fix: 摄像头翻转

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "cmake.sourceDirectory": "E:/code/code/code/new/new/new/new/new/nw/2.17.0/archery/cpp_ext"
+}

app.yaml

@@ -1,6 +1,6 @@
 id: t11
 name: t11
-version: 2.15.9
+version: 2.17.15
 author: t11
 icon: ''
 desc: t11
@@ -18,8 +18,8 @@ files:
   - laser_manager.py
   - logger_manager.py
   - main.py
-  - model_270139.cvimodel
+  - model_317828.cvimodel
-  - model_270139.mud
+  - model_317828.mud
   - network.py
   - ota_curl.sh
   - ota_manager.py

at_client.py

@@ -76,6 +76,7 @@ class ATClient:
         """
         expect_b = expect.encode() if isinstance(expect, str) else expect
         with self._cmd_lock:
+            with self._q_lock:
                 # 初始化等待
                 self._waiting = True
                 self._expect = expect_b
@@ -300,8 +301,8 @@ class ATClient:
                     if len(self._rx) > 512 * 1024:
                         self._rx = self._rx[-256 * 1024:]
                 else:
-                    if len(self._rx) > 16384:
+                    if len(self._rx) > 32768:
-                        self._rx = self._rx[-4096:]
+                        self._rx = self._rx[-16384:]
 
 
 

camera_manager.py

@@ -8,6 +8,15 @@ import threading
 import config
 from logger_manager import logger_manager
 
+_USE_CV = False
+try:
+    import cv2
+    import numpy as np
+    from maix import image as _maix_image
+    _USE_CV = True
+except ImportError:
+    pass
+
 
 class CameraManager:
     """相机管理器（单例）"""
@@ -101,7 +110,23 @@ class CameraManager:
         with self._camera_lock:
             if self._camera is None:
                 self.init_camera()
-            return self._camera.read()
+            frame = self._camera.read()
+            if frame is not None and _USE_CV:
+                try:
+                    v_flip = getattr(config, 'CAMERA_V_FLIP', False)
+                    h_mirror = getattr(config, 'CAMERA_H_MIRROR', False)
+                    if v_flip or h_mirror:
+                        img_cv = _maix_image.image2cv(frame, False, False)
+                        if v_flip and h_mirror:
+                            img_cv = cv2.flip(img_cv, -1)
+                        elif v_flip:
+                            img_cv = cv2.flip(img_cv, 0)
+                        elif h_mirror:
+                            img_cv = cv2.flip(img_cv, 1)
+                        frame = _maix_image.cv2image(img_cv, False, False)
+                except Exception:
+                    pass
+            return frame
     
     def show(self, image):
         """

config.py

@@ -15,6 +15,8 @@ LOCAL_FILENAME = APP_DIR + "/main_tmp.py"
 # 相机初始化分辨率（CameraManager / main.py 使用）
 CAMERA_WIDTH = 640
 CAMERA_HEIGHT = 480
+CAMERA_V_FLIP = True   # 摄像头垂直翻转（上下颠倒时设为 True）
+CAMERA_H_MIRROR = True  # 摄像头水平镜像（左右反了时设为 True）
 
 # 三角形检测缩图比例：默认按相机最长边缩到 1/2（性能更稳；可按需调整）
 # 取值范围建议 (0.25 ~ 1.0]；1.0 表示不缩图
@@ -24,7 +26,7 @@ TRIANGLE_DETECT_SCALE = 0.4
 # SERVER_IP = "stcp.shelingxingqiu.com"
 SERVER_IP = "www.shelingxingqiu.com"
 SERVER_PORT = 50005
-HEARTBEAT_INTERVAL = 15  # 心跳间隔（秒）
+HEARTBEAT_INTERVAL = 5  # 心跳间隔（秒）
 
 # WiFi 质量评估（开机先尝试 WiFi；质量差且 4G 可用则切到 4G，本次上电直至关机锁定 4G）
 WIFI_QUALITY_RTT_SAMPLES = 3  # 到业务服务器 TCP 建连耗时采样次数，取中位数
@@ -234,10 +236,10 @@ TRIANGLE_BLACKHAT_KERNEL_FRAC = 0.018  # 核大小 ≈ min(h,w)*frac，取奇数
 # ── YOLO(NPU) 靶环 ROI → 裁剪后再跑三角形（减小 CPU 处理面积）──────────────────
 # 日志里 net_in=W×H 来自 .mud 模型（det.input_width/height），不是这里配置的。
 TRIANGLE_YOLO_ROI_ENABLE = True
-TRIANGLE_YOLO_MODEL_PATH = APP_DIR + "/model_270139.mud"
+TRIANGLE_YOLO_MODEL_PATH = APP_DIR + "/model_317211.mud"
 # 参与 ROI 的类别：多类时只填「整靶/靶环」的 id；不要填角标类，否则 union 仍可对，但 largest 会偏小。
 TRIANGLE_YOLO_RING_CLASS_IDS = (0,)
-TRIANGLE_YOLO_CONF_TH = 0.7
+TRIANGLE_YOLO_CONF_TH = 0.9
 TRIANGLE_YOLO_IOU_TH = 0.45
 # YOLO 首次/临界帧可能在高阈值下 0 框；启用后仅在 0 候选时用较低阈值重试一次。
 # 后续仍会经过 min_box_side、ROI aspect、三角形几何校验，避免直接放大假阳性。
@@ -262,6 +264,16 @@ TRIANGLE_SAMPLE_PATCH_HALF_PX = 2
 # 开机阶段预加载 YOLO detector；detect 使用 dual_buff=False，避免返回上一帧结果。
 TRIANGLE_YOLO_PRELOAD_ON_BOOT = False
 
+# YOLO target size classification: class 0=20cm, class 1=40cm.
+TARGET_CLASS_YOLO_ENABLE = True
+TARGET_CLASS_YOLO_MODEL_PATH = APP_DIR + "/model_317828.mud"
+TARGET_CLASS_YOLO_LABELS = (20, 40)
+TARGET_CLASS_YOLO_CONF_TH = 0.66
+TARGET_CLASS_YOLO_IOU_TH = 0.45
+TARGET_CLASS_YOLO_RETRY_ON_EMPTY = False
+TARGET_CLASS_YOLO_RETRY_CONF_TH = 0.25
+TARGET_CLASS_YOLO_PRELOAD_ON_BOOT = True
+
 # ── 第二段 YOLO：仅在 Stage1 裁切出的靶环图上推理（与合成 stage2 训练数据一致）→ 子框内传统算法取直角点 ──
 # Stage1 靶环裁切内如何找黑三角标记（对比耗时时可切换）：
 # "yolo" — 调 Stage2 黑三角模型得子框，再子框内传统提取（需 TRIANGLE_BLACK_YOLO_ENABLE=True）。
@@ -308,14 +320,25 @@ LASER_COLOR = (0, 255, 0)  # RGB颜色
 LASER_THICKNESS = 1
 LASER_LENGTH = 2
 
+# ==================== 队列大小限制（防止内存泄漏） ====================
+MAX_SEND_QUEUE_SIZE = 500       # 发送队列上限
+MAX_TCP_PAYLOADS = 500          # AT TCP 载荷缓存上限
+MAX_HTTP_EVENTS = 200           # AT HTTP 事件缓存上限
+LOG_QUEUE_MAXSIZE = 10000       # 日志队列上限
+MAX_CMD_THREADS = 10            # 并发命令线程上限（防止服务器下发命令时无限创建线程）
+
 # ==================== 图像保存配置 ====================
 SAVE_IMAGE_ENABLED = True  # 是否保存图像（True=保存，False=不保存）
+SAVE_IMAGE_ON_FAILURE = False  # 检测失败时是否强制保存图像（供调试测试用）
 PHOTO_DIR = "/root/phot"    # 照片存储目录
 MAX_IMAGES = 1000
+SAVE_RAW_IMAGE_ENABLED = True  # 额外保存完整原始帧（不画框、不画点、不裁剪）
+RAW_IMAGE_DIR = PHOTO_DIR + "/raw"
+RAW_IMAGE_MAX_IMAGES = MAX_IMAGES
 # Stage2 调试目录（默认 PHOTO_DIR/stage2_roi）内 JPEG 最多保留张数；None 表示与 MAX_IMAGES 相同
 TRIANGLE_BLACK_YOLO_STAGE2_ROI_MAX_IMAGES = None
 
-SHOW_CAMERA_PHOTO_WHILE_SHOOTING = False  # 是否在拍摄时显示摄像头图像（True=显示，False=不显示），建议在连着USB测试过程中打开
+SHOW_CAMERA_PHOTO_WHILE_SHOOTING = True  # 是否在拍摄时显示摄像头图像（True=显示，False=不显示），建议在连着USB测试过程中打开
 
 # ==================== OTA配置 ====================
 MAX_BACKUPS = 5

logger_manager.py

@@ -65,8 +65,8 @@ class LoggerManager:
             backup_count = config.LOG_BACKUP_COUNT
         
         try:
-            # 创建日志队列（无界队列）
+            # 创建日志队列（有界队列，防止内存泄漏；满时自动丢弃旧日志）
-            self._log_queue = queue.Queue(-1)
+            self._log_queue = queue.Queue(maxsize=config.LOG_QUEUE_MAXSIZE)
             
             # 确保日志文件所在的目录存在
             log_dir = os.path.dirname(log_file)

main.py

@@ -120,9 +120,9 @@ def cmd_str():
 
     # ==================== 第二阶段：软件初始化 ====================
 
-    # 1. 初始化日志系统
+    # 1. 初始化日志系统（WARNING级别，不打印/写入INFO和DEBUG日志，提高执行流畅度）
     import logging
-    logger_manager.init_logging(log_level=logging.DEBUG)
+    logger_manager.init_logging(log_level=logging.WARNING)
     logger = logger_manager.logger
 
     # 补充：因为初始化的时候，激光会亮，先关了它
@@ -132,6 +132,7 @@ def cmd_str():
     sync_system_time_from_4g()
 
     # 2.1 WiFi 热点配网兜底：仅当 STA 与 4G 均不可用时起 AP + HTTP；提交后删 /boot/wifi.ap、建 wifi.sta 并 reboot
+    _ota_pending_path = f"{config.APP_DIR}/ota_pending.json"
     try:
         from wifi_config_httpd import maybe_start_wifi_ap_fallback
 
@@ -162,15 +163,21 @@ def cmd_str():
             and _loc_black == "yolo"
             and bool(getattr(config, "TRIANGLE_BLACK_YOLO_PRELOAD_ON_BOOT", True))
         )
-        _preload_yolo = _preload_yolo or _need_black_preload
+        _need_target_preload = (
-        if _preload_yolo:
+            bool(getattr(config, "TARGET_CLASS_YOLO_ENABLE", False))
+            and bool(getattr(config, "TARGET_CLASS_YOLO_PRELOAD_ON_BOOT", True))
+        )
+        _preload_yolo = _preload_yolo or _need_black_preload or _need_target_preload
+        if _preload_yolo and not os.path.exists(_ota_pending_path):
             preload_yolo_detector(logger)
+        elif _preload_yolo and logger:
+            logger.warning("[YOLO] ota_pending.json found; skip model preload until rollback check")
     except Exception as e:
         if logger:
             logger.warning(f"[YOLO-ROI] 启动预加载异常（不影响后续射箭）: {e}")
 
     # 3. 启动时检查：是否需要恢复备份
-    pending_path = f"{config.APP_DIR}/ota_pending.json"
+    pending_path = _ota_pending_path
     if os.path.exists(pending_path):
         try:
             with open(pending_path, 'r', encoding='utf-8') as f:
@@ -245,8 +252,12 @@ def cmd_str():
     # 4. 初始化设备ID（network_manager 内部会自动设置 device_id 和 password）
     network_manager.read_device_id()
 
-    # 5. 创建照片存储目录（如果启用图像保存）
+    # 5. 创建照片存储目录（如果启用图像保存或检测失败时强制保存）
-    if config.SAVE_IMAGE_ENABLED:
+    if (
+        config.SAVE_IMAGE_ENABLED
+        or getattr(config, "SAVE_IMAGE_ON_FAILURE", False)
+        or getattr(config, "SAVE_RAW_IMAGE_ENABLED", False)
+    ):
         photo_dir = config.PHOTO_DIR
         if photo_dir not in os.listdir("/root"):
             try:
@@ -278,46 +289,46 @@ def cmd_str():
         logger.info("系统准备完成...")
 
