a dF@s8ddlmZmZmZmZmZddlZddlmZddl m Z ddl m Z ddl mZddgZejed d d Zd eejeeejeefd ddZejejejdddZejejejejejdddZd!ejeeejdddZejejejejejejejdddZGdddZejjGdddZdS)")ListOptionalTupleUnioncastN)Tensor) validate_bbox)transform_points)eye_likeBoxesBoxes3D)dtypereturncCs|tjtjtjtjtjfvSN)torchfloat16float32float64Zbfloat16Zhalf)r r^/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/geometry/boxes.py_is_floating_point_dtype srpad)boxesmethodrcstdd|Ds,tddd|Dd|dkrntdd|Dfd d|D}tjjjj|d d }ntd |d ||fS)z&Merge a list of boxes into one tensor.css6|].}|jddtddgko,|dkVqdS)N)shaperSizedim.0Zboxrrr z"_merge_box_list..z5Input boxes must be a list of (N, 4, 2) shaped. Got: cSsg|] }|jqSrrr!rrr r$z#_merge_box_list...rcss|]}|jdVqdS)rNr%r!rrrr#r$csg|]}|jdqS)rr%r!Zmax_Nrrr&r$T)Z batch_first`z` is not implemented.) all TypeErrormaxrnnutilsZrnnZ pad_sequenceNotImplementedError)rrstatsoutputrr(r_merge_box_listsr2)rMrcCs|r |n|}|jdd\}}}|d|||}|jdkrH|n|d}|jd|jdkrtd|jdd|jddt||}||}|S) aTransforms 3D and 2D in kornia format by applying the transformation matrix M. Boxes and the transformation matrix could be batched or not. Args: boxes: 2D quadrilaterals or 3D hexahedrons in kornia format. M: the transformation matrix of shape :math:`(3, 3)` or :math:`(B, 3, 3)` for 2D and :math:`(4, 4)` or :math:`(B, 4, 4)` for 3D hexahedron. NrrzBatch size mismatch. Got z for boxes and z for the transformation matrix.) is_floating_pointfloatrviewndim unsqueeze ValueErrorr Zview_as)rr3Zboxes_per_batchZn_points_per_boxZcoordinates_dimensionZpointstransformed_boxesrrr_transform_boxes s   r=)xminyminwidthheightrcCs|j|jkr0|jkr0|jkr0dks:ntdtj|jd|jdddf|j|jd}|d|d<|d|d <|d |d7<|d |d7<|d |d7<|d |d7<|S)NrzXWe expect to create a batch of 2D boxes (quadrilaterals) in vertices format (B, N, 4, 2)rrdevicer r5.r.rB.rBr.rr.rrB.rrB)r9r;rzerosrrDr r:)r>r?r@rAZpolygonsrrr_boxes_to_polygons:s2(rLxyxyTrmodevalidate_boxesrc Cs|}|drl|jdk}d|jkr2dkrRnn|jddtddgkstd|d|jd nd|d r|jdk}d|jkrdkrnn|jd dkstd |d|jd ntd ||r|n|}|r|n| d}|dr|dkr^| }|ddddfd|ddddf<|ddddfd|ddddf<n"|dkrr| }ntd || pt |n|d r|dkr|d|d|d|d}}nb|dkr|d|dd|d|dd}}n,|dkr"|d|d}}ntd ||rb|dk rLtd|dk rbtd|d|d}}t ||||}ntd ||r|n|d}|S)z%Convert from boxes to quadrilaterals.verticesrrrNrz3Boxes shape must be (N, 4, 2) or (B, N, 4, 2) when z mode. Got r'Zxyr5z-Boxes shape must be (N, 4) or (B, N, 4) when Unknown mode r.rB vertices_plusrM.rrF.rrE xyxy_plusxywh%Some boxes have negative widths or 0.&Some boxes have negative heights or 0.)lower startswithr9rrrr;r6r7r:cloneranyrLsqueeze) rrOrPbatchedquadrilateralsrAr@r>r?rrr_boxes_to_quadrilateralsLsN  6  (  $&    $ , ra)r>r?zminr@rAdepthrc Cs,|j|jkrH|jkrH|jkrH|jkrH|jkrHdksRntdtj|jd|jdddf|j|jd}|d|d <|d|d <|d|d <|d |d7<|d |d7<|d|d7<|d|d7<|}|d |dd7<tj ||gdd}|S)NrzUWe expect to create a batch of 3D boxes (hexahedrons) in vertices format (B, N, 8, 3)rrBrrrCr5rErFrUrGrHrIrJrr ) r9r;rrKrrDr r:r\cat) r>r?rbr@rArcZfront_verticesZ back_verticesZ polygons3drrr_boxes3d_to_polygons3dsJ(rfc@seZdZdZd;eejeejfee