641 lines
23 KiB
Python
641 lines
23 KiB
Python
import click
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import cv2
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import json
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import csv
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import numpy as np
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import pyzed.sl as sl
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from pathlib import Path
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from typing import List, Dict, Any, Optional, Tuple
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from aruco.marker_geometry import (
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load_marker_geometry,
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validate_marker_geometry,
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load_face_mapping,
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)
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from aruco.svo_sync import SVOReader
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from aruco.detector import (
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create_detector,
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detect_markers,
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build_camera_matrix_from_zed,
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estimate_pose_from_detections,
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)
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from aruco.pose_math import rvec_tvec_to_matrix, invert_transform, matrix_to_rvec_tvec
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from aruco.pose_averaging import PoseAccumulator
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from aruco.preview import draw_detected_markers, draw_pose_axes, show_preview
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from aruco.depth_verify import verify_extrinsics_with_depth
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from aruco.depth_refine import refine_extrinsics_with_depth
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from aruco.alignment import (
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get_face_normal_from_geometry,
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detect_ground_face,
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rotation_align_vectors,
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apply_alignment_to_pose,
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)
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from loguru import logger
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ARUCO_DICT_MAP = {
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"DICT_4X4_50": cv2.aruco.DICT_4X4_50,
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"DICT_4X4_100": cv2.aruco.DICT_4X4_100,
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"DICT_4X4_250": cv2.aruco.DICT_4X4_250,
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"DICT_4X4_1000": cv2.aruco.DICT_4X4_1000,
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"DICT_5X5_50": cv2.aruco.DICT_5X5_50,
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"DICT_5X5_100": cv2.aruco.DICT_5X5_100,
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"DICT_5X5_250": cv2.aruco.DICT_5X5_250,
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"DICT_5X5_1000": cv2.aruco.DICT_5X5_1000,
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"DICT_6X6_50": cv2.aruco.DICT_6X6_50,
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"DICT_6X6_100": cv2.aruco.DICT_6X6_100,
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"DICT_6X6_250": cv2.aruco.DICT_6X6_250,
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"DICT_6X6_1000": cv2.aruco.DICT_6X6_1000,
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"DICT_7X7_50": cv2.aruco.DICT_7X7_50,
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"DICT_7X7_100": cv2.aruco.DICT_7X7_100,
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"DICT_7X7_250": cv2.aruco.DICT_7X7_250,
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"DICT_7X7_1000": cv2.aruco.DICT_7X7_1000,
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"DICT_ARUCO_ORIGINAL": cv2.aruco.DICT_ARUCO_ORIGINAL,
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"DICT_APRILTAG_16h5": cv2.aruco.DICT_APRILTAG_16h5,
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"DICT_APRILTAG_25h9": cv2.aruco.DICT_APRILTAG_25h9,
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"DICT_APRILTAG_36h10": cv2.aruco.DICT_APRILTAG_36h10,
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"DICT_APRILTAG_36h11": cv2.aruco.DICT_APRILTAG_36h11,
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}
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def apply_depth_verify_refine_postprocess(
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results: Dict[str, Any],
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verification_frames: Dict[str, Any],
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marker_geometry: Dict[int, Any],
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camera_matrices: Dict[str, Any],
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verify_depth: bool,
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refine_depth: bool,
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depth_confidence_threshold: int,
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report_csv_path: Optional[str] = None,
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) -> Tuple[Dict[str, Any], List[List[Any]]]:
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"""
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Apply depth verification and refinement to computed extrinsics.
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Returns updated results and list of CSV rows.
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"""
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csv_rows: List[List[Any]] = []
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if not (verify_depth or refine_depth):
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return results, csv_rows
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click.echo("\nRunning depth verification/refinement on computed extrinsics...")
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for serial, vf in verification_frames.items():
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if str(serial) not in results:
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continue
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frame = vf["frame"]
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ids = vf["ids"]
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# Use the FINAL COMPUTED POSE for verification
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pose_str = results[str(serial)]["pose"]
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T_mean = np.fromstring(pose_str, sep=" ").reshape(4, 4)
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cam_matrix = camera_matrices[serial]
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marker_corners_world = {
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int(mid): marker_geometry[int(mid)]
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for mid in ids.flatten()
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if int(mid) in marker_geometry
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}
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if marker_corners_world and frame.depth_map is not None:
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verify_res = verify_extrinsics_with_depth(
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T_mean,
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marker_corners_world,
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frame.depth_map,
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cam_matrix,
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confidence_map=frame.confidence_map,
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confidence_thresh=depth_confidence_threshold,
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)
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results[str(serial)]["depth_verify"] = {
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"rmse": verify_res.rmse,
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"mean_abs": verify_res.mean_abs,
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"median": verify_res.median,
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"depth_normalized_rmse": verify_res.depth_normalized_rmse,
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"n_valid": verify_res.n_valid,
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"n_total": verify_res.n_total,
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}
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click.echo(
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f"Camera {serial} verification: RMSE={verify_res.rmse:.3f}m, "
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f"Valid={verify_res.n_valid}/{verify_res.n_total}"
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)
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if refine_depth:
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if verify_res.n_valid < 4:
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click.echo(
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f"Camera {serial}: Not enough valid depth points for refinement ({verify_res.n_valid}). Skipping."