     last_adc_trigger = 0
+    trigger_adc_val = 0  # 触发时的气压值，气压需降回此值以下才能再次触发
+    # 读取一次ADC初始值，防止开机时传感器已有压力导致误触发
+    enable_check = True
+    try:
+        last_adc_val = hardware_manager.adc_obj.read()
+    except Exception:
+        last_adc_val = 0
     # 气压采样：减少日志频率（每 N 个点输出一条），避免 logger.debug 拖慢采样
     PRESSURE_BATCH_SIZE = 100
 
     pressure_buf = []
     pressure_sum = 0
-    pressure_abs_sum = 0
     pressure_min = 4095
     pressure_max = 0
     pressure_t0_ms = None
-    last_avg_abs = 0
 
     def _flush_pressure_buf(reason: str):
-        if not config.AIR_PRESSURE_lOG:
+        nonlocal pressure_buf, pressure_sum, pressure_min, pressure_max, pressure_t0_ms, logger
-            return
-        nonlocal pressure_buf, pressure_sum, pressure_min, pressure_max, pressure_t0_ms, logger, pressure_abs_sum, last_avg_abs
         if not pressure_buf:
             return
+        if config.AIR_PRESSURE_lOG:
             t1_ms = time.ticks_ms()
             n = len(pressure_buf)
             avg = (pressure_sum / n) if n else 0
-        avg_abs = (pressure_abs_sum / n) if n else 0
-        # 一行输出：方便后处理画曲线；同时带上统计信息便于快速看波峰
             line = (
                 f"[气压批量] reason={reason} "
                 f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
-            f"min={pressure_min} max={pressure_max} avg={avg:.1f} avg_abs={avg_abs:.3f} "
+                f"min={pressure_min} max={pressure_max} avg={avg:.1f} "
                 f"values={','.join(map(str, pressure_buf))}"
-            f" convert value (kpa): {(max(pressure_buf, key=lambda x: x[1])[1] - last_avg_abs) / (5 - 2.5) * config.AIR_PRESSURE_HARDWARE_MAX:.1f}"
             )
             if logger:
                 logger.debug(line)
             else:
                 print(line)
+        # 无论是否记录日志，都必须清空 buffer，否则内存泄漏
         pressure_buf = []
         pressure_sum = 0
-        pressure_abs_sum = 0
         pressure_min = 4095
         pressure_max = 0
         pressure_t0_ms = None
-        last_avg_abs = avg_abs
 
     # 主循环：检测扳机触发 → 拍照 → 分析 → 上报
     while not app.need_exit():
@@ -352,12 +363,10 @@ def cmd_str():
                 if network_manager.manual_trigger_flag:
                     network_manager.clear_manual_trigger()
                     adc_val = config.ADC_TRIGGER_THRESHOLD + 1
-                    adc_abs_val = 10
                     if logger:
                         logger.info("[TEST] TCP命令触发射箭")
                 else:
                     adc_val = hardware_manager.adc_obj.read()
-                    adc_abs_val = hardware_manager.adc_obj.read_vol()
             except Exception as e:
                 logger = logger_manager.logger
                 if logger:
@@ -368,25 +377,24 @@ def cmd_str():
             # ====== 气压采样缓存（每次循环都记录，批量输出日志）======
             if pressure_t0_ms is None:
                 pressure_t0_ms = current_time
-            pressure_buf.append((adc_val, adc_abs_val))
+            pressure_buf.append(adc_val)
             pressure_sum += adc_val
-            pressure_abs_sum += adc_abs_val
             if adc_val < pressure_min:
                 pressure_min = adc_val
             if adc_val > pressure_max:
                 pressure_max = adc_val
             if len(pressure_buf) >= PRESSURE_BATCH_SIZE:
                 _flush_pressure_buf("batch")
-            # if adc_val >= 2000:
+            # 突变增量检测：压力增量大于300时触发
-            # print(f"adc :{adc_val}")
+            # 触发后需等气压降到触发值以下才重新检测增量
-            if adc_val >= config.ADC_TRIGGER_THRESHOLD:
+            if adc_val < trigger_adc_val :
+                enable_check = True
+            if (adc_val - last_adc_val) > 500 and enable_check:
                 hardware_manager.start_idle_timer()  # 重新计时
-                diff_ms = current_time - last_adc_trigger
-                if diff_ms < 3000:
-                    logger.info(f"[MAIN] 扳机触发过于频繁, {diff_ms}ms")
-                    continue
                 last_adc_trigger = current_time
-                # 触发前先把缓存刷出来，避免波形被长耗时处理截断
+                trigger_adc_val = adc_val  # 记录触发时的气压值
+                last_adc_val = adc_val  # 更新基准值，防止连续增量误触发
+                enable_check = False
                 _flush_pressure_buf("before_trigger")
 
                 try:
@@ -404,10 +412,9 @@ def cmd_str():
                     try:
                         camera_manager.show(camera_manager.read_frame())
                     except Exception as e:
-                        logger = logger_manager.logger
+                        pass
-                        if logger:
+                time.sleep_ms(1)
-                            logger.error(f"[MAIN] 显示异常: {e}")
+            last_adc_val = adc_val
-                time.sleep_ms(5)
 
         except Exception as e:
             # 主循环的顶层异常捕获，防止程序静默退出

model_317189.mud

@@ -1,7 +1,7 @@
 
 [basic]
 type = cvimodel
-model = model_270139.cvimodel
+model = model_317189.cvimodel
 
 [extra]
 model_type = yolov5
@@ -9,5 +9,5 @@ input_type = rgb
 mean = 0, 0, 0
 scale = 0.00392156862745098, 0.00392156862745098, 0.00392156862745098
 anchors = 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326
-labels = 黑三角和圆环
+labels = circle, triangle
 

model_317211.mud

@@ -0,0 +1,13 @@
+
+[basic]
+type = cvimodel
+model = model_317211.cvimodel
+
+[extra]
+model_type = yolov5
+input_type = rgb
+mean = 0, 0, 0
+scale = 0.00392156862745098, 0.00392156862745098, 0.00392156862745098
+anchors = 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326
+labels = circle, triangle
+

model_317423.mud

@@ -0,0 +1,13 @@
+
+[basic]
+type = cvimodel
+model = model_317423.cvimodel
+
+[extra]
+model_type = yolov5
+input_type = rgb
+mean = 0, 0, 0
+scale = 0.00392156862745098, 0.00392156862745098, 0.00392156862745098
+anchors = 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326
+labels = 20, 10, 40
+

model_317704.mud

@@ -0,0 +1,13 @@
+
+[basic]
+type = cvimodel
+model = model_317704.cvimodel
+
+[extra]
+model_type = yolov5
+input_type = rgb
+mean = 0, 0, 0
+scale = 0.00392156862745098, 0.00392156862745098, 0.00392156862745098
+anchors = 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326
+labels = 40, circle, triangle
+

model_317828.mud

@@ -1,7 +1,7 @@
 
 [basic]
 type = cvimodel
-model = model_270820.cvimodel
+model = model_317828.cvimodel
 
 [extra]
 model_type = yolov5
@@ -9,5 +9,5 @@ input_type = rgb
 mean = 0, 0, 0
 scale = 0.00392156862745098, 0.00392156862745098, 0.00392156862745098
 anchors = 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326
-labels = triangle
+labels = 20, 40
 

network.py

@@ -18,7 +18,7 @@ import socket
 import config
 
 from hardware import hardware_manager
-from power import get_bus_voltage, voltage_to_percent
+from power import get_bus_voltage, voltage_to_percent, is_charging
 from logger_manager import logger_manager
 from wifi import wifi_manager
 import subprocess
@@ -72,6 +72,10 @@ class NetworkManager:
         self._raw_line_data = []
         self._manual_trigger_flag = False
 
+        # 限制并发命令线程数
+        self._cmd_thread_lock = threading.Lock()
+        self._cmd_thread_count = 0
+
         # 网络类型状态
         self._network_type = None  # "wifi" 或 "4G" 或 None
         # 本次上电曾因 WiFi 质量差切换到 4G 后，直至关机不再改回 WiFi
@@ -165,11 +169,15 @@ class NetworkManager:
         self._password = password
 
     def _enqueue(self, item, high=False):
-        """线程安全地加入队列（内部方法）"""
+        """线程安全地加入队列（内部方法），队列满时丢弃最旧消息"""
         with self._queue_lock:
             if high:
+                if len(self._high_send_queue) >= config.MAX_SEND_QUEUE_SIZE:
+                    self._high_send_queue.pop(0)
                 self._high_send_queue.append(item)
             else:
+                if len(self._normal_send_queue) >= config.MAX_SEND_QUEUE_SIZE:
+                    self._normal_send_queue.pop(0)
                 self._normal_send_queue.append(item)
         self._send_event.set()
 
@@ -198,6 +206,29 @@ class NetworkManager:
         """获取队列锁（用于with语句）"""
         return self._queue_lock
 
+    def _spawn_cmd_thread(self, target, args=()):
+        """安全创建命令线程，限制并发数，防止无限创建导致内存耗尽"""
+        with self._cmd_thread_lock:
+            if self._cmd_thread_count >= config.MAX_CMD_THREADS:
+                self.logger.warning(
+                    f"[NET] 并发命令线程已达上限({config.MAX_CMD_THREADS})，跳过: {getattr(target, '__name__', str(target))}"
+                )
+                return False
+            self._cmd_thread_count += 1
+
+        def _wrapper(*a):
+            try:
+                target(*a)
+            except Exception as e:
+                self.logger.error(f"[NET] 命令线程异常: {e}")
+            finally:
+                with self._cmd_thread_lock:
+                    self._cmd_thread_count -= 1
+
+        import _thread
+        _thread.start_new_thread(_wrapper, args)
+        return True
+
     # ==================== 业务方法 ====================
 
     def read_device_id(self):
@@ -593,6 +624,11 @@ class NetworkManager:
         password = inner_data.get("password")
         ota_res_url = inner_data.get("url")
         try:
+            for _f in ("/etc/wpa_supplicant.conf", "/boot/wpa_supplicant.conf", "/boot/wifi.ssid", "/boot/wifi.pass"):
+                try:
+                    os.remove(_f)
+                except OSError:
+                    pass
             w = network.wifi.Wifi()
             e = w.connect(ssid, password, wait=True, timeout=15)
             err.check_raise(e, "connect wifi failed")
@@ -627,6 +663,50 @@ class NetworkManager:
                 2,
             )
 
+    def _cmd600_conn_wifi(self, data_obj):
+        hardware_manager.start_idle_timer()
+        inner_data = data_obj.get("data", {}) if isinstance(data_obj, dict) else {}
+        self.logger.info(f"[conn wifi] cmd600 , data: {inner_data}")
+        ssid = inner_data.get("ssid")
+        password = inner_data.get("password")
+        # 停止旧的WiFi质量监测（无论当前是WiFi还是4G连接）
+        self._stop_wifi_quality_monitor()
+        try:
+            for _f in ("/etc/wpa_supplicant.conf", "/boot/wpa_supplicant.conf", "/boot/wifi.ssid", "/boot/wifi.pass"):
+                try:
+                    os.remove(_f)
+                except OSError:
+                    pass
+            w = network.wifi.Wifi()
+            e = w.connect(ssid, password, wait=True, timeout=15)
+            err.check_raise(e, "connect wifi failed")
+            if self.logger:
+                self.logger.info(f"[ota] Connect success, got ip{w.get_ip()}")
+            self.safe_enqueue(
+                {
+                    "cmd": 600,
+                    "result": "success",
+                    "wifi": w.get_ip(),
+                },
+                2,
+            )
+            self._session_force_4g = False
+            self.disconnect_server()
+            self._tcp_connected = False
+            self._network_type = None
+            self.logger.info("[conn wifi] WiFi已连接，等待主循环重新登录")
+        except Exception as e:
+            self.logger.error(f"cmd600 失败: {e}")
+            self.safe_enqueue(
+                {
+                    "cmd": 600,
+                    "result": "conn fail",
+                    "reason": str(e),
+                },
+                2,
+            )
+            self._switch_to_4g_due_to_poor_wifi()
+
     def safe_enqueue(self, data_dict, msg_type=2, high=False):
         """线程安全地将消息加入队列（公共方法）"""
         self._enqueue((msg_type, data_dict), high)
@@ -643,7 +723,7 @@ class NetworkManager:
             if self._network_type == "wifi":
                 return self._check_wifi_connection()
             elif self._network_type == "4g":
-                return True  # 4G连接状态由AT命令维护
+                return self._check_4g_connection()
             return False
 
