483 lines
16 KiB
Python
483 lines
16 KiB
Python
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import warnings
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from itertools import cycle
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import numpy as np
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from scipy import ndimage as ndi
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from .._shared.utils import check_nD
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__all__ = ['morphological_chan_vese',
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'morphological_geodesic_active_contour',
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'inverse_gaussian_gradient',
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'circle_level_set',
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'disk_level_set',
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'checkerboard_level_set'
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]
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class _fcycle(object):
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def __init__(self, iterable):
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"""Call functions from the iterable each time it is called."""
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self.funcs = cycle(iterable)
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def __call__(self, *args, **kwargs):
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f = next(self.funcs)
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return f(*args, **kwargs)
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# SI and IS operators for 2D and 3D.
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_P2 = [np.eye(3),
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np.array([[0, 1, 0]] * 3),
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np.flipud(np.eye(3)),
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np.rot90([[0, 1, 0]] * 3)]
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_P3 = [np.zeros((3, 3, 3)) for i in range(9)]
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_P3[0][:, :, 1] = 1
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_P3[1][:, 1, :] = 1
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_P3[2][1, :, :] = 1
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_P3[3][:, [0, 1, 2], [0, 1, 2]] = 1
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_P3[4][:, [0, 1, 2], [2, 1, 0]] = 1
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_P3[5][[0, 1, 2], :, [0, 1, 2]] = 1
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_P3[6][[0, 1, 2], :, [2, 1, 0]] = 1
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_P3[7][[0, 1, 2], [0, 1, 2], :] = 1
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_P3[8][[0, 1, 2], [2, 1, 0], :] = 1
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def sup_inf(u):
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"""SI operator."""
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if np.ndim(u) == 2:
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P = _P2
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elif np.ndim(u) == 3:
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P = _P3
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else:
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raise ValueError("u has an invalid number of dimensions "
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"(should be 2 or 3)")
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erosions = []
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for P_i in P:
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erosions.append(ndi.binary_erosion(u, P_i).astype(np.int8))
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return np.stack(erosions, axis=0).max(0)
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def inf_sup(u):
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"""IS operator."""
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if np.ndim(u) == 2:
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P = _P2
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elif np.ndim(u) == 3:
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P = _P3
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else:
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raise ValueError("u has an invalid number of dimensions "
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"(should be 2 or 3)")
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dilations = []
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for P_i in P:
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dilations.append(ndi.binary_dilation(u, P_i).astype(np.int8))
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return np.stack(dilations, axis=0).min(0)
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_curvop = _fcycle([lambda u: sup_inf(inf_sup(u)), # SIoIS
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lambda u: inf_sup(sup_inf(u))]) # ISoSI
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def _check_input(image, init_level_set):
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"""Check that shapes of `image` and `init_level_set` match."""
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check_nD(image, [2, 3])
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if len(image.shape) != len(init_level_set.shape):
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raise ValueError("The dimensions of the initial level set do not "
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"match the dimensions of the image.")
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def _init_level_set(init_level_set, image_shape):
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"""Auxiliary function for initializing level sets with a string.
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If `init_level_set` is not a string, it is returned as is.
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"""
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if isinstance(init_level_set, str):
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if init_level_set == 'checkerboard':
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res = checkerboard_level_set(image_shape)
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# TODO: remove me in 0.19.0
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elif init_level_set == 'circle':
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res = circle_level_set(image_shape)
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elif init_level_set == 'disk':
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res = disk_level_set(image_shape)
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else:
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raise ValueError("`init_level_set` not in "
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"['checkerboard', 'circle', 'disk']")
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else:
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res = init_level_set
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return res
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def circle_level_set(image_shape, center=None, radius=None):
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"""Create a circle level set with binary values.
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Parameters
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----------
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image_shape : tuple of positive integers
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Shape of the image
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center : tuple of positive integers, optional
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Coordinates of the center of the circle given in (row, column). If not
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given, it defaults to the center of the image.
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radius : float, optional
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Radius of the circle. If not given, it is set to the 75% of the
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smallest image dimension.
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Returns
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-------
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out : array with shape `image_shape`
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Binary level set of the circle with the given `radius` and `center`.
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Warns
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-----
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Deprecated:
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.. versionadded:: 0.17
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This function is deprecated and will be removed in scikit-image 0.19.
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Please use the function named ``disk_level_set`` instead.
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See Also
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--------
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checkerboard_level_set
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"""
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warnings.warn("circle_level_set is deprecated in favor of "
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"disk_level_set."
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"circle_level_set will be removed in version 0.19",
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FutureWarning, stacklevel=2)
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return disk_level_set(image_shape, center=center, radius=radius)
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def disk_level_set(image_shape, *, center=None, radius=None):
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"""Create a disk level set with binary values.
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Parameters
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----------
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image_shape : tuple of positive integers
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Shape of the image
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center : tuple of positive integers, optional
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Coordinates of the center of the disk given in (row, column). If not
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given, it defaults to the center of the image.
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radius : float, optional
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Radius of the disk. If not given, it is set to the 75% of the
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smallest image dimension.
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Returns
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-------
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out : array with shape `image_shape`
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Binary level set of the disk with the given `radius` and `center`.
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See Also
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--------
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checkerboard_level_set
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"""
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if center is None:
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center = tuple(i // 2 for i in image_shape)
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if radius is None:
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radius = min(image_shape) * 3.0 / 8.0
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grid = np.mgrid[[slice(i) for i in image_shape]]
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grid = (grid.T - center).T
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phi = radius - np.sqrt(np.sum((grid)**2, 0))
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res = np.int8(phi > 0)
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return res
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def checkerboard_level_set(image_shape, square_size=5):
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"""Create a checkerboard level set with binary values.
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Parameters
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----------
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image_shape : tuple of positive integers
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Shape of the image.
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square_size : int, optional
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Size of the squares of the checkerboard. It defaults to 5.
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Returns
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-------
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out : array with shape `image_shape`
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Binary level set of the checkerboard.
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See Also
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--------
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circle_level_set
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"""
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grid = np.mgrid[[slice(i) for i in image_shape]]
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grid = (grid // square_size)
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# Alternate 0/1 for even/odd numbers.
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grid = grid & 1
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checkerboard = np.bitwise_xor.reduce(grid, axis=0)
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res = np.int8(checkerboard)
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return res
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def inverse_gaussian_gradient(image, alpha=100.0, sigma=5.0):
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"""Inverse of gradient magnitude.
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Compute the magnitude of the gradients in the image and then inverts the
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result in the range [0, 1]. Flat areas are assigned values close to 1,
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while areas close to borders are assigned values close to 0.
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This function or a similar one defined by the user should be applied over
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the image as a preprocessing step before calling
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`morphological_geodesic_active_contour`.
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Parameters
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----------
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image : (M, N) or (L, M, N) array
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Grayscale image or volume.
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alpha : float, optional
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Controls the steepness of the inversion. A larger value will make the
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transition between the flat areas and border areas steeper in the
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resulting array.
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sigma : float, optional
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Standard deviation of the Gaussian filter applied over the image.
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Returns
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-------
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gimage : (M, N) or (L, M, N) array
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Preprocessed image (or volume) suitable for
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`morphological_geodesic_active_contour`.
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"""
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gradnorm = ndi.gaussian_gradient_magnitude(image, sigma, mode='nearest')
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return 1.0 / np.sqrt(1.0 + alpha * gradnorm)
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def morphological_chan_vese(image, iterations, init_level_set='checkerboard',
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smoothing=1, lambda1=1, lambda2=1,
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iter_callback=lambda x: None):
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"""Morphological Active Contours without Edges (MorphACWE)
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Active contours without edges implemented with morphological operators. It
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can be used to segment objects in images and volumes without well defined
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borders. It is required that the inside of the object looks different on
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average than the outside (i.e., the inner area of the object should be
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darker or lighter than the outer area on average).
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Parameters
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----------
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image : (M, N) or (L, M, N) array
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Grayscale image or volume to be segmented.
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iterations : uint
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Number of iterations to run
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init_level_set : str, (M, N) array, or (L, M, N) array
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Initial level set. If an array is given, it will be binarized and used
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as the initial level set. If a string is given, it defines the method
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to generate a reasonable initial level set with the shape of the
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`image`. Accepted values are 'checkerboard' and 'circle'. See the
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documentation of `checkerboard_level_set` and `circle_level_set`
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respectively for details about how these level sets are created.
