543 lines
20 KiB
Python
543 lines
20 KiB
Python
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"""extrema.py - local minima and maxima
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This module provides functions to find local maxima and minima of an image.
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Here, local maxima (minima) are defined as connected sets of pixels with equal
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gray level which is strictly greater (smaller) than the gray level of all
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pixels in direct neighborhood of the connected set. In addition, the module
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provides the related functions h-maxima and h-minima.
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Soille, P. (2003). Morphological Image Analysis: Principles and Applications
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(2nd ed.), Chapter 6. Springer-Verlag New York, Inc.
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"""
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import numpy as np
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from ..util import dtype_limits, invert, crop
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from .._shared.utils import warn
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from . import greyreconstruct, _util
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from ._extrema_cy import _local_maxima
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def _add_constant_clip(image, const_value):
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"""Add constant to the image while handling overflow issues gracefully.
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"""
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min_dtype, max_dtype = dtype_limits(image, clip_negative=False)
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if const_value > (max_dtype - min_dtype):
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raise ValueError("The added constant is not compatible"
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"with the image data type.")
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result = image + const_value
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result[image > max_dtype-const_value] = max_dtype
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return(result)
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def _subtract_constant_clip(image, const_value):
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"""Subtract constant from image while handling underflow issues.
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"""
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min_dtype, max_dtype = dtype_limits(image, clip_negative=False)
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if const_value > (max_dtype-min_dtype):
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raise ValueError("The subtracted constant is not compatible"
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"with the image data type.")
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result = image - const_value
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result[image < (const_value + min_dtype)] = min_dtype
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return(result)
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def h_maxima(image, h, selem=None):
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"""Determine all maxima of the image with height >= h.
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The local maxima are defined as connected sets of pixels with equal
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grey level strictly greater than the grey level of all pixels in direct
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neighborhood of the set.
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A local maximum M of height h is a local maximum for which
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there is at least one path joining M with an equal or higher local maximum
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on which the minimal value is f(M) - h (i.e. the values along the path
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are not decreasing by more than h with respect to the maximum's value)
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and no path to an equal or higher local maximum for which the minimal
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value is greater.
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The global maxima of the image are also found by this function.
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Parameters
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----------
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image : ndarray
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The input image for which the maxima are to be calculated.
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h : unsigned integer
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The minimal height of all extracted maxima.
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selem : ndarray, optional
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The neighborhood expressed as an n-D array of 1's and 0's.
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Default is the ball of radius 1 according to the maximum norm
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(i.e. a 3x3 square for 2D images, a 3x3x3 cube for 3D images, etc.)
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Returns
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-------
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h_max : ndarray
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The local maxima of height >= h and the global maxima.
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The resulting image is a binary image, where pixels belonging to
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the determined maxima take value 1, the others take value 0.
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See also
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--------
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skimage.morphology.extrema.h_minima
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skimage.morphology.extrema.local_maxima
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skimage.morphology.extrema.local_minima
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References
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----------
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.. [1] Soille, P., "Morphological Image Analysis: Principles and
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Applications" (Chapter 6), 2nd edition (2003), ISBN 3540429883.
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Examples
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--------
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>>> import numpy as np
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>>> from skimage.morphology import extrema
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We create an image (quadratic function with a maximum in the center and
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4 additional constant maxima.
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The heights of the maxima are: 1, 21, 41, 61, 81
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>>> w = 10
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>>> x, y = np.mgrid[0:w,0:w]
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>>> f = 20 - 0.2*((x - w/2)**2 + (y-w/2)**2)
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>>> f[2:4,2:4] = 40; f[2:4,7:9] = 60; f[7:9,2:4] = 80; f[7:9,7:9] = 100
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>>> f = f.astype(int)
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We can calculate all maxima with a height of at least 40:
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>>> maxima = extrema.h_maxima(f, 40)
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The resulting image will contain 3 local maxima.
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"""
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# Check for h value that is larger then range of the image. If this
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# is True then there are no h-maxima in the image.
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if h > np.ptp(image):
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return np.zeros(image.shape, dtype=np.uint8)
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# Check for floating point h value. For this to work properly
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# we need to explicitly convert image to float64.
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#
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# FIXME: This could give incorrect results if image is int64 and
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# has a very high dynamic range. The dtype of image is
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# changed to float64, and different integer values could
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# become the same float due to rounding.
