o oi+ @s8ddlmZddlmZmZmZmZddlZddlmZddl m Z ddl m Z d&ded ed e fd d Zd eed e fddZde d e fddZde d e fddZd'de d eded efddZGdddejZd(de deeded e fddZd ed!e fd ed!e ffd"d#Zd ed!e fd ed!e ffd$d%ZdS)))wraps)AnyCallableListOptionalN)nn) functional)TensorTimagekeepdimreturncCst|jdkst|jdkrtd|j}t|}t|dkr&|d}n&t|dkr4|ddd}nt|dkrE|dddd}d}ntd||sS|dS|S) aConvert a numpy image to a PyTorch 4d tensor image. Args: image: image of the form :math:`(H, W, C)`, :math:`(H, W)` or :math:`(B, H, W, C)`. keepdim: If ``False`` unsqueeze the input image to match the shape :math:`(B, H, W, C)`. Returns: tensor of the form :math:`(B, C, H, W)` if keepdim is ``False``, :math:`(C, H, W)` otherwise. Example: >>> img = np.ones((3, 3)) >>> image_to_tensor(img).shape torch.Size([1, 3, 3]) >>> img = np.ones((4, 4, 1)) >>> image_to_tensor(img).shape torch.Size([1, 4, 4]) >>> img = np.ones((4, 4, 3)) >>> image_to_tensor(img, keepdim=False).shape torch.Size([1, 3, 4, 4]) z9Input size must be a two, three or four dimensional arrayrTz Cannot process image with shape )lenshape ValueErrortorch from_numpy unsqueezepermute)r r input_shapetensorrF/home/ubuntu/.local/lib/python3.10/site-packages/kornia/utils/image.pyimage_to_tensors     rimagescs|stdg}|D]}t|st|}||q |djtdkr+tdtfdd|Dr:tdtj|dd ddd d S) aConvert a list of numpy images to a PyTorch 4d tensor image. Args: images: list of images, each of the form :math:`(H, W, C)`. Image shapes must be consistent Returns: tensor of the form :math:`(B, C, H, W)`. Example: >>> imgs = [np.ones((4, 4, 1)), np.zeros((4, 4, 1))] >>> image_list_to_tensor(imgs).shape torch.Size([2, 1, 4, 4]) zInput list of images is emptyrrz$Each image must have shape (H, W, C)c3s|]}|jkVqdSNr).0imgrrr isz'image_list_to_tensor..z#All images must have the same shapedimrr) rr is_tensor as_tensorappendrranystackr)rimages_tr!rrrimage_list_to_tensorMs     r+rcCst|tstdt|t|jdkrtd|jt|jdkr)|d}t|jdkr5|d}t|jdkrM|d|jd|jd |jd}|S) zConvert a PyTorch tensor image to BCHW format. Args: tensor (torch.Tensor): image of the form :math:`(*, H, W)`. Returns: input tensor of the form :math:`(B, C, H, W)`. Input type is not a Tensor. Got rz@Input size must be a two, three or four dimensional tensor. Got rrr isinstancer TypeErrortyperrrrviewrrrr_to_bchwps   "r6cCst|tstdt|t|jdkrtd|jt|jdkr)|d}t|jdkr5|d}t|jdkrQ|d|jd|jd |jd |jd}|S) zConvert a PyTorch tensor image to BCDHW format. Args: tensor (torch.Tensor): image of the form :math:`(*, D, H, W)`. Returns: input tensor of the form :math:`(B, C, D, H, W)`. r,rzAInput size must be a three, four or five dimensional tensor. Got rr r-r.r/r0r5rrr _to_bcdhws   *r9Fforce_contiguouscCst|tstdt|t|jdkst|jdkr td|j}|}t|dkr0nGt|dkrI|ddkrA| }n6| ddd}n.t|dkrp| dddd}|ddkrd|sd| d}|ddkro| d}ntd ||r}| }| S) aConvert a PyTorch tensor image to a numpy image. In case the tensor is in the GPU, it