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smoothing : uint, optional
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Number of times the smoothing operator is applied per iteration.
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Reasonable values are around 1-4. Larger values lead to smoother
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segmentations.
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lambda1 : float, optional
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Weight parameter for the outer region. If `lambda1` is larger than
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`lambda2`, the outer region will contain a larger range of values than
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the inner region.
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lambda2 : float, optional
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Weight parameter for the inner region. If `lambda2` is larger than
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`lambda1`, the inner region will contain a larger range of values than
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the outer region.
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iter_callback : function, optional
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If given, this function is called once per iteration with the current
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level set as the only argument. This is useful for debugging or for
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plotting intermediate results during the evolution.
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Returns
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-------
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out : (M, N) or (L, M, N) array
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Final segmentation (i.e., the final level set)
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See Also
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--------
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circle_level_set, checkerboard_level_set
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Notes
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-----
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This is a version of the Chan-Vese algorithm that uses morphological
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operators instead of solving a partial differential equation (PDE) for the
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evolution of the contour. The set of morphological operators used in this
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algorithm are proved to be infinitesimally equivalent to the Chan-Vese PDE
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(see [1]_). However, morphological operators are do not suffer from the
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numerical stability issues typically found in PDEs (it is not necessary to
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find the right time step for the evolution), and are computationally
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faster.
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The algorithm and its theoretical derivation are described in [1]_.
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References
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----------
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.. [1] A Morphological Approach to Curvature-based Evolution of Curves and
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Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE
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Transactions on Pattern Analysis and Machine Intelligence (PAMI),
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2014, :DOI:`10.1109/TPAMI.2013.106`
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"""
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init_level_set = _init_level_set(init_level_set, image.shape)
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_check_input(image, init_level_set)
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u = np.int8(init_level_set > 0)
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iter_callback(u)
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for _ in range(iterations):
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# inside = u > 0
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# outside = u <= 0
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c0 = (image * (1 - u)).sum() / float((1 - u).sum() + 1e-8)
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c1 = (image * u).sum() / float(u.sum() + 1e-8)
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# Image attachment
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du = np.gradient(u)
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abs_du = np.abs(du).sum(0)
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aux = abs_du * (lambda1 * (image - c1)**2 - lambda2 * (image - c0)**2)
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u[aux < 0] = 1
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u[aux > 0] = 0
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# Smoothing
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for _ in range(smoothing):
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u = _curvop(u)
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iter_callback(u)
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return u
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def morphological_geodesic_active_contour(gimage, iterations,
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init_level_set='circle', smoothing=1,
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threshold='auto', balloon=0,
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iter_callback=lambda x: None):
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"""Morphological Geodesic Active Contours (MorphGAC).
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Geodesic active contours implemented with morphological operators. It can
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be used to segment objects with visible but noisy, cluttered, broken
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borders.
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Parameters
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----------
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gimage : (M, N) or (L, M, N) array
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Preprocessed image or volume to be segmented. This is very rarely the
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original image. Instead, this is usually a preprocessed version of the
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original image that enhances and highlights the borders (or other
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structures) of the object to segment.
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`morphological_geodesic_active_contour` will try to stop the contour
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evolution in areas where `gimage` is small. See
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`morphsnakes.inverse_gaussian_gradient` as an example function to
|
||
|
|
perform this preprocessing. Note that the quality of
|
||
|
|
`morphological_geodesic_active_contour` might greatly depend on this
|
||
|
|
preprocessing.
|
||
|
|
iterations : uint
|
||
|
|
Number of iterations to run.
|
||
|
|
init_level_set : str, (M, N) array, or (L, M, N) array
|
||
|
|
Initial level set. If an array is given, it will be binarized and used
|
||
|
|
as the initial level set. If a string is given, it defines the method
|
||
|
|
to generate a reasonable initial level set with the shape of the
|
||
|
|
`image`. Accepted values are 'checkerboard' and 'circle'. See the
|
||
|
|
documentation of `checkerboard_level_set` and `circle_level_set`
|
||
|
|
respectively for details about how these level sets are created.
