o oi @sBddlZddlmmZddlmZd dededefddZdS) N)Tensordimagenum_iterationsreturnc Cs t|tstdt|t|tr|dkrtdt|jdks)|jddkr1td|j|jdd\}}|d d||}|dk}|j\}}}}t j d|||d|j |j d d d||f}d ||<t |D]}tj|dddd }t ||}qn||S) aCompute 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 Tensor. Got: z0Input num_iterations must be a positive integer.z*Input image shape must be (*,1,H,W). Got: N)devicedtyper) kernel_sizestridepadding) isinstancer TypeErrortypeintlenshape ValueErrorviewtorcharanger r rangeF max_pool2dmulview_as) rrHW image_viewmaskB_outr'W/home/ubuntu/.local/lib/python3.10/site-packages/kornia/contrib/connected_components.pyconnected_componentss 0   r))r) rtorch.nn.functionalnn functionalr kornia.corerrr)r'r'r'r(s