Update upload

app.py

@@ -36,6 +36,14 @@ COCO_TO_LABEL = {
 }
 DEFAULT_CLASSES = list(COCO_TO_LABEL.values())
 
+CSV_FIELDS = [
+    "frame", "timestamp_sec", "scene_name", "group_id",
+    "video_name", "track_id", "class_name", "confidence",
+    "bbox_x1", "bbox_y1", "bbox_x2", "bbox_y2",
+    "cx", "cy", "frame_width", "frame_height",
+    "crossed_line", "direction", "speed_px_s"
+]
+
 CLASS_COLORS = {
     "Vehicle":       (255, 120, 80),
     "Motorcycle":    (80,  200, 80),
@@ -242,6 +250,7 @@ def _worker(jid):
     job["progress"] = 0
     job["stats"] = {}
     classes_filter = set(job.get("classes") or DEFAULT_CLASSES)
+    counted_track_ids = set()
     
     print(f"\n[PROCESS] Opening video: {job['path']}")
     cap = cv2.VideoCapture(job["path"])
@@ -290,24 +299,36 @@ def _worker(jid):
         frame_count += 1
         timestamp_sec = (frame_count - 1) / fps if fps > 0 else 0
         
-        # Run inference
-        results = _model(frame, conf=CONF, iou=IOU, imgsz=INFER_SZ, verbose=False)
+        # Run tracking so each physical object gets a stable track_id.
+        results = _model.track(
+            frame,
+            conf=CONF,
+            iou=IOU,
+            imgsz=INFER_SZ,
+            persist=True,
+            verbose=False
+        )
         
         # Draw detections on frame
         annotated_frame = frame.copy()
-        if results[0].boxes:
-            for box in results[0].boxes:
+        if results and results[0].boxes:
+            for box_index, box in enumerate(results[0].boxes):
                 cls = int(box.cls[0])
                 conf = float(box.conf[0])
                 class_name = COCO_TO_LABEL.get(cls)
                 
                 if class_name and class_name in classes_filter:
-                    job["detections"][class_name] = job["detections"].get(class_name, 0) + 1
-                    
                     # Get bounding box coordinates
                     x1, y1, x2, y2 = map(int, box.xyxy[0])
                     cx = (x1 + x2) // 2
                     cy = (y1 + y2) // 2
+                    raw_track_id = box.id[0] if box.id is not None else None
+                    track_id = str(int(raw_track_id)) if raw_track_id is not None else f"untracked-{class_name}-{box_index}"
+                    unique_key = (class_name, track_id)
+
+                    if unique_key not in counted_track_ids:
+                        counted_track_ids.add(unique_key)
+                        job["detections"][class_name] = job["detections"].get(class_name, 0) + 1
                     
                     # Store detection data for CSV
                     frame_detections.append({
@@ -316,7 +337,7 @@ def _worker(jid):
                         "scene_name": jid,
                         "group_id": jid,
                         "video_name": job["name"],
-                        "track_id": "",
+                        "track_id": track_id,
                         "class_name": class_name,
                         "confidence": conf,
                         "bbox_x1": x1,
@@ -442,6 +463,42 @@ def api_logs():
     with _stats_lock:
         return jsonify(_global_stats["scenes"]), 200
 
+@app.route("/api/logs/rows")
+def api_logs_rows():
+    rows = []
+    for jid, job in _jobs.items():
+        for det in job.get("frame_detections", []):
+            rows.append({
+                "frame": det["frame"],
+                "frame_id": det["frame"],
+                "timestamp_sec": det["timestamp_sec"],
+                "timestamp_s": det["timestamp_sec"],
+                "scene_name": det["scene_name"],
+                "scene_id": jid,
+                "group_id": det["group_id"],
+                "video_name": det["video_name"],
+                "track_id": det["track_id"],
+                "class_name": det["class_name"],
+                "confidence": det["confidence"],
+                "bbox_x1": det["bbox_x1"],
+                "bbox_y1": det["bbox_y1"],
+                "bbox_x2": det["bbox_x2"],
+                "bbox_y2": det["bbox_y2"],
+                "x1": det["bbox_x1"],
+                "y1": det["bbox_y1"],
+                "x2": det["bbox_x2"],
+                "y2": det["bbox_y2"],
+                "cx": det["cx"],
+                "cy": det["cy"],
+                "frame_width": det["frame_width"],
+                "frame_height": det["frame_height"],
+                "crossed_line": det["crossed_line"],
+                "direction": det["direction"],
+                "speed_px_s": det["speed_px_s"]
+            })
+    rows.sort(key=lambda r: (r["scene_id"], r["frame"], str(r["track_id"])))
+    return jsonify(rows), 200
+
 @app.route("/api/logs/<scene_id>/csv")
 def api_logs_csv(scene_id):
     job = _jobs.get(scene_id)
@@ -449,41 +506,33 @@ def api_logs_csv(scene_id):
         return jsonify({"error": "not found"}), 404
 
