commit eee1893b9d244fe77efb7a8647155bb556ca3d21 Author: administrator Date: Sat Jun 27 08:50:25 2026 +0200 Add AprilTag calibration, assets, and tracking demo diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..9ad8ebd --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +__pycache__/ +*.py[cod] +*.mp4 diff --git a/analysis_output/apriltag_analysis_report.json b/analysis_output/apriltag_analysis_report.json new file mode 100644 index 0000000..b5bc87f --- /dev/null +++ b/analysis_output/apriltag_analysis_report.json @@ -0,0 +1,68 @@ +{ + "video_path": "C:\\devel\\slam_demo\\VID_20260626_142931.mp4", + "calibration_path": "C:\\devel\\slam_demo\\calibration_output\\camera_calibration.npz", + "tag_size_m": 0.2, + "precise_distance_m": 3.0, + "video": { + "fps": 29.490109925345436, + "frame_count": 3965, + "width": 1080, + "height": 1920, + "duration_sec": 134.45185555555557 + }, + "detections_total": 2043, + "frames_with_tags": 2042, + "frames_with_tags_ratio": 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Path + +import cv2 +import numpy as np + + +DICT_ID = cv2.aruco.DICT_APRILTAG_36h11 + + +@dataclass +class DetectionRow: + frame_index: int + time_sec: float + tag_id: int + center_x_px: float + center_y_px: float + side_px: float + distance_m: float + x_m: float + y_m: float + z_m: float + yaw_deg: float + pitch_deg: float + roll_deg: float + reproj_err_px: float + + +def load_calibration(path: Path) -> tuple[np.ndarray, np.ndarray]: + data = np.load(path) + return data["camera_matrix"], data["dist_coeffs"] + + +def build_object_points(tag_size_m: float) -> np.ndarray: + half = tag_size_m / 2.0 + return np.array( + [ + [-half, half, 0.0], + [half, half, 0.0], + [half, -half, 0.0], + [-half, -half, 0.0], + ], + dtype=np.float32, + ) + + +def pose_to_euler_deg(rvec: np.ndarray) -> tuple[float, float, float]: + rot, _ = cv2.Rodrigues(rvec) + yaw = math.degrees(math.atan2(rot[1, 0], rot[0, 0])) + pitch = math.degrees(math.atan2(-rot[2, 0], math.sqrt(rot[2, 1] ** 2 + rot[2, 2] ** 2))) + roll = math.degrees(math.atan2(rot[2, 1], rot[2, 2])) + return yaw, pitch, roll + + +def estimate_side_px(points: np.ndarray) -> float: + lengths = [] + for i in range(4): + p0 = points[i] + p1 = points[(i + 1) % 4] + lengths.append(float(np.linalg.norm(p1 - p0))) + return float(sum(lengths) / len(lengths)) + + +def reprojection_error( + object_points: np.ndarray, + image_points: np.ndarray, + rvec: np.ndarray, + tvec: np.ndarray, + camera_matrix: np.ndarray, + dist_coeffs: np.ndarray, +) -> float: + projected, _ = cv2.projectPoints(object_points, rvec, tvec, camera_matrix, dist_coeffs) + projected = projected.reshape(-1, 2) + err = np.linalg.norm(projected - image_points, axis=1) + return float(np.mean(err)) + + +def detect_video( + video_path: Path, + calibration_path: Path, + output_dir: Path, + tag_size_m: float, + precise_distance_m: float, +) -> dict: + output_dir.mkdir(parents=True, exist_ok=True) + camera_matrix, dist_coeffs = load_calibration(calibration_path) + object_points = build_object_points(tag_size_m) + + dictionary = cv2.aruco.getPredefinedDictionary(DICT_ID) + parameters = cv2.aruco.DetectorParameters() + detector = cv2.aruco.ArucoDetector(dictionary, parameters) + + cap = cv2.VideoCapture(str(video_path)) + if not cap.isOpened(): + raise RuntimeError(f"Cannot open video: {video_path}") + + fps = float(cap.get(cv2.CAP_PROP_FPS)) + frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + duration_sec = frame_count / fps if fps else 0.0 + + detections: list[DetectionRow] = [] + tag_summary: dict[int, dict] = {} + best_frames: dict[int, tuple[DetectionRow, np.ndarray]] = {} + frames_with_tags = 0 + + frame_index = 0 + while True: + ok, frame = cap.read() + if not ok: + break + + corners, ids, _ = detector.detectMarkers(frame) + if ids is not None and len(ids) > 0: + frames_with_tags += 1 + + if ids is not None: + for marker_corners, marker_id_arr in zip(corners, ids): + tag_id = int(marker_id_arr[0]) + image_points = marker_corners.reshape(4, 2).astype(np.float32) + ok_pnp, rvec, tvec = cv2.solvePnP( + object_points, + image_points, + camera_matrix, + dist_coeffs, + flags=cv2.SOLVEPNP_IPPE_SQUARE, + ) + if not ok_pnp: + continue + + x_m = float(tvec[0, 0]) + y_m = float(tvec[1, 0]) + z_m = float(tvec[2, 0]) + distance_m = float(np.linalg.norm(tvec)) + yaw_deg, pitch_deg, roll_deg = pose_to_euler_deg(rvec) + side_px = estimate_side_px(image_points) + center = image_points.mean(axis=0) + reproj_err_px = reprojection_error( + object_points, image_points, rvec, tvec, camera_matrix, dist_coeffs + ) + + row = DetectionRow( + frame_index=frame_index, + time_sec=frame_index / fps if fps else 0.0, + tag_id=tag_id, + center_x_px=float(center[0]), + center_y_px=float(center[1]), + side_px=side_px, + distance_m=distance_m, + x_m=x_m, + y_m=y_m, + z_m=z_m, + yaw_deg=yaw_deg, + pitch_deg=pitch_deg, + roll_deg=roll_deg, + reproj_err_px=reproj_err_px, + ) + detections.append(row) + + summary = tag_summary.setdefault( + tag_id, + { + "count": 0, + "distance_min_m": float("inf"), + "distance_max_m": 0.0, + "side_px_max": 0.0, + "precise_track_frames": 0, + "first_seen_sec": row.time_sec, + "last_seen_sec": row.time_sec, + }, + ) + summary["count"] += 1 + summary["distance_min_m"] = min(summary["distance_min_m"], distance_m) + summary["distance_max_m"] = max(summary["distance_max_m"], distance_m) + summary["side_px_max"] = max(summary["side_px_max"], side_px) + if distance_m <= precise_distance_m: + summary["precise_track_frames"] += 1 + summary["last_seen_sec"] = row.time_sec + + current_best = best_frames.get(tag_id) + if current_best is None or row.side_px > current_best[0].side_px: + best_frames[tag_id] = (row, frame.copy()) + + frame_index += 1 + + cap.release() + + csv_path = output_dir / "apriltag_detections.csv" + with csv_path.open("w", newline="", encoding="utf-8") as fh: + writer = csv.DictWriter(fh, fieldnames=list(asdict(detections[0]).keys()) if detections else list(DetectionRow.