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)
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else:
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click.echo(f"Camera {serial}: Refining extrinsics with depth...")
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T_refined, refine_stats = refine_extrinsics_with_depth(
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T_mean,
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marker_corners_world,
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frame.depth_map,
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cam_matrix,
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)
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verify_res_post = verify_extrinsics_with_depth(
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T_refined,
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marker_corners_world,
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frame.depth_map,
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cam_matrix,
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confidence_map=frame.confidence_map,
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confidence_thresh=depth_confidence_threshold,
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)
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pose_str_refined = " ".join(f"{x:.6f}" for x in T_refined.flatten())
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results[str(serial)]["pose"] = pose_str_refined
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results[str(serial)]["refine_depth"] = refine_stats
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results[str(serial)]["depth_verify_post"] = {
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"rmse": verify_res_post.rmse,
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"mean_abs": verify_res_post.mean_abs,
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"median": verify_res_post.median,
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"depth_normalized_rmse": verify_res_post.depth_normalized_rmse,
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"n_valid": verify_res_post.n_valid,
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"n_total": verify_res_post.n_total,
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}
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improvement = verify_res.rmse - verify_res_post.rmse
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results[str(serial)]["refine_depth"]["improvement_rmse"] = (
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improvement
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)
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click.echo(
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f"Camera {serial} refined: RMSE={verify_res_post.rmse:.3f}m "
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f"(Improved by {improvement:.3f}m). "
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f"Delta Rot={refine_stats['delta_rotation_deg']:.2f}deg, "
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f"Trans={refine_stats['delta_translation_norm_m']:.3f}m"
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)
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verify_res = verify_res_post
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if report_csv_path:
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for mid, cidx, resid in verify_res.residuals:
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csv_rows.append([serial, mid, cidx, resid])
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if report_csv_path and csv_rows:
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with open(report_csv_path, "w", newline="") as f:
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writer = csv.writer(f)
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writer.writerow(["serial", "marker_id", "corner_idx", "residual"])
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writer.writerows(csv_rows)
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click.echo(f"Saved depth verification report to {report_csv_path}")
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return results, csv_rows
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@click.command()
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@click.option("--svo", "-s", multiple=True, required=False, help="Path to SVO files.")
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@click.option("--markers", "-m", required=True, help="Path to markers parquet file.")
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@click.option("--output", "-o", default="extrinsics.json", help="Output JSON file.")
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@click.option(
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"--sample-interval", "-n", default=30, type=int, help="Sample every N frames."
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)
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@click.option(
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"--max-reproj-error",
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"-e",
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default=2.0,
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type=float,
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help="Max reprojection error for pose.",
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)
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@click.option("--preview/--no-preview", default=True, help="Show preview window.")
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@click.option(
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"--validate-markers/--no-validate", default=True, help="Validate marker geometry."
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)
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@click.option(
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"--self-check/--no-self-check", default=False, help="Perform self-check on result."
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)
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@click.option(
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"--verify-depth/--no-verify-depth", default=False, help="Enable depth verification."
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)
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@click.option(
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"--refine-depth/--no-refine-depth", default=False, help="Enable depth refinement."
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)
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@click.option(
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"--depth-mode",
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default="NEURAL",
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type=click.Choice(["NEURAL", "ULTRA", "PERFORMANCE", "NONE"]),
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help="Depth computation mode.",
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)
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@click.option(
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"--depth-confidence-threshold",
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default=50,
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type=int,
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help="Confidence threshold for depth filtering (lower = more confident).",
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)
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@click.option(
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"--report-csv", type=click.Path(), help="Optional path for per-frame CSV report."
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)
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@click.option(
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"--auto-align/--no-auto-align",
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default=False,
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help="Automatically align ground plane.",
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)
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@click.option(
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"--ground-face", type=str, help="Explicit face name for ground alignment."
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)
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@click.option(
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"--ground-marker-id", type=int, help="Explicit marker ID to define ground face."
