diff --git a/POSE_PIPELINE.md b/POSE_PIPELINE.md new file mode 100644 index 0000000..cc6f9a8 --- /dev/null +++ b/POSE_PIPELINE.md @@ -0,0 +1,112 @@ +# Pose-Erkennungs-Pipeline + +Aus Mehrkamera-Fotos eines Roboters werden die Gelenkwinkel (`x,y,z,a,b,c,e`) +geschätzt. Kern: Jeder ArUco-Marker wird über seine **4 triangulierten Ecken** +als volle 6-DOF-Pose (Position **+ gemessene Normale**) rekonstruiert; daraus +werden die Gelenkwinkel über die Kinematik (`robot_fk.py`) bestimmt. + +## Ergebnis (Stand: Nacht-Build) + +Benchmark über 10 valide Sim-Posen (Scene4–9b, 11, 12; Scene10 war fehlerhaft +gerendert und ausgeschlossen), Methode **hybrid**: + +| Metrik | Wert | +|---|---| +| Winkelfehler mittel | **0,25°** | +| Winkelfehler std (Stabilität) | 0,13° | +| Winkelfehler schlechtester | 1,57° | +| Positionsfehler mittel | 0,10 mm | + +Zwei Vorbedingungen waren entscheidend und sind erledigt: +1. **npz-fy-Bug** in `render_robot.py` gefixt (war ~15 % Brennweitenfehler bei 16:9). +2. **Eck- statt Center-Triangulation** — liefert pro Marker eine Normale (~1,2° genau, validiert in `benchmark/stage0_corner_normals.py`). + +## Pipeline (run/run_pipeline.bat) + +``` +1_detect_aruco_observations Fotos -> *_aruco_detection.json (inkl. 4 Ecken) +2_estimate_camera_from_observations -> *_camera_pose.json +3_multiview_bundle_adjustment_v4 -> aruco_positions_initial.json (Center; für Viewer/Vergleich) +3b_corner_marker_poses -> aruco_marker_poses.json (Eck-Pose + Normale) +pose_estimation -> robot_state.json (Gelenkwinkel + Konfidenz) +``` + +Aufruf: `.\run_pipeline.bat ..\data\simulation\Scene8` + +## Pose-Estimation (pipeline/pose_estimation.py) + +Parametrisiert über **Gelenk-Variablen** (nicht Links), damit Sonderfälle dieses +Roboters generisch funktionieren: +- Glieder ohne eigene Marker (Base/`x`, Hand/`b`, Palm/`c`) — über Kind-Marker bestimmt. +- Geteilte Variable (`e` für FingerA+FingerB) — eine Unbekannte. +- Verdeckte Mittelglieder — durch globales BA überbrückt. + +### Vier Methoden (robot.json → `pose_estimation.method`) + +| Methode | Idee | Benchmark (mean / worst) | +|---|---|---| +| `sequential_vector` | analytisch aus Marker-Paar-Vektoren | 0,32° / 1,72° | +| `sequential_fk` | blockweise 1D/Block-LS entlang der Kette | 0,43° / 1,84° | +| `global_ba` | alle Winkel gemeinsam, robuste Loss | 0,25° / 1,57° | +| **`hybrid`** (Default) | sequenzieller Init → globales BA | **0,25° / 1,57°** | + +### robot.json-Konfiguration + +```json +"pose_estimation": { + "method": "hybrid", + "marker_observation": "corner_pose", // oder "center_point" + "use_normals": true, + "normal_weight": 100.0, // höher = stabiler bei wenigen Markern + "robust_loss": "huber", + "huber_delta_mm": 8.0, + "max_iterations": 200, + "min_cameras_per_marker": 2, + "per_link_method": {} // optional: Methode pro Glied +} +``` + +`normal_weight=100` wurde empirisch gewählt: stabilisiert sicht-arme Posen +deutlich (Worst-Case-Winkel halbiert), kostet bei gut sichtbaren Posen nur ~0,2°. + +### Konfidenz pro Gelenk + +`robot_state.json → movements[v].confidence`: `high` (≥2 Marker/Variable im +Block), `medium` (≥1), `low` (unterbestimmt — misstrauen!), `none` (0 Marker). +Für die Kollisionssicherheit: Gelenke mit `low`/`none` nicht ungeprüft anfahren; +ggf. Homing/`a`-Drehung für mehr Sichtbarkeit (Multi-Pose). + +## Benchmark- & Eval-Werkzeuge (benchmark/) + +| Tool | Zweck | +|---|---| +| `stage0_corner_normals.py` | Eck-Normalen-Genauigkeit gegen GT (Go/No-Go) | +| `eval_pose.py` | geschätzte Gelenkwinkel vs. `pose.json` | +| `run_benchmark.py` | Scenes × Methoden → Matrix + Aggregat (CSV/JSON) | +| `pipeline/9_evaluateMarker.py` | Marker Position + Normale pro Glied vs. `render_*.json` | + +Beispiele: +``` +python benchmark/run_benchmark.py # alle bereiten Scenes, alle Methoden +python benchmark/run_benchmark.py --scenes 5 8 --methods hybrid +python benchmark/stage0_corner_normals.py --evalDir data/evaluations/Scene5 --gt data/simulation/Scene5/render_a.json +python benchmark/eval_pose.py data/evaluations/Scene5/robot_state.json data/simulation/Scene5/pose.json +``` + +## Viewer (run/robot_viewer.html) + +Lade `aruco_marker_poses.json` als „aruco" → der **Observed-normals**-Pfeil zeigt +jetzt die **gemessene** Normale, eingefärbt nach Winkelabweichung zur Modell- +Normale (grün <2° / gelb 2–5° / rot >5°). Statistik-Panel zeigt Normal mean/max. + +## Offene Punkte / Ideen + +- **Adaptive `normal_weight`**: pro Marker nach Sicht-Qualität gewichten → + best-case und worst-case gleichzeitig optimal (statt fester Kompromiss 100). +- **Multi-Pose-Fusion**: mehrere Aufnahmen bei verschiedenen `a`-Winkeln in + *eine* globale BA werfen — für Posen, in denen die Hand sonst verdeckt ist. +- **Reale Kameras**: Schritt 1 nutzt aktuell für alle Bilder `render_a.npz` + (Sim: alle Kameras gleich). Real bräuchte jede Kamera ihre eigene Intrinsik-npz. +- **`center_point`-Vergleich** im Benchmark (`--observation center_point`) braucht + `aruco_positions_initial.json` pro Scene (Schritt 3). +``` diff --git a/benchmark/benchmark_results.csv b/benchmark/benchmark_results.csv new file mode 100644 index 0000000..35d7891 --- /dev/null +++ b/benchmark/benchmark_results.csv @@ -0,0 +1,41 @@ +scene,method,mean_abs_deg,max_abs_deg,mean_abs_mm,max_abs_mm +Scene11,sequential_vector,0.21615071495813823,0.8314190755566813,0.008262698768396692,0.013081401253181468 +Scene11,sequential_fk,0.38749059657140494,0.7435712942532007,0.15624107163785528,0.2994007420225291 +Scene11,global_ba,0.24076193032735205,0.7758696886111238,0.05602993259134115,0.060046841579868726 +Scene11,hybrid,0.24076193032735205,0.7758696886111238,0.05602993259134115,0.060046841579868726 +Scene12,sequential_vector,0.1394358162818984,0.2923307824906374,0.05495893424309628,0.05495893424309628 +Scene12,sequential_fk,0.2887046847064975,0.700084532189635,0.05495893424309628,0.05495893424309628 +Scene12,global_ba,0.23421405631437628,0.6623398298570464,0.06225340961087511,0.06225340961087511 +Scene12,hybrid,0.23421405631437628,0.6623398298570464,0.06225340961087511,0.06225340961087511 +Scene4,sequential_vector,0.1689903806559073,0.37177692028527076,0.12196674018700904,0.12203581131754504 +Scene4,sequential_fk,0.2052561066616761,0.40043968827325216,0.20664534471432816,0.2912548781111113 +Scene4,global_ba,0.12764613973571387,0.21003754767065175,0.004541214773356117,0.007785849528890054 +Scene4,hybrid,0.12764613973571387,0.21003754767065175,0.004541214773356117,0.007785849528890054 +Scene5,sequential_vector,0.24751400376691776,0.5125580870892463,0.09353406132425324,0.1507674786526909 +Scene5,sequential_fk,0.37528621184521854,0.6967000968706998,0.12625134900152357,0.21620205400723158 +Scene5,global_ba,0.09060360042167304,0.1434928216381195,0.014419220965278257,0.020356040652554697 +Scene5,hybrid,0.09060360042167304,0.1434928216381195,0.014419220965278257,0.020356040652554697 +Scene6,sequential_vector,0.3720210667917684,0.7648573568365009,0.3613204413461384,0.712110322655052 +Scene6,sequential_fk,0.7316454589650221,1.7237275472800775,0.4305546445989883,0.8505787291607518 +Scene6,global_ba,0.3541493232707921,1.1662562506099334,0.14890611576813906,0.2013600383222438 +Scene6,hybrid,0.3541493232707921,1.1662562506099334,0.14890611576813906,0.2013600383222438 +Scene7,sequential_vector,0.5815188036541258,1.7172960028987632,0.17909586175084868,0.313891394909561 +Scene7,sequential_fk,0.6459864657291178,1.3044728301107398,0.05085403969021218,0.05740775078828797 +Scene7,global_ba,0.5322101800604117,1.5679743083230164,0.32619525048234,0.39010299334816345 +Scene7,hybrid,0.5322101800604117,1.5679743083230164,0.32619525048234,0.39010299334816345 +Scene8,sequential_vector,0.2597533931518797,0.6380771626184014,0.029322037687468328,0.032765746060491985 +Scene8,sequential_fk,0.45929803469902597,0.9239031754049449,0.05204031166694967,0.07820229401945467 +Scene8,global_ba,0.3073474311985535,0.6645055768230179,0.13313561563484289,0.19702884143382704 +Scene8,hybrid,0.3073474311985535,0.6645055768230179,0.13313561563484289,0.19702884143382704 +Scene9,sequential_vector,0.3923356692953973,1.4948777416184953,0.14356583700798886,0.2574687935528708 +Scene9,sequential_fk,0.5281710000926637,1.3165046155579603,0.2919599619770281,0.5542570434909493 +Scene9,global_ba,0.13197745617665077,0.19531145821179052,0.09420136800160872,0.11355612175911745 +Scene9,hybrid,0.13197745617665077,0.19531145821179052,0.09420136800160872,0.11355612175911745 +Scene9a,sequential_vector,0.34522769828549826,1.0414123806210682,0.2061424863522392,0.38298669630472126 +Scene9a,sequential_fk,0.5338944077748919,1.8379242756954568,0.17538586419238023,0.3214734519850033 +Scene9a,global_ba,0.380374450420976,1.1839749264013335,0.1337460792771763,0.145277731665594 +Scene9a,hybrid,0.380374450420976,1.1839749264013335,0.1337460792771763,0.145277731665594 +Scene9b,sequential_vector,0.4318049021823242,0.7961252335628615,0.24164404042724819,0.4507708686551588 +Scene9b,sequential_fk,0.1849035221198619,0.44040456936312466,0.035433954208512475,0.038350696217687386 +Scene9b,global_ba,0.13080989543460078,0.3566984466850158,0.05474286758364233,0.06138719680519422 +Scene9b,hybrid,0.13080989543460078,0.3566984466850158,0.05474286758364233,0.06138719680519422 diff --git a/benchmark/benchmark_results.json b/benchmark/benchmark_results.json new file mode 100644 index 0000000..ea2111b --- /dev/null +++ b/benchmark/benchmark_results.json @@ -0,0 +1,339 @@ +{ + "observation": "corner_pose", + "matrix": { + "Scene11": { + "sequential_vector": { + "n_joints": 7, + "mean_abs_deg": 0.21615071495813823, + "max_abs_deg": 0.8314190755566813, + "mean_abs_mm": 0.008262698768396692, + "max_abs_mm": 0.013081401253181468 + }, + "sequential_fk": { + "n_joints": 7, + 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+ } + }, + "Scene9a": { + "sequential_vector": { + "n_joints": 7, + "mean_abs_deg": 0.34522769828549826, + "max_abs_deg": 1.0414123806210682, + "mean_abs_mm": 0.2061424863522392, + "max_abs_mm": 0.38298669630472126 + }, + "sequential_fk": { + "n_joints": 7, + "mean_abs_deg": 0.5338944077748919, + "max_abs_deg": 1.8379242756954568, + "mean_abs_mm": 0.17538586419238023, + "max_abs_mm": 0.3214734519850033 + }, + "global_ba": { + "n_joints": 7, + "mean_abs_deg": 0.380374450420976, + "max_abs_deg": 1.1839749264013335, + "mean_abs_mm": 0.1337460792771763, + "max_abs_mm": 0.145277731665594 + }, + "hybrid": { + "n_joints": 7, + "mean_abs_deg": 0.380374450420976, + "max_abs_deg": 1.1839749264013335, + "mean_abs_mm": 0.1337460792771763, + "max_abs_mm": 0.145277731665594 + } + }, + "Scene9b": { + "sequential_vector": { + "n_joints": 7, + "mean_abs_deg": 0.4318049021823242, + "max_abs_deg": 0.7961252335628615, + "mean_abs_mm": 0.24164404042724819, + "max_abs_mm": 0.4507708686551588 + }, + "sequential_fk": { + "n_joints": 7, + "mean_abs_deg": 0.1849035221198619, + "max_abs_deg": 0.44040456936312466, + "mean_abs_mm": 0.035433954208512475, + "max_abs_mm": 0.038350696217687386 + }, + "global_ba": { + "n_joints": 7, + "mean_abs_deg": 0.13080989543460078, + "max_abs_deg": 0.3566984466850158, + "mean_abs_mm": 0.05474286758364233, + "max_abs_mm": 0.06138719680519422 + }, + "hybrid": { + "n_joints": 7, + "mean_abs_deg": 0.13080989543460078, + "max_abs_deg": 0.3566984466850158, + "mean_abs_mm": 0.05474286758364233, + "max_abs_mm": 0.06138719680519422 + } + } + }, + "aggregate": { + "sequential_vector": { + "mean_deg": 0.31547524490238554, + "std_deg": 0.1281946335848945, + "worst_deg": 1.7172960028987632, + "mean_mm": 0.1439813139094687, + "worst_mm": 0.712110322655052, + "n_scenes": 10 + }, + "sequential_fk": { + "mean_deg": 0.43406364891653804, + "std_deg": 0.17149696414532492, + "worst_deg": 1.8379242756954568, + "mean_mm": 0.15803254759308744, + "worst_mm": 0.8505787291607518, + "n_scenes": 10 + }, + "global_ba": { + "mean_deg": 0.25300944633611, + "std_deg": 0.13398997870322718, + "worst_deg": 1.5679743083230164, + "mean_mm": 0.10281710746886, + "worst_mm": 0.39010299334816345, + "n_scenes": 10 + }, + "hybrid": { + "mean_deg": 0.25300944633611, + "std_deg": 0.13398997870322718, + "worst_deg": 1.5679743083230164, + "mean_mm": 0.10281710746886, + "worst_mm": 0.39010299334816345, + "n_scenes": 10 + } + } +} \ No newline at end of file diff --git a/benchmark/eval_pose.py b/benchmark/eval_pose.py new file mode 100644 index 0000000..8cec1fd --- /dev/null +++ b/benchmark/eval_pose.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +""" +eval_pose.py +============ +Compare estimated joint angles (robot_state.json) against ground truth +(simulation/SceneX/pose.json -> "position"). + +Per-joint error: + revolute (y,z,a,b,c): angular error in degrees, wrap-aware (179 vs -179 = 2deg) + linear (x,e): error in millimetres + +Prints a table and optionally writes a JSON summary. Returns nonzero if any +observable joint exceeds a tolerance (for scripted regression checks). +""" +from __future__ import annotations + +import argparse +import json +import sys +from typing import Any, Dict + +LINEAR = {"x", "e"} +JOINTS = ["x", "y", "z", "a", "b", "c", "e"] + + +def load_estimate(path: str) -> Dict[str, Dict[str, Any]]: + d = json.load(open(path, "r", encoding="utf-8")) + mv = d.get("movements", {}) or {} + out: Dict[str, Dict[str, Any]] = {} + for v in JOINTS: + e = mv.get(v, {}) + # tolerate several historical schemas + val = e.get("value", e.get("value_mm", e.get("value_deg"))) + out[v] = { + "value": float(val) if val is not None else 0.0, + "observable": bool(e.get("observable", True)), + "n_markers": int(e.get("n_markers", -1)), + } + return out + + +def load_gt(path: str) -> Dict[str, float]: + d = json.load(open(path, "r", encoding="utf-8")) + pos = d.get("position", d) + return {v: float(pos[v]) for v in JOINTS if v in pos} + + +def joint_error(v: str, est: float, gt: float) -> float: + if v in LINEAR: + return abs(est - gt) + return abs(((est - gt + 180.0) % 360.0) - 180.0) + + +def evaluate(estimate_path: str, gt_path: str) -> Dict[str, Any]: + est = load_estimate(estimate_path) + gt = load_gt(gt_path) + + rows = [] + ang_errs, lin_errs = [], [] + for v in JOINTS: + if v not in gt: + continue + e = est.get(v, {"value": 0.0, "observable": False, "n_markers": -1}) + err = joint_error(v, e["value"], gt[v]) + unit = "mm" if v in LINEAR else "deg" + rows.append({"joint": v, "estimate": e["value"], "gt": gt[v], "error": err, + "unit": unit, "observable": e["observable"], "n_markers": e["n_markers"]}) + if e["observable"]: + (lin_errs if v in LINEAR else ang_errs).append(err) + + summary = { + "n_joints": len(rows), + "mean_abs_deg": (sum(ang_errs) / len(ang_errs)) if ang_errs else None, + "max_abs_deg": max(ang_errs) if ang_errs else None, + "mean_abs_mm": (sum(lin_errs) / len(lin_errs)) if lin_errs else None, + "max_abs_mm": max(lin_errs) if lin_errs else None, + } + return {"rows": rows, "summary": summary} + + +def main() -> int: + ap = argparse.ArgumentParser(description="Evaluate estimated joint angles vs ground truth") + ap.add_argument("estimate", help="robot_state.json") + ap.add_argument("gt", help="simulation/SceneX/pose.json") + ap.add_argument("--out", default=None) + ap.add_argument("--tolDeg", type=float, default=2.0) + ap.add_argument("--tolMm", type=float, default=3.0) + args = ap.parse_args() + + res = evaluate(args.estimate, args.gt) + print(f"{'joint':>6} | {'est':>9} | {'gt':>9} | {'error':>9} | obs | nMk") + print("-" * 58) + worst = 0.0 + for r in res["rows"]: + flag = " " if r["observable"] else "U" + print(f"{r['joint']:>6} | {r['estimate']:9.2f} | {r['gt']:9.2f} | " + f"{r['error']:7.2f}{r['unit']:>2} | {flag:>3} | {r['n_markers']:>3}") + s = res["summary"] + print("-" * 58) + md = f"{s['mean_abs_deg']:.2f}" if s["mean_abs_deg"] is not None else "-" + xd = f"{s['max_abs_deg']:.2f}" if s["max_abs_deg"] is not None else "-" + mm = f"{s['mean_abs_mm']:.2f}" if s["mean_abs_mm"] is not None else "-" + xm = f"{s['max_abs_mm']:.2f}" if s["max_abs_mm"] is not None else "-" + print(f"angles: mean {md}deg / max {xd}deg | linear: mean {mm}mm / max {xm}mm") + + if args.out: + json.dump(res, open(args.out, "w", encoding="utf-8"), indent=2) + print(f"[INFO] wrote {args.out}") + + over = [r for r in res["rows"] if r["observable"] and + r["error"] > (args.tolMm if r["joint"] in LINEAR else args.tolDeg)] + return 1 if over else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/benchmark/run_benchmark.py b/benchmark/run_benchmark.py new file mode 100644 index 0000000..cded739 --- /dev/null +++ b/benchmark/run_benchmark.py @@ -0,0 +1,183 @@ +#!/usr/bin/env python3 +""" +run_benchmark.py +================ +Pose-estimation benchmark over many scenes x methods against ground truth. + +For each scene it ensures corner marker poses exist (pipeline step 3b), then +runs each pose-estimation method and compares the estimated joint angles to +simulation/SceneX/pose.json. Produces a matrix (mean/max joint error) and a +per-method aggregate including the spread across scenes — the actual measure +of STABILITY. + +Usage: + python benchmark/run_benchmark.py + python benchmark/run_benchmark.py --scenes 4 5 8 --methods hybrid global_ba + python benchmark/run_benchmark.py --observation center_point # aruco_positions_initial.json +""" +from __future__ import annotations + +import argparse +import json +import os +import subprocess +import sys +from statistics import mean, pstdev +from typing import Any, Dict, List, Optional + +ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +PY = sys.executable +ALL_METHODS = ["sequential_vector", "sequential_fk", "global_ba", "hybrid"] + + +def run(cmd: List[str]) -> bool: + r = subprocess.run(cmd, cwd=ROOT, capture_output=True, text=True) + if r.returncode != 0: + print(f"[WARN] command failed: {' '.join(cmd)}\n{r.stderr.strip()[:300]}") + return False + return True + + +def discover_scenes() -> List[str]: + out = [] + sim = os.path.join(ROOT, "data", "simulation") + for s in sorted(os.listdir(sim)): + if not s.startswith("Scene"): + continue + ev = os.path.join(ROOT, "data", "evaluations", s) + if os.path.exists(os.path.join(sim, s, "pose.json")) and os.path.isdir(ev): + import glob + if glob.glob(os.path.join(ev, "*_aruco_detection.json")): + out.append(s) + return out + + +def ensure_marker_poses(scene: str, robot_path: str) -> Optional[str]: + eval_dir = os.path.join(ROOT, "data", "evaluations", scene) + mp = os.path.join(eval_dir, "aruco_marker_poses.json") + if not os.path.exists(mp): + ok = run([PY, "pipeline/3b_corner_marker_poses.py", "--evalDir", eval_dir, "--robot", robot_path]) + if not ok: + return None + return mp + + +def center_input(scene: str) -> Optional[str]: + p = os.path.join(ROOT, "data", "evaluations", scene, "aruco_positions_initial.json") + return p if os.path.exists(p) else None + + +def eval_one(scene: str, method: str, markers_path: str, robot_path: str) -> Optional[Dict[str, Any]]: + eval_dir = os.path.join(ROOT, "data", "evaluations", scene) + rs = os.path.join(eval_dir, f"bench_rs_{method}.json") + if not run([PY, "pipeline/pose_estimation.py", markers_path, "-robot", robot_path, + "--method", method, "-out", rs]): + return None + ev = os.path.join(eval_dir, f"bench_ev_{method}.json") + gt = os.path.join(ROOT, "data", "simulation", scene, "pose.json") + # eval_pose returns nonzero when over tolerance; that's fine, we read the JSON + run([PY, "benchmark/eval_pose.py", rs, gt, "--out", ev, "--tolDeg", "999", "--tolMm", "999"]) + if not os.path.exists(ev): + return None + return json.load(open(ev, "r", encoding="utf-8")) + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--scenes", nargs="*", default=None, help="scene names or numbers (default: all ready)") + ap.add_argument("--methods", nargs="*", default=ALL_METHODS) + ap.add_argument("--robot", default=os.path.join(ROOT, "data", "robot", "robot.json")) + ap.add_argument("--observation", choices=["corner_pose", "center_point"], default="corner_pose") + ap.add_argument("--out", default=os.path.join(ROOT, "benchmark", "benchmark_results.json")) + ap.add_argument("--csv", default=os.path.join(ROOT, "benchmark", "benchmark_results.csv")) + args = ap.parse_args() + + scenes = discover_scenes() + if args.scenes: + want = {s if s.startswith("Scene") else f"Scene{s}" for s in args.scenes} + scenes = [s for s in scenes if s in want] + print(f"[INFO] scenes: {scenes}") + print(f"[INFO] methods: {args.methods} | observation: {args.observation}\n") + + matrix: Dict[str, Dict[str, Any]] = {} + for scene in scenes: + if args.observation == "corner_pose": + mpath = ensure_marker_poses(scene, args.robot) + else: + mpath = center_input(scene) + if not mpath: + print(f"[WARN] {scene}: no {args.observation} input, skipping") + continue + matrix[scene] = {} + for m in args.methods: + ev = eval_one(scene, m, mpath, args.robot) + matrix[scene][m] = ev["summary"] if ev else None + + # ---- print matrix ---- + def fmt(s, key): + return f"{s[key]:5.2f}" if (s and s.get(key) is not None) else " - " + + print("\n" + "=" * 78) + print("POSE ERROR — mean angle [deg] (max) / mean linear [mm] (max)") + print("=" * 78) + header = f"{'scene':>8} | " + " | ".join(f"{m[:12]:>16}" for m in args.methods) + print(header) + print("-" * len(header)) + for scene in scenes: + if scene not in matrix: + continue + cells = [] + for m in args.methods: + s = matrix[scene][m] + if s: + cells.append(f"{fmt(s,'mean_abs_deg')}({fmt(s,'max_abs_deg').strip()})/{fmt(s,'mean_abs_mm').strip()}") + else: + cells.append(" FAIL ") + print(f"{scene:>8} | " + " | ".join(f"{c:>16}" for c in cells)) + + # ---- per-method aggregate (stability = spread across scenes) ---- + print("\n" + "=" * 78) + print("PER-METHOD AGGREGATE across scenes (lower = better, std = instability)") + print("=" * 78) + print(f"{'method':>16} | {'mean deg':>9} | {'std deg':>8} | {'worst deg':>9} | {'mean mm':>8} | {'worst mm':>8}") + print("-" * 78) + agg: Dict[str, Any] = {} + for m in args.methods: + degs = [matrix[s][m]["mean_abs_deg"] for s in matrix + if matrix[s].get(m) and matrix[s][m].get("mean_abs_deg") is not