         # 自动选择网络
@@ -660,6 +740,37 @@ class NetworkManager:
             return self._connect_tcp_via_4g()
         return False
 
+    def _check_4g_connection(self):
+        """检查4G TCP连接是否仍然有效（通过查询PDP地址验证网络附着状态）"""
+        try:
+            atc = hardware_manager.at_client
+            if atc is None:
+                return False
+            if not self._uart4g_lock.acquire(timeout=3000):
+                # 获取锁超时说明有其他操作在进行，视为连接仍有效
+                return True
+            try:
+                r = atc.send("AT+CGPADDR=1", "OK", 3000)
+                m = re.search(r'\+CGPADDR:\s*1,"([^"]+)"', r)
+                ip = m.group(1) if m else ""
+                if ip and ip != "0.0.0.0":
+                    return True
+                # 无IP或IP无效，尝试重新激活PDP
+                self.logger.warning("[4G-TCP] PDP地址无效，尝试重新激活")
+                atc.send("AT+MIPCALL=1,1", "OK", 15000)
+                r2 = atc.send("AT+CGPADDR=1", "OK", 3000)
+                m2 = re.search(r'\+CGPADDR:\s*1,"([^"]+)"', r2)
+                ip2 = m2.group(1) if m2 else ""
+                if ip2 and ip2 != "0.0.0.0":
+                    return True
+                self.logger.error("[4G-TCP] 重新激活PDP仍无有效IP，连接已断开")
+                return False
+            finally:
+                self._uart4g_lock.release()
+        except Exception as e:
+            self.logger.warning(f"[4G-TCP] 连接检查异常: {e}")
+            return True  # 异常时不误判断线
+
     def _wrap_wifi_tls(self, plain_sock, hostname):
         """
         在已建立的 TCP socket 上做 TLS（WiFi 走主机 ssl 库；4G 仍用模组 AT+SSL）。
@@ -1007,6 +1118,8 @@ class NetworkManager:
                     total += n
 
                 hardware_manager.uart4g.write(b"\x1A")
+                with hardware_manager.at_client._q_lock:
+                    hardware_manager.at_client._rx = b""
                 r = hardware_manager.at_client.send("", "OK", 8000)
                 if ("SEND OK" in r) or ("OK" in r) or ("+MIPSEND" in r):
                     return True
@@ -1696,8 +1809,6 @@ class NetworkManager:
 
     def tcp_main(self):
         """TCP 主通信循环：登录、心跳、处理指令、发送数据"""
-        import _thread
-
         self.logger.info("[NET] TCP主线程启动")
 
         send_hartbeat_fail_count = 0
@@ -1723,7 +1834,7 @@ class NetworkManager:
                     continue
 
                 if not self.connect_server():
-                    time.sleep_ms(5000)
+                    time.sleep_ms(1000)
                     continue
 
                 # 发送登录包
@@ -1744,7 +1855,7 @@ class NetworkManager:
                         self.disconnect_server()
                     except:
                         pass
-                    time.sleep_ms(2000)
+                    time.sleep_ms(500)
                     continue
 
                 self.logger.info("➡️ 登录包已发送，等待确认...")
@@ -1933,8 +2044,7 @@ class NetworkManager:
                                             self.logger.info(f"[IMAGE_UPLOAD] 准备上传: {target_image} -> {key}")
 
                                             # 在新线程中执行上传，避免阻塞主循环
-                                            import _thread
+                                            self._spawn_cmd_thread(
-                                            _thread.start_new_thread(
                                                 self._upload_image_file,
                                                 (target_image, upload_url, upload_token, key, shoot_id, outlink)
                                             )
@@ -1962,8 +2072,7 @@ class NetworkManager:
                                     else:
                                         self.logger.info(f"[LOG_UPLOAD] 收到日志上传命令，key: {key}")
                                         # 在新线程中执行上传，避免阻塞主循环
-                                        import _thread
+                                        self._spawn_cmd_thread(
-                                        _thread.start_new_thread(
                                             self._upload_log_file_v2,
                                             (upload_url, upload_token, key, outlink, include_rotated, max_files,
                                              archive_format)
@@ -2033,9 +2142,20 @@ class NetworkManager:
                                         battery_data = {
                                             "battery": battery_percent,
                                             "voltage": round(float(voltage), 3),
+                                            "netType": self.network_type,
                                         }
                                         self.safe_enqueue(battery_data, 2)
-                                        self.logger.info(f"电量上报: {battery_percent}%")
+                                        self.logger.info(f"电量上报: {battery_percent}% 充电: {is_charging()}")
+                                        if is_charging():
+                                            self.safe_enqueue(
+                                                {
+                                                    "cmd": 700,
+                                                },
+                                                2,
+                                            )
+                                    elif inner_cmd == 700:
+                                        self.logger.warning("服务器下发关机！！！")
+                                        exit(-1)
                                     elif inner_cmd == 5:  # OTA 升级
                                         inner_data = data_obj.get("data", {}) if isinstance(data_obj, dict) else {}
                                         ssid = inner_data.get("ssid")
@@ -2078,7 +2198,7 @@ class NetworkManager:
                                         if mode == "4g":
                                             ota_manager._set_ota_url(ota_url)  # 记录 OTA URL，供命令7使用
                                             ota_manager._start_update_thread()
-                                            _thread.start_new_thread(ota_manager.direct_ota_download_via_4g, (ota_url,))
+                                            self._spawn_cmd_thread(ota_manager.direct_ota_download_via_4g, (ota_url,))
                                         else:  # mode == "wifi"
                                             if not ssid or not password:
                                                 self.logger.error("ota wifi mode requires ssid and password")
@@ -2087,7 +2207,7 @@ class NetworkManager:
                                                 self.logger.info(f"ssid: {ssid}")
                                                 self.logger.info(f"password: {password}")
                                                 ota_manager._start_update_thread()
-                                                _thread.start_new_thread(ota_manager.handle_wifi_and_update,
+                                                self._spawn_cmd_thread(ota_manager.handle_wifi_and_update,
                                                                        (ssid, password, ota_url))
                                     elif inner_cmd == 6:
                                         try:
@@ -2148,20 +2268,22 @@ class NetworkManager:
                                         else:
                                             self.logger.info(f"[LOG_UPLOAD] 收到日志上传命令，目标URL: {upload_url}")
                                         # 在新线程中执行上传，避免阻塞主循环
-                                            import _thread
+                                        self._spawn_cmd_thread(
-                                            _thread.start_new_thread(
                                             self._upload_log_file,
                                             (upload_url, wifi_ssid, wifi_password, include_rotated, max_files,
                                              archive_format)
                                         )
                                     elif inner_cmd == 200:
                                         self.logger.info("[LASER] cmd200 在后台线程执行检测")
-                                        import _thread
+                                        self._spawn_cmd_thread(self._cmd200_detect_laser, ())
-                                        _thread.start_new_thread(self._cmd200_detect_laser, ())
                                     elif inner_cmd == 300:
                                         self.logger.info("[New Ota] cmd300 在后台线程执行OTA")
-                                        import _thread
+                                        self._spawn_cmd_thread(self._cmd300_ota, (data_obj,))
-                                        _thread.start_new_thread(self._cmd300_ota, (data_obj,))
+                                    elif inner_cmd == 600:
+                                        self.logger.info("[conn wifi] cmd600 在后台线程执行连接wifi: {data_obj}")
+                                        self._spawn_cmd_thread(self._cmd600_conn_wifi, (data_obj,))
+                                    elif inner_cmd == 601:
+                                        pass
                                 else:  # data的结构不是 dict
                                     self.logger.info(f"[NET] body={body}, {time.time()}")
                             else:
@@ -2190,11 +2312,13 @@ class NetworkManager:
                             msg_type, data_dict = item
                             pkt = self._netcore.make_packet(msg_type, data_dict)
                             if not self.tcp_send_raw(pkt):
-                                # 发送失败：将消息放回队首，触发重连（避免丢消息）
+                                # 发送失败：将消息放回队首（队列满则丢弃）
                                 with self.get_queue_lock():
                                     if item_is_high:
+                                        if len(self._high_send_queue) < config.MAX_SEND_QUEUE_SIZE:
                                             self._high_send_queue.insert(0, item)
                                     else:
+                                        if len(self._normal_send_queue) < config.MAX_SEND_QUEUE_SIZE:
                                             self._normal_send_queue.insert(0, item)
                                 self._tcp_connected = False
                                 try:
@@ -2230,8 +2354,8 @@ class NetworkManager:
                                     pass
                                 break
                             else:
-                                # 不立即断开，让下一轮心跳再试；同时缩短一点等待，提升恢复速度
+                                # 不立即断开，让下一轮心跳再试
-                                time.sleep_ms(200)
+                                time.sleep_ms(50)
                                 continue
                         else:
                             send_hartbeat_fail_count = 0
@@ -2261,8 +2385,8 @@ class NetworkManager:
                     self._send_event.clear()
 
                 self._tcp_connected = False
-                self.logger.error("连接异常，2秒后重连...")
+                self.logger.error("连接异常，50ms后重连...")
-                time.sleep_ms(2000)
+                time.sleep_ms(50)
 
             except Exception as e:
                 # TCP主循环的顶层异常捕获，防止线程静默退出
@@ -2270,7 +2394,7 @@ class NetworkManager:
                 import traceback
                 self.logger.error(traceback.format_exc())
                 self._tcp_connected = False
-                time.sleep_ms(5000)  # 等待5秒后重试连接
+                time.sleep_ms(500)  # 等待5秒后重试连接
 
 
 # 创建全局单例实例

power.py

@@ -5,10 +5,11 @@
 提供电压、电流监测和充电状态检测
 """
 import config
+import os
+import subprocess
 from logger_manager import logger_manager
 from maix import time as maix_time
 
-
 _INA226_PRESENT = None
 
 
@@ -85,7 +86,7 @@ def get_bus_voltage():
 def get_current():
     """
     读取电流（单位：mA）
-    正数表示充电，负数表示放电
+    当前电源板实测：正数表示放电，负数表示充电。
 
     INA226 电流计算公式：
     Current = (Current Register Value) × Current_LSB
@@ -96,13 +97,13 @@ def get_current():
             return 0.0
         raw = read_register(config.REG_CURRENT)
         # INA226 电流寄存器是16位有符号整数
-        # 最高位是符号位：0=正（充电），1=负（放电）
+        # 最高位是符号位；电流方向含义取决于电源板的采样电阻接线方向。
         # 计算 Current_LSB（根据 CALIBRATION_VALUE）
         current_lsb = 0.001 * config.CALIBRATION_VALUE / 4096  # 单位：A
         # 处理有符号数：如果最高位为1，转换为负数
-        if raw & 0x8000:  # 最高位为1，表示负数（放电）
+        if raw & 0x8000:
             signed_raw = raw - 0x10000  # 转换为有符号整数
-        else:  # 最高位为0，表示正数（充电）
+        else:
             signed_raw = raw
         # 转换为毫安
         current_ma = signed_raw * current_lsb * 1000
@@ -129,7 +130,7 @@ def is_charging(threshold_ma=10.0):
     """
     try:
         current = get_current()
-        is_charge = current > threshold_ma
+        is_charge = current < -abs(float(threshold_ma))
         return is_charge
     except Exception as e:
         logger = logger_manager.logger

shoot_manager.py

@@ -8,7 +8,12 @@ from laser_manager import laser_manager
 from logger_manager import logger_manager
 from network import network_manager
 from triangle_target import load_camera_from_xml, load_triangle_positions, try_triangle_scoring
-from vision import estimate_distance, detect_circle_v3, enqueue_save_shot
+from vision import (
+    estimate_distance,
+    detect_circle_v3,
+    enqueue_save_shot,
+    enqueue_save_raw_shot,
+)
 from maix import image, time
 
