1-Ph?ddlZddlmZddlmZddlZddlmZddlm Z ddl m Z m Z ddl mZdd lmZdd lmZdd lmZmZd d lmZmZdZdZejd ddddZdS)N)Iterable)warn)random)kmeans2)pdist squareform)utils)gaussian)rgb2lab) img_as_float regular_grid)"_enforce_label_connectivity_cython _slic_cythonc tjtj|tj}t jd}tjt|t}tj | |t|t|d}d}|r |j dz n|j }t||z|z} t|| kr+tj | || d} nt} t|| ||d\} } t!t#| } tj| tj| d }t+| | |d d fz d }| |fS) aFind regularly spaced centroids on a mask. Parameters ---------- mask : 3D ndarray The mask within which the centroids must be positioned. n_centroids : int The number of centroids to be returned. Returns ------- centroids : 2D ndarray The coordinates of the centroids with shape (n_centroids, 3). steps : 1D ndarray The approximate distance between two seeds in all dimensions. dtype{F)replace r)iterNr)nparraynonzerofloatTr RandomStatearangelenintsortchoiceminndimEllipsisrrr fill_diagonalinfargminabsmean)mask n_centroids multichannelcoordrngidx_fullidx dense_factor ndim_spatialn_dense idx_dense centroids_dist closest_ptsstepss e/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/segmentation/slic_superpixels.py_get_mask_centroidsr?s( HRZ%%U 3 3 3 5E  S ! !CyU3///H '#**Xs;E 'C'CU*SS T TCL$0?49q==diL<-<==G 5zzGGCJJx%JHHII  5+U3Za@@@LIq eI&& ' 'DT26"""++b//K  Ik111n55 6 6 ; ;A > >E e c|jdd\}}}tjd|d|d|f\}}}t|jdd|}||dtjf} ||dtjf} ||dtjf} tj| | | gd} tjd|D} | | fS)aFind regularly spaced centroids on the image. Parameters ---------- image : 2D, 3D or 4D ndarray Input image, which can be 2D or 3D, and grayscale or multichannel. n_centroids : int The (approximate) number of centroids to be returned. Returns ------- centroids : 2D ndarray The coordinates of the centroids with shape (~n_centroids, 3). steps : 1D ndarray The approximate distance between two seeds in all dimensions. 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Parameters ---------- image : (M, N[, P][, C]) ndarray Input image. Can be 2D or 3D, and grayscale or multichannel (see `channel_axis` parameter). Input image must either be NaN-free or the NaN's must be masked out. 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_num_iter : int, optional Maximum number of iterations of k-means. sigma : float or 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 if a manual voxel spacing is provided (see Notes section). If sigma is array-like, its size must match ``image``'s number of spatial dimensions. spacing : array-like of floats, optional The voxel spacing along each spatial dimension. By default, `slic` assumes uniform spacing (same voxel resolution along each spatial dimension). This parameter controls the weights of the distances along the spatial dimensions during k-means clustering. 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 ``channel_axis` is not None *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]_ start_label : int, optional The labels' index start. Should be 0 or 1. .. versionadded:: 0.17 ``start_label`` was introduced in 0.17 mask : ndarray, optional If provided, superpixels are computed only where mask is True, and seed points are homogeneously distributed over the mask using a k-means clustering strategy. Mask number of dimensions must be equal to image number of spatial dimensions. .. versionadded:: 0.17 ``mask`` was introduced in 0.17 channel_axis : int or None, optional If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels. .. versionadded:: 0.19 ``channel_axis`` was added in 0.19. 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. ValueError If ``start_label`` is not 0 or 1. ValueError If ``image`` contains unmasked NaN values. ValueError If ``image`` contains unmasked infinite values. ValueError If ``image`` is 2D but ``channel_axis`` is -1 (the default). 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 (masked values are ignored). * 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 `channel_axis=None`. * `start_label` is introduced to handle the issue [4]_. Label indexing starts at 1 by default. 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. :DOI:`10.1109/TPAMI.2012.120` .. [2] https://www.epfl.ch/labs/ivrl/research/slic-superpixels/#SLICO .. [3] Irving, Benjamin. "maskSLIC: regional superpixel generation with application to local pathology characterisation in medical images.", 2016, :arXiv:`1606.09518` .. [4] https://github.com/scikit-image/scikit-image/issues/3722 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) r Nz channel_axis=z| indicates multichannel, which is not supported for a two-dimensional image; use channel_axis=None if the image is grayscaleT)copyrrCz.unmasked NaN values in image are not supportedz3unmasked infinite values in image are not supportedF.rBz/Lab colorspace conversion requires a RGB image.)rrzstart_label should be 0 or 1.uint8z*image and mask should have the same shape.zdInput image is 2D: spacing number of elements must be 2. In the future, a ValueError will be raised.) stacklevelz#Input image is 2D, but spacing has z elements (expected 2).rrz#Input image is 3D, but spacing has z elements (expected 3).z!spacing must be None or iterable.zbInput image is 2D: sigma number of elements must be 2. 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