a de@sddlmZddlZddlmZddlmZddlmZddl m Z m Z dej ej ej eej e ej d d d Zej ej ej ej d d dZdS))OptionalN)transform_points)remap)create_meshgrid)distort_pointstilt_projection)pointsKdistnew_K num_itersreturncCs|dkr,|jddkr,td|jd|jdddkrPtd|jd|dur^|}n$|jdddkrtd |jd|jdd vrtd |jd|jdd krtjj|d d |jdg}|dd ddf}|ddddf}|dd dd f}|ddddf}|d||} |d||} t|dd ks`t|dd krt|d|dd} t | tj | | gdd d\} } | | } } t |D]}| | | | }d|dddf||dddf|||dddf|dd|dd df||dddf|||dddf|d}d|dddf| | |dddf|d| | |dddf||dddf||}|dddf|d| | d|dddf| | |dddf||dddf||}| ||} | ||} q|dd ddf}|ddddf}|dd dd f}|ddddf}|| |} || |} t | | gdS) aKCompensate 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]]]) zpoints shape is invalid. Got .NrK matrix shape is invalid. Got z#new_K matrix shape is invalid. Got r  /Invalid number of distortion coefficients. Got rr.r.r.r).r). T)dimr rrr r) r shape ValueErrortorchnnZ functionalpadanyrrstackZunbindrange)r r r r rZcxcyZfxZfyxyZinv_tiltZx0Zy0_Zr2Z inv_rad_polyZdeltaXZdeltaYZnew_cxZnew_cyZnew_fxZnew_fyr2n/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/geometry/calibration/undistort.pyundistort_points sh (" FF   r4)imager r rc Cst|jdkr td|jd|jdddkrDtd|jd|jdd vrhtd |jdd|std |jd|jdd |jddks|jdd |jddkr0t|jdd d k|jdddk|jdddkfs0td|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]) rzImage shape is invalid. Got: rrNrrrrrz:Invalid input image data type. Input should be float. Got )rr2zAInput shape is invalid. Input batch dimensions should match. Got z, FrrrT)Z align_corners) lenr&r'Zis_floating_pointZdtypeallZnumelrZdeviceZreshaperrZview_as) r5r r ZchannelsrowscolsBZxy_gridZptsZptsdZmapxZmapyoutr2r2r3undistort_imagess8::     r=)Nr )typingrr(Zkornia.geometry.linalgrZkornia.geometry.transformrZ kornia.utilsrZdistortrrZTensorintr4r=r2r2r2r3s     f