o oi+8@sddlmZddlZddlmZmZddlmZmZgdZ  dd ej d ej d ee e e fd e d e dej f ddZ d d ej d ee e e fd e d e dej f ddZ  dd ej dej dej d e d e dej f ddZ   d!d ej dej dee e e fde de d e dej fddZdS)")TupleN)infer_bbox_shape3dvalidate_bbox3d)get_perspective_transform3d warp_affine3d) center_crop3dcrop_and_resize3dcrop_by_boxes3dcrop_by_transform_mat3dbilinearFtensorboxessize interpolation align_cornersreturnc CsHt|tjstdt|t|tjstdt|t|ttfs2t|dkr2td|t|j dkrBt d|j d|d|d |d }}}|}tj gd |d ddg|d |d dgd|d dgdd|d g|d d|d g|d |d |d gd|d |d ggg|j |j d |j dd d } t||| ||S)aExtract crops from 3D volumes (5D tensor) and resize them. Args: tensor: the 3D volume tensor with shape (B, C, D, H, W). boxes: a tensor with shape (B, 8, 3) containing the coordinates of the bounding boxes to be extracted. The tensor must have the shape of Bx8x3, where each box is defined in the clockwise order: front-top-left, front-top-right, front-bottom-right, front-bottom-left, back-top-left, back-top-right, back-bottom-right, back-bottom-left. The coordinates must be in x, y, z order. size: a tuple with the height and width that will be used to resize the extracted patches. interpolation: Interpolation flag. align_corners: mode for grid_generation. Returns: tensor containing the patches with shape (Bx)CxN1xN2xN3. Example: >>> input = torch.arange(64, dtype=torch.float32).view(1, 1, 4, 4, 4) >>> input tensor([[[[[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [12., 13., 14., 15.]], [[16., 17., 18., 19.], [20., 21., 22., 23.], [24., 25., 26., 27.], [28., 29., 30., 31.]], [[32., 33., 34., 35.], [36., 37., 38., 39.], [40., 41., 42., 43.], [44., 45., 46., 47.]], [[48., 49., 50., 51.], [52., 53., 54., 55.], [56., 57., 58., 59.], [60., 61., 62., 63.]]]]]) >>> boxes = torch.tensor([[ ... [1., 1., 1.], ... [3., 1., 1.], ... [3., 3., 1.], ... [1., 3., 1.], ... [1., 1., 2.], ... [3., 1., 2.], ... [3., 3., 2.], ... [1., 3., 2.], ... ]]) # 1x8x3 >>> crop_and_resize3d(input, boxes, (2, 2, 2), align_corners=True) tensor([[[[[21.0000, 23.0000], [29.0000, 31.0000]], [[37.0000, 39.0000], [45.0000, 47.0000]]]]]) -Input tensor type is not a torch.Tensor. Got z,Input boxes type is not a torch.Tensor. Got 1Input size must be a tuple/list of length 3. Got 6Only tensor with shape (B, C, D, H, W) supported. Got .rrrrr)dtypedevice) isinstancetorchTensor TypeErrortypetuplelistlen ValueErrorshapeAssertionErrorr rrexpandr ) r rrrrdst_ddst_hdst_w points_src points_dstr/T/home/ubuntu/.local/lib/python3.10/site-packages/kornia/geometry/transform/crop3d.pyr s6 ?     r Tc Cst|tjstdt|t|jdkrtd|jdt|tt fs3t|dkr3t d||\}}}|jdd\}}} |d } |d } |d } |d } |d }| d }|| }|| }| | }||d }||d }||d }tj |||g|||g|||g|||g|||g|||g|||g|||ggg|j d }tj gd |d d d g|d |d d gd |d d gd d |d g|d d |d g|d |d |d gd |d |d ggg|j d  |jd dd}t|||j||j||S)aCrop the 3D volumes (5D tensor) at the center. Args: tensor: the 3D volume tensor with shape (B, C, D, H, W). size: a tuple with the expected depth, height and width of the output patch. interpolation: Interpolation flag. align_corners : mode for grid_generation. Returns: the output tensor with patches. Examples: >>> input = torch.arange(64, dtype=torch.float32).view(1, 1, 4, 4, 4) >>> input tensor([[[[[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [12., 13., 14., 15.]], [[16., 17., 18., 19.], [20., 21., 22., 23.], [24., 25., 26., 27.], [28., 29., 30., 31.]], [[32., 33., 34., 35.], [36., 37., 38., 39.], [40., 41., 42., 43.], [44., 45., 46., 47.]], [[48., 49., 50., 51.], [52., 53., 54., 55.], [56., 57., 58., 59.], [60., 61., 62., 63.]]]]]) >>> center_crop3d(input, (2, 2, 2), align_corners=True) tensor([[[[[21.0000, 22.0000], [25.0000, 26.0000]], [[37.0000, 