修复不关机补充

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "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: 2.17.18
+version: 2.15.15
 author: t11
 icon: ''
 desc: t11
@@ -18,13 +18,14 @@ files:
   - laser_manager.py
   - logger_manager.py
   - main.py
-  - model_317828.cvimodel
-  - model_317828.mud
+  - model_270139.cvimodel
+  - model_270139.mud
   - network.py
   - ota_curl.sh
   - ota_manager.py
   - power.py
   - server.pem
+  - set_autostart.py
   - shoot_manager.py
   - shot_id_generator.py
   - target_roi_yolo.py

at_client.py

@@ -76,11 +76,10 @@ 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
-                self._resp = b""
+            # 初始化等待
+            self._waiting = True
+            self._expect = expect_b
+            self._resp = b""
 
             # 发送
             if cmd:
@@ -301,8 +300,8 @@ class ATClient:
                     if len(self._rx) > 512 * 1024:
                         self._rx = self._rx[-256 * 1024:]
                 else:
-                    if len(self._rx) > 32768:
-                        self._rx = self._rx[-16384:]
+                    if len(self._rx) > 16384:
+                        self._rx = self._rx[-4096:]
 
 
 

config.py

@@ -24,7 +24,7 @@ TRIANGLE_DETECT_SCALE = 0.4
 # SERVER_IP = "stcp.shelingxingqiu.com"
 SERVER_IP = "www.shelingxingqiu.com"
 SERVER_PORT = 50005
-HEARTBEAT_INTERVAL = 5  # 心跳间隔（秒）
+HEARTBEAT_INTERVAL = 15  # 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 +234,10 @@ 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_317211.mud"
+TRIANGLE_YOLO_MODEL_PATH = APP_DIR + "/model_270139.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 = 0.9
+TRIANGLE_YOLO_CONF_TH = 0.7
 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。
@@ -262,16 +262,6 @@ 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）。
@@ -326,13 +316,9 @@ 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 = True  # 检测失败时是否强制保存图像（供调试测试用）
+SAVE_IMAGE_ENABLED = False  # 是否保存图像（True=保存，False=不保存）
 PHOTO_DIR = "/root/phot"    # 照片存储目录
 MAX_IMAGES = 1000
-SAVE_RAW_IMAGE_ENABLED = False  # 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
 
@@ -355,7 +341,7 @@ PIN_MAPPINGS = {
 }
 
 # ==================== 电源配置 ====================
-AUTO_POWER_OFF_IN_SECONDS = 10 * 60  # 自动关机时间（秒），0表示不自动关机
+AUTO_POWER_OFF_IN_SECONDS = 0  # 自动关机时间（秒），0表示不自动关机
 
 BATTERY_SOC_LPF_ALPHA = 0.5
 BATTERY_SOC_AVG_WINDOW = 5

hardware.py

@@ -29,7 +29,7 @@ class HardwareManager:
         self._adc_obj = None             # ADC对象
         self._at_client = None            # AT客户端
 
-        self._last_active_time = 0       # 用于记录用户的最后一次活跃的时间
+        self._last_active_ticks = None   # 上次活跃时刻（ticks_ms，单调递增，不受校时影响）
         self._stop_timer = False         # 用于停止定时器的标志
 
         self._initialized = True
@@ -111,7 +111,7 @@ class HardwareManager:
 
     def start_idle_timer(self):
         self._stop_timer = False
-        self._last_active_time = time.time()
+        self._last_active_ticks = time.ticks_ms()
 
     def stop_idle_timer(self):
         self._stop_timer = True
@@ -119,12 +119,13 @@ class HardwareManager:
     def get_idle_time_in_sec(self):
         if self._stop_timer:
             return 0
-        diff = time.time() - self._last_active_time
-        if diff < 0:
-            # 时间可能被重置了，重新计时
-            self._last_active_time = time.time()
+        if self._last_active_ticks is None:
             return 0
-        return diff
+        diff_ms = time.ticks_diff(time.ticks_ms(), self._last_active_ticks)
+        if diff_ms < 0:
+            self._last_active_ticks = time.ticks_ms()
+            return 0
+        return diff_ms / 1000.0
 
 
 # 创建全局单例实例

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（WARNING级别，不打印/写入INFO和DEBUG日志，提高执行流畅度）
+    # 1. 初始化日志系统
     import logging
-    logger_manager.init_logging(log_level=logging.WARNING)
+    logger_manager.init_logging(log_level=logging.DEBUG)
     logger = logger_manager.logger
 
