3juk SSKJrJrJr SSKrSSKJr SSKJr SSK J r J r SSK J r Jr "SS5r"S S \5rSS \R"S \S \R"4SjjrSS\R"S \S \R"4SjjrS \R"S\R"S \R"4SjrS \R"S \R"4SjrS\R"S\R"S \R"4SjrS\R"S\R"S\R"S \R"4SjrSS\R"S\R"S \S \R"4Sjjrg) )IterableListUnionNKORNIA_CHECK_SAME_DEVICE)_torch_inverse_cast)convert_points_from_homogeneousconvert_points_to_homogeneous)inverse_transformationtransform_pointsc\rSrSrSrS\R S\R S\R S\R SS4 S jr\S \ \R S\ 4S j5r \S \R S \ S\ 4Sj5r \S \R S \ S\ 4Sj5r\S \\R SS4Sj5rS\R"4Sjr\S\R 4Sj5r\S\R 4Sj5r\S\4Sj5r\S\R 4Sj5r\S\R 4Sj5r\S\R 4Sj5r\S\R 4Sj5r\S\R 4Sj5r\R8S\\R \4SS4Sj5r\S\R 4Sj5r\R8S\\R \4SS4Sj5r\S\R 4Sj5r \ R8S\\R \4SS4Sj5r \S\R 4S j5r!\S\R 4S!j5r"\S\R 4S"j5r#\S\R 4S#j5r$S:S$jr%S\R 4S%jr&S&\R SS4S'jr'S&\\\R 4SS4S(jr(S)\R S\R 4S*jr)S+\R S,\R S\R 4S-jr*\+S.\R S/\R S0\R S1\R S\S\S2\R S3\R S4\R S5\S6\\ \R"S4S7\RXSS4S8j5r-S9r.g); PinholeCameraaQClass that represents a Pinhole Camera model. Args: intrinsics: torch.Tensor with shape :math:`(B, 4, 4)` containing the full 4x4 camera calibration matrix. extrinsics: torch.Tensor with shape :math:`(B, 4, 4)` containing the full 4x4 rotation-translation matrix. height: torch.Tensor with shape :math:`(B)` containing the image height. width: torch.Tensor with shape :math:`(B)` containing the image width. .. note:: We assume that the class attributes are in batch form in order to take advantage of PyTorch parallelism to boost computing performance. intrinsics extrinsicsheightwidthreturnNcURXX4/5 URUS5 URUS5 URUS5 URUS5 URXX4/5 X0lX@lXlX lg)Nrrrr) _check_valid_check_valid_params_check_valid_shape_check_consistent_devicerr _intrinsics _extrinsics)selfrrrrs X/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/geometry/camera/pinhole.py__init__PinholeCamera.__init__-s :6AB   \:   \: 1 w/ %%zv&MN$* #( )3)3 data_itercH[SU55(d [S5eg)Nc3># UHoRSv M g7f)rN)shape).0datas r -PinholeCamera._check_valid..?s7YT::a=YszArguments shapes must matchT)all ValueError)r!s rrPinholeCamera._check_valid=s"7Y777:; ;r r& data_namec[UR5S;a.URSSS:wa[SUSUR35eg)N)r/r/ Argument z< shape must be in the following shape Bx4x4 or BxNx4x4. Got Tlenr$r*r&r,s rr!PinholeCamera._check_valid_paramsCsN tzz?& (TZZ_-FI;&bcgcmcmbno r cl[UR5S:Xd[SUSUR35eg)Nr2z- shape must be in the following shape B. Got Tr3r5s rr PinholeCamera._check_valid_shapeKs64::!#y 3`aeakak`lmn nr c6USnUHn[X!5 M g)Nrr)r!firstr&s rr&PinholeCamera._check_consistent_deviceQs! D $T 1r c.URR$)zYReturn the device for camera buffers. Returns: Union[str, torch.device, None] type )rdevicers rr>PinholeCamera.deviceWs&&&r chURURS5(d[eUR$)zXThe full 4x4 intrinsics matrix. Returns: torch.Tensor of shape :math:`(B, 4, 4)`. r)rrAssertionErrorr?s rrPinholeCamera.intrinsics`/''(8(8,GG r chURURS5(d[eUR$)zXThe full 4x4 extrinsics matrix. Returns: torch.Tensor of shape :math:`(B, 4, 4)`. r)rrrBr?s rrPinholeCamera.extrinsicslrDr c4URRS$)zQReturn the batch size of the storage. Returns: scalar with the batch size. r)rr$r?s r batch_sizePinholeCamera.batch_sizexs$$Q''r