o oi@sddlmZddlmZddlZddlmZddlmZddl m Z ddl m Z ddl mZd d lmZmZ ddddZdddZdS)) annotations)OptionalN)stack)KORNIA_CHECK_SHAPE)transform_points)remap)create_meshgrid)distort_pointstilt_projectionpoints torch.TensorKdistnew_KOptional[torch.Tensor] num_itersintreturncCst|gdt|gd|dkr$|jddkr$td|jd|dur+|}nt|gd|jddvrCtd |jd|jdd krYtjj|d d |jdg}|d d d df}|d d ddf}|d d d d f}|d d dd f}|d||} |d||} t|dd kst|dd krt |d|dd} t | t | | gdd d\} } | | } } t |D]}| | | | }d |d ddf||d ddf|||d ddf|dd |d d d f||d d df|||d ddf|d}d|d ddf| | |d ddf|d| | |d ddf||d ddf||}|d ddf|d| | d|d ddf| | |d ddf||d ddf||}| ||} | ||} q|d d d df}|d d ddf}|d d d d f}|d d dd f}|| |} || |} t | | gdS)aMCompensate for lens distortion a set of 2D image points. Radial :math:`(k_1, k_2, k_3, k_4, k_5, k_6)`, tangential :math:`(p_1, p_2)`, thin prism :math:`(s_1, s_2, s_3, s_4)`, and tilt :math:`(\tau_x, \tau_y)` distortion models are considered in this function. Args: points: Input image points with shape :math:`(*, N, 2)`. K: Intrinsic camera matrix with shape :math:`(*, 3, 3)`. dist: Distortion coefficients :math:`(k_1,k_2,p_1,p_2[,k_3[,k_4,k_5,k_6[,s_1,s_2,s_3,s_4[,\tau_x,\tau_y]]]])`. This is a vector with 4, 5, 8, 12 or 14 elements with shape :math:`(*, n)`. new_K: Intrinsic camera matrix of the distorted image. By default, it is the same as K but you may additionally scale and shift the result by using a different matrix. Shape: :math:`(*, 3, 3)`. Default: None. num_iters: Number of undistortion iterations. Default: 5. Returns: Undistorted 2D points with shape :math:`(*, N, 2)`. Example: >>> _ = torch.manual_seed(0) >>> x = torch.rand(1, 4, 2) >>> K = torch.eye(3)[None] >>> dist = torch.rand(1, 4) >>> undistort_points(x, K, dist) tensor([[[-0.1513, -0.1165], [ 0.0711, 0.1100], [-0.0697, 0.0228], [-0.1843, -0.1606]]]) )*N2)r3rzpoints shape is invalid. Got .Nr  /Invalid number of distortion coefficients. Got r!r.r .r.r ).r ). T)dimr rr r )rr&shape ValueErrortorchnn functionalpadanyr rrunbindrange)r rrrrcxcyfxfyxyinv_tiltx0y0_r2 inv_rad_polydeltaXdeltaYnew_cxnew_cynew_fxnew_fyrHY/home/ubuntu/.local/lib/python3.10/site-packages/kornia/geometry/calibration/undistort.pyundistort_points"sf"$   FF   rJimagec Cst|jdkrtd|jd|jdddkr"td|jd|jdd vr4td |jdd|sAtd |jd|jdd |jddks]|jdd |jddkrt|jdd d k|jdddk|jdddkfstd|jdd d|jddd|jddd|jd d\}}}||||}t||d|j|j}| dd}t |||} | d |||} | d |||} t | ||||| | dd} | |S)aCompensate an image for lens distortion. Radial :math:`(k_1, k_2, k_3, k_4, k_4, k_6)`, tangential :math:`(p_1, p_2)`, thin prism :math:`(s_1, s_2, s_3, s_4)`, and tilt :math:`(\tau_x, \tau_y)` distortion models are considered in this function. Args: image: Input image with shape :math:`(*, C, H, W)`. K: Intrinsic camera matrix with shape :math:`(*, 3, 3)`. dist: Distortion coefficients :math:`(k_1,k_2,p_1,p_2[,k_3[,k_4,k_5,k_6[,s_1,s_2,s_3,s_4[,\tau_x,\tau_y]]]])`. This is a vector with 4, 5, 8, 12 or 14 elements with shape :math:`(*, n)`. Returns: Undistorted image with shape :math:`(*, C, H, W)`. Example: >>> img = torch.rand(1, 3, 5, 5) >>> K = torch.eye(3)[None] >>> dist_coeff = torch.rand(1, 4) >>> out = undistort_image(img, K, dist_coeff) >>> out.shape torch.Size([1, 3, 5, 5]) r)zImage shape is invalid. Got: rN)r)r)zK matrix shape is invalid. Got rrr"z:Invalid input image data type. Input should be float. Got )r rHzAInput shape is invalid. Input batch dimensions should match. Got z, Frr#r$T) align_corners) lenr-r.is_floating_pointdtypeallnumelrdevicereshaper rview_as) rKrrchannelsrowscolsBxy_gridptsptsdmapxmapyoutrHrHrIundistort_images888      ra)Nr ) r rrrrrrrrrrr)rKrrrrrrr) __future__rtypingrr/ kornia.corerkornia.core.checkrkornia.geometry.linalgrkornia.geometry.transformr kornia.utilsrdistortr r rJrarHrHrHrIs        h