Pipeline complet Radiacode 103 - identification automatique d'isotopes

- VegaModel CNN-FCNN 34.5M params, 82 isotopes, val acc 99.89% - Generation 50k spectres synthetiques 1D (12-24h durees) - Entrainement 100 epochs sur RTX 5060 Ti (CUDA 12.8, Blackwell) - Detection continue avec soustraction du background - Capture background 24h avec gestion deconnexion - Docker Compose : conteneur train (GPU) + detect (CPU/USB) - Modele entraite inclus (vega_best.pt, 395 Mo) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-05-19 12:29:56 +02:00
commit 745a64b342
52 changed files with 17558 additions and 0 deletions
--- a/detect/capture_background.py
+++ b/detect/capture_background.py
@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""
+Capture le bruit de fond du détecteur sur 24h (sans source).
+Gère le débranchement/rebranchement du détecteur.
+À lancer séparément avant le moniteur :
+    docker-compose run --rm detect python capture_background.py
+"""
+import numpy as np
+import time
+import json
+import os
+
+SAMPLE_INTERVAL = int(os.environ.get("SAMPLE_INTERVAL", "60"))
+TARGET_DURATION = int(os.environ.get("TARGET_DURATION", str(86400)))  # 24h
+OUTPUT_PATH = os.environ.get("BACKGROUND_PATH", "/data/background_24h.npy")
+SNAPSHOT_PATH = os.environ.get("SNAPSHOT_PATH", "/data/background_snapshot.json")
+
+BG_COUNTS = np.zeros(1024, dtype=np.float64)
+BG_LIVE_TIME = 0.0
+device = None
+
+def save_snapshot():
+    """Save a human-readable snapshot of current background."""
+    cps = BG_COUNTS.sum() / BG_LIVE_TIME if BG_LIVE_TIME > 0 else 0
+    # Approximate energy calibration for RC-103: E ≈ 0.33 + 2.97*ch
+    peaks = []
+    max_c = BG_COUNTS.max()
+    if max_c > 0:
+        for i, c in enumerate(BG_COUNTS):
+            if c > max_c * 0.03:
+                energy = 0.33 + 2.97 * i
+                peaks.append({"channel": i, "energy_kev": round(energy, 1), "counts": round(float(c), 1)})
+
+    snapshot = {
+        "elapsed_hours": round((time.time() - start) / 3600, 2),
+        "live_time_s": round(BG_LIVE_TIME, 1),
+        "total_counts": round(float(BG_COUNTS.sum()), 0),
+        "cps": round(cps, 2),
+        "top_peaks": sorted(peaks, key=lambda x: -x["counts"])[:15],
+        "spectrum": [round(float(c), 1) for c in BG_COUNTS],
+    }
+    with open(SNAPSHOT_PATH, "w") as f:
+        json.dump(snapshot, f, indent=2)
+
+print(f"Capture du bruit de fond pendant {TARGET_DURATION/3600:.0f}h...")
+print("Assurez-vous qu'aucune source radioactive n'est a proximite du detecteur.")
+print()
+
+start = time.time()
+while (time.time() - start) < TARGET_DURATION:
+    time.sleep(SAMPLE_INTERVAL)
+    try:
+        if device is None:
+            from radiacode import RadiaCode
+
+            device = RadiaCode()
+            device.spectrum_reset()
+            print("Radiacode connecte.")
+
+        spectrum = device.spectrum()
+        BG_COUNTS += np.array(spectrum.counts, dtype=np.float64)
+        BG_LIVE_TIME += spectrum.duration.total_seconds()
+        device.spectrum_reset()
+        elapsed = time.time() - start
+        cps = BG_COUNTS.sum() / BG_LIVE_TIME if BG_LIVE_TIME > 0 else 0
+        print(
+            f"Background : {elapsed/3600:.1f}h / {TARGET_DURATION/3600:.1f}h "
+            f"({BG_LIVE_TIME:.0f}s live, {BG_COUNTS.sum():.0f} coups, {cps:.1f} CPS)",
+            flush=True,
+        )
+        save_snapshot()
+    except Exception as e:
+        print(f"\nErreur : {e}, reconnexion...")
+        device = None
+
+os.makedirs(os.path.dirname(OUTPUT_PATH) if os.path.dirname(OUTPUT_PATH) else ".", exist_ok=True)
+np.save(
+    OUTPUT_PATH,
+    {
+        "counts": BG_COUNTS,
+        "duration": BG_LIVE_TIME,
+        "timestamp": time.time(),
+    },
+)
+print(f"\n\nBackground sauvegarde : {OUTPUT_PATH}")
+print(f"  Duree live : {BG_LIVE_TIME/3600:.1f}h")
+print(f"  Total coups : {BG_COUNTS.sum():.0f}")
+print(f"  CPS moyen : {BG_COUNTS.sum()/BG_LIVE_TIME:.1f}")