camera_stream.py

@@ -13,19 +13,18 @@ load_dotenv(override=True)
 
 app = Flask(__name__)
 
+# Load-time debug for camera config (safe to leave; only prints on startup)
+print(
+    "[Surveillance] camera_ip_1=" + str(os.getenv("camera_ip_1")) +
+    " camera_ip_2=" + str(os.getenv("camera_ip_2"))
+)
+
 # ---------------------------------------------------------------------------
 # Configuration
 # ---------------------------------------------------------------------------
 USERNAME = os.getenv("username")
 PASSWORD = os.getenv("password")
-
-# Dynamically find all camera_ip_N variables in environment
-CAMERA_IPS = []
-for i in range(1, 101):  # Supports up to 100 cameras
-    ip = os.getenv(f"camera_ip_{i}")
-    if ip and ip.strip():
-        CAMERA_IPS.append(ip.strip())
-
+CAMERA_IPS = [os.getenv(f"camera_ip_{i}") for i in range(1, 3)]
 CAMERAS = [
     {
         "id": f"cam{i + 1}",
@@ -35,6 +34,7 @@ CAMERAS = [
     }
     for i, ip in enumerate(CAMERA_IPS)
 ]
+DEFAULT_CAMERA_ID = CAMERAS[0]["id"] if CAMERAS else "cam1"
 
 PROXIMITY_PX = 200        # max pixel distance to consider two people "together"
 GROUP_TIME_THRESHOLD = 20  # seconds before an alert fires
@@ -44,264 +44,470 @@ YOLO_CONF = 0.25
 INFERENCE_DEVICE = 0
 
 # ---------------------------------------------------------------------------
-# Shared state
+# Shared state (protected by lock)
 # ---------------------------------------------------------------------------
 lock = threading.Lock()
 state = {
-    "cameras": {},  # camera_id -> dict with frame, metadata, status
-    "grid_frame": None,
+    "frame": None,
+    "people_count": 0,
+    "groups": [],
+    "alert_active": False,
     "alerts": [],
-    "total_people_count": 0,
-    "alert_active": False
+    "fps": 0,
+    "stream_status": "connecting",
+    "selected_camera_id": DEFAULT_CAMERA_ID,
+    "active_camera_id": DEFAULT_CAMERA_ID,
 }
 
-# Initialize camera states
-for cam in CAMERAS:
-    state["cameras"][cam["id"]] = {
-        "frame": None,
-        "people_count": 0,
-        "groups": [],
-        "alert_active": False,
-        "fps": 0,
-        "status": "connecting",
-        "name": cam["name"]
-    }
-
 # ---------------------------------------------------------------------------
-# YOLO model
+# YOLO model (downloaded on first run)
 # ---------------------------------------------------------------------------
 if not torch.cuda.is_available():
-    print("[WARNING] CUDA not detected. Using CPU (this will be slow!).")
-    device = "cpu"
-else:
-    device = f"cuda:{INFERENCE_DEVICE}"
+    raise RuntimeError(
+        "CUDA GPU is required but not available. Install a CUDA-enabled PyTorch build "
+        "and verify NVIDIA drivers."
+    )
+model = YOLO("yolo26m.pt")
+model.to(f"cuda:{INFERENCE_DEVICE}")
+
+# Group tracking
+_group_trackers: dict = {}
+_next_group_id = 0
+_last_alert_time = 0.0
 
-model = YOLO("person_detector_best.pt")
-model.to(device)
 
 # ---------------------------------------------------------------------------
-# Detection Helpers
+# Detection helpers
 # ---------------------------------------------------------------------------
+
 def _centroids(boxes):
+    """Return list of (cx, cy) from xyxy boxes."""
     return [((b[0] + b[2]) / 2, (b[1] + b[3]) / 2) for b in boxes]
 
+
 def _find_groups(centroids, threshold):
+    """BFS clustering — returns list of index-lists with >= MIN_GROUP_SIZE."""
     n = len(centroids)
-    if n < MIN_GROUP_SIZE: return []
+    if n < MIN_GROUP_SIZE:
+        return []
+
     visited = set()
     groups = []
+
     for i in range(n):
-        if i in visited: continue
-        cluster, queue = [i], [i]
+        if i in visited:
+            continue
+        cluster = [i]
         visited.add(i)
+        queue = [i]
         while queue:
             cur = queue.pop(0)
             for j in range(n):
-                if j in visited: continue
-                dx, dy = centroids[cur][0] - centroids[j][0], centroids[cur][1] - centroids[j][1]
-                if (dx*dx + dy*dy)**0.5 < threshold:
-                    cluster.append(j); visited.add(j); queue.append(j)
-        if len(cluster) >= MIN_GROUP_SIZE: groups.append(cluster)
+                if j in visited:
+                    continue
+                dx = centroids[cur][0] - centroids[j][0]
+                dy = centroids[cur][1] - centroids[j][1]
+                if (dx * dx + dy * dy) ** 0.5 < threshold:
+                    cluster.append(j)
+                    visited.add(j)
+                    queue.append(j)
+        if len(cluster) >= MIN_GROUP_SIZE:
+            groups.append(cluster)
+
     return groups
 
