o oi 0@s ddlmZddlmZddlmZmZddlmZm Z m Z ddl m Z m Z ddlmZddlmZmZmZddlmZmZGd d d eZd!ddZeeeefZeeefZGdddZGdddeZGdddeZGdddeZGdddeZeeeeefZ GdddZ!d S)") annotations)Enum)AnyUnion)Tensorstack zeros_like)Vector2Vector3) ImageSize)AffineTransformBrownConradyTransformKannalaBrandtK3Transform)OrthographicProjection Z1Projectionc@seZdZdZdZdZdZdS)CameraModelTyperN)__name__ __module__ __qualname__PINHOLE BROWN_CONRADYKANNALA_BRANDT_K3 ORTHOGRAPHICrrV/home/ubuntu/.local/lib/python3.10/site-packages/kornia/sensors/camera/camera_model.pyrs r model_type image_sizer paramsrreturnCameraModelVariantscCsX|tjkr t||S|tjkrt||S|tjkrt||S|tjkr(t||St d)z!Get camera model from model type.zInvalid Camera Model Type) rr PinholeModelrBrownConradyModelrKannalaBrandtK3r Orthographic ValueError)rrr rrrget_model_from_type&s        r(c@seZdZdZd,d d Zed-ddZed.ddZed.ddZed/ddZ ed/ddZ ed/ddZ ed/ddZ ed/ddZ d/dd Zd/d!d"Zd0d&d'Zd1d)d*Zd+S)2CameraModelBaseaBase class to represent camera models based on distortion and projection types. Distortion is of 3 types: - Affine - Brown Conrady - Kannala Brandt K3 Projection is of 2 types: - Z1 - Orthographic Example: >>> params = torch.Tensor([328., 328., 320., 240.]) >>> cam = CameraModelBase(BrownConradyTransform(), Z1Projection(), ImageSize(480, 640), params) >>> cam.params tensor([328., 328., 320., 240.]) distortionCameraDistortionType projectionCameraProjectionTyperr r rr!NonecCs,||_||_||_|j|_|j|_||_dS)aConstruct CameraModelBase class. Args: distortion: Distortion type projection: Projection type image_size: Image size params: Camera parameters of shape :math:`(B, 4)` for PINHOLE Camera, :math:`(B, 12)` for Brown Conrady, :math:`(B, 8)` for Kannala Brandt K3. N)r*r, _image_sizeheight_heightwidth_width_params)selfr*r,rr rrr__init__Ks  zCameraModelBase.__init__cC|jS)z+Returns the image size of the camera model.)r/r5rrrrazCameraModelBase.image_size int | TensorcCr7)z Returns the height of the image.)r1r8rrrr0fr9zCameraModelBase.heightcCr7)zReturns the width of the image.)r3r8rrrr2kr9zCameraModelBase.widthcCr7)zReturns the camera parameters.r4r8rrrr pr9zCameraModelBase.paramscC |jdS)z(Returns the focal length in x direction.).rr;r8rrrfxu zCameraModelBase.fxcCr<)z(Returns the focal length in y direction.).rr;r8rrrfyzr>zCameraModelBase.fycCr<)z+Returns the principal point in x direction.).rr;r8rrrcxr>zCameraModelBase.cxcCr<)z+Returns the principal point in y direction.).rr;r8rrrcyr>zCameraModelBase.cycCstzReturn the camera matrix.)NotImplementedErrorr8rrrmatrixszCameraModelBase.matrixcCs|SrB)rDr8rrrKszCameraModelBase.Kpointsr r cCs|j|j|j|S)aProjects 3D points to 2D camera plane. Args: points: Vector3 representing 3D points. Returns: Vector2 representing the projected 2D points. Example: >>> points = Vector3(torch.Tensor([1.0, 1.0, 1.0])) >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.PINHOLE, torch.Tensor([328., 328., 320., 240.])) >>> cam.project(points) x: 648.0 y: 568.0 )r*distortr r,project)r5rFrrrrHszCameraModelBase.projectdepthcCs|j|j|j||S)aNUnprojects 2D points from camera plane to 3D. Args: points: Vector2 representing 2D points. depth: Depth of the points. Returns: Vector3 representing the unprojected 3D points. Example: >>> points = Vector2(torch.Tensor([1.0, 1.0])) >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.PINHOLE, torch.Tensor([328., 328., 320., 240.])) >>> cam.unproject(points, torch.Tensor([1.0])) x: tensor([-0.9726]) y: tensor([-0.7287]) z: tensor([1.]) )r, unprojectr* undistortr )r5rFrIrrrrJszCameraModelBase.unprojectN) r*r+r,r-rr r rr!r.)r!r )r!r:r!r)rFr r!r )rFr rIrr!r )rrr__doc__r6propertyrr0r2r r=r?r@rArDrErHrJrrrrr)8s.            r)cs6eZdZdZdfdd Zdd d Zdd dZZS)r#aClass to represent Pinhole Camera Model. The pinhole camera model describes the mathematical relationship between the coordinates of a point in three-dimensional space and its projection onto the image plane of an ideal pinhole camera, where the camera aperture is described as a point and no lenses are used to focus light. See more: https://en.wikipedia.org/wiki/Pinhole_camera_model Example: >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.PINHOLE, torch.Tensor([328., 328., 320., 240.])) >>> cam CameraModel(ImageSize(height=480, width=640), PinholeModel, tensor([328., 328., 320., 240.])) rr r rr!r.c>|jddkst|jdkrtdttt||dS)zConstruct PinholeModel class. Args: image_size: Image size params: Camera parameters of shape :math:`(B, 4)` of the form :math:`(fx, fy, cx, cy)`. rz1params must be of shape (B, 4) for PINHOLE CameraN)shapelenr'superr6r rr5rr  __class__rrr6zPinholeModel.