3jS]SSKJr SSKrSSKJr SSKrSSKJr /SQrSSjr SSjr SS jr SS jr SS jr SS jrSS jrSSjrSSSjjrSSjrg)) annotationsN)Optional)transform_points) bbox_generatorbbox_generator3d bbox_to_maskbbox_to_mask3dinfer_bbox_shapeinfer_bbox_shape3dnmstransform_bbox validate_bboxvalidate_bbox3dc:[UR5S;a)URSS[R"SS/5:Xdg[UR5S:XaUR SSS5nUSUS p!US US pCUS US peUSUSpX1- S-XW- S-pXB- S-Xh- S-p[R "X- 5n [R "X- 5n[R "U S:5(ag[R "US:5(agg)aValidate if a 2D bounding box usable or not. This function checks if the boxes are rectangular or not. Args: boxes: a tensor containing the coordinates of the bounding boxes to be extracted. The tensor must have the shape of Bx4x2, where each box is defined in the following ``clockwise`` order: top-left, top-right, bottom-right, bottom-left. The coordinates must be in the x, y order. NrF).rr).rr).rr).rr).rr).rr).rr).rrrg-C6?T)lenshapetorchSizeviewabsany)boxesx_tly_tlx_try_trx_bry_brx_bly_blwidth_twidth_bheight_theight_b width_diff height_diffs N/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/geometry/bbox.pyrr)s   &5;;rs+;uzz1a&?Q+Q 5;;1 2q!$y!5#3$y!5#3$y!5#3$y!5#3${Q aWq$+/h7,-J))H/0K yyd"## yyt#$$ c r[UR5S;a)URSS[R"SS/5:Xd[ SURS35e[UR5S:XaUR S SS5n[R "US [R"/S QUR[RS 95SS2SS2S 4n[R "US [R"/SQUR[RS 95SS2SS2S 4nX- S -n[R"URS S 5USS2S 45(d[ SUS35e[R "US [R"/SQUR[RS 95SS2SS2S 4n[R "US [R"/SQUR[RS 95SS2SS2S 4nXE- S -n[R"URS S 5USS2S 45(d[ SUS35eUSS2SS2S4USS2SS2S4- S -n[R"URS S 5USS2S 45(d[ SUS35eg)aValidate if a 3D bounding box usable or not. This function checks if the boxes are cube or not. Args: boxes: a tensor 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 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. The coordinates must be in the x, y, z order. rrNrz1Box shape must be (B, 8, 3) or (B, N, 8, 3). Got .rrrrrdevicedtyperrrrz4Boxes must have be cube, while get different widths rrr5r:rrrr4z5Boxes must have be cube, while get different heights rz4Boxes must have be cube, while get different depths T) rrrrAssertionErrorr index_selecttensorr7longallclosepermuterleftrightwidthsbotupperheightsdepthss r.rrNsX   &5;;rs+;uzz1a&?Q+QPQVQ\Q\P]]^_`` 5;;1 2q!$   eQ \%,,^c^h^h(i jklnoqrkr sD   ual5<<_d_i_i)j klmoprsls tE \A F >>&..A.q!t = =STZS[[\]^^   UAu||L]b]g]g'h ijkmnpqjq rC   ual5<<_d_i_i)j klmoprsls tEkAoG >>'//!Q/A ? ?TU\T]]^_`` 1ab!8_uQAX . 2F >>&..A.q!t = =STZS[[\]^^ r/c~[U5 USS2SS4USS2SS4- S-nUSS2SS4USS2SS4- S-nX!4$)a"Auto-infer the output sizes for the given 2D bounding boxes. Args: boxes: a tensor containing the coordinates of the bounding boxes to be extracted. The tensor must have the shape of Bx4x2, where each box is defined in the following ``clockwise`` order: top-left, top-right, bottom-right, bottom-left. The coordinates must be in the x, y order. Returns: - Bounding box heights, shape of :math:`(B,)`. - Boundingbox widths, shape of :math:`(B,)`. Example: >>> boxes = torch.tensor([[ ... [1., 1.], ... [2., 1.], ... [2., 2.], ... [1., 2.], ... ], [ ... [1., 1.], ... [3., 1.], ... [3., 2.], ... [1., 2.], ... ]]) # 2x4x2 >>> infer_bbox_shape(boxes) (tensor([2., 2.]), tensor([2., 3.])) Nrrr)r)rwidthheights r.r r qsZ8%1a.5Aq>9A=E Aq>E!Q'N:Q>F =r/c [U5 [R"US[R"/SQUR[R S95SS2SS2S4n[R"US[R"/SQUR[R S95SS2SS2S4nX- S-SS2S4n[R"US[R"/SQUR[R S95SS2SS2S4n[R"US[R"/SQUR[R S95SS2SS2S4nXE- S-SS2S4nUSS2S S2S 4USS2SS 2S 4- S-SS2S4nXvU4$) aAuto-infer the output sizes for the given 3D bounding boxes. Args: boxes: a tensor 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 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. The coordinates must be in the x, y, z order. Returns: - Bounding box depths, shape of :math:`(B,)`. - Bounding box heights, shape of :math:`(B,)`. - Bounding box widths, shape of :math:`(B,)`. Example: >>> boxes = torch.tensor([[[ 0, 1, 2], ... [10, 1, 2], ... [10, 21, 2], ... [ 0, 21, 2], ... [ 0, 1, 32], ... [10, 1, 32], ... [10, 21, 32], ... [ 0, 21, 32]], ... [[ 3, 4, 5], ... [43, 4, 5], ... [43, 54, 5], ... [ 3, 54, 5], ... [ 3, 4, 65], ... [43, 4, 65], ... [43, 54, 65], ... [ 3, 54, 65]]]) # 2x8x3 >>> infer_bbox_shape3d(boxes) (tensor([31, 61]), tensor([21, 51]), tensor([11, 41])) rr3r6Nrr9r;r<rr)rrr>r?r7r@rCs r.r r s_FE   eQ \%,,^c^h^h(i jklnoqrkr sD   ual5<<_d_i_i)j klmoprsls tElQ1 %F   UAu||L]b]g]g'h ijkmnpqjq rC   ual5<<_d_i_i)j klmoprsls tE{Q1%GAqr1Hoa!Qh/!3QT :F F ""r/c0[U5 [R"X RURS9R US5n[R"XRURS9R SU5nUSS2SS4R SSS5nUSS2SS4R SSS5nUSS2SS4R SSS5nUSS2SS4R SSS5nXE:XG:*-X6:-X8:*-n U R UR5$)aConvert 2D bounding boxes to masks. Covered area is 1. and the remaining is 0. Args: boxes: a tensor containing the coordinates of the bounding boxes to be extracted. The tensor must have the shape of Bx4x2, where each box is defined in the following ``clockwise`` order: top-left, top-right, bottom-right and bottom-left. The coordinates must be in the x, y order. width: width of the masked image. height: height of the masked image. Returns: the output mask tensor. Note: It is currently non-differentiable. Examples: >>> boxes = torch.tensor([[ ... [1., 1.], ... [3., 1.], ... [3., 2.], ... [1., 2.], ... ]]) # 1x4x2 >>> bbox_to_mask(boxes, 5, 5) tensor([[[0., 0., 0., 0., 0.], [0., 1., 1., 1., 0.], [0., 1., 1., 1., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]]]) r6rNrrr)rraranger7r8rto) rrLrMyyxxx_miny_minx_maxy_maxmasks r.r r s>% f\\ E J J6ST UB eLL D I I!U SB !Q'N  Aq )E !Q'N  Aq )E !Q'N  Aq )E !Q'N  Aq )E KBK (BK 8BK HD 775;; r/c[U5 Uup#nUSS2SS4R5nUSS2SS4R5nUSS2SS4R5nUSS2SS4R5nUSS2SS4R5n USS2SS4R5n [R"X R[RS9n [R"X0R[RS9n [R"X@R[RS9n U SSS24USS2S4:U SSS24USS2S4:*-SS2SSS2SS4U SSS24USS2S4:U SSS24USS2S4:*-SS2SSSS2S4-U SSS24U SS2S4:U SSS24U SS2S4:*-SS2SSSSS24-R 5nUR SSS 9R SSS 9nUR SSS 9R SSS 9nUR SSS 9R SSS 9nUU-U-nUR 5$) aConvert 3D bounding boxes to masks. Covered area is 1. and the remaining is 0. Args: boxes: a tensor 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 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. The coordinates must be in the x, y, z order. size: depth, height and width of the masked image. Returns: the output mask tensor. Examples: >>> boxes = torch.tensor([[ ... [1., 1., 1.], ... [2., 1., 1.], ... [2., 2., 1.], ... [1., 2., 1.], ... [1., 1., 2.], ... [2., 1., 2.], ... [2., 2., 2.], ... [1., 2., 2.], ... ]]) # 1x8x3 >>> bbox_to_mask3d(boxes, (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., 0., 0.], [0., 1., 1., 0., 0.], [0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]], [[0., 0., 0., 0., 0.], [0., 1., 1., 0., 0.], [0., 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., 0.], [0., 0., 0., 0., 0.]]]]]) Nrrrrr6rT)dimkeepdim)rr@rrPr7floatall)rsizeD0D1D2z_minz_maxrUrWrTrVzyxmcond1cond2cond3m_outs r.r r s[dEJBB !Q'N   !E !Q'N   !E !Q'N   !E !Q'N   !E !Q'N   !E !Q'N   !E R EJJ?A R EJJ?A R EJJ?A D!G*ag &1T1W:q$w+G H!TSTVZ\`J`a dAgJ%4. (QtQwZ5D>-I JAtUY[\^bLb c d dAgJ%4. (QtQwZ5D>-I JAtUY[_abLb c d eg  EEaE & * *q$ * ?E EEaE & * *q$ * ?E EEaE & * *q$ * ?E EME !E ;;=r/c URUR:XaUR5S;d[SUSUS35eURUR:XaUR5S;d[SUSUS35eURURs=:Xa!URs=:XaUR:XdBO [SURSURSURS URS 3 5eURURs=:Xa!URs=:XaUR:XdBO [S URSURSURS URS 3 5e[ R "S S /S S /S S /S S ///URURS 9RUR5S :XaSO [U5SS5nUSS2SS2S 4==URSS5- ss'USS2SS2S4==URSS5- ss'USS2SS 4==US- - ss'USS2SS 4==US- - ss'USS2SS4==US- - ss'USS2SS4==US- - ss'U$)aGenerate 2D bounding boxes according to the provided start coords, width and height. Args: x_start: a tensor containing the x coordinates of the bounding boxes to be extracted. Shape must be a scalar tensor or :math:`(B,)`. y_start: a tensor containing the y coordinates of the bounding boxes to be extracted. Shape must be a scalar tensor or :math:`(B,)`. width: widths of the masked image. Shape must be a scalar tensor or :math:`(B,)`. height: heights of the masked image. Shape must be a scalar tensor or :math:`(B,)`. Returns: the bounding box tensor. Examples: >>> x_start = torch.tensor([0, 1]) >>> y_start = torch.tensor([1, 0]) >>> width = torch.tensor([5, 3]) >>> height = torch.tensor([7, 4]) >>> bbox_generator(x_start, y_start, width, height) tensor([[[0, 1], [4, 1], [4, 7], [0, 7]], [[1, 0], [3, 0], [3, 3], [1, 3]]]) rrz6`x_start` and `y_start` must be a scalar or (B,). Got , r2z3`width` and `height` must be a scalar or (B,). Got 5All tensors must be in the same dtype. Got `x_start`( ), `y_start`( ), `width`( ), `height`().6All tensors must be in the same device. Got `x_start`(rr6rNrrr) rrZr=r8r7rr?repeatrr)x_starty_startrLrMbboxs r.rr?s[B MMW]] *w{{}/FUV]U^^`ah`iijkll KK6<< 'EIIK6,ARSXRYY[\b[ccdeff ==GMM HU[[ HFLL H  }W]]O;u{{m[ghnhthtguuw y   >>W^^ Lu|| Lv}} L  ( gnn5EF||nLr C  <<1a&1a&1a&1a&9:7>>Y`YfYf g n n [[]a S\1a D AqMW\\"a((MAqMW\\"a((MAqMUQYMAqMUQYMAqMVaZMAqMVaZM Kr/cURURs=:XaUR:XaO OUR5S;d[SUSUSUS35eURURs=:XaUR:XaO OUR5S;d[SUSUSUS35eURURs=:XaCURs=:Xa2URs=:Xa!URs=:XaUR:Xd\O [SURSURSURS URS URS URS 3 5eURURs=:XaCURs=:Xa2URs=:Xa!URs=:XaUR:Xd\O [S URSURSURS URS URS URS 3 5e[ R "/SQ/SQ/SQ/SQ//URURS9R[U5SS5nUSS2SS2S4==URSS5- ss'USS2SS2S4==URSS5- ss'USS2SS2S4==URSS5- ss'USS2SS4==U- ss'USS2SS4==U- ss'USS2SS4==U- ss'USS2SS4==U- ss'UR5nUSS2SS2S4==URSS9RSS5- ss'[ R"Xg/SS9nU$)aZGenerate 3D bounding boxes according to the provided start coords, width, height and depth. Args: x_start: a tensor containing the x coordinates of the bounding boxes to be extracted. Shape must be a scalar tensor or :math:`(B,)`. y_start: a tensor containing the y coordinates of the bounding boxes to be extracted. Shape must be a scalar tensor or :math:`(B,)`. z_start: a tensor containing the z coordinates of the bounding boxes to be extracted. Shape must be a scalar tensor or :math:`(B,)`. width: widths of the masked image. Shape must be a scalar tensor or :math:`(B,)`. height: heights of the masked image. Shape must be a scalar tensor or :math:`(B,)`. depth: depths of the masked image. Shape must be a scalar tensor or :math:`(B,)`. Returns: the 3d bounding box tensor :math:`(B, 8, 3)`. Examples: >>> x_start = torch.tensor([0, 3]) >>> y_start = torch.tensor([1, 4]) >>> z_start = torch.tensor([2, 5]) >>> width = torch.tensor([10, 40]) >>> height = torch.tensor([20, 50]) >>> depth = torch.tensor([30, 60]) >>> bbox_generator3d(x_start, y_start, z_start, width, height, depth) tensor([[[ 0, 1, 2], [10, 1, 2], [10, 21, 2], [ 0, 21, 2], [ 0, 1, 32], [10, 1, 32], [10, 21, 32], [ 0, 21, 32]], [[ 3, 4, 5], [43, 4, 5], [43, 54, 5], [ 3, 54, 5], [ 3, 4, 65], [43, 4, 65], [43, 54, 65], [ 3, 54, 65]]]) rmzA`x_start`, `y_start` and `z_start` must be a scalar or (B,). Got rnr2z<`width`, `height` and `depth` must be a scalar or (B,). Got rorpz ), `z_start`(rqrrz) and `depth`(rsrt)rrrr6rNrrrrrZr) rrZr=r8r7rr?rurrclone unsqueezecat)rvrwz_startrLrMdepthrx bbox_backs r.rr~s#f MMW]] ;gmm ; QW@WOPWyXZ[bZccefmenno p   KK6<< 65;; 6599;&;P[\a[bbdekdllnotnuuvwxx ==GMM hW]] hekk hV\\ h]b]h]h h $]]O=}U\UbUbTcd{{m< ~^EKK=XZ \  >>W^^ nw~~ n nQWQ^Q^ nbgbnbn n $^^,M'..9IW^WeWeVfg||nL~ell^[] _   << Y 9 56w~~U\UbUb  fS\1a   AqMW\\"a((MAqMW\\"a((MAqMW\\"a((MAqMUMAqMUMAqMVMAqMVM I aBh5??q?188A>> 99d&A .D