+
 def _group_centroid(centroids, indices):
     xs = [centroids[i][0] for i in indices]
     ys = [centroids[i][1] for i in indices]
     return (sum(xs) / len(xs), sum(ys) / len(ys))
 
+
+def _get_camera_by_id(camera_id):
+    for cam in CAMERAS:
+        if cam["id"] == camera_id:
+            return cam
+    return CAMERAS[0] if CAMERAS else None
+
+
+def _reset_tracking():
+    global _group_trackers, _next_group_id, _last_alert_time
+    _group_trackers = {}
+    _next_group_id = 0
+    _last_alert_time = 0.0
+
+
 # ---------------------------------------------------------------------------
-# Stream Processing (One per camera)
+# Main processing loop (runs in background thread)
 # ---------------------------------------------------------------------------
-def _process_stream(camera):
-    cam_id = camera["id"]
-    rtsp_url = camera["rtsp_url"]
-    group_trackers = {}
-    next_group_id = 0
-    last_alert_time = 0.0
+
+def _process_stream():
+    global _next_group_id, _last_alert_time
     cap = None
+    active_camera_id = None
     prev_time = time.time()
 
-    print(f"[Surveillance] Starting thread for {camera['name']}")
-
     while True:
-        if cap is None:
-            cap = cv2.VideoCapture(rtsp_url)
-            if not cap.isOpened():
-                print(f"[ERROR] {camera['name']} (IP: {camera['ip']}) failed to connect. Check if 401 Unauthorized or 403 Forbidden.")
-                with lock: state["cameras"][cam_id]["status"] = "error"
-                time.sleep(10); cap = None; continue
+        with lock:
+            selected_camera_id = state["selected_camera_id"]
+        selected_camera = _get_camera_by_id(selected_camera_id)
+        if not selected_camera:
+            with lock:
+                state["stream_status"] = "error"
+            time.sleep(2)
+            continue
 
-            print(f"[SUCCESS] {camera['name']} (IP: {camera['ip']}) connected and streaming over TCP.")
-            with lock: state["cameras"][cam_id]["status"] = "live"
+        if cap is None or active_camera_id != selected_camera["id"]:
+            if cap is not None:
+                cap.release()
+            cap = cv2.VideoCapture(selected_camera["rtsp_url"])
+            active_camera_id = selected_camera["id"]
+            _reset_tracking()
+            with lock:
+                state["active_camera_id"] = active_camera_id
+            if not cap.isOpened():
+                with lock:
+                    state["stream_status"] = "error"
+                print(f"[ERROR] Cannot open RTSP stream: {selected_camera['rtsp_url']}")
+                time.sleep(2)
+                cap = None
+                continue
+            with lock:
+                state["stream_status"] = "live"
 
         ret, frame = cap.read()
         if not ret:
-            with lock: state["cameras"][cam_id]["status"] = "reconnecting"
-            cap.release(); time.sleep(2); cap = cv2.VideoCapture(rtsp_url); continue
+            with lock:
+                state["stream_status"] = "reconnecting"
+            cap.release()
+            time.sleep(2)
+            cap = cv2.VideoCapture(selected_camera["rtsp_url"])
+            if not cap.isOpened():
+                with lock:
+                    state["stream_status"] = "error"
+                cap = None
+            else:
+                with lock:
+                    state["stream_status"] = "live"
+            continue
 
         now = time.time()
         fps = 1.0 / max(now - prev_time, 1e-6)
         prev_time = now
 
-        results = model(frame, classes=[0], verbose=False, conf=YOLO_CONF, device=device)
+        # --- YOLO inference (person = class 0) ---
+        results = model(
+            frame,
+            classes=[0],
+            verbose=False,
+            conf=YOLO_CONF,
+            device=INFERENCE_DEVICE,
+        )
+
         person_boxes = []
         for r in results:
             for box in r.boxes:
                 x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
-                person_boxes.append((float(x1), float(y1), float(x2), float(y2), float(box.conf[0])))
+                conf = float(box.conf[0])
+                person_boxes.append((float(x1), float(y1), float(x2), float(y2), conf))
 
         centroids = _centroids([(b[0], b[1], b[2], b[3]) for b in person_boxes])
         current_groups = _find_groups(centroids, PROXIMITY_PX)
 
