9i?SSKrSSKJr SSKJr SSKrSSKJr SSKJ r SSK J r J r SSK Jr SS KJr SS KJr SS KJrJr S S KJrJr SrSr\R6"SS9SSS.Sjj5rg)N)Iterable)warn)random)kmeans2)pdist squareform)utils)gaussian)rgb2lab) img_as_float regular_grid)"_enforce_label_connectivity_cython _slic_cythonc .[R"[R"U5[S9Rn[ R "S5n[R"[U5[S9n[R"URU[U[U55SS95nSnU(aURS- O URn[Xx-U-5n [U5U :a%[R"URXYSS95n O[n [X:X6SS9up[![#U 55n [R$"U [R&5 U R)S 5n[+XUS S 24- 5R-S 5nX4$) 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/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/segmentation/slic_superpixels.py_get_mask_centroidsr?s@( HHRZZ%U 3 5 5E   S !CyyU3/H ''#**Xs;E 'CU*S TCL$0499q=diiL<-<=G 5zGGGCJJx%JHI  5+UZa@LI eI& 'DT266"++b/K  k1n55 6 ; ;A >E  cBURSSup#n[RSU2SU2SU24upVn[URSSU5nXXR 5S[R 4n XhR 5S[R 4n XxR 5S[R 4n [R "XU /SS9n [R"UV s/sH&oRb[U R5OSPM( sn 5nX4$s sn f)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. N.raxis?) shapermgridrravelnewaxis concatenateasarraystepr)imager/dhwgrid_zgrid_ygrid_xslices centroids_z centroids_y centroids_xr9sr=s r>_get_grid_centroidsrYGs&kk"1oGA!XXbqb"1"bqbj1FF %++bq/; 7F.&&(bjj9K.&&(bjj9K.&&(bjj9K +FRPI JJfUf);aff DfU VE  Vs%-DF)multichannel_outputr) channel_axisc   URS:XaU b[SU S35e[U5n[R"UR 5nUR USS9nU bX[R"U [S9n U b5[R"XS9n[R"XR5nOU nXnOUnUR5nUR5n[R"U5(a [S 5e[R "U5(d[R "U5(a [S 5eUU-nUU:waUUU- -nU SLnUR nS nU SLnURS:Xa'U[R"S [R"4nSnO[URS :XaU(aU[R"S 4nSnO,URS :XaU(dUS [R"4nU(aMU(dUcCURU S :waU(a [S5eURU S :Xa [%U5nU S;a [S5eS nU(aU R'S5n U RS:Xa)[R"U [R"S 45n U RURSS :wa [S5e[)XU5unnSnO[+X5unnUc[R,"S US9nO[/U[05(a[R2"UUS9nU(abUR4S:wa9UR4S :Xa[7S[8SS9 O[[SUR4S35e[R:"USS5nO)UR4S :wa[SUR4S35e[R"UUS9nO [=S5e[R>"U5(a[R@"XDU/US9nXE-nO[/U[05(a[R2"UUS9nU(abUR4S:wa9UR4S :Xa[7S[8SS9 O[[SUR4S35e[R:"USS5nO)UR4S :wa[SUR4S35eUS:RC5(a[EU5S/-n[GXSS 9nURSn[R"[RH"U[RJ"UURS 45/S!S9US9n[U5nS"U- n[R"UU-US9nU(a[MUU UUUUU SU S#9 [MUU UUUUU S U S#9 nU(aiU(aU RO5U- nO&[PRR"URSS 5U- n[UUU-5n [UU U-5n![WUU U!U S$9nU(aUSnU$)%uSegments image using k-means clustering in Color-(x,y,z) space. 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. In the future, a ValueError will be raised.z!Input image is 2D, but sigma has z!Input image is 3D, but sigma has reflect)sigmamoderrE) ignore_color start_label)rd),r' ValueErrorr r _supported_float_typerastyperascontiguousarraybool expand_dims broadcast_torFr&maxisnanisinfrIr viewr?rYones isinstancerrKsizer FutureWarninginsert TypeErrorisscalarranylistr rJzerosrsummathprodr#r)"rM n_segments compactness max_num_iterraspacing convert2labenforce_connectivitymin_size_factormax_size_factor slic_zerordr.r[ float_dtypemask_ image_valuesiminimaxuse_maskris_2dr0update_centroidsr9r=r/segmentsrLratiolabels segment_sizemin_sizemax_sizes" r>slicrisn zzQ<3L>*% %   E--ekk:K LL4L 0E ##D5  #NN4;EOOE;;7EE|      D    D xx~~IJJ xx~~$NOO TME t| 4H KKE Et+L zzQbjj#rzz12 q\bjj#o& qc2::o& (; ;;| $ )kNO O [[ &! +ENE& 899yy! 99>''RZZ_(=>D ::Ra (IJ J.tN 5.uA 5''!5) GX & &**WE2 ||q <<1$*&#$ %="<<.(?A ))GQ2 \\Q 5<<. 79 &&we<;<< {{5%.e<  E8 $ $ 5. zzQ<<1$*&#$ %; ::,&=?  %A. 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