__init__cCs^t|j}t|j||jfd}t||j|jfd}t|||fd}t|||fd}d|d<|S)aReturn the camera matrix. The matrix is of the form: .. math:: \begin{bmatrix} fx & 0 & cx \\ 0 & fy & cy \\ 0 & 0 & 1\end{bmatrix} Example: >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.PINHOLE, torch.Tensor([1.0, 2.0, 3.0, 4.0])) >>> cam.matrix() tensor([[1., 0., 3.], [0., 2., 4.], [0., 0., 1.]]) rPg?).rPrP)rr=rr@r?rA)r5zrow1row2row3rErrrrDs zPinholeModel.matrix scale_factorcCs^|j|}|j|}|j|}|j|}t||||fd}t|jj||jj|}t ||S)aScales the camera model by a scale factor. Args: scale_factor: Scale factor to scale the camera model. Returns: Scaled camera model. Example: >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.PINHOLE, torch.Tensor([328., 328., 320., 240.])) >>> cam_scaled = cam.scale(2) >>> cam_scaled.params tensor([656., 656., 640., 480.]) rP) r=r?r@rArr rr0r2r#)r5r^r=r?r@rAr rrrrscales     zPinholeModel.scalerr r rr!r.rL)r^rr!r#)rrrrMr6rDr_ __classcell__rrrVrr#s  r#c"eZdZdZd fdd ZZS) r$zBrown Conrady Camera Model.rr r rr!r.crO)zConstruct BrownConradyModel class. Args: image_size: Image size params: Camera parameters of shape :math:`(B, 12)` of the form :math:`(fx, fy, cx, cy, kb0, kb1, kb2, kb3, k1, k2, k3, k4)`. rP rz8params must be of shape (B, 12) for BROWN_CONRADY CameraN)rRrSr'rTr6r rrUrVrrr6 s zBrownConradyModel.__init__r`rrrrMr6rarrrVrr$ r$crb) r%zKannala Brandt K3 Camera Model.rr r rr!r.crO)zConstruct KannalaBrandtK3 class. Args: image_size: Image size params: Camera parameters of shape :math:`(B, 8)` of the form :math:`(fx, fy, cx, cy, kb0, kb1, kb2, kb3)`. rPrz9params must be of shape B, 8 for KANNALA_BRANDT_K3 CameraN)rRrSr'rTr6rrrUrVrrr6rXzKannalaBrandtK3.__init__r`rdrrrVrr%rer%crb) r&zOrthographic Camera Model.rr r rr!r.cs>ttt|||jddkst|jdkrtddS)zConstruct Orthographic class. Args: image_size: Image size params: Camera parameters of shape :math:`(B, 4)` of the form :math:`(fx, fy, cx, cy)`. rPrQrz4params must be of shape B, 4 for ORTHOGRAPHIC CameraN)rTr6r rrRrSr'rUrVrrr6-szOrthographic.__init__r`rdrrrVrr&*rer&c@s.eZdZdZdd d ZdddZdddZdS) CameraModelaGClass to represent camera models. Example: >>> # Pinhole Camera Model >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.PINHOLE, torch.Tensor([328., 328., 320., 240.])) >>> # Brown Conrady Camera Model >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.BROWN_CONRADY, torch.Tensor([1.0, 1.0, 1.0, 1.0, ... 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])) >>> # Kannala Brandt K3 Camera Model >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.KANNALA_BRANDT_K3, torch.Tensor([1.0, 1.0, 1.0, ... 1.0, 1.0, 1.0, 1.0, 1.0])) >>> # Orthographic Camera Model >>> cam = CameraModel(ImageSize(480, 640), CameraModelType.ORTHOGRAPHIC, torch.Tensor([328., 328., 320., 240.])) >>> cam.params tensor([328., 328., 320., 240.]) rr rrr rr!r.cCst||||_dS)zConstruct CameraModel class. Args: image_size: Image size model_type: Camera model type params: Camera parameters of shape :math:`(B, N)`. N)r(_model)r5rrr rrrr6Ps zCameraModel.__init__namestrrcCs t|j|S)N)getattrrh)r5rirrr __getattr__[s zCameraModel.__getattr__cCs"d|jd|jjjd|jdS)Nz CameraModel(z, ))rrhrWrr r8rrr__repr__^s"zCameraModel.__repr__N)rr rrr rr!r.)rirjr!r)r!rj)rrrrMr6rlrnrrrrrg=s   rgN)rrrr r rr!r")" __future__renumrtypingrr kornia.corerrrkornia.geometry.vectorr r kornia.imager &kornia.sensors.camera.distortion_modelr r r&kornia.sensors.camera.projection_modelrrrr(r+r-r)r#r$r%r&r"rgrrrrs(     O