c URS$)z^Return the focal length in the x-direction. Returns: torch.Tensor of shape :math:`(B)`. .rrrr?s rfxPinholeCamera.fxy))r c URS$)z^Return the focal length in the y-direction. Returns: torch.Tensor of shape :math:`(B)`. .r8r8rLr?s rfyPinholeCamera.fyrOr c URS$)zbReturn the x-coordinate of the principal point. Returns: torch.Tensor of shape :math:`(B)`. .rrLr?s rcxPinholeCamera.cxrOr c URS$)zbReturn the y-coordinate of the principal point. Returns: torch.Tensor of shape :math:`(B)`. .r8rVrLr?s rcyPinholeCamera.cyrOr c URS$)zeReturn the x-coordinate of the translation vector. Returns: torch.Tensor of shape :math:`(B)`. .rrr?s rtxPinholeCamera.txz**r valuec XRS'g)zDSet the x-coordinate of the translation vector with the given value.r^Nr`rrds rrarb', #r c URS$)zeReturn the y-coordinate of the translation vector. Returns: torch.Tensor of shape :math:`(B)`. .r8r_r`r?s rtyPinholeCamera.tyrcr c XRS'g)DSet the y-coordinate of the translation vector with the given value.riNr`rfs rrjrkrgr c URS$)zfReturns the z-coordinate of the translation vector. Returns: torch.Tensor of shape :math:`(B)`. .rVr_r`r?s rtzPinholeCamera.tzrcr c XRS'g)rmroNr`rfs rrprqrgr c.URSSS2SS24$)zdReturn the 3x4 rotation-translation matrix. Returns: torch.Tensor of shape :math:`(B, 3, 4)`. .Nr.r/r`r?s r rt_matrixPinholeCamera.rt_matrix!sBQB{++r c.URSSS2SS24$)zpReturn the 3x3 camera matrix containing the intrinsics. Returns: torch.Tensor of shape :math:`(B, 3, 3)`. .Nr.rLr?s r camera_matrixPinholeCamera.camera_matrixrvr c.URSSS2SS24$)zlReturn the 3x3 rotation matrix from the extrinsics. Returns: torch.Tensor of shape :math:`(B, 3, 3)`. .Nr.r`r?s rrotation_matrixPinholeCamera.rotation_matrixrvr c.URSSS2SS24$)zkReturn the translation vector from the extrinsics. Returns: torch.Tensor of shape :math:`(B, 3, 1)`. .Nr.r_r`r?s rtranslation_vector PinholeCamera.translation_vectorssBQB|,,r cURR5nURR5nURR5nURR5n[ X4X5$)z2Return a deep copy of the current object instance.)rclonerrrr)rrrrrs rrPinholeCamera.clonesW#{{002"jj..0#'??#8#8#: #'??#8#8#: ZVCCr c6URR5$)ziReturn the inverse of the 4x4 instrisics matrix. Returns: torch.Tensor of shape :math:`(B, 4, 4)`. )rinverser?s rintrinsics_inverse PinholeCamera.intrinsics_inverses&&((r scale_factorc:URR5nUS==U-ss'US==U-ss'US==U-ss'US==U-ss'XRR5-nXRR5-n[ X R X45$)zScale the pinhole model. Args: scale_factor: a torch.Tensor with the scale factor. It has to be broadcastable with class members. The expected shape is :math:`(B)` or :math:`(1)`. Returns: the camera model with scaled parameters. rKrQrUrZ)rrrrrr)rrrrrs rscalePinholeCamera.scales$(??