Source code for kornia.geometry.calibration.distort

import torch


# Based on https://github.com/opencv/opencv/blob/master/modules/calib3d/src/distortion_model.hpp#L75
[docs]def tilt_projection(taux: torch.Tensor, tauy: torch.Tensor, return_inverse: bool = False) -> torch.Tensor: r"""Estimate the tilt projection matrix or the inverse tilt projection matrix. Args: taux: Rotation angle in radians around the :math:`x`-axis with shape :math:`(*, 1)`. tauy: Rotation angle in radians around the :math:`y`-axis with shape :math:`(*, 1)`. return_inverse: False to obtain the the tilt projection matrix. True for the inverse matrix. Returns: torch.Tensor: Inverse tilt projection matrix with shape :math:`(*, 3, 3)`. """ if taux.shape != tauy.shape: raise ValueError(f'Shape of taux {taux.shape} and tauy {tauy.shape} do not match.') ndim: int = taux.dim() taux = taux.reshape(-1) tauy = tauy.reshape(-1) cTx = torch.cos(taux) sTx = torch.sin(taux) cTy = torch.cos(tauy) sTy = torch.sin(tauy) zero = torch.zeros_like(cTx) one = torch.ones_like(cTx) Rx = torch.stack([one, zero, zero, zero, cTx, sTx, zero, -sTx, cTx], -1).reshape(-1, 3, 3) Ry = torch.stack([cTy, zero, -sTy, zero, one, zero, sTy, zero, cTy], -1).reshape(-1, 3, 3) R = Ry @ Rx if return_inverse: invR22 = 1 / R[..., 2, 2] invPz = torch.stack( [invR22, zero, R[..., 0, 2] * invR22, zero, invR22, R[..., 1, 2] * invR22, zero, zero, one], -1 ).reshape(-1, 3, 3) inv_tilt = R.transpose(-1, -2) @ invPz if ndim == 0: inv_tilt = torch.squeeze(inv_tilt) return inv_tilt Pz = torch.stack( [R[..., 2, 2], zero, -R[..., 0, 2], zero, R[..., 2, 2], -R[..., 1, 2], zero, zero, one], -1 ).reshape(-1, 3, 3) tilt = Pz @ R.transpose(-1, -2) if ndim == 0: tilt = torch.squeeze(tilt) return tilt
[docs]def distort_points(points: torch.Tensor, K: torch.Tensor, dist: torch.Tensor) -> torch.Tensor: r"""Distortion of a set of 2D points based on the lens distortion model. 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: 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)`. Returns: Undistorted 2D points with shape :math:`(*, N, 2)`. Example: >>> points = torch.rand(1, 1, 2) >>> K = torch.eye(3)[None] >>> dist_coeff = torch.rand(1, 4) >>> points_dist = distort_points(points, K, dist_coeff) """ if points.dim() < 2 and points.shape[-1] != 2: raise ValueError(f'points shape is invalid. Got {points.shape}.') if K.shape[-2:] != (3, 3): raise ValueError(f'K matrix shape is invalid. Got {K.shape}.') if dist.shape[-1] not in [4, 5, 8, 12, 14]: raise ValueError(f'Invalid number of distortion coefficients. Got {dist.shape[-1]}') # Adding zeros to obtain vector with 14 coeffs. if dist.shape[-1] < 14: dist = torch.nn.functional.pad(dist, [0, 14 - dist.shape[-1]]) # Convert 2D points from pixels to normalized camera coordinates cx: torch.Tensor = K[..., 0:1, 2] # princial point in x (Bx1) cy: torch.Tensor = K[..., 1:2, 2] # princial point in y (Bx1) fx: torch.Tensor = K[..., 0:1, 0] # focal in x (Bx1) fy: torch.Tensor = K[..., 1:2, 1] # focal in y (Bx1) # This is equivalent to K^-1 [u,v,1]^T x: torch.Tensor = (points[..., 0] - cx) / fx # (BxN - Bx1)/Bx1 -> BxN or (N,) y: torch.Tensor = (points[..., 1] - cy) / fy # (BxN - Bx1)/Bx1 -> BxN or (N,) # Distort points r2 = x * x + y * y rad_poly = (1 + dist[..., 0:1] * r2 + dist[..., 1:2] * r2 * r2 + dist[..., 4:5] * r2 ** 3) / ( 1 + dist[..., 5:6] * r2 + dist[..., 6:7] * r2 * r2 + dist[..., 7:8] * r2 ** 3 ) xd = ( x * rad_poly + 2 * dist[..., 2:3] * x * y + dist[..., 3:4] * (r2 + 2 * x * x) + dist[..., 8:9] * r2 + dist[..., 9:10] * r2 * r2 ) yd = ( y * rad_poly + dist[..., 2:3] * (r2 + 2 * y * y) + 2 * dist[..., 3:4] * x * y + dist[..., 10:11] * r2 + dist[..., 11:12] * r2 * r2 ) # Compensate for tilt distortion if torch.any(dist[..., 12] != 0) or torch.any(dist[..., 13] != 0): tilt = tilt_projection(dist[..., 12], dist[..., 13]) # Transposed untilt points (instead of [x,y,1]^T, we obtain [x,y,1]) points_untilt = torch.stack([xd, yd, torch.ones_like(xd)], -1) @ tilt.transpose(-2, -1) xd = points_untilt[..., 0] / points_untilt[..., 2] yd = points_untilt[..., 1] / points_untilt[..., 2] # Convert points from normalized camera coordinates to pixel coordinates x = fx * xd + cx y = fy * yd + cy return torch.stack([x, y], -1)