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#
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# >>> ii64 = np.iinfo(np.int64)
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# >>> a = np.array([ii64.max, ii64.max - 2])
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# >>> a[0] == a[1]
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# False
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# >>> b = a.astype(np.float64)
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# >>> b[0] == b[1]
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# True
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#
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if np.issubdtype(type(h), np.floating) and \
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np.issubdtype(image.dtype, np.integer):
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if ((h % 1) != 0):
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warn('possible precision loss converting image to '
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'floating point. To silence this warning, '
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'ensure image and h have same data type.',
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stacklevel=2)
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image = image.astype(float)
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else:
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h = image.dtype.type(h)
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if (h == 0):
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raise ValueError("h = 0 is ambiguous, use local_maxima() "
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"instead?")
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if np.issubdtype(image.dtype, np.floating):
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# The purpose of the resolution variable is to allow for the
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# small rounding errors that inevitably occur when doing
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# floating point arithmetic. We want shifted_img to be
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# guaranteed to be h less than image. If we only subtract h
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# there may be pixels were shifted_img ends up being
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# slightly greater than image - h.
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#
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# The resolution is scaled based on the pixel values in the
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# image because floating point precision is relative. A
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# very large value of 1.0e10 will have a large precision,
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# say +-1.0e4, and a very small value of 1.0e-10 will have
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# a very small precision, say +-1.0e-16.
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#
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resolution = 2 * np.finfo(image.dtype).resolution * np.abs(image)
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shifted_img = image - h - resolution
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else:
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shifted_img = _subtract_constant_clip(image, h)
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rec_img = greyreconstruct.reconstruction(shifted_img, image,
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method='dilation', selem=selem)
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residue_img = image - rec_img
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return (residue_img >= h).astype(np.uint8)
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def h_minima(image, h, selem=None):
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"""Determine all minima of the image with depth >= h.
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The local minima are defined as connected sets of pixels with equal
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grey level strictly smaller than the grey levels of all pixels in direct
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neighborhood of the set.
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A local minimum M of depth h is a local minimum for which
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there is at least one path joining M with an equal or lower local minimum
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on which the maximal value is f(M) + h (i.e. the values along the path
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are not increasing by more than h with respect to the minimum's value)
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and no path to an equal or lower local minimum for which the maximal
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value is smaller.
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The global minima of the image are also found by this function.
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Parameters
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----------
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image : ndarray
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The input image for which the minima are to be calculated.
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h : unsigned integer
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The minimal depth of all extracted minima.
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selem : ndarray, optional
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The neighborhood expressed as an n-D array of 1's and 0's.
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Default is the ball of radius 1 according to the maximum norm
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(i.e. a 3x3 square for 2D images, a 3x3x3 cube for 3D images, etc.)
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Returns
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-------
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h_min : ndarray
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The local minima of depth >= h and the global minima.
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The resulting image is a binary image, where pixels belonging to
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the determined minima take value 1, the others take value 0.
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See also
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--------
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skimage.morphology.extrema.h_maxima
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skimage.morphology.extrema.local_maxima
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skimage.morphology.extrema.local_minima
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References
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----------
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.. [1] Soille, P., "Morphological Image Analysis: Principles and
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Applications" (Chapter 6), 2nd edition (2003), ISBN 3540429883.
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Examples
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--------
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>>> import numpy as np
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>>> from skimage.morphology import extrema
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We create an image (quadratic function with a minimum in the center and
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4 additional constant maxima.
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The depth of the minima are: 1, 21, 41, 61, 81
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>>> w = 10
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>>> x, y = np.mgrid[0:w,0:w]
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>>> f = 180 + 0.2*((x - w/2)**2 + (y-w/2)**2)
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>>> f[2:4,2:4] = 160; f[2:4,7:9] = 140; f[7:9,2:4] = 120; f[7:9,7:9] = 100
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>>> f = f.astype(int)
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We can calculate all minima with a depth of at least 40:
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>>> minima = extrema.h_minima(f, 40)
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The resulting image will contain 3 local minima.