-        # Match Groups
-        matched_ids = set()
+        # --- Match current groups to tracked groups ---
+        matched_ids: set = set()
         frame_group_data = []
+
         for grp_indices in current_groups:
             gc = _group_centroid(centroids, grp_indices)
+
             best_id, best_dist = None, float("inf")
-            for gid, gdata in group_trackers.items():
-                if gid in matched_ids: continue
-                dist = ((gc[0]-gdata["centroid"][0])**2 + (gc[1]-gdata["centroid"][1])**2)**0.5
+            for gid, gdata in _group_trackers.items():
+                if gid in matched_ids:
+                    continue
+                dx = gc[0] - gdata["centroid"][0]
+                dy = gc[1] - gdata["centroid"][1]
+                dist = (dx * dx + dy * dy) ** 0.5
                 if dist < PROXIMITY_PX * 2 and dist < best_dist:
-                    best_dist, best_id = dist, gid
+                    best_dist = dist
+                    best_id = gid
 
             if best_id is not None:
-                group_trackers[best_id].update({"centroid": gc, "last_seen": now, "member_count": len(grp_indices)})
+                _group_trackers[best_id]["centroid"] = gc
+                _group_trackers[best_id]["last_seen"] = now
+                _group_trackers[best_id]["member_count"] = len(grp_indices)
                 matched_ids.add(best_id)
                 frame_group_data.append((best_id, grp_indices, gc))
             else:
-                gid = next_group_id
-                next_group_id += 1
-                group_trackers[gid] = {"centroid": gc, "first_seen": now, "last_seen": now, "member_count": len(grp_indices), "alerted": False}
+                gid = _next_group_id
+                _next_group_id += 1
+                _group_trackers[gid] = {
+                    "centroid": gc,
+                    "first_seen": now,
+                    "last_seen": now,
+                    "member_count": len(grp_indices),
+                    "alerted": False,
+                }
                 frame_group_data.append((gid, grp_indices, gc))
 
-        stale = [gid for gid, gd in group_trackers.items() if now - gd["last_seen"] > 3]
-        for gid in stale: del group_trackers[gid]
+        # Remove stale groups (not seen for > 3 s)
+        stale = [gid for gid, gd in _group_trackers.items() if now - gd["last_seen"] > 3]
+        for gid in stale:
+            del _group_trackers[gid]
 
-        # Alerting
+        # --- Alert logic (mark + metadata; save after drawing overlays) ---
         alert_this_frame = False
-        for gid, gdata in group_trackers.items():
-            if (gdata["last_seen"] - gdata["first_seen"]) >= GROUP_TIME_THRESHOLD and not gdata["alerted"]:
-                if now - last_alert_time >= ALERT_COOLDOWN:
+        pending_alerts = []  # list of (gid, people_count, duration_seconds)
+        for gid, gdata in _group_trackers.items():
+            duration = gdata["last_seen"] - gdata["first_seen"]
+            if duration >= GROUP_TIME_THRESHOLD and not gdata["alerted"]:
+                if now - _last_alert_time >= ALERT_COOLDOWN:
                     gdata["alerted"] = True
                     alert_this_frame = True
-                    last_alert_time = now
-                    os.makedirs("alerts", exist_ok=True)
-                    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-                    path = f"alerts/alert_{ts}_{cam_id}_gid{gid}.jpg"
-                    cv2.imwrite(path, frame)
-                    with lock:
-                        state["alerts"].insert(0, {"time": datetime.now().strftime("%H:%M:%S"), "camera": camera["name"], "people": gdata["member_count"], "duration": round(gdata["last_seen"]-gdata["first_seen"],1), "image": path})
-                        state["alerts"] = state["alerts"][:50]
+                    _last_alert_time = now
+                    pending_alerts.append((gid, gdata["member_count"], duration))
 
