o :ήcp @sLddlZddlmZddlmZejddejdd dd dddZdS)N)_felzenszwalb_cython)utilsF)multichannel_output)multichannel_position皙?T) channel_axiscCs4|dur |jdkr tdt|}t||||dS)aComputes 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 (standard deviation) 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. This argument is deprecated: specify `channel_axis` instead. 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 ------- 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 Notes ----- The `k` parameter used in the original paper renamed to `scale` here. 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) Nrz@This algorithm works only on single or multi-channel 2d images. )scalesigmamin_size)ndim ValueErrornp atleast_3dr)imager rr multichannelr rI/tmp/pip-target-vg8gfxp4/lib/python/skimage/segmentation/_felzenszwalb.py felzenszwalbs > r)rr r T) numpyr_felzenszwalb_cyr_sharedrchannel_as_last_axisdeprecate_multichannel_kwargrrrrrs