None] + maxdegs = [matrix[s][m]["max_abs_deg"] for s in matrix + if matrix[s].get(m) and matrix[s][m].get("max_abs_deg") is not None] + mms = [matrix[s][m]["mean_abs_mm"] for s in matrix + if matrix[s].get(m) and matrix[s][m].get("mean_abs_mm") is not None] + maxmms = [matrix[s][m]["max_abs_mm"] for s in matrix + if matrix[s].get(m) and matrix[s][m].get("max_abs_mm") is not None] + a = { + "mean_deg": mean(degs) if degs else None, + "std_deg": pstdev(degs) if len(degs) > 1 else 0.0, + "worst_deg": max(maxdegs) if maxdegs else None, + "mean_mm": mean(mms) if mms else None, + "worst_mm": max(maxmms) if maxmms else None, + "n_scenes": len(degs), + } + agg[m] = a + def f(x): + return f"{x:9.3f}" if x is not None else " - " + print(f"{m:>16} | {f(a['mean_deg'])} | {a['std_deg']:8.3f} | {f(a['worst_deg'])} | " + f"{f(a['mean_mm'])} | {f(a['worst_mm'])}") + + json.dump({"observation": args.observation, "matrix": matrix, "aggregate": agg}, + open(args.out, "w", encoding="utf-8"), indent=2) + # CSV + with open(args.csv, "w", encoding="utf-8") as f: + f.write("scene,method,mean_abs_deg,max_abs_deg,mean_abs_mm,max_abs_mm\n") + for scene in matrix: + for m in args.methods: + s = matrix[scene].get(m) + if s: + f.write(f"{scene},{m},{s.get('mean_abs_deg','')},{s.get('max_abs_deg','')}," + f"{s.get('mean_abs_mm','')},{s.get('max_abs_mm','')}\n") + print(f"\n[INFO] wrote {args.out} and {args.csv}") + + +if __name__ == "__main__": + main() diff --git a/data/evaluations/Scene10/aruco_marker_poses.json b/data/evaluations/Scene10/aruco_marker_poses.json new file mode 100644 index 0000000..8ae3d0d --- /dev/null +++ b/data/evaluations/Scene10/aruco_marker_poses.json @@ -0,0 +1,2247 @@ +{ + "schema_version": "1.0", + "stage": "corner_marker_poses", + 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b/data/evaluations/Scene10/render_a_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:49:00Z", + "created_utc": "2026-06-01T23:08:38Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene10\\render_a_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" @@ -9,14 +9,14 @@ "camera_id": "a", "camera_matrix": [ [ - 1777.77783203125, + 2000.0, 0.0, - 640.0 + 720.0 ], [ 0.0, - 1500.0, - 360.0 + 2000.0, + 540.0 ], [ 0.0, @@ -36,459 +36,540 @@ "method": "single_camera_marker_center_lm", "description": "Rigid init from per-marker pose estimates, followed by LM on normalized marker-center reprojection residuals.", "marker_size_m": 0.025, - "num_used_markers": 21, + "num_used_markers": 27, "used_marker_ids": [ + 97, + 95, + 103, + 54, + 55, + 51, 47, 210, 85, - 105, 79, + 105, 215, - 62, - 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b/data/evaluations/Scene10/render_e_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:49:02Z", + "created_utc": "2026-06-01T23:08:40Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene10\\render_e_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" @@ -9,14 +9,14 @@ "camera_id": "e", "camera_matrix": [ [ - 1777.77783203125, + 2000.0, 0.0, - 640.0 + 720.0 ], [ 0.0, - 1500.0, - 360.0 + 2000.0, + 540.0 ], [ 0.0, @@ -36,31 +36,37 @@ "method": "single_camera_marker_center_lm", "description": "Rigid init from per-marker pose estimates, followed by LM on normalized marker-center reprojection residuals.", "marker_size_m": 0.025, - "num_used_markers": 23, + "num_used_markers": 29, "used_marker_ids": [ + 63, + 48, + 57, + 59, + 102, 92, 217, 85, 105, 206, + 47, + 54, 97, 96, - 47, 62, 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b/data/evaluations/Scene10/render_g_camera_pose.json index 7ee35cc..c496a6d 100644 --- a/data/evaluations/Scene10/render_g_camera_pose.json +++ b/data/evaluations/Scene10/render_g_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:49:02Z", + "created_utc": "2026-06-01T23:08:41Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene10\\render_g_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" @@ -9,14 +9,14 @@ "camera_id": "g", "camera_matrix": [ [ - 1777.77783203125, + 2000.0, 0.0, - 640.0 + 720.0 ], [ 0.0, - 1500.0, - 360.0 + 2000.0, + 540.0 ], [ 0.0, @@ -36,603 +36,568 @@ "method": "single_camera_marker_center_lm", "description": "Rigid init from per-marker pose estimates, followed by LM on normalized marker-center reprojection residuals.", "marker_size_m": 0.025, - 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a/data/evaluations/Scene5/aruco_positions_initial.json b/data/evaluations/Scene5/aruco_positions_initial.json index 0ea79c6..44153d9 100644 --- a/data/evaluations/Scene5/aruco_positions_initial.json +++ b/data/evaluations/Scene5/aruco_positions_initial.json @@ -1,7 +1,7 @@ { "schema_version": "1.2", "stage": "initial_triangulation", - "created_utc": "2026-06-01T19:02:41Z", + "created_utc": "2026-06-01T20:35:33Z", "summary": { "num_cameras": 7, "num_markers": 56, diff --git a/data/evaluations/Scene5/aruco_positions_optimized.json b/data/evaluations/Scene5/aruco_positions_optimized.json index 13d25d5..f2e22f0 100644 --- a/data/evaluations/Scene5/aruco_positions_optimized.json +++ b/data/evaluations/Scene5/aruco_positions_optimized.json @@ -1,6 +1,6 @@ { "schema_version": "1.2", - "created_utc": "2026-06-01T19:02:43Z", + "created_utc": "2026-06-01T20:35:34Z", "summary": { "num_cameras": 7, "num_markers": 56, diff --git a/data/evaluations/Scene5/render_a_aruco_detection.json b/data/evaluations/Scene5/render_a_aruco_detection.json index a582a1d..e188040 100644 --- a/data/evaluations/Scene5/render_a_aruco_detection.json +++ b/data/evaluations/Scene5/render_a_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:32Z", + "created_utc": "2026-06-01T20:35:27Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "26009bc3-d9ad-4c34-87b9-bfa9c97f1809", + "observation_id": "ca96ea7e-e926-4f18-9c6d-fe84f50c3707", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9534814647781058 }, { - "observation_id": "6b79f1d9-c62f-4758-be9c-496b38d8367b", + "observation_id": "f30377f8-ef4a-4bd7-a46f-a9986a14c212", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.8810013242977345 }, { - "observation_id": "0aecba85-0635-40f4-a6a4-abb8d39a93ab", + "observation_id": "76e52235-7c40-468e-a279-3f0f545c22fb", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.7733614724853967 }, { - "observation_id": "9d38c2b0-a4e5-422e-af5c-1b5b1149c476", + "observation_id": "0b3ffa9e-8b87-4b50-939a-f61f421e78f8", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.12146268776527554 }, { - "observation_id": "01042af9-314d-4cfa-a121-0456db293478", + "observation_id": "20bf72a5-4dfd-49b0-af87-fd35a518e472", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.5819518243971115 }, { - "observation_id": "446026e6-472e-4012-8c21-baf7f11b961b", + "observation_id": "dedc1780-f0e5-4a0d-8333-d5167b03c8bd", "type": "aruco", "marker_id": 242, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.20467827127148344 }, { - "observation_id": "09eb9e19-9c53-4cc2-9035-41f841102748", + "observation_id": "3da682e2-f2a5-402b-b3d0-db5ffc8faab4", "type": 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"259173de-7c7b-447f-9fde-744beb32c069", + "observation_id": "5e45c408-8ab1-429f-a359-faa885095c5d", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.35589175865715006 }, { - "observation_id": "99df37b0-d7b8-4f95-b240-6c41aa4e7763", + "observation_id": "ed5e9bf1-8b59-488a-aafd-3a1b1ef10ab9", "type": "aruco", "marker_id": 74, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.27942076592245474 }, { - "observation_id": "a0944862-fa96-41dd-b059-665cafeeeb39", + "observation_id": "d3a937a5-be3d-4093-86e2-3a3d15c88c60", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.31777455864589965 }, { - "observation_id": "ccac453d-e92d-4e4a-a1cd-e77569e892af", + "observation_id": "72f068ef-de99-41a0-94be-1757d6f96315", "type": "aruco", "marker_id": 81, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.2741936898356918 }, { - "observation_id": "4a3c178a-7305-4e0f-bbe4-11977beb0d88", + "observation_id": "054cc74b-a2ad-4732-9c36-3c98cb6ff7f3", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.3029173397022545 }, { - "observation_id": "cd5ba542-05a5-4178-8f82-1ea950585dcb", + "observation_id": "1b8ddf1f-c620-49fd-936b-7f110fa91bd7", "type": "aruco", "marker_id": 61, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.1527342148240039 }, { - "observation_id": "665192cf-6547-4c06-bd09-07aa65da7224", + "observation_id": "ecad545a-1274-419d-85f6-6a6f87518069", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.21946523336267398 }, { - "observation_id": "8875e653-aab9-4520-8101-e6de9f049ec1", + "observation_id": "681f108a-1e94-48ad-afa0-6c309d80a450", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.19743498130596515 }, { - "observation_id": "3b59a578-dcc4-4de4-a849-6b4132bd3438", + "observation_id": "8d7e3ae3-4b6a-4239-8100-be1946b3415c", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.2849187633475991 }, { - "observation_id": "f200c492-4233-4e4f-8a1c-109ca3b5a148", + "observation_id": "ef077bf3-ef87-44af-abca-9fcc9f4352ca", "type": "aruco", "marker_id": 82, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.20388582934250105 }, { - "observation_id": "85be9b24-6407-4cb9-84eb-0bc7c75ee3cf", + "observation_id": "a3652769-26d1-40fa-b0fd-e1d3fd40540e", "type": "aruco", "marker_id": 207, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.24627951083020566 }, { - "observation_id": "1fb82fcd-3125-4532-ac91-bc23f3daa1b1", + "observation_id": "484453af-849b-44c5-9277-5fac49f34626", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.2345502390230936 }, { - "observation_id": "232bab2a-b3d3-4773-ae3f-e29a0dd5da01", + "observation_id": "70249d21-df28-4c9e-8614-cf57f869fd0c", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.17208874842045807 }, { - "observation_id": "b098d41f-e52b-4dff-9177-c02f10e845cb", + "observation_id": "afc2a732-9063-4019-ba74-47ffec832d37", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.19579935125865724 }, { - "observation_id": "e23125e5-4ba4-466e-b085-01cfaf82612a", + "observation_id": "e9852a79-d064-4727-b6cb-7d8c7de0eb65", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.2119841467910161 }, { - "observation_id": "bef29c51-654b-4589-b882-f35cc31684b8", + "observation_id": "c6c548c8-6876-428a-8298-e6879d3f1d95", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -1936,7 +1936,7 @@ "confidence": 0.18860386268790535 }, { - "observation_id": "47f5165b-33c0-45ce-b3e9-0d1a01c894e7", + "observation_id": "79308088-674b-4a26-adae-c323d456ed28", "type": "aruco", "marker_id": 93, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_a_camera_pose.json b/data/evaluations/Scene5/render_a_camera_pose.json index 3bfdf69..e2e4794 100644 --- a/data/evaluations/Scene5/render_a_camera_pose.json +++ b/data/evaluations/Scene5/render_a_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:38Z", + "created_utc": "2026-06-01T20:35:31Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_a_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_b_aruco_detection.json b/data/evaluations/Scene5/render_b_aruco_detection.json index 93d94d4..1203847 100644 --- a/data/evaluations/Scene5/render_b_aruco_detection.json +++ b/data/evaluations/Scene5/render_b_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:33Z", + "created_utc": "2026-06-01T20:35:28Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "f332309f-478b-463a-8107-f360eafdd213", + "observation_id": "20ce9a30-e58d-450f-8316-be79e2791e66", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.5544269284959954 }, { - "observation_id": "96d1ef06-b66f-4c75-a06e-f4bb7f0a978c", + "observation_id": "710ee1ca-e400-49f2-8262-8dc9a7a928d2", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.5466352941176471 }, { - "observation_id": "484e57c7-7eab-4790-830b-ac85b9dfb3c3", + "observation_id": "31a0ff35-88b9-4092-a453-432c7bbf619d", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.6668638701841824 }, { - "observation_id": "aa12605b-fde5-44a0-88df-39e22897f0d3", + "observation_id": "f37b01e0-e2ff-4158-8dc9-3a8c0acb6275", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.5937058777020776 }, { - "observation_id": "894608c5-3a26-4c17-9781-5d82b2d7e1ce", + "observation_id": "cbba41cd-8f52-429d-8240-3e7fd0626f27", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.6074766727947272 }, { - "observation_id": "8790b313-081b-42e5-b428-613a390c1313", + "observation_id": "62098a09-7088-48d5-acb6-c975729bfb2d", "type": "aruco", "marker_id": 63, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.1539593300495616 }, { - "observation_id": "dec18b38-f329-40c2-981e-f960289e6ef3", + "observation_id": "4e931631-c4d2-4353-9fe6-1bc06920cb8d", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.39637168760361213 }, { - "observation_id": "24bf73ba-6ce7-4e8b-b725-00d9c56ea313", + "observation_id": "5506cedd-282b-48e6-a7b9-588331f879fa", "type": 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-694,7 +694,7 @@ "confidence": 0.44372569385651617 }, { - "observation_id": "2ea2d1a7-d6d6-44a5-aecf-b4420258de6e", + "observation_id": "b24a46cb-eed8-4d85-a77a-cddfa62a697b", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.45199802144368495 }, { - "observation_id": "4f3e8c75-3450-4784-970a-40fbfde23d82", + "observation_id": "f84b2a53-6250-4eec-be15-522e119a202f", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.45199802144368495 }, { - "observation_id": "980079aa-9ad2-4f02-951a-76d19bb0f561", + "observation_id": "7dd7d3cf-dfc3-4f9d-91e6-473fcc9472c7", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.44348322079976393 }, { - "observation_id": "b5e7bd43-0be1-427c-8996-6f90539f370c", + "observation_id": "43e56bdf-de78-41d3-a1ab-d1a555970166", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.33469798269642576 }, { - "observation_id": "7419fc98-76c2-46ce-8745-5417ac0cfb74", + "observation_id": "52130770-77c4-40ff-9a11-5b3dabb42bfd", "type": "aruco", "marker_id": 242, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.3252974596452519 }, { - "observation_id": "7ba1ebbf-6a2a-436e-8a01-46d8a7bd0119", + "observation_id": "3eefbbf1-7d95-457c-b315-1acc33c54cb8", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.41815762699787656 }, { - "observation_id": "94cd91d0-f47c-49ca-bc73-9470de0d7be3", + "observation_id": "c89b5be6-2298-4c60-83f8-9b3e102e0491", "type": "aruco", "marker_id": 205, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.3958181457519531 }, { - "observation_id": "6dee4404-e3c2-44e6-9647-47e25f45f01b", + "observation_id": "689a5823-03f8-44b4-97f7-fc999bce3bb5", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.4015868133874269 }, { - "observation_id": "60b87193-0968-400c-9685-42bc16b8688e", + "observation_id": "4dc5a47f-1e4b-42d0-ae39-6e48344b3220", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.38377189776715764 }, { - "observation_id": "092fcd86-d44c-4c85-8f54-25735fbf8c1c", + "observation_id": "4ca73fe7-1281-4eed-b87d-7767767d0519", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.3320910299473707 }, { - "observation_id": "56ad5c2f-b4c7-46d2-a9fd-93b8a9fdec5a", + "observation_id": "72ca6db8-cea9-413e-8501-4098fcd56dd2", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.3579602826436361 }, { - "observation_id": "f6e1f748-f2a0-4453-a79d-4e3447ee4829", + "observation_id": "9d7c49c4-25c7-4535-a201-6b387e92a408", "type": "aruco", "marker_id": 207, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.343462485354258 }, { - "observation_id": "c9d9132a-71f4-4d63-a4c5-88871cea9f1a", + "observation_id": "0fcddf75-7dff-4119-94ad-d220b2a5fb13", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.3524419738837484 }, { - "observation_id": "4c0b8592-3a5c-4b44-87bb-7709eab5de5a", + "observation_id": "671de797-9841-47a9-b9c1-a2057533bafe", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.1293030348501416 }, { - "observation_id": "c8af20e3-9333-4a0c-b770-316f09f2020c", + "observation_id": "cde71283-3e14-4b73-90f7-4bc598666494", "type": "aruco", "marker_id": 94, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.06057156860351561 }, { - "observation_id": "8bee69da-7677-42b6-adf3-a50699c64290", + "observation_id": "a9176993-05d3-4773-bedd-8295cc9c74aa", "type": "aruco", "marker_id": 76, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.2459421895345052 }, { - "observation_id": "ceddd6ad-92d0-4b73-a57e-962cf33b7558", + "observation_id": "f4f86666-e42a-4511-ad29-c89b0939f005", "type": "aruco", "marker_id": 100, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.24407473894265982 }, { - "observation_id": "bbf182f3-9650-4f9c-89fb-bc884319aedf", + "observation_id": "fb4402b7-82ee-4be5-975c-905daf07335d", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.060547802731330126 }, { - "observation_id": "a75b3501-c220-4896-9686-ecbe621f074a", + "observation_id": "f8198c27-6057-4799-b010-2f54a7f49b40", "type": "aruco", "marker_id": 81, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.24033422651029265 }, { - "observation_id": "311a0611-d6d5-4d12-8ed3-25f0069fc4f3", + "observation_id": "237409c3-36e1-41d2-b799-a75be30a11b3", "type": "aruco", "marker_id": 75, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.23896265492072474 }, { - "observation_id": "ed9d5bfb-cab9-447f-88f8-832da0d5e9d2", + "observation_id": "4d0cfd89-0b45-439f-8233-6d0abfa293c4", "type": "aruco", "marker_id": 68, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.24909137483284013 }, { - "observation_id": "d318de30-1035-4217-b9cd-30a3e552c8c0", + "observation_id": "7586e8ba-888d-43e9-9490-623305b5e1a8", "type": "aruco", "marker_id": 77, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.23734254719660833 }, { - "observation_id": "f45dd642-2a5a-45a6-92bd-4be23b478342", + "observation_id": "2080430b-5a19-4294-b749-095980f5c1c0", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -1936,7 +1936,7 @@ "confidence": 0.23734254719660833 }, { - "observation_id": "7a47f997-16e8-4fe1-84f5-4b6ff1daf0fa", + "observation_id": "628c5463-32d0-42af-b573-e5a858a8d268", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -1990,7 +1990,7 @@ "confidence": 0.236365170112023 }, { - "observation_id": "f02f7b22-3161-49d6-bba9-c497030a526d", + "observation_id": "83d2947c-8b5b-47a1-a026-2338b5f26263", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -2044,7 +2044,7 @@ "confidence": 0.21925684197423947 }, { - "observation_id": "78ee5151-9ea2-4203-9d7e-4f19a622d677", + "observation_id": "bd3fdf34-cc77-409d-998e-aa42f451a56b", "type": "aruco", "marker_id": 50, "marker_size_m": 0.025, @@ -2098,7 +2098,7 @@ "confidence": 0.21921579106955483 }, { - "observation_id": "808a41bf-c146-44ec-b1cd-42b837cee9ef", + "observation_id": "da867ce4-1a22-4673-924c-90bfb2f7545e", "type": "aruco", "marker_id": 104, "marker_size_m": 0.025, @@ -2152,7 +2152,7 @@ "confidence": 0.22417849731445313 }, { - "observation_id": "0d04f29b-796d-4ecf-a778-2085c36e4a05", + "observation_id": "d270261b-9b65-47f3-8737-f69d0e53ae97", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -2206,7 +2206,7 @@ "confidence": 0.21217281752366285 }, { - "observation_id": "e9dd5851-74bb-4526-9c77-3ebfc8072af6", + "observation_id": "f764303b-30c2-4a48-b770-83818c3919a4", "type": "aruco", "marker_id": 67, "marker_size_m": 0.025, @@ -2260,7 +2260,7 @@ "confidence": 0.19627771759033205 }, { - "observation_id": "650693aa-a14b-4c22-b95d-415d5590b9c1", + "observation_id": "a8406610-3f0f-4779-80db-fe3de3f87082", "type": "aruco", "marker_id": 88, "marker_size_m": 0.025, @@ -2314,7 +2314,7 @@ "confidence": 0.20782158780899712 }, { - "observation_id": "67e43b95-2c10-432c-bb35-d0e6a5e7b5cf", + "observation_id": "d989c9b2-5117-462e-8d91-2be04de2ef9b", "type": "aruco", "marker_id": 70, "marker_size_m": 0.025, @@ -2368,7 +2368,7 @@ "confidence": 0.193995418548584 }, { - "observation_id": "8c07028a-1447-4b30-b408-99a46d535bbd", + "observation_id": "f0a03815-0282-4902-8857-b2ee8c2cc5ca", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -2422,7 +2422,7 @@ "confidence": 0.1722355942663244 }, { - "observation_id": "d6594bec-6360-4e69-ae52-a30b15879bc5", + "observation_id": "a7317da2-b5d0-41fa-901d-866147c5f55a", "type": "aruco", "marker_id": 98, "marker_size_m": 0.025, @@ -2476,7 +2476,7 @@ "confidence": 0.1899538473288218 }, { - "observation_id": "6e55e80f-3f2c-4653-ab27-7e431fdf8b94", + "observation_id": "10287560-a99f-4a18-be48-c8d37f3da68e", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -2530,7 +2530,7 @@ "confidence": 0.17676838843571283 }, { - "observation_id": "e12868a2-1e6d-40f8-8d2a-1337506d6f99", + "observation_id": "0d3d6702-7a5e-41c4-b314-b00f0278f3de", "type": "aruco", "marker_id": 61, "marker_size_m": 0.025, @@ -2584,7 +2584,7 @@ "confidence": 0.1668050360320266 }, { - "observation_id": "ee69775f-81e3-41de-891c-b5817978e6e9", + "observation_id": "dd0821aa-c501-42c0-a8e2-032f7e96055f", "type": "aruco", "marker_id": 90, "marker_size_m": 0.025, @@ -2638,7 +2638,7 @@ "confidence": 0.17612778902053833 }, { - "observation_id": "bc186931-8abe-462e-b65c-22412ea9210f", + "observation_id": "42e356e4-4f2c-4941-b5d6-d50ce9b7e488", "type": "aruco", "marker_id": 91, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_b_camera_pose.json b/data/evaluations/Scene5/render_b_camera_pose.json index 2b6b246..047f4f9 100644 --- a/data/evaluations/Scene5/render_b_camera_pose.json +++ b/data/evaluations/Scene5/render_b_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:39Z", + "created_utc": "2026-06-01T20:35:31Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_b_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_c_aruco_detection.json b/data/evaluations/Scene5/render_c_aruco_detection.json index 27edd28..5e0a910 100644 --- a/data/evaluations/Scene5/render_c_aruco_detection.json +++ b/data/evaluations/Scene5/render_c_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:34Z", + "created_utc": "2026-06-01T20:35:28Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "f3707ba8-05a8-4e1c-8000-4481c8854c51", + "observation_id": "8c4c85dc-a848-4a39-a570-bbc1111c32f4", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9034560154563519 }, { - "observation_id": "dcfec6db-e399-49f4-833f-2716c85c7cd6", + "observation_id": "3e35619f-8669-4fcb-8311-510d9ade5000", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.9035563368771673 }, { - "observation_id": "8f66d42a-0476-4c73-8120-938a7c870ef1", + "observation_id": "7cda7476-6714-497e-ab59-c68e3e4f8eb1", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.9539329593288292 }, { - "observation_id": "59d42771-0e0e-43c3-8e3c-48c8f667ebd6", + "observation_id": "c02cef06-edba-498f-b9a0-6632dd47d1a9", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.7440653293809782 }, { - "observation_id": "9348c8e6-c992-43c8-8414-b110e8ac00e4", + "observation_id": "6dac2dfd-f225-4cf0-a48a-234d9a45633c", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.663361454729631 }, { - "observation_id": "b0b023b3-0f28-4873-8b0b-d6ea12c83403", + "observation_id": "a28e19fc-85bb-4609-b688-9f05c023f2c9", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.386044632761102 }, { - "observation_id": "3ce40fbb-63e5-400c-9365-4b5e53c31969", + "observation_id": "25983a9b-a6ff-491f-b2cb-67cdf38ed91f", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.2964796698261984 }, { - "observation_id": "04c888f4-c7db-4ed5-a2e2-d47a2eb0adc6", + "observation_id": "1ad405c3-2ba5-4afa-a619-f268855a5081", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.2743742952602821 }, { - "observation_id": "1affa52b-859c-4cb0-9a69-9208f41541a9", + "observation_id": "0fff5336-0b48-4019-9a57-9c69bf04f2cd", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.21966133686197709 }, { - "observation_id": "b8e67d0c-b9c1-4f85-9400-93b58034fab0", + "observation_id": "6125eb74-bd4c-4986-9dd4-9775ead33100", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.233781757000696 }, { - "observation_id": "2ab998aa-191e-4edd-a4d3-29384e453e58", + "observation_id": "a77ff326-6910-431a-8b58-f17f2ded2ca2", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.21604275474161502 }, { - "observation_id": "08f2d0f6-1b83-44d8-818d-91b1528f0b19", + "observation_id": "729aa284-8886-46f2-a315-eca590bc8740", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_c_camera_pose.json b/data/evaluations/Scene5/render_c_camera_pose.json index 5aa5b16..7b9d2f5 100644 --- a/data/evaluations/Scene5/render_c_camera_pose.json +++ b/data/evaluations/Scene5/render_c_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:39Z", + "created_utc": "2026-06-01T20:35:31Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_c_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_d_aruco_detection.json b/data/evaluations/Scene5/render_d_aruco_detection.json index 842385f..ad5b263 100644 --- a/data/evaluations/Scene5/render_d_aruco_detection.json +++ b/data/evaluations/Scene5/render_d_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:35Z", + "created_utc": "2026-06-01T20:35:29Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "354f756e-945a-4abb-9028-0c6dc23719a3", + "observation_id": "727b3bd8-5e65-4ba7-91f8-3fe1af46a0fc", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9823660345732603 }, { - "observation_id": "c989f6a4-b6be-42e7-87ae-f231ef72c130", + "observation_id": "32f0806b-af41-4ca4-ba86-1aba52548055", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.8483779676539408 }, { - "observation_id": "ab0d1a85-ae05-4a3a-83f1-4180c59a2eba", + "observation_id": "84a22337-beea-4d1b-979a-fe3c90a586c4", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.773184725081227 }, { - "observation_id": "2c39803a-938d-44d4-ac2c-bd2bdfd7868a", + "observation_id": "a50add6f-25f9-4b99-a4d9-8d90eb32d0c9", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.6280285196076234 }, { - "observation_id": "f03d5110-966e-4303-9edb-e1cead599df9", + "observation_id": "5cd67637-96cc-447b-8cd3-6a5eee82200b", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.3116200930635104 }, { - "observation_id": "7ad68aaf-c8d3-49c4-ac07-8eb379c1237c", + "observation_id": "3a582385-f80c-4b74-b91d-d461d3a5e784", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.5885341085448238 }, { - "observation_id": "67f0de2c-1021-42f6-9a3b-ff9885637a01", + "observation_id": "9bf67ddf-6d95-4956-9d9f-77b64a6b5434", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.06739671544456982 }, { - "observation_id": "8009ea43-67e3-4327-8e7d-87b0440cb6ad", + "observation_id": "e75fdf7d-d6b4-48ad-870e-4cdc8c87bbeb", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.17416420855033052 }, { - "observation_id": "2335011e-8a1a-4ef4-9fcb-3d49a1d31565", + "observation_id": "3aa505b1-1400-45b8-b759-1a5e95fe5b98", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.24540823915500745 }, { - "observation_id": "69359ff2-4120-4ed8-ab59-c05b317e9cd5", + "observation_id": "9ab0c51a-2bb0-4101-bcc5-1b3c240fa942", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.43828628344822296 }, { - "observation_id": "c7d1f9ce-ce15-44dc-8cff-e9f3ec0973e8", + "observation_id": "e280a46a-1891-483f-9141-223e684cb166", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.32624039952470835 }, { - "observation_id": "e3c9cad3-2d15-4a47-9fb9-2800c3afa5bb", + "observation_id": "6b4952ad-4243-429d-b121-817790b1d489", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.4432762787121197 }, { - "observation_id": "fd0db7a0-dc0a-4ea3-92c6-44244e92fad5", + "observation_id": "8d6bb58d-7225-432e-a854-b49516e140a4", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.3071912693949274 }, { - "observation_id": "839100c6-8b84-485b-8254-222a02ce4f78", + "observation_id": "e8d55704-5fdb-464c-a569-24d10d450836", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.018333813047618722 }, { - "observation_id": "e36a1ab6-9293-4063-85c5-74b0c177233e", + "observation_id": "1dc79051-2078-4f89-9b88-06066e90d662", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.2483175954884472 }, { - "observation_id": "18de463c-fb33-4876-b400-8f0448d4df24", + "observation_id": "ba24dffd-0bf3-404c-ac4e-d38ba462f81d", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.07285288505606423 }, { - "observation_id": "eb16ec5e-ce70-477f-9a50-8072fba874f8", + "observation_id": "899960b6-4e98-40df-8208-ab0a1d57c4a6", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_d_camera_pose.json b/data/evaluations/Scene5/render_d_camera_pose.json index 6300601..8f8bae2 100644 --- a/data/evaluations/Scene5/render_d_camera_pose.json +++ b/data/evaluations/Scene5/render_d_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:40Z", + "created_utc": "2026-06-01T20:35:32Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_d_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_e_aruco_detection.json b/data/evaluations/Scene5/render_e_aruco_detection.json index 6fcbce2..4ab3d5d 100644 --- a/data/evaluations/Scene5/render_e_aruco_detection.json +++ b/data/evaluations/Scene5/render_e_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:35Z", + "created_utc": "2026-06-01T20:35:29Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "1c9e6116-fe1c-44db-8e76-6a127c8114cd", + "observation_id": "ca7131c8-1b93-4593-932f-8b93346cda38", "type": "aruco", "marker_id": 42, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.8333996992934779 }, { - "observation_id": "55c722d1-6740-4e40-a4da-82f183ac0e7d", + "observation_id": "efb89654-b831-4261-8728-b8145986a644", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.7377098688391879 }, { - "observation_id": "3677f895-43ef-4d13-95be-05c3235445d8", + "observation_id": "e97a1452-41bd-40b6-9331-de9d48d3e335", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.15369374989294518 }, { - "observation_id": "9d35b7bc-f4a1-47ba-9164-9f5a82922920", + "observation_id": "a39eb2dc-a76f-4ae3-9f75-2073396614e7", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.3233651345714012 }, { - "observation_id": "80cd594b-b77a-4e91-9ebb-d6f307e971f9", + "observation_id": "87194e79-c259-4ee2-9282-6be5d07c0ede", "type": "aruco", "marker_id": 105, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.420622587927407 }, { - "observation_id": "f6517f80-7320-4ae9-8f72-49df2f61085d", + "observation_id": "f73afdd1-7f01-4aa1-8110-fcb344577723", "type": "aruco", "marker_id": 85, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.3128949822399185 }, { - "observation_id": "ca588b82-9c92-4e63-aa4d-ae433e33c12c", + "observation_id": "3e7a5960-1bff-4426-87cb-7e5d21cf8372", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.15136171428561934 }, { - "observation_id": "f3d0a797-25d9-4f8b-87aa-464f7ff42c6f", + "observation_id": "da4dadf5-4754-4da2-b6fd-3e03f9a92d92", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.22596816505744277 }, { - "observation_id": "0707701b-b539-4fa7-bfaa-7d98f15e3890", + "observation_id": "cd6fd42d-6d06-4b72-a8f6-09d08534153c", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.24626971873720385 }, { - "observation_id": "b7e9216c-b3a0-4197-8006-9ea8d772fd60", + "observation_id": "394750af-e89f-41f1-a580-83fa32300c62", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.2205630261207473 }, { - "observation_id": "9c4aeded-a42a-4ceb-a95b-9565eaa72098", + "observation_id": "6cfb1049-bbfa-4c5f-8833-b5048cabcfc2", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.2664122029520185 }, { - "observation_id": "c0986cbd-91a7-49a4-886d-4296534d24db", + "observation_id": "7795dd0e-80fa-4f13-95cd-8fb09bc88daa", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.2093822185726055 }, { - "observation_id": "0487d851-772a-43bf-a2ff-a1e2463d1309", + "observation_id": "5f0f3ddc-45d9-48d6-b050-b7fcd9dd7aef", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.16725963004706537 }, { - "observation_id": "eddce12f-e51d-444b-9468-799d8acf382d", + "observation_id": "fd9f31c2-82e7-4fda-a46a-3aafd5c36531", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.22298824455832925 }, { - "observation_id": "4dad3e32-203b-4d2f-88c5-1face665cb8b", + "observation_id": "342c6b28-9aa7-42ae-8d6a-ed72e8057a2e", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.16812950449251388 }, { - "observation_id": "f0211f9b-e76f-4271-8747-9002d73bbfb5", + "observation_id": "6c31fcad-2243-4bef-8c2d-e6536f018adb", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.1697872859143744 }, { - "observation_id": "6a7bdfba-d6d8-40ad-be9b-035f8d04d32e", + "observation_id": "9360a540-df92-4c7e-a2be-3d5dbda17d59", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.12157403976480062 }, { - "observation_id": "7aefd025-f8c1-4a0b-91fc-a28d8a475273", + "observation_id": "13552c61-dbc9-4e36-a8da-32c0ee7dd67b", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.14922985097126407 }, { - "observation_id": "69d4e29b-54cb-4122-abf1-080f5f4b8f01", + "observation_id": "284ad561-0ec9-4e16-9216-4a1c7382ca71", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.12136831092318832 }, { - "observation_id": "dd1957f4-068f-44c7-846c-7179f4c35a1a", + "observation_id": "3ff12017-669b-4a15-971e-5de531dd7956", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.16137616188441373 }, { - "observation_id": "52368e2b-efe1-4053-b783-db120b603b54", + "observation_id": "5c5ed2eb-23c2-4bf5-a4b0-061e22f19522", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.13595850692924102 }, { - "observation_id": "37c65bcf-37af-47d5-a069-13d7af09ffa5", + "observation_id": "c9f2b38e-e2bd-486e-ba77-8da8a5c92a68", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.09610181165120435 }, { - "observation_id": "976c8f0a-d2cc-4616-80df-78ccf79ad223", + "observation_id": "33d2c2dc-8d34-45b2-a29f-33b500958818", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.13307488759358727 }, { - "observation_id": "673e02e7-a9ab-452d-9275-4bd6d7b2e46d", + "observation_id": "51cf6c91-3095-4065-805f-9c020e88b98e", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.09071710124676984 }, { - "observation_id": "b207aa5c-86dd-4ad3-92af-ee490f1b9815", + "observation_id": "714ec2f4-ab2b-45e4-872d-55c16e3e7104", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.1273664826027396 }, { - "observation_id": "5c5a1717-e366-4b69-8aee-32c19162d55c", + "observation_id": "351968c8-956b-4d57-9ef5-dc4dcd7a28aa", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.09857192946593334 }, { - "observation_id": "b9875f7e-a878-49d4-a250-c3acee639497", + "observation_id": "0a21b723-37af-4355-8b7b-3d5ebcf92004", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.1006702290166984 }, { - "observation_id": "b6cbaada-20d5-49b2-bdd1-49cb4a82665c", + "observation_id": "01ec339b-6a64-4ca1-99ac-2fd80ec2512e", "type": "aruco", "marker_id": 116, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_e_camera_pose.json b/data/evaluations/Scene5/render_e_camera_pose.json index d2eaf94..1c686b0 100644 --- a/data/evaluations/Scene5/render_e_camera_pose.json +++ b/data/evaluations/Scene5/render_e_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:40Z", + "created_utc": "2026-06-01T20:35:32Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_e_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_f_aruco_detection.json b/data/evaluations/Scene5/render_f_aruco_detection.json index bfe29a0..e7b5fa6 100644 --- a/data/evaluations/Scene5/render_f_aruco_detection.json +++ b/data/evaluations/Scene5/render_f_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:36Z", + "created_utc": "2026-06-01T20:35:30Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "47bfc303-e1a6-454d-8eba-ec2a93b73cd9", + "observation_id": "b91f3035-fad8-4fcc-82a1-0b66135543f0", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9768332578354781 }, { - "observation_id": "bfc032b2-3e95-4403-8f23-df604b306938", + "observation_id": "b4cabdb3-dcb6-43e4-b540-e399494ef696", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 1.0 }, { - "observation_id": "175c6b4d-63bc-4587-a3fb-fe4e1672dae4", + "observation_id": "8aa057bb-f8b4-4643-8db0-5a31682027c5", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.9517473802485018 }, { - "observation_id": "e885ee71-12a5-4156-a121-9f017dd90fd3", + "observation_id": "2fc01814-533c-4f08-9fa3-f6fcdfcd8a43", "type": "aruco", "marker_id": 231, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.7952277652013182 }, { - "observation_id": "123ac3b1-2ce5-49fa-8e86-ec99a775e78d", + "observation_id": "ef05865b-17d8-4c31-ac3b-d2b5df20cfee", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.5995617283681293 }, { - "observation_id": "bb81b7fb-8971-4a4e-9a24-bee041b73ac2", + "observation_id": "8901329b-c64a-454e-9a94-aaca77e574a6", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.569827819836196 }, { - "observation_id": "f16a091e-3b99-4fc4-b708-f89376c85e50", + "observation_id": "e7513b9b-22a9-46b5-80ac-ba8fe44bbd78", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.2988751797371847 }, { - "observation_id": "1d406025-a867-4986-8d04-8279fca630d3", + "observation_id": "eb73f2c6-50ea-4059-8ebd-697ca2aad6ef", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.5697620483284124 }, { - "observation_id": "c789674e-2ef1-47bb-9c56-b722b16eb8c9", + "observation_id": "bd9e57cc-0754-49fd-845b-f6c65a9e27cc", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.5768583617164568 }, { - "observation_id": "c446717f-90d1-4075-a6ce-41107213cfa2", + "observation_id": "6528e36f-77c8-477c-a498-be10df5ff84e", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.5593704450665263 }, { - "observation_id": "ac7cc8ca-0894-48a2-b6b6-24da9163ff68", + "observation_id": "92021ceb-e372-4faf-9be9-92bc1a8f56eb", "type": "aruco", "marker_id": 54, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.5766374039623903 }, { - "observation_id": "9194577e-ffbb-4db2-a18d-184bff2362cd", + "observation_id": "a59f1a06-27a7-4add-b2d6-7f71e0e02b97", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.21364841201201337 }, { - "observation_id": "012bf26d-c557-45ce-99e6-96f1dd2fea4c", + "observation_id": "930404c1-11ff-44f6-8a33-0a3a762c0695", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.44403921568627447 }, { - "observation_id": "18c8df14-ca0f-4667-9105-ddab66ea870f", + "observation_id": "f613c1a3-7ab0-48f8-abea-88e9ee688cc7", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.5705029634947052 }, { - "observation_id": "24bb3da7-0f2f-4fc6-9d47-9b1a8762bcc6", + "observation_id": "e36212db-58a1-45a6-9d16-a809ac5467e6", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.5440516905787338 }, { - "observation_id": "4a40aee1-0a4d-4589-aa41-fb53912b09c1", + "observation_id": "2a71d54c-eaba-491a-8ffa-ad44c6237831", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.551983363957988 }, { - "observation_id": "25906109-5c6c-4105-abee-c5ec1a4a1fb8", + "observation_id": "1419f47d-1801-4a1e-ba56-04228cb10d20", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.5459402660246769 }, { - "observation_id": "68f6029c-6c3b-48d0-b6e3-d00856725e67", + "observation_id": "4c84134d-c31b-4ad6-9543-e1304909fb00", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.06519221426443914 }, { - "observation_id": "cf4be45a-ab31-4572-ad20-bda77927d4b7", + "observation_id": "0125ec51-9752-4325-b69d-270a874d461f", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.5349107016469481 }, { - "observation_id": "27a7fb6e-5a06-44eb-8139-bd5ca7f136c4", + "observation_id": "a57005f7-98ec-4630-bf55-8cedf138e977", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.31811398390251094 }, { - "observation_id": "1823652c-943f-43bd-9853-ec990a828a6a", + "observation_id": "34f9df01-e44a-4348-bbb4-842ac224aa7b", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.5227826547122322 }, { - "observation_id": "eed4751d-d197-4f3f-adc2-79925c287432", + "observation_id": "a51d3c0f-021f-4614-8850-5fb227cec442", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.5111500432161571 }, { - "observation_id": "a0b85cfa-87b8-4ee0-a200-d0d5b20bf810", + "observation_id": "6fe142dc-d7d3-424f-8339-a9ec89f56db1", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.5046333482067283 }, { - "observation_id": "1724477a-81db-4041-b84d-4806eda85bda", + "observation_id": "e08907e3-c630-417c-bd63-9c0a6000d26c", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.13607001327160623 }, { - "observation_id": "2413b0f3-fd46-4d0b-8219-6fe63c47af79", + "observation_id": "bed29f47-a1ca-4a33-b767-b50657a91ffe", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.46988879526830113 }, { - "observation_id": "07728bc7-aaac-4342-8d9d-beaac7818b95", + "observation_id": "ba54ebd5-0755-4294-8ee0-7b971287adcb", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.4655541127751829 }, { - "observation_id": "7875ce9f-65c5-4311-86ee-87e57bfa6841", + "observation_id": "5a64101b-7eb2-4115-954a-177d9094fedf", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.4655541127751829 }, { - "observation_id": "a0d976b3-0d88-4d7a-8797-0a00cf2a4114", + "observation_id": "11ce8d49-6226-416c-9989-9b7f21ca059c", "type": "aruco", "marker_id": 82, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.49001748422717006 }, { - "observation_id": "68b3c0e8-b738-46c6-b216-2bc1dab7ce8a", + "observation_id": "9686173a-1b48-4e1d-9038-f5d460eab788", "type": "aruco", "marker_id": 101, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.46225891142368675 }, { - "observation_id": "5ae7bd2d-f236-4475-92e7-5f0034e28255", + "observation_id": "49d47604-6664-418a-b308-75fd38433e3a", "type": "aruco", "marker_id": 74, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.4548532843889077 }, { - "observation_id": "69b381a0-7ace-4047-b691-74a7573ab8c2", + "observation_id": "b030b1b9-a5e0-42fd-8342-4e21f6aa2f2d", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.43211414091114625 }, { - "observation_id": "2285ed27-0713-4fd4-a30a-f8c124ddc41d", + "observation_id": "cc9e1ecb-c985-4927-8561-e4a3fbf82c52", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.45818248585417487 }, { - "observation_id": "0b2d1922-8202-4481-af4a-8792e4efbdb1", + "observation_id": "e879498c-9ea8-4a6b-b56f-bd30aaf3f33a", "type": "aruco", "marker_id": 77, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.422956152719696 }, { - "observation_id": "da819b13-2f74-4c56-b8fe-29f883d47e0e", + "observation_id": "eb7482bd-564d-4c75-91aa-d50041df9c50", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.4377857805109236 }, { - "observation_id": "4d0787d2-2463-460b-baf2-05a46306c9f0", + "observation_id": "10f0b572-fec0-40c8-b3a2-38916e451d48", "type": "aruco", "marker_id": 75, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene5/render_f_camera_pose.json b/data/evaluations/Scene5/render_f_camera_pose.json index e1103a1..a2f36d9 100644 --- a/data/evaluations/Scene5/render_f_camera_pose.json +++ b/data/evaluations/Scene5/render_f_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:41Z", + "created_utc": "2026-06-01T20:35:32Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene5\\render_f_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene5/render_g_aruco_detection.json b/data/evaluations/Scene5/render_g_aruco_detection.json index af90a66..f62203e 100644 --- a/data/evaluations/Scene5/render_g_aruco_detection.json +++ b/data/evaluations/Scene5/render_g_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T19:02:37Z", + "created_utc": "2026-06-01T20:35:30Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "c8b67f2c-77e2-4235-929f-5021d49ebf5c", + "observation_id": "35d85584-01b9-4206-8bb5-ae43d73ff419", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.7460587402140983 }, { - "observation_id": "6a411337-36b3-45d8-a9fa-a480270e702d", + "observation_id": "4409df1d-53c1-47fc-a61f-ef7878209b3b", "type": "aruco", "marker_id": 231, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.7064691664889216 }, { - "observation_id": "3f4b274a-06d9-4efb-8418-e3c3c0c6f450", + "observation_id": "09fa70b6-4bb2-450c-a579-0133f83fc8ae", "type": "aruco", "marker_id": 247, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.5903116652341175 }, { - "observation_id": "650946c3-7952-4677-9410-10bd0304ebab", + "observation_id": "18f51517-f6bd-424c-bb40-ea1d284b7e75", "type": "aruco", "marker_id": 246, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.5514992115017867 }, { - "observation_id": "ef6db564-921d-4406-8c76-b78735320619", + "observation_id": "d251d385-29b7-429d-9782-283dae66266c", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.4449530931867355 }, { - "observation_id": "6562dc2c-802e-4bb0-8e79-ba0c762f209f", + "observation_id": "b5bd3c0e-0a63-4e86-adc6-b8c0d8f89b74", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.3490767193505014 }, { - "observation_id": "72b30a81-f94a-4c86-b6e0-7f3a0b37866d", + "observation_id": "3b3ce018-1b7c-4b09-a3c2-2b799ea69ba0", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.5072111596185911 }, { - "observation_id": "14bb0583-ed5a-4b71-b0bc-5bdd796e78dc", + "observation_id": "8737e104-0435-477d-b38c-828ecc408ffa", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.43254677700170213 }, { - "observation_id": "0f04ad07-f789-44e2-8238-364f8cc73d71", + "observation_id": "74573a9a-9f43-486d-958a-b752d5c4fa82", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.36252498796515575 }, { - "observation_id": "19a345f3-14b3-4386-ab04-c1ffb3e41a76", + "observation_id": "d515c3fe-3164-46f1-911a-7bbc775a2141", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.27711751756945874 }, { - "observation_id": "8c075a1b-bf77-410a-b26e-0ae784f59094", + "observation_id": "59a2933b-56b7-4494-9270-f41105376d5f", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.48073279309443956 }, { - "observation_id": "0fad80ec-0ae6-437d-9dc0-5d7e49e16d2f", + "observation_id": "0e1c2510-ebaf-4482-aa14-53a73101c6c1", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.45546210617301425 }, { - "observation_id": "170c8228-0591-439a-81f7-80d0b2a53fe1", + "observation_id": "ee283b81-a8f6-4c53-afed-65fc4aee9eb5", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.4795917219326271 }, { - "observation_id": "c90d9c89-bd9c-4e01-9be4-268bf636fa09", + "observation_id": "f2e39e53-66be-472f-b9fc-5352b67f13ba", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.4668259742313624 }, { - "observation_id": "e4ab776c-6bd8-4310-bd00-8f26cdec6b8a", + "observation_id": "e999197f-5c1d-478d-9e82-7f2423860b8c", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.39662908657084783 }, { - "observation_id": "0e703621-9303-4563-beba-91b826ff908e", + "observation_id": "a9e22f5f-6ac5-4ff0-acbd-9cb40b11fddf", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.4561015937422635 }, { - "observation_id": "e49b8e7f-2090-4949-816f-aca3abdd5cd6", + "observation_id": "9dc6fdf2-af6b-4959-9ec6-254a3b18063b", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.1698001278724927 }, { - "observation_id": "b6caf319-c90f-4cf4-9fdd-32fce45a3f14", + "observation_id": "72c27a1a-30ca-45f3-8a5b-518ace108125", "type": "aruco", "marker_id": 68, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.07718187630567852 }, { - "observation_id": "e4fd266d-87d7-43c7-a87f-88916c80ad15", + "observation_id": "881b105b-07be-48de-82a1-da54c4f00b91", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.4316478958478293 }, { - "observation_id": "0c227523-c3bf-4f57-a1be-f85ec8f0ddb3", + "observation_id": "157be5d7-0917-4f9c-9aec-4786be197f72", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.34608319473201704 }, { - "observation_id": "bafc12bb-d297-484a-8e2c-13a2dfa7907b", + "observation_id": "c3959e0d-79b7-48f4-ac12-5910fd6e8fad", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.40793535669071385 }, { - "observation_id": "6b4332b8-f05c-4b32-a8fa-b2c7fbc4b727", + "observation_id": "cdabd390-73ba-4b62-9694-3aebdd54af0f", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.4181682027393045 }, { - "observation_id": "45993dcf-5fd1-4cf6-b1dc-2d2edeb49e45", + "observation_id": "550ac006-076b-4c0c-ae17-dba932c036de", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.39668368657567366 }, { - "observation_id": "780b876c-8d94-4614-8251-c2d34d063c4a", + "observation_id": "e80bf8a5-1547-4ae7-ade4-dd47d456767c", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.3577321830843512 }, { - "observation_id": "dc3d0bf6-d2a3-48c8-908d-50f1d4db511a", + "observation_id": "bb7f44a7-086b-46a1-8d5d-8440ee081c6a", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.3829765144945479 }, { - "observation_id": "3b2d7ba5-2335-43a7-9022-bf7387788763", + "observation_id": "f638a3f7-60e6-41b6-8921-2df581ca5df1", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.3382587531306593 }, { - "observation_id": "9a5f12f9-02b5-437f-aa95-f6783202a17e", + "observation_id": "be966f0d-7e8b-4207-ae4d-3da6ee2a1a85", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.3623898971459892 }, { - "observation_id": "8873a268-a991-4b71-adcc-7c343cc6ceda", + "observation_id": "89c8f1c6-5d7a-45ca-ad3c-0e18f61bdbfa", "type": "aruco", "marker_id": 54, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.3590718541167963 }, { - "observation_id": "f3b1c533-6f71-4f51-ac64-44b8365df01b", + "observation_id": "928fd3c0-e2c1-495a-a08f-cabf369dd831", "type": "aruco", "marker_id": 43, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.2738204676258435 }, { - "observation_id": "d2e831fc-101c-489c-9ffd-d521063366bc", + "observation_id": "b0060ffc-2890-4c09-92c1-239a16b426dd", "type": "aruco", "marker_id": 205, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.29591817114314195 }, { - "observation_id": "3fbbe428-cdac-47c2-831c-02cfced6aeda", + "observation_id": "d05ed423-6cd2-462e-9806-a54df153e6fd", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.3158166687011719 }, { - "observation_id": "08090d85-7ebb-4abb-9c24-7c121fd96cc7", + "observation_id": "a879a1aa-cf44-4b89-8ecb-a5da644a009f", "type": "aruco", "marker_id": 105, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.31800273344664126 }, { - "observation_id": "2060ada4-c465-41dd-a763-068156e28d56", + "observation_id": "341ec4cf-b699-45dd-9ee5-e1a7f6e2b9c9", "type": "aruco", "marker_id": 85, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.2969848480224609 }, { - "observation_id": "08ea919e-c408-4140-81ee-cd9bb43150ff", + "observation_id": "02d265e8-cfb5-45fb-90a8-44d0ab3def80", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.3170967427530611 }, { - "observation_id": "759cba9c-5500-4e55-9f93-db1ddf41d22a", + "observation_id": "7373ea07-d4d8-469e-be87-0f3e64d6b64c", "type": "aruco", "marker_id": 59, "marker_size_m": 0.025, @@ -1936,7 +1936,7 @@ "confidence": 0.3170967427530611 }, { - "observation_id": "bc77d3b5-8da6-4175-b693-979d2b5d9054", + "observation_id": "b7d49d7e-1b78-480e-a9a2-c2a5ccb3a3aa", "type": "aruco", "marker_id": 48, "marker_size_m": 0.025, @@ -1990,7 +1990,7 @@ "confidence": 0.17828567290874034 }, { - "observation_id": "34435999-494e-4b1e-8c3b-9e5379ce2097", + "observation_id": "5e095398-c882-4308-89c9-ccbb7bc05100", "type": "aruco", "marker_id": 57, "marker_size_m": 0.025, @@ -2044,7 +2044,7 @@ "confidence": 0.3167426670523156 }, { - "observation_id": "99b4197b-d20a-41e9-8471-4f92416012c3", + "observation_id": "60f6cd58-61b3-443d-a274-3b46d6c48725", "type": "aruco", "marker_id": 63, "marker_size_m": 0.025, @@ -2098,7 +2098,7 @@ "confidence": 0.15449597168691093 }, { - "observation_id": "70eb2ee6-c957-498d-adaa-714779a77ad9", + "observation_id": 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"5cb337f6-ce3a-4fac-8cad-ddb325a94a6f", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -346,14 +454,14 @@ "area_px": 819.5, "perimeter_px": 126.28364562988281, "sharpness": { - "laplacian_var": 1416.0191627572412 + "laplacian_var": 1407.315787979266 }, "contrast": { "p05": 14.0, - "p95": 139.89999999999998, - "dynamic_range": 125.89999999999998, - "mean_gray": 75.03907637655418, - "std_gray": 53.12746728688665 + "p95": 139.0, + "dynamic_range": 125.0, + "mean_gray": 74.94671403197158, + "std_gray": 53.053182228965504 }, "geometry": { "distance_to_center_norm": 0.48465821146965027, @@ -370,7 +478,7 @@ "confidence": 0.21966133686197709 }, { - "observation_id": "df4241e5-0c1b-4d34-b4ff-4978aa4b4615", + "observation_id": "e8d8deac-4d88-4321-b4c2-55a50f2cf5ec", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -400,14 +508,14 @@ "area_px": 745.5, "perimeter_px": 126.15118789672852, "sharpness": { - "laplacian_var": 2617.828996875531 + "laplacian_var": 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b/data/evaluations/Scene6/render_d_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T08:27:17Z", + "created_utc": "2026-06-01T20:35:47Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene6\\render_d_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" @@ -15,7 +15,7 @@ ], [ 0.0, - 1500.0, + 1777.77783203125, 360.0 ], [ @@ -59,10 +59,10 @@ 3 ], "rms": [ - 0.007824168563366577, - 0.0017174779903767997, - 0.001686304523576421, - 0.001686304408434461 + 0.007119340264402724, + 0.00030920270496480193, + 0.00010110639735161258, + 0.0001011028748367772 ], "lambda": [ 0.001, @@ -71,135 +71,135 @@ 0.000125 ] }, - "residual_rms_px": 3.5910223694678898, - "residual_median_px": 2.9200512819165123, - "residual_max_px": 7.227451375473963, - "sigma2_normalized": 3.791496743872021e-06 + 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+1,7 @@ { "schema_version": "1.2", "stage": "initial_triangulation", - "created_utc": "2026-06-01T18:52:09Z", + "created_utc": "2026-06-01T20:36:23Z", "summary": { "num_cameras": 7, "num_markers": 56, diff --git a/data/evaluations/Scene7/aruco_positions_optimized.json b/data/evaluations/Scene7/aruco_positions_optimized.json index 2b71557..0dcd36c 100644 --- a/data/evaluations/Scene7/aruco_positions_optimized.json +++ b/data/evaluations/Scene7/aruco_positions_optimized.json @@ -1,6 +1,6 @@ { "schema_version": "1.2", - "created_utc": "2026-06-01T18:52:19Z", + "created_utc": "2026-06-01T20:36:33Z", "summary": { "num_cameras": 7, "num_markers": 56, diff --git a/data/evaluations/Scene7/render_a_aruco_detection.json b/data/evaluations/Scene7/render_a_aruco_detection.json index c4702f3..acd41bc 100644 --- a/data/evaluations/Scene7/render_a_aruco_detection.json +++ b/data/evaluations/Scene7/render_a_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:02Z", + "created_utc": "2026-06-01T20:36:16Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "10afab06-cad6-40f5-b175-8c8711a9bb38", + "observation_id": "f964c55f-43d4-4751-9a22-20ab94326b26", "type": "aruco", "marker_id": 45, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.933333344056117 }, { - "observation_id": "4503577d-47c6-42ce-8f31-474f4247768c", + "observation_id": "51270879-cef3-45a1-a23a-aca87097927b", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.8316061635117153 }, { - "observation_id": "2aa66b9a-c7ce-44b3-a366-986b70d8a82f", + "observation_id": "8df5a72f-024b-4337-9cd7-3fd7ed8c03c5", "type": "aruco", "marker_id": 124, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.864344467203455 }, { - "observation_id": "ba278d3f-7cae-4201-b90f-5bcd5a23ef8a", + "observation_id": "15178740-87f4-480c-851e-c8cf9b7bb7dd", "type": "aruco", "marker_id": 44, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.47551060221792857 }, { - "observation_id": "c8741af7-529f-4c59-a7e0-58e90b60d6ed", + "observation_id": "a9f1ef5e-4f34-42fa-8619-0bb24e7179db", "type": "aruco", "marker_id": 122, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.6838307339774958 }, { - "observation_id": "1efec4df-f9b3-4cf8-81b0-fd6bb60b7a8d", + "observation_id": "b76acde0-52df-4ce9-844e-631ce8c467af", "type": "aruco", "marker_id": 229, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.6663683735229249 }, { - "observation_id": "182ec1cb-0844-452a-b436-03db4e80c29a", + "observation_id": "76931319-2e1a-4f5b-87af-bdd57dcb4cf4", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.12146268776527554 }, { - "observation_id": "5cf0d300-c32c-436b-b25a-876ed3f76fd0", + "observation_id": "7e06c2f7-475c-4358-92d8-23ef0f4bb78e", "type": "aruco", "marker_id": 248, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.5327431878174796 }, { - "observation_id": "94f12c51-f8c5-49d5-b9c3-c837d65269bb", + "observation_id": "09009ec0-744a-4b2d-b975-5bdca7877fe8", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.4563796230965866 }, { - "observation_id": "dd27fc85-532a-40e1-a5fe-7844e54fdba5", + "observation_id": "07b28336-ebff-462e-b07b-87a19090546f", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.46829700346999537 }, { - "observation_id": "350fc6e8-b4b8-466a-8e50-9c00831554ca", + "observation_id": "ba6142b9-e5db-4f5f-a8c0-fff3098dfaa8", "type": "aruco", "marker_id": 245, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.5199863882378979 }, { - "observation_id": "ce76f802-a0c2-4f5e-863d-9fa56708424c", + "observation_id": "b23a2be3-3146-41b4-8e55-9742d02e2ef5", "type": "aruco", "marker_id": 198, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 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"9823e332-dad7-44b2-bcbe-55c170534373", + "observation_id": "102f9251-e36f-4f89-969e-875244b44a2f", "type": "aruco", "marker_id": 105, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.43622925667631085 }, { - "observation_id": "a81c2209-5c04-4cc1-af87-021d1ababad8", + "observation_id": "5f0ba448-65ff-4fa6-bf79-2b13be6114bb", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.402108970929575 }, { - "observation_id": "7780d8ad-c866-48cd-bff6-b8ec89192478", + "observation_id": "431fff5d-67b3-4dc6-a82c-9ac30a12f53b", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.4435967685123575 }, { - "observation_id": "1cf791b0-fb7a-45dc-ac49-43f22b4a0bb6", + "observation_id": "77876da5-3b1c-4149-8835-6616adb365a1", "type": "aruco", "marker_id": 75, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.29844141482805986 }, { - "observation_id": "58e56cb0-a6d9-48c3-b4b6-ab4419a4200a", + "observation_id": "4a427a4f-d70a-4a57-b706-dd5d13f8dc44", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.2521714913889437 }, { - "observation_id": "237370d3-77b0-4c71-8652-c12a6d0e4420", + "observation_id": "61e9e701-36a0-4c9a-9f32-a66f368ae8c0", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.31777455864589965 }, { - "observation_id": "48d82e54-7873-49ba-b297-204742c9322f", + "observation_id": "14bda017-d54d-4436-8e0b-7a87ca6ad8b0", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.3029173397022545 }, { - "observation_id": "92db358d-d787-4444-850d-ff36ee284cb1", + "observation_id": "9d7a84c6-9e5b-43b4-9d54-9cc59a31fe01", "type": "aruco", "marker_id": 61, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.1527342148240039 }, { - "observation_id": "97e33310-8524-4f6e-965e-c50510668944", + "observation_id": "ae9a45d4-c589-42dd-9534-c2f942d96969", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.19743498130596515 }, { - "observation_id": "c4d75170-f9cd-4186-89e4-133ffabf9339", + "observation_id": "1395f53b-f439-467c-ad28-15836125c3d1", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.2849187633475991 }, { - "observation_id": "16ae02ef-0573-4844-ace7-c34ab17f620d", + "observation_id": "4d92d6a7-9d59-49a9-aeb6-e5271f9f6d72", "type": "aruco", "marker_id": 207, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.24627951083020566 }, { - "observation_id": "b320d9a5-7e77-4245-a7eb-0af8fcf347cc", + "observation_id": "0c3de08d-33b5-4461-a083-d903294b8481", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.17208874842045807 }, { - "observation_id": "69a34de3-b56c-4773-98f8-a29c01800999", + "observation_id": "ad0504e4-8b64-42b1-a192-d739f5f2dce8", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.1988568274481309 }, { - "observation_id": "2ecb4c05-cc11-4e46-9ec9-fc668b006f6e", + "observation_id": "597e2380-1452-4507-bbcb-e126ce89b22a", "type": "aruco", "marker_id": 68, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.22045556467782906 }, { - "observation_id": "6fecf543-e273-4b80-bfbe-7f1fb26968e7", + "observation_id": "860c0fff-de11-4b0f-9966-1704b3f583f2", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.17646704338372862 }, { - "observation_id": "493b6081-f759-4709-a919-13ade4db5719", + "observation_id": "e220d217-34cd-47db-9606-927ae76c6f70", "type": "aruco", "marker_id": 93, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene7/render_a_camera_pose.json b/data/evaluations/Scene7/render_a_camera_pose.json index 85e6fca..320f019 100644 --- a/data/evaluations/Scene7/render_a_camera_pose.json +++ b/data/evaluations/Scene7/render_a_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:06Z", + "created_utc": "2026-06-01T20:36:20Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene7\\render_a_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene7/render_b_aruco_detection.json b/data/evaluations/Scene7/render_b_aruco_detection.json index a836ac0..7b149ab 100644 --- a/data/evaluations/Scene7/render_b_aruco_detection.json +++ b/data/evaluations/Scene7/render_b_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:02Z", + "created_utc": "2026-06-01T20:36:17Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "21cc6a5f-aac0-498c-b937-4b2b09d64779", + "observation_id": "aa99123c-033a-46aa-aa80-56e8845350ab", "type": "aruco", "marker_id": 44, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.8821394157128136 }, { - "observation_id": "1587ea11-d826-4727-bb82-a052036aceee", + "observation_id": "dd3c1a7b-3569-4a13-af99-e15865129b62", "type": "aruco", "marker_id": 45, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.5269880306489465 }, { - "observation_id": "e8b5d103-e25e-4f88-bf90-d9aec0ecde2b", + "observation_id": "8997cef2-b458-4e5f-b1d0-a1e252e67dd6", "type": "aruco", "marker_id": 63, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.1539593300495616 }, { - "observation_id": "e32c4185-6956-4158-83cd-ced29ba6c7eb", + "observation_id": "98df6c3c-2421-4f47-a101-2b0d6248b541", "type": "aruco", "marker_id": 229, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.4599225075242942 }, { - "observation_id": "8e07856b-3677-4dd1-adb0-1349ce397e44", + "observation_id": "3a1cc7d6-7a48-4f50-9678-1012744794f8", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.39637168760361213 }, { - "observation_id": "aa098d00-9953-44b9-93fb-f83904c337cd", + "observation_id": "3a004e5a-e06c-4210-9dc5-06a421075950", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.3638956157928849 }, { - "observation_id": "259fb1c6-1a06-4e71-914a-95b95a24ffdc", + "observation_id": "84d09036-8054-4bb2-a6cb-acb5fbce2b31", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.4534700649674178 }, { - "observation_id": "fbb43e0c-1c4a-4e0c-a23e-f5b6e937106f", + "observation_id": "cdb09709-4aa7-4e7b-b46d-3e14c2c91fe5", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.46896024883690896 }, { - "observation_id": "79bd5703-32d1-483c-a81b-03ccd9a4769d", + "observation_id": 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@@ -964,7 +964,7 @@ "confidence": 0.4015868133874269 }, { - "observation_id": "97f1080b-07da-4712-a8d3-a0bd974a0654", + "observation_id": "0cc6047f-5610-4c9f-81ad-27f84f534184", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.3776 }, { - "observation_id": "ee486ff9-0ddb-4fba-a2af-6ccff6f1a543", + "observation_id": "95cec3c3-738e-490d-894b-1dfc76dd5634", "type": "aruco", "marker_id": 40, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.13187467173526163 }, { - "observation_id": "d9227cdf-39ba-4ebe-88bb-04d919323231", + "observation_id": "736c7c8f-737d-4cbe-8074-b5bcd24bbd58", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.3320910299473707 }, { - "observation_id": "d928a683-a12b-4b9c-a76e-b179a19f72ae", + "observation_id": "bce0d66b-3a74-438d-95c5-c9f582b3411e", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.3579602826436361 }, { - "observation_id": "d4fb71e6-6eb3-4831-b0b2-993f6ec45a6f", + "observation_id": "a3ac0361-d5bf-4926-a4d5-e38670db6ead", "type": "aruco", "marker_id": 207, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.343462485354258 }, { - "observation_id": "19611478-918a-4d8d-b71e-bf0c0808937e", + "observation_id": "8a700163-6cee-48bc-8b62-69a98adb6abb", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.31710344827586207 }, { - "observation_id": "05a3e652-1470-4142-98b0-dfc831e8479d", + "observation_id": "c1724660-fa50-4cd2-87bc-9e4c202ba365", "type": "aruco", "marker_id": 248, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.21732925415039062 }, { - "observation_id": "c62bfa2a-7a4b-42df-bbc6-42d6e4226411", + "observation_id": "84aa7e9e-b81e-411a-874c-2b52f168890d", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.15502133641776936 }, { - "observation_id": "6a2008b2-215e-4ebc-b632-5df08708f633", + "observation_id": "7b4fd3cd-ce94-49d4-9df9-63bb5af4b36b", "type": "aruco", "marker_id": 245, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.21633306884765627 }, { - "observation_id": "c116b571-c10a-4085-9ccf-ea27b8aa6ed7", + "observation_id": "5fe91ba0-1b11-465f-9830-bfec3bfc7296", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.1584702743164707 }, { - "observation_id": "27e82996-93ef-4e04-a494-4942ae82a76a", + "observation_id": "9feb325d-eccf-4738-8b02-3f3be6d5624e", "type": "aruco", "marker_id": 94, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.06057156860351561 }, { - "observation_id": "7617b6ec-6bf3-4b7c-a7ab-839c519a8aa3", + "observation_id": "713bcdc4-34e9-4696-bad9-f467496e985f", "type": "aruco", "marker_id": 76, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.2459421895345052 }, { - "observation_id": "b33846ed-4064-4ddc-831e-1a50b3561b81", + "observation_id": "9ca4c1e5-c736-46da-8183-ff0bd20032f1", "type": "aruco", "marker_id": 100, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.24407473894265982 }, { - "observation_id": "262c11aa-5aee-40cb-b807-6cb9e0532013", + "observation_id": "3ec5e4c7-7943-4ae4-8456-fca0d4de238d", "type": "aruco", "marker_id": 68, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.24909137483284013 }, { - "observation_id": "ac866509-ffec-4edf-9162-f417563c77ff", + "observation_id": "e3362dbd-3f0a-4bcc-aa15-8059e1ae6985", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.23734254719660833 }, { - "observation_id": "d261b501-d6f2-4fad-a792-aed82971cbe2", + "observation_id": "b1aaf976-32ec-4b48-bacd-0fd6a434d507", "type": "aruco", "marker_id": 50, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.21965114789131363 }, { - "observation_id": "68d6f7ce-a6f8-4eac-8be3-a641ed068452", + "observation_id": "ae833c24-3218-4f76-b34f-8a2b1a0475d1", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -1882,7 +1882,7 @@ "confidence": 0.21841319450965294 }, { - "observation_id": "83e64639-29e4-4a2b-9da0-13a066c289ea", + "observation_id": "eef14837-780b-4742-a54a-7e85842d263c", "type": "aruco", "marker_id": 104, "marker_size_m": 0.025, @@ -1936,7 +1936,7 @@ "confidence": 0.22417849731445313 }, { - "observation_id": "bf6022ba-36da-4d4e-805d-68b4d245f485", + "observation_id": "deb49bc2-b50b-4de6-8c2a-1c01a0a5d7c4", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -1990,7 +1990,7 @@ "confidence": 0.22367025973033955 }, { - "observation_id": "61cb69a0-6e57-47be-9867-9a1ecd918682", + "observation_id": "8cb650f2-5157-495d-ae9e-031dab93fc88", "type": "aruco", "marker_id": 60, "marker_size_m": 0.025, @@ -2044,7 +2044,7 @@ "confidence": 0.2000031054889577 }, { - "observation_id": "4148cdf5-aca6-4f29-b4d8-0f93abfc2b72", + "observation_id": "ee98dc68-da30-4b27-ad1a-e36d5e07a43b", "type": "aruco", "marker_id": 67, "marker_size_m": 0.025, @@ -2098,7 +2098,7 @@ "confidence": 0.19627771759033205 }, { - "observation_id": "711116a6-1a9a-4dad-b46e-2efc4718b0f7", + "observation_id": "8340b659-c8ae-4ee0-8bbb-c7f5b830e388", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, @@ -2152,7 +2152,7 @@ "confidence": 0.1764160816420665 }, { - "observation_id": "5b4e3306-7931-49c3-8723-cfd24a6ad45d", + "observation_id": "5b598d5b-37c1-4932-843a-c8c297fa04b3", "type": "aruco", "marker_id": 88, "marker_size_m": 0.025, @@ -2206,7 +2206,7 @@ "confidence": 0.20782158780899712 }, { - "observation_id": "c0a9aaa6-b8e9-43ca-b416-aeb0e529b0b5", + "observation_id": "c69812f0-7222-4d6f-8239-6feea5d6b0ca", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -2260,7 +2260,7 @@ "confidence": 0.1929997138977051 }, { - "observation_id": "29dab72e-d063-46ec-a5c0-ede55a2f7cb4", + "observation_id": "701f9447-aa5c-405a-a65d-f9043a7b1bd9", "type": "aruco", "marker_id": 82, "marker_size_m": 0.025, @@ -2314,7 +2314,7 @@ "confidence": 0.19249963767972095 }, { - "observation_id": "c8bfb42c-4fa1-4438-b8c3-ebb2d5a1d761", + "observation_id": "a95bd6de-5c4c-4d41-ac67-5f8e32edbf94", "type": "aruco", "marker_id": 98, "marker_size_m": 0.025, @@ -2368,7 +2368,7 @@ "confidence": 0.1899538473288218 }, { - "observation_id": "af6704f9-92c7-4761-829b-6ceaef4f48fb", + "observation_id": "f084d2a5-643d-4f03-84c9-ba3f420c60fa", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -2422,7 +2422,7 @@ "confidence": 0.17676838843571283 }, { - "observation_id": "6fc34d5f-a3a7-4cac-ad43-a840ebe205ce", + "observation_id": "28fdb693-f26d-4513-89c7-1d6b13d7ab2d", "type": "aruco", "marker_id": 61, "marker_size_m": 0.025, @@ -2476,7 +2476,7 @@ "confidence": 0.1668050360320266 }, { - "observation_id": "09480647-3aae-4cd5-ae92-b78f6de91dcc", + "observation_id": "2cb4b6dd-bf60-4310-8461-a9175f0bbb3d", "type": "aruco", "marker_id": 90, "marker_size_m": 0.025, @@ -2530,7 +2530,7 @@ "confidence": 0.17612778902053833 }, { - "observation_id": "aa2be298-66e1-4064-90ac-ed4d274f78c0", + "observation_id": "b3802ebb-c3c3-41b9-8cd3-945272e9afd5", "type": "aruco", "marker_id": 91, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene7/render_b_camera_pose.json b/data/evaluations/Scene7/render_b_camera_pose.json index d6f17ef..7158113 100644 --- a/data/evaluations/Scene7/render_b_camera_pose.json +++ b/data/evaluations/Scene7/render_b_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:06Z", + "created_utc": "2026-06-01T20:36:21Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene7\\render_b_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene7/render_c_aruco_detection.json b/data/evaluations/Scene7/render_c_aruco_detection.json index 424bb19..52a359d 100644 --- a/data/evaluations/Scene7/render_c_aruco_detection.json +++ b/data/evaluations/Scene7/render_c_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:03Z", + "created_utc": "2026-06-01T20:36:17Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "26864f97-26d1-4da2-a27b-7e6215f7098b", + "observation_id": "f84f1d10-99cd-41ba-8bf7-4335564b5cf3", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.8641129878529331 }, { - "observation_id": "d20ad25c-dc0c-4db4-9231-514e056fba29", + "observation_id": "d0400a85-a1f6-48cb-aa17-54810677b037", "type": "aruco", "marker_id": 245, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.9079230840829977 }, { - "observation_id": "0a3a75ac-d2dc-4c3c-815d-570e76177a54", + "observation_id": "4ff7968a-845b-4783-b28c-8e9d78c4fec6", "type": "aruco", "marker_id": 248, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.9245002946548448 }, { - "observation_id": "a418f4b5-2950-40a7-b564-c4882e613ce1", + "observation_id": "cdf425d1-218d-43d4-965b-a27c2bba504d", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.9049975452490508 }, { - "observation_id": "5329b1d8-daa7-42d6-81b4-c4789bc1f156", + "observation_id": "8db68d52-989e-42b3-9380-ff5295e1ad55", "type": "aruco", "marker_id": 114, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.41522739398786884 }, { - "observation_id": "b384f0f1-42b0-45c8-9fe8-5080a39fd05e", + "observation_id": "cd3985f9-a0d4-48ae-b3ed-92b8be67af9a", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.48468101391144364 }, { - "observation_id": "c94394f1-1501-4c49-90b8-74f635a8741a", + "observation_id": "da8b9216-d686-487a-af30-12681c674cc0", "type": "aruco", "marker_id": 229, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.31933343447029705 }, { - "observation_id": "b66193ec-0752-4fea-bb02-97d2748917a1", + "observation_id": "c656419e-7d8a-4948-8075-00295186a9af", "type": "aruco", "marker_id": 232, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.24808403242708815 }, { - "observation_id": "ff9ac091-f94b-4cd1-b10c-070157818297", + "observation_id": "29e267d4-5253-4b68-b145-32e3019c1728", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.2964796698261984 }, { - "observation_id": "13d8d000-0bb2-483d-b128-9708510073b5", + "observation_id": "405fe550-c3e2-42f4-992c-d3ebf9b16bb7", "type": "aruco", "marker_id": 198, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.18915498788222326 }, { - "observation_id": "80fd81ef-3ddb-42f6-87a7-8e3dce5e8520", + "observation_id": "27bbea3d-b406-4ae8-aa7a-0695532a6a78", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.21966133686197709 }, { - "observation_id": "6a2c4d70-a97b-4d35-a5fe-ef687528b499", + "observation_id": "edaa346a-6260-43e6-9802-5ae8ffc4f807", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.233781757000696 }, { - "observation_id": "67db30f1-3b3b-4ce0-bd8f-32b57efac143", + "observation_id": "99126d01-74b8-42e6-8aed-7d67412206b0", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene7/render_c_camera_pose.json b/data/evaluations/Scene7/render_c_camera_pose.json index 74a025b..c3f3207 100644 --- a/data/evaluations/Scene7/render_c_camera_pose.json +++ b/data/evaluations/Scene7/render_c_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:07Z", + "created_utc": "2026-06-01T20:36:21Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene7\\render_c_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene7/render_d_aruco_detection.json b/data/evaluations/Scene7/render_d_aruco_detection.json index c74a1a6..40dc6b9 100644 --- a/data/evaluations/Scene7/render_d_aruco_detection.json +++ b/data/evaluations/Scene7/render_d_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:04Z", + "created_utc": "2026-06-01T20:36:18Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "bdeb975c-168d-463b-918e-506fb711d298", + "observation_id": "eabbdbe5-1b82-4c28-ae6b-b97f43420fbc", "type": "aruco", "marker_id": 114, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.7875369142907499 }, { - "observation_id": "2ff28b10-4e16-478c-bc43-c1bc39e1183c", + "observation_id": "e72a5664-6b3a-412a-bf46-e97999a56333", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.8549197945530235 }, { - "observation_id": "3f020c4c-01a6-4d48-8ee1-94d5e2bc9a90", + "observation_id": "d93c2d9d-45ee-43af-99b4-bb601ea2581b", "type": "aruco", "marker_id": 245, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.8898073693817129 }, { - "observation_id": "aef0a66e-0b43-46dd-b646-eef6de91a4ab", + "observation_id": "044e11dd-ffee-46dd-9c70-cace5cf77cf7", "type": "aruco", "marker_id": 248, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.8621616769344248 }, { - "observation_id": "44aef80a-3a57-47d7-9774-b1c28cf90f9d", + "observation_id": "b67843fc-9b86-4e67-9d86-ccdf2e6f4aa3", "type": "aruco", "marker_id": 243, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.8690627158366645 }, { - "observation_id": "b9074860-462f-4bad-96fe-d76697a03d18", + "observation_id": "a6417cdc-3a57-4a5d-a0e6-cd7ffbfb7bd5", "type": "aruco", "marker_id": 244, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.519508460732346 }, { - "observation_id": "cba44a75-15a4-4077-bdf0-08ffb49b80b6", + "observation_id": "164a4b05-1a2b-4f93-b7ed-fbb92a70c9e7", "type": "aruco", "marker_id": 115, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.40433054682109365 }, { - "observation_id": "8a2b64e1-4d97-4ca3-b2e0-04d9e60cc708", + "observation_id": "ab4900ad-6a1e-48b1-a199-da2810a27efb", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.06739671544456982 }, { - "observation_id": "ddd869fe-e892-4378-b4e4-d14e33bfe144", + "observation_id": "7339e930-76d7-4c24-b597-cb93e2e1c644", "type": "aruco", "marker_id": 232, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.31112697734150224 }, { - "observation_id": "0b69ea03-7faf-450c-aa78-ceeb6e06f231", + "observation_id": "47d7d6bb-1934-4875-9a36-13505b6259fc", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.17416420855033052 }, { - "observation_id": "285a9edc-10ef-4696-8b94-fbacdf8da13a", + "observation_id": "75a69418-e30c-4cb8-9f25-e3d2fd4d8974", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.24540823915500745 }, { - "observation_id": "01b83416-848f-4183-bbaa-40b738207337", + "observation_id": "55e9cdf5-59f1-4dba-83cb-6ce2882c12fe", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.3780219194740923 }, { - "observation_id": "4625907d-47ef-4518-9703-5c70238d90c3", + "observation_id": "46e83112-a855-4dd7-bda1-e28f3fd4da8a", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.33466528453625716 }, { - "observation_id": "eba18b85-4f4e-48cc-97b5-061cd1139ebc", + "observation_id": "5207b2f8-23c2-4eea-8f92-778c59c8abe7", "type": "aruco", "marker_id": 219, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.0749290408725987 }, { - "observation_id": "9d1aa856-4d10-49c9-86b5-32fc556af46a", + "observation_id": "f3fa1927-a8a0-48df-b539-844bec3920c1", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.4478501628460533 }, { - "observation_id": "56f845f1-be99-496a-825e-8e188b406174", + "observation_id": "ea575956-5a56-4d1c-8642-abd7f22b9b18", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.3071912693949274 }, { - "observation_id": "e2485caf-47df-4505-b962-4a92e2293a03", + "observation_id": "5c7f672e-666f-46ff-85b5-19ff6ccd8e79", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.018959818235601233 }, { - "observation_id": "24e6792f-514a-4232-b163-63c5b370fb83", + "observation_id": "36eec318-78ed-411e-9c23-c86a5c8b40af", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.07779094550673546 }, { - "observation_id": "81e70943-0a84-4345-90d3-70dbc35a905a", + "observation_id": "d83d81d4-04be-4ba1-bcc2-c86057abd9ab", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene7/render_d_camera_pose.json b/data/evaluations/Scene7/render_d_camera_pose.json index 8a1adfe..81bb9ff 100644 --- a/data/evaluations/Scene7/render_d_camera_pose.json +++ b/data/evaluations/Scene7/render_d_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:07Z", + "created_utc": "2026-06-01T20:36:22Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene7\\render_d_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene7/render_e_aruco_detection.json b/data/evaluations/Scene7/render_e_aruco_detection.json index ea36953..51b705d 100644 --- a/data/evaluations/Scene7/render_e_aruco_detection.json +++ b/data/evaluations/Scene7/render_e_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:04Z", + "created_utc": "2026-06-01T20:36:19Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "1e48793b-b3fa-4cf6-a418-fc56afe11a71", + "observation_id": "a6d6d0b7-3fed-4d9c-a7ed-4c1327828f1a", "type": "aruco", "marker_id": 114, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.754802504315709 }, { - "observation_id": "7b4166d7-cf54-4f51-86ec-288f09b104a4", + "observation_id": "4fadd12c-d81d-481d-b658-7c476b920b08", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.7694801395214592 }, { - "observation_id": "d87224df-5d3a-44d3-a405-cb157126c394", + "observation_id": "9d9b0d45-4a18-4d81-a0b0-aa8b47dc928e", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.15369374989294518 }, { - "observation_id": "87cb8457-881d-456d-b045-3e2b5b04f4b6", + "observation_id": "fc99c204-8cf2-4946-beea-9a1f40cc41d3", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.3233651345714012 }, { - "observation_id": "13d25259-1fbf-412d-ba6a-477d8e308d86", + "observation_id": "c9eff4f1-a03c-4911-91a4-ad7822e7091b", "type": "aruco", "marker_id": 105, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.420622587927407 }, { - "observation_id": "a1d6bcfe-f3d1-425c-98b9-b25aefc5be2a", + "observation_id": "785272f7-6893-412c-9a64-73cb1502bf60", "type": "aruco", "marker_id": 85, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.3128949822399185 }, { - "observation_id": "0ce527a2-476b-4cea-b92f-6cb16fdd2714", + "observation_id": "a323ef48-03c2-4e49-926b-bceae07a28c4", "type": "aruco", "marker_id": 206, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.15136171428561934 }, { - "observation_id": "f1bb2e47-d37f-4a80-b41f-6a7f8c085a79", + "observation_id": "16b974c7-e6ab-4adf-a8d6-f6acd8468b4f", "type": "aruco", "marker_id": 244, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.45499836375360975 }, { - "observation_id": "feb000d2-e13e-4da9-9fb5-3cf5bdd8d4a4", + "observation_id": "a2c2e0ef-d4e4-4e65-913c-1294bdaf3ea2", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.24626971873720385 }, { - "observation_id": "e44c993f-28a5-4a63-90f3-9b94ce6873af", + "observation_id": "f7252776-71f0-429e-b63b-7fedf49dd25e", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.22596816505744277 }, { - "observation_id": "5f5f80fc-b811-42dd-a197-355c5cfbabe1", + "observation_id": "f11444c2-9ec4-4f1e-a9cc-7e49f22229f9", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.28140192555287236 }, { - "observation_id": "0711646f-06fc-4357-9573-30320333cede", + "observation_id": "1d1bfa6b-df7d-480d-882b-ed56cf8e8745", "type": "aruco", "marker_id": 245, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.28539378587467984 }, { - "observation_id": "8bc77187-3921-4394-9714-c5711362acd4", + "observation_id": "7c89f1fd-6c96-4af1-acb0-1f107b947f88", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.2664122029520185 }, { - "observation_id": "3cf66db6-086f-44a0-ab29-6d921810d2cd", + "observation_id": "1d731fc4-e446-4f8c-84ff-d0fd254950d0", "type": "aruco", "marker_id": 219, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.16961540871655734 }, { - "observation_id": "b2e959ae-000f-42c0-9dc6-5f47b4db1a95", + "observation_id": "d82188be-ca24-4bba-a1e1-9aa6384b4385", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.2093822185726055 }, { - "observation_id": "141231f1-c807-42be-a683-bde8278f1497", + "observation_id": "844fe7ed-dcd7-4b3e-a4d0-1ee97c832c47", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.16725963004706537 }, { - "observation_id": "397d03a2-8ed8-4fbf-8e5b-5fe3e638edf6", + "observation_id": "3179d794-270d-46bb-9997-40ea4c3cf5d8", "type": "aruco", "marker_id": 248, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.2812873397283331 }, { - "observation_id": "64800420-23b1-4e32-877f-2c38f77cad54", + "observation_id": "4890dbbd-2888-4e3d-91e2-96bcdb7498fa", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.22298824455832925 }, { - "observation_id": "150cdab0-9f7d-4d54-824a-8aae0db17b99", + "observation_id": "b527fba1-b0d8-4f83-a004-2a4093396f41", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.13595850692924102 }, { - "observation_id": "9df50c0c-7917-4e6b-afea-3851542f83cf", + "observation_id": "8528c786-9086-47c0-a0e2-03ba26b8754f", "type": "aruco", "marker_id": 51, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.12157403976480062 }, { - "observation_id": "6b8cfc9c-92c5-42f5-9e91-ce3982211f17", + "observation_id": "0ef6c873-6767-4324-b7de-be91e2050d4c", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.14675484287524884 }, { - "observation_id": "7cf84dcc-0d0f-404f-8f44-e95c4f9a75fc", + "observation_id": "eed1053d-bd7c-4840-8a1e-c42fc5cd3958", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.14922985097126407 }, { - "observation_id": "044a636b-02e5-459c-baff-8e40c4cf6fd5", + "observation_id": "32fcde41-9c1d-48e9-b1f7-783b742992c9", "type": "aruco", "marker_id": 214, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.1697872859143744 }, { - "observation_id": "a3bbd7b1-321e-441a-a19f-bebf3e4a9bf6", + "observation_id": "37b1d3b3-5d2b-436c-ad13-689f44493f04", "type": "aruco", "marker_id": 232, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.12910024435238043 }, { - "observation_id": "6f436abd-a851-4957-b6ae-955da43c6b0e", + "observation_id": "8f43fb1f-0696-4bbf-b451-5f761c7eac40", "type": "aruco", "marker_id": 60, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.1208904643109979 }, { - "observation_id": "0e7d743d-aee1-444e-bdc6-40a50cee236c", + "observation_id": "1c531c89-213e-4631-8a72-5f15504506ca", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.10609680160172753 }, { - "observation_id": "0fc60864-7bdd-4a25-95fa-3827202686be", + "observation_id": "68761099-b324-436c-a6ed-bc46d389adca", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.1403163344678544 }, { - "observation_id": "66242007-3c9c-4b3d-b558-fdb098797190", + "observation_id": "90d8e124-fdda-4b64-9b67-774b545de334", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.08927835602899967 }, { - "observation_id": "c201c1cb-2242-455f-8050-7f5e7dceae94", + "observation_id": "814a3bd9-7e0e-4afd-bc86-3e8da4be722e", "type": "aruco", "marker_id": 211, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.13884955866439178 }, { - "observation_id": "c4de8297-c3ed-44e6-9832-9c6a73baa725", + "observation_id": "55f03aac-2b5a-4c97-97e6-98af97e7ea3a", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.13255097071329755 }, { - "observation_id": "1221516d-caa4-4ca1-acb7-4a32b2046a45", + "observation_id": "9944f714-deed-4fc8-b601-38c386a94260", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.09449207516023345 }, { - "observation_id": "a783b3e2-344c-4851-adb4-f7bfebf133f5", + "observation_id": "bc2af503-2560-4434-ba09-71f7320e66ae", "type": "aruco", "marker_id": 86, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.11774828338549573 }, { - "observation_id": "c4980591-bf45-402f-ab62-f06b8e5987c2", + "observation_id": "5ed9579b-ceca-4bd4-ac35-0de097390656", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -1828,7 +1828,7 @@ "confidence": 0.10636273412018452 }, { - "observation_id": "c7d9acf6-ba39-49f5-9f93-60159fb3b235", + "observation_id": "6ce14d49-a4e3-4704-b595-f7abdd7334b1", "type": "aruco", "marker_id": 101, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene7/render_e_camera_pose.json b/data/evaluations/Scene7/render_e_camera_pose.json index a4d74d4..b83c77a 100644 --- a/data/evaluations/Scene7/render_e_camera_pose.json +++ b/data/evaluations/Scene7/render_e_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:08Z", + "created_utc": "2026-06-01T20:36:22Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene7\\render_e_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene7/render_f_aruco_detection.json b/data/evaluations/Scene7/render_f_aruco_detection.json index c296bbc..2c4c26e 100644 --- a/data/evaluations/Scene7/render_f_aruco_detection.json +++ b/data/evaluations/Scene7/render_f_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:05Z", + "created_utc": "2026-06-01T20:36:19Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "c4413bed-f0aa-4393-b1d5-a02f2a397a9e", + "observation_id": "5aba4221-4cc2-44ce-9246-a04b854e53fd", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.9355335674502584 }, { - "observation_id": "e747e812-5d79-47f5-917d-aa63c971f0f9", + "observation_id": "72144813-92fb-4f61-85e2-559378b97909", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.2988751797371847 }, { - "observation_id": "135021a6-d8c0-4aaf-b9b2-54a0a5769476", + "observation_id": "11ec4495-8abe-47c1-b312-7665056d90ee", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.5697620483284124 }, { - "observation_id": "62936b73-9575-4d74-a10a-07b6ca42ecd8", + "observation_id": "60bf9a66-0228-4688-94d7-bb3b596630a6", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.5829305549976826 }, { - "observation_id": "986d14c3-19ba-442e-b949-b1002de24f55", + "observation_id": "5289f509-34b2-4781-aceb-f846235d5418", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.5711737708098855 }, { - "observation_id": "90b8f805-2be6-4cfd-b52c-dc4214d46bd5", + "observation_id": "da8cc296-a17b-4df7-aa28-15ad0ef090ab", "type": "aruco", "marker_id": 47, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.5768583617164568 }, { - "observation_id": "8ce155ea-fbf2-44c2-b8ff-8e98fd29a712", + "observation_id": "ead26db2-d2cd-4c1a-a823-9d94a5737824", "type": "aruco", "marker_id": 54, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.5766374039623903 }, { - "observation_id": "c26b599f-4d70-4e5c-b244-7d34892d6d3e", + "observation_id": "7dac8609-b8e3-47e7-8356-c7ec568890c8", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.21364841201201337 }, { - "observation_id": "85769bb4-9675-48eb-b042-5714934faff4", + "observation_id": "f81481e1-aea7-47b9-8f21-90862e0a2e62", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.5705029634947052 }, { - "observation_id": "01db305e-db45-4add-87c7-a2f4331fd476", + "observation_id": "c177b94c-59a7-4bc2-95f5-1db5ec5bcccd", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.5440516905787338 }, { - "observation_id": "c3215d38-18d3-4dab-bf23-ff56f24ea7a9", + "observation_id": "5d3f8afd-f228-45f2-a366-ef1f3cca4bbd", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.5461315019106076 }, { - "observation_id": "ff10f2d7-544e-441e-8354-d38c00f39137", + "observation_id": "8481944e-1f29-4719-b067-bcac7e548a3e", "type": "aruco", "marker_id": 96, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.5763709732716893 }, { - "observation_id": "339d1360-6dca-4b40-8b86-985edae149c9", + "observation_id": "3b1a2070-d7f5-4582-946c-02fd3c613575", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.06519221426443914 }, { - "observation_id": "381caf08-a06b-45aa-a1bb-b4194a9c1d2b", + "observation_id": "50c80c88-ec1c-4844-8eb2-55c1aea057fd", "type": "aruco", "marker_id": 79, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.5460974493377216 }, { - "observation_id": "95609a3b-a27a-4fa1-8798-a3590d92ece0", + "observation_id": "28e4ac84-452c-4cf2-9ba3-e60a0849abc0", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.5349107016469481 }, { - "observation_id": "535c5cd6-0c1d-4bbd-9344-6f5039444028", + "observation_id": "7d279515-fc36-45ab-959b-9b0a422d6ffb", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.5227826547122322 }, { - "observation_id": "22fed7f6-67af-49f7-a583-4cfcf3daf071", + "observation_id": "3f48b0c1-efe2-4b2d-8e59-0979fc510d9f", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.5046333482067283 }, { - "observation_id": "a6c7aa46-bbde-4e6f-aa97-24b288233e3e", + "observation_id": "41723c6c-ca13-4e93-9ff0-f9b22d3970af", "type": "aruco", "marker_id": 73, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.13607001327160623 }, { - "observation_id": "386322ea-953d-4517-bdd8-049730db5838", + "observation_id": "57b643cd-e129-4fee-90f1-9f43d7f4782b", "type": "aruco", "marker_id": 210, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.46988879526830113 }, { - "observation_id": "2bcd08ee-a607-4117-9645-356174b80117", + "observation_id": "ff26aa23-eca1-4955-940f-684683cf9b5e", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.4655541127751829 }, { - "observation_id": "133a9c30-246c-4ba7-88a2-e990b8f33b18", + "observation_id": "ef970fc3-2389-448b-9426-e2c4f9c6df3c", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.4655541127751829 }, { - "observation_id": "e6f43a1a-8f51-404a-b818-dacf73834701", + "observation_id": "ba55b06b-6012-4d6d-987b-d1e4ce60e9ba", "type": "aruco", "marker_id": 82, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.49001748422717006 }, { - "observation_id": "0b87ada6-8082-437f-a207-fb610a815460", + "observation_id": "71937873-f179-4e7d-bd9b-7bd85b002129", "type": "aruco", "marker_id": 101, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.46225891142368675 }, { - "observation_id": "65c117fb-c723-4f2c-b3e9-05f15bcbe27c", + "observation_id": "138b05cf-2f42-4150-9f9f-39e5c8880c1c", "type": "aruco", "marker_id": 81, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.4548532843889077 }, { - "observation_id": "0bb64099-5d1f-4dfb-ba1c-394e49990e8d", + "observation_id": "e1465f48-d7b7-4beb-a1cd-4d6f3dfacaf6", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.43211414091114625 }, { - "observation_id": "0d5ba706-e28b-4e8f-8dfa-e931f1f1c6e9", + "observation_id": "874052a0-d71b-43f1-8418-e3276130e6b4", "type": "aruco", "marker_id": 52, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.45818248585417487 }, { - "observation_id": "4bd8ecac-c4d7-4304-94e3-ddadeb163e86", + "observation_id": "a5b06cd5-3a3d-4a68-b001-0545a67fd34d", "type": "aruco", "marker_id": 83, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.4377857805109236 }, { - "observation_id": "281d6d4d-eaef-4de3-a2ff-ef9692e62457", + "observation_id": "3ac82360-c1ff-442d-b14a-42c3db063609", "type": "aruco", "marker_id": 120, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene7/render_f_camera_pose.json b/data/evaluations/Scene7/render_f_camera_pose.json index 1eb9cfa..ee885a7 100644 --- a/data/evaluations/Scene7/render_f_camera_pose.json +++ b/data/evaluations/Scene7/render_f_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:08Z", + "created_utc": "2026-06-01T20:36:22Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene7\\render_f_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene7/render_g_aruco_detection.json b/data/evaluations/Scene7/render_g_aruco_detection.json index b03d77a..a7105ae 100644 --- a/data/evaluations/Scene7/render_g_aruco_detection.json +++ b/data/evaluations/Scene7/render_g_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:05Z", + "created_utc": "2026-06-01T20:36:20Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -46,7 +46,7 @@ }, "detections": [ { - "observation_id": "490a383a-f497-422a-bda4-38168b57a39f", + "observation_id": "8a7e0779-ffa9-49b7-8f5c-22d1c2237201", "type": "aruco", "marker_id": 41, "marker_size_m": 0.025, @@ -100,7 +100,7 @@ "confidence": 0.7199168185011974 }, { - "observation_id": "8cabd1f9-6c64-4158-8739-f3eed072e356", + "observation_id": "07461a95-f046-42f2-817d-f53bc7efd4ac", "type": "aruco", "marker_id": 198, "marker_size_m": 0.025, @@ -154,7 +154,7 @@ "confidence": 0.5734079332884925 }, { - "observation_id": "c9d13f6c-7f95-443b-bc92-b476c459c719", + "observation_id": "92b3c18c-b1a7-489c-8e14-5b775d4ce9e5", "type": "aruco", "marker_id": 229, "marker_size_m": 0.025, @@ -208,7 +208,7 @@ "confidence": 0.5595404377699383 }, { - "observation_id": "9f15a298-9bb3-47ce-90b5-00ebe1a15fd9", + "observation_id": "f7b13a0d-6bec-46fd-ac5c-9a52cbd7398c", "type": "aruco", "marker_id": 84, "marker_size_m": 0.025, @@ -262,7 +262,7 @@ "confidence": 0.3490767193505014 }, { - "observation_id": "8bb329d9-fccc-4d25-a218-03b4b5e6994b", + "observation_id": "32f1f1da-3c12-47a6-8291-990b68dfefbd", "type": "aruco", "marker_id": 64, "marker_size_m": 0.025, @@ -316,7 +316,7 @@ "confidence": 0.5331747682067169 }, { - "observation_id": "969cab49-23fc-4fa7-9f79-86d15d88df8e", + "observation_id": "63adcc54-15dd-429d-8dda-74760ec1a4c5", "type": "aruco", "marker_id": 72, "marker_size_m": 0.025, @@ -370,7 +370,7 @@ "confidence": 0.4347053164175514 }, { - "observation_id": "d2731f2b-b80f-40aa-b1e0-1888f1fb8c40", + "observation_id": "37d15171-0867-4f9b-90e7-6adccc124a39", "type": "aruco", "marker_id": 53, "marker_size_m": 0.025, @@ -424,7 +424,7 @@ "confidence": 0.27711751756945874 }, { - "observation_id": "e5532763-cf8c-476e-8d13-38bbacd49200", + "observation_id": "6a02671f-105d-4ec7-8c48-fdb35d05fdbc", "type": "aruco", "marker_id": 58, "marker_size_m": 0.025, @@ -478,7 +478,7 @@ "confidence": 0.48073279309443956 }, { - "observation_id": "3e7ffb9b-f154-4545-8bdd-d7e1c5f85061", + "observation_id": "0d912ce9-50a9-4223-9e28-0132d11d9223", "type": "aruco", "marker_id": 69, "marker_size_m": 0.025, @@ -532,7 +532,7 @@ "confidence": 0.45546210617301425 }, { - "observation_id": "6d442b23-d9c0-4a30-8896-a8383b40c802", + "observation_id": "d6325a7d-87f1-4230-91f9-95886b7d61e0", "type": "aruco", "marker_id": 103, "marker_size_m": 0.025, @@ -586,7 +586,7 @@ "confidence": 0.4795917219326271 }, { - "observation_id": "245e4267-23a4-4a13-8416-06c27a6a59a2", + "observation_id": "18d64a56-ca06-4b30-bc01-1928ec64789a", "type": "aruco", "marker_id": 56, "marker_size_m": 0.025, @@ -640,7 +640,7 @@ "confidence": 0.39662908657084783 }, { - "observation_id": "ff87a220-e12a-452c-ad71-336415e4f0c7", + "observation_id": "a2a9337c-98e2-4bcd-b5ff-5293beeb79e7", "type": "aruco", "marker_id": 215, "marker_size_m": 0.025, @@ -694,7 +694,7 @@ "confidence": 0.42189397421159375 }, { - "observation_id": "e8fb0802-583c-4221-b8e3-a3055b32f147", + "observation_id": "57cd9483-df8d-45bb-b0de-bdf30cab96ea", "type": "aruco", "marker_id": 46, "marker_size_m": 0.025, @@ -748,7 +748,7 @@ "confidence": 0.1698001278724927 }, { - "observation_id": "2e120c61-fc10-4bbb-997b-4bd378e52a99", + "observation_id": "a74ffbf9-bc83-4f3e-939c-eeea2bcc708b", "type": "aruco", "marker_id": 68, "marker_size_m": 0.025, @@ -802,7 +802,7 @@ "confidence": 0.07718187630567852 }, { - "observation_id": "b9540fef-ab99-4320-b21f-7ae9a55febd5", + "observation_id": "62385ecb-a62a-454e-9310-1a0b9bd1a574", "type": "aruco", "marker_id": 208, "marker_size_m": 0.025, @@ -856,7 +856,7 @@ "confidence": 0.4316478958478293 }, { - "observation_id": "0eb9ffb9-7144-4a4b-ad5e-01dbcb1db22e", + "observation_id": "2593884a-5d00-4ab9-9216-7e3443e18edc", "type": "aruco", "marker_id": 95, "marker_size_m": 0.025, @@ -910,7 +910,7 @@ "confidence": 0.4181682027393045 }, { - "observation_id": "860a8131-9d6b-448b-a972-e083c013eb2a", + "observation_id": "acbb661d-70bf-4a69-bf4d-6a4a8b15df1e", "type": "aruco", "marker_id": 55, "marker_size_m": 0.025, @@ -964,7 +964,7 @@ "confidence": 0.39668368657567366 }, { - "observation_id": "93ab7acb-7291-4296-8f2a-52dbd0366112", + "observation_id": "cfda43d9-4c0d-439d-bd94-98cfe66d2c23", "type": "aruco", "marker_id": 66, "marker_size_m": 0.025, @@ -1018,7 +1018,7 @@ "confidence": 0.3829765144945479 }, { - "observation_id": "76532e43-bd96-4b90-889d-147d142942e6", + "observation_id": "d854e0a0-2ca4-41ff-b4f7-c07043ac8425", "type": "aruco", "marker_id": 217, "marker_size_m": 0.025, @@ -1072,7 +1072,7 @@ "confidence": 0.3382587531306593 }, { - "observation_id": "3d2b8639-182f-4bb9-a354-c62c0ee7d2db", + "observation_id": "64fe23a5-be15-46a1-b675-f0f052d82d13", "type": "aruco", "marker_id": 62, "marker_size_m": 0.025, @@ -1126,7 +1126,7 @@ "confidence": 0.3632179345093538 }, { - "observation_id": "84d24b52-8e55-453f-ae59-291a02f91329", + "observation_id": "abeb8fd2-bfe7-40ae-a2c9-5e1113cd5bd4", "type": "aruco", "marker_id": 97, "marker_size_m": 0.025, @@ -1180,7 +1180,7 @@ "confidence": 0.3623898971459892 }, { - "observation_id": "a2195cc1-1c29-43ad-bf7b-85f93f0b413e", + "observation_id": "02a63f38-ef55-4129-b854-6b001764345a", "type": "aruco", "marker_id": 54, "marker_size_m": 0.025, @@ -1234,7 +1234,7 @@ "confidence": 0.3590718541167963 }, { - "observation_id": "84ffb95f-06f4-4c11-afa8-6b073758a728", + "observation_id": "c0170000-4b2a-4b5e-9dff-5f5dd5f3b3e6", "type": "aruco", "marker_id": 205, "marker_size_m": 0.025, @@ -1288,7 +1288,7 @@ "confidence": 0.29591817114314195 }, { - "observation_id": "4c9fae0f-608d-4a3c-8450-04ce64166411", + "observation_id": "20a86955-aed6-4010-8161-e690ef9ac4c6", "type": "aruco", "marker_id": 92, "marker_size_m": 0.025, @@ -1342,7 +1342,7 @@ "confidence": 0.3158166687011719 }, { - "observation_id": "9db54152-4964-42c3-8534-959baca5cec9", + "observation_id": "a25426ac-91cb-4c64-a39b-98e8293a9a86", "type": "aruco", "marker_id": 113, "marker_size_m": 0.025, @@ -1396,7 +1396,7 @@ "confidence": 0.07997148327617512 }, { - "observation_id": "04aea3a3-5ef3-4ca2-99ff-79f7c3b37244", + "observation_id": "c6886a45-3bbe-4a70-873c-788612f44a80", "type": "aruco", "marker_id": 105, "marker_size_m": 0.025, @@ -1450,7 +1450,7 @@ "confidence": 0.31800273344664126 }, { - "observation_id": "0c2a9e54-aa2a-4c5d-8d7e-5d8a0ff5653c", + "observation_id": "de879805-19f2-4070-a68c-9547f1a06a16", "type": "aruco", "marker_id": 85, "marker_size_m": 0.025, @@ -1504,7 +1504,7 @@ "confidence": 0.2969848480224609 }, { - "observation_id": "dc595c66-cb9e-4e8a-a04e-721a3f6da8bf", + "observation_id": "47ed24d6-767f-47b7-b6f3-fbcd97683de8", "type": "aruco", "marker_id": 102, "marker_size_m": 0.025, @@ -1558,7 +1558,7 @@ "confidence": 0.3170967427530611 }, { - "observation_id": "91e796f4-817a-47f3-8368-b66aa180e7a3", + "observation_id": "ab81ae9e-92f4-4001-bf6f-b22ae86078b6", "type": "aruco", "marker_id": 59, "marker_size_m": 0.025, @@ -1612,7 +1612,7 @@ "confidence": 0.3170967427530611 }, { - "observation_id": "83fd0aa9-ba5e-43e7-ba5e-1b55829f5136", + "observation_id": "fdbc4253-de75-4aaf-9c8d-317fe20a3090", "type": "aruco", "marker_id": 48, "marker_size_m": 0.025, @@ -1666,7 +1666,7 @@ "confidence": 0.17828567290874034 }, { - "observation_id": "9c909a4e-cfb8-4a70-8564-e8e2714c7c45", + "observation_id": "0fdb06de-8018-4dc3-9aef-a5e7b94b1c9f", "type": "aruco", "marker_id": 57, "marker_size_m": 0.025, @@ -1720,7 +1720,7 @@ "confidence": 0.3167426670523156 }, { - "observation_id": "206a623f-b1ea-4541-ab10-abb476432399", + "observation_id": "22d68d02-0347-409f-9255-d175df710a15", "type": "aruco", "marker_id": 63, "marker_size_m": 0.025, @@ -1774,7 +1774,7 @@ "confidence": 0.15449597168691093 }, { - "observation_id": "0a1f622e-447c-4ce6-b52e-24803869a1df", + "observation_id": "5168ee39-f49c-465f-995c-5b1e3f878500", "type": "aruco", "marker_id": 71, "marker_size_m": 0.025, diff --git a/data/evaluations/Scene7/render_g_camera_pose.json b/data/evaluations/Scene7/render_g_camera_pose.json index ae92f4f..3e31450 100644 --- a/data/evaluations/Scene7/render_g_camera_pose.json +++ b/data/evaluations/Scene7/render_g_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-06-01T18:52:08Z", + "created_utc": "2026-06-01T20:36:23Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene7\\render_g_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" diff --git a/data/evaluations/Scene7/robot_state.json b/data/evaluations/Scene7/robot_state.json index 8f348d2..a2d0fe0 100644 --- a/data/evaluations/Scene7/robot_state.json +++ b/data/evaluations/Scene7/robot_state.json @@ -1,7 +1,7 @@ { "schema_version": "1.0", "method": "deterministic_geometric_sequential_prefix", - "created_utc": "2026-06-01T18:52:19Z", + "created_utc": "2026-06-01T20:36:34Z", "root_pose": { "translation_m": [ 2.522185138842392e-05, diff --git a/data/evaluations/Scene8/marker_eval.json b/data/evaluations/Scene8/marker_eval.json new file mode 100644 index 0000000..04cde91 --- /dev/null +++ b/data/evaluations/Scene8/marker_eval.json @@ -0,0 +1,396 @@ +{ + "n_original": 94, + "n_recognized": 56, + "n_missing": 38, + 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a/data/evaluations/Scene9b/render_d_aruco_detection.json b/data/evaluations/Scene9b/render_d_aruco_detection.json index 40c8908..f860647 100644 --- a/data/evaluations/Scene9b/render_d_aruco_detection.json +++ b/data/evaluations/Scene9b/render_d_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T14:41:29Z", + "created_utc": "2026-06-01T21:51:48Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -10,14 +10,14 @@ "intrinsics_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\simulation\\Scene9b\\render_a.npz", "camera_matrix": [ [ - 2000.0, + 6800.0, 0.0, - 720.0 + 2448.0 ], [ 0.0, - 2250.0, - 540.0 + 6800.0, + 1632.0 ], [ 0.0, @@ -35,18 +35,18 @@ }, "image": { "image_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\simulation\\Scene9b\\render_d.png", - "image_sha256": "e3644c746532bbbaaa82655c334d4dc1218c4d81547db237ee20330e89ebcdea", + 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b/data/evaluations/Scene9b/render_e_camera_pose.json index bddc965..ba478f4 100644 --- a/data/evaluations/Scene9b/render_e_camera_pose.json +++ b/data/evaluations/Scene9b/render_e_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T14:41:44Z", + "created_utc": "2026-06-01T21:52:03Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene9b\\render_e_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" @@ -9,14 +9,14 @@ "camera_id": "e", "camera_matrix": [ [ - 2000.0, + 6800.0, 0.0, - 720.0 + 2448.0 ], [ 0.0, - 2250.0, - 540.0 + 6800.0, + 1632.0 ], [ 0.0, @@ -68,222 +68,150 @@ 0, 1, 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27 + 3 ], "rms": [ - 0.35421553530940286, - 0.1841731718117656, - 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a/data/evaluations/Scene9b/render_f_camera_pose.json b/data/evaluations/Scene9b/render_f_camera_pose.json index cee5f9d..7a43352 100644 --- a/data/evaluations/Scene9b/render_f_camera_pose.json +++ b/data/evaluations/Scene9b/render_f_camera_pose.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T14:41:45Z", + "created_utc": "2026-06-01T21:52:04Z", "source": { "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene9b\\render_f_aruco_detection.json", "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json" @@ -9,14 +9,14 @@ "camera_id": "f", "camera_matrix": [ [ - 2000.0, + 6800.0, 0.0, - 720.0 + 2448.0 ], [ 0.0, - 2250.0, - 540.0 + 6800.0, + 1632.0 ], [ 0.0, @@ -75,155 +75,152 @@ 1, 2, 3, - 4, - 5 + 4 ], "rms": [ - 0.09977649187186353, - 0.03441444627218159, - 0.0257326434322241, - 0.02571489269823018, - 0.02571488791422037, - 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b/data/evaluations/Scene9b/render_g_aruco_detection.json index 7b6b889..b521641 100644 --- a/data/evaluations/Scene9b/render_g_aruco_detection.json +++ b/data/evaluations/Scene9b/render_g_aruco_detection.json @@ -1,6 +1,6 @@ { "schema_version": "1.0", - "created_utc": "2026-05-31T14:41:43Z", + "created_utc": "2026-06-01T21:52:01Z", "vision_config": { "MarkerType": "DICT_4X4_250", "MarkerSize": 0.025 @@ -10,14 +10,14 @@ "intrinsics_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\simulation\\Scene9b\\render_a.npz", "camera_matrix": [ [ - 2000.0, + 6800.0, 0.0, - 720.0 + 2448.0 ], [ 0.0, - 2250.0, - 540.0 + 6800.0, + 1632.0 ], [ 0.0, @@ -35,7 +35,7 @@ }, "image": { "image_file": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\simulation\\Scene9b\\render_g.png", - "image_sha256": "2b93c246788686c90d9e726cf50e5ab1ccf5cf0f308ec8e863d3b40675ba3690", + "image_sha256": 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a/doc/pipeline.pdf b/doc/pipeline.pdf new file mode 100644 index 0000000..ae032c2 Binary files /dev/null and b/doc/pipeline.pdf differ diff --git a/doc/pipeline.tex b/doc/pipeline.tex new file mode 100644 index 0000000..c078c7e --- /dev/null +++ b/doc/pipeline.tex @@ -0,0 +1,500 @@ +\documentclass[11pt,a4paper]{article} + +\usepackage[utf8]{inputenc} +\usepackage[T1]{fontenc} +\usepackage[ngerman]{babel} +\usepackage[margin=2.4cm]{geometry} +\usepackage{amsmath,amssymb,bm,mathtools} +\usepackage{booktabs} +\usepackage{xcolor} +\usepackage{listings} +\usepackage{graphicx} +\usepackage{enumitem} +\usepackage[hidelinks]{hyperref} + +\definecolor{codebg}{rgb}{0.96,0.96,0.97} +\definecolor{kw}{rgb}{0.2,0.3,0.7} +\lstset{ + basicstyle=\ttfamily\small, + backgroundcolor=\color{codebg}, + frame=single, rulecolor=\color{gray!40}, + breaklines=true, columns=fullflexible, + keepspaces=true, showstringspaces=false, + numberstyle=\tiny, xleftmargin=4pt, xrightmargin=4pt, aboveskip=6pt, belowskip=6pt, +} + +\newcommand{\R}{\mathbb{R}} +\newcommand{\vx}{\mathbf{x}} +\newcommand{\vX}{\mathbf{X}} +\newcommand{\vu}{\mathbf{u}} +\newcommand{\vn}{\mathbf{n}} +\newcommand{\vp}{\mathbf{p}} +\newcommand{\vt}{\mathbf{t}} +\newcommand{\vq}{\mathbf{q}} +\newcommand{\va}{\hat{\mathbf{a}}} +\newcommand{\vY}{\mathbf{Y}} +\newcommand{\vv}{\mathbf{v}} +\newcommand{\vr}{\mathbf{r}} +\newcommand{\vd}{\mathbf{d}} +\newcommand{\norm}[1]{\left\lVert #1 \right\rVert} + +\title{\textbf{Roboter-Pose-Erkennung aus Mehrkamera-ArUco-Aufnahmen}\\[2pt] +\large Pipeline, mathematische Grundlagen und Konfiguration} +\author{appRobotRendering --- technische Dokumentation} +\date{\today} + +\begin{document} +\maketitle + +\begin{abstract} +\noindent +Diese Dokumentation beschreibt eine Bildverarbeitungs-Pipeline, die aus +synchronen Fotos eines Roboterarms von mehreren kalibrierten Kameras die +Gelenkstellung (sieben Achsen $x,y,z,a,b,c,e$) rekonstruiert. Auf dem Roboter +und einer Referenzplatte (\emph{Board}) sind ArUco-Marker angebracht. Aus den +\emph{vier Eckpunkten} jedes Markers wird per Mehrsicht-Triangulation eine volle +6-DOF-Markerpose (Position \emph{und} Flächennormale) bestimmt; aus diesen +Beobachtungen werden über das Vorwärtskinematik-Modell die Gelenkwinkel +geschätzt. Es werden vier austauschbare Schätzverfahren beschrieben und an +simulierten Szenen mit bekannter Grundwahrheit validiert (mittlerer +Winkelfehler $0{,}25^\circ$, Positionsfehler $0{,}10$\,mm). +\end{abstract} + +\tableofcontents +\bigskip + +% ===================================================================== +\section{Einführung und Problemstellung} +% ===================================================================== +Gegeben sind $C$ kalibrierte Kameras, die denselben Roboter gleichzeitig +fotografieren. Der Roboter trägt eine kinematische Kette starrer Glieder +(\emph{Links}); auf den Gliedern und auf der fixen Bodenplatte sitzen ArUco-Marker +bekannter Größe. Das \emph{Board} ist die Wurzel der Kette und definiert das +Weltkoordinatensystem $W$. Gesucht ist der Gelenkvektor +\[ + \vq = (x, y, z, a, b, c, e) \in \R^7, +\] +mit linearen Achsen $x,e$ (in mm) und rotatorischen Achsen $y,z,a,b,c$ (in Grad). + +Die zentrale Idee ist, dass ein ArUco-Marker \emph{kein Punkt} ist, sondern ein +ebenes Quadrat bekannter Kantenlänge mit vier detektierten Eckpunkten. Werden +diese vier Ecken über mehrere Kameras trianguliert, liefert jeder einzelne +Marker neben seiner Position auch eine \emph{gemessene} Orientierung. Dadurch +genügt bereits ein einziger sichtbarer Marker, um den Winkel des Gliedes zu +bestimmen, auf dem er sitzt. + +% ===================================================================== +\section{Notation und Koordinatensysteme} +% ===================================================================== +Punkte im Weltsystem $W$ werden mit $\vX\in\R^3$ bezeichnet (Einheit mm). +Eine Kamera $c$ besitzt die Intrinsikmatrix $K_c\in\R^{3\times3}$, die +Verzeichnungskoeffizienten $D_c$ sowie die Extrinsik $(R_c, \vt_c)$ in +\emph{world-to-camera}-Konvention, +\[ + \vX^{\mathrm{cam}} = R_c\,\vX + \vt_c , \qquad R_c\in SO(3). +\] +Das Kamerazentrum im Weltsystem ist $\mathbf{C}_c = -R_c^{\top}\vt_c$. Die +normierte (intrinsikfreie) Projektion eines Weltpunkts lautet +\begin{equation} + \tilde{\pi}(\vX;R_c,\vt_c)= + \frac{1}{[\vX^{\mathrm{cam}}]_3}\begin{pmatrix}[\vX^{\mathrm{cam}}]_1\\[2pt][\vX^{\mathrm{cam}}]_2\end{pmatrix}\in\R^2 . + \label{eq:proj} +\end{equation} +Eine beobachtete Pixelkoordinate $\vu$ wird mittels $\tilde{\vu}=\mathrm{undistort}(\vu;K_c,D_c)$ +in dieselbe normierte Ebene überführt. Rotationen werden bei der Optimierung über +den Rodrigues-Vektor $\bm{\omega}\in\R^3$ mit $R=\exp([\bm{\omega}]_\times)$ +parametrisiert. + +% ===================================================================== +\section{Pipeline-Architektur} +% ===================================================================== +Die Verarbeitung erfolgt in vier Stufen (Skript \texttt{run/run\_pipeline.bat}): + +\begin{center} +\begin{tabular}{@{}lll@{}} +\toprule +Stufe & Skript & Ausgabe \\ +\midrule +1 & \texttt{1\_detect\_aruco\_observations.py} & \texttt{*\_aruco\_detection.json} (Ecken) \\ +2 & \texttt{2\_estimate\_camera\_from\_observations.py} & \texttt{*\_camera\_pose.json} \\ +3 & \texttt{3\_multiview\_bundle\_adjustment\_v4.py} & \texttt{aruco\_positions\_initial.json} \\ +3b & \texttt{3b\_corner\_marker\_poses.py} & \texttt{aruco\_marker\_poses.json} \\ +4 & \texttt{pose\_estimation.py} & \texttt{robot\_state.json} \\ +\bottomrule +\end{tabular} +\end{center} + +Stufe 3 (reine Center-Triangulation) wird für Visualisierung und Vergleich +beibehalten; die eigentliche Posenschätzung nutzt die in Stufe 3b erzeugten +Eckposen. + +% ===================================================================== +\section{Stufe 1: ArUco-Detektion} +% ===================================================================== +Pro Bild liefert der OpenCV-ArUco-Detektor für jeden erkannten Marker $m$ die +vier Eckpunkte $\vu_{m,0},\dots,\vu_{m,3}$ im Uhrzeigersinn sowie deren +Mittelpunkt. Zusätzlich werden Qualitätsmaße (Fläche, Schärfe als +Laplace-Varianz, Kontrast, Randabstand, Kantenverhältnis) berechnet und eine +heuristische Konfidenz abgeleitet. Die Eckpunkte $\vu_{m,k}$ sind die +\emph{primären} Messungen; der Mittelpunkt ist nur ihr Mittelwert. + +% ===================================================================== +\section{Stufe 2: Kamera-Pose-Schätzung} +% ===================================================================== +Die Board-Marker haben bekannte Weltpositionen $\vX_i$ (aus \texttt{robot.json}). +Aus ihren beobachteten Mittelpunkten wird die Pose jeder Kamera geschätzt. +Ein robuster Anfangswert ergibt sich aus \texttt{solvePnP} (IPPE-Square) je +Marker, gefolgt von einem starren Ausgleich der per-Marker-Kamerazentren. +Verfeinert wird mit Levenberg--Marquardt auf den normierten +Reprojektionsresiduen: +\begin{equation} + \min_{\bm{\theta}}\ \sum_{i} \norm{\,\tilde{\vu}_i - \tilde{\pi}\!