o oi&@sddlmZmZmZmZddlZddlmZmZm Z m Z m Z ddl m Z mZmZddlmZddlmZddlmZdd gZGd ddeZd e d efd dZd e de d efddZdd e dee d efdd ZdS))IteratorOptionalTupleUnionN)Module ParameterTensor normalizewhere) KORNIA_CHECKKORNIA_CHECK_IS_TENSORKORNIA_CHECK_SHAPE)batched_dot_product) Hyperplane)_torch_svd_castParametrizedLinefit_linec s*eZdZdZdededdffdd Zdefdd Zdefd d Zd e defd dZ de efddZ e defddZe defddZde fddZedededdfddZdeeefdefddZdedefdd Zdedefd!d"Zdedefd#d$Zd*d&ed'edeeeffd(d)ZZS)+rzClass that describes a parametrize line. A parametrized line is defined by an origin point :math:`o` and a unit direction vector :math:`d` such that the line corresponds to the set .. math:: l(t) = o + t * d origin directionreturnNcs"tt||_t||_dS)alInitialize a parametrized line of direction and origin. Args: origin: any point on the line of any dimension. direction: the normalized vector direction of any dimension. Example: >>> o = torch.tensor([0.0, 0.0]) >>> d = torch.tensor([1.0, 1.0]) >>> l = ParametrizedLine(o, d) N)super__init__r_origin _direction)selfrr __class__H/home/ubuntu/.local/lib/python3.10/site-packages/kornia/geometry/line.pyr,s zParametrizedLine.__init__cCsd|jd|jS)NzOrigin: z Direction: rrrrrr__str__=zParametrizedLine.__str__cCst|SN)strr rrr__repr__@szParametrizedLine.__repr__idxcCs|dkr|jS|jS)Nrr)rr&rrr __getitem__Cr"zParametrizedLine.__getitem__ccs|j|jfEdHdSr#rr rrr__iter__FszParametrizedLine.__iter__cC|jS)zReturn the line origin point.)rr rrrrIzParametrizedLine.origincCr))z!Return the line direction vector.)rr rrrrNr*zParametrizedLine.directioncCs |jjdS)z-Return the dimension in which the line holds.)rshaper rrrdimSs zParametrizedLine.dimp0p1cCst|t||dddS)aConstruct a parametrized line going from a point :math:`p0` to :math:`p1`. Args: p0: tensor with first point :math:`(B, D)` where `D` is the point dimension. p1: tensor with second point :math:`(B, D)` where `D` is the point dimension. Example: >>> p0 = torch.tensor([0.0, 0.0]) >>> p1 = torch.tensor([1.0, 1.0]) >>> l = ParametrizedLine.through(p0, p1) r+)pr-)rr )clsr.r/rrrthroughWszParametrizedLine.throughtcCs|j|j|S)adGet the point at :math:`t` along this line. Args: t: step along the line. Return: tensor with the point. Example: >>> p0 = torch.tensor([0.0, 0.0]) >>> p1 = torch.tensor([1.0, 1.0]) >>> l = ParametrizedLine.through(p0, p1) >>> p2 = l.point_at(0.1) r)rr4rrrpoint_atgszParametrizedLine.point_atpointcCs|j|j||j|jS)zvReturn the projection of a point onto the line. Args: point: the point to be projected. rrr6rrr projectionyszParametrizedLine.projectioncCs<||j}tj||jdd}tj||dd}|||S)zReturn the squared distance of a point to its projection onte the line. Args: point: the point to calculate the distance onto the line. r+r-)rtorchsumr)rr6dproj sq_norm_drrrsquared_distances  z!ParametrizedLine.squared_distancecCs||S)zReturn the distance of a point to its projections onto the line. Args: point: the point to calculate the distance into the line. )r?sqrtr7rrrdistanceszParametrizedLine.distanceư>planeepscCs\t|jj|jj}||k}t||jt|jj|jj |t |}| |}||fS)aDReturn the intersection point between the line and a given plane. Args: plane: the plane to compute the intersection point. eps: epsilon for numerical stability. Return: - the lambda value used to compute the look at point. - the intersected point. ) rnormaldatarabsr offsetrr: empty_liker5)rrCrDdot_prod dot_prod_mask res_lambda res_pointrrr intersects  zParametrizedLine.intersect)rB)__name__ __module__ __qualname____doc__rrr$r!r%intr'rr(propertyrrr- classmethodr3rfloatr5r8r?rArrrN __classcell__rrrrr!s&   (pointsrc Cs|d}|d}|jddd}|jddd}||}||}||jddd}t|dk||jddd|t|}t|dktjt||gddtjdd g|jd  |j d d } | | j ddd} tj||gdd} t | | S) N.r.r+Tr-keepdim:0yE>r9?)devicerr0) meanr;r:r zeros_likecat ones_liketensorraexpandr,normr) rXxyx_meany_meandxdydenomsloperrrrr_fit_line_ols_2ds *  rqweightscCs |d}|d}|jddd}||jddd|}||jddd|}||}||}|||} |||} | jddd} | jddd| } tt| | t| } | dk} tjt| | gdd}tjdd g|j|j d }||| d<||j ddd}tj||gdd}t ||S) NrYrZr+Tr\r^r9r_r`)radtype) r;r:r isfinitercrdrerfrarssqueezerhr)rXrrrirjw_sumrkrlrmrn weighted_dx2 weighted_dxdyrorp is_verticalr replacementrrrr_fit_line_weighted_ols_2ds&   r{c Cs6t|dt|gd|j\}}}|dkr;|dur7t|dt|ddgt|jd|jdkt||St|S|d d }||}|durot|dt|ddgt|jd|jdk|d d t ||}n|d d |}t |\}}}|d d }|d dddf} |d dddf} t | | S) aiFit a line from a set of points. Args: points: tensor containing a batch of sets of n-dimensional points. The expected shape of the tensor is :math:`(B, N, D)`. weights: weights to use to solve the equations system. The expected shape of the tensor is :math:`(B, N)`. Return: A tensor containing the direction of the fitted line of shape :math:`(B, D)`. Example: >>> points = torch.rand(2, 10, 3) >>> weights = torch.ones(2, 10) >>> line = fit_line(points, weights) >>> line.direction.shape torch.Size([2, 3]) zpoints must be a tensor)BNDr0Nzweights must be a tensorr|r}rTr+.) r r r,r r{rqrb transposer: diag_embedrr) rXrr_B_Nr~rbA_Vrrrrrrs.        r#)typingrrrrr: kornia.corerrrr r kornia.core.checkr r r kornia.geometry.linalgrkornia.geometry.planerkornia.utils.helpersr__all__rrqr{rrrrrs