a d0@sddlmZmZddlZddlmZddlmZddlm Z ddl m Z dej e eej ej fdd d Zdej ej ej eej e ej d d dZdS))OptionalTupleN)convert_points_to_homogeneous)transform_points)eye_like) linalg_qr:0yE>)pointsepsreturnc CsFt|tjstdt|t|jdkr>td|jdtj|ddd}||jdd d jdd }|jd}tj |jdtj |j d }t |||}t |d|}tj|tj|j d }|d d ||f|d d d f|d d ||f<|d d ||f|d d d f |d d d|f|d d ||f<t||}||fS)a\Normalizes points. Args: points : Tensor containing the points to be normalized with shape :math:`(B, N, D)`. eps : Small value to avoid division by zero error. Returns: Tuple containing the normalized points in the shape :math:`(B, N, D)` and the transformation matrix in the shape :math:`(B, D+1, D+1)`. z=points is not an instance of torch.Tensor. Type of points is z-points must be of shape (B, N, D). Got shape .T)dimZkeepdim)rprdtypedeviceNr) isinstancetorchTensorAssertionErrortypelenshapemeannormZtensorfloat64rsqrtrZarangeint64r) r r Zx_meanZscaleZD_intZD_floatZ transformZidxsZ points_normr#h/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/geometry/calibration/pnp.py_mean_isotropic_scale_normalize s  0D r%-C6?) world_points img_points intrinsicsweightssvd_epsr c#Cst|tjstdt|t|tjs>> world_points = torch.tensor([[ ... [ 5. , -5. , 0. ], [ 0. , 0. , 1.5], ... [ 2.5, 3. , 6. ], [ 9. , -2. , 3. ], ... [-4. , 5. , 2. ], [-5. , 5. , 1. ], ... ]], dtype=torch.float64) >>> >>> img_points = torch.tensor([[ ... [1409.1504, -800.936 ], [ 407.0207, -182.1229], ... [ 392.7021, 177.9428], [1016.838 , -2.9416], ... [ -63.1116, 142.9204], [-219.3874, 99.666 ], ... ]], dtype=torch.float64) >>> >>> intrinsics = torch.tensor([[ ... [ 500., 0., 250.], ... [ 0., 500., 250.], ... [ 0., 0., 1.], ... ]], dtype=torch.float64) >>> >>> print(world_points.shape, img_points.shape, intrinsics.shape) torch.Size([1, 6, 3]) torch.Size([1, 6, 2]) torch.Size([1, 3, 3]) >>> >>> pred_world_to_cam = kornia.geometry.solve_pnp_dlt(world_points, img_points, intrinsics) >>> >>> print(pred_world_to_cam.shape) torch.Size([1, 3, 4]) >>> >>> pred_world_to_cam tensor([[[ 0.9392, -0.3432, -0.0130, 1.6734], [ 0.3390, 0.9324, -0.1254, -4.3634], [ 0.0552, 0.1134, 0.9920, 3.7785]]], dtype=torch.float64) zIworld_points is not an instance of torch.Tensor. Type of world_points is zEimg_points is not an instance of torch.Tensor. Type of img_points is zEintrinsics is not an instance of torch.Tensor. Type of intrinsics is Nz_If weights is not None, then weights should be an instance of torch.Tensor. Type of weights is z"Type of svd_eps is not float. Got z3world_points must have one of the following dtypes z. Currently it has r z1img_points must have one of the following dtypes z1intrinsics must have one of the following dtypes r rz3world_points must be of shape (B, N, 3). Got shape z1img_points must be of shape (B, N, 2). Got shape r)r r z1intrinsics must be of shape (B, 3, 3). Got shape z=world_points and img_points must have equal number of points.rzFworld_points, img_points and intrinsics must have the same batch size.z9At least 6 points are required to use this function. Got z points.rzQThe last singular value of one/more of the elements of the batch is smaller than z. This function cannot be used if all world_points (of any element of the batch) lie on a line or if all world_points (of any element of the batch) lie on a plane. r.).r)inputrrr)rrrrrfloatfloat32r rrrr%ZsvdanyZinverserrzerosrZreshaperZbmmdetZ ones_likewhererrsigncat)#r'r(r)r*r+Zaccepted_dtypesBNZworld_points_normZworld_transform_norm_sZintrinsics_invZworld_points_norm_hZimg_points_invZimg_points_normZimg_transform_normZinv_img_transform_normsystemvZsolutionZ solution_4x4Z intermediater5ZonesZsign_fixZnorm_colZ mul_factortempZorthorightmaskZ col_sign_fixZrot_matZpred_world_to_camr#r#r$ solve_pnp_dlt-sT           $,     00  "  ""  (rB)r)Nr&)typingrrrZkornia.geometry.conversionsrZkornia.geometry.linalgrZ kornia.utilsrZkornia.utils._compatrrr1r%rBr#r#r#r$s     "&