\big(\vX_i;\,R(\bm{\omega}),\vt\big)}^2, + \qquad \bm{\theta}=(\bm{\omega},\vt)\in\R^6 . + \label{eq:campose} +\end{equation} +Aus der Jacobi-Matrix am Optimum wird eine $6\times6$-Kovarianz und daraus die +Unsicherheit von Kamerazentrum und Orientierung propagiert. Bemerkung: Da die +Board-Marker nahezu koplanar sind ($z\approx0$), ist die Tiefe schlecht +konditioniert --- eine korrekte Intrinsik ist deshalb essenziell (siehe +Abschnitt~\ref{sec:limits}). + +% ===================================================================== +\section{Stufe 3b: Eck-Triangulation und Normalenschätzung} +% ===================================================================== +\subsection{Mehrsicht-Triangulation (DLT)} +Sei $V_{m,k}\subseteq\{1,\dots,C\}$ die Menge der Kameras, die Ecke $k$ von +Marker $m$ sehen. Mit der normierten Projektionsmatrix $P_c=[\,R_c \mid \vt_c\,]$ +(Zeilen $\vp_{c,1}^\top,\vp_{c,2}^\top,\vp_{c,3}^\top$) und der entzerrten +Beobachtung $\tilde{\vu}=(x,y)$ liefert jede Kamera zwei lineare Gleichungen in +den homogenen Punktkoordinaten $\vX_h$: +\begin{equation} + x\,\vp_{c,3}^\top \vX_h - \vp_{c,1}^\top \vX_h = 0,\qquad + y\,\vp_{c,3}^\top \vX_h - \vp_{c,2}^\top \vX_h = 0 . +\end{equation} +Das Stapeln über alle $c\in V_{m,k}$ ergibt $A\,\vX_h=\mathbf 0$; die Lösung ist +der rechte Singulärvektor zum kleinsten Singulärwert von $A$, gefolgt von der +Dehomogenisierung $\vX = \vX_h^{1:3}/\vX_h^{4}$. Dies wird für alle vier Ecken +ausgeführt und liefert $\vY_0,\dots,\vY_3$. + +\subsection{Position und Normale} +Die Markerposition ist der Schwerpunkt $\bar{\vY}=\tfrac14\sum_k\vY_k$. Die +Normale ist der Normalenvektor der Ausgleichsebene durch die vier Ecken: mit +$M=[\vY_0-\bar{\vY};\dots;\vY_3-\bar{\vY}]\in\R^{4\times3}$ und der +Singulärwertzerlegung $M=U\Sigma V^\top$ ist +\begin{equation} + \vn = \pm\,\vv_3 \quad(\text{kleinster Singulärvektor}), +\end{equation} +wobei das Vorzeichen über die ArUco-Eckreihenfolge so gewählt wird, dass $\vn$ +zur Kamera weist (Konvention wie in der Grundwahrheit): +$\vn\leftarrow-\vn$, falls $\vn\cdot\big((\vY_1-\vY_0)\times(\vY_2-\vY_0)\big)>0$. +Die Validierung (\texttt{benchmark/stage0\_corner\_normals.py}) ergibt einen +Median-Normalenfehler von $1{,}2^\circ$ ($p_{90}=3{,}0^\circ$); die +Aufschlüsselung je Glied (Tabelle~\ref{tab:normals}) zeigt, dass selbst die +kleinen, weit entfernten Fingermarker eine Orientierung von rund $1^\circ$ +liefern --- die Grundlage dafür, dass ein einzelner Marker seinen Gelenkwinkel +bestimmen kann. + +\begin{table}[h] +\centering +\begin{tabular}{@{}lccc@{}} +\toprule +Glied & \# Marker & Normalenfehler Mittel [$^\circ$] & Max [$^\circ$] \\ +\midrule +Arm1 & 3 & 0{,}64 & 0{,}94 \\ +Arm2 & 4 & 0{,}78 & 1{,}04 \\ +Ellbow & 4 & 0{,}96 & 1{,}99 \\ +FingerA & 2 & 0{,}96 & 1{,}14 \\ +FingerB & 2 & 1{,}43 & 1{,}44 \\ +Board & 41 & 1{,}79 & 7{,}07 \\ +\bottomrule +\end{tabular} +\caption{Genauigkeit der aus Ecken abgeleiteten Normale je Glied (Scene8).} +\label{tab:normals} +\end{table} + +% ===================================================================== +\section{Vorwärtskinematik} +% ===================================================================== +Die Links werden topologisch (Wurzel zuerst) sortiert. Für Link $l$ mit Eltern-% +link $p$ gilt +\begin{equation} + T_l \;=\; T_p \cdot \underbrace{\mathrm{Tr}(\mathbf{m}_l)\,R_{xyz}(\bm{\rho}_l)}_{\text{Mount}} + \cdot \underbrace{\mathrm{Tr}(\mathbf{o}_l)\,R_{xyz}(\bm{\sigma}_l)}_{\text{Gelenkursprung}} + \cdot Q_l(q_l), +\end{equation} +mit der Gelenkbewegung +\begin{equation} + Q_l(q_l)= + \begin{cases} + R(\hat{\mathbf{s}}_l,\,q_l) & \text{revolut (Achse }\hat{\mathbf{s}}_l,\text{ Winkel }q_l),\\ + \mathrm{Tr}(\hat{\mathbf{s}}_l\, q_l) & \text{linear (Verschiebung }q_l),\\ + I & \text{fest.} + \end{cases} +\end{equation} +Ein Marker mit lokaler Position $\vp_m^{\text{lok}}$ und lokaler Normale +$\vn_m^{\text{lok}}$ erscheint im Weltsystem als +\begin{equation} + \vp_m(\vq)=T_{l(m)}(\vq)\begin{psmallmatrix}\vp_m^{\text{lok}}\\1\end{psmallmatrix}, + \qquad + \vn_m(\vq)=R\big(T_{l(m)}(\vq)\big)\,\vn_m^{\text{lok}} . + \label{eq:fkmarker} +\end{equation} +Die Kette dieses Roboters ist in Tabelle~\ref{tab:chain} zusammengefasst. +Bemerkenswert: \emph{Base}, \emph{Hand} und \emph{Palm} tragen keine eigenen +Marker, und beide Finger teilen sich die Variable $e$ (mit entgegengesetzten +Achsen --- ein Greifer). + +\begin{table}[h] +\centering +\begin{tabular}{@{}llllc@{}} +\toprule +Link & Eltern & Gelenktyp & Variable & Achse \\ +\midrule +Board & --- & (Wurzel) & --- & --- \\ +Base & Board & linear & $x$ & $[1,0,0]$ \\ +Arm1 & Base & revolut & $y$ & $[-1,0,0]$ \\ +Ellbow & Arm1 & revolut & $z$ & $[-1,0,0]$ \\ +Arm2 & Ellbow & revolut & $a$ & $[0,-1,0]$ \\ +Hand & Arm2 & revolut & $b$ & $[1,0,0]$ \\ +Palm & Hand & revolut & $c$ & $[0,-1,0]$ \\ +FingerA & Palm & linear & $e$ & $[1,0,0]$ \\ +FingerB & Palm & linear & $e$ & $[-1,0,0]$ \\ +\bottomrule +\end{tabular} +\caption{Kinematische Kette: sieben Freiheitsgrade, davon zwei linear ($x,e$).} +\label{tab:chain} +\end{table} + +% ===================================================================== +\section{Pose-Estimation: Vier Schätzverfahren} +% ===================================================================== +Die Beobachtungen sind die Eckposen $\{(\vY_m, \vn^{o}_m)\}_{m\in\mathcal M}$ +aus Stufe 3b. Alle Verfahren parametrisieren über die \emph{Gelenkvariablen} +$\vq$ (nicht über Links), wodurch geteilte Variablen ($e$) und markerlose +Glieder ($x,b,c$) generisch behandelt werden. Das gemeinsame +Residuum eines Markers kombiniert Position und Normale, +\begin{equation} + \vr_m(\vq)=\Big[\;\vp_m(\vq)-\vY_m\,;\;\; w_n\big(\vn_m(\vq)-\vn^{o}_m\big)\Big]\in\R^6, + \label{eq:res} +\end{equation} +mit der Normalengewichtung $w_n$ (Parameter \texttt{normal\_weight}). + +\subsection{Blockstruktur der Kette} +Für die sequenziellen Verfahren wird die Kette in Blöcke zerlegt: Von der Wurzel +fortschreitend werden die Variablen markerloser Gelenke gesammelt und mit der +Variable des nächsten markertragenden Gelenks zu einem Block zusammengefasst, +der aus den Markern dieses Gliedes (zuzüglich Geschwistern mit geteilter +Variable) geschätzt wird. Für diesen Roboter ergeben sich +\[ + B_1=\{x,y\}\ \text{(Arm1)},\quad B_2=\{z\}\ \text{(Ellbow)},\quad + B_3=\{a\}\ \text{(Arm2)},\quad B_4=\{b,c,e\}\ \text{(Finger)} . +\] + +\subsection{Verfahren A --- \texttt{sequential\_vector}} +Analytische Winkelbestimmung pro rotatorischem Gelenk mit $\ge2$ sichtbaren +Markern auf demselben Glied. Mit der Weltachse $\va$ des Gelenks (aus der FK bei +$q=0$) bildet man für Markerpaare $(i,j)$ den Modellvektor +$\vd^{\text{mod}}_{ij}=\vp_j(q{=}0)-\vp_i(q{=}0)$ (im Weltsystem, da der lokale +Linkframe durch die Elterngelenke bereits rotiert ist) und den Beobachtungs-% +vektor $\vd^{\text{obs}}_{ij}=\vY_j-\vY_i$. Nach Projektion senkrecht zur Achse, +$\vd^{\perp}=\vd-(\vd\cdot\va)\va$, ist der vorzeichenbehaftete Drehwinkel +\begin{equation} + \varphi_{ij}=\operatorname{atan2}\!\Big(\va\cdot(\vd^{\text{mod},\perp}_{ij}\times \vd^{\text{obs},\perp}_{ij}),\; + \vd^{\text{mod},\perp}_{ij}\cdot \vd^{\text{obs},\perp}_{ij}\Big). +\end{equation} +Der Gelenkwinkel ist das mit $w_{ij}=\norm{\vd^{\text{mod},\perp}_{ij}}\,\norm{\vd^{\text{obs},\perp}_{ij}}$ +gewichtete \emph{zirkuläre} Mittel +$q^*=\operatorname{atan2}(\sum w\sin\varphi,\ \sum w\cos\varphi)$. +Lineare, markerlose oder Einzelmarker-Gelenke werden über den Block-FK-Löser +(Verfahren B) ergänzt. Sehr schnell, keine lokalen Minima, benötigt aber +$\ge2$ Marker pro Gelenk. + +\subsection{Verfahren B --- \texttt{sequential\_fk}} +Blockweiser nichtlinearer Ausgleich entlang der Kette. Für Block $B$ mit +Variablen $\vq_B$ und Markern $\mathcal M_B$ werden die vorhergehenden Blöcke +$\vq_{5^\circ$). + +% ===================================================================== +\section{Validierung und Ergebnisse} +% ===================================================================== +\subsection{Methodik} +Die Validierung nutzt synthetische Szenen aus einem Blender-Generator +(\texttt{run/run\_sceneGenerator.bat}). Dieser rendert pro Pose $C=7$ Ansichten +und schreibt neben Bildern (\texttt{render\_*.png}) und Intrinsiken +(\texttt{render\_*.npz}) zwei Grundwahrheits-Dateien: \texttt{render\_*.json} mit +den wahren Marker-Weltposen und \texttt{pose.json} mit den wahren Gelenkwinkeln. +Drei Werkzeuge vergleichen die Rekonstruktion gegen diese Wahrheit: +\texttt{stage0\_corner\_normals.py} (Normalenfehler der Triangulation), +\texttt{9\_evaluateMarker.py} (Positions-/Normalenfehler je Marker und Glied) +sowie \texttt{eval\_pose.py} (Gelenkwinkelfehler). Der Treiber +\texttt{run\_benchmark.py} aggregiert über Szenen und Verfahren. + +\subsection{Ergebnisse} +Über zehn simulierte Posen mit bekannter Grundwahrheit (eine fehlerhaft +gerenderte Pose ausgeschlossen) ergeben sich die folgenden mittleren +Gelenk-Winkelfehler. Die Standardabweichung über die Szenen ist das Maß für die +\emph{Stabilität}. + +\begin{center} +\begin{tabular}{@{}lccccc@{}} +\toprule +Verfahren & Mittel [$^\circ$] & Std [$^\circ$] & Schlechtest. [$^\circ$] & Mittel [mm] & Schlechtest. [mm]\\ +\midrule +\texttt{sequential\_vector} & 0{,}315 & 0{,}128 & 1{,}717 & 0{,}144 & 0{,}712\\ +\texttt{sequential\_fk} & 0{,}434 & 0{,}171 & 1{,}838 & 0{,}158 & 0{,}851\\ +\texttt{global\_ba} & 0{,}253 & 0{,}134 & 1{,}568 & 0{,}103 & 0{,}390\\ +\textbf{\texttt{hybrid}} & \textbf{0{,}253} & \textbf{0{,}134} & \textbf{1{,}568} & \textbf{0{,}103} & \textbf{0{,}390}\\ +\bottomrule +\end{tabular} +\end{center} + +Alle Verfahren liegen unter $0{,}5^\circ$ Mittelwert; \texttt{hybrid}/\texttt{global\_ba} +sind am genauesten und am stabilsten und rekonstruieren auch die markerlosen +Gelenke $b,c$ rein aus den Fingermarkern. + +% ===================================================================== +\section{Grenzen und Ausblick} +\label{sec:limits} +% ===================================================================== +\begin{itemize}[leftmargin=1.4em,itemsep=2pt] +\item \textbf{Intrinsik.} Da die Board-Marker koplanar sind, ist die Kamerantiefe + schlecht konditioniert; ein fehlerhaftes $f_y$ (z.\,B. ein früherer + Brennweitenfehler von $\sim$15\,\% bei 16:9) verfälscht alle Höhen. Korrekte + npz-Intrinsiken sind Voraussetzung. +\item \textbf{Sicht-arme Posen.} Sind zu wenige Marker eines Gliedes sichtbar, + ist der zugehörige Winkel schlecht bestimmt (\texttt{confidence=low}). Abhilfe: + Multi-Pose durch Drehen der Achse $a$. +\item \textbf{Adaptive Normalengewichtung.} Ein festes $w_n$ ist ein Kompromiss; + eine pro-Marker nach Sichtqualität skalierte Gewichtung könnte Best- und + Worst-Case gleichzeitig optimieren. +\item \textbf{Reale Kameras.} Stufe 1 nutzt derzeit dieselbe Intrinsik-npz für + alle Bilder (Simulation: identische Kameras); reale Aufbauten benötigen je + Kamera eine eigene Kalibrierung. +\end{itemize} + +\end{document} diff --git a/pipeline/9_evaluateMarker.py b/pipeline/9_evaluateMarker.py index d7ab7bf..6e1c655 100644 --- a/pipeline/9_evaluateMarker.py +++ b/pipeline/9_evaluateMarker.py @@ -1,73 +1,145 @@ +#!/usr/bin/env python3 +""" +9_evaluateMarker.py +=================== +Compare reconstructed markers against ground-truth (render_*.json). + +Reports, per link and overall: + * 3D position error (mm) + * orientation error (deg) between measured and GT normal — only meaningful + when the detected file carries a MEASURED normal (aruco_marker_poses.json). + +Backwards compatible: the original positional CLI + python 9_evaluateMarker.py detected.json original.json +still works and prints the familiar summary; --out adds a JSON report. +""" +from __future__ import annotations + +import argparse import json import math -import argparse +from collections import defaultdict +from typing import Any, Dict, List, Optional -def calculate_distance(point1, point2): - """Berechnet den euklidischen Abstand zwischen zwei 3D-Punkten.""" - return math.sqrt(sum([(a - b) ** 2 for a, b in zip(point1, point2)])) -def analyze_marker_detection(detected_markers_file, original_markers_file): - """Analysiert die Markererkennung und gibt Erkennungsrate und Genauigkeit aus.""" +def dist3(a, b) -> float: + return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b))) - with open(detected_markers_file, 'r') as f: - detected_data = json.load(f) - with open(original_markers_file, 'r') as f: - original_data = json.load(f) +def angle_deg(a, b) -> Optional[float]: + na = math.sqrt(sum(x * x for x in a)) + nb = math.sqrt(sum(x * x for x in b)) + if na < 1e-9 or nb < 1e-9: + return None + c = sum(x * y for x, y in zip(a, b)) / (na * nb) + c = max(-1.0, min(1.0, c)) + ang = math.degrees(math.acos(c)) + return min(ang, 180.0 - ang) # flip-invariant - detected_marker_ids = {marker['marker_id'] for marker in detected_data['markers']} - original_marker_ids = {marker['id'] for marker in original_data} - # Erkannte und nicht erkannte Marker - recognized_markers = len(detected_marker_ids.intersection(original_marker_ids)) - missing_markers = len(original_marker_ids.difference(detected_marker_ids)) - - # Distanzen berechnen - distances = [] - for original_marker in original_data: - marker_id = original_marker['id'] - - # Gefundener Marker suchen - detected_marker = next((m for m in detected_data['markers'] if m['marker_id'] == marker_id), None) - - if detected_marker: - distance = calculate_distance(original_marker['position_m'], detected_marker['position_m']) - distances.append(distance) +def _pct(values: List[float], q: float) -> float: + if not values: + return 0.0 + s = sorted(values) + idx = max(0, min(len(s) - 1, int(q * len(s)) - 1)) + return s[idx] - if not distances: + +def analyze(detected_file: str, original_file: str) -> Dict[str, Any]: + detected = json.load(open(detected_file, "r", encoding="utf-8")) + original = json.load(open(original_file, "r", encoding="utf-8")) + + det_markers = detected.get("markers", detected if isinstance(detected, list) else []) + det_by_id = {int(m.get("marker_id", m.get("id", -1))): m for m in det_markers} + orig_by_id = {int(m["id"]): m for m in original} + + det_ids = set(det_by_id) - {-1} + orig_ids = set(orig_by_id) + recognized = det_ids & orig_ids + missing = orig_ids - det_ids + + per_link_pos: Dict[str, List[float]] = defaultdict(list) + per_link_ang: Dict[str, List[float]] = defaultdict(list) + rows = [] + for mid in sorted(recognized): + o = orig_by_id[mid] + d = det_by_id[mid] + link = o.get("link", d.get("link", "?")) + pos_err = dist3(o["position_m"], d["position_m"]) * 1000.0 # mm + per_link_pos[link].append(pos_err) + ang_err = None + if o.get("normal") is not None and d.get("normal") is not None: + ang_err = angle_deg(o["normal"], d["normal"]) + if ang_err is not None: + per_link_ang[link].append(ang_err) + rows.append({"marker_id": mid, "link": link, "pos_err_mm": pos_err, "normal_err_deg": ang_err}) + + all_pos = [r["pos_err_mm"] for r in rows] + all_ang = [r["normal_err_deg"] for r in rows if r["normal_err_deg"] is not None] + + return { + "n_original": len(orig_ids), + "n_recognized": len(recognized), + "n_missing": len(missing), + "recognition_rate": (len(recognized) / len(orig_ids) * 100.0) if orig_ids else 0.0, + "per_link": { + ln: { + "n": len(per_link_pos[ln]), + "pos_mean_mm": sum(per_link_pos[ln]) / len(per_link_pos[ln]), + "pos_max_mm": max(per_link_pos[ln]), + "normal_mean_deg": (sum(per_link_ang[ln]) / len(per_link_ang[ln])) if per_link_ang.get(ln) else None, + "normal_max_deg": max(per_link_ang[ln]) if per_link_ang.get(ln) else None, + } for ln in sorted(per_link_pos, key=lambda k: -len(per_link_pos[k])) + }, + "overall": { + "pos_mean_mm": (sum(all_pos) / len(all_pos)) if all_pos else 0.0, + "pos_p90_mm": _pct(all_pos, 0.9), + "pos_max_mm": max(all_pos) if all_pos else 0.0, + "normal_mean_deg": (sum(all_ang) / len(all_ang)) if all_ang else None, + "normal_p90_deg": _pct(all_ang, 0.9) if all_ang else None, + "normal_max_deg": max(all_ang) if all_ang else None, + }, + "rows": rows, + } + + +def print_report(r: Dict[str, Any]) -> None: + # familiar summary (backwards compatible wording) + print(f"Erkannte Marker: {r['n_recognized']}") + print(f"Nicht erkannte Marker: {r['n_missing']}") + print(f"Gesamtzahl der Original-Marker: {r['n_original']}") + print(f"Erkennungsrate: {r['recognition_rate']:.2f}%") + o = r["overall"] + print(f"Gemittelter 3D-Abstand: {o['pos_mean_mm']/1000.0:.4f}m") + print(f"90%-Radius: {o['pos_p90_mm']/1000.0:.4f}m") + print(f"Schlechtester Abstand: {o['pos_max_mm']/1000.0:.4f}m") + if o["normal_mean_deg"] is not None: + print(f"Normalen-Fehler: mean {o['normal_mean_deg']:.2f}deg / p90 {o['normal_p90_deg']:.2f}deg / max {o['normal_max_deg']:.2f}deg") + + print("\nPro Glied:") + print(f" {'link':>10} | {'n':>3} | {'pos mean/max [mm]':>18} | {'normal mean/max [deg]':>21}") + print(" " + "-" * 60) + for ln, s in r["per_link"].items(): + nd = f"{s['normal_mean_deg']:6.2f} /{s['normal_max_deg']:6.2f}" if s["normal_mean_deg"] is not None else " - " + print(f" {ln:>10} | {s['n']:>3} | {s['pos_mean_mm']:7.2f} /{s['pos_max_mm']:7.2f} | {nd:>21}") + + +def main() -> None: + ap = argparse.ArgumentParser(description="Analysiert die Markererkennung (Position + Orientierung).") + ap.add_argument("detected_file", help="aruco_marker_poses.json oder aruco_positions_*.json") + ap.add_argument("original_file", help="Ground-Truth render_*.json") + ap.add_argument("--out", default=None, help="optional JSON-Report") + args = ap.parse_args() + + r = analyze(args.detected_file, args.original_file) + if r["n_recognized"] == 0: print("Keine gemeinsamen Marker gefunden, um die Genauigkeit zu bewerten.") return - - # Statistiken berechnen - distances.sort() - mean_distance = sum(distances) / len(distances) - - # 90%-Radius - n = len(distances) - index_90 = int(0.9 * n) - 1 - radius_90 = distances[index_90] - - # 80%-Radius - index_80 = int(0.8 * n) - 1 - radius_80 = distances[index_80] - - # Schlechtester Abstand - worst_distance = distances[-1] - - print(f"Erkannte Marker: {recognized_markers}") - print(f"Nicht erkannte Marker: {missing_markers}") - print(f"Gesamtzahl der Original-Marker: {len(original_marker_ids)}") - print(f"Erkennungsrate: {recognized_markers / len(original_marker_ids) * 100:.2f}%") - print(f"Gemittelter 3D-Abstand: {mean_distance:.4f}m") - print(f"90%-Radius: {radius_90:.4f}m") - print(f"80%-Radius: {radius_80:.4f}m") - print(f"Schlechtester Abstand: {worst_distance:.4f}m") + print_report(r) + if args.out: + json.dump(r, open(args.out, "w", encoding="utf-8"), indent=2) + print(f"\n[INFO] wrote {args.out}") if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Analysiert die Markererkennung.") - parser.add_argument("detected_file", help="Pfad zur JSON-Datei mit den erkannten Markern.") - parser.add_argument("original_file", help="Pfad zur JSON-Datei mit den originalen Markern.") - args = parser.parse_args() - - analyze_marker_detection(args.detected_file, args.original_file) \ No newline at end of file + main() diff --git a/pipeline/__pycache__/pose_estimation.cpython-311.pyc b/pipeline/__pycache__/pose_estimation.cpython-311.pyc new file mode 100644 index 0000000..7cb4eb3 Binary files /dev/null and b/pipeline/__pycache__/pose_estimation.cpython-311.pyc differ diff --git a/pipeline/pose_estimation.py b/pipeline/pose_estimation.py new file mode 100644 index 0000000..8bf703c --- /dev/null +++ b/pipeline/pose_estimation.py @@ -0,0 +1,539 @@ +#!