feat: Add detection count tracking and display in the UI

app/inference/worker.py

@@ -42,6 +42,7 @@ class ResultPacket(NamedTuple):
     detections: list       # list of (x1, y1, x2, y2, conf, label) tuples
     width: int             # source frame width (for overlay scaling)
     height: int            # source frame height
+    elapsed_ms: float = 0.0  # inference wall-clock time in milliseconds
 
 
 # ---------------------------------------------------------------------------
@@ -145,7 +146,9 @@ def _select_device() -> str:
 
 
 def _infer(model, packet: FramePacket) -> ResultPacket:
-    """Run model on one frame, return ResultPacket."""
+    """Run model on one frame, return ResultPacket with elapsed_ms."""
+    import time  # noqa: PLC0415
+
     import numpy as np  # noqa: PLC0415
 
     frame_np = np.frombuffer(packet.raw_bytes, dtype=np.uint8).reshape(
@@ -153,7 +156,9 @@ def _infer(model, packet: FramePacket) -> ResultPacket:
     )
 
     device = _select_device()
+    t0 = time.perf_counter()
     results = model(frame_np, device=device, verbose=False)
+    elapsed_ms = (time.perf_counter() - t0) * 1000.0
 
     detections = []
     for r in results:
@@ -180,6 +185,7 @@ def _infer(model, packet: FramePacket) -> ResultPacket:
         detections=detections,
         width=packet.width,
         height=packet.height,
+        elapsed_ms=elapsed_ms,
     )
 
 

app/inference/worker_manager.py

@@ -55,6 +55,7 @@ class InferenceManager(QObject):
     """
 
     detections_ready = Signal(object, object)   # list[Detection], tuple[int,int]
+    detection_count_updated = Signal(int)       # total frames with detections so far
     inference_started = Signal()
     inference_stopped = Signal()
     inference_error = Signal(str)
@@ -79,6 +80,9 @@ class InferenceManager(QObject):
         # Paused flag — inference can be suspended without stopping the process
         self._paused: bool = False
 
+        # Detection counter — frames on which at least one detection occurred
+        self._detection_frame_count: int = 0
+
         # QTimers (GUI thread)
         self._poll_timer = QTimer(self)
         self._poll_timer.setInterval(INFERENCE_POLL_INTERVAL_MS)
@@ -104,6 +108,7 @@ class InferenceManager(QObject):
         self._model_path = model_path
         self._restart_count = 0
         self._paused = False
+        self._detection_frame_count = 0
         self._start_worker()
 
     def stop(self) -> None:
@@ -199,7 +204,7 @@ class InferenceManager(QObject):
         try:
             self._input_queue.put_nowait(packet)
             self._busy = True
-            logger.debug("InferenceManager: submitted frame %d", self._frame_id)
+            # logger.debug("InferenceManager: submitted frame %d", self._frame_id)
         except Exception as exc:
             logger.warning("InferenceManager: could not enqueue frame: %s", exc)
 
@@ -289,10 +294,20 @@ class InferenceManager(QObject):
                 ]
                 source_size = (packet.width, packet.height)
 
-                logger.debug(
-                    "InferenceManager: frame %d → %d detections",
-                    packet.frame_id, len(detections),
-                )
+                if detections:
+                    self._detection_frame_count += 1
+                    conf_summary = ", ".join(
+                        f"{d.label} {d.conf:.2f}" for d in detections
+                    )
+                    logger.info(
+                        "frame %d: %d detection(s) in %.1f ms — %s",
+                        packet.frame_id,
+                        len(detections),
+                        packet.elapsed_ms,
+                        conf_summary,
+                    )
+                    self.detection_count_updated.emit(self._detection_frame_count)
+
                 self.detections_ready.emit(detections, source_size)
 
         except Exception: