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| from typing import Tuple, Union | |
| import numpy as np | |
| import torch | |
| from .convert_convention import convert_camera_matrix | |
| def convert_perspective_to_weakperspective( | |
| K: Union[torch.Tensor, np.ndarray], | |
| zmean: Union[torch.Tensor, np.ndarray, float, int], | |
| resolution: Union[int, Tuple[int, int], torch.Tensor, | |
| np.ndarray] = None, | |
| in_ndc: bool = False, | |
| convention: str = 'opencv') -> Union[torch.Tensor, np.ndarray]: | |
| """Convert perspective to weakperspective intrinsic matrix. | |
| Args: | |
| K (Union[torch.Tensor, np.ndarray]): input intrinsic matrix, shape | |
| should be (batch, 4, 4) or (batch, 3, 3). | |
| zmean (Union[torch.Tensor, np.ndarray, int, float]): zmean for object. | |
| shape should be (batch, ) or singleton number. | |
| resolution (Union[int, Tuple[int, int], torch.Tensor, np.ndarray], | |
| optional): (height, width) of image. Defaults to None. | |
| in_ndc (bool, optional): whether defined in ndc. Defaults to False. | |
| convention (str, optional): camera convention. Defaults to 'opencv'. | |
| Returns: | |
| Union[torch.Tensor, np.ndarray]: output weakperspective pred_cam, | |
| shape is (batch, 4) | |
| """ | |
| assert K is not None, 'K is required.' | |
| K, _, _ = convert_camera_matrix(K=K, | |
| convention_src=convention, | |
| convention_dst='pytorch3d', | |
| is_perspective=True, | |
| in_ndc_src=in_ndc, | |
| in_ndc_dst=True, | |
| resolution_src=resolution) | |
| if isinstance(zmean, np.ndarray): | |
| zmean = torch.Tensor(zmean) | |
| elif isinstance(zmean, (float, int)): | |
| zmean = torch.Tensor([zmean]) | |
| zmean = zmean.view(-1) | |
| num_frame = max(zmean.shape[0], K.shape[0]) | |
| new_K = torch.eye(4, 4)[None].repeat(num_frame, 1, 1) | |
| fx = K[:, 0, 0] | |
| fy = K[:, 0, 0] | |
| cx = K[:, 0, 2] | |
| cy = K[:, 1, 2] | |
| new_K[:, 0, 0] = fx / zmean | |
| new_K[:, 1, 1] = fy / zmean | |
| new_K[:, 0, 3] = cx | |
| new_K[:, 1, 3] = cy | |
| return new_K | |
| def convert_weakperspective_to_perspective( | |
| K: Union[torch.Tensor, np.ndarray], | |
| zmean: Union[torch.Tensor, np.ndarray, int, float], | |
| resolution: Union[int, Tuple[int, int], torch.Tensor, | |
| np.ndarray] = None, | |
| in_ndc: bool = False, | |
| convention: str = 'opencv') -> Union[torch.Tensor, np.ndarray]: | |
| """Convert perspective to weakperspective intrinsic matrix. | |
| Args: | |
| K (Union[torch.Tensor, np.ndarray]): input intrinsic matrix, shape | |
| should be (batch, 4, 4) or (batch, 3, 3). | |
| zmean (Union[torch.Tensor, np.ndarray, int, float]): zmean for object. | |
| shape should be (batch, ) or singleton number. | |
| resolution (Union[int, Tuple[int, int], torch.Tensor, np.ndarray], | |
| optional): (height, width) of image. Defaults to None. | |
| in_ndc (bool, optional): whether defined in ndc. Defaults to False. | |
| convention (str, optional): camera convention. Defaults to 'opencv'. | |
| Returns: | |
| Union[torch.Tensor, np.ndarray]: output weakperspective pred_cam, | |
| shape is (batch, 4) | |
| """ | |
| if K.ndim == 2: | |
| K = K[None] | |
| if isinstance(zmean, np.ndarray): | |
| zmean = torch.Tensor(zmean) | |
| elif isinstance(zmean, (float, int)): | |
| zmean = torch.Tensor([zmean]) | |
| zmean = zmean.view(-1) | |
| _N = max(K.shape[0], zmean.shape[0]) | |
| s1 = K[:, 0, 0] | |
| s2 = K[:, 1, 1] | |
| c1 = K[:, 0, 3] | |
| c2 = K[:, 1, 3] | |
| new_K = torch.zeros(_N, 4, 4) | |
| new_K[:, 0, 0] = zmean * s1 | |
| new_K[:, 1, 1] = zmean * s2 | |
| new_K[:, 0, 2] = c1 | |
| new_K[:, 1, 2] = c2 | |
| new_K[:, 2, 3] = 1 | |
| new_K[:, 3, 2] = 1 | |
| new_K, _, _ = convert_camera_matrix(K=new_K, | |
| convention_src=convention, | |
| convention_dst='pytorch3d', | |
| is_perspective=True, | |
| in_ndc_src=in_ndc, | |
| in_ndc_dst=True, | |
| resolution_src=resolution) | |
| return new_K | |