3jSSKJr SSKJrJrJr SSKrSSKJr SSKJ r SSK J r SSK J r SS /rSS jrSSS jjrSS jrSS jrSSSjjrSSjr"SS5r"SS\5r"SS 5rg)) annotations)OptionalTuplecastN)Size)eye_like) validate_bbox)transform_pointsBoxesBoxes3DcU[R[R[R[R[R 4;$N)torchfloat16float32float64bfloat16half)dtypes O/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/geometry/boxes.py_is_floating_point_dtyper s/ U]]EMM5==%..RWR\R\] ]]c[SU55(d)[SUVs/sHo"RPM snS35eUS:Xah[SU55nUVs/sHo#URS- PM nn[R R RRUSS9nXT4$[S US 35es snfs snf) z&Merge a list of boxes into one tensor.c3# UHFoRSS[R"SS/5:H=(a UR5S:Hv MH g7f)N)shaperrdim.0boxs r "_merge_box_list..&s>XRW3yy~QF!33F QFRWsAAz5Input boxes must be a list of (N, 4, 2) shaped. Got: .padc3># UHoRSv M g7f)rN)rr!s rr$r%*s2ESIIaLEsrT) batch_first`z` is not implemented.) all TypeErrorrmaxrnnutilsrnn pad_sequenceNotImplementedError)boxesmethodr#max_Nstatsoutputs r_merge_box_listr8$s XRWX X XOfkPlfk_bQZQZfkPlOmmnopp 2E22167#1%7##00D0I ="AfX-B"CDDQm8s B> CcUR5(aUOUR5nURSSup#nUS:XaU$URSX2-U5nURS:XaUOUR S5nURSURS:wa,[ SURSSURSS35e[X5nURU5nU$) amTransform 3D and 2D in kornia format by applying the transformation matrix M. Boxes and the transformation matrix could be batched or not. Args: boxes: 2D quadrilaterals or 3D hexahedrons in kornia format. M: the transformation matrix of shape :math:`(3, 3)` or :math:`(B, 3, 3)` for 2D and :math:`(4, 4)` or :math:`(B, 4, 4)` for 3D hexahedron. NrrzBatch size mismatch. Got z for boxes and z for the transformation matrix.) is_floating_pointfloatrviewndim unsqueeze ValueErrorr view_as)r3Mboxes_per_batchn_points_per_boxcoordinates_dimensionpointstransformed_boxess r_transform_boxesrI3s " " A@E{{23?O@U VF VVq[akk!nA ||A!''!*$' Q'8PQ |Sr s  '7q&A)11%8 rcURURs=:Xa(URs=:XaURs=:XaS:Xd O [S5e[R"URSURSSS4UR UR S9nURS5US'URS5US 'US ==US- - ss'US ==US- - ss'US ==US- - ss'US ==US- - ss'U$)NrzXWe expect to create a batch of 2D boxes (quadrilaterals) in vertices format (B, N, 4, 2)rrdevicerr;.r.rK.rKr.rr.rrK.rrK)r?rArzerosrrMrr@)xminyminwidthheightpolygonss r_boxes_to_polygonsrZQs 99 CUZZ C6;; C! Cstt{{DJJqM4::a=!Q? [_[e[efH~~b)HV~~b)HV Y519$ Y519$ Y6A:% Y6A:% OrcUR5nURS5(aoURS:HnSURs=::aS::a,O O)URSS[R "SS/5:Xd[ SUSURS 35eO}URS 5(aYURS:HnSURs=::aS::aO OURS S:Xd[ S USURS 35eO[ S U35eUR5(aUOUR5nU(aUOURS5nURS5(aUS:Xa;UR5nUSSS2S4S- USSS2S4'USSS2S4S- USSS2S4'O%US:XaUR5nO[ S U35eU(+=(d [U5 OURS 5(aUS:XaUSUS- USUS- peO@US:XaUSUS- S-USUS- S-peOUS:Xa USUSpeO[ S U35eU(aFUS:*R5(a [ S5eUS:*R5(a [ S5eUSUSp[XxXe5nO[ S U35eU(aUnU$URS5nU$)z%Convert from boxes to quadrilaterals.verticesrrrNrz3Boxes shape must be (N, 4, 2) or (B, N, 4, 2) when z mode. Got r&xyr;z-Boxes shape must be (N, 4) or (B, N, 4) when Unknown mode r.rK vertices_plusxyxy.rrO.rrN xyxy_plusxywh%Some boxes have negative widths or 0.