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)
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@click.option(
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"--aruco-dictionary",
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default="DICT_4X4_50",
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type=click.Choice(list(ARUCO_DICT_MAP.keys())),
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help="ArUco dictionary to use.",
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)
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@click.option(
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"--min-markers",
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default=1,
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type=int,
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help="Minimum markers required for pose estimation.",
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)
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@click.option(
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"--debug/--no-debug",
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default=False,
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help="Enable verbose debug logging.",
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)
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@click.option(
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"--max-samples",
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default=None,
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type=int,
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help="Maximum number of samples to process before stopping.",
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)
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def main(
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svo: tuple[str, ...],
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markers: str,
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output: str,
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sample_interval: int,
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max_reproj_error: float,
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preview: bool,
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validate_markers: bool,
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self_check: bool,
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verify_depth: bool,
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refine_depth: bool,
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depth_mode: str,
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depth_confidence_threshold: int,
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report_csv: str | None,
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auto_align: bool,
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ground_face: str | None,
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ground_marker_id: int | None,
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aruco_dictionary: str,
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min_markers: int,
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debug: bool,
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max_samples: int | None,
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):
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"""
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Calibrate camera extrinsics relative to a global coordinate system defined by ArUco markers.
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"""
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# Configure logging level
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logger.remove()
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logger.add(
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lambda msg: click.echo(msg, nl=False),
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level="DEBUG" if debug else "INFO",
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format="{message}",
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)
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depth_mode_map = {
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"NEURAL": sl.DEPTH_MODE.NEURAL,
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"ULTRA": sl.DEPTH_MODE.ULTRA,
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"PERFORMANCE": sl.DEPTH_MODE.PERFORMANCE,
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"NONE": sl.DEPTH_MODE.NONE,
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}
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sl_depth_mode = depth_mode_map.get(depth_mode, sl.DEPTH_MODE.NONE)
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if not (verify_depth or refine_depth):
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sl_depth_mode = sl.DEPTH_MODE.NONE
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# Expand SVO paths (files or directories)
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expanded_svo = []
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for path_str in svo:
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path = Path(path_str)
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if path.is_dir():
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click.echo(f"Searching for SVO files in {path}...")
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found = sorted(
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[
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str(p)
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for p in path.iterdir()
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if p.is_file() and p.suffix.lower() in (".svo", ".svo2")
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]
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)
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if found:
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click.echo(f"Found {len(found)} SVO files in {path}")
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expanded_svo.extend(found)
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else:
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click.echo(f"Warning: No .svo/.svo2 files found in {path}", err=True)
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elif path.is_file():
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expanded_svo.append(str(path))
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else:
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click.echo(f"Warning: Path not found: {path}", err=True)
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if not expanded_svo:
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if validate_markers:
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click.echo("Marker validation successful. No SVOs provided, exiting.")
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return
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else:
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click.echo(
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"Error: --svo is required unless --validate-markers is used.", err=True
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)
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raise click.UsageError("Missing option '--svo' / '-s'.")
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# 1. Load Marker Geometry
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try:
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marker_geometry = load_marker_geometry(markers)
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if validate_markers:
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validate_marker_geometry(marker_geometry)
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click.echo(f"Loaded {len(marker_geometry)} markers from {markers}")
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# Load face mapping if available
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face_marker_map = load_face_mapping(markers)
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if face_marker_map:
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click.echo(f"Loaded face mapping for {len(face_marker_map)} faces.")
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else:
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click.echo("No face mapping found in parquet (missing 'name'/'ids').")
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face_marker_map = None
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except Exception as e:
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click.echo(f"Error loading markers: {e}", err=True)
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raise SystemExit(1)
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# 2. Initialize SVO Reader
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reader = SVOReader(expanded_svo, depth_mode=sl_depth_mode)
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if not reader.cameras:
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click.echo("No SVO files could be opened.", err=True)
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return
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# Align SVOs
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reader.sync_to_latest_start()
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# Calculate max frames to process to avoid infinite loop
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max_frames = 10000 # Default safety limit
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if reader.cameras:
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remaining = []
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for i, cam in enumerate(reader.cameras):
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total = reader.camera_info[i]["total_frames"]
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if total > 0:
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current = cam.get_svo_position()
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remaining.append(total - current)
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else:
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# If any total_frames is unknown (<= 0), use a hard limit
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remaining = [10000]
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break
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if remaining:
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max_frames = min(remaining)
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else:
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click.echo(
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"Warning: Could not determine SVO lengths, using safety limit of 10,000 frames."