 # 缓存相机标定与三角形位置，避免每次射箭重复读磁盘
@@ -320,9 +325,28 @@ def process_shot(adc_val):
     logger = logger_manager.logger
 
     try:
-        network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True)
         frame = camera_manager.read_frame()
 
+        # Copy the untouched frame before any detection or drawing.
+        from shot_id_generator import shot_id_generator
+        shot_id = shot_id_generator.generate_id()
+        enqueue_save_raw_shot(frame, shot_id)
+
+        # 网络事件移到拍照之后，避免阻塞拍照
+        network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True)
+
+        # Classify only the current shot frame; never reuse a previous result.
+        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)
 
@@ -366,10 +390,6 @@ def process_shot(adc_val):
         if dx is None and dy is None and logger:
             logger.warning("[MAIN] 未检测到偏移量（三角形与圆形均失败），但会保存图像")
 
-        # 生成射箭ID
-        from shot_id_generator import shot_id_generator
-        shot_id = shot_id_generator.generate_id()
-
         if logger:
             logger.info(f"[MAIN] 射箭ID: {shot_id}")
 
@@ -382,11 +402,25 @@ 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,
@@ -413,7 +447,19 @@ def process_shot(adc_val):
             inner_data["ellipse_center_x"] = None
             inner_data["ellipse_center_y"] = None
 
+        # 记录这组 inner_data 即将进入上报队列的本地时间，精确到毫秒。
+        upload_time_ms = int(time_std.time() * 1000)
+        upload_time_sec, upload_time_millis = divmod(upload_time_ms, 1000)
+        inner_data["upload_time"] = (
+            time_std.strftime("%Y-%m-%d %H:%M:%S", time_std.localtime(upload_time_sec))
+            + f".{upload_time_millis:03d}"
+        )
         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
@@ -518,6 +564,7 @@ def process_shot(adc_val):
             laser_manager.flash_laser(config.FLASH_LASER_DURATION_MS)
 
         # 保存图像（异步队列，与 main.py 一致）
+        _force_save = (dx is None and dy is None) and getattr(config, "SAVE_IMAGE_ON_FAILURE", False)
         enqueue_save_shot(
             result_img,
             center,
@@ -527,8 +574,9 @@ def process_shot(adc_val):
             (x, y),
             distance_m,
             shot_id=shot_id,
-            photo_dir=config.PHOTO_DIR if config.SAVE_IMAGE_ENABLED else None,
+            photo_dir=config.PHOTO_DIR if (config.SAVE_IMAGE_ENABLED or _force_save) else None,
             yolo_roi_xyxy=yolo_roi_xyxy if draw_yolo_roi else None,
+            force_save=_force_save,
         )
 
         if logger:

target_roi_yolo.py

@@ -89,6 +89,29 @@ def _stage2_roi_crop_save_worker(
 _detector_by_path = {}
 
 
+def _resolve_model_path(model_path: str):
+    """Resolve a model in either the installed app or MaixVision run directory."""
+    model_path = (model_path or "").strip()
+    if model_path and os.path.isfile(model_path):
+        return model_path
+    if not model_path:
+        return ""
+    name = os.path.basename(model_path)
+    module_dir = os.path.dirname(os.path.abspath(__file__))
+    candidates = (
+        os.path.join(module_dir, name),
+        os.path.join(module_dir, "test", name),
+        os.path.join("/tmp/maixpy_run", name),
+        os.path.join("/tmp/maixpy_run", "test", name),
+        os.path.join(os.getcwd(), name),
+        os.path.join(os.getcwd(), "test", name),
+    )
+    for candidate in candidates:
+        if os.path.isfile(candidate):
+            return candidate
+    return model_path
+
+
 def reset_yolo_detector_cache():
     """切换模型路径时可调用（通常不必）。"""
     global _detector_by_path
@@ -103,10 +126,19 @@ def _get_detector(model_path: str):
         return _detector_by_path[model_path]
     try:
         from maix import nn
-    except ImportError:
+    except Exception:
         return None
-    _detector_by_path[model_path] = nn.YOLOv5(model=model_path, dual_buff=False)
+    # YOLO is an optional capability. A broken/incompatible model must not
-    return _detector_by_path[model_path]
+    # abort boot (especially before the OTA rollback check).
+    try:
+        detector = nn.YOLOv5(model=model_path, dual_buff=False)
+    except Exception:
+        # Cache the failure to avoid retrying a broken native load every frame.
+        # reset_yolo_detector_cache() clears this after a model replacement.
+        _detector_by_path[model_path] = None
+        return None
+    _detector_by_path[model_path] = detector
+    return detector
 
 
 def preload_yolo_detector(logger=None):
@@ -175,6 +207,23 @@ def preload_yolo_detector(logger=None):
                 % (_loc_black,)
             )
 
+    if bool(getattr(cfg, "TARGET_CLASS_YOLO_ENABLE", False)) and bool(
+        getattr(cfg, "TARGET_CLASS_YOLO_PRELOAD_ON_BOOT", True)
+    ):
+        class_model_path = _resolve_model_path(
+            getattr(cfg, "TARGET_CLASS_YOLO_MODEL_PATH", "") or ""
+        )
+        class_detector = _get_detector(class_model_path)
+        if class_detector is None:
+            if logger:
+                logger.warning(
+                    f"[YOLO-TARGET] 预加载失败：无法加载模型 {class_model_path}"
+                )
+        else:
+            ok = True
+            if logger:
+                logger.info(f"[YOLO-TARGET] 靶规格模型已预加载: {class_model_path}")
+
     return ok
 
 
@@ -206,8 +255,10 @@ def _det_obj_class_id(o):
             if v is None:
                 continue
             try:
+                if callable(v):
+                    v = v()
                 return int(float(v))
-            except (TypeError, ValueError):
+            except (TypeError, ValueError, AttributeError):
                 continue
     return None
 
@@ -242,6 +293,106 @@ def _normalize_objs(objs):
     return out
 
 
+def _det_obj_score(o):
+    """Return confidence across supported Maix YOLO result formats."""
+    for key in ("score", "confidence", "conf", "prob"):
+        if hasattr(o, key):
+            try:
+                value = getattr(o, key)
+                if callable(value):
+                    value = value()
+                value = float(value)
+                if value == value:
+                    return value
+            except (TypeError, ValueError, AttributeError):
+                pass
+    return 0.0
+
+
+def try_get_target_class_from_yolo(maix_frame, logger=None):
+    """Classify the current target as 20cm or 40cm; return None if unknown."""
+    try:
+        import config as cfg
+    except Exception:
+        return None
+
+    if not bool(getattr(cfg, "TARGET_CLASS_YOLO_ENABLE", False)):
+        return None
+    model_path = _resolve_model_path(
+        getattr(cfg, "TARGET_CLASS_YOLO_MODEL_PATH", "") or ""
+    )
+    if not os.path.isfile(model_path):
+        if logger:
+            logger.warning(f"[YOLO-TARGET] 模型文件不存在: {model_path}")
+        return None
+    detector = _get_detector(model_path)
+    if detector is None:
+        if logger:
+            logger.warning("[YOLO-TARGET] 无法加载 nn.YOLOv5")
+        return None
+
+    conf_th = float(getattr(cfg, "TARGET_CLASS_YOLO_CONF_TH", 0.5))
+    iou_th = float(getattr(cfg, "TARGET_CLASS_YOLO_IOU_TH", 0.45))
+    labels = getattr(cfg, "TARGET_CLASS_YOLO_LABELS", (20, 40))
+    if isinstance(labels, str):
+        labels = tuple(x.strip() for x in labels.split(",") if x.strip())
+    labels = tuple(labels)
+
+    def _detect(threshold):
+        try:
+            raw = detector.detect(maix_frame, conf_th=threshold, iou_th=iou_th)
+        except Exception as exc:
+            if logger:
+                logger.warning(f"[YOLO-TARGET] detect 异常: {exc}")
+            return []
+        return _normalize_objs(raw if raw is not None else [])
+
+    def _candidates(objs):
+        found = []
+        for obj in objs:
+            class_id = _det_obj_class_id(obj)
+            if class_id is None or class_id < 0 or class_id >= len(labels):
+                continue
+            try:
+                label = int(float(labels[class_id]))
+            except (TypeError, ValueError):
+                continue
+            if label in (20, 40):
+                found.append((label, class_id, _det_obj_score(obj)))
+        return found
+
+    objects = _detect(conf_th)
+    candidates = _candidates(objects)
+    if logger and objects:
+        logger.info(
+            "[YOLO-TARGET] 原始框=%d, 解析类别=%s"
+            % (
+                len(objects),
+                [(_det_obj_class_id(o), _det_obj_score(o)) for o in objects[:8]],
+            )
+        )
+    if not candidates and bool(
+        getattr(cfg, "TARGET_CLASS_YOLO_RETRY_ON_EMPTY", False)
+    ):
+        retry_th = float(getattr(cfg, "TARGET_CLASS_YOLO_RETRY_CONF_TH", conf_th))
+        if 0 < retry_th < conf_th:
+            candidates = _candidates(_detect(retry_th))
+
+    if not candidates:
+        if logger:
+            logger.warning("[YOLO-TARGET] 当前帧未识别到 20/40，按未知处理")
+        return None
+
+    label, class_id, confidence = max(candidates, key=lambda item: item[2])
+    result = {"label": label, "class_id": class_id, "confidence": confidence}
+    if logger:
+        logger.info(
+            f"[YOLO-TARGET] 当前帧分类={label}, class_id={class_id}, "
+            f"conf={confidence:.3f}"
+        )
+    return result
+
+
 def _det_to_src_xyxy(o, coord_mode: str, src_w: int, src_h: int, net_w: int, net_h: int):
     """把单个检测框转为全图坐标系下的 xyxy（半开区间语义与后续 clip 一致）。"""
     x, y, w, h = float(o.x), float(o.y), float(o.w), float(o.h)