&Some boxes have negative heights or 0.)lower startswithr?rrrrAr<r=r@cloner anyrZsqueeze) r3modevalidate_boxesbatchedquadrilateralsrXrWrUrVs r_boxes_to_quadrilateralsrpcs ::=/00,,..EEKKMEE%//!"4E z"" : "[[]N+9ac1*E*IN3!Q; ')7QR )Ca)GN3A: & _ $"[[]N}TF34 4 ;mN;    6>!&ME&M95=5QW=;XE [ !&ME&M9A=uV}uU[}?\_`?`E V^!&M5=E}TF34 4  !! !HII!   "" !IJJ6]E&Md+DF=/00'.^N 5C4J4J14MN rcURURs=:XaJURs=:Xa9URs=:Xa(URs=:XaURs=:XaS:Xd O [S5e[R"URSURSSS4UR UR S9nURS5US 'URS5US 'URS5US 'US ==US- - ss'US ==US- - ss'US==US- - ss'US==US- - ss'UR5nUS ==URS5S- - ss'[R"Xg/SS9nU$)NrzUWe expect to create a batch of 3D boxes (hexahedrons) in vertices format (B, N, 8, 3)rrKrrrLr;rNrOrbrPrQrRrSrr ) r?rArrTrrMrr@ricat) rUrVzminrWrXdepthfront_vertices back_vertices polygons3ds r_boxes3d_to_polygons3drysR 99 ^TYY ^%** ^ ^uzz ^]^ ^pqq[[$**Q-A1!EdkkaeakaklN!^^B/N6!^^B/N6!^^B/N69*9*9!+9!+#((*M&U__R0144N:CJ rc\rSrSrSrSS SjjrS!SjrS"Sjr\S#Sj5r S$Sjr S%S&Sjjr S%S'S jjr S(S jr S(S jrS)S*S jjrS+S,SjjrS)S-SjjrS.Sjr\S/S0Sjj5rS1S2SjjrS3SjrS%S4SjjrS5SjrS6S7Sjjr\S.Sj5r\S8Sj5r\S9Sj5r\S:Sj5rS;SSjr Sr!g )?r 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. cSUl[U[5(a[U5uol[U[R 5(d[ S[U5S35eUR5(d/U(a[SUR35eUR5n[UR5S:XaURS5nSURs=::aS::aO OURSSS :Xd[S URS35eURS:XaS OS UlXlX0lg) N"Input boxes is not a Tensor. Got: r&+Coordinates must be in floating point. Got r)r;rrrr)rrz3Boxes shape must be (N, 4, 2) or (B, N, 4, 2). Got FT)_N isinstancelistr8rTensorr,typer<rArr=lenrreshaper? _is_batched_data_modeselfr3raise_if_not_floating_pointrls r__init__Boxes.__init__s (, eT " ",U3NE7%..@e QOP P&&((* #Nu{{m!\]]KKME u{{ q MM'*EUZZ$1$RS)9V)CRSXS^S^R__`ab b$)JJ!O5  rcf[U5"URUS5nURUlU$NF)rrrrkeynew_boxs r __getitem__Boxes.__getitem__s*t*TZZ_e4  rc8URURU'U$rrrrvalues r __setitem__Boxes.__setitem__++ 3 rc.URR$rdatarrs rr Boxes.shapeyyrcj[[RURSSS95nUSUSp2X24$)afCompute 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.]])) rdTas_padded_sequencerbra)rrr to_tensor)r boxes_xywhwidthsheightss rget_boxes_shapeBoxes.get_boxes_shapes;%,,vRV(WX $V,j.@rc[R"URUR/SS9nU(aX0lU$UR 5nX4lU$)zMerge boxes. Say, current instance holds :math:`(B, N, 4, 2)` and the incoming boxes holds :math:`(B, M, 4, 2)`, the merge results in :math:`(B, N + M, 4, 2)`. Args: boxes: 2D boxes. inplace: do transform in-place and return self. rKrr)rrsrrri)rr3inplacerobjs rmerge Boxes.mergesCyy$**ejj1q9 JKjjl  rc$U(a URnOURR5n[U[5(aUR XR 5 OUR X5 U(aU$UR5nXElU$r)rrirr