-        # Overlays
+        # --- Draw overlays ---
         display = frame.copy()
-        cv2.putText(display, camera["name"], (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-        for x1, y1, x2, y2, conf in person_boxes:
-            cv2.rectangle(display, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
+
+        pending_gid_set = {gid for gid, _, _ in pending_alerts}
+        alert_person_indices = set()
+        for gid, grp_indices, _gc in frame_group_data:
+            if gid in pending_gid_set:
+                alert_person_indices.update(grp_indices)
+
+        # Live overlay: mark any person that belongs to any alerting group.
+        if pending_alerts:
+            max_people = max(people for _gid, people, _dur in pending_alerts)
+            max_dur = max(dur for _gid, _people, dur in pending_alerts)
+            cv2.putText(
+                display,
+                f"ALERT: {len(pending_alerts)} group(s) | {max_people} people | {int(max_dur)}s",
+                (12, 32),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.8,
+                (0, 0, 255),
+                3,
+            )
+
+        for idx, (x1, y1, x2, y2, conf) in enumerate(person_boxes):
+            is_alert_person = idx in alert_person_indices
+            box_color = (0, 0, 255) if is_alert_person else (0, 255, 0)
+            cv2.rectangle(display, (int(x1), int(y1)), (int(x2), int(y2)), box_color, 2)
+            cv2.putText(
+                display, f"{conf:.0%}",
+                (int(x1), int(y1) - 6),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.5, box_color, 2,
+            )
+
         for gid, grp_indices, gc in frame_group_data:
-            cv2.circle(display, (int(gc[0]), int(gc[1])), 50, (0, 165, 255), 2)
+            gdata = _group_trackers.get(gid)
+            if gdata is None:
+                continue
+            duration = gdata["last_seen"] - gdata["first_seen"]
+            radius = int(PROXIMITY_PX * 0.6)
+            is_alert = duration >= GROUP_TIME_THRESHOLD
+            color = (0, 0, 255) if is_alert else (0, 165, 255)
+
+            cv2.circle(display, (int(gc[0]), int(gc[1])), radius, color, 2)
+            label = f"Group: {len(grp_indices)} | {duration:.0f}s"
+            cv2.putText(
+                display, label,
+                (int(gc[0]) - 70, int(gc[1]) - radius - 10),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.55, color, 2,
+            )
+            if is_alert:
+                cv2.putText(
+                    display, "ALERT",
+                    (int(gc[0]) - 35, int(gc[1]) + radius + 25),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3,
+                )
+
+        # --- Save annotated alerts (separate per group) ---
+        if pending_alerts:
+            os.makedirs("alerts", exist_ok=True)
+            ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+            frame_group_by_id = {gid: (grp_indices, gc) for gid, grp_indices, gc in frame_group_data}
+
+            for gid, people, duration in pending_alerts:
+                grp_indices, gc = frame_group_by_id.get(gid, (None, None))
+                if grp_indices is None:
+                    continue
+
+                alert_display = frame.copy()
+                alert_person_set = set(grp_indices)
+
+                # Header annotation for this specific alert group.
+                cv2.putText(
+                    alert_display,
+                    f"ALERT GROUP {gid} | {people} people | {int(duration)}s",
+                    (12, 32),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.9,
+                    (0, 0, 255),
+                    3,
+                )
+
+                # Draw only the person boxes relevant to this group.
+                for idx, (x1, y1, x2, y2, conf) in enumerate(person_boxes):
+                    is_alert_person = idx in alert_person_set
+                    box_color = (0, 0, 255) if is_alert_person else (0, 255, 0)
+                    cv2.rectangle(alert_display, (int(x1), int(y1)), (int(x2), int(y2)), box_color, 2)
+                    cv2.putText(
+                        alert_display,
+                        f"{conf:.0%}",
+                        (int(x1), int(y1) - 6),
+                        cv2.FONT_HERSHEY_SIMPLEX,
+                        0.5,
+                        box_color,
+                        2,
+                    )
+
+                # Draw only the circle/label for this group.
+                radius = int(PROXIMITY_PX * 0.6)
+                cv2.circle(alert_display, (int(gc[0]), int(gc[1])), radius, (0, 0, 255), 2)
+                cv2.putText(
+                    alert_display,
+                    f"Group: {len(grp_indices)} | {duration:.0f}s",
+                    (int(gc[0]) - 70, int(gc[1]) - radius - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.55,
+                    (0, 0, 255),
+                    2,
+                )
+                cv2.putText(
+                    alert_display,
+                    "ALERT",
+                    (int(gc[0]) - 35, int(gc[1]) + radius + 25),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.8,
+                    (0, 0, 255),
+                    3,
+                )
+
+                # Include group id to avoid collisions when multiple groups alert in one second.
+                alert_path = f"alerts/alert_{ts}_gid{gid}.jpg"
+                cv2.imwrite(alert_path, alert_display)
+
+                alert_info = {
+                    "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                    "people": people,
+                    "duration": round(duration, 1),
+                    "image": alert_path,
+                }
+                with lock:
+                    state["alerts"].insert(0, alert_info)
+                    state["alerts"] = state["alerts"][:50]
+
+        # --- Update shared state ---
+        groups_json = []
+        for gid, gi, gc in frame_group_data:
+            gdata = _group_trackers.get(gid)
+            if gdata:
+                groups_json.append({
+                    "id": gid,
+                    "count": len(gi),
+                    "duration": round(gdata["last_seen"] - gdata["first_seen"], 1),
+                })
 
         with lock:
-            state["cameras"][cam_id].update({
-                "frame": display,
-                "people_count": len(person_boxes),
-                "groups": [{"id": gid, "count": len(gi), "duration": round(group_trackers[gid]["last_seen"]-group_trackers[gid]["first_seen"], 1)} for gid, gi, gc in frame_group_data],
-                "alert_active": alert_this_frame,
-                "fps": round(fps, 1)
-            })
+            state["frame"] = display
+            state["people_count"] = len(person_boxes)
+            state["groups"] = groups_json
+            state["alert_active"] = alert_this_frame or any(
+                (gd["last_seen"] - gd["first_seen"]) >= GROUP_TIME_THRESHOLD
+                for gd in _group_trackers.values()
+            )
+            state["fps"] = round(fps, 1)
 
-        # --- 1 FPS RATE LIMIT ---
-        # ensures this specific camera thread only loops once per second
-        elapsed = time.time() - now
-        time.sleep(max(0, 1.0 - elapsed))
 
 # ---------------------------------------------------------------------------
-# Grid View Generator
+# MJPEG generator
 # ---------------------------------------------------------------------------
-def _update_grid_frame():
+
+def _generate_frames():
     while True:
-        frames = []
         with lock:
-            for cam_id in state["cameras"]:
-                if state["cameras"][cam_id]["frame"] is not None:
-                    frames.append(cv2.resize(state["cameras"][cam_id]["frame"], (640, 480)))
+            frame = state["frame"]
+        if frame is not None:
+            ok, buf = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, 80])
+            if ok:
+                yield (
+                    b"--frame\r\n"
+                    b"Content-Type: image/jpeg\r\n\r\n" + buf.tobytes() + b"\r\n"
+                )
+        time.sleep(0.033)
 