ddddZ e ejejfdd d Z deeddddZd?eeeeeddddZejdddZed@eejeejfe eddddZdAee eeejeejfddd Zeeejd!d"d#ZdBejedd$d%d&Zejdd'd(d)ZdCee edd+d,d-Zeejdd.d/Zee dd0d1Zeejdd2d3Zeej dd4d5Z dDeejeej dd6d7d8Z!ddd9d:Z"dS)Er a2D boxes containing N or BxN boxes. Args: boxes: 2D boxes, shape of :math:`(N, 4, 2)`, :math:`(B, N, 4, 2)` or a list of :math:`(N, 4, 2)`. See below for more details. raise_if_not_floating_point: flag to control floating point casting behaviour when `boxes` is not a floating point tensor. True to raise an error when `boxes` isn't a floating point tensor, False to cast to float. mode: the box format of the input boxes. Note: **2D boxes format** is defined as a floating data type tensor of shape ``Nx4x2`` or ``BxNx4x2`` where each box is a `quadrilateral `_ defined by it's 4 vertices coordinates (A, B, C, D). Coordinates must be in ``x, y`` order. The height and width of a box is defined as ``width = xmax - xmin + 1`` and ``height = ymax - ymin + 1``. Examples of `quadrilaterals `_ are rectangles, rhombus and trapezoids. TrSNrraise_if_not_floating_pointrOrcCsd|_t|trt|\}|_t|tjs>tdt|d|sb|rZt d|j | }t |j dkrz|d}d|jkrdkrnn|j ddd kst d |j d|jdkrd nd |_||_||_dS) N"Input boxes is not a Tensor. Got: r'+Coordinates must be in floating point. Got r)r5rrrr)rrz3Boxes shape must be (N, 4, 2) or (B, N, 4, 2). Got FT)_N isinstancelistr2rrr+typer6r;r r7lenrreshaper9 _is_batched_data_modeselfrrhrOrrr__init__s    ,zBoxes.__init__rcCs0ttj|jddd}|d|d}}||fS)aCompute boxes heights and widths. Returns: - Boxes heights, shape of :math:`(N,)` or :math:`(B,N)`. - Boxes widths, shape of :math:`(N,)` or :math:`(B,N)`. Example: >>> boxes_xyxy = torch.tensor([[[1,1,2,2],[1,1,3,2]]]) >>> boxes = Boxes.from_tensor(boxes_xyxy) >>> boxes.get_boxes_shape() (tensor([[1., 1.]]), tensor([[1., 2.]])) rWTas_padded_sequencerUrT)rrr to_tensor)ruZ boxes_xywhwidthsheightsrrrget_boxes_shapes zBoxes.get_boxes_shapeF)rinplacercCs.tj|j|jgdd}|r$||_|St|dS)a"Merges boxes. Say, current instance holds :math:`(B, N, 4, 2)` and the incoming boxes holds :math:`(B, M, 4, 2)`, the merge results in :math:`(B, N + M, 4, 2)`. Args: boxes: 2D boxes. inplace: do transform in-place and return self. rBrdF)rrerrdatar )rurr~rrrrmerges z Boxes.merge)topleftbotrightr~rc CsZt|trt|tst|r$|j}n |j}|dddddfd|dd}||d|k|d|d|k<|dddddfd|dd}||d|k|d|d|k<|dddddfd|dd}||d|k|d|d|k<|dddddfd|dd}||d|k|d|d|k<|rP|St|dS)NrBrrErFF)rlrr/rrr\repeatsizer ) rurrr~rrZ topleft_xZ topleft_yZ botright_xZ botright_yrrrclamps  & & & & z Boxes.clamp)correspondence_preserver~rcCstdS)aTrim out zero padded boxes. Given box arrangements of shape :math:`(4, 4, Box)`: -- Box -- Box -- Box -- Box -- -- 0 -- 0 -- Box -- Box -- -- 0 -- Box -- 0 -- 0 -- -- 0 -- 0 -- 0 -- 0 -- Nothing will change if correspondence_preserve is True. Only pure zero layers will be removed, resulting in shape :math:`(4, 3, Box)`: -- Box -- Box -- Box -- Box -- -- 0 -- 0 -- Box -- Box -- -- 