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)
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serials = [info["serial"] for info in reader.camera_info]
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accumulators = {serial: PoseAccumulator() for serial in serials}
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camera_matrices = {
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serial: build_camera_matrix_from_zed(cam)
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for serial, cam in zip(serials, reader.cameras)
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}
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# Store verification frames for post-process check
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verification_frames = {}
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# Track all visible marker IDs for heuristic ground detection
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all_visible_ids = set()
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detector = create_detector(dictionary_id=ARUCO_DICT_MAP[aruco_dictionary])
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frame_count = 0
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sampled_count = 0
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click.echo(f"Processing SVOs: {serials}")
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try:
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while frame_count < max_frames:
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frames = reader.grab_synced()
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if not any(frames):
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break
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if frame_count % sample_interval == 0:
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preview_frames = {}
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for i, frame in enumerate(frames):
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if frame is None:
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continue
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serial = frame.serial_number
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K = camera_matrices[serial]
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# Detect markers
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corners, ids = detect_markers(frame.image, detector)
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if ids is not None:
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all_visible_ids.update(ids.flatten().tolist())
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logger.debug(
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f"Cam {serial}: Detected {len(ids)} markers: {ids.flatten()}"
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)
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else:
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logger.debug(f"Cam {serial}: No markers detected")
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if ids is None:
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if preview:
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preview_frames[serial] = frame.image
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continue
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# Estimate pose (T_cam_from_world)
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pose_res = estimate_pose_from_detections(
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corners, ids, marker_geometry, K, min_markers=min_markers
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)
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if pose_res:
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rvec, tvec, reproj_err, n_markers = pose_res
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if reproj_err <= max_reproj_error:
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T_cam_world = rvec_tvec_to_matrix(rvec, tvec)
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# We want T_world_from_cam
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T_world_cam = invert_transform(T_cam_world)
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# Save latest valid frame for verification
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if (
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verify_depth or refine_depth
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) and frame.depth_map is not None:
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verification_frames[serial] = {
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"frame": frame,
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"ids": ids,
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"corners": corners,
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}
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accumulators[serial].add_pose(
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T_world_cam, reproj_err, frame_count
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)
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logger.debug(
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f"Cam {serial}: Pose accepted. Reproj={reproj_err:.3f}, Markers={n_markers}"
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)
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else:
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logger.debug(
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f"Cam {serial}: Pose rejected. Reproj {reproj_err:.3f} > {max_reproj_error}"
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)
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if preview:
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img = draw_detected_markers(
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frame.image.copy(), corners, ids
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)
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img = draw_pose_axes(img, rvec, tvec, K, length=0.2)
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preview_frames[serial] = img
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else:
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if ids is not None:
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logger.debug(
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f"Cam {serial}: Pose estimation failed (insufficient markers < {min_markers} or solver failure)"
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)
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elif preview:
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preview_frames[serial] = frame.image
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if preview and preview_frames:
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key = show_preview(preview_frames)
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if key == 27 or key == ord("q"):
|
|
break
|
|
|
|
sampled_count += 1
|
|
if max_samples is not None and sampled_count >= max_samples:
|
|
click.echo(f"\nReached max samples ({max_samples}). Stopping.")
|
|
break
|
|
|
|
frame_count += 1
|
|
if frame_count % 100 == 0:
|
|
counts = [len(acc.poses) for acc in accumulators.values()]
|
|
click.echo(
|
|
f"Frame {frame_count}, Accepted Poses: {dict(zip(serials, counts))}"
|
|
)
|
|
|
|
except KeyboardInterrupt:
|
|
click.echo("\nInterrupted by user.")
|
|
finally:
|
|
reader.close()
|
|
cv2.destroyAllWindows()
|
|
|
|
# 3. Compute Final Poses
|
|
results = {}
|
|
for serial, acc in accumulators.items():
|
|
if not acc.poses:
|
|
click.echo(f"Warning: No valid poses for camera {serial}")
|
|
continue
|
|
|
|
# Use RANSAC to find best consensus
|
|
inliers = acc.ransac_filter()
|
|
T_mean, stats = acc.compute_robust_mean(inliers)
|
|
|
|
# Flatten for JSON as space-separated string
|
|
pose_str = " ".join(f"{x:.6f}" for x in T_mean.flatten())
|
|
|
|
results[str(serial)] = {"pose": pose_str, "stats": stats}
|
|
click.echo(
|
|
f"Camera {serial}: {stats['n_inliers']}/{stats['n_total']} inliers, median error: {stats['median_reproj_error']:.3f}"
|
|
)
|
|
|
|
if not results:
|
|
click.echo("No extrinsics computed.", err=True)
|
|
return
|
|
|
|
# 4. Run Depth Verification if requested
|
|
apply_depth_verify_refine_postprocess(
|
|
results,
|
|
verification_frames,
|
|
marker_geometry,
|
|
camera_matrices,
|
|
verify_depth,
|
|
refine_depth,
|
|
depth_confidence_threshold,
|
|
report_csv,
|
|
)
|
|
|
|
# 5. Optional Ground Plane Alignment
|
|
if auto_align:
|
|
click.echo("\nPerforming ground plane alignment...")
