a d@s6ddlZddlmmZdejeejdddZdS)Nd)imagenum_iterationsreturnc Cst|tjstdt|t|tr0|dkr8tdt|jdksT|jddkrdtd|j|jdd\}}| d d||}|dk}|j\}}}}tj ||||j |j d  d d||f}d ||<t |D]}tj|dddd |||<q||S) aComputes the Connected-component labelling (CCL) algorithm. .. image:: https://github.com/kornia/data/raw/main/cells_segmented.png The implementation is an adaptation of the following repository: https://gist.github.com/efirdc/5d8bd66859e574c683a504a4690ae8bc .. warning:: This is an experimental API subject to changes and optimization improvements. .. note:: See a working example `here `__. Args: image: the binarized input image with shape :math:`(*, 1, H, W)`. The image must be in floating point with range [0, 1]. num_iterations: the number of iterations to make the algorithm to converge. Return: The labels image with the same shape of the input image. Example: >>> img = torch.rand(2, 1, 4, 5) >>> img_labels = connected_components(img, num_iterations=100) z,Input imagetype is not a torch.Tensor. Got: z0Input num_iterations must be a positive integer.z*Input image shape must be (*,1,H,W). Got: N)devicedtyper)Z kernel_sizeZstridepadding) isinstancetorchTensor TypeErrortypeintlenshape ValueErrorviewZaranger r rangeFZ max_pool2dZview_as) rrHWZ image_viewmaskB_outr l/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/contrib/connected_components.pyconnected_componentss *  r")r)rZtorch.nn.functionalnnZ functionalrrrr"r r r r!s