# coding=utf-8 from __future__ import division import collections as coll import numpy as np from scipy import ndimage as ndi from ..util import img_as_float, regular_grid from ..segmentation._slic import (_slic_cython, _enforce_label_connectivity_cython) from ..color import rgb2lab def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=0, spacing=None, multichannel=True, convert2lab=None, enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3, slic_zero=False): """Segments image using k-means clustering in Color-(x,y,z) space. Parameters ---------- image : 2D, 3D or 4D ndarray Input image, which can be 2D or 3D, and grayscale or multichannel (see `multichannel` parameter). n_segments : int, optional The (approximate) number of labels in the segmented output image. compactness : float, optional Balances color proximity and space proximity. Higher values give more weight to space proximity, making superpixel shapes more square/cubic. In SLICO mode, this is the initial compactness. This parameter depends strongly on image contrast and on the shapes of objects in the image. We recommend exploring possible values on a log scale, e.g., 0.01, 0.1, 1, 10, 100, before refining around a chosen value. max_iter : int, optional Maximum number of iterations of k-means. sigma : float or (3,) array-like of floats, optional Width of Gaussian smoothing kernel for pre-processing for each dimension of the image. The same sigma is applied to each dimension in case of a scalar value. Zero means no smoothing. Note, that `sigma` is automatically scaled if it is scalar and a manual voxel spacing is provided (see Notes section). spacing : (3,) array-like of floats, optional The voxel spacing along each image dimension. By default, `slic` assumes uniform spacing (same voxel resolution along z, y and x). This parameter controls the weights of the distances along z, y, and x during k-means clustering. multichannel : bool, optional Whether the last axis of the image is to be interpreted as multiple channels or another spatial dimension. convert2lab : bool, optional Whether the input should be converted to Lab colorspace prior to segmentation. The input image *must* be RGB. Highly recommended. This option defaults to ``True`` when ``multichannel=True`` *and* ``image.shape[-1] == 3``. enforce_connectivity: bool, optional Whether the generated segments are connected or not min_size_factor: float, optional Proportion of the minimum segment size to be removed with respect to the supposed segment size ```depth*width*height/n_segments``` max_size_factor: float, optional Proportion of the maximum connected segment size. A value of 3 works in most of the cases. slic_zero: bool, optional Run SLIC-zero, the zero-parameter mode of SLIC. [2]_ Returns ------- labels : 2D or 3D array Integer mask indicating segment labels. Raises ------ ValueError If ``convert2lab`` is set to ``True`` but the last array dimension is not of length 3. Notes ----- * If `sigma > 0`, the image is smoothed using a Gaussian kernel prior to segmentation. * If `sigma` is scalar and `spacing` is provided, the kernel width is divided along each dimension by the spacing. For example, if ``sigma=1`` and ``spacing=[5, 1, 1]``, the effective `sigma` is ``[0.2, 1, 1]``. This ensures sensible smoothing for anisotropic images. * The image is rescaled to be in [0, 1] prior to processing. * Images of shape (M, N, 3) are interpreted as 2D RGB images by default. To interpret them as 3D with the last dimension having length 3, use `multichannel=False`. References ---------- .. [1] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, TPAMI, May 2012. .. [2] http://ivrg.epfl.ch/research/superpixels#SLICO Examples -------- >>> from skimage.segmentation import slic >>> from skimage.data import astronaut >>> img = astronaut() >>> segments = slic(img, n_segments=100, compactness=10) Increasing the compactness parameter yields more square regions: >>> segments = slic(img, n_segments=100, compactness=20) """ image = img_as_float(image) is_2d = False if image.ndim == 2: # 2D grayscale image image = image[np.newaxis, ..., np.newaxis] is_2d = True elif image.ndim == 3 and multichannel: # Make 2D multichannel image 3D with depth = 1 image = image[np.newaxis, ...] is_2d = True elif image.ndim == 3 and not multichannel: # Add channel as single last dimension image = image[..., np.newaxis] if spacing is None: spacing = np.ones(3) elif isinstance(spacing, (list, tuple)): spacing = np.array(spacing, dtype=np.double) if not isinstance(sigma, coll.Iterable): sigma = np.array([sigma, sigma, sigma], dtype=np.double) sigma /= spacing.astype(np.double) elif isinstance(sigma, (list, tuple)): sigma = np.array(sigma, dtype=np.double) if (sigma > 0).any(): # add zero smoothing for multichannel dimension sigma = list(sigma) + [0] image = ndi.gaussian_filter(image, sigma) if multichannel and (convert2lab or convert2lab is None): if image.shape[-1] != 3 and convert2lab: raise ValueError("Lab colorspace conversion requires a RGB image.") elif image.shape[-1] == 3: image = rgb2lab(image) depth, height, width = image.shape[:3] # initialize cluster centroids for desired number of segments grid_z, grid_y, grid_x = np.mgrid[:depth, :height, :width] slices = regular_grid(image.shape[:3], n_segments) step_z, step_y, step_x = [int(s.step if s.step is not None else 1) for s in slices] segments_z = grid_z[slices] segments_y = grid_y[slices] segments_x = grid_x[slices] segments_color = np.zeros(segments_z.shape + (image.shape[3],)) segments = np.concatenate([segments_z[..., np.newaxis], segments_y[..., np.newaxis], segments_x[..., np.newaxis], segments_color], axis=-1).reshape(-1, 3 + image.shape[3]) segments = np.ascontiguousarray(segments) # we do the scaling of ratio in the same way as in the SLIC paper # so the values have the same meaning step = float(max((step_z, step_y, step_x))) ratio = 1.0 / compactness image = np.ascontiguousarray(image * ratio) labels = _slic_cython(image, segments, step, max_iter, spacing, slic_zero) if enforce_connectivity: segment_size = depth * height * width / n_segments min_size = int(min_size_factor * segment_size) max_size = int(max_size_factor * segment_size) labels = _enforce_label_connectivity_cython(labels, min_size, max_size) if is_2d: labels = labels[0] return labels