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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch | |
| from .camera_base import CAMERAS, SingleCameraBase | |
| class SimpleCameraTorch(SingleCameraBase): | |
| """Camera model to calculate coordinate transformation with given | |
| intrinsic/extrinsic camera parameters. | |
| Notes: | |
| The keypoint coordinate should be an np.ndarray with a shape of | |
| [...,J, C] where J is the keypoint number of an instance, and C is | |
| the coordinate dimension. For example: | |
| [J, C]: shape of joint coordinates of a person with J joints. | |
| [N, J, C]: shape of a batch of person joint coordinates. | |
| [N, T, J, C]: shape of a batch of pose sequences. | |
| Args: | |
| param (dict): camera parameters including: | |
| - R: 3x3, camera rotation matrix (camera-to-world) | |
| - T: 3x1, camera translation (camera-to-world) | |
| - K: (optional) 2x3, camera intrinsic matrix | |
| - k: (optional) nx1, camera radial distortion coefficients | |
| - p: (optional) mx1, camera tangential distortion coefficients | |
| - f: (optional) 2x1, camera focal length | |
| - c: (optional) 2x1, camera center | |
| if K is not provided, it will be calculated from f and c. | |
| Methods: | |
| world_to_camera: Project points from world coordinates to camera | |
| coordinates | |
| camera_to_pixel: Project points from camera coordinates to pixel | |
| coordinates | |
| world_to_pixel: Project points from world coordinates to pixel | |
| coordinates | |
| """ | |
| def __init__(self, param, device): | |
| self.param = {} | |
| # extrinsic param | |
| R = torch.tensor(param['R'], device=device) | |
| T = torch.tensor(param['T'], device=device) | |
| assert R.shape == (3, 3) | |
| assert T.shape == (3, 1) | |
| # The camera matrices are transposed in advance because the joint | |
| # coordinates are stored as row vectors. | |
| self.param['R_c2w'] = R.T | |
| self.param['T_c2w'] = T.T | |
| self.param['R_w2c'] = R | |
| self.param['T_w2c'] = -self.param['T_c2w'] @ self.param['R_w2c'] | |
| # intrinsic param | |
| if 'K' in param: | |
| K = torch.tensor(param['K'], device=device) | |
| assert K.shape == (2, 3) | |
| self.param['K'] = K.T | |
| self.param['f'] = torch.tensor([[K[0, 0]], [K[1, 1]]], | |
| device=device) | |
| self.param['c'] = torch.tensor([[K[0, 2]], [K[1, 2]]], | |
| device=device) | |
| elif 'f' in param and 'c' in param: | |
| f = torch.tensor(param['f'], device=device) | |
| c = torch.tensor(param['c'], device=device) | |
| assert f.shape == (2, 1) | |
| assert c.shape == (2, 1) | |
| self.param['K'] = torch.cat([torch.diagflat(f), c], dim=-1).T | |
| self.param['f'] = f | |
| self.param['c'] = c | |
| else: | |
| raise ValueError('Camera intrinsic parameters are missing. ' | |
| 'Either "K" or "f"&"c" should be provided.') | |
| # distortion param | |
| if 'k' in param and 'p' in param: | |
| self.undistortion = True | |
| self.param['k'] = torch.tensor(param['k'], device=device).view(-1) | |
| self.param['p'] = torch.tensor(param['p'], device=device).view(-1) | |
| assert len(self.param['k']) in {3, 6} | |
| assert len(self.param['p']) == 2 | |
| else: | |
| self.undistortion = False | |
| def world_to_camera(self, X): | |
| assert isinstance(X, torch.Tensor) | |
| assert X.ndim >= 2 and X.shape[-1] == 3 | |
| return X @ self.param['R_w2c'] + self.param['T_w2c'] | |
| def camera_to_world(self, X): | |
| assert isinstance(X, torch.Tensor) | |
| assert X.ndim >= 2 and X.shape[-1] == 3 | |
| return X @ self.param['R_c2w'] + self.param['T_c2w'] | |
| def camera_to_pixel(self, X): | |
| assert isinstance(X, torch.Tensor) | |
| assert X.ndim >= 2 and X.shape[-1] == 3 | |
| _X = X / X[..., 2:] | |
| if self.undistortion: | |
| k = self.param['k'] | |
| p = self.param['p'] | |
| _X_2d = _X[..., :2] | |
| r2 = (_X_2d**2).sum(-1) | |
| radial = 1 + sum(ki * r2**(i + 1) for i, ki in enumerate(k[:3])) | |
| if k.size == 6: | |
| radial /= 1 + sum( | |
| (ki * r2**(i + 1) for i, ki in enumerate(k[3:]))) | |
| tangential = 2 * (p[1] * _X[..., 0] + p[0] * _X[..., 1]) | |
| _X[..., :2] = _X_2d * (radial + tangential)[..., None] + torch.ger( | |
| r2, p.flip([0])).reshape(_X_2d.shape) | |
| return _X @ self.param['K'] | |