test/test_decect_circle_v4.py

@@ -0,0 +1,635 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+离线测试脚本：直接复用 detect_circle 逻辑进行测试
+运行环境：MaixPy (Sipeed MAIX)
+"""
+import sys
+import os
+# import time
+from maix import image, time
+import cv2
+import numpy as np
+
+# ==================== 全局配置 (与 test_main.py 保持一致) ====================
+REAL_RADIUS_CM = 20  # 靶心实际半径（厘米）
+
+
+# ==================== 复制的核心算法 ====================
+# 注意：这里直接复制了 detect_circle 的逻辑，避免 import main 导致的冲突
+
+
+def detect_circle_v3(frame, laser_point=None):
+    """检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本
+    增加红色圆圈检测，验证黄色圆圈是否为真正的靶心
+    如果提供 laser_point，会选择最接近激光点的目标
+    
+    Args:
+        frame: 图像帧
+        laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择
+    
+    Returns:
+        (result_img, best_center, best_radius, method, best_radius1, ellipse_params)
+    """
+    img_cv = image.image2cv(frame, False, False)
+
+    best_center = best_radius = best_radius1 = method = None
+    ellipse_params = None
+
+    # HSV 黄色掩码检测（模糊靶心）
+    hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    h, s, v = cv2.split(hsv)
+
+    # 调整饱和度策略：稍微增强，不要过度
+    s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
+
+    hsv = cv2.merge((h, s, v))
+
+    # 放宽 HSV 阈值范围（针对模糊图像的关键调整）
+    lower_yellow = np.array([7, 80, 0])  # 饱和度下限降低，捕捉淡黄色
+    upper_yellow = np.array([32, 255, 255])  # 亮度上限拉满
+
+    mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # 调整形态学操作
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
+
+    contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # 存储所有有效的黄色-红色组合
+    valid_targets = []
+
+    if contours_yellow:
+        for cnt_yellow in contours_yellow:
+            area = cv2.contourArea(cnt_yellow)
+            perimeter = cv2.arcLength(cnt_yellow, True)
+
+            # 计算圆度
+            if perimeter > 0:
+                circularity = (4 * np.pi * area) / (perimeter * perimeter)
+            else:
+                circularity = 0
+
+            if area > 50 and circularity > 0.7:
+                print(f"[target] -> 面积:{area}, 圆度:{circularity:.2f}")
+                # 尝试拟合椭圆
+                yellow_center = None
+                yellow_radius = None
+                yellow_ellipse = None
+
+                if len(cnt_yellow) >= 5:
+                    (x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
+                    yellow_ellipse = ((x, y), (width, height), angle)
+                    axes_minor = min(width, height)
+                    radius = axes_minor / 2
+                    yellow_center = (int(x), int(y))
+                    yellow_radius = int(radius)
+                else:
+                    (x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
+                    yellow_center = (int(x), int(y))
+                    yellow_radius = int(radius)
+                    yellow_ellipse = None
+
+                # 如果检测到黄色圆圈，再检测红色圆圈进行验证
+                if yellow_center and yellow_radius:
+                    # HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围）
+                    # 红色范围1: 0-12度（接近0度的红色）
+                    # 放宽S/V阈值：S>=30, V>=20 以捕获淡红/暗红
+                    lower_red1 = np.array([0, 30, 20])
+                    upper_red1 = np.array([12, 255, 255])
+                    mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
+
+                    # 红色范围2: 168-180度（接近180度的红色）
+                    lower_red2 = np.array([168, 30, 20])
+                    upper_red2 = np.array([180, 255, 255])
+                    mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2)
+
+                    # 合并两个红色掩码
+                    mask_red = cv2.bitwise_or(mask_red1, mask_red2)
+
+                    # 形态学操作：先CLOSE填充空洞，再DILATE加厚环状区域
+                    kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+                    mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
+                    mask_red = cv2.dilate(mask_red, kernel_red, iterations=1)
+
+                    contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+                    red_pixel_count = np.sum(mask_red > 0)
+                    print(f"Debug -> 红色掩码: {red_pixel_count} 像素, {len(contours_red)} 个轮廓")
+
+                    found_valid_red = False
+
+                    if contours_red:
+                        for cnt_red in contours_red:
+                            area_red = cv2.contourArea(cnt_red)
+                            perimeter_red = cv2.arcLength(cnt_red, True)
+
+                            if perimeter_red > 0:
+                                circularity_red = (4 * np.pi * area_red) / (perimeter_red * perimeter_red)
+                            else:
+                                circularity_red = 0
+
+                            # 环状轮廓圆度可能偏低，放宽到0.2
+                            print(f"Debug -> 红轮廓: 面积={area_red:.1f}, 圆度={circularity_red:.2f}" +
+                                  f" (面积>15={area_red > 15}, 圆度>0.2={circularity_red > 0.2})")
+                            if area_red > 15 and circularity_red > 0.2:
+                                if len(cnt_red) >= 5:
+                                    (x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red)
+                                    radius_red = min(w_red, h_red) / 2
+                                    red_center = (int(x_red), int(y_red))
+                                    red_radius = int(radius_red)
+                                else:
+                                    (x_red, y_red), radius_red = cv2.minEnclosingCircle(cnt_red)
+                                    red_center = (int(x_red), int(y_red))
+                                    red_radius = int(radius_red)
+
+                                if red_center:
+                                    dx = yellow_center[0] - red_center[0]
+                                    dy = yellow_center[1] - red_center[1]
+                                    distance = np.sqrt(dx * dx + dy * dy)
+
+                                    max_distance = yellow_radius * 2.0
+                                    min_r = min(red_radius, yellow_radius)
+                                    max_r = max(red_radius, yellow_radius)
+                                    size_ratio = min_r / max_r if max_r > 0 else 0
+                                    print(f"Debug -> 圆心距={distance:.1f}(阈值={max_distance:.1f}), "
+                                          f"大小比={size_ratio:.2f}(阈值=0.4), "
+                                          f"距离OK={distance < max_distance}, 大小OK={size_ratio >= 0.4}")
+
+                                    # 允许红圈在黄圈外侧或内侧，只要大小相近（较小/较大 >= 0.5）
+                                    if distance < max_distance and size_ratio >= 0.4:
+                                        found_valid_red = True
+                                        print(
+                                            f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), 红心({red_center}), 距离:{distance:.1f}, 黄半径:{yellow_radius}, 红半径:{red_radius}")
+
+                                        valid_targets.append({
+                                            'center': yellow_center,
+                                            'radius': yellow_radius,
+                                            'ellipse': yellow_ellipse,
+                                            'area': area
+                                        })
+                                        break
+
+                    if not found_valid_red:
+                        # 如果黄圈非常可靠（大且圆），在没有红圈验证时仍接受
+                        if area > 30 and circularity > 0.85:
+                            print(f"[target] -> 黄圈高置信度(面积:{area:.0f}, 圆度:{circularity:.2f})，跳过红圈验证直接接受")
+                            valid_targets.append({
+                                'center': yellow_center,
+                                'radius': yellow_radius,
+                                'ellipse': yellow_ellipse,
+                                'area': area
+                            })
+                        else:
+                            print("Debug -> 未找到匹配的红色圆圈，可能是误识别")
+
+    # 从所有有效目标中选择最佳目标
+    if valid_targets:
+        if laser_point:
+            # 如果有激光点，选择最接近激光点的目标
+            best_target = None
+            min_distance = float('inf')
+            for target in valid_targets:
+                dx = target['center'][0] - laser_point[0]
+                dy = target['center'][1] - laser_point[1]
+                distance = np.sqrt(dx * dx + dy * dy)
+                if distance < min_distance:
+                    min_distance = distance
+                    best_target = target
+            if best_target:
+                best_center = best_target['center']
+                best_radius = best_target['radius']
+                ellipse_params = best_target['ellipse']
+                method = "v3_ellipse_red_validated_laser_selected"
+                best_radius1 = best_radius * 5
+        else:
+            # 如果没有激光点，选择面积最大的目标
+            best_target = max(valid_targets, key=lambda t: t['area'])
+            best_center = best_target['center']
+            best_radius = best_target['radius']
+            ellipse_params = best_target['ellipse']
+            method = "v3_ellipse_red_validated"
+            best_radius1 = best_radius * 5
+
+    result_img = image.cv2image(img_cv, False, False)
+    return result_img, best_center, best_radius, method, best_radius1, ellipse_params
+
+
+def detect_circle(frame):
+    """检测图像中的靶心（优先清晰轮廓，其次黄色区域）"""
+    img_cv = image.image2cv(frame, False, False)
+    # gray = cv2.cvtColor(img_cv, cv2.COLOR_RGB2GRAY)
+    # blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    # edged = cv2.Canny(blurred, 50, 150)
+    # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    # ceroded = cv2.erode(cv2.dilate(edged, kernel), kernel)
+
+    # contours, _ = cv2.findContours(ceroded, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    # best_center = best_radius = best_radius1 = method = None
+
+    # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    # h, s, v = cv2.split(hsv)
+    # s = np.clip(s * 2, 0, 255).astype(np.uint8)
+    # hsv = cv2.merge((h, s, v))
+    # lower_yellow = np.array([7, 80, 0])
+    # upper_yellow = np.array([32, 255, 182])
+    # mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+    # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)
+    # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # if contours:
+    #     largest = max(contours, key=cv2.contourArea)
+    #     if cv2.contourArea(largest) > 50:
+    #         (x, y), radius = cv2.minEnclosingCircle(largest)
+    #         best_center = (int(x), int(y))
+    #         best_radius = int(radius)
+    #         best_radius1 = radius * 5
+    #         method = "v2"
+
+    # auto
+    # R:31 M:v2 D:2.410110127692767
+    # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    # h, s, v = cv2.split(hsv)
+
+    # # 1. 增强饱和度（模糊照片需要更强的增强）
+    # s = np.clip(s * 2.5, 0, 255).astype(np.uint8)  # 从2.0改为2.5
+
+    # # 2. 增强亮度（模糊照片可能偏暗）
+    # v = np.clip(v * 1.2, 0, 255).astype(np.uint8)  # 新增：提升亮度
+
+    # hsv = cv2.merge((h, s, v))
+
+    # # 3. 放宽HSV颜色范围（特别是模糊照片）
+    # # 降低饱和度下限，提高亮度上限
+    # lower_yellow = np.array([5, 50, 30])   # H:5-35, S:50-255, V:30-255
+    # upper_yellow = np.array([35, 255, 255])
+
+    # mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # # 4. 增强形态学操作（连接被分割的区域）
+    # kernel_small = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    # kernel_large = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 9))  # 更大的核
+
+    # # 先开运算去除噪声
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel_small)
+    # # 多次膨胀连接区域（模糊照片需要更多膨胀）
+    # mask = cv2.dilate(mask, kernel_large, iterations=2)  # 增加迭代次数
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel_large)  # 闭运算填充空洞
+
+    # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # if contours:
+    #     largest = max(contours, key=cv2.contourArea)
+    #     area = cv2.contourArea(largest)
+    #     if area > 50:
+    #         # 5. 使用面积计算等效半径（更准确）
+    #         equivalent_radius = np.sqrt(area / np.pi)
+
+    #         # 6. 同时使用minEnclosingCircle作为备选（取较大值）
+    #         (x, y), enclosing_radius = cv2.minEnclosingCircle(largest)
+
+    #         # 取两者中的较大值，确保不遗漏
+    #         radius = max(equivalent_radius, enclosing_radius)
+
+    #         best_center = (int(x), int(y))
+    #         best_radius = int(radius)
+    #         best_radius1 = radius * 5
+    #         method = "v2"
+
+    # codegee
+    # R:24 M:v2 D:3.061493895819174
+    # R:22 M:v2 D:3.3644971681267077  np.clip(s * 1.1, 0, 255)
+    hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    h, s, v = cv2.split(hsv)
+
+    # 2. 调整饱和度策略：
+    # 不要暴力翻倍，可以尝试稍微增强，或者使用 CLAHE 增强亮度/对比度
+    # 这里我们稍微增加一点饱和度，并确保不溢出
+    s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
+    # 对亮度通道 v 也可以做一点 CLAHE 处理来增强对比度（可选）
+    # clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    # v = clahe.apply(v)
+
+    hsv = cv2.merge((h, s, v))
+
+    # 3. 放宽 HSV 阈值范围（针对模糊图像的关键调整）
+    # 降低 S 的下限 (80 -> 35)，提高 V 的上限 (182 -> 255)
+    lower_yellow = np.array([7, 80, 0])  # 饱和度下限降低，捕捉淡黄色
+    upper_yellow = np.array([32, 255, 255])  # 亮度上限拉满
+
+    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # 4. 调整形态学操作
+    # 去掉 MORPH_OPEN，因为它会减小面积。
+    # 使用 MORPH_CLOSE (先膨胀后腐蚀) 来填充内部小黑洞，连接近邻区域
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    # 再进行一次膨胀，确保边缘被包含进来
+    # mask = cv2.dilate(mask, kernel, iterations=1) 
+
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    if contours:
+        largest = max(contours, key=cv2.contourArea)
+
+        # 这里可以适当降低面积阈值，或者保持不变
+        if cv2.contourArea(largest) > 50:
+            # (x, y), radius = cv2.minEnclosingCircle(largest)
+            # best_center = (int(x), int(y))
+            # best_radius = int(radius)
+
+            # --- 核心修改开始 ---
+            # 1. 尝试拟合椭圆 (需要轮廓点至少为5个)
+            if len(largest) >= 5:
+                # 返回值: ((中心x, 中心y), (长轴, 短轴), 旋转角度)
+                (x, y), (axes_major, axes_minor), angle = cv2.fitEllipse(largest)
+
+                # 2. 计算半径
+                # 选项A：取长短轴的平均值 (比较稳健)
+                # radius = (axes_major + axes_minor) / 4
+
+                # 选项B：直接取短轴的一半 (抗模糊最强，推荐)
+                radius = axes_minor / 2
+
+                best_center = (int(x), int(y))
+                best_radius = int(radius)
+                method = "v2_ellipse"
+            else:
+                # 如果点太少无法拟合椭圆，降级回 minEnclosingCircle
+                (x, y), radius = cv2.minEnclosingCircle(largest)
+                best_center = (int(x), int(y))
+                best_radius = int(radius)
+                method = "v2"
+            # --- 核心修改结束 ---
+
+            # 你的后续逻辑
+            best_radius1 = radius * 5
+
+    # operas 4.5
+    # R:25 M:v2 D:2.9554872521538527
+    # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    # h, s, v = cv2.split(hsv)
+
+    # # 1. 适度增强饱和度（不要过度，否则噪声也会增强）
+    # s = np.clip(s * 1.5, 0, 255).astype(np.uint8)
+    # hsv = cv2.merge((h, s, v))
+
+    # # 2. 放宽 HSV 阈值范围（关键改动）
+    # # - 饱和度下限从 80 降到 40（捕捉淡黄色）
+    # # - 亮度上限从 182 提高到 255（允许更亮的黄色）
+    # lower_yellow = np.array([7, 40, 30])
+    # upper_yellow = np.array([35, 255, 255])
+
+    # mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # # 3. 调整形态学操作：用 CLOSE 替代 OPEN
+    # # CLOSE（先膨胀后腐蚀）：填充内部空洞，连接相邻区域
+    # # OPEN（先腐蚀后膨胀）：会缩小区域，不适合模糊图像
+    # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))  # 稍大的核
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    # mask = cv2.dilate(mask, kernel, iterations=1)  # 额外膨胀，确保边缘被包含
+
+    # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # if contours:
+    #     largest = max(contours, key=cv2.contourArea)
+    #     if cv2.contourArea(largest) > 50:
+    #         (x, y), radius = cv2.minEnclosingCircle(largest)
+    #         best_center = (int(x), int(y))
+    #         best_radius = int(radius)
+    #         best_radius1 = radius * 5
+    #         method = "v2"
+
+    # # --- 新增：将 Mask 叠加到原图上用于调试 ---
+    # # 创建一个彩色掩码（红色通道为255，其他为0）
+    # mask_overlay = np.zeros_like(img_cv)
+    # mask_overlay[:, :, 2] = mask  # 将掩码放在红色通道 (BGR中的R)
+    #
+    # cv2.addWeighted(img_cv, 0.6, mask_overlay, 0.4, 0, img_cv)
+
+    result_img = image.cv2image(img_cv, False, False)
+    return result_img, best_center, best_radius, method, best_radius1
+
+
+def detect_circle_v2(frame):
+    """检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本"""
+    global REAL_RADIUS_CM
+    img_cv = image.image2cv(frame, False, False)
+
+    best_center = best_radius = best_radius1 = method = None
+    ellipse_params = None  # 存储椭圆参数 ((x, y), (axes_major, axes_minor), angle)
+
+    # HSV 黄色掩码检测（模糊靶心）
+    hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    h, s, v = cv2.split(hsv)
+
+    # 调整饱和度策略：稍微增强，不要过度
+    s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
+
+    hsv = cv2.merge((h, s, v))
+
+    # 放宽 HSV 阈值范围（针对模糊图像的关键调整）
+    lower_yellow = np.array([7, 80, 0])  # 饱和度下限降低，捕捉淡黄色
+    upper_yellow = np.array([32, 255, 255])  # 亮度上限拉满
+
+    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # 调整形态学操作
+    # 使用 MORPH_CLOSE (先膨胀后腐蚀) 来填充内部小黑洞，连接近邻区域
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    if contours:
+        largest = max(contours, key=cv2.contourArea)
+
+        if cv2.contourArea(largest) > 50:
+            # 尝试拟合椭圆 (需要轮廓点至少为5个)
+            if len(largest) >= 5:
+                # 返回值: ((中心x, 中心y), (width, height), 旋转角度)
+                # 注意：width 和 height 是外接矩形的尺寸，不是长轴和短轴
+                (x, y), (width, height), angle = cv2.fitEllipse(largest)
+
+                # 保存椭圆参数（保持原始顺序，用于绘制）
+                ellipse_params = ((x, y), (width, height), angle)
+
+                # 计算半径：使用较小的尺寸作为短轴
+                axes_minor = min(width, height)
+                radius = axes_minor / 2
+
+                best_center = (int(x), int(y))
+                best_radius = int(radius)
+                method = "v2_ellipse"
+            else:
+                # 如果点太少无法拟合椭圆，降级回 minEnclosingCircle
+                (x, y), radius = cv2.minEnclosingCircle(largest)
+                best_center = (int(x), int(y))
+                best_radius = int(radius)
+                method = "v2"
+                ellipse_params = None  # 圆形，没有椭圆参数
+
+            best_radius1 = radius * 5
+
+    result_img = image.cv2image(img_cv, False, False)
+    return result_img, best_center, best_radius, method, best_radius1, ellipse_params
+
+
+# ==================== 测试逻辑 ====================
+
+def run_offline_test(image_path):
+    """读取图片，检测圆，绘制结果，保存图片"""
+
+    # 1. 检查文件是否存在
+    if not os.path.exists(image_path):
+        print(f"[ERROR] 找不到图片文件: {image_path}")
+        return
+
+    # 2. 使用 maix.image 读取图片 (适配 MaixPy v4)
+    try:
+        # 使用 image.load 读取文件，返回 Image 对象
+        img = image.load(image_path)
+        print(f"[INFO] 成功读取图片: {image_path} (尺寸: {img.width()}x{img.height()})")
+    except Exception as e:
+        print(f"[ERROR] 读取图片失败: {e}")
+        print("提示：请确认 MaixPy 版本是否为 v4，且图片路径正确。")
+        return
+
+    # 3. 调用 detect_circle_v2 函数
+    print("[INFO] 正在调用 detect_circle_v2 进行检测...")
+    start_time = time.ticks_ms()
+
+    result_img, center, radius, method, radius1, ellipse_params = detect_circle_v3(img)
+
+    cost_time = time.ticks_ms() - start_time
+    print(f"[INFO] 检测完成，耗时: {cost_time}ms")
+    print(f"       结果 -> 圆心: {center}, 半径: {radius}, 方法: {method}")
+    if ellipse_params:
+        (ell_center, (width, height), angle) = ellipse_params
+        print(
+            f"       椭圆 -> 中心: ({ell_center[0]:.1f}, {ell_center[1]:.1f}), 长轴: {max(width, height):.1f}, 短轴: {min(width, height):.1f}, 角度: {angle:.1f}°")
+
+    # 4. 绘制辅助线（可选，用于调试）
+    if center and radius:
+        # 为了绘制椭圆，需要转换回 cv2 图像
+        img_cv = image.image2cv(result_img, False, False)
+
+        cx, cy = center
+
+        # 如果有椭圆参数，绘制椭圆
+        if ellipse_params:
+            (ell_center, (width, height), angle) = ellipse_params
+            cx_ell, cy_ell = int(ell_center[0]), int(ell_center[1])
+
+            # 确定长轴和短轴
+            if width >= height:
+                # width 是长轴，height 是短轴
+                axes_major = width
+                axes_minor = height
+                major_angle = angle  # 长轴角度就是 angle
+                minor_angle = angle + 90  # 短轴角度 = 长轴角度 + 90度
+            else:
+                # height 是长轴，width 是短轴
+                axes_major = height
+                axes_minor = width
+                major_angle = angle + 90  # 长轴角度 = width角度 + 90度
+                minor_angle = angle  # 短轴角度就是 angle
+
+            # 使用 OpenCV 绘制椭圆（绿色，线宽2）
+            cv2.ellipse(img_cv,
+                        (cx_ell, cy_ell),  # 中心点
+                        (int(width / 2), int(height / 2)),  # 半宽、半高
+                        angle,  # 旋转角度（OpenCV需要原始angle）
+                        0, 360,  # 起始和结束角度
+                        (0, 255, 0),  # 绿色 (RGB格式)
+                        2)  # 线宽
+
+            # 绘制椭圆中心点（红色）
+            cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1)
+
+            import math
+            # 绘制短轴（蓝色线条）
+            minor_length = axes_minor / 2
+            minor_angle_rad = math.radians(minor_angle)
+            dx_minor = minor_length * math.cos(minor_angle_rad)
+            dy_minor = minor_length * math.sin(minor_angle_rad)
+            pt1_minor = (int(cx_ell - dx_minor), int(cy_ell - dy_minor))
+            pt2_minor = (int(cx_ell + dx_minor), int(cy_ell + dy_minor))
+            cv2.line(img_cv, pt1_minor, pt2_minor, (0, 0, 255), 2)  # 蓝色 (RGB格式)
+        else:
+            # 如果没有椭圆参数，绘制圆形（红色）
+            cv2.circle(img_cv, (cx, cy), radius, (0, 0, 255), 2)
+            cv2.circle(img_cv, (cx, cy), 2, (0, 0, 255), -1)
+
+        # 转换回 maix image
+        result_img = image.cv2image(img_cv, False, False)
+
+        # 定义颜色对象用于文字
+        try:
+            color_black = image.Color.from_rgb(0, 0, 0)
+        except AttributeError:
+            color_black = image.Color(0, 0, 0)
+
+        # D. 添加文字信息
+        FOCAL_LENGTH_PIX = 1900
+        d = (REAL_RADIUS_CM * FOCAL_LENGTH_PIX) / radius1 / 100.0
+        info_str = f"R:{radius} M:{method} D:{d:.2f}"
+        print(info_str)
+
+        # 计算文字位置，防止超出图片边界
+        r_outer = int(radius * 11.0) if radius else 100
+        text_y = cy - r_outer - 20 if cy > r_outer + 20 else cy + r_outer + 20
+
+        # 调用 draw_string
+        result_img.draw_string(0, 0, info_str, color=color_black, scale=1.0)
+
+    # 5. 保存结果图片
+    output_path = image_path.replace(".bmp", "_result.bmp")
+    output_path = image_path.replace(".jpg", "_result.jpg")
+    try:
+        result_img.save(output_path, quality=100)
+        print(f"[SUCCESS] 结果已保存至: {output_path}")
+    except Exception as e:
+        print(f"[ERROR] 保存图片失败: {e}")
+
+
+if __name__ == "__main__":
+    # ================= 配置区域 =================
+
+    # 1. 设置要测试的图片路径
+    # 建议将图片放在与脚本同级目录，或者使用绝对路径
+    TARGET_IMAGE = "/root/phot/shot_1830921_0_no_target.jpg"
+
+    TARGET_DIR = "/root/phot"  # 修改为你想要读取的目录路径
+
+    # 支持的图片格式
+    IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.bmp']
+
+    # ================= 执行区域 =================
+    if 'TARGET_DIR' in locals():
+        # 读取目录下所有图片文件，过滤掉 _result.jpg 后缀的文件
+        image_files = []
+        if os.path.exists(TARGET_DIR) and os.path.isdir(TARGET_DIR):
+            for filename in os.listdir(TARGET_DIR):
+                # 检查文件扩展名
+                if any(filename.lower().endswith(ext) for ext in IMAGE_EXTENSIONS):
+                    # 过滤掉 _result.jpg 后缀的文件
+                    if filename.endswith('no_target.jpg'):
+                        filepath = os.path.join(TARGET_DIR, filename)
+                        if os.path.isfile(filepath):
+                            image_files.append(filepath)
+
+            # 按文件名排序（可选）
+            image_files.sort()
+
+            print(f"[INFO] 在目录 {TARGET_DIR} 中找到 {len(image_files)} 张图片")
+
+            # 处理每张图片
+            for img_path in image_files:
+                print(f"\n{'=' * 10} 开始处理: {img_path} {'=' * 10}")
+                run_offline_test(img_path)
+        else:
+            print(f"[ERROR] 目录不存在或不是有效目录: {TARGET_DIR}")
+
+    else:
+        run_offline_test(TARGET_IMAGE)