__dataclass_fields__.keys())) + writer.writeheader() + for row in detections: + writer.writerow(asdict(row)) + + preview_paths = {} + for tag_id, (row, frame) in best_frames.items(): + corners, ids, _ = detector.detectMarkers(frame) + annotated = frame.copy() + if ids is not None: + cv2.aruco.drawDetectedMarkers(annotated, corners, ids) + text = ( + f"ID {tag_id} t={row.time_sec:.1f}s d={row.distance_m:.2f}m " + f"x={row.x_m:+.2f} y={row.y_m:+.2f} z={row.z_m:+.2f} yaw={row.yaw_deg:+.1f}" + ) + cv2.putText(annotated, text, (30, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2, cv2.LINE_AA) + preview_path = output_dir / f"best_tag_{tag_id:02d}.jpg" + cv2.imwrite(str(preview_path), annotated) + preview_paths[tag_id] = str(preview_path) + + if detections: + all_distances = [row.distance_m for row in detections] + report = { + "video_path": str(video_path), + "calibration_path": str(calibration_path), + "tag_size_m": tag_size_m, + "precise_distance_m": precise_distance_m, + "video": { + "fps": fps, + "frame_count": frame_count, + "width": width, + "height": height, + "duration_sec": duration_sec, + }, + "detections_total": len(detections), + "frames_with_tags": frames_with_tags, + "frames_with_tags_ratio": frames_with_tags / frame_count if frame_count else 0.0, + "detected_tag_ids": sorted(int(k) for k in tag_summary.keys()), + "distance_global_min_m": min(all_distances), + "distance_global_max_m": max(all_distances), + "tag_summary": tag_summary, + "best_frame_previews": preview_paths, + } + else: + report = { + "video_path": str(video_path), + "calibration_path": str(calibration_path), + "tag_size_m": tag_size_m, + "precise_distance_m": precise_distance_m, + "video": { + "fps": fps, + "frame_count": frame_count, + "width": width, + "height": height, + "duration_sec": duration_sec, + }, + "detections_total": 0, + "frames_with_tags": 0, + "frames_with_tags_ratio": 0.0, + "detected_tag_ids": [], + "tag_summary": {}, + "best_frame_previews": {}, + } + + report_path = output_dir / "apriltag_analysis_report.json" + report_path.write_text(json.dumps(report, indent=2), encoding="utf-8") + return report + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--video", required=True) + parser.add_argument("--calibration", required=True) + parser.add_argument("--output-dir", required=True) + parser.add_argument("--tag-size-m", type=float, default=0.20) + parser.add_argument("--precise-distance-m", type=float, default=3.0) + args = parser.parse_args() + + report = detect_video( + Path(args.video), + Path(args.calibration), + Path(args.output_dir), + args.tag_size_m, + args.precise_distance_m, + ) + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/apriltag_tracking_demo.py b/apriltag_tracking_demo.py new file mode 100644 index 0000000..af5e97a --- /dev/null +++ b/apriltag_tracking_demo.py @@ -0,0 +1,385 @@ +from __future__ import annotations + +import argparse +import math +from collections import deque +from dataclasses import dataclass +from pathlib import Path + +import cv2 +import numpy as np + + +DICT_ID = cv2.aruco.DICT_APRILTAG_36h11 + + +@dataclass +class PoseEstimate: + tag_id: int + frame_index: int + time_sec: float + corners: np.ndarray + center_x_px: float + center_y_px: float + side_px: float + x_m: float + y_m: float + z_m: float + distance_m: float + yaw_deg: float + pitch_deg: float + roll_deg: float + + +def load_calibration(path: Path) -> tuple[np.ndarray, np.ndarray]: + data = np.load(path) + return data["camera_matrix"], data["dist_coeffs"] + + +def build_object_points(tag_size_m: float) -> np.ndarray: + half = tag_size_m / 2.0 + return np.array( + [ + [-half, half, 0.0], + [half, half, 0.0], + [half, -half, 0.0], + [-half, -half, 0.0], + ], + dtype=np.float32, + ) + + +def pose_to_euler_deg(rvec: np.ndarray) -> tuple[float, float, float]: + rot, _ = cv2.Rodrigues(rvec) + yaw = math.degrees(math.atan2(rot[1, 0], rot[0, 0])) + pitch = math.degrees(math.atan2(-rot[2, 0], math.sqrt(rot[2, 1] ** 2 + rot[2, 2] ** 2))) + roll = math.degrees(math.atan2(rot[2, 1], rot[2, 2])) + return yaw, pitch, roll + + +def estimate_side_px(points: np.ndarray) -> float: + lengths = [] + for i in range(4): + p0 = points[i] + p1 = points[(i + 1) % 4] + lengths.append(float(np.linalg.norm(p1 - p0))) + return float(sum(lengths) / len(lengths)) + + +def detect_primary_pose( + frame: np.ndarray, + frame_index: int, + time_sec: float, + detector: cv2.aruco.ArucoDetector, + object_points: np.ndarray, + camera_matrix: np.ndarray, + dist_coeffs: np.ndarray, +) -> tuple[PoseEstimate | None, list[PoseEstimate]]: + corners, ids, _ = detector.detectMarkers(frame) + if ids is None or len(ids) == 0: + return None, [] + + detections: list[PoseEstimate] = [] + for marker_corners, marker_id_arr in zip(corners, ids): + tag_id = int(marker_id_arr[0]) + image_points = marker_corners.reshape(4, 2).astype(np.float32) + ok_pnp, rvec, tvec = cv2.solvePnP( + object_points, + image_points, + camera_matrix, + dist_coeffs, + flags=cv2.SOLVEPNP_IPPE_SQUARE, + ) + if not ok_pnp: + continue + x_m = float(tvec[0, 0]) + y_m = float(tvec[1, 0]) + z_m = float(tvec[2, 0]) + distance_m = float(np.linalg.norm(tvec)) + yaw_deg, pitch_deg, roll_deg = pose_to_euler_deg(rvec) + center = image_points.mean(axis=0) + detections.append( + PoseEstimate( + tag_id=tag_id, + frame_index=frame_index, + time_sec=time_sec, + corners=image_points, + center_x_px=float(center[0]), + center_y_px=float(center[1]), + side_px=estimate_side_px(image_points), + x_m=x_m, + y_m=y_m, + z_m=z_m, + distance_m=distance_m, + yaw_deg=yaw_deg, + pitch_deg=pitch_deg, + roll_deg=roll_deg, + ) + ) + + if not detections: + return None, [] + + detections.sort(key=lambda d: (d.side_px, -d.distance_m), reverse=True) + return detections[0], detections + + +def tracking_state(pose: PoseEstimate | None, precise_distance_m: float) -> str: + if pose is None: + return "SEARCH" + if pose.distance_m > precise_distance_m: + return "APPROACH" + if abs(pose.x_m) < 0.08 and abs(pose.y_m) < 0.08 and abs(pose.yaw_deg) < 4.0: + return "LOCK" + return "PRECISE_TRACK" + + +def draw_pose_axes(panel: np.ndarray, pose: PoseEstimate | None) -> None: + h, w = panel.shape[:2] + cx, cy = w // 2, h // 2 + cv2.line(panel, (cx - 180, cy), (cx + 180, cy), (70, 70, 70), 1, cv2.LINE_AA) + cv2.line(panel, (cx, cy - 180), (cx, cy + 180), (70, 70, 70), 1, cv2.LINE_AA) + cv2.circle(panel, (cx, cy), 4, (200, 200, 200), -1, cv2.LINE_AA) + cv2.putText(panel, "X laterale", (cx + 20, cy - 8), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (170, 170, 170), 1, cv2.LINE_AA) + cv2.putText(panel, "Y verticale", (cx + 20, cy + 22), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (170, 170, 170), 1, cv2.LINE_AA) + if pose is None: + return + px = int(np.clip(cx + pose.x_m * 220, 40, w - 40)) + py = int(np.clip(cy + pose.y_m * 220, 40, h - 40)) + cv2.circle(panel, (px, py), 12, (0, 220, 255), -1, cv2.LINE_AA) + cv2.line(panel, (cx, cy), (px, py), (0, 220, 255), 2, cv2.LINE_AA) + + +def draw_tracking_panel(panel: np.ndarray, pose: PoseEstimate | None, state: str, precise_distance_m: float) -> None: + panel[:] = (24, 24, 28) + cv2.putText(panel, "AUTOTRACKING", (24, 42), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (240, 240, 240), 2, cv2.LINE_AA) + color = { + "SEARCH": (110, 110, 110), + "APPROACH": (0, 200, 255), + "PRECISE_TRACK": (0, 220, 0), + "LOCK": (255, 200, 0), + }[state] + cv2.rectangle(panel, (24, 62), (250, 110), color, -1) + cv2.putText(panel, state, (38, 95), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (10, 10, 10), 2, cv2.LINE_AA) + + if pose is None: + cv2.putText(panel, "Nessun tag visibile", (24, 156), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (220, 220, 220), 2, cv2.LINE_AA) + draw_pose_axes(panel, None) + return + + lines = [ + f"Tag attivo: {pose.tag_id}", + f"Distanza: {pose.distance_m:.2f} m", + f"Tracking preciso sotto: {precise_distance_m:.2f} m", + f"Offset X: {pose.x_m:+.2f} m", + f"Offset Y: {pose.y_m:+.2f} m", + f"Profondita Z: {pose.z_m:+.2f} m", + f"Yaw: {pose.yaw_deg:+.1f} deg", + f"Pitch: {pose.pitch_deg:+.1f} deg", + f"Roll: {pose.roll_deg:+.1f} deg", + ] + y = 156 + for line in lines: + cv2.putText(panel, line, (24, y), cv2.FONT_HERSHEY_SIMPLEX, 0.72, (220, 220, 220), 2, cv2.LINE_AA) + y += 38 + + draw_pose_axes(panel, pose) + + +def fmt_motion(value: float, pos_label: str, neg_label: str, unit: str = "m/s") -> str: + if abs(value) < 1e-3: + return f"FERMO 0.00 {unit}" + label = pos_label if value > 0 else neg_label + return f"{label} {abs(value):.2f} {unit}" + + +def draw_motion_bar(panel: np.ndarray, y: int, label: str, value: float, scale: float, color: tuple[int, int, int]) -> None: + left = 220 + center = 420 + width = 180 + cv2.putText(panel, label, (24, y + 8), cv2.FONT_HERSHEY_SIMPLEX, 0.72, (220, 220, 220), 2, cv2.LINE_AA) + cv2.line(panel, (center - width, y), (center + width, y), (80, 80, 80), 2, cv2.LINE_AA) + cv2.line(panel, (center, y - 16), (center, y + 16), (170, 170, 170), 2, cv2.LINE_AA) + px = int(np.clip(center + (value / scale) * width, center - width, center + width)) + cv2.circle(panel, (px, y), 10, color, -1, cv2.LINE_AA) + + +def draw_motion_panel(panel: np.ndarray, motion: dict, active_tag_id: int | None) -> None: + panel[:] = (20, 22, 26) + cv2.putText(panel, "COMANDI APPENA ESEGUITI", (24, 42), cv2.FONT_HERSHEY_SIMPLEX, 0.95, (240, 240, 240), 2, cv2.LINE_AA) + tag_text = f"Tag di riferimento: {active_tag_id}" if active_tag_id is not None else "Tag di riferimento: nessuno" + cv2.putText(panel, tag_text, (24, 82), cv2.FONT_HERSHEY_SIMPLEX, 0.72, (200, 200, 200), 2, cv2.LINE_AA) + cv2.putText(panel, "I valori descrivono cosa il drone ha appena fatto fra due pose successive.", (24, 116), cv2.FONT_HERSHEY_SIMPLEX, 0.58, (170, 170, 170), 1, cv2.LINE_AA) + + lines = [ + ("Longitudinale", motion["forward_mps"], "AVANTI", "INDIETRO", 0.8, (0, 220, 255)), + ("Laterale", motion["right_mps"], "DESTRA", "SINISTRA", 0.6, (255, 180, 0)), + ("Verticale", motion["up_mps"], "SU", "GIU", 0.4, (120, 220, 120)), + ("Yaw", motion["yaw_rate_dps"], "RUOTA DX", "RUOTA SX", 20.0, (220, 120, 220)), + ] + y = 190 + for label, value, pos_label, neg_label, scale, color in lines: + text = fmt_motion(value, pos_label, neg_label, "deg/s" if label == "Yaw" else "m/s") + cv2.putText(panel, f"{label}: {text}", (24, y - 22), cv2.FONT_HERSHEY_SIMPLEX, 0.66, (225, 225, 225), 2, cv2.LINE_AA) + draw_motion_bar(panel, y, "", value, scale, color) + y += 105 + + +def annotate_video_frame(frame: np.ndarray, detections: list[PoseEstimate], primary: PoseEstimate | None, state: str) -> np.ndarray: + out = frame.copy() + if detections: + corners = [d.corners.reshape(1, 4, 2).astype(np.float32) for d in detections] + ids = np.array([[d.tag_id] for d in detections], dtype=np.int32) + cv2.aruco.drawDetectedMarkers(out, corners, ids) + + if primary is not None: + cv2.putText( + out, + f"ACTIVE TAG {primary.tag_id} | d={primary.distance_m:.2f}m | x={primary.x_m:+.2f} y={primary.y_m:+.2f} z={primary.z_m:+.2f} | yaw={primary.yaw_deg:+.1f}", + (20, 40), + cv2.FONT_HERSHEY_SIMPLEX, + 0.75, + (0, 255, 0), + 2, + cv2.LINE_AA, + ) + cv2.putText( + out, + f"STATE {state}", + (20, 76), + cv2.FONT_HERSHEY_SIMPLEX, + 0.8, + (0, 220, 255) if state != "LOCK" else (0, 255, 255), + 2, + cv2.LINE_AA, + ) + return out + + +def run_demo( + video_path: Path, + calibration_path: Path, + tag_size_m: float, + precise_distance_m: float, + write_composite: Path | None, + max_frames: int | None, + headless: bool, +) -> None: + camera_matrix, dist_coeffs = load_calibration(calibration_path) + object_points = build_object_points(tag_size_m) + detector = cv2.aruco.ArucoDetector( + cv2.aruco.getPredefinedDictionary(DICT_ID), + cv2.aruco.DetectorParameters(), + ) + + cap = cv2.VideoCapture(str(video_path)) + if not cap.isOpened(): + raise RuntimeError(f"Cannot open video: {video_path}") + + fps = float(cap.get(cv2.CAP_PROP_FPS)) + width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + track_size = (700, 620) + motion_size = (840, 620) + writer = None + if write_composite is not None: + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + writer = cv2.VideoWriter(str(write_composite), fourcc, fps if fps > 0 else 25.0, (width + track_size[0] + motion_size[0], max(height, track_size[1], motion_size[1]))) + + prev_pose: PoseEstimate | None = None + smoothed_motion = { + "forward_mps": 0.0, + "right_mps": 0.0, + "up_mps": 0.0, + "yaw_rate_dps": 0.0, + } + alpha = 0.22 + frame_index = 0 + + while True: + ok, frame = cap.read() + if not ok: + break + if max_frames is not None and frame_index >= max_frames: + break + + time_sec = frame_index / fps if fps else 0.0 + primary, detections = detect_primary_pose( + frame, frame_index, time_sec, detector, object_points, camera_matrix, dist_coeffs + ) + state = tracking_state(primary, precise_distance_m) + + if primary is not None and prev_pose is not None and prev_pose.tag_id == primary.tag_id: + dt = max(primary.time_sec - prev_pose.time_sec, 1e-6) + instant = { + "forward_mps": (prev_pose.z_m - primary.z_m) / dt, + "right_mps": (prev_pose.x_m - primary.x_m) / dt, + "up_mps": (primary.y_m - prev_pose.y_m) / dt, + "yaw_rate_dps": -(primary.yaw_deg - prev_pose.yaw_deg) / dt, + } + for key, value in instant.items(): + smoothed_motion[key] = alpha * value + (1.0 - alpha) * smoothed_motion[key] + else: + for key in smoothed_motion: + smoothed_motion[key] *= 0.85 + + prev_pose = primary if primary is not None else prev_pose + + video_view = annotate_video_frame(frame, detections, primary, state) + tracking_panel = np.zeros((track_size[1], track_size[0], 3), dtype=np.uint8) + motion_panel = np.zeros((motion_size[1], motion_size[0], 3), dtype=np.uint8) + draw_tracking_panel(tracking_panel, primary, state, precise_distance_m) + draw_motion_panel(motion_panel, smoothed_motion, primary.tag_id if primary is not None else None) + + if writer is not None: + canvas_h = max(video_view.shape[0], tracking_panel.shape[0], motion_panel.shape[0]) + canvas_w = video_view.shape[1] + tracking_panel.shape[1] + motion_panel.shape[1] + canvas = np.zeros((canvas_h, canvas_w, 3), dtype=np.uint8) + canvas[: video_view.shape[0], : video_view.shape[1]] = video_view + x1 = video_view.shape[1] + canvas[: tracking_panel.shape[0], x1 : x1 + tracking_panel.shape[1]] = tracking_panel + x2 = x1 + tracking_panel.shape[1] + canvas[: motion_panel.shape[0], x2 : x2 + motion_panel.shape[1]] = motion_panel + writer.write(canvas) + + if not headless: + cv2.imshow("AprilTag Video", video_view) + cv2.imshow("Autotracking", tracking_panel) + cv2.imshow("Comandi Simulati", motion_panel) + key = cv2.waitKey(1) & 0xFF + if key in (27, ord("q")): + break + frame_index += 1 + + cap.release() + if writer is not None: + writer.release() + if not headless: + cv2.destroyAllWindows() + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--video", required=True) + parser.add_argument("--calibration", required=True) + parser.add_argument("--tag-size-m", type=float, default=0.20) + parser.add_argument("--precise-distance-m", type=float, default=3.0) + parser.add_argument("--write-composite") + parser.add_argument("--max-frames", type=int) + parser.add_argument("--headless", action="store_true") + args = parser.parse_args() + + run_demo( + Path(args.video), + Path(args.calibration), + args.tag_size_m, + args.precise_distance_m, + Path(args.write_composite) if args.write_composite else None, + args.max_frames, + args.headless, + ) + + +if __name__ == "__main__": + main() diff --git a/assets/IMG_20260606_093157.jpg b/assets/IMG_20260606_093157.jpg new file mode 100644 index 0000000..faaf2f5 Binary files /dev/null and b/assets/IMG_20260606_093157.jpg differ diff --git a/assets/IMG_20260606_093204.jpg 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100644 index 0000000..9e655c5 --- /dev/null +++ b/calibrate_charuco.py @@ -0,0 +1,174 @@ +import argparse +import json +from pathlib import Path + +import cv2 +import numpy as np + + +IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"} + + +def parse_args() -> argparse.Namespace: + ap = argparse.ArgumentParser(description="Select good ChArUco calibration images and calibrate the camera.") + ap.add_argument("--assets-dir", default="assets", help="Directory containing calibration images.") + ap.add_argument("--output-dir", default="calibration_output", help="Directory where reports and calibration files are written.") + ap.add_argument("--squares-x", type=int, default=7, help="Number of chessboard squares along X.") + ap.add_argument("--squares-y", type=int, default=5, help="Number of chessboard squares along Y.") + ap.add_argument("--square-length-mm", type=float, default=25.0, help="Square side length in millimeters.") + ap.add_argument("--marker-length-mm", type=float, default=18.75, help="Inner marker side length in millimeters.") + ap.add_argument("--dictionary", default="DICT_4X4_50", help="OpenCV aruco dictionary name.") + ap.add_argument("--min-charuco-corners", type=int, default=12, help="Minimum detected ChArUco corners required to accept an image.") + ap.add_argument("--min-markers", type=int, default=8, help="Minimum detected ArUco markers required to accept an image.") + ap.add_argument("--min-images", type=int, default=8, help="Minimum accepted images required before calibration.") + return ap.parse_args() + + +def get_dictionary(name: str): + if not hasattr(cv2.aruco, name): + raise ValueError(f"Unknown dictionary: {name}") + return cv2.aruco.getPredefinedDictionary(getattr(cv2.aruco, name)) + + +def collect_images(assets_dir: Path) -> list[Path]: + return sorted([p for p in assets_dir.iterdir() if p.is_file() and p.suffix.lower() in IMAGE_EXTS]) + + +def write_yaml(path: Path, camera_matrix: np.ndarray, dist_coeffs: np.ndarray) -> None: + fs = cv2.FileStorage(str(path), cv2.FILE_STORAGE_WRITE) + fs.write("camera_matrix", camera_matrix) + fs.write("dist_coeffs", dist_coeffs) + fs.release() + + +def main() -> int: + args = parse_args() + assets_dir = Path(args.assets_dir) + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + if not assets_dir.exists(): + raise SystemExit(f"Assets directory not found: {assets_dir}") + + dictionary = get_dictionary(args.dictionary) + board = cv2.aruco.CharucoBoard( + (args.squares_x, args.squares_y), + args.square_length_mm / 1000.0, + args.marker_length_mm / 1000.0, + dictionary, + ) + detector = cv2.aruco.CharucoDetector(board) + + files = collect_images(assets_dir) + if not files: + raise SystemExit(f"No images found in {assets_dir}") + + accepted: list[dict] = [] + rejected: list[dict] = [] + all_charuco_corners = [] + all_charuco_ids = [] + image_size = None + + for path in files: + img = cv2.imread(str(path)) + if img is None: + rejected.append({"file": path.name, "reason": "read_fail", "markers": 0, "charuco_corners": 0}) + continue + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + if image_size is None: + image_size = (gray.shape[1], gray.shape[0]) + + charuco_corners, charuco_ids, marker_corners, marker_ids = detector.detectBoard(gray) + markers = 0 if marker_ids is None else len(marker_ids) + corners = 0 if charuco_ids is None else len(charuco_ids) + record = { + "file": path.name, + "markers": int(markers), + "charuco_corners": int(corners), + "width": int(gray.shape[1]), + "height": int(gray.shape[0]), + } + + if markers >= args.min_markers and corners >= args.min_charuco_corners: + accepted.append(record) + all_charuco_corners.append(charuco_corners) + all_charuco_ids.append(charuco_ids) + else: + reasons = [] + if markers < args.min_markers: + reasons.append(f"markers<{args.min_markers}") + if corners < args.min_charuco_corners: + reasons.append(f"charuco<{args.min_charuco_corners}") + record["reason"] = ",".join(reasons) if reasons else "rejected" + rejected.append(record) + + (output_dir / "accepted_images.json").write_text(json.dumps(accepted, indent=2), encoding="utf-8") + (output_dir / "rejected_images.json").write_text(json.dumps(rejected, indent=2), encoding="utf-8") + + summary = { + "assets_dir": str(assets_dir.resolve()), + "dictionary": args.dictionary, + "squares_x": args.squares_x, + "squares_y": args.squares_y, + "square_length_mm": args.square_length_mm, + "marker_length_mm": args.marker_length_mm, + "min_markers": args.min_markers, + "min_charuco_corners": args.min_charuco_corners, + "total_images": len(files), + "accepted_images": len(accepted), + "rejected_images": len(rejected), + } + + print(f"Images found: {len(files)}") + print(f"Accepted: {len(accepted)}") + print(f"Rejected: {len(rejected)}") + + if len(accepted) < args.min_images: + summary["status"] = "not_enough_images" + (output_dir / "calibration_report.json").write_text(json.dumps(summary, indent=2), encoding="utf-8") + raise SystemExit(f"Not enough accepted images for calibration: {len(accepted)} < {args.min_images}") + + retval, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.aruco.calibrateCameraCharuco( + charucoCorners=all_charuco_corners, + charucoIds=all_charuco_ids, + board=board, + imageSize=image_size, + cameraMatrix=None, + distCoeffs=None, + ) + + np.savez( + output_dir / "camera_calibration.npz", + camera_matrix=camera_matrix, + dist_coeffs=dist_coeffs, + image_width=image_size[0], + image_height=image_size[1], + dictionary=args.dictionary, + squares_x=args.squares_x, + squares_y=args.squares_y, + square_length_mm=args.square_length_mm, + marker_length_mm=args.marker_length_mm, + ) + write_yaml(output_dir / "camera_calibration.yaml", camera_matrix, dist_coeffs) + + summary.update( + { + "status": "ok", + "image_width": image_size[0], + "image_height": image_size[1], + "rms_reprojection_error": float(retval), + "camera_matrix": camera_matrix.tolist(), + "dist_coeffs": dist_coeffs.tolist(), + } + ) + (output_dir / "calibration_report.json").write_text(json.dumps(summary, indent=2), encoding="utf-8") + + print(f"Calibration RMS reprojection error: {retval:.6f}") + print(f"Report written to: {output_dir / 'calibration_report.json'}") + print(f"NPZ written to: {output_dir / 'camera_calibration.npz'}") + print(f"YAML written to: {output_dir / 'camera_calibration.yaml'}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/calibration_output/accepted_images.json b/calibration_output/accepted_images.json new file mode 100644 index 0000000..79fad23 --- /dev/null +++ b/calibration_output/accepted_images.json @@ -0,0 +1,191 @@ +[ + { + "file": "IMG_20260606_093157.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093204.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093212.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093219.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093237.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093257.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093305.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093421.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093611.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093615.jpg", + "markers": 13, + "charuco_corners": 15, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093627.jpg", + "markers": 16, + "charuco_corners": 22, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093646.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093656.jpg", + "markers": 13, + "charuco_corners": 13, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093714.jpg", + "markers": 15, + "charuco_corners": 18, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093751.jpg", + "markers": 16, + "charuco_corners": 20, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093815.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_093935.jpg", + "markers": 16, + "charuco_corners": 20, + "width": 4000, + "height": 3000 + }, + { + "file": "IMG_20260606_093944.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 4000, + "height": 3000 + }, + { + "file": "IMG_20260606_093948.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_094016.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_094028.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_094032.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_094037.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 3000, + "height": 4000 + }, + { + "file": "IMG_20260606_094042.jpg", + "markers": 16, + "charuco_corners": 20, + "width": 4000, + "height": 3000 + }, + { + "file": "IMG_20260606_094047.jpg", + "markers": 16, + "charuco_corners": 22, + "width": 4000, + "height": 3000 + }, + { + "file": "IMG_20260606_094052.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 4000, + "height": 3000 + }, + { + "file": "IMG_20260606_094057.jpg", + "markers": 17, + "charuco_corners": 24, + "width": 4000, + "height": 3000 + } +] \ No newline at end of file diff --git a/calibration_output/calibration_report.json b/calibration_output/calibration_report.json new file mode 100644 index 0000000..50125b1 --- /dev/null +++ b/calibration_output/calibration_report.json @@ -0,0 +1,43 @@ +{ + "assets_dir": "C:\\devel\\slam_demo\\assets", + "dictionary": "DICT_4X4_50", + "squares_x": 7, + "squares_y": 5, + "square_length_mm": 25.0, + "marker_length_mm": 18.75, + "min_markers": 8, + "min_charuco_corners": 12, + "total_images": 43, + "accepted_images": 27, + "rejected_images": 16, + "status": "ok", + "image_width": 3000, + "image_height": 4000, + "rms_reprojection_error": 0.4331525306786906, + "camera_matrix": [ + [ + 2922.5451296678484, + 0.0, + 1513.228003099643 + ], + [ + 0.0, + 2929.578022490368, + 1987.1398774988904 + ], + [ + 0.0, + 0.0, + 1.0 + ] + ], + "dist_coeffs": [ + [ + 0.06569042692273651, + -0.12285829543468728, + -0.0017248793581868354, + -0.00019726508785207679, + 0.07981683482477274 + ] + ] +} \ No newline at end of file diff --git a/calibration_output/camera_calibration.npz b/calibration_output/camera_calibration.npz new file mode 100644 index 0000000..544bda9 Binary files /dev/null and b/calibration_output/camera_calibration.npz differ diff --git a/calibration_output/camera_calibration.yaml b/calibration_output/camera_calibration.yaml new file mode 100644 index 0000000..1b79c5d --- /dev/null +++ b/calibration_output/camera_calibration.yaml @@ -0,0 +1,15 @@ +%YAML:1.0 +--- +camera_matrix: !!opencv-matrix + rows: 3 + cols: 3 + dt: d + data: [ 2922.5451296678484, 0., 1513.228003099643, 0., + 2929.5780224903679, 1987.1398774988904, 0., 0., 1. ] +dist_coeffs: !!opencv-matrix + rows: 1 + cols: 5 + dt: d + data: [ 0.065690426922736508, -0.12285829543468728, + -0.0017248793581868354, -0.00019726508785207679, + 0.079816834824772739 ] diff --git a/calibration_output/rejected_images.json b/calibration_output/rejected_images.json new file mode 100644 index 0000000..4f3ab16 --- /dev/null +++ b/calibration_output/rejected_images.json @@ -0,0 +1,130 @@ +[ + { + "file": "IMG_20260606_093320.