-        if not frames:
-            time.sleep(0.1); continue
-
-        n = len(frames)
-        if n == 0:
-            time.sleep(0.1); continue
-            
-        # Calculate grid dimensions (rows/cols)
-        cols = int(np.ceil(np.sqrt(n)))
-        rows = int(np.ceil(n / cols))
-        
-        # Determine cell size
-        # Use 320x240 for large grids to prevent the output frame from being too massive
-        cell_w, cell_h = 320, 240
-        if n <= 1: cell_w, cell_h = 640, 480
-        elif n <= 4: cell_w, cell_h = 480, 360
-
-        grid_rows = []
-        for r in range(rows):
-            row_items = []
-            for c in range(cols):
-                idx = r * cols + c
-                if idx < n:
-                    row_items.append(cv2.resize(frames[idx], (cell_w, cell_h)))
-                else:
-                    row_items.append(np.zeros((cell_h, cell_w, 3), dtype=np.uint8))
-            grid_rows.append(np.hstack(row_items))
-        
-        grid = np.vstack(grid_rows)
-
-        with lock:
-            state["grid_frame"] = grid
-            state["total_people_count"] = sum(c["people_count"] for c in state["cameras"].values())
-            state["alert_active"] = any(c["alert_active"] for c in state["cameras"].values())
-        time.sleep(0.04)
 
 # ---------------------------------------------------------------------------
 # Routes
 # ---------------------------------------------------------------------------
+
 @app.route("/")
-def dashboard(): return render_template("dashboard.html")
+def dashboard():
+    return render_template("dashboard.html")
+
 
 @app.route("/video_feed")
 def video_feed():
-    def gen():
-        while True:
-            with lock: 
-                frame = state["grid_frame"]
-            if frame is not None:
-                _, buf = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, 80])
-                yield (b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + buf.tobytes() + b"\r\n")
-            time.sleep(0.04)
-    return Response(gen(), mimetype="multipart/x-mixed-replace; boundary=frame")
+    return Response(
+        _generate_frames(),
+        mimetype="multipart/x-mixed-replace; boundary=frame",
+    )
+
 
 @app.route("/api/status")
 def api_status():
     with lock:
-        cams_info = {cid: {k:v for k,v in c.items() if k != "frame"} for cid, c in state["cameras"].items()}
+        active_camera = _get_camera_by_id(state["active_camera_id"])
         return jsonify({
-            "total_people_count": state["total_people_count"],
-            "cameras": cams_info,
+            "people_count": state["people_count"],
+            "groups": state["groups"],
             "alert_active": state["alert_active"],
-            "alerts": state["alerts"][:20]
+            "alerts": state["alerts"][:20],
+            "fps": state["fps"],
+            "stream_status": state["stream_status"],
+            "selected_camera_id": state["selected_camera_id"],
+            "active_camera_name": active_camera["name"] if active_camera else "Unknown",
         })
 
+
 @app.route("/api/cameras")
 def api_cameras():
-    return jsonify({"cameras": [{"id": c["id"], "name": c["name"], "ip": c["ip"]} for c in CAMERAS]})
+    return jsonify({
+        "cameras": [{"id": c["id"], "name": c["name"], "ip": c["ip"]} for c in CAMERAS]
+    })
+
+
+@app.route("/api/camera/select", methods=["POST"])
+def api_camera_select():
+    data = request.get_json(silent=True) or {}
+    camera_id = data.get("camera_id")
+    camera = _get_camera_by_id(camera_id)
+    if camera is None:
+        return jsonify({"ok": False, "error": "Invalid camera id"}), 400
+    with lock:
+        state["selected_camera_id"] = camera["id"]
+        state["frame"] = None
+        state["groups"] = []
+        state["people_count"] = 0
+        state["alert_active"] = False
+        state["fps"] = 0
+        state["stream_status"] = "connecting"
+    return jsonify({"ok": True})
+
 
 @app.route("/alerts/<path:filename>")
-def serve_alert_image(filename): return send_from_directory("alerts", filename)
+def serve_alert_image(filename):
+    return send_from_directory("alerts", filename)
+
+
+# ---------------------------------------------------------------------------
+# Entry point
+# ---------------------------------------------------------------------------
 
 if __name__ == "__main__":
-    for cam in CAMERAS: threading.Thread(target=_process_stream, args=(cam,), daemon=True).start()
-    time.sleep(1.5)
-    threading.Thread(target=_update_grid_frame, daemon=True).start()
-    print("\n  Grid View Dashboard → http://localhost:5000\n")
+    threading.Thread(target=_process_stream, daemon=True).start()
+    print(f"\n  Surveillance Dashboard → http://localhost:5000\n")
     app.run(host="0.0.0.0", port=5000, debug=False, threaded=True)

templates/dashboard.html

@@ -3,7 +3,7 @@
 <head>
 <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Surveillance Dashboard - Multi-Cam Grid</title>
+<title>Surveillance Dashboard</title>
 <style>
   *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
 
