@ -470,10 +517,8 @@ def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
|
||||
>>> from skimage import data
|
||||
>>> from skimage.morphology import disk
|
||||
>>> from skimage.filter.rank import enhance_contrast
|
||||
>>> # Load test image
|
||||
>>> ima = data.camera()
|
||||
>>> # Local mean
|
||||
>>> avg = enhance_contrast(ima, disk(20))
|
||||
>>> img = data.camera()
|
||||
>>> out = enhance_contrast(img, disk(5))
|
||||
|
||||
"""
|
||||
|
||||
@@ -482,17 +527,19 @@ def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
|
||||
|
||||
|
||||
def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
"""Return the number (population) of pixels actually inside the
|
||||
neighborhood.
|
||||
"""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 : ndarray (uint8, uint16)
|
||||
Image array.
|
||||
selem : ndarray
|
||||
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 : ndarray (same dtype as input)
|
||||
If None, a new array will be allocated.
|
||||
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).
|
||||
@@ -503,19 +550,19 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray (same dtype as input image)
|
||||
out : 2-D array (same dtype as input image)
|
||||
Output image.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage.morphology import square
|
||||
>>> import skimage.filter.rank as rank
|
||||
>>> ima = 255 * np.array([[0, 0, 0, 0, 0],
|
||||
>>> 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(ima, square(3))
|
||||
>>> rank.pop(img, square(3))
|
||||
array([[4, 6, 6, 6, 4],
|
||||
[6, 9, 9, 9, 6],
|
||||
[6, 9, 9, 9, 6],
|
||||
@@ -527,17 +574,18 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
return _apply(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 sum of pixels inside the neighborhood (truncated to uint8 or uint16).
|
||||
"""Return the local sum of pixels.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : ndarray (uint8, uint16)
|
||||
Image array.
|
||||
selem : ndarray
|
||||
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 : ndarray (same dtype as input)
|
||||
If None, a new array will be allocated.
|
||||
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).
|
||||
@@ -548,19 +596,19 @@ def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray (same dtype as input image)
|
||||
out : 2-D array (same dtype as input image)
|
||||
Output image.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage.morphology import square
|
||||
>>> import skimage.filter.rank as rank
|
||||
>>> ima = np.array([[0, 0, 0, 0, 0],
|
||||
>>> 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(ima, square(3))
|
||||
>>> rank.sum(img, square(3))
|
||||
array([[1, 2, 3, 2, 1],
|
||||
[2, 4, 6, 4, 2],
|
||||
[3, 6, 9, 6, 3],
|
||||
@@ -574,16 +622,19 @@ def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
|
||||
def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
"""Return greyscale local threshold of an image.
|
||||
"""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 : ndarray (uint8, uint16)
|
||||
Image array.
|
||||
selem : ndarray
|
||||
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 : ndarray (same dtype as input)
|
||||
If None, a new array will be allocated.
|
||||
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).
|
||||
@@ -594,20 +645,19 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray (same dtype as input image)
|
||||
out : 2-D array (same dtype as input image)
|
||||
Output image.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> # Local threshold
|
||||
>>> from skimage.morphology import square
|
||||
>>> from skimage.filter.rank import threshold
|
||||
>>> ima = 255 * np.array([[0, 0, 0, 0, 0],
|
||||
>>> 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(ima, square(3))
|
||||
>>> threshold(img, square(3))
|
||||
array([[0, 0, 0, 0, 0],
|
||||
[0, 1, 1, 1, 0],
|
||||
[0, 1, 0, 1, 0],
|
||||
@@ -621,16 +671,19 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
|
||||
def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
"""Return greyscale local tophat of an image.
|
||||
"""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 : ndarray (uint8, uint16)
|
||||
Image array.
|
||||
selem : ndarray
|
||||
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 : ndarray (same dtype as input)
|
||||
If None, a new array will be allocated.
|
||||
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).