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093434.jpg", + "markers": 1, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093444.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093455.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093505.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093515.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093525.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093726.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093738.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093758.jpg", + "markers": 28, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "charuco<12" + }, + { + "file": "IMG_20260606_093804.jpg", + "markers": 36, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "charuco<12" + }, + { + "file": "IMG_20260606_093825.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093835.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093902.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093939.jpg", + "markers": 5, + "charuco_corners": 3, + "width": 4000, + "height": 3000, + "reason": "markers<8,charuco<12" + }, + { + "file": "IMG_20260606_093959.jpg", + "markers": 0, + "charuco_corners": 0, + "width": 3000, + "height": 4000, + "reason": "markers<8,charuco<12" + } +] \ No newline at end of file diff --git a/generate_apriltag_set.py b/generate_apriltag_set.py new file mode 100644 index 0000000..9384b5a --- /dev/null +++ b/generate_apriltag_set.py @@ -0,0 +1,107 @@ +from pathlib import Path + +import cv2 +import numpy as np +from PIL import Image, ImageDraw, ImageFont + +A4_WIDTH_PX = 2480 +A4_HEIGHT_PX = 3508 +DPI = 300 +TAG_SIDE_MM = 200.0 +TAG_SIDE_PX = round(TAG_SIDE_MM / 25.4 * DPI) +DICT_NAME = "DICT_APRILTAG_36h11" +DICT_ID = cv2.aruco.DICT_APRILTAG_36h11 + +TAGS = [ + (1, "base_home", "BASE_HOME", "Tag base drone / home position"), + (10, "fronte_scaffali_centrali", "FRONTE_SCAFFALI", "Fronte 2 scaffali centrali"), + (21, "inizio_scaffale_basso", "START_LOW", "Inizio scaffale quota 1.0 m"), + (22, "inizio_scaffale_medio", "START_MID", "Inizio scaffale quota 2.0 m"), + (23, "inizio_scaffale_alto", "START_HIGH", "Inizio scaffale quota 3.0 m"), + (31, "fine_scaffale_basso", "END_LOW", "Fine scaffale quota 1.0 m"), + (32, "fine_scaffale_medio", "END_MID", "Fine scaffale quota 2.0 m"), + (33, "fine_scaffale_alto", "END_HIGH", "Fine scaffale quota 3.0 m"), +] + +OUT_DIR = Path(r"C:\devel\slam_demo\tags_set_a4_20cm") +OUT_DIR.mkdir(parents=True, exist_ok=True) + +aruco_dict = cv2.aruco.getPredefinedDictionary(DICT_ID) + +try: + title_font = ImageFont.truetype("arial.ttf", 72) + meta_font = ImageFont.truetype("arial.ttf", 46) + small_font = ImageFont.truetype("arial.ttf", 34) +except OSError: + title_font = ImageFont.load_default() + meta_font = ImageFont.load_default() + small_font = ImageFont.load_default() + +info_lines = [ + f"Dictionary: {DICT_NAME}", + f"A4 page: {A4_WIDTH_PX}x{A4_HEIGHT_PX}px @ {DPI} dpi", + f"Marker side target: {TAG_SIDE_MM:.1f} mm", + "Print settings: 100% scale, no fit-to-page, no auto-resize.", + "Verification after print: measure the black square side, it must be 200 mm.", + "", + "Tag set and placement:", +] + +contact_sheet = Image.new("RGB", (A4_WIDTH_PX * 2, A4_HEIGHT_PX * 4), "white") + +for index, (tag_id, slug, short_name, meaning) in enumerate(TAGS): + marker = cv2.aruco.generateImageMarker(aruco_dict, tag_id, TAG_SIDE_PX) + marker_rgb = cv2.cvtColor(marker, cv2.COLOR_GRAY2RGB) + marker_img = Image.fromarray(marker_rgb) + + page = Image.new("RGB", (A4_WIDTH_PX, A4_HEIGHT_PX), "white") + draw = ImageDraw.Draw(page) + + title = f"APRILTAG {tag_id:02d}" + subtitle = short_name + footer = meaning + verify = "Verifica stampa: lato quadrato nero = 200 mm" + + title_bbox = draw.textbbox((0, 0), title, font=title_font) + subtitle_bbox = draw.textbbox((0, 0), subtitle, font=meta_font) + footer_bbox = draw.textbbox((0, 0), footer, font=small_font) + verify_bbox = draw.textbbox((0, 0), verify, font=small_font) + + top_y = 120 + draw.text(((A4_WIDTH_PX - (title_bbox[2] - title_bbox[0])) / 2, top_y), title, fill="black", font=title_font) + draw.text(((A4_WIDTH_PX - (subtitle_bbox[2] - subtitle_bbox[0])) / 2, top_y + 90), subtitle, fill="black", font=meta_font) + + marker_x = (A4_WIDTH_PX - TAG_SIDE_PX) // 2 + marker_y = 360 + page.paste(marker_img, (marker_x, marker_y)) + + footer_y = marker_y + TAG_SIDE_PX + 80 + draw.text(((A4_WIDTH_PX - (footer_bbox[2] - footer_bbox[0])) / 2, footer_y), footer, fill="black", font=small_font) + draw.text(((A4_WIDTH_PX - (verify_bbox[2] - verify_bbox[0])) / 2, footer_y + 60), verify, fill="black", font=small_font) + + png_path = OUT_DIR / f"apriltag_{tag_id:02d}_{slug}_a4_20cm.png" + page.save(png_path, dpi=(DPI, DPI)) + + info_lines.append(f"- ID {tag_id:02d} | {short_name} | {meaning}") + + thumb = page.copy() + thumb.thumbnail((A4_WIDTH_PX, A4_HEIGHT_PX)) + row = index // 2 + col = index % 2 + contact_sheet.paste(thumb, (col * A4_WIDTH_PX, row * A4_HEIGHT_PX)) + +info_lines.extend([ + "", + "Placement instructions:", + "- ID 01 BASE_HOME: attach near the drone base/home position, clearly visible from the parking/start pose.", + "- ID 10 FRONTE_SCAFFALI: attach on the wall or fixed support in front of the two central shelves, at approx. 