|
|
target_face = ground_face
|
|
|
|
# Use loaded map or skip if None
|
|
if face_marker_map is None:
|
|
click.echo(
|
|
"Warning: No face mapping available (missing 'name'/'ids' in parquet). Skipping alignment.",
|
|
err=True,
|
|
)
|
|
# Skip alignment logic by ensuring loop below doesn't run and heuristic fails gracefully
|
|
mapping_to_use = {}
|
|
else:
|
|
mapping_to_use = face_marker_map
|
|
|
|
if not target_face and ground_marker_id is not None:
|
|
# Map marker ID to face
|
|
for face, ids in mapping_to_use.items():
|
|
if ground_marker_id in ids:
|
|
target_face = face
|
|
logger.info(
|
|
f"Mapped ground-marker-id {ground_marker_id} to face '{face}' (markers={ids})"
|
|
)
|
|
break
|
|
|
|
ground_normal = None
|
|
if target_face:
|
|
ground_normal = get_face_normal_from_geometry(
|
|
target_face, marker_geometry, face_marker_map=face_marker_map
|
|
)
|
|
if ground_normal is not None:
|
|
ids = mapping_to_use.get(target_face, [])
|
|
logger.info(
|
|
f"Using explicit ground face '{target_face}' (markers={ids})"
|
|
)
|
|
else:
|
|
# Heuristic detection
|
|
heuristic_res = detect_ground_face(
|
|
all_visible_ids, marker_geometry, face_marker_map=face_marker_map
|
|
)
|
|
if heuristic_res:
|
|
target_face, ground_normal = heuristic_res
|
|
ids = mapping_to_use.get(target_face, [])
|
|
logger.info(
|
|
f"Heuristically detected ground face '{target_face}' (markers={ids})"
|
|
)
|
|
|
|
if ground_normal is not None:
|
|
R_align = rotation_align_vectors(ground_normal, np.array([0, 1, 0]))
|
|
logger.info(f"Computed alignment rotation for face '{target_face}'")
|
|
|
|
for serial, data in results.items():
|
|
T_mean = np.fromstring(data["pose"], sep=" ").reshape(4, 4)
|
|
T_aligned = apply_alignment_to_pose(T_mean, R_align)
|
|
data["pose"] = " ".join(f"{x:.6f}" for x in T_aligned.flatten())
|
|
logger.debug(f"Applied alignment to camera {serial}")
|
|
else:
|
|
click.echo(
|
|
"Warning: Could not determine ground normal. Skipping alignment."
|
|
)
|
|
|
|
# 6. Save to JSON
|
|
with open(output, "w") as f:
|
|
json.dump(results, f, indent=4, sort_keys=True)
|
|
click.echo(f"Saved extrinsics to {output}")
|
|
|
|
# 7. Optional Self-Check
|
|
if self_check:
|
|
# Verify reprojection error
|
|
for serial, data in results.items():
|
|
if data["stats"]["median_reproj_error"] > max_reproj_error:
|
|
click.echo(
|
|
f"Error: Camera {serial} failed self-check (median error {data['stats']['median_reproj_error']:.3f} > {max_reproj_error})",
|
|
err=True,
|
|
)
|
|
raise SystemExit(1)
|
|
|
|
# Simple check: verify distance between cameras if multiple
|
|
if len(results) >= 2:
|
|
serials_list = sorted(results.keys())
|
|
for i in range(len(serials_list)):
|
|
for j in range(i + 1, len(serials_list)):
|
|
s1 = serials_list[i]
|
|
s2 = serials_list[j]
|
|
p1 = np.fromstring(results[s1]["pose"], sep=" ").reshape(4, 4)[
|
|
:3, 3
|
|
]
|
|
p2 = np.fromstring(results[s2]["pose"], sep=" ").reshape(4, 4)[
|
|
:3, 3
|
|
]
|
|
dist = np.linalg.norm(p1 - p2)
|
|
click.echo(f"Self-check: Distance {s1} <-> {s2}: {dist:.3f}m")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() # pylint: disable=no-value-for-parameter
|