#8#8#: 9-9-9-9-+kk.?.?.AA*ZZ-=-=-??Z&HHr cURS==U-ss'URS==U-ss'URS==U-ss'URS==U-ss'U=RU-slU=RU-slU$)aScale the pinhole model in-place. Args: scale_factor: a torch.Tensor with the scale factor. It has to be broadcastable with class members. The expected shape is :math:`(B)` or :math:`(1)`. Returns: the camera model with scaled parameters. rKrQrUrZ)rrr)rrs rscale_PinholeCamera.scale_'su  "l2"  "l2"  "l2"  "l2" |#  l"  r point_3dc\URUR-n[[X!55$)agProject a 3d point in world coordinates onto the 2d camera plane. Args: point_3d: torch.Tensor containing the 3d points to be projected to the camera plane. The shape of the torch.Tensor can be :math:`(*, 3)`. Returns: torch.Tensor of (u, v) cam coordinates with shape :math:`(*, 2)`. Example: >>> _ = torch.manual_seed(0) >>> X = torch.rand(1, 3) >>> K = torch.eye(4)[None] >>> E = torch.eye(4)[None] >>> h = torch.ones(1) >>> w = torch.ones(1) >>> pinhole = kornia.geometry.camera.PinholeCamera(K, E, h, w) >>> pinhole.project(X) tensor([[5.6088, 8.6827]]) )rrr r )rrPs rprojectPinholeCamera.project=s', OOdoo -./?/LMMr point_2ddepthczURUR-n[U5n[U[ U5U-5$)aUnproject a 2d point in 3d. Transform coordinates in the pixel frame to the world frame. Args: point_2d: torch.Tensor containing the 2d to be projected to world coordinates. The shape of the torch.Tensor can be :math:`(*, 2)`. depth: torch.Tensor containing the depth value of each 2d points. The torch.Tensor shape must be equal to point2d :math:`(*, 1)`. normalize: whether to F.normalize the pointcloud. This must be set to `True` when the depth is represented as the Euclidean ray length from the camera position. Returns: torch.Tensor of (x, y, z) world coordinates with shape :math:`(*, 3)`. Example: >>> _ = torch.manual_seed(0) >>> x = torch.rand(1, 2) >>> depth = torch.ones(1, 1) >>> K = torch.eye(4)[None] >>> E = torch.eye(4)[None] >>> h = torch.ones(1) >>> w = torch.ones(1) >>> pinhole = kornia.geometry.camera.PinholeCamera(K, E, h, w) >>> pinhole.unproject(x, depth) tensor([[0.4963, 0.7682, 1.0000]]) )rrrr r )rrrrP_invs r unprojectPinholeCamera.unprojectVs9< OOdoo -#A&'DX'NQV'VWWr rMrRrWr[rarjrprHr>dtypec [R"U SSXS9n U S==U- ss'U S==U- ss'U S==U- ss'U S==U- ss'U S==S- ss'U S ==S- ss'[R"SXS9RU S S 5nUS ==U- ss'US ==U- ss'US ==U - ss'[R"XU S9nUS==U- ss'[R"XU S9nUS==U- ss'U"XUU5$)Nr/r>rrKrQrUrZ).rVrV?).r.r.r8r^riro.r)torchzeroseyerepeat)rrMrRrWr[rrrarjrprHr>rrr height_tmp width_tmps rfrom_parametersPinholeCamera.from_parametersys"[[Q&N 9#9#9#9#9$9$YYq=DDZQRTUV :"$:"$:"$[[%H 6f$KK G &U"JJ BBr rrrr)rr)/__name__ __module__ __qualname____firstlineno____doc__rTensorr staticmethodrboolrstrrrrrr>propertyrrintrHrMrRrWr[rasetterrfloatrjrprtrxr{r~rrrrrr classmethodrr__static_attributes__r rrrs 4,,449LL4JO,,4_d_k_k4 4  64 %,,34#$ 2D,>2422 ' '  ELL      ELL    (C((*ELL***ELL***ELL***ELL**+ELL++YY,ellE12,t,,+ELL++YY,ellE12,t,,+ELL++YY,ellE12,t,,,5<<,,,u||,,,,,-ELL--D)ELL)I%,,I?I.5 )<#=/,N NN2 X%,, Xu|| X XF!C LL!C LL!C LL !C LL !C  !C!C LL!C LL!C LL!C!Cc5<<-.!C{{!C !C!Cr rcr\rSrSrSrS\\SS4SjrS\\SS4Sjr\ S\ 4S j5r S \ S\4S jr S r g) PinholeCamerasListiaQClass that represents a list of pinhole cameras. The class inherits from :class:`~kornia.PinholeCamera` meaning that it will keep the same class properties but with an extra dimension. .. note:: The underlying data torch.Tensor will be stacked in the first dimension. That's it, given a list of two camera instances, the intrinsics torch.Tensor will have a shape :math:`(B, N, 4, 4)` where :math:`B` is the batch size and :math:`N` is the numbers of cameras (in this case two). Args: pinholes_list: a python tuple or list containing a set of `PinholeCamera` instances. pinholes_listrNc&URU5 g)N)_initialize_parameters)rrs rrPinholeCamerasList.