119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""
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benchmark/camera_count/analyze.py
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===================================
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Liest camera_study.json und erstellt:
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- Konsolentabelle: k → Mittelwert / Median / Std / Min / Max des Fehlers
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- Beste und schlechteste Kamera-Subsets je k
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- Boxplot: Anzahl Kameras vs. Gelenkwinkelfehler (PNG)
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Aufruf:
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python benchmark/camera_count/analyze.py
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python benchmark/camera_count/analyze.py --input benchmark/camera_count/results/camera_study.json
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python benchmark/camera_count/analyze.py --metric max_abs_deg
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"""
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from __future__ import annotations
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import argparse
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import json
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import statistics
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from pathlib import Path
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RESULTS_DIR = Path(__file__).resolve().parent / "results"
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def load(path: str) -> list[dict]:
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data = json.loads(Path(path).read_text(encoding="utf-8"))
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if not data:
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print("[ERROR] Keine Ergebnisse in der Datei.")
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return data
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def group_by_k(data: list[dict], metric: str) -> dict[int, list[float]]:
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by_k: dict[int, list[float]] = {}
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for row in data:
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v = row.get(metric)
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if v is None:
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continue
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by_k.setdefault(row["k"], []).append(v)
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return by_k
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def print_table(by_k: dict[int, list[float]], metric: str) -> None:
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print(f"\nKamera-Anzahl vs. {metric}")
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print(f"{'k':>4} | {'n':>5} | {'Mittel':>8} | {'Median':>8} | "
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f"{'Std':>8} | {'Min':>8} | {'Max':>8}")
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print("-" * 65)
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for k in sorted(by_k):
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vals = by_k[k]
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med = statistics.median(vals)
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std = statistics.pstdev(vals)
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print(f"{k:>4} | {len(vals):>5} | "
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f"{statistics.mean(vals):8.3f} | {med:8.3f} | "
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f"{std:8.3f} | {min(vals):8.3f} | {max(vals):8.3f}")
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def print_best_worst(data: list[dict], metric: str) -> None:
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ks = sorted({r["k"] for r in data})
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print(f"\nBeste / schlechteste Subsets je k ({metric}):")
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for k in ks:
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rows_k = [r for r in data if r["k"] == k and r.get(metric) is not None]
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if not rows_k:
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continue
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best = min(rows_k, key=lambda r: r[metric])
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worst = max(rows_k, key=lambda r: r[metric])
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print(f" k={k}: best [{best['scene']}] {best['subset']} "
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f"({best[metric]:.3f}) "
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f"worst [{worst['scene']}] {worst['subset']} ({worst[metric]:.3f})")
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def plot(by_k: dict[int, list[float]], metric: str, out_path: str) -> None:
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try:
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import matplotlib.pyplot as plt
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except ImportError:
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print("\n[INFO] matplotlib nicht installiert — Plot übersprungen.")
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return
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ks = sorted(by_k)
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data_plot = [by_k[k] for k in ks]
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fig, ax = plt.subplots(figsize=(8, 5))
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ax.boxplot(data_plot, labels=[str(k) for k in ks], patch_artist=True)
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ax.set_xlabel("Anzahl Kameras")
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ax.set_ylabel(f"{metric}")
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ax.set_title("Anzahl Kameras vs. Pose-Schätzfehler")
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ax.grid(True, axis="y", alpha=0.35)
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fig.tight_layout()
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fig.savefig(out_path, dpi=150)
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print(f"\n[INFO] Plot gespeichert: {out_path}")
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def main() -> None:
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ap = argparse.ArgumentParser(description="Auswertung der Kamera-Anzahl-Studie")
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ap.add_argument("--input", default=str(RESULTS_DIR / "camera_study.json"))
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ap.add_argument("--metric",
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choices=["mean_abs_deg", "max_abs_deg", "mean_abs_mm", "max_abs_mm",
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"finger_error_mm"],
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default="finger_error_mm",
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help="Metrik für Tabelle und Plot (Standard: finger_error_mm)")
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ap.add_argument("--out-plot",
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default=str(RESULTS_DIR / "camera_count_vs_error.png"))
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args = ap.parse_args()
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data = load(args.input)
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if not data:
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return
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by_k = group_by_k(data, args.metric)
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if not by_k:
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print(f"[ERROR] Keine Werte für Metrik '{args.metric}' gefunden.")
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return
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print_table(by_k, args.metric)
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print_best_worst(data, args.metric)
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plot(by_k, args.metric, args.out_plot)
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if __name__ == "__main__":
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main()
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