     output = io.StringIO()
-    writer = csv.writer(output)
-    
-    # Write CSV header - SCHEMA_EXAMPLE.csv format
-    writer.writerow([
-        "frame", "timestamp_sec", "scene_name", "group_id", 
-        "video_name", "track_id", "class_name", "confidence",
-        "bbox_x1", "bbox_y1", "bbox_x2", "bbox_y2", 
-        "cx", "cy", "frame_width", "frame_height",
-        "crossed_line", "direction", "speed_px_s"
-    ])
+    writer = csv.DictWriter(output, fieldnames=CSV_FIELDS, lineterminator="\n")
+    writer.writeheader()
     
     # Write detections for each frame
     frame_detections = job.get("frame_detections", [])
     for det in frame_detections:
-        writer.writerow([
-            det["frame"],
-            f"{det['timestamp_sec']:.3f}",
-            det["scene_name"],
-            det["group_id"],
-            det["video_name"],
-            det["track_id"],
-            det["class_name"],
-            f"{det['confidence']:.3f}",
-            det["bbox_x1"],
-            det["bbox_y1"],
-            det["bbox_x2"],
-            det["bbox_y2"],
-            det["cx"],
-            det["cy"],
-            det["frame_width"],
-            det["frame_height"],
-            det["crossed_line"],
-            det["direction"],
-            f"{det['speed_px_s']:.1f}"
-        ])
+        writer.writerow({
+            "frame": det["frame"],
+            "timestamp_sec": f"{det['timestamp_sec']:.3f}",
+            "scene_name": det["scene_name"],
+            "group_id": det["group_id"],
+            "video_name": det["video_name"],
+            "track_id": det["track_id"],
+            "class_name": det["class_name"],
+            "confidence": f"{det['confidence']:.3f}",
+            "bbox_x1": det["bbox_x1"],
+            "bbox_y1": det["bbox_y1"],
+            "bbox_x2": det["bbox_x2"],
+            "bbox_y2": det["bbox_y2"],
+            "cx": det["cx"],
+            "cy": det["cy"],
+            "frame_width": det["frame_width"],
+            "frame_height": det["frame_height"],
+            "crossed_line": det["crossed_line"],
+            "direction": det["direction"],
+            "speed_px_s": f"{det['speed_px_s']:.1f}"
+        })
 
     return Response(
         output.getvalue(),

templates/logs.html

@@ -207,6 +207,7 @@ async function loadLogs(){
             video_name: s.video_name||'', class_name: cls,
             confidence: 0, frame_id: 0, timestamp_s: 0,
             track_id: 0, x1:0, y1:0, x2:0, y2:0, cx:0, cy:0,
+            frame_width:0, frame_height:0,
             direction:'unknown', speed_px_s:0, crossed_line:false
           });
         });
@@ -307,17 +308,18 @@ function getCsvFilename(){
   return `${g}_${scenes.length===1?scenes[0]:'all'}.csv`;
 }
 function buildCsvContent(){
-  // Format exact du prof
-  const hdr = 'frame,timestamp_sec,scene_name,group_id,video_name,track_id,class_name,confidence,bbox_x1,bbox_y1,bbox_x2,bbox_y2,cx,cy,crossed_line,direction,speed_px_s\n';
+  // Same schema as SCHEMA_EXAMPLE.csv.
+  const hdr = 'frame,timestamp_sec,scene_name,group_id,video_name,track_id,class_name,confidence,bbox_x1,bbox_y1,bbox_x2,bbox_y2,cx,cy,frame_width,frame_height,crossed_line,direction,speed_px_s\n';
   const g = getGroupName();
   const rows = filteredRows.map(r => [
-    r.frame_id??'', r.timestamp_s??'',
+    r.frame??r.frame_id??'', r.timestamp_sec??r.timestamp_s??'',
     r.scene_name||r.scene_id||'',
-    g,
+    r.group_id||g,
     `"${(r.video_name||'').replace(/"/g,'""')}"`,
     r.track_id??'', r.class_name||'', r.confidence??'',
-    r.x1??'', r.y1??'', r.x2??'', r.y2??'',
+    r.bbox_x1??r.x1??'', r.bbox_y1??r.y1??'', r.bbox_x2??r.x2??'', r.bbox_y2??r.y2??'',
     r.cx??'', r.cy??'',
+    r.frame_width??'', r.frame_height??'',
     r.crossed_line??false,
     r.direction||'unknown',
     r.speed_px_s??0

test_csv_format.py

@@ -75,7 +75,7 @@ frame_detections = [
 
 # Generate CSV
 output = StringIO()
-writer = csv.writer(output)
+writer = csv.writer(output, lineterminator="\n")
 
 # Header
 writer.writerow([