ó  ‰\c @ s˘ddlmZddlZddlZddlmZddl m Z m Z ddl m Z mZddlmZdd d d deded d ed„ ZdS(i˙˙˙˙(tdivisionN(tndimagei(t img_as_floatt regular_grid(t _slic_cythont"_enforce_label_connectivity_cython(trgb2labidg$@i igŕ?ic #C s t|ƒ}t} |jdkrC|tjdtjf}t} n]|jdkrt|rt|tjdf}t} n,|jdkr | r |dtjf}n|dkržtjdƒ}n0t|t t fƒrîtj |dtj ƒ}nt|t jƒs:tj |||gdtj ƒ}||jtj ƒ}n0t|t t fƒrjtj |dtj ƒ}n|dkjƒr¤t |ƒdg}tj||ƒ}n|r |sź|dkr |jddkrä|rätdƒ‚q |jddkr t|ƒ}q n|jd \} }}tjd| …d|…d|…f\}}}t|jd |ƒ}g|D]*}t|jdk rŽ|jnd ƒ^qm\}}}||}||}||}tj|j|jdfƒ}tj|dtjf|dtjf|dtjf|gd dƒjdd|jdƒ}tj|ƒ}tt|||fƒƒ}d |}tj||ƒ}t |||||| ƒ}|rň| |||} t| | ƒ}!t| | ƒ}"t!||!|"ƒ}n| r|d}n|S( s"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 SĂźsstrunk, 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) i.itdtypeii˙˙˙˙s/Lab colorspace conversion requires a RGB image.Nitaxisgđ?("RtFalsetndimtnptnewaxistTruetNonetonest isinstancetlistttupletarraytdoubletcolltIterabletastypetanytnditgaussian_filtertshapet ValueErrorRtmgridRtinttsteptzerost concatenatetreshapetascontiguousarraytfloattmaxRR(#timaget n_segmentst compactnesstmax_itertsigmatspacingt multichannelt convert2labtenforce_connectivitytmin_size_factortmax_size_factort slic_zerotis_2dtdepththeighttwidthtgrid_ztgrid_ytgrid_xtslicestststep_ztstep_ytstep_xt segments_zt segments_yt segments_xtsegments_colortsegmentsRtratiotlabelst segment_sizetmin_sizetmax_size((sDlib/python2.7/site-packages/skimage/segmentation/slic_superpixels.pytslicspd    !1=         (t __future__Rt collectionsRtnumpyR tscipyRRtutilRRtsegmentation._slicRRtcolorRRR R RH(((sDlib/python2.7/site-packages/skimage/segmentation/slic_superpixels.pyts