yolo

shoot_manager.py

@@ -323,6 +323,18 @@ def process_shot(adc_val):
         frame = camera_manager.read_frame()
         network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True)
 
+        # 每箭只识别当前帧，不复用上一箭的靶规格结果。
+        target_class_result = None
+        try:
+            from target_roi_yolo import try_get_target_class_from_yolo
+
+            target_class_result = try_get_target_class_from_yolo(frame, logger=logger)
+            if logger:
+                logger.info(f"[YOLO-TARGET] 当前箭业务结果: {target_class_result}")
+        except Exception as exc:
+            if logger:
+                logger.warning(f"[YOLO-TARGET] 当前箭分类失败，按未知处理: {exc}")
+
         # 调用算法分析
         analysis_result = analyze_shot(frame)
 
@@ -382,11 +394,25 @@ def process_shot(adc_val):
         srv_y = round(float(dy), 4) if dy is not None else 200.0
 
         # 构造上报数据
+        target_label = (
+            target_class_result.get("label")
+            if isinstance(target_class_result, dict)
+            else None
+        )
+        target_confidence = (
+            target_class_result.get("confidence")
+            if isinstance(target_class_result, dict)
+            else None
+        )
         inner_data = {
             "shot_id": shot_id,
             "x": srv_x,
             "y": srv_y,
             "r": 20.0,  # 保留字段（服务端当前忽略，物理外环半径 cm）
+            "target_class": target_label,
+            "target_class_confidence": (
+                float(target_confidence) if target_confidence is not None else None
+            ),
             "d": round((distance_m or 0.0) * 100),
             "d_laser": round((laser_distance_m or 0.0) * 100),
             "d_laser_quality": laser_signal_quality,
@@ -414,6 +440,11 @@ def process_shot(adc_val):
             inner_data["ellipse_center_y"] = None
 
         report_data = {"cmd": 1, "data": inner_data}
+        if logger:
+            logger.info(
+                f"[REPORT-TARGET] enqueue shot_id={shot_id}, "
+                f"target_class={target_label}, confidence={target_confidence}"
+            )
         network_manager.safe_enqueue(report_data, msg_type=2, high=True)
 
         # 数据上报后再画标注，不干扰检测阶段的原始画面