index_put_r)rindicesvaluesrrrs r index_putBoxes.index_put"sl JJEJJ$$&E fe $ $   Wkk 2   W - Kjjl  rc[UR5S:XaURS5S:Xd[SURS35eURS==USSSS24R URR S 9- ss'URS ==USSSS 24R URR S 9- ss'U$ z5Pad a bounding box. Args: padding_size: (B, 4) rrKrz%Expected padding_size as (B, 4). Got r&rN.NrMrOrrrsize RuntimeErrorrtorMr padding_sizes rr' Boxes.pad9L&&'1,1B1B11E1J!F|GYGYFZZ[\] ] 6l3bqb=9<:==TZZEVEV=WW rc[UR5S:XaURS5S:Xd[SURS35eURS==USSSS24R URR S 9-ss'URS ==USSSS 24R URR S 9-ss'U$rrrs runpad Boxes.unpadFrrNc8[U[R5(a[U[R5(d[eU(a URnOURR 5nUSS2SSS24R SURS5S5nXTSU:USUSU:'USS2SSS24R SURS5S5nXdSU:USUSU:'USS2SSS24R SURS5S5nXtSU:USUSU:'USS2SSS24R SURS5S5nXSU:USUSU:'U(aU$UR 5n XIlU $)NrKrrNrO)rrrr2rrirepeatr) rtopleftbotrightrr topleft_x topleft_y botright_x botright_yrs rclamp Boxes.clampSs 7ELL11j5<<6X6X% % JJEJJ$$&EAtRaRK(//5::a=!D 3<6]Y=V3Wf eFmi/0AtQRK(//5::a=!D 3<6]Y=V3Wf eFmi/0arrk*11!UZZ]AF 4>V}z?Y4Zf eFmj01aqrk*11!UZZ]AF 4>V}z?Y4Zf eFmj01 Kjjl  rc[e)aTrim out zero padded boxes. Given box arrangements of shape :math:`(4, 4, Box)`: == === == === == === == === == -- Box -- Box -- Box -- Box -- -- 0 -- 0 -- Box -- Box -- -- 0 -- Box -- 0 -- 0 -- -- 0 -- 0 -- 0 -- 0 -- == === == === == === == === == Nothing will change if correspondence_preserve is True. Only pure zero layers will be removed, resulting in shape :math:`(4, 3, Box)`: == === == === == === == === == -- Box -- Box -- Box -- Box -- -- 0 -- 0 -- Box -- Box -- -- 0 -- Box -- 0 -- 0 -- == === == === == === == === == Otherwise, you will get :math:`(4, 2, Box)`: == === == === == === == === == -- Box -- Box -- Box -- Box -- -- 0 -- Box -- Box -- Box -- == === == === == === == === == )r2)rcorrespondence_preservers rtrim Boxes.trimqs 8"!rcUR5nU(a URnOURR5nUbSXTU:'UbSXTU:'U(aU$UR5nXVlU$)Ng) compute_arearri)rmin_areamax_areararearrs rfilter_boxes_by_areaBoxes.filter_boxes_by_areasq  " JJEJJ$$&E  %(E/ "  %(E/ " Kjjl  rc URRS:XaURRS5O URnURSSS9n[R "USUS- USUS- 5n[R "USSS9upE[R"USURS 5RS S S 55nUSUSpS [R"[R"U[R"USS5-U[R"USS5-- SS 95-n URRS:Xa(U RURRS S 5$U $)zReturn :math:`(B, N)`.r)r;rrrKT)r keepdimrOrN)r descendingr;rg?rrN)rr?r>meanratan2sortgatherr@expandabssumrollr) rcoordscentroidangles_clockwise_indicesordered_cornersxyrs rrBoxes.compute_areas>04 10D,$**;;1d;3VF^hv.