test/test_target_yolo_maixvision.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Run from MaixVision on PC to inspect the box's live 20/40 YOLO output."""
+
+import os
+
+from maix import app, camera, display, image, nn, time
+
+
+# This file is sent to /tmp/maixpy_run by MaixVision. Keep the model path
+# absolute so the script uses the model already installed on the box.
+MODEL_PATH = "/maixapp/apps/t11/model_317181.mud"
+CAMERA_WIDTH = 640
+CAMERA_HEIGHT = 480
+CONF_TH = 0.65
+IOU_TH = 0.45
+
+
+def _flatten_objects(raw):
+    if raw is None:
+        return []
+    if isinstance(raw, (list, tuple)):
+        result = []
+        for item in raw:
+            if isinstance(item, (list, tuple)):
+                result.extend(_flatten_objects(item))
+            else:
+                result.append(item)
+        return result
+    return [raw]
+
+
+def main():
+    if not os.path.isfile(MODEL_PATH):
+        raise FileNotFoundError("model not found on box: " + MODEL_PATH)
+
+    detector = nn.YOLOv5(model=MODEL_PATH, dual_buff=False)
+    cam = camera.Camera(CAMERA_WIDTH, CAMERA_HEIGHT)
+    disp = display.Display()
+
+    labels = tuple(str(label) for label in detector.labels)
+    print("[YOLO] model:", MODEL_PATH)
+    print("[YOLO] labels:", labels)
+    print("[YOLO] conf=%.2f iou=%.2f" % (CONF_TH, IOU_TH))
+
+    fps = 0.0
+    frame_count = 0
+    last_log_ms = time.ticks_ms()
+
+    while not app.need_exit():
+        loop_start_ms = time.ticks_ms()
+        img = cam.read()
+
+        detect_start_ms = time.ticks_ms()
+        raw = detector.detect(img, conf_th=CONF_TH, iou_th=IOU_TH)
+        detect_ms = max(0, time.ticks_diff(time.ticks_ms(), detect_start_ms))
+        objects = _flatten_objects(raw)
+
+        candidates = []
+        for obj in objects:
+            class_id = int(obj.class_id)
+            score = float(obj.score)
+            label = labels[class_id] if 0 <= class_id < len(labels) else "unknown"
+            color = image.COLOR_GREEN if label in ("20", "40") else image.COLOR_RED
+
+            img.draw_rect(obj.x, obj.y, obj.w, obj.h, color=color)
+            img.draw_string(
+                obj.x,
+                max(0, obj.y - 16),
+                "%scm %.2f" % (label, score),
+                color=color,
+            )
+            if label in ("20", "40"):
+                candidates.append((score, label))
+
+        loop_ms = max(1, time.ticks_diff(time.ticks_ms(), loop_start_ms))
+        instant_fps = 1000.0 / float(loop_ms)
+        fps = instant_fps if frame_count == 0 else fps * 0.9 + instant_fps * 0.1
+
+        if candidates:
+            best_score, best_label = max(candidates, key=lambda item: item[0])
+            status = "TARGET %scm %.2f" % (best_label, best_score)
+            status_color = image.COLOR_GREEN
+        else:
+            status = "TARGET UNKNOWN"
+            status_color = image.COLOR_RED
+
+        img.draw_string(5, 5, status, color=status_color)
+        img.draw_string(
+            5,
+            25,
+            "infer=%dms fps=%.1f boxes=%d" % (detect_ms, fps, len(objects)),
+            color=image.COLOR_YELLOW,
+        )
+        disp.show(img)
+
+        frame_count += 1
+        now_ms = time.ticks_ms()
+        if time.ticks_diff(now_ms, last_log_ms) >= 1000:
+            print(
+                "[YOLO] %s infer=%dms fps=%.1f boxes=%d"
+                % (status, detect_ms, fps, len(objects))
+            )
+            last_log_ms = now_ms
+
+
+if __name__ == "__main__":
+    main()

