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| import numpy as np | |
| import torch | |
| from tools.utils import wrap | |
| from common.quaternion import qort, qinverse | |
| def normalize_screen_coordinates(X, w, h): | |
| assert X.shape[-1] == 2 | |
| # Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio | |
| return X/w*2 - [1, h/w] | |
| def image_coordinates(X, w, h): | |
| assert X.shape[-1] == 2 | |
| # Reverse camera frame normalization | |
| return (X + [1, h/w]) * w / 2 | |
| def world_to_camera(X, R, t): | |
| Rt = wrap(qinverse, R) # Invert rotation | |
| return wrap(qort, np.tile(Rt, (*X.shape[:-1], 1)), X - t) # Rotate and translate | |
| def camera_to_world(X, R, t): | |
| return wrap(qort, np.tile(R, (*X.shape[:-1], 1)), X) + t | |
| def project_to_2d(X, camera_params): | |
| """ | |
| Project 3D points to 2D using the Human3.6M camera projection function. | |
| This is a differentiable and batched reimplementation of the original MATLAB script. | |
| Arguments: | |
| X -- 3D points in *camera space* to transform (N, *, 3) | |
| camera_params -- intrinsic parameteres (N, 2+2+3+2=9) | |
| """ | |
| assert X.shape[-1] == 3 | |
| assert len(camera_params.shape) == 2 | |
| assert camera_params.shape[-1] == 9 | |
| assert X.shape[0] == camera_params.shape[0] | |
| while len(camera_params.shape) < len(X.shape): | |
| camera_params = camera_params.unsqueeze(1) | |
| f = camera_params[..., :2] | |
| c = camera_params[..., 2:4] | |
| k = camera_params[..., 4:7] | |
| p = camera_params[..., 7:] | |
| # XX = torch.clamp(X[..., :2] / X[..., 2:], min=-1, max=1) | |
| XX = X[..., :2] / X[..., 2:] | |
| r2 = torch.sum(XX[..., :2]**2, dim=len(XX.shape)-1, keepdim=True) | |
| radial = 1 + torch.sum(k * torch.cat((r2, r2**2, r2**3), dim=len(r2.shape)-1), dim=len(r2.shape)-1, keepdim=True) | |
| tan = torch.sum(p*XX, dim=len(XX.shape)-1, keepdim=True) | |
| XXX = XX*(radial + tan) + p*r2 | |
| return f*XXX + c | |