Kr/c[U[5(d[S[U535eUS;a[ SU35eUc>UR SS[ R"SS/5:Xd[R"SS S 9 US :XaUS US -US 'USUS-US'[XRUR SSS55nURU5nUbU(aUR SS[ R"SS/5:XdUR5n[ R"USSS/4SS9SUS '[ R"USS S/4SS9SUS'[ R"USSS/4SS9SUS '[ R"USS S/4SS9SUS'UnUS :XaUS US - US 'USUS- US'U$)a>Apply a transformation matrix to a box or batch of boxes. Args: trans_mat: The transformation matrix to be applied with a shape of :math:`(3, 3)` or batched as :math:`(B, 3, 3)`. boxes: The boxes to be transformed with a common shape of :math:`(N, 4)` or batched as :math:`(B, N, 4)`, the polygon shape of :math:`(B, N, 4, 2)` is also supported. mode: The format in which the boxes are provided. If set to 'xyxy' the boxes are assumed to be in the format ``xmin, ymin, xmax, ymax``. If set to 'xywh' the boxes are assumed to be in the format ``xmin, ymin, width, height`` restore_coordinates: In case the boxes are flipped, adding a post processing step to restore the coordinates to a valid bounding box. Returns: The set of transformed points in the specified mode zMode must be a string. Got )xyxyxywhz(Mode must be one of 'xyxy', 'xywh'. Got NrrraPPrevious behaviour produces incorrect box coordinates if a flip transformation performed on boxes.The previous wrong behaviour has been corrected and will be removed in the future versions.If you wish to keep the previous behaviour, please set `restore_coordinates=False`.Otherwise, set `restore_coordinates=True` as an acknowledgement.r) stacklevelr).r).r).r).rrr.rzr) isinstancestr TypeErrortype ValueErrorrrrwarningswarnrrview_asr{minmax) trans_matrmoderestore_coordinatestransformed_boxesrestored_boxess r.rrs( dC 5d4j\BCC ##CD6JKK"EKK,< Aq6@R,R  O   v~f f 5f f f 5f &6y**U[[YZ^]_abBc&d)11%8#':U[[QSQTEUY^YcYcefhidjYkEk*002!&+>> boxes = torch.tensor([ ... [10., 10., 20., 20.], ... [15., 5., 15., 25.], ... [100., 100., 200., 200.], ... [100., 100., 200., 200.]]) >>> scores = torch.tensor([0.9, 0.8, 0.7, 0.9]) >>> nms(boxes, scores, iou_threshold=0.8) tensor([0, 3, 1]) rrrzboxes expected as Nx4. Got: r2rzscores expected as N. Got: rz+boxes and scores mus have same shape. Got: T) descendingNg)r) rrrunbindsortappendrrrclampwherestackr?)rscores iou_thresholdx1y1x2y2areas_orderkeepixx1yy1xx2yy2whinterovrindss r.r r s, 5;;1RA!57 }AFGG 6<<A6v||nAFGG {{1~a(Fu{{TZT`T`G`Faabcdd\\"%NBB W !E{{d{+HA D ++a.1  !H Aiir)}-iir)}-iir)}-iir)}- KK s + KK s +Aux%ab "22U:;{{3-/03dQh ++a.1   4y1}{{4  << r/)r torch.Tensorreturnbool)rrrz!tuple[torch.Tensor, torch.Tensor])rrrz/tuple[torch.Tensor, torch.Tensor, torch.Tensor])rrrLintrMrrr)rrr^ztuple[int, int, int]rr) rvrrwrrLrrMrrr)rvrrwrr~rrLrrMrrrrr)rN) rrrrrrrzOptional[bool]rr)rrrrrr\rr) __future__rrtypingrrlinalgr__all__rrr r r r rrrr r/r.rs$# $ "J FD.#b( VM`< <$0<9E<O[<<~X X XX  X  X  XXxmq99$098;9[i99x8r/