@@ -78,11 +78,20 @@
   @keyframes pulse { 0%,100%{ opacity:1 } 50%{ opacity:.35 } }
 
   .clock { font-size: .85rem; color: var(--text-dim); font-variant-numeric: tabular-nums; }
+  .camera-select {
+    background: var(--surface-2);
+    color: var(--text);
+    border: 1px solid var(--border);
+    border-radius: 8px;
+    padding: 6px 10px;
+    font-size: .85rem;
+    min-width: 170px;
+  }
 
   /* -------- Layout -------- */
   .container {
     display: grid;
-    grid-template-columns: 1fr 340px;
+    grid-template-columns: 1fr 320px;
     grid-template-rows: auto 1fr;
     gap: 20px;
     padding: 20px 28px 28px;
@@ -123,7 +132,6 @@
     overflow: hidden;
     position: relative;
     min-height: 480px;
-    aspect-ratio: 16/9;
   }
   .video-panel img {
     width: 100%;
@@ -165,8 +173,10 @@
     letter-spacing: .5px;
     box-shadow: 0 8px 30px rgba(239,68,68,.35);
     z-index: 200;
+    animation: bannerIn .4s ease-out;
   }
   .alert-banner.show { display: flex; align-items: center; gap: 10px; }
+  @keyframes bannerIn { from { opacity:0; transform:translateX(-50%) translateY(-12px); } }
 
   /* -------- Sidebar -------- */
   .sidebar {
@@ -191,6 +201,7 @@
   }
   .panel-body { padding: 12px 16px; }
 
+  /* Groups panel */
   .group-item {
     display: flex;
     justify-content: space-between;
@@ -200,8 +211,8 @@
   }
   .group-item:last-child { border-bottom: none; }
   .group-meta { display: flex; flex-direction: column; gap: 2px; }
-  .group-meta span:first-child { font-weight: 600; font-size: .95rem; }
-  .group-meta .cam-name { font-size: .75rem; color: var(--accent); }
+  .group-meta span:first-child { font-weight: 600; font-size: .9rem; }
+  .group-meta span:last-child  { font-size: .75rem; color: var(--text-dim); }
   .group-timer {
     font-variant-numeric: tabular-nums;
     font-weight: 700;
@@ -210,152 +221,282 @@
     border-radius: 6px;
     background: var(--surface-2);
   }
+  .group-timer.warning { color: var(--orange); background: rgba(245,158,11,.1); }
   .group-timer.danger  { color: var(--red);    background: rgba(239,68,68,.1);  }
+  .no-groups { color: var(--text-dim); font-size: .85rem; padding: 8px 0; }
 
-  .alert-log { max-height: 480px; overflow-y: auto; }
+  /* Alert history */
+  .alert-log {
+    max-height: 380px;
+    overflow-y: auto;
+    scrollbar-width: thin;
+    scrollbar-color: var(--border) transparent;
+  }
   .alert-entry {
     display: flex;
     gap: 12px;
-    padding: 12px 8px;
+    padding: 10px 0;
     border-bottom: 1px solid var(--border);
     cursor: pointer;
+    transition: background .2s;
+    border-radius: 6px;
+    padding: 10px 8px;
   }
   .alert-entry:hover { background: var(--surface-2); }
-  .alert-thumb { width: 80px; height: 50px; border-radius: 4px; object-fit: cover; }
+  .alert-entry:last-child { border-bottom: none; }
+  .alert-thumb {
+    width: 64px;
+    height: 44px;
+    border-radius: 6px;
+    object-fit: cover;
+    border: 1px solid var(--border);
+    flex-shrink: 0;
+  }
+  .alert-info { display: flex; flex-direction: column; gap: 2px; }
   .alert-info .time { font-size: .75rem; color: var(--text-dim); }
-  .alert-info .desc { font-size: .85rem; font-weight: 600; display: block; }
+  .alert-info .desc { font-size: .82rem; font-weight: 600; }
+  .alert-info .duration { font-size: .72rem; color: var(--orange); }
+
+  /* -------- Lightbox -------- */
+  .lightbox {
+    display: none;
+    position: fixed;
+    inset: 0;
+    background: rgba(0,0,0,.85);
+    z-index: 300;
+    justify-content: center;
+    align-items: center;
+    cursor: pointer;
+  }
+  .lightbox.open { display: flex; }
+  .lightbox img {
+    max-width: 90vw;
+    max-height: 85vh;
+    border-radius: 8px;
+    box-shadow: 0 12px 60px rgba(0,0,0,.6);
+  }
+
+  /* -------- Responsive -------- */
+  @media (max-width: 960px) {
+    .container {
+      grid-template-columns: 1fr;
+    }
+    .kpi-strip {
+      grid-template-columns: repeat(2, 1fr);
+    }
+  }
 </style>
 </head>
 <body>
 