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"""
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if h > np.ptp(image):
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return np.zeros(image.shape, dtype=np.uint8)
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if np.issubdtype(type(h), np.floating) and \
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np.issubdtype(image.dtype, np.integer):
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if ((h % 1) != 0):
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warn('possible precision loss converting image to '
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'floating point. To silence this warning, '
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'ensure image and h have same data type.',
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stacklevel=2)
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image = image.astype(float)
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else:
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h = image.dtype.type(h)
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if (h == 0):
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raise ValueError("h = 0 is ambiguous, use local_minima() "
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"instead?")
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if np.issubdtype(image.dtype, np.floating):
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resolution = 2 * np.finfo(image.dtype).resolution * np.abs(image)
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shifted_img = image + h + resolution
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else:
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shifted_img = _add_constant_clip(image, h)
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rec_img = greyreconstruct.reconstruction(shifted_img, image,
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method='erosion', selem=selem)
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residue_img = rec_img - image
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return (residue_img >= h).astype(np.uint8)
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def local_maxima(image, selem=None, connectivity=None, indices=False,
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allow_borders=True):
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"""Find local maxima of n-dimensional array.
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The local maxima are defined as connected sets of pixels with equal gray
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level (plateaus) strictly greater than the gray levels of all pixels in the
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neighborhood.
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Parameters
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----------
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image : ndarray
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An n-dimensional array.
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selem : ndarray, optional
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A structuring element used to determine the neighborhood of each
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evaluated pixel (``True`` denotes a connected pixel). It must be a
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boolean array and have the same number of dimensions as `image`. If
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neither `selem` nor `connectivity` are given, all adjacent pixels are
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considered as part of the neighborhood.
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connectivity : int, optional
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A number used to determine the neighborhood of each evaluated pixel.
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Adjacent pixels whose squared distance from the center is less than or
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equal to `connectivity` are considered neighbors. Ignored if
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`selem` is not None.
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indices : bool, optional
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If True, the output will be a tuple of one-dimensional arrays
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representing the indices of local maxima in each dimension. If False,
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the output will be a boolean array with the same shape as `image`.
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allow_borders : bool, optional
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If true, plateaus that touch the image border are valid maxima.
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Returns
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-------
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maxima : ndarray or tuple[ndarray]
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If `indices` is false, a boolean array with the same shape as `image`
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is returned with ``True`` indicating the position of local maxima
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(``False`` otherwise). If `indices` is true, a tuple of one-dimensional
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arrays containing the coordinates (indices) of all found maxima.
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Warns
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-----
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UserWarning
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If `allow_borders` is false and any dimension of the given `image` is
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shorter than 3 samples, maxima can't exist and a warning is shown.
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See Also
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--------
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skimage.morphology.local_minima
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skimage.morphology.h_maxima
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skimage.morphology.h_minima
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Notes
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-----
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This function operates on the following ideas:
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1. Make a first pass over the image's last dimension and flag candidates
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for local maxima by comparing pixels in only one direction.
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If the pixels aren't connected in the last dimension all pixels are
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flagged as candidates instead.
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For each candidate:
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2. Perform a flood-fill to find all connected pixels that have the same
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gray value and are part of the plateau.
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3. Consider the connected neighborhood of a plateau: if no bordering sample
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has a higher gray level, mark the plateau as a definite local maximum.
