mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 01:11:56 +08:00
Nelson Brown
328 lines
10 KiB
Python
328 lines
10 KiB
Python
import warnings
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from skimage import img_as_ubyte
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from .misc import default_fallback
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from . import cmorph
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__all__ = ['erosion', 'dilation', 'opening', 'closing', 'white_tophat',
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'black_tophat']
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@default_fallback
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def erosion(image, selem=None, out=None, shift_x=False, shift_y=False):
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"""Return greyscale morphological erosion of an image.
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Morphological erosion sets a pixel at (i,j) to the minimum over all pixels
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in the neighborhood centered at (i,j). Erosion shrinks bright regions and
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enlarges dark regions.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray, optional
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The neighborhood expressed as a 2-D array of 1's and 0's.
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If None, use cross-shaped structuring element (connectivity=1).
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out : ndarrays, optional
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The array to store the result of the morphology. If None is
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passed, a new array will be allocated.
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shift_x, shift_y : bool, optional
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shift structuring element about center point. This only affects
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eccentric structuring elements (i.e. selem with even numbered sides).
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Returns
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-------
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eroded : uint8 array
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The result of the morphological erosion.
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Examples
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--------
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>>> # Erosion shrinks bright regions
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>>> import numpy as np
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>>> from skimage.morphology import square
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>>> bright_square = 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.uint8)
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>>> erosion(bright_square, square(3))
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array([[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]], dtype=uint8)
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"""
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if image is out:
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raise NotImplementedError("In-place erosion not supported!")
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image = img_as_ubyte(image)
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selem = img_as_ubyte(selem)
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return cmorph._erode(image, selem, out=out,
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shift_x=shift_x, shift_y=shift_y)
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@default_fallback
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def dilation(image, selem=None, out=None, shift_x=False, shift_y=False):
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"""Return greyscale morphological dilation of an image.
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Morphological dilation sets a pixel at (i,j) to the maximum over all pixels
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in the neighborhood centered at (i,j). Dilation enlarges bright regions
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and shrinks dark regions.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray, optional
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The neighborhood expressed as a 2-D array of 1's and 0's.
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If None, use cross-shaped structuring element (connectivity=1).
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out : ndarray, optional
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The array to store the result of the morphology. If None, is
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passed, a new array will be allocated.
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shift_x, shift_y : bool, optional
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shift structuring element about center point. This only affects
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eccentric structuring elements (i.e. selem with even numbered sides).
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Returns
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-------
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dilated : uint8 array
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The result of the morphological dilation.
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Examples
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--------
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>>> # Dilation enlarges bright regions
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>>> import numpy as np
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>>> from skimage.morphology import square
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>>> bright_pixel = np.array([[0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 0],
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... [0, 0, 1, 0, 0],
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... [0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 0]], dtype=np.uint8)
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>>> dilation(bright_pixel, square(3))
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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=uint8)
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"""
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if image is out:
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raise NotImplementedError("In-place dilation not supported!")
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image = img_as_ubyte(image)
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selem = img_as_ubyte(selem)
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return cmorph._dilate(image, selem, out=out,
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shift_x=shift_x, shift_y=shift_y)
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@default_fallback
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def opening(image, selem=None, out=None):
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"""Return greyscale morphological opening of an image.
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The morphological opening on an image is defined as an erosion followed by
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a dilation. Opening can remove small bright spots (i.e. "salt") and connect
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small dark cracks. This tends to "open" up (dark) gaps between (bright)
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features.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray, optional
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The neighborhood expressed as a 2-D array of 1's and 0's.
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If None, use cross-shaped structuring element (connectivity=1).
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out : ndarray, optional
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The array to store the result of the morphology. If None
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is passed, a new array will be allocated.
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Returns
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-------
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opening : uint8 array
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The result of the morphological opening.
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Examples
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--------
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>>> # Open up gap between two bright regions (but also shrink regions)
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>>> import numpy as np
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>>> from skimage.morphology import square
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>>> bad_connection = np.array([[1, 0, 0, 0, 1],
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... [1, 1, 0, 1, 1],
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... [1, 1, 1, 1, 1],
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... [1, 1, 0, 1, 1],
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... [1, 0, 0, 0, 1]], dtype=np.uint8)
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>>> opening(bad_connection, square(3))
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array([[0, 0, 0, 0, 0],
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[1, 1, 0, 1, 1],
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[1, 1, 0, 1, 1],
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[1, 1, 0, 1, 1],
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[0, 0, 0, 0, 0]], dtype=uint8)
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"""
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h, w = selem.shape
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shift_x = True if (w % 2) == 0 else False
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shift_y = True if (h % 2) == 0 else False
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eroded = erosion(image, selem)
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out = dilation(eroded, selem, out=out, shift_x=shift_x, shift_y=shift_y)
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return out
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@default_fallback
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def closing(image, selem=None, out=None):
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"""Return greyscale morphological closing of an image.
