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