+<!-- Alert banner -->
 <div class="alert-banner" id="alertBanner">
   <svg width="20" height="20" fill="none" stroke="currentColor" stroke-width="2.2" viewBox="0 0 24 24"><path d="M10.29 3.86L1.82 18a2 2 0 001.71 3h16.94a2 2 0 001.71-3L13.71 3.86a2 2 0 00-3.42 0z"/><line x1="12" y1="9" x2="12" y2="13"/><line x1="12" y1="17" x2="12.01" y2="17"/></svg>
-  GATHERING DETECTED — Multiple groups active
+  GATHERING ALERT — People grouped for over 20 seconds
 </div>
 
 <header>
   <div class="logo">
     <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.8"><path d="M23 19a2 2 0 01-2 2H3a2 2 0 01-2-2V8a2 2 0 012-2h4l2-3h6l2 3h4a2 2 0 012 2z"/><circle cx="12" cy="13" r="4"/></svg>
-    Surveillance Dash: Multi-Grid
+    Surveillance Dashboard
   </div>
   <div class="header-right">
-    <div class="status-badge live" id="statusBadge">
+    <select id="cameraSelect" class="camera-select"></select>
+    <div class="status-badge connecting" id="statusBadge">
       <span class="dot"></span>
-      <span id="statusText">System Live</span>
+      <span id="statusText">Connecting</span>
     </div>
     <div class="clock" id="clock"></div>
   </div>
 </header>
 
 <div class="container">
+  <!-- KPI strip -->
   <div class="kpi-strip">
-    <div class="kpi">
-      <span class="kpi-label">People (Total)</span>
+    <div class="kpi" id="kpiPeople">
+      <span class="kpi-label">People Detected</span>
       <span class="kpi-value accent" id="valPeople">0</span>
     </div>
-    <div class="kpi">
+    <div class="kpi" id="kpiGroups">
       <span class="kpi-label">Active Groups</span>
       <span class="kpi-value orange" id="valGroups">0</span>
     </div>
     <div class="kpi" id="kpiAlerts">
-      <span class="kpi-label">Global Alerts</span>
+      <span class="kpi-label">Total Alerts</span>
       <span class="kpi-value red" id="valAlerts">0</span>
     </div>
     <div class="kpi">
-      <span class="kpi-label">Node Status</span>
-      <span class="kpi-value green" id="valNodes">Online</span>
+      <span class="kpi-label">FPS</span>
+      <span class="kpi-value green" id="valFps">0</span>
     </div>
   </div>
 
+  <!-- Video feed -->
   <div class="video-panel">
-    <img id="videoFeed" src="/video_feed" alt="Simultaneous Feed">
+    <img id="videoFeed" src="/video_feed" alt="Live Feed">
     <div class="video-overlay">
-      <span class="overlay-tag">GRID VIEW</span>
-      <span class="overlay-tag" id="overlayPeople">0 People Total</span>
+      <span class="overlay-tag">LIVE</span>
+      <span class="overlay-tag" id="overlayPeople">0 People</span>
     </div>
   </div>
 
+  <!-- Sidebar -->
   <div class="sidebar">
+    <!-- Active groups -->
     <div class="panel">
-      <div class="panel-header">Active Groups (All Cams)</div>
+      <div class="panel-header">Active Groups</div>
       <div class="panel-body" id="groupList">
-        <div style="color:var(--text-dim); font-size:.8rem;">Monitoring...</div>
+        <div class="no-groups">No groups detected</div>
       </div>
     </div>
 
+    <!-- Alert history -->
     <div class="panel" style="flex:1;">
-      <div class="panel-header">Global Alert History</div>
+      <div class="panel-header">Alert History</div>
       <div class="panel-body alert-log" id="alertLog">
-        <div style="color:var(--text-dim); font-size:.8rem;">Scanning for events...</div>
+        <div class="no-groups">No alerts yet</div>
       </div>
     </div>
   </div>
 </div>
 
+<!-- Lightbox for alert images -->
+<div class="lightbox" id="lightbox" onclick="this.classList.remove('open')">
+  <img id="lightboxImg" src="" alt="Alert snapshot">
+</div>
+
 <script>
 function updateClock() {
   const now = new Date();
-  document.getElementById('clock').textContent = now.toLocaleTimeString();
+  document.getElementById('clock').textContent = now.toLocaleString('en-US', {
+    hour: '2-digit', minute: '2-digit', second: '2-digit', hour12: false,
+    year: 'numeric', month: 'short', day: 'numeric'
+  });
 }
 setInterval(updateClock, 1000);
 updateClock();
 
 let prevAlertActive = false;
+let bannerTimeout = null;
+let camerasLoaded = false;
+
+async function loadCameras() {
+  try {
+    const res = await fetch('/api/cameras');
+    const data = await res.json();
+    const select = document.getElementById('cameraSelect');
+    select.innerHTML = data.cameras
+      .map(c => `<option value="${c.id}">${c.name} (${c.ip})</option>`)
+      .join('');
+    camerasLoaded = true;
+  } catch (e) {
+    console.error('Failed to load cameras', e);
+  }
+}
+
+async function selectCamera(cameraId) {
+  try {
+    await fetch('/api/camera/select', {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({ camera_id: cameraId })
+    });
+  } catch (e) {
+    console.error('Failed to switch camera', e);
+  }
+}
 
 async function poll() {
   try {
     const res = await fetch('/api/status');
     const d = await res.json();
 