|
||
|
|
smoothing : uint, optional
|
||
|
|
Number of times the smoothing operator is applied per iteration.
|
||
|
|
Reasonable values are around 1-4. Larger values lead to smoother
|
||
|
|
segmentations.
|
||
|
|
threshold : float, optional
|
||
|
|
Areas of the image with a value smaller than this threshold will be
|
||
|
|
considered borders. The evolution of the contour will stop in this
|
||
|
|
areas.
|
||
|
|
balloon : float, optional
|
||
|
|
Balloon force to guide the contour in non-informative areas of the
|
||
|
|
image, i.e., areas where the gradient of the image is too small to push
|
||
|
|
the contour towards a border. A negative value will shrink the contour,
|
||
|
|
while a positive value will expand the contour in these areas. Setting
|
||
|
|
this to zero will disable the balloon force.
|
||
|
|
iter_callback : function, optional
|
||
|
|
If given, this function is called once per iteration with the current
|
||
|
|
level set as the only argument. This is useful for debugging or for
|
||
|
|
plotting intermediate results during the evolution.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
out : (M, N) or (L, M, N) array
|
||
|
|
Final segmentation (i.e., the final level set)
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
inverse_gaussian_gradient, circle_level_set, checkerboard_level_set
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
This is a version of the Geodesic Active Contours (GAC) algorithm that uses
|
||
|
|
morphological operators instead of solving partial differential equations
|
||
|
|
(PDEs) for the evolution of the contour. The set of morphological operators
|
||
|
|
used in this algorithm are proved to be infinitesimally equivalent to the
|
||
|
|
GAC PDEs (see [1]_). However, morphological operators are do not suffer
|
||
|
|
from the numerical stability issues typically found in PDEs (e.g., it is
|
||
|
|
not necessary to find the right time step for the evolution), and are
|
||
|
|
computationally faster.
|
||
|
|
|
||
|
|
The algorithm and its theoretical derivation are described in [1]_.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
.. [1] A Morphological Approach to Curvature-based Evolution of Curves and
|
||
|
|
Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE
|
||
|
|
Transactions on Pattern Analysis and Machine Intelligence (PAMI),
|
||
|
|
2014, :DOI:`10.1109/TPAMI.2013.106`
|
||
|
|
"""
|
||
|
|
|
||
|
|
image = gimage
|
||
|
|
init_level_set = _init_level_set(init_level_set, image.shape)
|
||
|
|
|
||
|
|
_check_input(image, init_level_set)
|
||
|
|
|
||
|
|
if threshold == 'auto':
|
||
|
|
threshold = np.percentile(image, 40)
|
||
|
|
|
||
|
|
structure = np.ones((3,) * len(image.shape), dtype=np.int8)
|
||
|
|
dimage = np.gradient(image)
|
||
|
|
# threshold_mask = image > threshold
|
||
|
|
if balloon != 0:
|
||
|
|
threshold_mask_balloon = image > threshold / np.abs(balloon)
|
||
|
|
|
||
|
|
u = np.int8(init_level_set > 0)
|
||
|
|
|
||
|
|
iter_callback(u)
|
||
|
|
|
||
|
|
for _ in range(iterations):
|
||
|
|
|
||
|
|
# Balloon
|
||
|
|
if balloon > 0:
|
||
|
|
aux = ndi.binary_dilation(u, structure)
|
||
|
|
elif balloon < 0:
|
||
|
|
aux = ndi.binary_erosion(u, structure)
|
||
|
|
if balloon != 0:
|
||
|
|
u[threshold_mask_balloon] = aux[threshold_mask_balloon]
|
||
|
|
|
||
|
|
# Image attachment
|
||
|
|
aux = np.zeros_like(image)
|
||
|
|
du = np.gradient(u)
|
||
|
|
for el1, el2 in zip(dimage, du):
|
||
|
|
aux += el1 * el2
|
||
|
|
u[aux > 0] = 1
|
||
|
|
u[aux < 0] = 0
|
||
|
|
|
||
|
|
# Smoothing
|
||
|
|
for _ in range(smoothing):
|
||
|
|
u = _curvop(u)
|
||
|
|
|
||
|
|
iter_callback(u)
|
||
|
|
|
||
|
|
return u
|