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Examples
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--------
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>>> from skimage.morphology import local_maxima
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>>> image = np.zeros((4, 7), dtype=int)
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>>> image[1:3, 1:3] = 1
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>>> image[3, 0] = 1
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>>> image[1:3, 4:6] = 2
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>>> image[3, 6] = 3
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>>> image
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array([[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 0, 2, 2, 0],
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[0, 1, 1, 0, 2, 2, 0],
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[1, 0, 0, 0, 0, 0, 3]])
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Find local maxima by comparing to all neighboring pixels (maximal
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connectivity):
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>>> local_maxima(image)
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array([[False, False, False, False, False, False, False],
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[False, True, True, False, False, False, False],
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[False, True, True, False, False, False, False],
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[ True, False, False, False, False, False, True]])
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>>> local_maxima(image, indices=True)
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(array([1, 1, 2, 2, 3, 3]), array([1, 2, 1, 2, 0, 6]))
|
||
|
|
|
||
|
|
Find local maxima without comparing to diagonal pixels (connectivity 1):
|
||
|
|
|
||
|
|
>>> local_maxima(image, connectivity=1)
|
||
|
|
array([[False, False, False, False, False, False, False],
|
||
|
|
[False, True, True, False, True, True, False],
|
||
|
|
[False, True, True, False, True, True, False],
|
||
|
|
[ True, False, False, False, False, False, True]])
|
||
|
|
|
||
|
|
and exclude maxima that border the image edge:
|
||
|
|
|
||
|
|
>>> local_maxima(image, connectivity=1, allow_borders=False)
|
||
|
|
array([[False, False, False, False, False, False, False],
|
||
|
|
[False, True, True, False, True, True, False],
|
||
|
|
[False, True, True, False, True, True, False],
|
||
|
|
[False, False, False, False, False, False, False]])
|
||
|
|
"""
|
||
|
|
image = np.asarray(image, order="C")
|
||
|
|
if image.size == 0:
|
||
|
|
# Return early for empty input
|
||
|
|
if indices:
|
||
|
|
# Make sure that output is a tuple of 1 empty array per dimension
|
||
|
|
return np.nonzero(image)
|
||
|
|
else:
|
||
|
|
return np.zeros(image.shape, dtype=bool)
|
||
|
|
|
||
|
|
if allow_borders:
|
||
|
|
# Ensure that local maxima are always at least one smaller sample away
|
||
|
|
# from the image border
|
||
|
|
image = _util._fast_pad(image, image.min())
|
||
|
|
|
||
|
|
# Array of flags used to store the state of each pixel during evaluation.
|
||
|
|
# See _extrema_cy.pyx for their meaning
|
||
|
|
flags = np.zeros(image.shape, dtype=np.uint8)
|
||
|
|
_util._set_border_values(flags, value=3)
|
||
|
|
|
||
|
|
if any(s < 3 for s in image.shape):
|
||
|
|
# Warn and skip if any dimension is smaller than 3
|
||
|
|
# -> no maxima can exist & structuring element can't be applied
|
||
|
|
warn(
|
||
|
|
"maxima can't exist for an image with any dimension smaller 3 "
|
||
|
|
"if borders aren't allowed",
|
||
|
|
stacklevel=3
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
selem = _util._resolve_neighborhood(selem, connectivity, image.ndim)
|
||
|
|
neighbor_offsets = _util._offsets_to_raveled_neighbors(
|
||
|
|
image.shape, selem, center=((1,) * image.ndim)
|
||
|
|
)
|
||
|
|
|
||
|
|
try:
|
||
|
|
_local_maxima(image.ravel(), flags.ravel(), neighbor_offsets)
|
||
|
|
except TypeError:
|
||
|
|
if image.dtype == np.float16:
|
||
|
|
# Provide the user with clearer error message
|
||
|
|
raise TypeError("dtype of `image` is float16 which is not "
|
||
|
|
"supported, try upcasting to float32")
|
||
|
|
else:
|
||
|
|
raise # Otherwise raise original message
|
||
|
|
|
||
|
|
if allow_borders:
|
||
|
|
# Revert padding performed at the beginning of the function
|
||
|
|
flags = crop(flags, 1)
|
||
|
|
else:
|
||
|
|
# No padding was performed but set edge values back to 0
|
||
|
|
_util._set_border_values(flags, value=0)
|
||
|
|
|
||
|
|
if indices:
|
||
|
|
return np.nonzero(flags)
|
||
|
|
else:
|
||
|
|
return flags.view(bool)
|
||
|
|
|
||
|
|
|
||
|
|
def local_minima(image, selem=None, connectivity=None, indices=False,
|
||
|
|
allow_borders=True):
|
||
|
|
"""Find local minima of n-dimensional array.
|
||
|
|
|
||
|
|
The local minima are defined as connected sets of pixels with equal gray
|
||
|
|
level (plateaus) strictly smaller than the gray levels of all pixels in the
|
||
|
|
neighborhood.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
image : ndarray
|
||
|
|
An n-dimensional array.
|
||
|
|
selem : ndarray, optional
|
||
|
|
A structuring element used to determine the neighborhood of each
|
||
|
|
evaluated pixel (``True`` denotes a connected pixel). It must be a
|
||
|
|
boolean array and have the same number of dimensions as `image`. If
|
||
|
|
neither `selem` nor `connectivity` are given, all adjacent pixels are
|
||
|
|
considered as part of the neighborhood.
|
||
|
|
connectivity : int, optional
|
||
|
|
A number used to determine the neighborhood of each evaluated pixel.
