Update App.py

app.py

@@ -25,11 +25,11 @@ UPLOAD_DIR = BASE_DIR / "static" / "uploads"
 DATABASE_PATH = BASE_DIR / "instance" / "traffic_signs.sqlite3"
 MODEL_PATH = BASE_DIR / "traffic_classifier.h5"
 ALLOWED_EXTENSIONS = {"png", "jpg", "jpeg", "webp"}
-NORMALIZE_INPUT = os.environ.get("NORMALIZE_INPUT", "false").lower() in {"1", "true", "yes"}
+NORMALIZE_INPUT = os.environ.get("NORMALIZE_INPUT", "true").lower() in {"1", "true", "yes"}  # le modèle a été entraîné sur des pixels /255
 MC_DROPOUT_PASSES = int(os.environ.get("MC_DROPOUT_PASSES", "1"))
-UNKNOWN_CONFIDENCE_THRESHOLD = float(os.environ.get("UNKNOWN_CONFIDENCE_THRESHOLD", "0.999"))
-UNKNOWN_MARGIN_THRESHOLD = float(os.environ.get("UNKNOWN_MARGIN_THRESHOLD", "0.0"))
-UNKNOWN_VOTE_THRESHOLD = float(os.environ.get("UNKNOWN_VOTE_THRESHOLD", "0.0"))
+UNKNOWN_CONFIDENCE_THRESHOLD = float(os.environ.get("UNKNOWN_CONFIDENCE_THRESHOLD", "0.85"))   # en dessous → "Unknown"
+UNKNOWN_MARGIN_THRESHOLD = float(os.environ.get("UNKNOWN_MARGIN_THRESHOLD", "0.10"))             # écart min entre 1er et 2e choix
+UNKNOWN_VOTE_THRESHOLD = float(os.environ.get("UNKNOWN_VOTE_THRESHOLD", "0.0"))                  # accord MC-Dropout (0 = désactivé)
 URL_CONTENT_TYPES = {
     "image/jpeg": "jpg",
     "image/png": "png",
@@ -282,21 +282,29 @@ def predict_probabilities(batch):
 
 def predict_sign(path):
     if model is None:
-        return "Model unavailable", 0.0
+        return "Model unavailable", 0.0, False
+
     probabilities, vote_agreement = predict_probabilities(prepare_image(path))
-    ranked_indices = np.argsort(probabilities)[::-1]
-    class_index = int(ranked_indices[0])
-    confidence = float(probabilities[class_index])
+    ranked_indices   = np.argsort(probabilities)[::-1]
+    class_index      = int(ranked_indices[0])
+    confidence       = float(probabilities[class_index])
     second_confidence = float(probabilities[int(ranked_indices[1])]) if len(ranked_indices) > 1 else 0.0
-    margin = confidence - second_confidence
-    if (
-        confidence < UNKNOWN_CONFIDENCE_THRESHOLD
-        or margin < UNKNOWN_MARGIN_THRESHOLD
-        or vote_agreement < UNKNOWN_VOTE_THRESHOLD
-    ):
-        return "Unknown traffic sign", confidence
+    margin           = confidence - second_confidence
+
+    # ── Rejet : image inconnue ou hors-distribution ───────────────────────────
+    # Le softmax force toujours une prédiction ; ces trois critères détectent
+    # les cas où le modèle n'est pas suffisamment sûr de lui.
+    is_unknown = (
+        confidence < UNKNOWN_CONFIDENCE_THRESHOLD   # confiance globale trop faible
+        or margin < UNKNOWN_MARGIN_THRESHOLD         # trop proche du 2e candidat
+        or vote_agreement < UNKNOWN_VOTE_THRESHOLD   # MC-Dropout désaccord
+    )
+
+    if is_unknown:
+        return "Unknown traffic sign", confidence, True
+
     label = TRAFFIC_SIGN_CLASSES[class_index] if class_index < len(TRAFFIC_SIGN_CLASSES) else f"Class {class_index}"
-    return label, confidence
+    return label, confidence, False
 
 
 def format_confidence(confidence):
@@ -416,7 +424,7 @@ def predict():
 
 
 def create_prediction(save_path, image_path):
-    label, confidence = predict_sign(save_path)
+    label, confidence, is_unknown = predict_sign(save_path)
     with get_db() as conn:
         cursor = conn.execute(
             """
@@ -438,6 +446,7 @@ def create_prediction(save_path, image_path):
         "image_path": image_path,
         "predicted_class": label,
         "confidence": confidence,
+        "is_unknown": is_unknown,   # utilisé dans le template pour afficher l'avertissement
     }