test_traditional_target.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Offline baseline for traditional target-paper detection.
+
+Dataset format: sibling .txt files use YOLO boxes and classes.txt maps ids
+(the supplied dataset uses 0=40, 1=20, 2=10).  This intentionally simple
+baseline uses grayscale segmentation and contour geometry; it is useful as a
+reference before adding more specialized black-triangle grouping.
+"""
+from __future__ import annotations
+
+import argparse
+import csv
+import glob
+import itertools
+import os
+
+import cv2
+import numpy as np
+
+
+def detect_white_papers(image: np.ndarray) -> list[tuple[int, int, int, int]]:
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    h, w = gray.shape[:2]
+    mask = cv2.inRange(gray, 120, 255)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, np.ones((9, 9), np.uint8))
+    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8))
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    out = []
+    for contour in contours:
+        x, y, bw, bh = cv2.boundingRect(contour)
+        area = float(bw * bh)
+        if area < 0.05 * w * h or min(bw, bh) < 80:
+            continue
+        fill = cv2.contourArea(contour) / max(area, 1.0)
+        aspect = bw / max(float(bh), 1.0)
+        if fill >= 0.45 and 0.4 <= aspect <= 2.5:
+            out.append((x, y, x + bw, y + bh))
+    return out
+
+
+def detect_black_triangle_papers(image: np.ndarray):
+    """Infer paper boxes from the four small black corner marks."""
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    mask = cv2.inRange(gray, 0, 100)
+    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, np.ones((2, 2), np.uint8))
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    points = []
+    for contour in contours:
+        x, y, bw, bh = cv2.boundingRect(contour)
+        area = cv2.contourArea(contour)
+        vertices = cv2.approxPolyDP(contour, 0.08 * cv2.arcLength(contour, True), True)
+        if 60 <= area <= 400 and 8 <= bw <= 24 and 8 <= bh <= 24:
+            if 3 <= len(vertices) <= 5 and 0.5 <= bw / max(bh, 1) <= 2.0:
+                points.append((x + bw / 2.0, y + bh / 2.0))
+    candidates = []
+    for group in itertools.combinations(points, 4):
+        xs = sorted(p[0] for p in group)
+        ys = sorted(p[1] for p in group)
+        span_x, span_y = xs[-1] - xs[0], ys[-1] - ys[0]
+        if span_x < 50 or span_y < 50 or not 0.45 < span_x / span_y < 1.5:
+            continue
+        corners = ((xs[0], ys[0]), (xs[-1], ys[0]),
+                   (xs[0], ys[-1]), (xs[-1], ys[-1]))
+        error = max(min(np.hypot(p[0] - c[0], p[1] - c[1]) for c in corners)
+                    for p in group) / max(span_x, span_y)
+        if error > 0.22:
+            continue
+        ex, ey = 0.12 * span_x, 0.12 * span_y
+        candidates.append((xs[0] - ex, ys[0] - ey,
+                           xs[-1] + ex, ys[-1] + ey, error))
+    # A colored target ring supplies an independent center check. Hough is
+    # deliberately low-cost here because it runs only on the already small
+    # candidate list's source frame.
+    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+    color = cv2.inRange(hsv, (0, 70, 45), (179, 255, 255))
+    color = cv2.morphologyEx(color, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8))
+    ring_centers = []
+    for contour in cv2.findContours(color, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]:
+        area = cv2.contourArea(contour)
+        if area < 150:
+            continue
+        moments = cv2.moments(contour)
+        if moments["m00"]:
+            ring_centers.append((moments["m10"] / moments["m00"], moments["m01"] / moments["m00"]))
+    checked = []
+    for box in candidates:
+        if not ring_centers:
+            checked.append(box)
+            continue
+        x0, y0, x1, y1, err = box
+        inside = any(x0 - .15 * (x1 - x0) <= cx <= x1 + .15 * (x1 - x0)
+                     and y0 - .15 * (y1 - y0) <= cy <= y1 + .15 * (y1 - y0)
+                     for cx, cy in ring_centers)
+        if inside:
+            checked.append(box)
+    return sorted(checked, key=lambda x: x[-1])
+
+
+def iou(a, b):
+    x0, y0 = max(a[0], b[0]), max(a[1], b[1])
+    x1, y1 = min(a[2], b[2]), min(a[3], b[3])
+    inter = max(0, x1 - x0) * max(0, y1 - y0)
+    aa = max(0, a[2] - a[0]) * max(0, a[3] - a[1])
+    bb = max(0, b[2] - b[0]) * max(0, b[3] - b[1])
+    return inter / max(aa + bb - inter, 1)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("dataset", help="directory containing jpg and YOLO txt files")
+    ap.add_argument("--iou", type=float, default=0.5)
+    ap.add_argument("--out", default="traditional_eval_results.csv",
+                    help="CSV output path; relative paths are next to the dataset")
+    ap.add_argument("--vis-dir", default="traditional_eval_images",
+                    help="directory for annotated result images; empty disables")
+    args = ap.parse_args()
+    stats = {0: [0, 0], 1: [0, 0]}
+    rows = []
+    # OpenCV on some Windows builds cannot decode non-ASCII filenames. Work
+    # relative to the dataset directory so the supplied Chinese path is safe.
+    dataset = os.path.abspath(args.dataset)
+    os.chdir(dataset)
+    # cwd is now the dataset, so a relative output avoids Windows console
+    # encoding issues with the Chinese parent path.
+    vis_dir = args.vis_dir if args.vis_dir else ""
+    if vis_dir:
+        os.makedirs(vis_dir, exist_ok=True)
+    files = glob.glob(os.path.join("**", "*.jpg"), recursive=True)
+    for image_path in files:
+        label_path = os.path.splitext(image_path)[0] + ".txt"
+        if not os.path.isfile(label_path):
+            continue
+        image = cv2.imread(image_path)
+        if image is None:
+            continue
+        h, w = image.shape[:2]
+        predictions = detect_black_triangle_papers(image)
+        vis = image.copy()
+        for p in predictions:
+            cv2.rectangle(vis, (int(p[0]), int(p[1])), (int(p[2]), int(p[3])), (0, 255, 255), 2)
+        for line in open(label_path, encoding="utf-8", errors="ignore"):
+            z = line.split()
+            if len(z) < 5 or int(float(z[0])) not in stats:
+                continue
+            cls, cx, cy, bw, bh = int(float(z[0])), *map(float, z[1:5])
+            truth = (int((cx - bw / 2) * w), int((cy - bh / 2) * h),
+                     int((cx + bw / 2) * w), int((cy + bh / 2) * h))
+            best = max((iou(truth, p) for p in predictions), default=0.0)
+            best_box = max(predictions, key=lambda p: iou(truth, p), default=())
+            stats[cls][0] += 1
+            stats[cls][1] += int(best >= args.iou)
+            rows.append({
+                "image": image_path,
+                "class_id": cls,
+                "truth_xyxy": ",".join(map(str, truth[:4])),
+                "pred_xyxy": ",".join(map(str, best_box[:4])) if best_box else "",
+                "iou": f"{best:.4f}",
+                "pass": int(best >= args.iou),
+            })
+            color = (0, 255, 0) if best >= args.iou else (0, 0, 255)
+            cv2.rectangle(vis, truth[:2], truth[2:4], color, 2)
+            cv2.putText(vis, f"GT {cls} IoU {best:.2f}",
+                        (truth[0], max(16, truth[1] - 4)),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 1, cv2.LINE_AA)
+        if vis_dir:
+            name = os.path.splitext(os.path.basename(image_path))[0] + "_result.jpg"
+            cv2.imwrite(os.path.join(vis_dir, name), vis)
+    total = sum(v[0] for v in stats.values())
+    good = sum(v[1] for v in stats.values())
+    print(f"paper objects: {good}/{total} = {good / max(total, 1):.2%} (IoU >= {args.iou})")
+    for cls, (n, ok) in stats.items():
+        print(f"class {cls}: {ok}/{n} = {ok / max(n, 1):.2%}")
+    out_path = args.out if os.path.isabs(args.out) else os.path.join(dataset, args.out)
+    with open(out_path, "w", newline="", encoding="utf-8-sig") as fp:
+        writer = csv.DictWriter(fp, fieldnames=("image", "class_id", "truth_xyxy",
+                                                "pred_xyxy", "iou", "pass"))
+        writer.writeheader()
+        writer.writerows(rows)
+    print(f"details csv: {out_path}")
+
+
+if __name__ == "__main__":
+    main()

version.md

@@ -20,3 +20,21 @@
 # 2.15.7 更新版本号
 # 2.15.8 启动不加载预加载yolo
 # 2.15.9 20cm
+# 2.15.10 不保存图片
+# 2.15.11 优化内存
+# 2.15.12 优化算法
+# 2.15.13 优化算法
+# 2.15.14 优化算法
+# 2.15.15 优化wifi连接
+# 2.15.16 修复wifi连接问题
+# 2.15.17 修复wifi连接问题
+# 2.15.18 wifi连接成功重新登录
+# 2.16.4  优化射箭延迟
+# 2.17.0  yolo标靶类别识别
+# 2.17.1  26-08-19 17：39 压力传感修改 增量方式 
+
+# 2.17.2 26-08-24 17：56 靶纸识别模型更替
+
+# 2.17.3 26-08-25 9：57 原图拍摄开关
+
+# 2.17.4 26-08-25 14：57 模型修改

version.py

@@ -4,6 +4,6 @@
 应用版本号
 每次 OTA 更新时，只需要更新这个文件中的版本号
 """
-VERSION = '2.15.9'
+VERSION = '2.17.15'
 