will be copied back to CPU. Args: tensor: image of the form :math:`(H, W)`, :math:`(C, H, W)` or :math:`(B, C, H, W)`. keepdim: If ``False`` squeeze the input image to match the shape :math:`(H, W, C)` or :math:`(H, W)`. force_contiguous: If ``True`` call `contiguous` to the tensor before Returns: image of the form :math:`(H, W)`, :math:`(H, W, C)` or :math:`(B, H, W, C)`. Example: >>> img = torch.ones(1, 3, 3) >>> tensor_to_image(img).shape (3, 3) >>> img = torch.ones(3, 4, 4) >>> tensor_to_image(img).shape (4, 4, 3) r,r rz:Input size must be a two, three or four dimensional tensorrrrr-z!Cannot process tensor with shape ) r1r r2r3rrrcpudetachsqueezer contiguousnumpy)rr r:rr rrrtensor_to_images.          r@cs>eZdZdZd deddffdd Zdedefd d ZZ S) ImageToTensorzConverts a numpy image to a PyTorch 4d tensor image. Args: keepdim: If ``False`` unsqueeze the input image to match the shape :math:`(B, H, W, C)`. Fr r Ncst||_dSr)super__init__r )selfr  __class__rrrCs  zImageToTensor.__init__xcCst||jdS)N)r )rr )rDrGrrrforwardszImageToTensor.forward)F) __name__ __module__ __qualname____doc__boolrCrr rH __classcell__rrrErrAsrArn_rowpaddingcCs>t|tjs td|j\}}}}|dur&tttj|tjd }||d|}||}||} t |d|d|f} ||} | |kratj | |||| f|j|jd} tj| | fdd} | } | ||||| } | ddd dd } | ||||| }|||}|| |}|ddd|d|f}|S) a?Convert a batched tensor to one image with padding in between. Args: tensor: A batched tensor of shape (B, C, H, W). n_row: Number of images displayed in each row of the grid. padding: The amount of padding to add between images. Returns: Tensor: The combined image grid. z&Input tensor must be a PyTorch tensor.N)dtyperr)rQdevicer#rrr )r1rr r2rintsqrtrfloat32ceilitemFpadzerosrQrRcatr>r4r)rrOrPBCHWn_colpadded_Hpadded_W tensor_paddedtotal pad_tilesgridcombined combined_H combined_Wrrr make_grids* "   rjf.c *tdtdtdtdtffdd }|S)zApply `f` to an image of arbitrary leading dimensions `(*, C, H, W)`. It works by first viewing the image as `(B, C, H, W)`, applying the function and re-viewing the image as original shape. inputargskwargsr cst|tstdt||jdkrtd|j}t|}|g|Ri|}t|dkr5|d}t|dkr?|d}t|dkrU|j |dd|jdd}|S) N&Input input type is not a Tensor. Got r"Invalid input tensor, it is empty.rrrrr r.) r1r r2r3rnumelrr6rr4rmrnroroutputrkrr_wrapper-s     z*perform_keep_shape_image.._wrapperrr rrkrwrrvrperform_keep_shape_image& rzc rl)zApply `f` to an image of arbitrary leading dimensions `(*, C, D, H, W)`. It works by first viewing the image as `(B, C, D, H, W)`, applying the function and re-viewing the image as original shape. rmrnror cst|tstdt||dkrtd|j}t|}|g|Ri|}t|dkr4|d}t|dkr>|d}t|dkrT|j |dd|jdd}|S) Nrprrqr rrrr7r8) r1r r2r3rsrrr9rr4rtrvrrrwMs      z*perform_keep_shape_video.._wrapperrxryrrvrperform_keep_shape_videoFr{r|)T)FF)Nr) functoolsrtypingrrrrrrtorch.nnrrX kornia.corer rMrr+r6r9r@ModulerArSrjrzr|rrrrs    1#= "1&