>>vQYZ`Qa@ab$zz&aDI,,vq2C2M2Mb2Q2X2XY[]_ab2cdv&(?1UYYuyy!ejjAq.A*Aa%**UVXY[\J]F])^defgg26**//Q2Ftyy))"1-.PDPrc[U[R5(a [XUS9nOUVs/sHn[XRU5PM nnU"USU5$s snf)aCreate :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. validate_boxes: Check if boxes are valid. Default is True. * '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.]]]) rlrmF)rrrrp)clsr3rlrmror#s r from_tensorBoxes.from_tensorsW` eU\\ * *5eWefN]bc]bVY6s.Q]bNc>5$//dsAc UR(a UROURRS5n[R"UR SS9UR SS9/SS9RURSURSS5nUc URnUR5nUS;aO8US;a$USUS - S-US US - S-peXdS 'XTS'O[S U35eUS ;a0[R"/SQURURS9nXG-nURS5(a[!US US US US5nUR"b{U(dt[%X@R"5VV s/sHPup[R&R(R+U[-UR5S- SS/-SU */-5PMR nnn U$UR(aUOUR/S5nU$s sn nf)aCast :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 whilst `from_tensor`. 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() rrrrrKr)r`rc)rdr\r_rarOrbrNr^)r`r\)rrrKrKrLr\)rrr@rstackaminamaxr>rrlrgrA as_tensorrMrrhrZrzipr. functionalr'rrk) rrlr batched_boxesr3rXrWoffsetons rrBoxes.to_tensorsH'+&6&6 DJJ>> 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`.cudarrKrrMr`Tr.Nrr;rrr)r requires_gradrrrrMrrrTrrrrclamp_rr>roundintlongraranger@r)rrXrW is_batchedrrMmaskclipped_boxes_xyxy mask_channelbox_xyxy out_shaper`x1y1x2y2ysxsy_maskx_maskmaskss rto_mask Boxes.to_mask1s: :: # #u %%   ;;& {{ZZ%%a($***:*:1*=vMUYU_U_hlhshs{{DJJ$4$4Q$7#Gtzzbfbmbmn"&ellDNN6^bN4c!d  sCaCx ( / /5 9 sADqDy ) 0 0F ;+. "f,.@.E.Eb!.L.R.R.T.X.X.Z+& VW a[8A;6 hqk8QQR + K A 1 vEIA6I!%,,vZ^0_`3!8$++Au5319%,,Q7!&&r1-335::<adT!Q$ZadT!Q$ZG XXa  XXa  XXa  XXa  \\& 0 \\% /T1W+AtG,D!Gr!T'{1JKT1W+AtG,D!Gr!T'{1JK!!!$v'7'7'::>>uEzz9%%rcSURs=::aS::aO OURSSS:wa[SURS35e[URU5nU(aX0lU$UR 5nX4lU$)zApply a transformation matrix to the 2D boxes. Args: M: The transformation matrix to be applied, shape of :math:`(3, 3)` or :math:`(B, 3, 3)`. inplace: do transform in-place and return self. Returns: The transformed boxes. rrrN)rrzAThe transformation matrix shape must be (3, 3) or (B, 3, 3). Got r&)r?rrArIrri)rrCrrHrs rtransform_boxesBoxes.transform_boxesswAFFa17723<6#9`abahah`iijkl l,TZZ; *JKjjl%  rc"URUSS9$)z1Inplace version of :func:`Boxes.transform_boxes`.TrrrrCs rtransform_boxes_Boxes.transform_boxes_##At#44rcUS:Xa[eUS:XaO[e[SU5nXSS2SS2S4'URXCS9$)zTranslate boxes by the provided size. Args: size: translate size for x, y direction, shape of :math:`(B, 2)`. method: "warp" or "fast". inplace: do transform in-place and return self. Returns: The transformed boxes. fastwarprNrr)r2rr)rrr4rrCs r translateBoxes.translatesT V % % v  % %"1d+!RaR( ##A#77rcUR$rrrs rr Boxes.data zzrcUR$rrrs rrl Boxes.moder*rc.URR$zReturns boxes device.rrMrs rrM Boxes.devicezz   rc.URR$zReturns boxes dtype.rrrs rr Boxes.dtypezzrc~Ub[U5(d [S5eURRXS9UlU$z)Like :func:`torch.nn.Module.to()` method.zBoxes must be in floating pointrLrrArrrrMrs rrBoxes.to<  %=e%D%D>? ?ZZ]]&]>  rc[U5"URR5S5nURUlURUlUR UlU$r)rrrirrrrrs rri Boxes.clonesG4j))+U3JJ ** rcFURRU5UlU$r)rr)rrs rr Boxes.typesZZ__U+  