2.0 m height.", + "- IDs 21/22/23 START_*: attach vertically on the start side testata scaffale at 1.0 m, 2.0 m, 3.0 m.", + "- IDs 31/32/33 END_*: attach vertically on the end side testata scaffale at 1.0 m, 2.0 m, 3.0 m.", + "- Keep each tag planar, rigid, and orthogonal to the expected viewing direction.", + "- Avoid reflections, wrinkles, and partial occlusions.", + "- Keep at least 10-15 cm vertical separation between printed sheets when mounting the stacked tags.", +]) + +(OUT_DIR / "apriltag_semantics_and_placement.txt").write_text("\n".join(info_lines), encoding="utf-8") +contact_sheet.save(OUT_DIR / "apriltag_contact_sheet.png", dpi=(DPI, DPI)) +print(f"Generated {len(TAGS)} tags in {OUT_DIR}") diff --git a/generate_apriltag_set_pdf.py b/generate_apriltag_set_pdf.py new file mode 100644 index 0000000..540f04d --- /dev/null +++ b/generate_apriltag_set_pdf.py @@ -0,0 +1,131 @@ +from pathlib import Path + +import cv2 +from PIL import Image, ImageDraw, ImageFont + + +A4_WIDTH_PX = 2480 +A4_HEIGHT_PX = 3508 +DPI = 300 +TAG_SIDE_MM = 200.0 +TAG_SIDE_PX = round(TAG_SIDE_MM / 25.4 * DPI) +DICT_NAME = "DICT_APRILTAG_36h11" +DICT_ID = cv2.aruco.DICT_APRILTAG_36h11 + +TAGS = [ + (1, "base_home", "BASE_HOME", "Tag base drone / home position"), + (10, "fronte_scaffali_centrali", "FRONTE_SCAFFALI", "Fronte 2 scaffali centrali"), + (21, "inizio_scaffale_basso", "START_LOW", "Inizio scaffale quota 1.0 m"), + (22, "inizio_scaffale_medio", "START_MID", "Inizio scaffale quota 2.0 m"), + (23, "inizio_scaffale_alto", "START_HIGH", "Inizio scaffale quota 3.0 m"), + (31, "fine_scaffale_basso", "END_LOW", "Fine scaffale quota 1.0 m"), + (32, "fine_scaffale_medio", "END_MID", "Fine scaffale quota 2.0 m"), + (33, "fine_scaffale_alto", "END_HIGH", "Fine scaffale quota 3.0 m"), +] + +OUT_DIR = Path(r"C:\devel\slam_demo\tags_set_a4_20cm_pdf") +OUT_DIR.mkdir(parents=True, exist_ok=True) + + +def mm_to_px(mm: float) -> int: + return round(mm / 25.4 * DPI) + + +def load_font(name: str, size: int): + try: + return ImageFont.truetype(name, size) + except OSError: + return ImageFont.load_default() + + +def draw_centered(draw: ImageDraw.ImageDraw, y: int, text: str, font, fill="black") -> None: + bbox = draw.textbbox((0, 0), text, font=font) + w = bbox[2] - bbox[0] + x = (A4_WIDTH_PX - w) // 2 + draw.text((x, y), text, fill=fill, font=font) + + +def draw_measure_line(draw: ImageDraw.ImageDraw, x: int, y: int, width_px: int, label: str, font) -> None: + tick = 20 + draw.line((x, y, x + width_px, y), fill="black", width=3) + draw.line((x, y - tick, x, y + tick), fill="black", width=3) + draw.line((x + width_px, y - tick, x + width_px, y + tick), fill="black", width=3) + bbox = draw.textbbox((0, 0), label, font=font) + text_w = bbox[2] - bbox[0] + draw.text((x + (width_px - text_w) // 2, y - 60), label, fill="black", font=font) + + +def generate(): + aruco_dict = cv2.aruco.getPredefinedDictionary(DICT_ID) + + title_font = load_font("arial.ttf", 72) + meta_font = load_font("arial.ttf", 46) + small_font = load_font("arial.ttf", 34) + + info_lines = [ + f"Dictionary: {DICT_NAME}", + f"A4 page: {A4_WIDTH_PX}x{A4_HEIGHT_PX}px @ {DPI} dpi", + f"Marker side target: {TAG_SIDE_MM:.1f} mm", + "Print settings: actual size / 100%, no fit-to-page, no shrink, no borderless auto-scale.", + "Verification after print: black square side must be 200 mm. Secondary ruler line must be 100 mm.", + "", + "Tag set and placement:", + ] + + pages = [] + + for tag_id, slug, short_name, meaning in TAGS: + marker = cv2.aruco.generateImageMarker(aruco_dict, tag_id, TAG_SIDE_PX) + marker_img = Image.fromarray(marker).convert("RGB") + + page = Image.new("RGB", (A4_WIDTH_PX, A4_HEIGHT_PX), "white") + draw = ImageDraw.Draw(page) + + draw_centered(draw, 120, f"APRILTAG {tag_id:02d}", title_font) + draw_centered(draw, 210, short_name, meta_font) + + marker_x = (A4_WIDTH_PX - TAG_SIDE_PX) // 2 + marker_y = 360 + page.paste(marker_img, (marker_x, marker_y)) + + draw_centered(draw, marker_y + TAG_SIDE_PX + 70, meaning, small_font) + draw_centered(draw, marker_y + TAG_SIDE_PX + 120, "Verifica stampa: lato quadrato nero = 200 mm", small_font) + + line_width = mm_to_px(100.0) + line_x = (A4_WIDTH_PX - line_width) // 2 + line_y = marker_y + TAG_SIDE_PX + 230 + draw_measure_line(draw, line_x, line_y, line_width, "Linea campione = 100 mm", small_font) + + pdf_path = OUT_DIR / f"apriltag_{tag_id:02d}_{slug}_a4_20cm.pdf" + png_path = OUT_DIR / f"apriltag_{tag_id:02d}_{slug}_a4_20cm_preview.png" + page.save(pdf_path, "PDF", resolution=DPI) + page.save(png_path, dpi=(DPI, DPI)) + pages.append(page.copy()) + + info_lines.append(f"- ID {tag_id:02d} | {short_name} | {meaning}") + + info_lines.extend( + [ + "", + "Placement instructions:", + "- ID 01 BASE_HOME: attach near the drone base/home position, clearly visible from the parking/start pose.", + "- ID 10 FRONTE_SCAFFALI: attach on the wall or fixed support in front of the two central shelves, at approx. 2.0 m height.", + "- IDs 21/22/23 START_*: attach vertically on the start side testata scaffale at 1.0 m, 2.0 m, 3.0 m.", + "- IDs 31/32/33 END_*: attach vertically on the end side testata scaffale at 1.0 m, 2.0 m, 3.0 m.", + "- Keep each tag planar, rigid, and orthogonal to the expected viewing direction.", + "- Avoid reflections, wrinkles, and partial occlusions.", + "- If measured size is wrong, do not compensate in software: fix the printer scaling first.", + ] + ) + (OUT_DIR / "apriltag_print_and_placement_instructions.txt").write_text( + "\n".join(info_lines), encoding="utf-8" + ) + + if pages: + bundle_path = OUT_DIR / "apriltag_bundle_a4_20cm.pdf" + first, rest = pages[0], pages[1:] + first.save(bundle_path, "PDF", resolution=DPI, save_all=True, append_images=rest) + + +if __name__ == "__main__": + generate() diff --git a/good_asset/area6_scaffali.bmp b/good_asset/area6_scaffali.bmp new file mode 100644 index 0000000..3cfe8d7 Binary files /dev/null and b/good_asset/area6_scaffali.bmp differ diff --git a/good_asset/calibration_contact_sheet.jpg b/good_asset/calibration_contact_sheet.jpg new file mode 100644 index 0000000..cc3427d Binary files /dev/null and b/good_asset/calibration_contact_sheet.jpg differ diff --git a/good_asset/demo_missione.bmp b/good_asset/demo_missione.bmp new file mode 100644 index 0000000..1ffd835 Binary files /dev/null and b/good_asset/demo_missione.bmp differ diff --git a/tags/apriltag_10_posizione_fronte_laterale_scaffali.png b/tags/apriltag_10_posizione_fronte_laterale_scaffali.png new