B \ @sbddlmZddlZddlZddlmZddl m Z m Z ddl m Z mZddlmZdddZdS))divisionN)ndimage) img_as_float regular_grid) _slic_cython"_enforce_label_connectivity_cython)rgb2labd$@ T?Fc "Cst|}d} |jdkr.|tjdtjf}d} n>|jdkrP|rP|tjdf}d} n|jdkrl|sl|dtjf}|dkrtd}nt|ttfrtj|tj d}t|t j stj|||gtj d}|| tj }nt|ttfrtj|tj d}|dk rt|dg}t||}|rf|s.|dkrf|jd dkrN|rNtd n|jd dkrft|}|jdd\} }}tjd| d|d|f\}}}t|jdd|}d d |D\}}}||}||}||}t|j|jdf}tj|dtjf|dtjf|dtjf|gd d d d|jd}t|}tt|||f}d|}t||}t|||||| }|r| |||}t| |} t| |}!t|| |!}| r|d}|S)u"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) Fr.TrN)Zdtyperz/Lab colorspace conversion requires a RGB image.cSs$g|]}t|jdk r|jndqS)N)intstep).0srDlib/python3.7/site-packages/skimage/segmentation/slic_superpixels.py szslic..)Zaxisg?)rndimnpZnewaxisZones isinstancelisttupleZarrayZdoublecollIterableZastypeanyndiZgaussian_filtershape ValueErrorr ZmgridrZzerosZ concatenateZreshapeZascontiguousarrayfloatmaxrrr)"ZimageZ n_segmentsZ compactnessZmax_iterZsigmaZspacingZ multichannelZ convert2labZenforce_connectivityZmin_size_factorZmax_size_factorZ slic_zeroZis_2dZdepthZheightwidthZgrid_zZgrid_yZgrid_xZslicesZstep_zZstep_yZstep_xZ segments_zZ segments_yZ segments_xZsegments_colorZsegmentsrZratiolabelsZ segment_sizeZmin_sizeZmax_sizerrrslicspd     "     r') r r r rNTNTr rF)Z __future__r collectionsrZnumpyrZscipyrr utilrrZsegmentation._slicrrZcolorr r'rrrrs