o €o™i‡ã@s’ddlmZmZddlmZddlmZddlmZm Z ddedeee dfd efd d „Z d ed efd d„Z Gdd„deƒZ Gdd„deƒZdS)é)ÚOptionalÚTuple)Ú ImageModule)ÚTensor)Ú KORNIA_CHECKÚKORNIA_CHECK_SHAPENÚinputÚdim.ÚreturncCs`t|ddgƒ|dur d}tt|ƒdkdƒtt|ƒt|jƒkdƒ|}|D]}| |¡}q&|S)aCalculate integral of the input tensor. The algorithm computes the integral image by summing over the specified dimensions. In case dim is specified, the contained dimensions must be unique and sorted in ascending order and not exceed the number of dimensions of the input tensor. Args: input: the input tensor with shape :math:`(*, D)`. Where D is the number of dimensions. dim: the dimension to be summed. Returns: Integral tensor for the input tensor with shape :math:`(*, D)`. Examples: >>> input = torch.ones(3, 5) >>> output = integral_tensor(input, (-2, -1)) >>> output tensor([[ 1., 2., 3., 4., 5.], [ 2., 4., 6., 8., 10.], [ 3., 6., 9., 12., 15.]]) Ú*ÚDN)éÿÿÿÿrzdim must be a non-empty tuple.z-dim must be a tuple of length <= input.shape.)rrÚlenÚshapeÚcumsum)rr ÚoutputÚi©rúK/home/ubuntu/.local/lib/python3.10/site-packages/kornia/enhance/integral.pyÚintegral_tensors rÚimagecCst|gd¢ƒt|dƒS)atCalculate integral of the input image tensor. This particular version sums over the last two dimensions. Args: image: the input image tensor with shape :math:`(*, H, W)`. Returns: Integral tensor for the input image tensor with shape :math:`(*, H, W)`. Examples: >>> input = torch.ones(1, 5, 5) >>> output = integral_image(input) >>> output tensor([[[ 1., 2., 3., 4., 5.], [ 2., 4., 6., 8., 10.], [ 3., 6., 9., 12., 15.], [ 4., 8., 12., 16., 20.], [ 5., 10., 15., 20., 25.]]]) )r ÚHÚW)éþÿÿÿr )rr)rrrrÚintegral_image?s rcsJeZdZdZd deeedfddf‡fdd„ Zdedefd d „Z ‡Z S) ÚIntegralTensora=Calculates integral of the input tensor. Args: image: the input tensor with shape :math:`(B,C,H,W)`. Returns: Integral tensor for the input tensor with shape :math:`(B,C,H,W)`. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` Examples: >>> input = torch.ones(3, 5) >>> dim = (-2, -1) >>> output = IntegralTensor(dim)(input) >>> output tensor([[ 1., 2., 3., 4., 5.], [ 2., 4., 6., 8., 10.], [ 3., 6., 9., 12., 15.]]) Nr .r cstƒ ¡||_dS©N)ÚsuperÚ__init__r )Úselfr ©Ú __class__rrrrs  zIntegralTensor.__init__rcCs t||jƒSr)rr ©rrrrrÚforwardvs zIntegralTensor.forwardr) Ú__name__Ú __module__Ú __qualname__Ú__doc__rrÚintrrr#Ú __classcell__rrr rrZs$rc@s"eZdZdZdedefdd„ZdS)Ú IntegralImageaÏCalculates integral of the input image tensor. This particular version sums over the last two dimensions. Args: image: the input image tensor with shape :math:`(B,C,H,W)`. Returns: Integral tensor for the input image tensor with shape :math:`(B,C,H,W)`. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` Examples: >>> input = torch.ones(1, 5, 5) >>> output = IntegralImage()(input) >>> output tensor([[[ 1., 2., 3., 4., 5.], [ 2., 4., 6., 8., 10.], [ 3., 6., 9., 12., 15.], [ 4., 8., 12., 16., 20.], [ 5., 10., 15., 20., 25.]]]) rr cCst|ƒSr)rr"rrrr#•szIntegralImage.forwardN)r$r%r&r'rr#rrrrr*zsr*r)ÚtypingrrÚ kornia.corerÚModulerÚkornia.core.checkrrr(rrrr*rrrrÚs  $&