file mode 100644 index 0000000..520c8aa Binary files /dev/null and b/tags/apriltag_10_posizione_fronte_laterale_scaffali.png differ diff --git a/tags/apriltag_21_start_scan_2m_sx_dx.png b/tags/apriltag_21_start_scan_2m_sx_dx.png new file mode 100644 index 0000000..5bf8ea0 Binary files /dev/null and b/tags/apriltag_21_start_scan_2m_sx_dx.png differ diff --git a/tags/apriltag_info.txt b/tags/apriltag_info.txt new file mode 100644 index 0000000..3228d28 --- /dev/null +++ b/tags/apriltag_info.txt @@ -0,0 +1,7 @@ +AprilTag dictionary: DICT_APRILTAG_36h11 +Print note: print at 100% scale, no fit-to-page, no auto-resize +Suggested print target: A4, centered, with white margins preserved + +ID 10 -> POS_FRONTE_LATERALE_SCAFFALI +ID 21 -> START_SCAN_L2M_LR + meaning: start scan at 2.0 m height, left to right \ No newline at end of file diff --git a/tags_a4_20cm/apriltag_10_a4_20cm.png b/tags_a4_20cm/apriltag_10_a4_20cm.png new file mode 100644 index 0000000..4321a00 Binary files /dev/null and b/tags_a4_20cm/apriltag_10_a4_20cm.png differ diff --git a/tags_a4_20cm/apriltag_21_a4_20cm.png b/tags_a4_20cm/apriltag_21_a4_20cm.png new file mode 100644 index 0000000..3426544 Binary files /dev/null and b/tags_a4_20cm/apriltag_21_a4_20cm.png differ diff --git a/tags_a4_20cm/apriltag_print_info.txt b/tags_a4_20cm/apriltag_print_info.txt new file mode 100644 index 0000000..c67802d --- /dev/null +++ b/tags_a4_20cm/apriltag_print_info.txt @@ -0,0 +1,8 @@ +AprilTag dictionary: DICT_APRILTAG_36h11 +Target print size: marker side = 200 mm +Paper: A4 portrait +Print at 100% scale, no fit-to-page, no auto-resize +After printing, measure the black square side: it must be 200 mm + +ID 10 -> POS_FRONTE_LATERALE_SCAFFALI +ID 21 -> START_SCAN_L2M_LR \ No newline at end of file diff --git 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a/tags_set_a4_20cm/apriltag_33_fine_scaffale_alto_a4_20cm.png b/tags_set_a4_20cm/apriltag_33_fine_scaffale_alto_a4_20cm.png new file mode 100644 index 0000000..c031b0c Binary files /dev/null and b/tags_set_a4_20cm/apriltag_33_fine_scaffale_alto_a4_20cm.png differ diff --git a/tags_set_a4_20cm/apriltag_contact_sheet.png b/tags_set_a4_20cm/apriltag_contact_sheet.png new file mode 100644 index 0000000..91225ca Binary files /dev/null and b/tags_set_a4_20cm/apriltag_contact_sheet.png differ diff --git a/tags_set_a4_20cm/apriltag_semantics_and_placement.txt b/tags_set_a4_20cm/apriltag_semantics_and_placement.txt new file mode 100644 index 0000000..f176c5f --- /dev/null +++ b/tags_set_a4_20cm/apriltag_semantics_and_placement.txt @@ -0,0 +1,24 @@ +Dictionary: DICT_APRILTAG_36h11 +A4 page: 2480x3508px @ 300 dpi +Marker side target: 200.0 mm +Print settings: 100% scale, no fit-to-page, no auto-resize. +Verification after print: measure the black square side, it must be 200 mm. + +Tag set and placement: +- ID 01 | BASE_HOME | Tag base drone / home position +- ID 10 | FRONTE_SCAFFALI | Fronte 2 scaffali centrali +- ID 21 | START_LOW | Inizio scaffale quota 1.0 m +- ID 22 | START_MID | Inizio scaffale quota 2.0 m +- ID 23 | START_HIGH | Inizio scaffale quota 3.0 m +- ID 31 | END_LOW | Fine scaffale quota 1.0 m +- ID 32 | END_MID | Fine scaffale quota 2.0 m +- ID 33 | END_HIGH | Fine scaffale quota 3.0 m + +Placement instructions: +- ID 01 BASE_HOME: attach near the drone base/home position, clearly visible from the parking/start pose. +- ID 10 FRONTE_SCAFFALI: attach on the wall or fixed support in front of the two central shelves, at approx. 2.0 m height. +- IDs 21/22/23 START_*: attach vertically on the start side testata scaffale at 1.0 m, 2.0 m, 3.0 m. +- IDs 31/32/33 END_*: attach vertically on the end side testata scaffale at 1.0 m, 2.0 m, 3.0 m. +- Keep each tag planar, rigid, and orthogonal to the expected viewing direction. +- Avoid reflections, wrinkles, and partial occlusions. +- Keep at least 10-15 cm vertical separation between printed sheets when mounting the stacked tags. \ No newline at end of file diff --git a/tags_set_a4_20cm/tags_set_a4_20cm.zip b/tags_set_a4_20cm/tags_set_a4_20cm.zip new file mode 100644 index 0000000..b0aaa50 Binary files /dev/null and b/tags_set_a4_20cm/tags_set_a4_20cm.zip differ diff --git a/tags_set_a4_20cm_pdf/apriltag_01_base_home_a4_20cm.pdf b/tags_set_a4_20cm_pdf/apriltag_01_base_home_a4_20cm.pdf new file mode 100644 index 0000000..1720121 Binary files /dev/null and b/tags_set_a4_20cm_pdf/apriltag_01_base_home_a4_20cm.pdf differ diff --git a/tags_set_a4_20cm_pdf/apriltag_01_base_home_a4_20cm_preview.png b/tags_set_a4_20cm_pdf/apriltag_01_base_home_a4_20cm_preview.png new file mode 100644 index 0000000..945d760 Binary files /dev/null and b/tags_set_a4_20cm_pdf/apriltag_01_base_home_a4_20cm_preview.png differ diff --git a/tags_set_a4_20cm_pdf/apriltag_10_fronte_scaffali_centrali_a4_20cm.pdf 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borderless auto-scale. +Verification after print: black square side must be 200 mm. Secondary ruler line must be 100 mm. + +Tag set and placement: +- ID 01 | BASE_HOME | Tag base drone / home position +- ID 10 | FRONTE_SCAFFALI | Fronte 2 scaffali centrali +- ID 21 | START_LOW | Inizio scaffale quota 1.0 m +- ID 22 | START_MID | Inizio scaffale quota 2.0 m +- ID 23 | START_HIGH | Inizio scaffale quota 3.0 m +- ID 31 | END_LOW | Fine scaffale quota 1.0 m +- ID 32 | END_MID | Fine scaffale quota 2.0 m +- ID 33 | END_HIGH | Fine scaffale quota 3.0 m + +Placement instructions: +- ID 01 BASE_HOME: attach near the drone base/home position, clearly visible from the parking/start pose. +- ID 10 FRONTE_SCAFFALI: attach on the wall or fixed support in front of the two central shelves, at approx. 2.0 m height. +- IDs 21/22/23 START_*: attach vertically on the start side testata scaffale at 1.0 m, 2.0 m, 3.0 m. +- IDs 31/32/33 END_*: attach vertically on the end side testata scaffale at 1.0 m, 2.0 m, 3.0 m. +- Keep each tag planar, rigid, and orthogonal to the expected viewing direction. +- Avoid reflections, wrinkles, and partial occlusions. +- If measured size is wrong, do not compensate in software: fix the printer scaling first. \ No newline at end of file diff --git a/tags_set_a4_20cm_pdf/tags_set_a4_20cm_pdf.rar b/tags_set_a4_20cm_pdf/tags_set_a4_20cm_pdf.rar new file mode 100644 index 0000000..d64758c Binary files /dev/null and b/tags_set_a4_20cm_pdf/tags_set_a4_20cm_pdf.rar differ