38.0000], [41.0000, 42.0000]]]]]) rrrrrrNrr)rrrr)rrr r!r"r%r'r(r#r$r&r rr)r tor)r rrrr*r+r,src_dsrc_hsrc_w dst_d_half dst_h_half dst_w_half src_d_half src_h_half src_w_halfstart_xstart_ystart_zend_xend_yend_zr-r.r/r/r0rsf -         rsrc_boxdst_boxcCs(t|t|t|jdkrtd|jdt||j||j}||jddd|}t |}|d|ddk rZ|d|ddk rZ|d|ddk sntd|dd |dd |ddt ||t |dd t |dd t |dd f||d }|S) a Perform crop transform on 3D volumes (5D tensor) by bounding boxes. Given an input tensor, this function selected the interested areas by the provided bounding boxes (src_box). Then the selected areas would be fitted into the targeted bounding boxes (dst_box) by a perspective transformation. So far, the ragged tensor is not supported by PyTorch right now. This function hereby requires the bounding boxes in a batch must be rectangles with same width, height and depth. Args: tensor : the 3D volume tensor with shape (B, C, D, H, W). src_box : a tensor with shape (B, 8, 3) containing the coordinates of the bounding boxes to be extracted. The tensor must have the shape of Bx8x3, where each box is defined in the clockwise order: front-top-left, front-top-right, front-bottom-right, front-bottom-left, back-top-left, back-top-right, back-bottom-right, back-bottom-left. The coordinates must be in x, y, z order. dst_box: a tensor with shape (B, 8, 3) containing the coordinates of the bounding boxes to be placed. The tensor must have the shape of Bx8x3, where each box is defined in the clockwise order: front-top-left, front-top-right, front-bottom-right, front-bottom-left, back-top-left, back-top-right, back-bottom-right, back-bottom-left. The coordinates must be in x, y, z order. interpolation: Interpolation flag. align_corners: mode for grid_generation. Returns: the output tensor with patches. Examples: >>> input = torch.tensor([[[ ... [[ 0., 1., 2., 3.], ... [ 4., 5., 6., 7.], ... [ 8., 9., 10., 11.], ... [12., 13., 14., 15.]], ... [[16., 17., 18., 19.], ... [20., 21., 22., 23.], ... [24., 25., 26., 27.], ... [28., 29., 30., 31.]], ... [[32., 33., 34., 35.], ... [36., 37., 38., 39.], ... [40., 41., 42., 43.], ... [44., 45., 46., 47.]]]]]) >>> src_box = torch.tensor([[ ... [1., 1., 1.], ... [3., 1., 1.], ... [3., 3., 1.], ... [1., 3., 1.], ... [1., 1., 2.], ... [3., 1., 2.], ... [3., 3., 2.], ... [1., 3., 2.], ... ]]) # 1x8x3 >>> dst_box = torch.tensor([[ ... [0., 0., 0.], ... [2., 0., 0.], ... [2., 2., 0.], ... [0., 2., 0.], ... [0., 0., 1.], ... [2., 0., 1.], ... [2., 2., 1.], ... [0., 2., 1.], ... ]]) # 1x8x3 >>> crop_by_boxes3d(input, src_box, dst_box, interpolation='nearest', align_corners=True) tensor([[[[[21., 22., 23.], [25., 26., 27.], [29., 30., 31.]], [[37., 38., 39.], [41., 42., 43.], [45., 46., 47.]]]]]) rrrrrrrzJCropping height, width and depth must be exact same in a batch.Got height z, width z and depth )moder)rr%r'r(rr2rr)type_asrallr intitem)r rBrCrr dst_trans_srcbboxpatchesr/r/r0r s2JH8r zeros transformout_sizerD padding_modecCsB||jddd}t||ddddddf||||d}|S)azPerform crop transform on 3D volumes (5D tensor) given a perspective transformation matrix. Args: tensor: the 2D image tensor with shape (B, C, H, W). transform: a perspective transformation matrix with shape (B, 4, 4). out_size: size of the output image (depth, height, width). mode: interpolation mode to calculate output values ``'bilinear'`` | ``'nearest'``. padding_mode: padding mode for outside grid values ``'zeros'`` | ``'border'`` | ``'reflection'``. align_corners: mode for grid_generation. Returns: the output tensor with patches. rrNr)flagsrOr)r)r'r)r rMrNrDrOrrIrKr/r/r0r _s "r )r F)r T)r rLT)typingrrkornia.geometry.bboxrrimgwarprr__all__r rGstrboolr rr r r/r/r/r0s   g  x l