r)rrrr)Tr_)r3!torch.Tensor | list[torch.Tensor]rboolrlstrreturnNone)rslice | int | torch.TensorrFr )rrHrr rFr rFztuple[int, ...] | Size)rFz!tuple[torch.Tensor, torch.Tensor]F)r3r rrDrFr )rz-tuple[torch.Tensor, ...] | list[torch.Tensor]rztorch.Tensor | BoxesrrDrFr )r torch.TensorrFr )NNF)r(Optional[torch.Tensor | tuple[int, int]]rrLrrDrFr )FF)rrDrrDrFr )rOptional[float]rrMrrDrFr rFrKr`T)r3rCrlrErmrDrFr r)rl Optional[str]rrDrFrC)rXrrWrrFrK)rCrKrrDrFr )rCrKrFr )r%F)rrKr4rErrDrFr rFrErFz torch.devicerF torch.dtypeNN)rMOptional[torch.device]rOptional[torch.dtype]rFr )rFr )rrTrFr )"__name__ __module__ __qualname____firstlineno____doc__rrrpropertyrrrrr'rrrrr classmethodrrrrr r&rrlrMrrrir__static_attributes__rrr r s,-1# 0&*   > $0 >%   .  =A=A 9;   <">ch':I[_ &Q bf40540=@40[_40 4040nFKF!F>BF *FP\&|.58.!!  rcj^\rSrSr%S\S'\SS Sjj5rS S U4SjjjrS SjrSr U=r $) VideoBoxesirtemporal_channel_sizec [U[45(d=UR5S:wd)URSS[R "SS/5:wa [ S5eURS5n[URURS5URS5-SURS 5URS55S US 9nU"US S 5nX5l U$) NrrrzQInput box type is not yet supported. Please input an `BxTxNx4x2` tensor directly.rKrr;rr_rF) rrr rrrrArrpr>rc)rr3rmrcroouts rrVideoBoxes.from_tensors edW % %%))+*:ekk"#>NRWR\R\^_ab]cRd>dpq q % 1 1 JJuzz!}uzz!}4b%**Q-TU W )  .%9$9! rc 4>[TU]USS9n[U[R5(a+UR "SUR /URSSQ76$UVs/sH-o3R "SUR /URSSQ76PM/ sn$s snf)NFrr;rK)superrrrrr>rcr)rrlrf_out __class__s rrVideoBoxes.to_tensorsg? c5<< ( (88B : :KSYYqr]K KWZ[WZt "d88J4::ab>JWZ[[[s4Bc[U5"URR5S5nURUlURUlUR UlUR UlU$r)rrrirrrrcr?s rriVideoBoxes.clonesU4j))+U3JJ **$($>$>! rr`)T)r3rCrmrDrFrbr)rlrPrFrC)rFrb) rXrYrZr[__annotations__r^rrrir_ __classcell__)rks@rrbrbsLNR5GK "\\rrbc\rSrSrSrSSSjjrSSjrSSjr\SSj5r SSjr \ SSSjj5r SSS jjr SS jrSS S jjrS!S jr\S"S j5r\S#Sj5r\S$Sj5r\S%Sj5rS&S'SjjrSrg)(r i ay3D 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. c&[U[R5(d[S[ U5S35eUR 5(d0U(a[ SURS35eUR5n[UR5S:XaURS5nSURs=::aS::aO OURSSS :Xd[ S URS35eURS:XaS OS Ul XlX0lg) Nr}r&r~r)r;rrr)rz53D bbox shape must be (N, 8, 3) or (B, N, 8, 3). Got FT)rrrr,rr<rArr=rrrr?rrrrs rrBoxes3D.__init__s%..@e QOP P&&((* #Nu{{m[\!]^^KKME u{{ q MM'*EUZZ$1$RS)9V)CTUZU`U`Taabcd d$)JJ!O5  rcX[URUSSS9nURUlU$)NF xyzxyz_plusrl)r rrrs rrBoxes3D.__getitem__4s($**S/5}E  rc8URURU'U$rrrs rrBoxes3D.