0 -- Box -- 0 -- 0 -- Otherwise, you will get :math:`(4, 2, Box)`: -- Box -- Box -- Box -- Box -- -- 0 -- Box -- Box -- Box -- N)r/)rurr~rrrtrimsz Boxes.trim)min_areamax_arear~rcCsX|}|r|j}n |j}|dur2d|||k<|durFd|||k<|rN|St|dS)NgF) compute_arearrr\r )rurrr~arearrrrrfilter_boxes_by_area-s   zBoxes.filter_boxes_by_areacCsp|jddddddf|jddddddf}|jddddddf|jddddddf}||S)zReturns :math:`(B, N)`.NrBrrrr)ruwhrrrr=s44zBoxes.compute_arearMrNcs<t|tjrt|d}nfdd|D}||dS)ay Helper method to easily create :class:`Boxes` from boxes stored in another format. Args: boxes: 2D boxes, shape of :math:`(N, 4)`, :math:`(B, N, 4)`, :math:`(N, 4, 2)` or :math:`(B, N, 4, 2)`. mode: The format in which the boxes are provided. * 'xyxy': boxes are assumed to be in the format ``xmin, ymin, xmax, ymax`` where ``width = xmax - xmin`` and ``height = ymax - ymin``. With shape :math:`(N, 4)`, :math:`(B, N, 4)`. * 'xyxy_plus': similar to 'xyxy' mode but where box width and length are defined as ``width = xmax - xmin + 1`` and ``height = ymax - ymin + 1``. With shape :math:`(N, 4)`, :math:`(B, N, 4)`. * 'xywh': boxes are assumed to be in the format ``xmin, ymin, width, height`` where ``width = xmax - xmin`` and ``height = ymax - ymin``. With shape :math:`(N, 4)`, :math:`(B, N, 4)`. * 'vertices': boxes are defined by their vertices points in the following ``clockwise`` order: *top-left, top-right, bottom-right, bottom-left*. Vertices coordinates are in (x,y) order. Finally, box width and height are defined as ``width = xmax - xmin`` and ``height = ymax - ymin``. With shape :math:`(N, 4, 2)` or :math:`(B, N, 4, 2)`. * 'vertices_plus': similar to 'vertices' mode but where box width and length are defined as ``width = xmax - xmin + 1`` and ``height = ymax - ymin + 1``. ymin + 1``. With shape :math:`(N, 4, 2)` or :math:`(B, N, 4, 2)`. validate_boxes: check if boxes are valid rectangles or not. Valid rectangles are those with width and height >= 1 (>= 2 when mode ends with '_plus' suffix). Returns: :class:`Boxes` class containing the original `boxes` in the format specified by ``mode``. Examples: >>> boxes_xyxy = torch.as_tensor([[0, 3, 1, 4], [5, 1, 8, 4]]) >>> boxes = Boxes.from_tensor(boxes_xyxy, mode='xyxy') >>> boxes.data # (2, 4, 2) tensor([[[0., 3.], [0., 3.], [0., 3.], [0., 3.]], [[5., 1.], [7., 1.], [7., 3.], [5., 3.]]]) rOrPcsg|]}t|qSr)rar!rrrr&tr$z%Boxes.from_tensor..F)rlrrra)clsrrOrPr`rrr from_tensorCs. zBoxes.from_tensor)rOryrcCs\|jr |jn |jd}tj|jdd|jddgdd|jd|jdd}|dur^|j }| }|dvrpnR|dvr|d |d d|d |d d}}||d <||d <nt d ||dvrtj gd|j |jd}||}|drt|d |d |d |d }|jdurB|sBtddt||jD}n|jrN|n|d}|S)a^Cast :class:`Boxes` to a tensor. ``mode`` controls which 2D boxes format should be use to represent boxes in the tensor. Args: mode: the output box format. It could be: * 'xyxy': boxes are defined as ``xmin, ymin, xmax, ymax`` where ``width = xmax - xmin`` and ``height = ymax - ymin``. * 'xyxy_plus': similar to 'xyxy' mode but where box