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| import torch | |
| import torch.nn.functional as F | |
| import numpy as np | |
| def pad_camera_extrinsics_4x4(extrinsics): | |
| if extrinsics.shape[-2] == 4: | |
| return extrinsics | |
| padding = torch.tensor([[0, 0, 0, 1]]).to(extrinsics) | |
| if extrinsics.ndim == 3: | |
| padding = padding.unsqueeze(0).repeat(extrinsics.shape[0], 1, 1) | |
| extrinsics = torch.cat([extrinsics, padding], dim=-2) | |
| return extrinsics | |
| def center_looking_at_camera_pose(camera_position: torch.Tensor, look_at: torch.Tensor = None, up_world: torch.Tensor = None): | |
| """ | |
| Create OpenGL camera extrinsics from camera locations and look-at position. | |
| camera_position: (M, 3) or (3,) | |
| look_at: (3) | |
| up_world: (3) | |
| return: (M, 3, 4) or (3, 4) | |
| """ | |
| # by default, looking at the origin and world up is z-axis | |
| if look_at is None: | |
| look_at = torch.tensor([0, 0, 0], dtype=torch.float32) | |
| if up_world is None: | |
| up_world = torch.tensor([0, 0, 1], dtype=torch.float32) | |
| if camera_position.ndim == 2: | |
| look_at = look_at.unsqueeze(0).repeat(camera_position.shape[0], 1) | |
| up_world = up_world.unsqueeze(0).repeat(camera_position.shape[0], 1) | |
| # OpenGL camera: z-backward, x-right, y-up | |
| z_axis = camera_position - look_at | |
| z_axis = F.normalize(z_axis, dim=-1).float() | |
| x_axis = torch.linalg.cross(up_world, z_axis, dim=-1) | |
| x_axis = F.normalize(x_axis, dim=-1).float() | |
| y_axis = torch.linalg.cross(z_axis, x_axis, dim=-1) | |
| y_axis = F.normalize(y_axis, dim=-1).float() | |
| extrinsics = torch.stack([x_axis, y_axis, z_axis, camera_position], dim=-1) | |
| extrinsics = pad_camera_extrinsics_4x4(extrinsics) | |
| return extrinsics | |
| def spherical_camera_pose(azimuths: np.ndarray, elevations: np.ndarray, radius=2.5): | |
| azimuths = np.deg2rad(azimuths) | |
| elevations = np.deg2rad(elevations) | |
| xs = radius * np.cos(elevations) * np.cos(azimuths) | |
| ys = radius * np.cos(elevations) * np.sin(azimuths) | |
| zs = radius * np.sin(elevations) | |
| cam_locations = np.stack([xs, ys, zs], axis=-1) | |
| cam_locations = torch.from_numpy(cam_locations).float() | |
| c2ws = center_looking_at_camera_pose(cam_locations) | |
| return c2ws | |
| def get_circular_camera_poses(M=120, radius=2.5, elevation=30.0): | |
| # M: number of circular views | |
| # radius: camera dist to center | |
| # elevation: elevation degrees of the camera | |
| # return: (M, 4, 4) | |
| assert M > 0 and radius > 0 | |
| elevation = np.deg2rad(elevation) | |
| camera_positions = [] | |
| for i in range(M): | |
| azimuth = 2 * np.pi * i / M | |
| x = radius * np.cos(elevation) * np.cos(azimuth) | |
| y = radius * np.cos(elevation) * np.sin(azimuth) | |
| z = radius * np.sin(elevation) | |
| camera_positions.append([x, y, z]) | |
| camera_positions = np.array(camera_positions) | |
| camera_positions = torch.from_numpy(camera_positions).float() | |
| extrinsics = center_looking_at_camera_pose(camera_positions) | |
| return extrinsics | |
| def FOV_to_intrinsics(fov, device='cpu'): | |
| """ | |
| Creates a 3x3 camera intrinsics matrix from the camera field of view, specified in degrees. | |
| Note the intrinsics are returned as normalized by image size, rather than in pixel units. | |
| Assumes principal point is at image center. | |
| """ | |
| focal_length = 0.5 / np.tan(np.deg2rad(fov) * 0.5) | |
| intrinsics = torch.tensor([[focal_length, 0, 0.5], [0, focal_length, 0.5], [0, 0, 1]], device=device) | |
| return intrinsics | |
| def get_zero123plus_input_cameras(batch_size=1, radius=4.0, fov=30.0): | |
| """ | |
| Get the input camera parameters. | |
| """ | |
| azimuths = np.array([30, 90, 150, 210, 270, 330]).astype(float) | |
| elevations = np.array([20, -10, 20, -10, 20, -10]).astype(float) | |
| c2ws = spherical_camera_pose(azimuths, elevations, radius) | |
| c2ws = c2ws.float().flatten(-2) | |
| Ks = FOV_to_intrinsics(fov).unsqueeze(0).repeat(6, 1, 1).float().flatten(-2) | |
| extrinsics = c2ws[:, :12] | |
| intrinsics = torch.stack([Ks[:, 0], Ks[:, 4], Ks[:, 2], Ks[:, 5]], dim=-1) | |
| cameras = torch.cat([extrinsics, intrinsics], dim=-1) | |
| return cameras.unsqueeze(0).repeat(batch_size, 1, 1) | |