ó  ‰\c@s5ddlZddlmZddded„ZdS(iÿÿÿÿNi(t_felzenszwalb_cythongš™™™™™é?icCsP| r%|jdkr%tdƒ‚ntj|ƒ}t|d|d|d|ƒS(sñComputes Felsenszwalb's efficient graph based image segmentation. Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter ``scale`` sets an observation level. Higher scale means less and larger segments. ``sigma`` is the diameter of a Gaussian kernel, used for smoothing the image prior to segmentation. The number of produced segments as well as their size can only be controlled indirectly through ``scale``. Segment size within an image can vary greatly depending on local contrast. For RGB images, the algorithm uses the euclidean distance between pixels in color space. Parameters ---------- image : (width, height, 3) or (width, height) ndarray Input image. scale : float Free parameter. Higher means larger clusters. sigma : float Width of Gaussian kernel used in preprocessing. min_size : int Minimum component size. Enforced using postprocessing. multichannel : bool, optional (default: True) Whether the last axis of the image is to be interpreted as multiple channels. A value of False, for a 3D image, is not currently supported. Returns ------- segment_mask : (width, height) ndarray Integer mask indicating segment labels. References ---------- .. [1] Efficient graph-based image segmentation, Felzenszwalb, P.F. and Huttenlocher, D.P. International Journal of Computer Vision, 2004 Examples -------- >>> from skimage.segmentation import felzenszwalb >>> from skimage.data import coffee >>> img = coffee() >>> segments = felzenszwalb(img, scale=3.0, sigma=0.95, min_size=5) is@This algorithm works only on single or multi-channel 2d images. tscaletsigmatmin_size(tndimt ValueErrortnpt atleast_3dR(timageRRRt multichannel((sAlib/python2.7/site-packages/skimage/segmentation/_felzenszwalb.pyt felzenszwalbs 0(tnumpyRt_felzenszwalb_cyRtTrueR (((sAlib/python2.7/site-packages/skimage/segmentation/_felzenszwalb.pyts