width and length are defined as ``width = xmax - xmin + 1`` and ``height = ymax - ymin + 1``. * 'xywh': boxes are defined as ``xmin, ymin, width, height`` where ``width = xmax - xmin`` and ``height = ymax - ymin``. * 'vertices': boxes are defined by their vertices points in the following ``clockwise`` order: *top-left, top-right, bottom-right, bottom-left*. Vertices coordinates are in (x,y) order. Finally, box width and height are defined as ``width = xmax - xmin`` and ``height = ymax - ymin``. * 'vertices_plus': similar to 'vertices' mode but where box width and length are defined as ``width = xmax - xmin + 1`` and ``height = ymax - ymin + 1``. ymin + 1``. as_padded_sequence: whether to keep the pads for a list of boxes. This parameter is only valid if the boxes are from a box list. Returns: Boxes tensor in the ``mode`` format. The shape depends with the ``mode`` value: * 'vertices' or 'verticies_plus': :math:`(N, 4, 2)` or :math:`(B, N, 4, 2)`. * Any other value: :math:`(N, 4)` or :math:`(B, N, 4)`. Examples: >>> boxes_xyxy = torch.as_tensor([[0, 3, 1, 4], [5, 1, 8, 4]]) >>> boxes = Boxes.from_tensor(boxes_xyxy) >>> assert (boxes_xyxy == boxes.to_tensor(mode='xyxy')).all() rrrdrBrN)rMrV)rWrQrSrTrFrUrErR)rMrQ)rrrBrBrCrQcss>|]6\}}tjj|t|jdddgd| gVqdS)rBrN)rr-Z functionalrror)r"onrrrr#sz"Boxes.to_tensor..)rqrrr:rstackaminamaxr8rrOrZr; as_tensorrDr r[rLrkrmzipr^)rurOry batched_boxesrrAr@offsetrrrrzzs2""*   zBoxes.to_tensor)rAr@rcCs|jjrtd|jrDtj|jjd|jjd||f|j|jd}n"tj|jjd||f|j|jd}t tj |j ddd}|dd d d f d||ddd d f d|t |d |||d d D],\}}d||d|d |d|d f<q|S)aConvert 2D boxes to masks. Covered area is 1 and the remaining is 0. Args: height: height of the masked image/images. width: width of the masked image/images. Returns: the output mask tensor, shape of :math:`(N, width, height)` or :math:`(B,N, width, height)` and dtype of :func:`Boxes.dtype` (it can be any floating point dtype). Note: It is currently non-differentiable. Examples: >>> boxes = Boxes(torch.tensor([[ # Equivalent to boxes = Boxes.from_tensor([[1,1,4,3]]) ... [1., 1.], ... [4., 1.], ... [4., 3.], ... [1., 3.], ... ]])) # 1x4x2 >>> boxes.to_mask(5, 5) tensor([[[0., 0., 0., 0., 0.], [0., 1., 1., 1., 1.], [0., 1., 1., 1., 1.], [0., 1., 1., 1., 1.], [0., 0., 0., 0., 0.]]]) cBoxes.to_tensor isn't differentiable. Please, create boxes from tensors with `requires_grad=False`.rrBr rDrMTrx.Nrr5rr)rr requires_grad RuntimeErrorrqrrKrr rDrrrzclamp_rr8roundint)rurAr@maskZclipped_boxes_xyxy mask_channelZbox_xyxyrrrto_masks"",&z Boxes.to_maskr3r~rcCsbd|jkrdkr,nn|jdddkr>td|jdt|j|}|rX||_|St|dS) aApply a transformation matrix to the 2D boxes. Args: M: The transformation matrix to be applied, shape of :math:`(3, 3)` or :math:`(B, 3, 3)`. inplace: do transform in-place and return self. Returns: The transformed boxes. rrrN)rrzAThe transformation matrix shape must be (3, 3) or (B, 3, 3). Got r'F)r9rr;r=rrr rur3r~r<rrrtransform_boxess , zBoxes.transform_boxesr3rcCs|j|ddS)z0Inplace version of :func:`Boxes.transform_boxes`Tr~rrur3rrrtransform_boxes_ szBoxes.transform_boxes_warp)rrr~rcCsJ|dkrtn|dkrnttd|}||dddddf<|j||dS)a#Translates boxes by the provided size. Args: size: translate size for x, y direction, shape of :math:`(B, 2)`. method: "warp" or "fast". inplace: do transform in-place and return self. Returns: The transformed boxes. fastrrNrr)r/r r)rurrr~r3rrr translates  zBoxes.translatecCs|jSrrrurrrr'sz Boxes.datacCs|jSrrsrrrrrO+sz Boxes.modecCs|jjSzReturns boxes device.rrrDrrrrrD/sz Boxes.devicecCs|jjSzReturns boxes dtype.rrr rrrrr 4sz Boxes.dtyperDr rcCs.|durt|std|jj||d|_|Sz)Like :func:`torch.nn.Module.to()` method.NzBoxes must be in floating pointrCrr;rrtorurDr rrrr9szBoxes.tocCst|jdS)NF)r rrr\rrrrr\Asz Boxes.clone)TrS)F)NNF)FF)NNF)rMT)NF)F)rF)NN)#__name__ __module__ __qualname____doc__rrrrboolstrrvrr}rrrrrr7rr classmethodrrzrrrrpropertyrrOrDr rr\rrrrr sh "  7 H5 c@s"eZdZdZd'ejeeddddZe ejejejfdd d Z e d(ejeedd d dZ d)eejdddZ eeeejdddZd*ejeddddZejddddZeejdddZeedddZeejdd d!Zeejdd"d#Zd+eejeejdd$d%d&ZdS),r a3D boxes containing N or BxN boxes. Args: boxes: 3D boxes, shape of :math:`(N,8,3)` or :math:`(B,N,8,3)`. See below for more details. raise_if_not_floating_point: flag to control floating point casting behaviour when `boxes` is not a floating point tensor. True to raise an error when `boxes` isn't a floating point tensor, False to cast to float. Note: **3D boxes format** is defined as a floating data type tensor of shape ``Nx8x3`` or ``BxNx8x3`` where each box is a `hexahedron `_ defined by it's 8 vertices coordinates. Coordinates must be in ``x, y, z`` order. The height, width and depth of a box is defined as ``width = xmax - xmin + 1``, ``height = ymax - ymin + 1`` and ``depth = zmax - zmin + 1``. Examples of `hexahedrons `_ are cubes and rhombohedrons. T xyzxyz_plusNrgcCst|tjs tdt|d|sF|r>td|jd|}t |j dkr^| d}d|j krtdkrnn|j ddd kstd |j d|j dkrd nd |_ ||_||_dS) Nrir'rjr)r5rrr)rz53D bbox shape must be (N, 8, 3) or (B, N, 8, 3). Got FT)rlrrr+rnr6r;r r7rorrpr9rqrrrsrtrrrrvVs  ,zBoxes3D.__init__rwcCs2|jdd}|d|d|d}}}|||fS)a)Compute boxes heights and widths. Returns: - Boxes depths, shape of :math:`(N,)` or :math:`(B,N)`. - Boxes heights, shape of :math:`(N,)` or :math:`(B,N)`. - Boxes widths, shape of :math:`(N,)` or :math:`(B,N)`. Example: >>> boxes_xyzxyz = torch.tensor([[ 0, 1, 2, 10, 21, 32], [3, 4, 5, 43, 54, 65]]) >>> boxes3d = Boxes3D.from_tensor(boxes_xyzxyz) >>> boxes3d.get_boxes_shape() (tensor([30., 60.]), tensor([20., 50.]), tensor([10., 40.])) xyzwhd)rOrT.r.)rz)ruZ boxes_xyzwhdr{r|Zdepthsrrrr}os zBoxes3D.get_boxes_shapexyzxyzrNc Csd|jkrdkr(nn|jddks:td|jd|jdk}|rL|n|d}|rb|n|}|d|d |d }}}|}|d kr|d |d}|d |d } |d|d } n~|dkr|d |dd}|d |d d} |d|d d} n6|dkr8|d |d |d} } }ntd||r|dkrbtd| dkrxtd| dkrtdt||||| | } |r| n| d} || d|dS)aHelper method to easily create :class:`Boxes3D` from 3D boxes stored in another format. Args: boxes: 3D