__setitem__9rrc.URR$rrrs rr Boxes3D.shape=rrcDURSS9nUSUSUSpCnXCU4$)aCompute 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.])) xyzwhdrxra.r.re)r)r boxes_xyzwhdrrdepthss rrBoxes3D.get_boxes_shapeAs:~~8~4 ".v"6 V8Ll[aNb&&rcSURs=::aS::aO OURSS:Xd[SURS35eURS:HnU(aUOURS5nUR 5(aUOUR 5nUSUS US pvnUR 5nUS :Xa"US US- nUS US - n USUS - n OTUS:Xa+US US- S-nUS US - S-n USUS - S-n O#US:XaUS US USpn O[SU35eU(aiUS:*R5(a [S5eU S:*R5(a [S5eU S:*R5(a [S5e[XVXxX5n U(aU OU RS5n U"U SUS9$)aCreate :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.]]]) rrr;rsz,BBox shape must be (N, 6) or (B, N, 6). Got r&rrNrOrbxyzxyzrarrrwrKrr^rerfz%Some boxes have negative depths or 0.F)rrl) r?rrAr@r<r=rgrjryrk) rr3rlrmrnrUrVrtrWrXru hexahedronss rrBoxes3D.from_tensorTsXUZZ$1$RA)=KEKK=XYZ[ [**/ eooa&80022  =%-vDzz| 8 &ME&M1E6]U6]2F&ME&M1E ] "&ME&M1A5E6]U6]2Q6F&ME&M1A5E X #(=%-v5E5}TF34 4  !! !HII!   "" !IJJ !! !HII,TfT %,k+2E2Ea2H ;EMMrctURR(a [S5eUR(a UROURR S5n[ R "URSS9URSS9/SS9RURSURSS5nUR5nUS;aOKUS;a7US US - S-nUS US - S-nUS US- S-nXCS 'XSS 'XcS 'O[SU35eUS;a0[ R"/SQURURS9nX7-nUR!S5(a)US US USpnUS US US pen[#XXXV5nUR(aUnU$UR%S5nU$)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.rrrrrKrs)rrw)rr\r_rarNrrOrrbr^)rr\)rrrrKrKrKrLr\)rrrrr@rrrrr>rrgrArrMrrhryrk) rrlrr3rWrXrurrUrVrts rrBoxes3D.to_tensorsL :: # #D '+&6&6 DJJ>> 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.]]]]) rrrKrr.Nrrr;rsrer)rrrrrrTrrrMrrrr>rr)rrurXrWr clipped_boxes_xyzxyzr  box_xyzxyzs rrBoxes3D.to_masksh :: # #u    ;;!!!$djj&6&6q&95%Pjj&&zz((D ;;!!!$eU;4::CSCS\`\f\f\m\mD $~~h7S#A#X&--a7S!$Q$Y'..q&9S!$Q$Y'..q%8), IIb% /1E1J1J2q1Q1W1W1Y1]1]1_) $L  1 1 -z!}z!}/LjYZm^hij^kNkk  )  rcSURs=::aS::aO OURSSS:wa[SURS35e[URU5nU(aX0lU$[ USS 5$) zApply a transformation matrix to the 3D boxes. Args: M: The transformation matrix to be applied, shape of :math:`(4, 4)` or :math:`(B, 4, 4)`. inplace: do transform in-place and return self. Returns: The transformed boxes. rrrN)rrzAThe transformation matrix shape must be (4, 4) or (B, 4, 4). Got r&Frw)r?rrArIrr )rrCrrHs rrBoxes3D.transform_boxesGsrAFFa17723<6#9`abahah`iijkl l,TZZ; *JK(%??rc"URUSS9$)z3Inplace version of :func:`Boxes3D.transform_boxes`.Trrrs rr Boxes3D.transform_boxes_\r"rcUR$rrrs rr Boxes3D.data`r*rcUR$rr,rs rrl Boxes3D.modedr*rc.URR$r/r0rs rrMBoxes3D.devicehr2rc.URR$r4r5rs rr Boxes3D.dtypemr7rNc~Ub[U5(d [S5eURRXS9UlU$r9r:r;s rr Boxes3D.torr=r)rrr)Trw)r3rKrrDrlrErFrG)rrHrFr )rrHrr rFr rI)rFz/tuple[torch.Tensor, torch.Tensor, torch.Tensor])rT)r3rKrlrErmrDrFr )r)rlrErFrK)rurrXrrWrrFrKrJ)rCrKrrDrFr )rCrKrFr rNrQrRrSrU)rMrVrrWrFr )rXrYrZr[r\rrrr]rrr^rrrrr rrlrMrrr_r`rrr r s"Zg!@DSV . '&KNKNZM^Tl@*5!!  r)rrTrFrD)r')r3zlist[torch.Tensor]r4rErFztuple[torch.Tensor, list[int]])r3rKrCrKrFrK) rUrKrVrKrWrKrXrKrFrKrO)r3rKrlrErmrDrFrK)rUrKrVrKrtrKrWrKrXrKrurKrFrK) __future__rtypingrrrrrkornia.core.opsrkornia.geometry.bboxr kornia.geometry.linalgr __all__rr8rIrZrpryr rbr r`rrrs$#(( $.3 I ^ < *3?IU$7t         >hhV""Jllr