mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 02:47:23 +08:00
Johannes Schönberger
138 lines
3.9 KiB
Python
138 lines
3.9 KiB
Python
import numpy as np
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from scipy import ndimage
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def binary_erosion(image, selem, out=None):
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"""Return fast binary morphological erosion of an image.
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This function returns the same result as greyscale erosion but performs
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faster for binary images.
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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
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray
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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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Returns
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-------
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eroded : bool array
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The result of the morphological erosion.
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"""
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conv = ndimage.convolve(image > 0, selem, output=out,
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mode='constant', cval=1)
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if conv is not None:
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out = conv
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return np.equal(out, np.sum(selem), out=out)
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def binary_dilation(image, selem, out=None):
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"""Return fast binary morphological dilation of an image.
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This function returns the same result as greyscale dilation but performs
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faster for binary images.
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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
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray
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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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Returns
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-------
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dilated : bool array
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The result of the morphological dilation.
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"""
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conv = ndimage.convolve(image > 0, selem, output=out,
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mode='constant', cval=0)
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if conv is not None:
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out = conv
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return np.not_equal(out, 0, out=out)
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def binary_opening(image, selem, out=None):
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"""Return fast binary morphological opening of an image.
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This function returns the same result as greyscale opening but performs
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faster for binary images.
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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
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray
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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 : bool array
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The result of the morphological opening.
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"""
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eroded = binary_erosion(image, selem)
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out = binary_dilation(eroded, selem, out=out)
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return out
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def binary_closing(image, selem, out=None):
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"""Return fast binary morphological closing of an image.
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This function returns the same result as greyscale closing but performs
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faster for binary images.
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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
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The neighborhood expressed as a 2-D array of 1's and 0's.
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out : ndarray
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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 : bool array
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The result of the morphological closing.
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"""
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dilated = binary_dilation(image, selem)
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out = binary_erosion(dilated, selem, out=out)
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return out
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