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The morphological closing on an image is defined as a dilation followed by
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an erosion. Closing can remove small dark spots (i.e. "pepper") and connect
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small bright cracks. This tends to "close" up (dark) gaps between (bright)
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features.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray, optional
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The neighborhood expressed as a 2-D array of 1's and 0's.
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If None, use cross-shaped structuring element (connectivity=1).
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out : ndarray, optional
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The array to store the result of the morphology. If None,
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is passed, a new array will be allocated.
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Returns
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-------
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closing : uint8 array
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The result of the morphological closing.
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Examples
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--------
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>>> # Close a gap between two bright lines
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>>> import numpy as np
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>>> from skimage.morphology import square
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>>> broken_line = np.array([[0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 0],
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... [1, 1, 0, 1, 1],
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... [0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 0]], dtype=np.uint8)
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>>> closing(broken_line, square(3))
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array([[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[1, 1, 1, 1, 1],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]], dtype=uint8)
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"""
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h, w = selem.shape
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shift_x = True if (w % 2) == 0 else False
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shift_y = True if (h % 2) == 0 else False
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dilated = dilation(image, selem)
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out = erosion(dilated, selem, out=out, shift_x=shift_x, shift_y=shift_y)
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return out
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@default_fallback
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def white_tophat(image, selem=None, out=None):
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"""Return white top hat of an image.
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The white top hat of an image is defined as the image minus its
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morphological opening. This operation returns the bright spots of the image
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that are smaller than the structuring element.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray, optional
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The neighborhood expressed as a 2-D array of 1's and 0's.
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If None, use cross-shaped structuring element (connectivity=1).
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out : ndarray, optional
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The array to store the result of the morphology. If None
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is passed, a new array will be allocated.
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Returns
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-------
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opening : uint8 array
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The result of the morphological white top hat.
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Examples
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--------
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>>> # Subtract grey background from bright peak
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>>> import numpy as np
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>>> from skimage.morphology import square
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>>> bright_on_grey = np.array([[2, 3, 3, 3, 2],
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... [3, 4, 5, 4, 3],
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... [3, 5, 9, 5, 3],
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... [3, 4, 5, 4, 3],
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... [2, 3, 3, 3, 2]], dtype=np.uint8)
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>>> white_tophat(bright_on_grey, square(3))
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array([[0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0],
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[0, 1, 5, 1, 0],
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[0, 0, 1, 0, 0],
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[0, 0, 0, 0, 0]], dtype=uint8)
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"""
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if image is out:
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raise NotImplementedError("Cannot perform white top hat in place.")
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out = opening(image, selem, out=out)
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out = image - out
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return out
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@default_fallback
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def black_tophat(image, selem=None, out=None):
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"""Return black top hat of an image.
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The black top hat of an image is defined as its morphological closing minus
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the original image. This operation returns the dark spots of the image that
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are smaller than the structuring element. Note that dark spots in the
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original image are bright spots after the black top hat.
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Parameters
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----------
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image : ndarray
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Image array.
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selem : ndarray, optional
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The neighborhood expressed as a 2-D array of 1's and 0's.
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If None, use cross-shaped structuring element (connectivity=1).
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out : ndarray, optional
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The array to store the result of the morphology. If None
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is passed, a new array will be allocated.
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Returns
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-------
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opening : uint8 array
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The result of the black top filter.
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Examples
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--------
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>>> # Change dark peak to bright peak and subtract background
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>>> import numpy as np
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>>> from skimage.morphology import square
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>>> dark_on_grey = np.array([[7, 6, 6, 6, 7],
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... [6, 5, 4, 5, 6],
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... [6, 4, 0, 4, 6],
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... [6, 5, 4, 5, 6],
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... [7, 6, 6, 6, 7]], dtype=np.uint8)
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>>> black_tophat(dark_on_grey, square(3))
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array([[0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0],
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[0, 1, 5, 1, 0],
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[0, 0, 1, 0, 0],
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[0, 0, 0, 0, 0]], dtype=uint8)
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"""
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if image is out:
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raise NotImplementedError("Cannot perform white top hat in place.")
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out = closing(image, selem, out=out)
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out = out - image
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return out
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