a d @sdddlZddlZddlmZddlmZddlmZd eje e ejdddZ Gd d d ej Z dS) N)filter2d)create_meshgridffffff?)image kernel_sizehreturnc Cst|tjstdt|t|jdks>> tensor = torch.zeros(1, 1, 5, 5) >>> tensor[:,:, 1, 2] = 1 >>> dt = kornia.contrib.distance_transform(tensor) z&image type is not a torch.Tensor. Got z-Invalid image shape, we expect BxCxHxW. Got: rz"Kernel size must be an odd number.rF)Znormalized_coordinatesdevicedtypeNZ replicate)Z border_typeg)Zposinfg?) isinstancetorchTensor TypeErrortypelenshape ValueErrormathceilmaxfloorrr r hypotexpZ unsqueezeZ zeros_likecloneZ ones_likerangerlogZ nan_to_numwheresum) rrrZn_itersZgridkerneloutboundaryZ signal_onesiZcdtmaskoffsetr(j/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/contrib/distance_transform.pydistance_transform s6  *4    r*cs>eZdZdZd eedfdd Zejejddd Z Z S) DistanceTransformzModule that approximates the Manhattan (city block) distance transform of images using convolutions. Args: kernel_size: size of the convolution kernel. h: value that influence the approximation of the min function. rr)rrcst||_||_dS)N)super__init__rr)selfrr __class__r(r)r-Rs zDistanceTransform.__init__)rr cCsF|jddkr,|dd|jd|jd}n|}t||j|j|S)Nr)rviewr*rrZview_as)r.rZimage_inr(r(r)forwardWszDistanceTransform.forward)rr) __name__ __module__ __qualname____doc__intfloatr-rrr4 __classcell__r(r(r/r)r+Jsr+)rr)rrZtorch.nnnnZkornia.filtersrZ kornia.utilsrrr9r:r*Moduler+r(r(r(r)s    @