Update app.py

app.py

@@ -1,5 +1,5 @@
 """
-app.py — TrackIQ Backend (v6 — CORRIGÉ : Human/Person, CONF, modèle)
+app.py TrackIQ Backend 
 """
 import os
 os.environ.setdefault("YOLO_CONFIG_DIR", "/tmp")
@@ -30,10 +30,9 @@ LOG_DIR    = BASE / "logs";    LOG_DIR.mkdir(exist_ok=True)
 MODELS_DIR = BASE / "models";  MODELS_DIR.mkdir(exist_ok=True)
 JOB_STATE_PATH = LOG_DIR / "jobs_state.json"
 
-# ── CORRECTION 1 : "Person" → "Human" pour matcher l'UI ─────────────────────
-# Mapping COCO réel → nom affiché (index COCO officiel)
+#
 COCO_TO_LABEL = {
-    0: "Human",        # ← WAS "Person" — doit correspondre à ce que l'UI envoie
+    0: "Human",       
     1: "Bicycle",
     2: "Vehicle",
     3: "Motorcycle",
@@ -58,17 +57,17 @@ CLASS_COLORS = {
     "Motorcycle":    (80,  200, 80),
     "Truck":         (0,   180, 255),
     "Bus":           (220, 80,  255),
-    "Human":         (236, 72,  153),   # ← WAS "Person"
+    "Human":         (236, 72,  153),   
     "Bicycle":       (6,   182, 212),
     "Traffic light": (239, 68,  68),
     "Road sign":     (249, 115, 22),
 }
 
 # ── CORRECTION 2 : seuil de confiance abaissé + résolution augmentée ─────────
-CONF     = 0.25   # ← WAS 0.40  (trop élevé pour webcam intérieure)
+CONF     = 0.25   #  WAS 0.40  (trop élevé pour webcam intérieure)
 IOU      = 0.45
 SKIP     = 1
-INFER_SZ = 640    # ← WAS 320   (plus de précision)
+INFER_SZ = 640   
 
 _jobs: Dict[str, dict] = {}
 _sse_queues: Dict[str, queue.Queue] = {}
@@ -902,7 +901,7 @@ def api_webcam_start():
         _webcam_current_counts      = defaultdict(int)
         _webcam_seen_track_ids      = set()
 
-    print(f"✅ Webcam started with classes: {classes}")
+    print(f" Webcam started with classes: {classes}")
     return jsonify({"status": "started"}), 200
 
 @app.route("/api/webcam/frame", methods=["POST"])
@@ -978,17 +977,20 @@ def api_webcam_stop():
 @app.route("/api/webcam/stats")
 def api_webcam_stats():
     with _webcam_lock:
-        by_class = dict(_webcam_detections_by_class)
+        # unique_counts = objets uniques vus depuis le début 
+        unique_only = dict(_webcam_detections_by_class)
+        total_unique = sum(unique_only.values())
+        # frame_counts = ce qui est visible sur la frame courante seulement
         frame_counts = dict(_webcam_current_counts)
         return jsonify({
             "active":               _webcam_active,
             "frame":                _webcam_frame_count,
-            "detections":           _webcam_detections,
-            "detections_by_class":  by_class,
+            # detections = total uniques, pas cumul frames
+            "detections":           total_unique,
+            "detections_by_class":  unique_only,
             "frame_counts":         frame_counts,
-            "unique_counts":        by_class
+            "unique_counts":        unique_only,
         }), 200
-
 # ── Video streaming endpoints ─────────────────────────────────────────────────
 
 @app.route("/api/mjpeg/<jid>")