增加一些耗时操作打印

algorithm/checkpoint/npu_yolo_pose_onnx.py

@@ -246,6 +246,7 @@ class YOLOv8_Pose_ONNX:
     # -------------------------------------------------
     def __call__(self, frame):
         inp = self.preprocess(frame)
+        # 耗时操作
         pred = self.session.run(None, {self.input_name: inp})[0]
         return self.postprocess(pred, frame.shape[:2])
 

biz/checkpoint/checkpoint_biz.py

@@ -227,17 +227,27 @@ class KadianDetector:
         self.width, self.height = w, h
 
         self.current_frame_idx += 1
+        
+        # 性能计时开始
+        # total_start = time.time()
+        
         # ========= 每帧动态获取正确的 ROI（int32）=========
         roi_points_int32 = self._get_roi_points(w, h)  # shape: (4, 2), dtype: int32
         roi_points_draw = roi_points_int32.reshape((-1, 1, 2))  # shape: (4, 1, 2) 用于绘制
 
-
         current_time_sec = timestamp
 
+        # ========= 骨骼检测 =========
+        # pose_start = time.time()
+        # 耗时操作
         pose_results = self.pose_detector(frame)
+        # pose_time = (time.time() - pose_start) * 1000
 
         # ========= 主检测 =========
+        # detect_start = time.time()
+        # 耗时操作
         detections = self.detector(frame)
+        # detect_time = (time.time() - detect_start) * 1000
 
         dets_xyxy = []
         dets_roles = []
@@ -272,7 +282,8 @@ class KadianDetector:
             [self.height, self.width]
         )
         # logger.debug("tracks: {}".format(tracks))
-        # 绘制骨骼
+
+        # ========= 绘制骨骼 =========
         frame = YOLOv8_Pose_ONNX.draw_keypoints(frame, pose_results)
         # ========= 绘制 ROI =========
         cv2.polylines(frame, [roi_points_draw], isClosed=True, color=(255, 0, 0), thickness=3)
@@ -666,9 +677,16 @@ class KadianDetector:
             self.draw_alert(frame, alert_text, (0, 0, 255), offset_y=alert_offset)
             alert_offset += 100
 
+        # # ========= 性能统计和输出 =========
+        # total_time = (time.time() - total_start) * 1000
+
+        # logger.info(f"[PERF_DETAIL] Camera {camera_id} - ProcessFrame Total: {total_time:.1f}ms | "
+        #            f"PoseDetect: {pose_time:.1f}ms | "
+        #            f"MainDetect: {detect_time:.1f}ms | "
+        #            )
+        
         return {
             "image": frame,
-
             "alerts":current_frame_alerts
         }
 
@@ -717,7 +735,9 @@ class FrameProcessorWorker(threading.Thread):
                 detector = self.kadian_detectors[cam_id]
 
                 # 执行检测
+                # detect_start = time.time()
                 result = detector.process_frame(frame.copy(), cam_id, ts)
+                # detect_time = (time.time() - detect_start) * 1000
 
                 result_img = result["image"]
                 result_type = result["alerts"]
@@ -741,11 +761,13 @@ class FrameProcessorWorker(threading.Thread):
                         self.last_alert_push_time[cam_id][action] = current_time
 
                 # 通过 WebSocket 发送帧结果
+                # encode_start = time.time()
                 try:
                     img_b64 = self._encode_base64(result_img)
                 except Exception as e:
                     logger.error(f"[ERROR] Encode image failed: {e}")
                     img_b64 = None
+                # encode_time = (time.time() - encode_start) * 1000
 
                 if img_b64 is not None:
                     # 将abnormal_actions对象数组转换为字符串数组
@@ -765,10 +787,15 @@ class FrameProcessorWorker(threading.Thread):
                         #     self.ws_queue_2.put(msg, timeout=1.0)
                     except queue.Full:
                         logger.warning("[WARN] ws_send_queue full, drop frame message")
+                    
+                    # # 打印关键操作的耗时
+                    # total_time = detect_time + encode_time
+                    # logger.info(f"[PERF] Camera {cam_id} - Total: {total_time:.1f}ms | "
+                    #            f"Detect: {detect_time:.1f}ms | "
+                    #            f"Encode: {encode_time:.1f}ms | ")
+                self.raw_queue.task_done()
 
             except Exception as e:
                 logger.error(f"[ERROR] Frame processing failed for camera {cam_id if 'cam_id' in locals() else 'unknown'}: {e}")
                 logger.exception("Exception details:")  # 打印完整的堆栈跟踪
                 # 继续处理下一帧，不要退出循环
-            finally:
-                self.raw_queue.task_done()