__init__s ##M2r pinholesc[U[[45(d[S[ U535e//p2//pTUHn[U[ 5(d[S[ U535eUR UR5 UR UR5 UR UR5 UR UR5 M [R"USS9Ul[R"USS9Ul[R"USS9Ul [R"USS9UlU$)z5Initialise the class attributes given a cameras list.z(pinhole must of type list or tuple. Got z6Argument pinhole must be from type PinholeCamera. Got r8dim) isinstancelisttuple TypeErrortyperappendrrrrrstackrr)rrrrrrpinholes rr)PinholeCamerasList._initialize_parameterss(T5M22FtH~FVWX XB!#RJGg}55"XY]^eYfXg hii MM'.. ) LL '   g00 1   g00 1  %*KKA$> #(;;u!#< ).ZQ)G).ZQ)G r chSnURb"[URRS5nU$)z0Return the number of pinholes cameras per batch.r_r8)rrr$)r num_camerass rrPinholeCamerasList.num_camerass2 ?? &doo33A67Kr idxcURSU4nURSU4nURSS2U4nURSS2U4n[ XEX#5$)z>> rng = torch.manual_seed(0) >>> pinhole = torch.rand(1, 12) # Nx12 >>> pinhole_matrix(pinhole) # Nx4x4 tensor([[[4.9626e-01, 1.0000e-06, 8.8477e-02, 1.0000e-06], [1.0000e-06, 7.6822e-01, 1.3203e-01, 1.0000e-06], [1.0000e-06, 1.0000e-06, 1.0000e+00, 1.0000e-06], [1.0000e-06, 1.0000e-06, 1.0000e-06, 1.0000e+00]]]) rVr8 .Nr/rrrr. r4r$rBrchunkrr>rviewr)rrrMrRrWr[ks rpinhole_matrixrs8  1 $):b)@X^^,,[[#rr'!2A1=NBB !HOO8>>BSHA q!Qx~~a0!Q7Ac1ackNc1ackNc1ackNc1ackN Hr rc[UR5S:XaURSS:Xd[UR5e[R"USSS24SSS9up#pE[R "SUR URS9nURSSS5RURS SS5nS X!-- USS S S24'S X1-- USSSS24'S U-X!-- USS SS 24'S U-X1-- USSSS 24'U$) aReturn the inverted pinhole matrix from a pinhole model. .. note:: This method is going to be deprecated in version 0.2 in favour of :attr:`kornia.PinholeCamera.intrinsics_inverse()`. Args: pinhole: torch.Tensor with pinhole models. eps: epsilon for numerical stability. Returns: torch.Tensor of inverted pinhole matrices. Shape: - Input: :math:`(N, 12)` - Output: :math:`(N, 4, 4)` Example: >>> rng = torch.manual_seed(0) >>> pinhole = torch.rand(1, 12) # Nx12 >>> inverse_pinhole_matrix(pinhole) # Nx4x4 tensor([[[ 2.0151, 0.0000, -0.1783, 0.0000], [ 0.0000, 1.3017, -0.1719, 0.0000], [ 0.0000, 0.0000, 1.0000, 0.0000], [ 0.0000, 0.0000, 0.0000, 1.0000]]]) rVr8r.Nr/rrrrgr.r)rrrMrRrWr[rs rinverse_pinhole_matrixrs8  ! # a(8B(>W]]++[[bqb!11!>> rng = torch.manual_seed(0) >>> pinhole_i = torch.rand(1, 12) # Nx12 >>> scales = 2.0 * torch.ones(1) # N >>> scale_pinhole(pinhole_i, scales) # Nx12 tensor([[0.9925, 1.5364, 0.1770, 0.2641, 0.6148, 1.2682, 0.4901, 0.8964, 0.4556, 0.6323, 0.3489, 0.4017]]) rVr8r.Nr_)r4r$rBr unsqueeze)rrpinholes_scaleds r scale_pinholer-s6  1 $):b)@X^^,, 5;;1U[[))nn&O'RaR05??23FFOC!G r c[UR5S:XaURSS:Xd[UR5e[e)aCompute extrinsic transformation matrices for pinholes. Args: pinholes: torch.Tensor of form [fx fy cx cy h w rx ry rz tx ty tz] of size (N, 12). Returns: torch.Tensor of extrinsic transformation matrices of size (N, 4, 4). rVr8r)r4r$rBNotImplementedError)rs rget_optical_pose_baserQs:  1 $):b)@X^^,, r pinhole_i pinhole_refc [UR5S:XaURSS:Xd[UR5eURUR:wa[UR5e[U5n[U5n[R "U[ U55n[R "[U5[R "U[U555$)aHomography from reference to ith pinhole. .. note:: The pinhole model is represented in a single vector as follows: .. math:: pinhole = (f_x, f_y, c_x, c_y, height, width, r_x, r_y, r_z, t_x, t_y, t_z) torch.where: :math:`(r_x, r_y, r_z)` is the rotation vector in angle-axis convention. :math:`(t_x, t_y, t_z)` is the translation vector. .. math:: H_{ref}^{i} = K_{i} * T_{ref}^{i} * K_{ref}^{-1} Args: pinhole_i: torch.Tensor with pinhole model for ith frame. pinhole_ref: torch.Tensor with pinhole model for reference frame. Returns: tensors that convert depth points (u, v, d) from pinhole_ref to pinhole_i. Shape: - Input: :math:`(N, 12)` and :math:`(N, 12)` - Output: :math:`(N, 4, 4)` Example: pinhole_i = torch.rand(1, 12) # Nx12 pinhole_ref = torch.rand(1, 12) # Nx12 homography_i_H_ref(pinhole_i, pinhole_ref) # Nx4x4 rVr8r) r4r$rBrrmatmulr rr)rr i_pose_base ref_pose_base i_pose_refs rhomography_i_H_refrgsL   A %)//!*<*BY__--++++[..//' 2K)+6Mk+A-+PQJ <<y15<< LbcnLo3p qqr rintrinsics_inv pixel_coordsc[UR5S:Xd+URSS:Xa[SUR35e[UR5S:Xd[SUR35e[UR5S:Xd+URSS:Xa[SUR35e[USS2S4U5nX0R SS SS5-$) anTransform coordinates in the pixel frame to the camera frame. Args: depth: the source depth maps. Shape must be Bx1xHxW. intrinsics_inv: the inverse intrinsics camera matrix. Shape must be Bx4x4. pixel_coords: the grid with (u, v, 1) pixel coordinates. Shape must be BxHxWx3. Returns: torch.Tensor of shape BxHxWx3 with (x, y, z) cam coordinates. r/r8z3Input depth has to be in the shape of Bx1xHxW. Got r.z:Input intrinsics_inv has to be in the shape of Bx4x4. Got z:Input pixel_coords has to be in the shape of BxHxWx3. Got NrrV)r4r$r*r permute)rrr cam_coordss r pixel2camrs u{{ q U[[^q%8Nu{{m\]] ~## $ )UVdVjVjUklmm |!! "a 'L,>,>q,AQ,FUVdVjVjUklmm/q$w0GVJ  aAq1 11r cam_coords_src dst_proj_srcc[UR5S:Xd+URSS:Xa[SUR35e[UR5S:Xd+URSSS:Xa[SUR35e[USS2S4U5nUSnUS nUS nXFU-- nXVU-- n[R "Xx/S S 9n U $) aTransform coordinates in the camera frame to the pixel frame. Args: cam_coords_src: (x, y, z) coordinates defined in the first camera coordinates system. Shape must be BxHxWx3. dst_proj_src: the projection matrix between the reference and the non reference camera frame. Shape must be Bx4x4. eps: small value to avoid division by zero error. Returns: torch.Tensor of shape BxHxWx2 with (u, v) pixel coordinates. r/r.z,>rs,Cv,MST`TfTfSghii!1,q$w2G!XL(0G(0G(0G$}5G#}5G&+[['1C%L r )gư>)g-q=)typingrrrrkornia.core.checkrkornia.core.utilsrkornia.geometry.conversionsr r kornia.geometry.linalgr r rrrrrrrrrrrrr rrsW$)( 61fKCCD 6D6Dr( U\\( ( ( V( ELL( u(  ( V!ELL!!%,,!HELLU\\,-r%,,-rU\\-rell-rh2U\\25<<2u||2`e`l`l24ell%,,U_d_k_kB10r