import warnings from itertools import cycle import numpy as np from scipy import ndimage as ndi from .._shared.utils import check_nD __all__ = ['morphological_chan_vese', 'morphological_geodesic_active_contour', 'inverse_gaussian_gradient', 'circle_level_set', 'disk_level_set', 'checkerboard_level_set' ] class _fcycle(object): def __init__(self, iterable): """Call functions from the iterable each time it is called.""" self.funcs = cycle(iterable) def __call__(self, *args, **kwargs): f = next(self.funcs) return f(*args, **kwargs) # SI and IS operators for 2D and 3D. _P2 = [np.eye(3), np.array([[0, 1, 0]] * 3), np.flipud(np.eye(3)), np.rot90([[0, 1, 0]] * 3)] _P3 = [np.zeros((3, 3, 3)) for i in range(9)] _P3[0][:, :, 1] = 1 _P3[1][:, 1, :] = 1 _P3[2][1, :, :] = 1 _P3[3][:, [0, 1, 2], [0, 1, 2]] = 1 _P3[4][:, [0, 1, 2], [2, 1, 0]] = 1 _P3[5][[0, 1, 2], :, [0, 1, 2]] = 1 _P3[6][[0, 1, 2], :, [2, 1, 0]] = 1 _P3[7][[0, 1, 2], [0, 1, 2], :] = 1 _P3[8][[0, 1, 2], [2, 1, 0], :] = 1 def sup_inf(u): """SI operator.""" if np.ndim(u) == 2: P = _P2 elif np.ndim(u) == 3: P = _P3 else: raise ValueError("u has an invalid number of dimensions " "(should be 2 or 3)") erosions = [] for P_i in P: erosions.append(ndi.binary_erosion(u, P_i)) return np.array(erosions, dtype=np.int8).max(0) def inf_sup(u): """IS operator.""" if np.ndim(u) == 2: P = _P2 elif np.ndim(u) == 3: P = _P3 else: raise ValueError("u has an invalid number of dimensions " "(should be 2 or 3)") dilations = [] for P_i in P: dilations.append(ndi.binary_dilation(u, P_i)) return np.array(dilations, dtype=np.int8).min(0) _curvop = _fcycle([lambda u: sup_inf(inf_sup(u)), # SIoIS lambda u: inf_sup(sup_inf(u))]) # ISoSI def _check_input(image, init_level_set): """Check that shapes of `image` and `init_level_set` match.""" check_nD(image, [2, 3]) if len(image.shape) != len(init_level_set.shape): raise ValueError("The dimensions of the initial level set do not " "match the dimensions of the image.") def _init_level_set(init_level_set, image_shape): """Auxiliary function for initializing level sets with a string. If `init_level_set` is not a string, it is returned as is. """ if isinstance(init_level_set, str): if init_level_set == 'checkerboard': res = checkerboard_level_set(image_shape) # TODO: remove me in 0.19.0 elif init_level_set == 'circle': res = circle_level_set(image_shape) elif init_level_set == 'disk': res = disk_level_set(image_shape) else: raise ValueError("`init_level_set` not in " "['checkerboard', 'circle', 'disk']") else: res = init_level_set return res def circle_level_set(image_shape, center=None, radius=None): """Create a circle level set with binary values. Parameters ---------- image_shape : tuple of positive integers Shape of the image center : tuple of positive integers, optional Coordinates of the center of the circle given in (row, column). If not given, it defaults to the center of the image. radius : float, optional Radius of the circle. If not given, it is set to the 75% of the smallest image dimension. Returns ------- out : array with shape `image_shape` Binary level set of the circle with the given `radius` and `center`. Warns ----- Deprecated: .. versionadded:: 0.17 This function is deprecated and will be removed in scikit-image 0.19. Please use the function named ``disk_level_set`` instead. See also -------- checkerboard_level_set """ warnings.warn("circle_level_set is deprecated in favor of " "disk_level_set." "circle_level_set will be removed in version 0.19", FutureWarning, stacklevel=2) return disk_level_set(image_shape, center=center, radius=radius) def disk_level_set(image_shape, *, center=None, radius=None): """Create a disk level set with binary values. Parameters ---------- image_shape : tuple of positive integers Shape of the image center : tuple of positive integers, optional Coordinates of the center of the disk given in (row, column). If not given, it defaults to the center of the image. radius : float, optional Radius of the disk. If not given, it is set to the 75% of the smallest image dimension. Returns ------- out : array with shape `image_shape` Binary level set of the disk with the given `radius` and `center`. See also -------- checkerboard_level_set """ if center is None: center = tuple(i // 2 for i in image_shape) if radius is None: radius = min(image_shape) * 3.0 / 8.0 grid = np.mgrid[[slice(i) for i in image_shape]] grid = (grid.T - center).T phi = radius - np.sqrt(np.sum((grid)**2, 0)) res = np.int8(phi > 0) return res def checkerboard_level_set(image_shape, square_size=5): """Create a checkerboard level set with binary values. Parameters ---------- image_shape : tuple of positive integers Shape of the image. square_size : int, optional Size of the squares of the checkerboard. It defaults to 5. Returns ------- out : array with shape `image_shape` Binary level set of the checkerboard. See also -------- circle_level_set """ grid = np.mgrid[[slice(i) for i in image_shape]] grid = (grid // square_size) # Alternate 0/1 for even/odd numbers. grid = grid & 1 checkerboard = np.bitwise_xor.reduce(grid, axis=0) res = np.int8(checkerboard) return res def inverse_gaussian_gradient(image, alpha=100.0, sigma=5.0): """Inverse of gradient magnitude. Compute the magnitude of the gradients in the image and then inverts the result in the range [0, 1]. Flat areas are assigned values close to 1, while areas close to borders are assigned values close to 0. This function or a similar one defined by the user should be applied over the image as a preprocessing step before calling `morphological_geodesic_active_contour`. Parameters ---------- image : (M, N) or (L, M, N) array Grayscale image or volume. alpha : float, optional Controls the steepness of the inversion. A larger value will make the transition between the flat areas and border areas steeper in the resulting array. sigma : float, optional Standard deviation of the Gaussian filter applied over the image. Returns ------- gimage : (M, N) or (L, M, N) array Preprocessed image (or volume) suitable for `morphological_geodesic_active_contour`. """ gradnorm = ndi.gaussian_gradient_magnitude(image, sigma, mode='nearest') return 1.0 / np.sqrt(1.0 + alpha * gradnorm) def morphological_chan_vese(image, iterations, init_level_set='checkerboard', smoothing=1, lambda1=1, lambda2=1, iter_callback=lambda x: None): """Morphological Active Contours without Edges (MorphACWE) Active contours without edges implemented with morphological operators. It can be used to segment objects in images and volumes without well defined borders. It is required that the inside of the object looks different on average than the outside (i.e., the inner area of the object should be darker or lighter than the outer area on average). Parameters ---------- image : (M, N) or (L, M, N) array Grayscale image or volume to be segmented. 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. lambda1 : float, optional Weight parameter for the outer region. If `lambda1` is larger than `lambda2`, the outer region will contain a larger range of values than the inner region. lambda2 : float, optional Weight parameter for the inner region. If `lambda2` is larger than `lambda1`, the inner region will contain a larger range of values than the outer region. 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 -------- circle_level_set, checkerboard_level_set Notes ----- This is a version of the Chan-Vese algorithm that uses morphological operators instead of solving a partial differential equation (PDE) for the evolution of the contour. The set of morphological operators used in this algorithm are proved to be infinitesimally equivalent to the Chan-Vese PDE (see [1]_). However, morphological operators are do not suffer from the numerical stability issues typically found in PDEs (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` """ init_level_set = _init_level_set(init_level_set, image.shape) _check_input(image, init_level_set) u = np.int8(init_level_set > 0) iter_callback(u) for _ in range(iterations): # inside = u > 0 # outside = u <= 0 c0 = (image * (1 - u)).sum() / float((1 - u).sum() + 1e-8) c1 = (image * u).sum() / float(u.sum() + 1e-8) # Image attachment du = np.gradient(u) abs_du = np.abs(du).sum(0) aux = abs_du * (lambda1 * (image - c1)**2 - lambda2 * (image - c0)**2) u[aux < 0] = 1 u[aux > 0] = 0 # Smoothing for _ in range(smoothing): u = _curvop(u) iter_callback(u) return u def morphological_geodesic_active_contour(gimage, iterations, init_level_set='circle', smoothing=1, threshold='auto', balloon=0, iter_callback=lambda x: None): """Morphological Geodesic Active Contours (MorphGAC). Geodesic active contours implemented with morphological operators. It can be used to segment objects with visible but noisy, cluttered, broken borders. Parameters ---------- gimage : (M, N) or (L, M, N) array Preprocessed image or volume to be segmented. This is very rarely the original image. Instead, this is usually a preprocessed version of the original image that enhances and highlights the borders (or other structures) of the object to segment. `morphological_geodesic_active_contour` will try to stop the contour evolution in areas where `gimage` is small. See `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