boxes, shape of :math:`(N,6)` or :math:`(B,N,6)`. mode: The format in which the 3D boxes are provided. * 'xyzxyz': boxes are assumed to be in the format ``xmin, ymin, zmin, xmax, ymax, zmax`` where ``width = xmax - xmin``, ``height = ymax - ymin`` and ``depth = zmax - zmin``. * 'xyzxyz_plus': similar to 'xyzxyz' mode but where box width, length and depth are defined as ``width = xmax - xmin + 1``, ``height = ymax - ymin + 1`` and ``depth = zmax - zmin + 1``. * 'xyzwhd': boxes are assumed to be in the format ``xmin, ymin, zmin, width, height, depth`` where ``width = xmax - xmin``, ``height = ymax - ymin`` and ``depth = zmax - zmin``. validate_boxes: check if boxes are valid rectangles or not. Valid rectangles are those with width, height and depth >= 1 (>= 2 when mode ends with '_plus' suffix). Returns: :class:`Boxes3D` class containing the original `boxes` in the format specified by ``mode``. Examples: >>> boxes_xyzxyz = torch.as_tensor([[0, 3, 6, 1, 4, 8], [5, 1, 3, 8, 4, 9]]) >>> boxes = Boxes3D.from_tensor(boxes_xyzxyz, mode='xyzxyz') >>> boxes.data # (2, 8, 3) tensor([[[0., 3., 6.], [0., 3., 6.], [0., 3., 6.], [0., 3., 6.], [0., 3., 7.], [0., 3., 7.], [0., 3., 7.], [0., 3., 7.]], [[5., 1., 3.], [7., 1., 3.], [7., 3., 3.], [5., 3., 3.], [5., 1., 8.], [7., 1., 8.], [7., 3., 8.], [5., 3., 8.]]]) rrr5rz,BBox shape must be (N, 6) or (B, N, 6). Got r'rrErFrUrrTrrrrBrrRrXrYz%Some boxes have negative depths or 0.F)rhrO) r9rr;r:r6r7rZr]rfr^) rrrOrPr_r>r?rbr@rArcZ hexahedronsrrrrs8+(   zBoxes3D.from_tensor)rOrc Csx|jjrtd|jr|jn |jd}tj|jdd|jddgdd |j d|j dd}| }|dvrrnl|dvr|d |d d}|d |d d}|d |dd}||d <||d <||d <nt d||dvrtj gd|j|jd}||}|dr^|d |d |d}} } |d |d |d }}}t|| | |||}|jrj|n|d}|S)aCast :class:`Boxes3D` to a tensor. ``mode`` controls which 3D boxes format should be use to represent boxes in the tensor. Args: mode: The format in which the boxes are provided. * 'xyzxyz': boxes are assumed to be in the format ``xmin, ymin, zmin, xmax, ymax, zmax`` where ``width = xmax - xmin``, ``height = ymax - ymin`` and ``depth = zmax - zmin``. * 'xyzxyz_plus': similar to 'xyzxyz' mode but where box width, length and depth are defined as ``width = xmax - xmin + 1``, ``height = ymax - ymin + 1`` and ``depth = zmax - zmin + 1``. * 'xyzwhd': boxes are assumed to be in the format ``xmin, ymin, zmin, width, height, depth`` where ``width = xmax - xmin``, ``height = ymax - ymin`` and ``depth = zmax - zmin``. * 'vertices': boxes are defined by their vertices points in the following ``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*. Vertices coordinates are in (x,y, z) order. Finally, box width, height and depth are defined as ``width = xmax - xmin``, ``height = ymax - ymin`` and ``depth = zmax - zmin``. * 'vertices_plus': similar to 'vertices' mode but where box width, length and depth are defined as ``width = xmax - xmin + 1`` and ``height = ymax - ymin + 1``. Returns: 3D Boxes tensor in the ``mode`` format. The