/usr/bin/env python3 +""" +pose_estimation.py +================== +Estimate robot joint angles (x, y, z, a, b, c, e) from triangulated marker +poses, using the kinematic model in robot.json (via robot_fk.py). + +Design +------ +The estimator is parametrised over JOINT VARIABLES, not links. This handles the +tricky cases of this robot family generically: + * Links with zero own markers (Base/x, Hand/b, Palm/c) — their variable is + observable only through descendant markers. + * A variable shared by several links (FingerA & FingerB share 'e'). + * Occluded middle links — global BA reconstructs them from the fingers. + +Four switchable methods (robot.json -> pose_estimation.method): + sequential_vector : analytic per joint from marker-pair / normal vectors (fast) + sequential_fk : block-wise least squares along the chain (robust, 1 marker ok) + global_ba : all variables jointly, position + normal residuals, robust loss + hybrid : sequential_fk init -> global_ba refine (default, most stable) + +Observation input: + marker_observation = "corner_pose" -> aruco_marker_poses.json (pos + measured normal) + marker_observation = "center_point" -> aruco_positions_*.json (pos only) + +Both the engine (estimate_pose) and a CLI (main) live here. +""" +from __future__ import annotations + +import argparse +import json +import math +import os +import sys +import time +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np + +sys.path.insert(0, str(Path(__file__).parent)) +from robot_fk import RobotFK, STATE_KEYS # noqa: E402 + +try: + from scipy.optimize import least_squares + HAVE_SCIPY = True +except ImportError: + HAVE_SCIPY = False + + +# ================================================================== +# Config +# ================================================================== + +DEFAULT_CFG: Dict[str, Any] = { + "method": "hybrid", + "marker_observation": "corner_pose", + "use_normals": True, + "normal_weight": 100.0, + "robust_loss": "huber", + "huber_delta_mm": 8.0, + "max_iterations": 200, + "min_cameras_per_marker": 2, + "finger_block_joints": ["b", "c", "e"], + "per_link_method": {}, +} + + +def load_pose_cfg(robot_data: Dict[str, Any]) -> Dict[str, Any]: + cfg = dict(DEFAULT_CFG) + cfg.update(robot_data.get("pose_estimation", {}) or {}) + return cfg + + +# ================================================================== +# Observations +# ================================================================== + +def load_observations(path: str, use_normals: bool, min_cams: int = 2) -> Dict[int, Dict[str, Any]]: + """ + Load marker observations. Accepts aruco_marker_poses.json (with measured + normal + num_cameras) or aruco_positions_*.json (position only). + Returns: marker_id -> {pos_mm:(3,), normal:(3,)|None, link:str, n_cams:int} + """ + data = json.load(open(path, "r", encoding="utf-8")) + out: Dict[int, Dict[str, Any]] = {} + for m in data.get("markers", []): + mid = int(m.get("marker_id", m.get("id", -1))) + if mid < 0: + continue + n_cams = int(m.get("num_cameras", 99)) + if n_cams < min_cams: + continue + if "position_mm" in m: + pos = np.array(m["position_mm"], dtype=float) + elif "position_m" in m: + pos = np.array(m["position_m"], dtype=float) * 1000.0 + else: + continue + nrm = None + if use_normals and m.get("normal") is not None: + nv = np.array(m["normal"], dtype=float) + nn = np.linalg.norm(nv) + if nn > 1e-9: + nrm = nv / nn + out[mid] = {"pos_mm": pos, "normal": nrm, "link": m.get("link", "?"), "n_cams": n_cams} + return out + + +# ================================================================== +# Kinematic chain analysis +# ================================================================== + +def analyze_chain(fk: RobotFK) -> Dict[str, Any]: + """ + Derive, generically from the FK topology: + ordered_vars : movable joint variables, root->tip order, de-duplicated + var_links : variable -> list of links it drives + link_markers : link -> [model marker ids] + blocks : sequential estimation blocks; each block groups the + zero-marker ancestor variables with the next marker- + bearing joint variable, estimated from that link's own + markers (+ siblings sharing the same variable). + """ + links = fk.links + topo = fk._topo + + link_markers: Dict[str, List[int]] = {} + for ln, ld in links.items(): + ids = [] + for mk in ld.get("markers", []) or []: + if "id" in mk and "position" in mk: + ids.append(int(mk["id"])) + link_markers[ln] = ids + + link_var: Dict[str, str] = {} + for ln, ld in links.items(): + j = ld.get("jointToParent", {}) or {} + if str(j.get("type", "")).lower() in ("revolute", "linear"): + v = str(j.get("variable", "")).lower() + if v: + link_var[ln] = v + + var_type: Dict[str, str] = {} + var_links: Dict[str, List[str]] = defaultdict(list) + for ln, v in link_var.items(): + var_links[v].append(ln) + var_type[v] = str(links[ln].get("jointToParent", {}).get("type", "")).lower() + + ordered_vars: List[str] = [] + for ln in topo: + if ln in link_var and link_var[ln] not in ordered_vars: + ordered_vars.append(link_var[ln]) + + # ---- build blocks ---- + blocks: List[Dict[str, Any]] = [] + var_block: Dict[str, int] = {} + pending: List[str] = [] + for ln in topo: + if ln not in link_var: + continue + v = link_var[ln] + own = link_markers.get(ln, []) + if v in var_block: + # shared variable already in a block -> add this link's markers there + if own: + blocks[var_block[v]]["markers"].extend(own) + continue + if own: + bvars = [] + for x in pending + [v]: + if x not in bvars and x not in var_block: + bvars.append(x) + blocks.append({"vars": bvars, "markers": list(own), "anchor": ln}) + for x in bvars: + var_block[x] = len(blocks) - 1 + pending = [] + else: + if v not in pending: + pending.append(v) + if pending: + blocks.append({"vars": pending, "markers": [], "anchor": None}) + for x in pending: + var_block[x] = len(blocks) - 1 + + return { + "ordered_vars": ordered_vars, + "var_type": var_type, + "var_links": dict(var_links), + "link_markers": link_markers, + "blocks": blocks, + } + + +# ================================================================== +# Residuals +# ================================================================== + +def model_markers(fk: RobotFK, state: Dict[str, float]) -> Dict[int, Dict[str, np.ndarray]]: + T = fk.compute(state) + return fk.all_markers_world(T) # mid -> {world_mm, normal_world, link, local_mm} + + +def residual_vector(state: Dict[str, float], fk: RobotFK, obs: Dict[int, Dict[str, Any]], + marker_ids: List[int], cfg: Dict[str, Any]) -> np.ndarray: + """Position (mm) + optional normal (scaled) residuals over the given markers.""" + model = model_markers(fk, state) + res: List[float] = [] + w_n = float(cfg.get("normal_weight", 30.0)) + use_n = bool(cfg.get("use_normals", True)) + for mid in marker_ids: + if mid not in model or mid not in obs: + continue + mm = model[mid] + dp = np.asarray(mm["world_mm"], float) - obs[mid]["pos_mm"] + res.extend(dp.tolist()) + if use_n and obs[mid]["normal"] is not None and "normal_world" in mm: + dn = (np.asarray(mm["normal_world"], float) - obs[mid]["normal"]) * w_n + res.extend(dn.tolist()) + return np.asarray(res, dtype=float) + + +def _state_from_vec(var_names: List[str], vec: np.ndarray, base: Dict[str, float]) -> Dict[str, float]: + s = dict(base) + for name, val in zip(var_names, vec): + s[name] = float(val) + return s + + +# ================================================================== +# Method: global bundle adjustment +# ================================================================== + +def estimate_global_ba(fk: RobotFK, obs: Dict[int, Dict[str, Any]], var_names: List[str], + x0: Dict[str, float], cfg: Dict[str, Any]) -> Dict[str, float]: + if not HAVE_SCIPY: + print("[WARN] scipy missing — global_ba skipped, returning init") + return dict(x0) + marker_ids = list(obs.keys()) + base = {k: 0.0 for k in STATE_KEYS} + base.update(x0) + vec0 = np.array([base.get(v, 0.0) for v in var_names], dtype=float) + + def fun(vec): + st = _state_from_vec(var_names, vec, base) + return residual_vector(st, fk, obs, marker_ids, cfg) + + loss = cfg.get("robust_loss", "huber") + f_scale = float(cfg.get("huber_delta_mm", 8.0)) + try: + sol = least_squares(fun, vec0, loss=loss, f_scale=f_scale, + max_nfev=int(cfg.get("max_iterations", 200)) * max(1, len(var_names))) + return _state_from_vec(var_names, sol.x, base) + except Exception as exc: + print(f"[WARN] global_ba failed: {exc}") + return dict(base) + + +# ================================================================== +# Method: sequential block-wise FK fit +# ================================================================== + +def _multistart_values(vtype: str) -> List[float]: + # revolute: scan the circle to escape local minima at large angles + if vtype == "revolute": + return [0.0, 60.0, 120.0, 180.0, 240.0, 300.0] + return [0.0] + + +def estimate_sequential_fk(fk: RobotFK, obs: Dict[int, Dict[str, Any]], chain: Dict[str, Any], + cfg: Dict[str, Any]) -> Dict[str, float]: + """Estimate block by block along the chain, freezing already-solved variables.""" + state = {k: 0.0 for k in STATE_KEYS} + var_type = chain["var_type"] + + for block in chain["blocks"]: + bvars = block["vars"] + bmarkers = [m for m in block["markers"] if m in obs] + if not bvars: + continue + if not bmarkers: + # unobservable block: leave at 0, flag later + continue + + if not HAVE_SCIPY: + continue + + base = dict(state) + + def fun(vec, _bvars=bvars, _bm=bmarkers, _base=base): + st = _state_from_vec(_bvars, vec, _base) + return residual_vector(st, fk, obs, _bm, cfg) + + # multi-start over the first revolute variable in the block + starts = [[0.0] * len(bvars)] + lead_type = var_type.get(bvars[0], "linear") + if lead_type == "revolute": + starts = [] + for a0 in _multistart_values("revolute"): + s = [0.0] * len(bvars) + s[0] = a0 + starts.append(s) + + best, best_cost = None, float("inf") + for s0 in starts: + try: + sol = least_squares(fun, np.array(s0, dtype=float), + loss=cfg.get("robust_loss", "huber"), + f_scale=float(cfg.get("huber_delta_mm", 8.0)), + max_nfev=200 * max(1, len(bvars))) + if sol.cost < best_cost: + best_cost, best = sol.cost, sol.x + except Exception: + continue + if best is not None: + for name, val in zip(bvars, best): + state[name] = float(val) + + # wrap revolute angles to (-180, 180] + for v, vt in var_type.items(): + if vt == "revolute": + state[v] = (state[v] + 180.0) % 360.0 - 180.0 + return state + + +# ================================================================== +# Method: sequential analytic vector (per revolute joint) +# ================================================================== + +def estimate_sequential_vector(fk: RobotFK, obs: Dict[int, Dict[str, Any]], chain: Dict[str, Any], + cfg: Dict[str, Any]) -> Dict[str, float]: + """ + Analytic angle from marker geometry where possible. For revolute joints with + >=2 markers on the link, use the perpendicular marker-pair vector. Falls back + to the FK block solver for linear / zero-marker / single-marker cases, so it + always returns a full state (still cheaper than full sequential_fk because + well-populated joints are solved in closed form). + """ + state = {k: 0.0 for k in STATE_KEYS} + var_type = chain["var_type"] + link_markers = chain["link_markers"] + var_links = chain["var_links"] + + for block in chain["blocks"]: + bvars = block["vars"] + if len(bvars) == 1 and var_type.get(bvars[0]) == "revolute": + v = bvars[0] + ln = var_links[v][0] + mids = [m for m in link_markers.get(ln, []) if m in obs] + if len(mids) >= 2: + # model vectors must be expressed in the WORLD frame at angle=0 + # (the link frame is already rotated by the parents y,z,...), so + # use FK marker world positions with this joint set to 0. + state_v0 = dict(state) + state_v0[v] = 0.0 + model_v0 = model_markers(fk, state_v0) + axis_world = fk.joint_axis_world(ln, state_v0) + ang = _angle_from_pairs_world(mids, model_v0, obs, axis_world) + if ang is not None: + state[v] = ang + continue + # fallback: block FK fit for this single block + _fit_single_block(fk, obs, block, var_type, cfg, state) + + for v, vt in var_type.items(): + if vt == "revolute": + state[v] = (state[v] + 180.0) % 360.0 - 180.0 + return state + + +def _angle_from_pairs_world(mids: List[int], model_v0: Dict[int, Dict[str, np.ndarray]], + obs: Dict[int, Dict[str, Any]], axis_world: np.ndarray) -> Optional[float]: + from itertools import combinations + a = np.asarray(axis_world, float) + a = a / (np.linalg.norm(a) + 1e-12) + angs, ws = [], [] + for i, j in combinations(mids, 2): + if i not in model_v0 or j not in model_v0: + continue + vm = np.asarray(model_v0[j]["world_mm"], float) - np.asarray(model_v0[i]["world_mm"], float) # world @ angle 0 + vo = obs[j]["pos_mm"] - obs[i]["pos_mm"] # observed vector (world, mm) + vm_p = vm - np.dot(vm, a) * a + vo_p = vo - np.dot(vo, a) * a + if np.linalg.norm(vm_p) < 5 or np.linalg.norm(vo_p) < 5: + continue + ang = math.atan2(float(np.dot(a, np.cross(vm_p, vo_p))), float(np.dot(vm_p, vo_p))) + angs.append(ang) + ws.append(np.linalg.norm(vm_p) * np.linalg.norm(vo_p)) + if not angs: + return None + s = sum(w * math.sin(x) for w, x in zip(ws, angs)) + c = sum(w * math.cos(x) for w, x in zip(ws, angs)) + return math.degrees(math.atan2(s, c)) + + +def _fit_single_block(fk, obs, block, var_type, cfg, state): + if not HAVE_SCIPY: + return + bvars = block["vars"] + bmarkers = [m for m in block["markers"] if m in obs] + if not bvars or not bmarkers: + return + base = dict(state) + + def fun(vec): + return residual_vector(_state_from_vec(bvars, vec, base), fk, obs, bmarkers, cfg) + + starts = [[0.0] * len(bvars)] + if var_type.get(bvars[0]) == "revolute": + starts = [[a0] + [0.0] * (len(bvars) - 1) for a0 in _multistart_values("revolute")] + best, best_cost = None, float("inf") + for s0 in starts: + try: + sol = least_squares(fun, np.array(s0, float), loss=cfg.get("robust_loss", "huber"), + f_scale=float(cfg.get("huber_delta_mm", 8.0)), max_nfev=200 * max(1, len(bvars))) + if sol.cost < best_cost: + best_cost, best = sol.cost, sol.x + except Exception: + continue + if best is not None: + for name, val in zip(bvars, best): + state[name] = float(val) + + +# ================================================================== +# Dispatch +# ================================================================== + +def observability(chain: Dict[str, Any], obs: Dict[int, Dict[str, Any]]) -> Dict[str, Dict[str, Any]]: + """ + Per-variable confidence from how well its estimation block is determined. + A block groups coupled variables (e.g. b,c,e on the fingers); confidence is + driven by markers-per-variable in that block: + high : >= 2 markers per variable (well over-determined) + medium : >= 1 marker per variable + low : fewer markers than variables (under-determined — distrust!) + none : no markers at all (variable left at 0) + """ + info: Dict[str, Dict[str, Any]] = {} + for block in chain["blocks"]: + seen = [m for m in block["markers"] if m in obs] + nvars = max(1, len(block["vars"])) + ratio = len(seen) / nvars + if len(seen) == 0: + conf = "none" + elif ratio >= 2.0: + conf = "high" + elif ratio >= 1.0: + conf = "medium" + else: + conf = "low" + for v in block["vars"]: + info[v] = {"observable": len(seen) > 0, "n_markers": len(seen), + "block_vars": len(block["vars"]), "confidence": conf, + "block_anchor": block["anchor"]} + return info + + +def estimate_pose(fk: RobotFK, obs: Dict[int, Dict[str, Any]], cfg: Dict[str, Any]) -> Dict[str, Any]: + chain = analyze_chain(fk) + var_names = chain["ordered_vars"] + method = str(cfg.get("method", "hybrid")).lower() + obsv = observability(chain, obs) + + if method == "sequential_vector": + state = estimate_sequential_vector(fk, obs, chain, cfg) + elif method == "sequential_fk": + state = estimate_sequential_fk(fk, obs, chain, cfg) + elif method == "global_ba": + init = estimate_sequential_fk(fk, obs, chain, cfg) # cheap robust init + state = estimate_global_ba(fk, obs, var_names, init, cfg) + else: # hybrid (default) + init = estimate_sequential_fk(fk, obs, chain, cfg) + state = estimate_global_ba(fk, obs, var_names, init, cfg) + + # final residual stats over all observed markers + final_res = residual_vector(state, fk, obs, list(obs.keys()), cfg) + rms = float(np.sqrt(np.mean(final_res ** 2))) if final_res.size else 0.0 + + return {"state": state, "method": method, "observability": obsv, + "residual_rms": rms, "num_markers": len(obs)} + + +# ================================================================== +# CLI +# ================================================================== + +def main() -> None: + ap = argparse.ArgumentParser(description="Estimate robot joint angles from marker poses") + ap.add_argument("markers", help="aruco_marker_poses.json (corner_pose) or aruco_positions_*.json (center)") + ap.add_argument("-robot", "--robot", required=True) + ap.add_argument("-out", "--out", default=None) + ap.add_argument("--method", default=None, help="override robot.json method") + args = ap.parse_args() + + robot_data = json.load(open(args.robot, "r", encoding="utf-8")) + cfg = load_pose_cfg(robot_data) + if args.method: + cfg["method"] = args.method + + fk = RobotFK(robot_data) + obs = load_observations(args.markers, cfg.get("use_normals", True), + int(cfg.get("min_cameras_per_marker", 2))) + print(f"[INFO] method={cfg['method']} | observed markers={len(obs)} | use_normals={cfg.get('use_normals')}") + + result = estimate_pose(fk, obs, cfg) + st = result["state"] + + print("\nEstimated joint values:") + for v in ["x", "y", "z", "a", "b", "c", "e"]: + ob = result["observability"].get(v, {}) + unit = "mm" if v in ("x", "e") else "deg" + conf = ob.get("confidence", "?") + tag = "" if ob.get("observable", False) else " [UNOBSERVABLE -> 0]" + print(f" {v}: {st.get(v, 0.0):8.2f} {unit} (markers={ob.get('n_markers','?')}, conf={conf}){tag}") + print(f"\n[INFO] residual RMS over {result['num_markers']} markers: {result['residual_rms']:.3f}") + + out = { + "schema_version": "1.0", + "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + "method": result["method"], + "movements": {v: {"value": st.get(v, 0.0), + "unit": "mm" if v in ("x", "e") else "deg", + "observable": result["observability"].get(v, {}).get("observable", False), + "confidence": result["observability"].get(v, {}).get("confidence", "none"), + "n_markers": result["observability"].get(v, {}).get("n_markers", 0)} + for v in ["x", "y", "z", "a", "b", "c", "e"]}, + "residual_rms": result["residual_rms"], + "num_markers": result["num_markers"], + } + out_path = args.out or os.path.join(os.path.dirname(args.markers), "robot_state.json") + json.dump(out, open(out_path, "w", encoding="utf-8"), indent=2) + print(f"[INFO] wrote {out_path}") + + +if __name__ == "__main__": + main() diff --git a/run/robot_viewer.html b/run/robot_viewer.html index 8893e92..b95f297 100644 --- a/run/robot_viewer.html +++ b/run/robot_viewer.html @@ -796,6 +796,7 @@ function rebuild() { } const errors = []; + const normalErrors = []; for (const [midStr, {pos: opos, nor: oNor}] of Object.entries(obs)) { const mid = parseInt(midStr); const mpos = modelPositions[mid]; @@ -811,11 +812,20 @@ function rebuild() { gObserved.add(makeSphere(op, 0.006, col)); - // observed normal arrow — orange if available + // observed normal arrow — coloured by angular deviation from the model normal if (oNor) { try { - const obsArrow = makeNormalArrow(op, oNor, 0.018, 0xffaa00); - gObsNormals.add(obsArrow); + let normCol = 0xffaa00; // no model to compare -> orange + const mn = modelNormals[mid]; + if (mn) { + const dot = (oNor[0]*mn[0] + oNor[1]*mn[1] + oNor[2]*mn[2]) / + ((Math.hypot(oNor[0],oNor[1],oNor[2]) * Math.hypot(mn[0],mn[1],mn[2])) || 1); + let a = Math.acos(Math.max(-1, Math.min(1, dot))) * 180 / Math.PI; + a = Math.min(a, 180 - a); // flip-invariant + normalErrors.push(a); + normCol = a < 2 ? 0x3ecf6b : a < 5 ? 0xf59e0b : 0xff4f4f; + } + gObsNormals.add(makeNormalArrow(op, oNor, 0.018, normCol)); } catch (e) { console.warn('Invalid normal for marker', mid, oNor, e); } @@ -833,12 +843,13 @@ function rebuild() { } // ── stats ── - updateStats(errors, Object.keys(obs).length, Object.keys(modelPositions).length); + updateStats(errors, normalErrors, Object.keys(obs).length, Object.keys(modelPositions).length); } // ─── statistics panel ────────────────────────────────────────── -function updateStats(errArr, nObs, nModel) { +function updateStats(errArr, normArr, nObs, nModel) { const el = document.getElementById('stats-content'); + normArr = normArr || []; if (errArr.length === 0) { el.innerHTML = `
Observed:${nObs}
Model:${nModel}
@@ -854,6 +865,15 @@ function updateStats(errArr, nObs, nModel) { const cls = v => v < 2 ? 'stat-ok' : v < 5 ? 'stat-warn' : 'stat-err'; + let normHtml = ''; + if (normArr.length) { + const nmean = normArr.reduce((s,v)=>s+v,0) / normArr.length; + const nmax = Math.max(...normArr); + const ncls = v => v < 2 ? 'stat-ok' : v < 5 ? 'stat-warn' : 'stat-err'; + normHtml = `
Normal mean:${nmean.toFixed(1)}°
+
Normal max:${nmax.toFixed(1)}°
`; + } + el.innerHTML = `
Observed:${nObs}
Matched:${errArr.length}
@@ -862,8 +882,9 @@ function updateStats(errArr, nObs, nModel) {
Median:${p50.toFixed(1)} mm
p90:${p90.toFixed(1)} mm
Max:${max.toFixed(1)} mm
+ ${normHtml}
- 🟢 <2mm   🟡 2–5mm   🔴 >5mm
`; + 🟢 <2mm/°   🟡 2–5   🔴 >5`; } function setStatus(msg) { diff --git a/run/run_pipeline.bat b/run/run_pipeline.bat index 228d063..fc86f41 100644 --- a/run/run_pipeline.bat +++ b/run/run_pipeline.bat @@ -91,10 +91,17 @@ python3 "..\pipeline\3_multiview_bundle_adjustment_v4.py" ^ echo. -echo [STEP 4] Robotics Pose calculation of angles and joint positions +echo [STEP 3b] Corner-based marker poses (position + measured normal) + +python3 "..\pipeline\3b_corner_marker_poses.py" ^ + --evalDir "%OUT_DIR%" ^ + --robot "%ROBOT_JSON%" -python3 "..\pipeline\4_robotState_estimation_v6.py" ^ - "%OUT_DIR%\aruco_positions_initial.json" ^ +echo. +echo [STEP 4] Robot pose estimation (joint angles) from marker poses + +python3 "..\pipeline\pose_estimation.py" ^ + "%OUT_DIR%\aruco_marker_poses.json" ^ -robot "%ROBOT_JSON%" ^ -out "%OUT_DIR%\robot_state.json"