|
||
|
|
Adjacent pixels whose squared distance from the center is less than or
|
||
|
|
equal to `connectivity` are considered neighbors. Ignored if
|
||
|
|
`selem` is not None.
|
||
|
|
indices : bool, optional
|
||
|
|
If True, the output will be a tuple of one-dimensional arrays
|
||
|
|
representing the indices of local minima in each dimension. If False,
|
||
|
|
the output will be a boolean array with the same shape as `image`.
|
||
|
|
allow_borders : bool, optional
|
||
|
|
If true, plateaus that touch the image border are valid minima.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
minima : ndarray or tuple[ndarray]
|
||
|
|
If `indices` is false, a boolean array with the same shape as `image`
|
||
|
|
is returned with ``True`` indicating the position of local minima
|
||
|
|
(``False`` otherwise). If `indices` is true, a tuple of one-dimensional
|
||
|
|
arrays containing the coordinates (indices) of all found minima.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
skimage.morphology.local_maxima
|
||
|
|
skimage.morphology.h_maxima
|
||
|
|
skimage.morphology.h_minima
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
This function operates on the following ideas:
|
||
|
|
|
||
|
|
1. Make a first pass over the image's last dimension and flag candidates
|
||
|
|
for local minima by comparing pixels in only one direction.
|
||
|
|
If the pixels aren't connected in the last dimension all pixels are
|
||
|
|
flagged as candidates instead.
|
||
|
|
|
||
|
|
For each candidate:
|
||
|
|
|
||
|
|
2. Perform a flood-fill to find all connected pixels that have the same
|
||
|
|
gray value and are part of the plateau.
|
||
|
|
3. Consider the connected neighborhood of a plateau: if no bordering sample
|
||
|
|
has a smaller gray level, mark the plateau as a definite local minimum.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from skimage.morphology import local_minima
|
||
|
|
>>> image = np.zeros((4, 7), dtype=int)
|
||
|
|
>>> image[1:3, 1:3] = -1
|
||
|
|
>>> image[3, 0] = -1
|
||
|
|
>>> image[1:3, 4:6] = -2
|
||
|
|
>>> image[3, 6] = -3
|
||
|
|
>>> image
|
||
|
|
array([[ 0, 0, 0, 0, 0, 0, 0],
|
||
|
|
[ 0, -1, -1, 0, -2, -2, 0],
|
||
|
|
[ 0, -1, -1, 0, -2, -2, 0],
|
||
|
|
[-1, 0, 0, 0, 0, 0, -3]])
|
||
|
|
|
||
|
|
Find local minima by comparing to all neighboring pixels (maximal
|
||
|
|
connectivity):
|
||
|
|
|
||
|
|
>>> local_minima(image)
|
||
|
|
array([[False, False, False, False, False, False, False],
|
||
|
|
[False, True, True, False, False, False, False],
|
||
|
|
[False, True, True, False, False, False, False],
|
||
|
|
[ True, False, False, False, False, False, True]])
|
||
|
|
>>> local_minima(image, indices=True)
|
||
|
|
(array([1, 1, 2, 2, 3, 3]), array([1, 2, 1, 2, 0, 6]))
|
||
|
|
|
||
|
|
Find local minima without comparing to diagonal pixels (connectivity 1):
|
||
|
|
|
||
|
|
>>> local_minima(image, connectivity=1)
|
||
|
|
array([[False, False, False, False, False, False, False],
|
||
|
|
[False, True, True, False, True, True, False],
|
||
|
|
[False, True, True, False, True, True, False],
|
||
|
|
[ True, False, False, False, False, False, True]])
|
||
|
|
|
||
|
|
and exclude minima that border the image edge:
|
||
|
|
|
||
|
|
>>> local_minima(image, connectivity=1, allow_borders=False)
|
||
|
|
array([[False, False, False, False, False, False, False],
|
||
|
|
[False, True, True, False, True, True, False],
|
||
|
|
[False, True, True, False, True, True, False],
|
||
|
|
[False, False, False, False, False, False, False]])
|
||
|
|
"""
|
||
|
|
return local_maxima(
|
||
|
|
image=invert(image),
|
||
|
|
selem=selem,
|
||
|
|
connectivity=connectivity,
|
||
|
|
indices=indices,
|
||
|
|
allow_borders=allow_borders
|
||
|
|
)
|