shape depends with the ``mode`` value: * 'vertices' or 'verticies_plus': :math:`(N, 8, 3)` or :math:`(B, N, 8, 3)`. * Any other value: :math:`(N, 6)` or :math:`(B, N, 6)`. Note: It is currently non-differentiable due to a bug. See github issue `#1304 `_. Examples: >>> boxes_xyzxyz = torch.as_tensor([[0, 3, 6, 1, 4, 8], [5, 1, 3, 8, 4, 9]]) >>> boxes = Boxes3D.from_tensor(boxes_xyzxyz, mode='xyzxyz') >>> assert (boxes.to_tensor(mode='xyzxyz') == boxes_xyzxyz).all() aBoxes3D.to_tensor doesn't support computing gradients since they aren't accurate. Please, create boxes from tensors with `requires_grad=False`. This is a known bug. Help is needed to fix it. For more information, see https://github.com/kornia/kornia/issues/1396.rrrdrBr)rr)rrQrSrTrErrFrrUrR)rrQ)rrrrBrBrBrCrQ)rrrrrqr:rrrrr8rrZr;rrDr r[rfr^) rurOrrr@rArcrr>r?rbrrrrzs8$"   zBoxes3D.to_tensor)rcrAr@rcCs0|jjrtd|jrJtj|jjd|jjd|||f|jj|jjd}n(tj|jjd|||f|jj|jjd}| d}|ddddf d||ddddf d||dd ddf d|t | d |||| d d  D]:\}}d||d |d |d|d |d|df<q|S)uConvert ·D boxes to masks. Covered area is 1 and the remaining is 0. Args: depth: depth of the masked image/images. height: height of the masked image/images. width: width of the masked image/images. Returns: the output mask tensor, shape of :math:`(N, depth, width, height)` or :math:`(B,N, depth, width, height)` and dtype of :func:`Boxes3D.dtype` (it can be any floating point dtype). Note: It is currently non-differentiable. Examples: >>> boxes = Boxes3D(torch.tensor([[ # Equivalent to boxes = Boxes.3Dfrom_tensor([[1,1,1,3,3,2]]) ... [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 >>> boxes.to_mask(4, 5, 5) tensor([[[[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]], [[0., 0., 0., 0., 0.], [0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 0., 0., 0., 0.]], [[0., 0., 0., 0., 0.], [0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 0., 0., 0., 0.]], [[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]]]]) rrrBrr.Nrrr5rrr)rrrrrqrrKrr rDrzrrr8rr)rurcrAr@rZclipped_boxes_xyzxyzrZ box_xyzxyzrrrrs23  ,zBoxes3D.to_maskFrcCsdd|jkrdkr,nn|jdddkr>td|jdt|j|}|rX||_|St|dd S) aApply a transformation matrix to the 3D boxes. Args: M: The transformation matrix to be applied, shape of :math:`(4, 4)` or :math:`(B, 4, 4)`. inplace: do transform in-place and return self. Returns: The transformed boxes. rrrN)rrzAThe transformation matrix shape must be (4, 4) or (B, 4, 4). Got r'Fr)r9rr;r=rrr rrrrrrs , zBoxes3D.transform_boxesrcCs|j|ddS)z2Inplace version of :func:`Boxes3D.transform_boxes`TrrrrrrrszBoxes3D.transform_boxes_cCs|jSrrrrrrrsz Boxes3D.datacCs|jSrrrrrrrOsz Boxes3D.modecCs|jjSrrrrrrrDszBoxes3D.devicecCs|jjSrrrrrrr sz Boxes3D.dtypercCs.|durt|std|jj||d|_|Srrrrrrrsz Boxes3D.to)Tr)rT)r)F)NN)rrrrrrrrrvrr}rrrzrrrrrrrOrDr rrrrrrr Es, NMU)r)rMT)typingrrrrrrZ kornia.corerZkornia.geometry.bboxrZkornia.geometry.linalgr Z kornia.utilsr __all__r rrrrr2r=rLrarfr Zjitscriptr rrrrs6    ( ; !!