 

vision.py

@@ -570,11 +570,13 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
 
     # -- 3. 红色掩码：在循环外只算一次
     mask_red = cv2.bitwise_or(
-        cv2.inRange(hsv, np.array([0, 50, 40]),   np.array([10,  255, 255])),
+        cv2.inRange(hsv, np.array([0, 30, 20]),   np.array([12,  255, 255])),
-        cv2.inRange(hsv, np.array([170, 50, 40]), np.array([180, 255, 255])),
+        cv2.inRange(hsv, np.array([168, 30, 20]), np.array([180, 255, 255])),
     )
     kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
     mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
+    # 再加一次膨胀，加厚环状区域避免碎片化
+    mask_red = cv2.dilate(mask_red, kernel_red, iterations=1)
     contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     # 预先把红色轮廓筛选成 (center, radius) 列表，后续直接查表
     red_candidates = []
@@ -583,7 +585,7 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
         if ar <= 10:
             continue
         pr = cv2.arcLength(cnt_r, True)
-        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.3:
+        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.2:
             continue
         if len(cnt_r) >= 5:
             (xr, yr), (wr, hr), _ = cv2.fitEllipse(cnt_r)
@@ -625,7 +627,11 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
             ddx = yellow_center[0] - rc["center"][0]
             ddy = yellow_center[1] - rc["center"][1]
             dist_centers = math.hypot(ddx, ddy)
-            if dist_centers < yellow_radius * 1.5 and rc["radius"] > yellow_radius * 0.7:
+            max_dist = yellow_radius * 2.0
+            min_r = min(rc["radius"], yellow_radius)
+            max_r = max(rc["radius"], yellow_radius)
+            size_ratio = min_r / max_r if max_r > 0 else 0
+            if dist_centers < max_dist and size_ratio >= 0.4:
                 if logger:
                     logger.info(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), "
                                 f"红心({rc['center']}), 距离:{dist_centers:.1f}, "
@@ -638,7 +644,16 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
                 })
                 matched = True
                 break
-        if not matched and logger:
+        if not matched:
+            # 黄圈高置信度兜底：大且圆时跳过红圈验证
+            if area > 30 and circularity > 0.8:
+                valid_targets.append({
+                    "center": yellow_center,
+                    "radius": yellow_radius,
+                    "ellipse": yellow_ellipse,
+                    "area": area,
+                })
+            elif logger:
                 logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
 
     logger.debug(f"[detect_circle_v3] step 4 fin {datetime.now()}")
@@ -782,12 +797,12 @@ def estimate_pixel(physical_distance_cm, target_distance_m):
 
 def _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
                           laser_point, distance_m, shot_id=None, photo_dir=None,
-                          yolo_roi_xyxy=None):
+                          yolo_roi_xyxy=None, force_save=False):
     """
     内部实现：在 img_cv (numpy HWC RGB) 上绘制标注并保存。
     由 save_shot_image（同步）和存图 worker（异步）调用。
     """
-    if not config.SAVE_IMAGE_ENABLED:
+    if not config.SAVE_IMAGE_ENABLED and not force_save:
         return None
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -893,6 +908,11 @@ def _save_worker_loop():
             item = _save_queue.get()
             if item is None:
                 break
+            if isinstance(item, dict) and item.get("kind") == "raw":
+                _save_raw_image_impl(
+                    item["img_cv"], item["shot_id"], item["photo_dir"]
+                )
+            else:
                 _save_shot_image_impl(*item)
         except Exception as e:
             logger = logger_manager.logger
@@ -921,13 +941,64 @@ def start_save_shot_worker():
         logger.info("[VISION] 存图 worker 线程已启动")
 
 
+def _save_raw_image_impl(img_cv, shot_id, photo_dir):
+    """保存相机完整原始帧，不添加任何检测标注。"""
+    logger = logger_manager.logger
+    try:
+        os.makedirs(photo_dir, exist_ok=True)
+        filename = os.path.join(photo_dir, f"shot_{shot_id}_raw.jpg")
+        image.cv2image(img_cv, False, False).save(filename)
+        prune_old_images_in_dir(
+            photo_dir,
+            getattr(config, "RAW_IMAGE_MAX_IMAGES", config.MAX_IMAGES),
+            logger,
+            "[VISION-RAW]",
+        )
+        if logger:
+            logger.info(f"[VISION-RAW] 已保存纯原图: {filename}")
+        return filename
+    except Exception as e:
+        if logger:
+            logger.error(f"[VISION-RAW] 保存纯原图失败: {e}")
+        return None
+
+
+def enqueue_save_raw_shot(frame, shot_id, photo_dir=None):
+    """立即复制相机帧并异步保存，避免后续识别和绘图修改原图。"""
+    if not getattr(config, "SAVE_RAW_IMAGE_ENABLED", False):
+        return
+    if photo_dir is None:
+        photo_dir = getattr(
+            config, "RAW_IMAGE_DIR", os.path.join(config.PHOTO_DIR, "raw")
+        )
+    try:
+        img_copy = np.copy(image.image2cv(frame, False, False))
+        _save_queue.put_nowait(
+            {
+                "kind": "raw",
+                "img_cv": img_copy,
+                "shot_id": shot_id,
+                "photo_dir": photo_dir,
+            }
+        )
+    except queue.Full:
+        logger = logger_manager.logger
+        if logger:
+            logger.warning("[VISION-RAW] 存图队列已满，跳过本次纯原图保存")
+    except Exception as e:
+        logger = logger_manager.logger
+        if logger:
+            logger.error(f"[VISION-RAW] 复制纯原图失败: {e}")
+
+
 def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
                       laser_point, distance_m, shot_id=None, photo_dir=None,
-                      yolo_roi_xyxy=None):
+                      yolo_roi_xyxy=None, force_save=False):
     """
     将存图任务放入队列，由 worker 异步保存。主线程传入 result_img 的复制，不阻塞。
+    force_save=True 时，忽略 SAVE_IMAGE_ENABLED 配置强制保存（用于检测失败时的调试图像）。
     """
-    if not config.SAVE_IMAGE_ENABLED:
+    if not config.SAVE_IMAGE_ENABLED and not force_save:
         return
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -950,6 +1021,7 @@ def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
         shot_id,
         photo_dir,
         yolo_roi_xyxy,
+        force_save,
     )
     try:
         _save_queue.put_nowait(task)
@@ -961,12 +1033,12 @@ def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
 
 def save_shot_image(result_img, center, radius, method, ellipse_params,
                     laser_point, distance_m, shot_id=None, photo_dir=None,
-                    yolo_roi_xyxy=None):
+                    yolo_roi_xyxy=None, force_save=False):
     """
     保存射击图像（带标注）。同步调用，会阻塞。
     主流程建议使用 enqueue_save_shot；此处保留供校准、测试等场景使用。
     """
-    if not config.SAVE_IMAGE_ENABLED:
+    if not config.SAVE_IMAGE_ENABLED and not force_save:
         return None
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -983,6 +1055,7 @@ def save_shot_image(result_img, center, radius, method, ellipse_params,
             shot_id,
             photo_dir,
             yolo_roi_xyxy,
+            force_save,
         )
     except Exception as e:
         logger = logger_manager.logger

wifi.py

@@ -41,6 +41,7 @@ class WiFiManager:
         # WiFi 质量监测（后台线程）
         self._wifi_quality_monitor_thread = None
         self._wifi_quality_stop_event = threading.Event()
+        self._wifi_quality_lock = threading.Lock()
         self._last_wifi_rtt_ms = None  # 最近一次测量的 RTT
         self._last_wifi_rssi_dbm = None  # 最近一次测量的 RSSI
         
@@ -238,7 +239,6 @@ class WiFiManager:
         old_conf = _read_text(conf_path)
         old_boot_ssid = _read_text(ssid_file)
         old_boot_pass = _read_text(pass_file)
-        old_boot_wpa = _read_text(boot_wpa_path) if os.path.exists(boot_wpa_path) else None
 
         try:
             try:
@@ -250,9 +250,13 @@ class WiFiManager:
                 _write_text(conf_path, full_conf)
             except Exception:
                 pass
-            _write_text(boot_wpa_path, full_conf)
+            # 删除 wpa_supplicant.conf，让 S30wifi 回退读 ssid/pass
+            try:
+                if os.path.exists(boot_wpa_path):
+                    os.remove(boot_wpa_path)
+            except Exception:
+                pass
 
-            # 仍写入 ssid/pass，便于其它脚本/人工查看；S30wifi 优先使用 wpa_supplicant.conf
             _write_text(ssid_file, ssid.strip())
             _write_text(pass_file, password.strip())
 
@@ -292,7 +296,6 @@ class WiFiManager:
                     if not persist:
                         # 不持久化：把 /boot 恢复成旧值（不重启，当前连接保持不变）
                         _restore_boot(old_boot_ssid, old_boot_pass)
-                        _restore_boot_wpa(old_boot_wpa)
                         self.logger.info("[WIFI] 网络验证通过，但按 persist=False 回滚 /boot 凭证（不重启）")
                     else:
                         self.logger.info("[WIFI] 网络验证通过，/boot 凭证已保留（持久化）")
@@ -306,7 +309,6 @@ class WiFiManager:
         except Exception as e:
             # 失败：回滚 /boot 和 /etc，重启 WiFi 恢复旧网络
             _restore_boot(old_boot_ssid, old_boot_pass)
-            _restore_boot_wpa(old_boot_wpa)
             try:
                 if old_conf is not None:
                     _write_text(conf_path, old_conf)
@@ -351,7 +353,11 @@ class WiFiManager:
             else:
                 full_conf = build_sta_conf_open(ssid)
             _write_text(conf_path, full_conf)
-            _write_text(boot_wpa_path, full_conf)
+            try:
+                if os.path.exists(boot_wpa_path):
+                    os.remove(boot_wpa_path)
+            except Exception:
+                pass
         except ValueError as e:
             return False, str(e)
         except Exception as e:
@@ -542,7 +548,8 @@ class WiFiManager:
             network_type_callback: 获取当前网络类型的回调函数
             on_poor_quality_callback: WiFi质量差时的回调函数
         """
-        if self._wifi_quality_monitor_thread is not None:
+        with self._wifi_quality_lock:
+            if self._wifi_quality_monitor_thread is not None and self._wifi_quality_monitor_thread.is_alive():
                 self.logger.warning("[WiFi Monitor] 监测线程已在运行")
                 return
             
@@ -559,15 +566,25 @@ class WiFiManager:
     
     def stop_quality_monitor(self):
         """停止 WiFi 质量监测线程"""
-        if self._wifi_quality_monitor_thread is None:
+        with self._wifi_quality_lock:
+            t = self._wifi_quality_monitor_thread
+            if t is None:
+                return
+            if not t.is_alive():
+                self._wifi_quality_monitor_thread = None
                 return
         
         self._wifi_quality_stop_event.set()
         try:
-            self._wifi_quality_monitor_thread.join(timeout=2.0)
+            t.join(timeout=2.0)
         except Exception as e:
             self.logger.error(f"[WiFi Monitor] 停止线程失败：{e}")
-        finally:
+        
+        with self._wifi_quality_lock:
+            if t is self._wifi_quality_monitor_thread:
+                if t.is_alive():
+                    self.logger.warning("[WiFi Monitor] 线程未在超时内退出，保留引用防止重复创建")
+                else:
                     self._wifi_quality_monitor_thread = None
                     self.logger.info("[WiFi Monitor] 已停止后台监测线程")