| import numpy as np
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| from typing import List, Tuple
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| from dataclasses import dataclass
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| from pathlib import Path
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| from zipfile import ZipFile
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|
|
|
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| @dataclass
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| class Pose:
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| image_name: str
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| q: np.ndarray
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| t: np.ndarray
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| inliers: float
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|
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| def __str__(self) -> str:
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| formatter = {'float': lambda v: f'{v:.6f}'}
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| max_line_width = 1000
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| q_str = np.array2string(self.q, formatter=formatter, max_line_width=max_line_width)[1:-1]
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| t_str = np.array2string(self.t, formatter=formatter, max_line_width=max_line_width)[1:-1]
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| return f'{self.image_name} {q_str} {t_str} {self.inliers}'
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|
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|
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| def save_submission(results_dict: dict, output_path: Path):
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| with ZipFile(output_path, 'w') as zip:
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| for scene, poses in results_dict.items():
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| poses_str = '\n'.join((str(pose) for pose in poses))
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| zip.writestr(f'pose_{scene}.txt', poses_str.encode('utf-8'))
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|
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| def project(pts: np.ndarray, K: np.ndarray, img_size: List[int] or Tuple[int] = None) -> np.ndarray:
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| """Projects 3D points to image plane.
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|
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| Args:
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| - pts [N, 3/4]: points in camera coordinates (homogeneous or non-homogeneous)
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| - K [3, 3]: intrinsic matrix
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| - img_size (width, height): optional, clamp projection to image borders
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| Outputs:
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| - uv [N, 2]: coordinates of projected points
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| """
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|
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| assert len(pts.shape) == 2, 'incorrect number of dimensions'
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| assert pts.shape[1] in [3, 4], 'invalid dimension size'
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| assert K.shape == (3, 3), 'incorrect intrinsic shape'
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|
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| uv_h = (K @ pts[:, :3].T).T
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| uv = uv_h[:, :2] / uv_h[:, -1:]
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|
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| if img_size is not None:
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| uv[:, 0] = np.clip(uv[:, 0], 0, img_size[0])
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| uv[:, 1] = np.clip(uv[:, 1], 0, img_size[1])
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|
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| return uv
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|
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|
|
| def get_grid_multipleheight() -> np.ndarray:
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|
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| ar_grid_step = 0.3
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| ar_grid_num_x = 7
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| ar_grid_num_y = 4
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| ar_grid_num_z = 7
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| ar_grid_z_offset = 1.8
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| ar_grid_y_offset = 0
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|
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| ar_grid_x_pos = np.arange(0, ar_grid_num_x)-(ar_grid_num_x-1)/2
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| ar_grid_x_pos *= ar_grid_step
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|
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| ar_grid_y_pos = np.arange(0, ar_grid_num_y)-(ar_grid_num_y-1)/2
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| ar_grid_y_pos *= ar_grid_step
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| ar_grid_y_pos += ar_grid_y_offset
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|
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| ar_grid_z_pos = np.arange(0, ar_grid_num_z).astype(float)
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| ar_grid_z_pos *= ar_grid_step
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| ar_grid_z_pos += ar_grid_z_offset
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|
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| xx, yy, zz = np.meshgrid(ar_grid_x_pos, ar_grid_y_pos, ar_grid_z_pos)
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| ones = np.ones(xx.shape[0]*xx.shape[1]*xx.shape[2])
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| eye_coords = np.concatenate([c.reshape(-1, 1)
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| for c in (xx, yy, zz, ones)], axis=-1)
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| return eye_coords
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|
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|
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| eye_coords_glob = get_grid_multipleheight()
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|
|
|
|
| def reprojection_error(
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| R_est: np.ndarray, t_est: np.ndarray, R_gt: np.ndarray, t_gt: np.ndarray, K: np.ndarray,
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| W: int, H: int) -> float:
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| eye_coords = eye_coords_glob
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|
|
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| uv_gt = project(eye_coords, K, (W, H))
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|
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|
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| cam2w_est = np.eye(4)
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| if not np.isnan(R_est).any():
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| cam2w_est[:3, :3] = R_est
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| cam2w_est[:3, -1] = t_est
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| cam2w_gt = np.eye(4)
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| cam2w_gt[:3, :3] = R_gt
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| cam2w_gt[:3, -1] = t_gt
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|
|
|
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| eyes_residual = (np.linalg.inv(cam2w_est) @ cam2w_gt @ eye_coords.T).T
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| uv_pred = project(eyes_residual, K, (W, H))
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|
|
|
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| repr_err = np.linalg.norm(uv_gt - uv_pred, ord=2, axis=1)
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| mean_repr_err = float(repr_err.mean().item())
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| return mean_repr_err
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|
|