-    document.getElementById('valPeople').textContent = d.total_people_count;
+    // KPIs
+    document.getElementById('valPeople').textContent = d.people_count;
+    document.getElementById('valGroups').textContent  = d.groups.length;
     document.getElementById('valAlerts').textContent  = d.alerts.length;
-    document.getElementById('overlayPeople').textContent = d.total_people_count + ' People Total';
+    document.getElementById('valFps').textContent     = d.fps;
+    document.getElementById('overlayPeople').textContent = d.people_count + ' People';
 
-    let allGroups = [];
-    Object.keys(d.cameras).forEach(id => {
-        d.cameras[id].groups.forEach(g => allGroups.push({...g, camName: d.cameras[id].name}));
-    });
+    // Stream status badge
+    const badge = document.getElementById('statusBadge');
+    const stext = document.getElementById('statusText');
+    badge.className = 'status-badge ' + d.stream_status;
+    stext.textContent = d.stream_status === 'live' ? 'Live' :
+                        d.stream_status === 'error' ? 'Error' : 'Connecting';
+    if (d.active_camera_name) {
+      stext.textContent = `${stext.textContent} - ${d.active_camera_name}`;
+    }
 
-    document.getElementById('valGroups').textContent = allGroups.length;
-    document.getElementById('kpiAlerts').classList.toggle('alert-glow', d.alert_active);
+    if (camerasLoaded) {
+      const cameraSelect = document.getElementById('cameraSelect');
+      if (cameraSelect.value !== d.selected_camera_id) {
+        cameraSelect.value = d.selected_camera_id;
+      }
+    }
 
+    // Alert KPI glow
+    const kpiAlerts = document.getElementById('kpiAlerts');
+    kpiAlerts.classList.toggle('alert-glow', d.alert_active);
+
+    // Alert banner
     const banner = document.getElementById('alertBanner');
-    if (d.alert_active && !prevAlertActive) banner.classList.add('show');
-    else if (!d.alert_active) banner.classList.remove('show');
+    if (d.alert_active && !prevAlertActive) {
+      banner.classList.add('show');
+      clearTimeout(bannerTimeout);
+      bannerTimeout = setTimeout(() => banner.classList.remove('show'), 8000);
+    }
     prevAlertActive = d.alert_active;
 
+    // Groups list
     const gl = document.getElementById('groupList');
-    if (allGroups.length === 0) gl.innerHTML = '<div style="color:var(--text-dim); font-size:.8rem;">No groups detected</div>';
-    else {
-      gl.innerHTML = allGroups.map(g => `
-        <div class="group-item">
+    if (d.groups.length === 0) {
+      gl.innerHTML = '<div class="no-groups">No groups detected</div>';
+    } else {
+      gl.innerHTML = d.groups.map(g => {
+        const cls = g.duration >= 20 ? 'danger' : g.duration >= 10 ? 'warning' : '';
+        return `<div class="group-item">
           <div class="group-meta">
             <span>${g.count} People</span>
-            <span class="cam-name">${g.camName}</span>
+            <span>Group #${g.id}</span>
           </div>
-          <span class="group-timer ${g.duration > 20 ? 'danger' : ''}">${g.duration}s</span>
-        </div>
-      `).join('');
+          <span class="group-timer ${cls}">${g.duration}s</span>
+        </div>`;
+      }).join('');
     }
 
+    // Alert log
     const al = document.getElementById('alertLog');
-    if (d.alerts.length === 0) al.innerHTML = '<div style="color:var(--text-dim); font-size:.8rem;">No alerts recorded</div>';
-    else {
+    if (d.alerts.length === 0) {
+      al.innerHTML = '<div class="no-groups">No alerts yet</div>';
+    } else {
       al.innerHTML = d.alerts.map(a => `
-        <div class="alert-entry">
+        <div class="alert-entry" onclick="openLightbox('/${a.image}')">
           <img class="alert-thumb" src="/${a.image}" alt="Alert">
           <div class="alert-info">
-            <span class="time">${a.time} - ${a.camera}</span>
+            <span class="time">${a.time}</span>
             <span class="desc">${a.people} people gathered</span>
+            <span class="duration">Duration: ${a.duration}s</span>
           </div>
         </div>
       `).join('');
     }
 
-  } catch (e) { console.error('Poll error', e); }
+  } catch (e) {
+    console.error('Poll error', e);
+  }
 }
 
-setInterval(poll, 1500);
+function openLightbox(src) {
+  document.getElementById('lightboxImg').src = src;
+  document.getElementById('lightbox').classList.add('open');
+}
+
+setInterval(poll, 1000);
+document.getElementById('cameraSelect').addEventListener('change', (e) => {
+  selectCamera(e.target.value);
+});
+loadCameras().then(poll);
 poll();
 </script>
 </body>