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Improve doc strings of bilateral rank filters
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Johannes Schönberger
@@ -53,21 +53,22 @@ def mean_bilateral(image, selem, out=None, mask=None, shift_x=False,
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pixels based on their spatial closeness and radiometric similarity.
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Spatial closeness is measured by considering only the local pixel
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neighborhood given by a structuring element (selem).
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neighborhood given by a structuring element.
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Radiometric similarity is defined by the greylevel interval [g-s0, g+s1]
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where g is the current pixel greylevel. Only pixels belonging to the
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structuring element AND having a greylevel inside this interval are
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averaged. Return greyscale local bilateral_mean of an image.
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where g is the current pixel greylevel.
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Only pixels belonging to the structuring element and having a greylevel
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inside this interval are averaged.
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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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@@ -81,22 +82,20 @@ def mean_bilateral(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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See also
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--------
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skimage.filter.denoise_bilateral for a gaussian bilateral filter.
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skimage.filter.denoise_bilateral for a Gaussian bilateral filter.
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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 bilateral_mean
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>>> # Load test image / cast to uint16
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>>> ima = data.camera().astype(np.uint16)
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>>> # bilateral filtering of cameraman image using a flat kernel
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>>> bilat_ima = bilateral_mean(ima, disk(20), s0=10,s1=10)
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>>> from skimage.filter.rank import mean_bilateral
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>>> img = data.camera().astype(np.uint16)
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>>> bilat_img = mean_bilateral(img, disk(20), s0=10,s1=10)
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"""
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@@ -106,18 +105,22 @@ def mean_bilateral(image, selem, out=None, mask=None, shift_x=False,
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def pop_bilateral(image, selem, out=None, mask=None, shift_x=False,
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shift_y=False, s0=10, s1=10):
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"""Return the number (population) of pixels actually inside the bilateral
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neighborhood, i.e. being inside the structuring element AND having a gray
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level inside the interval [g-s0, g+s1].
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"""Return the local number (population) of pixels.
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The number of pixels is defined as the number of pixels which are included
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in the structuring element and the mask. Additionally the must have a
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greylevel inside the interval [g-s0, g+s1] where g is the greyvalue of the
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center pixel.
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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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@@ -131,20 +134,19 @@ def pop_bilateral(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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>>> # Local mean
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>>> from skimage.morphology import square
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>>> import skimage.filter.rank as rank
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>>> ima16 = 255 * np.array([[0, 0, 0, 0, 0],
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... [0, 1, 1, 1, 0],
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... [0, 1, 1, 1, 0],
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... [0, 1, 1, 1, 0],
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... [0, 0, 0, 0, 0]], dtype=np.uint16)
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>>> rank.bilateral_pop(ima16, square(3), s0=10,s1=10)
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>>> img = 255 * np.array([[0, 0, 0, 0, 0],
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... [0, 1, 1, 1, 0],
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... [0, 1, 1, 1, 0],
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... [0, 1, 1, 1, 0],
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... [0, 0, 0, 0, 0]], dtype=np.uint16)
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>>> rank.pop_bilateral(imgsquare(3), s0=10, s1=10)
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array([[3, 4, 3, 4, 3],
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[4, 4, 6, 4, 4],
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[3, 6, 9, 6, 3],
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@@ -167,19 +169,19 @@ def sum_bilateral(image, selem, out=None, mask=None, shift_x=False,
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neighborhood given by a structuring element (selem).
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Radiometric similarity is defined by the greylevel interval [g-s0, g+s1]
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where g is the current pixel greylevel. Only pixels belonging to the
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structuring element AND having a greylevel inside this interval are
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summed. Return greyscale local bilateral sum of an image.
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Result is truncated (8bit or 16bit).
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where g is the current pixel greylevel.
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Only pixels belonging to the structuring element AND having a greylevel
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inside this interval are summed.
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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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@@ -193,22 +195,20 @@ def sum_bilateral(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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See also
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--------
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skimage.filter.denoise_bilateral for a gaussian bilateral filter.
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skimage.filter.denoise_bilateral for a Gaussian bilateral filter.
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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 sum_bilateral
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>>> # Load test image / cast to uint16
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>>> ima = data.camera().astype(np.uint16)
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>>> # bilateral filtering of cameraman image using a flat kernel
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>>> bilat_ima = sum_bilateral(ima, disk(20), s0=10,s1=10)
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>>> img = data.camera().astype(np.uint16)
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>>> bilat_img = sum_bilateral(img, disk(10), s0=10, s1=10)
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"""
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