|
||||
@@ -641,9 +694,17 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray (same dtype as input image)
|
||||
out : 2-D array (same dtype as input image)
|
||||
Output image.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage import data
|
||||
>>> from skimage.morphology import disk
|
||||
>>> from skimage.filter.rank import tophat
|
||||
>>> img = data.camera()
|
||||
>>> out = tophat(img, disk(5))
|
||||
|
||||
"""
|
||||
|
||||
return _apply(generic_cy._tophat, image, selem,
|
||||
@@ -652,16 +713,16 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
def noise_filter(image, selem, out=None, mask=None, shift_x=False,
|
||||
shift_y=False):
|
||||
"""Returns the noise feature as described in [Hashimoto12]_
|
||||
"""Noise feature as described in [Hashimoto12]_.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : ndarray (uint8, uint16)
|
||||
Image array.
|
||||
selem : ndarray
|
||||
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 : ndarray (same dtype as input)
|
||||
If None, a new array will be allocated.
|
||||
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).
|
||||
@@ -677,9 +738,17 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False,
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray (same dtype as input image)
|
||||
out : 2-D array (same dtype as input image)
|
||||
Output image.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from skimage import data
|
||||
>>> from skimage.morphology import disk
|
||||
>>> from skimage.filter.rank import noise_filter
|
||||
>>> img = data.camera()
|
||||
>>> out = noise_filter(img, disk(5))
|
||||
|
||||
"""
|
||||
|
||||
# ensure that the central pixel in the structuring element is empty
|
||||
@@ -694,18 +763,19 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False,
|
||||
|
||||
|
||||
def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
"""Returns the entropy [1]_ computed locally. Entropy is computed
|
||||
using base 2 logarithm i.e. the filter returns the minimum number of
|
||||
bits needed to encode local greylevel distribution.
|
||||
"""Local entropy [1]_.
|
||||
|
||||
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 : ndarray (uint8, uint16)
|
||||
Image array.
|
||||
selem : ndarray
|
||||
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 : ndarray (same dtype as input)
|
||||
If None, a new array will be allocated.
|
||||
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).
|
||||
@@ -721,16 +791,15 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] http://en.wikipedia.org/wiki/Entropy_(information_theory)>
|
||||
.. [1] http://en.wikipedia.org/wiki/Entropy_(information_theory)
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> # Local entropy
|
||||
>>> from skimage import data
|
||||
>>> from skimage.filter.rank import entropy
|
||||
>>> from skimage.morphology import disk
|
||||
>>> a8 = data.camera()
|
||||
>>> ent8 = entropy(a8, disk(5))
|
||||
>>> img = data.camera()
|
||||
>>> ent = entropy(img, disk(5))
|
||||
|
||||
"""
|
||||
|
||||
@@ -740,13 +809,13 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
|
||||
def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
"""Returns the Otsu's threshold value for each pixel.
|
||||
"""Local Otsu's threshold value for each pixel.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : ndarray
|
||||
Image array (uint8 array).
|
||||
selem : ndarray
|
||||
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.
|
||||
@@ -760,7 +829,7 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
Returns
|
||||
-------
|
||||
out : ndarray (same dtype as input image)
|
||||
out : 2-D array (same dtype as input image)
|
||||
Output image.
|
||||
|
||||
References
|
||||
@@ -769,14 +838,12 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> # Local entropy
|
||||
>>> from skimage import data
|
||||
>>> from skimage.filter.rank import otsu
|
||||
>>> from skimage.morphology import disk
|
||||
>>> # defining a 8-bit test images
|
||||
>>> a8 = data.camera()
|
||||
>>> loc_otsu = otsu(a8, disk(5))
|
||||
>>> thresh_image = a8 >= loc_otsu
|
||||
>>> img = data.camera()
|
||||
>>> local_otsu = otsu(img, disk(5))
|
||||
>>> thresh_image = img >= local_otsu
|
||||
|
||||
"""
|
||||
|
||||
|
||||
@@ -221,6 +221,7 @@ cdef inline double _kernel_pop(Py_ssize_t* histo, double pop, dtype_t g,
|
||||
|
||||
return pop
|
||||
|
||||
|
||||
cdef inline double _kernel_sum(Py_ssize_t* histo, double pop,dtype_t g,
|
||||
Py_ssize_t max_bin, Py_ssize_t mid_bin,
|
||||
double p0, double p1,
|
||||
|
||||
Reference in New Issue
Block a user