     # 补充：因为初始化的时候，激光会亮，先关了它
@@ -132,7 +132,6 @@ 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
 
@@ -163,21 +162,15 @@ def cmd_str():
             and _loc_black == "yolo"
             and bool(getattr(config, "TRIANGLE_BLACK_YOLO_PRELOAD_ON_BOOT", True))
         )
-        _need_target_preload = (
-            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 = _preload_yolo or _need_black_preload
+        if _preload_yolo:
             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 = _ota_pending_path
+    pending_path = f"{config.APP_DIR}/ota_pending.json"
     if os.path.exists(pending_path):
         try:
             with open(pending_path, 'r', encoding='utf-8') as f:
@@ -252,12 +245,8 @@ 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（如果启用图像保存或检测失败时强制保存）
-    if (
-        config.SAVE_IMAGE_ENABLED
-        or getattr(config, "SAVE_IMAGE_ON_FAILURE", False)
-        or getattr(config, "SAVE_RAW_IMAGE_ENABLED", False)
-    ):
+    # 5. 创建照片存储目录（如果启用图像保存）
+    if config.SAVE_IMAGE_ENABLED:
         photo_dir = config.PHOTO_DIR
         if photo_dir not in os.listdir("/root"):
             try:
@@ -286,46 +275,50 @@ def cmd_str():
     hardware_manager.start_idle_timer()
 
     if logger:
+        _auto_power_off = int(getattr(config, "AUTO_POWER_OFF_IN_SECONDS", 0) or 0)
+        if _auto_power_off <= 0:
+            logger.info("[MAIN] 自动关机已禁用 (AUTO_POWER_OFF_IN_SECONDS=0)")
+        else:
+            logger.info(f"[MAIN] 自动关机: {_auto_power_off} 秒无活动")
         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):
-        nonlocal pressure_buf, pressure_sum, pressure_min, pressure_max, pressure_t0_ms, logger
+        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
             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} "
+                f"min={pressure_min} max={pressure_max} avg={avg:.1f} avg_abs={avg_abs:.3f} "
                 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)
+            last_avg_abs = avg_abs
         # 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
@@ -350,7 +343,10 @@ def cmd_str():
             # 不在 OTA 状态下，检测是否空闲足够长，自动关机
             # print(f"[MAIN] 空闲时间: {hardware_manager.get_idle_time_in_sec() }秒")
             # print(f"配置关机时间：{config.AUTO_POWER_OFF_IN_SECONDS} 秒")
-            if hardware_manager.get_idle_time_in_sec() > config.AUTO_POWER_OFF_IN_SECONDS:
+            if (
+                config.AUTO_POWER_OFF_IN_SECONDS > 0
+                and hardware_manager.get_idle_time_in_sec() > config.AUTO_POWER_OFF_IN_SECONDS
+            ):
                 logger.info("[MAIN] 超过设定时间未检测活动，自动关机")
                 network_manager.safe_enqueue({"poweroff": "超过设定时间未检测活动，自动关机"}, 2)
                 time.sleep_ms(100)
@@ -363,10 +359,12 @@ 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:
@@ -377,24 +375,25 @@ 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)
+            pressure_buf.append((adc_val, adc_abs_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")
-            # 突变增量检测：压力增量大于400时触发
-            # 触发后需等气压降到触发值以下才重新检测增量
-            if adc_val < trigger_adc_val :
-                enable_check = True
-            if (adc_val - last_adc_val) > 200 and enable_check:
+            # if adc_val >= 2000:
+            # print(f"adc :{adc_val}")
+            if adc_val >= config.ADC_TRIGGER_THRESHOLD:
                 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
-                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
+                # 触发前先把缓存刷出来，避免波形被长耗时处理截断
                 _flush_pressure_buf("before_trigger")
 
                 try:
@@ -412,9 +411,10 @@ def cmd_str():
                     try:
                         camera_manager.show(camera_manager.read_frame())
                     except Exception as e:
-                        pass
-                time.sleep_ms(1)
-            last_adc_val = adc_val
+                        logger = logger_manager.logger
+                        if logger:
+                            logger.error(f"[MAIN] 显示异常: {e}")
+                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_270139.mud

@@ -1,7 +1,7 @@
 
 [basic]
 type = cvimodel
-model = model_317189.cvimodel
+model = model_270139.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 = circle, triangle
+labels = 黑三角和圆环
 

model_270820.mud

@@ -1,7 +1,7 @@
 
 [basic]
 type = cvimodel
-model = model_317828.cvimodel
+model = model_270820.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 = 20, 40
+labels = triangle
 

model_317211.mud

@@ -1,13 +0,0 @@
-
-[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

@@ -1,13 +0,0 @@
-
-[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

@@ -1,13 +0,0 @@
-
-[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
-

network.py

@@ -18,7 +18,7 @@ import socket
 import config
 
 from hardware import hardware_manager
-from power import get_bus_voltage, voltage_to_percent, is_charging
+from power import get_bus_voltage, voltage_to_percent
 from logger_manager import logger_manager
 from wifi import wifi_manager
 import subprocess
@@ -474,10 +474,10 @@ class NetworkManager:
     def select_network(self, prefer_wifi=None):
         """
         自动选择网络（WiFi优先）
-
+        
         Args:
             prefer_wifi: 是否优先使用WiFi（None表示使用默认策略）
-
+        
         Returns:
             "wifi" 或 "4g" 或 None（无可用网络）
         """
@@ -624,11 +624,6 @@ 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")
@@ -669,14 +664,7 @@ class NetworkManager:
         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")
@@ -690,11 +678,6 @@ class NetworkManager:
                 },
                 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(
@@ -714,7 +697,7 @@ class NetworkManager:
     def connect_server(self):
         """
         连接到服务器（自动选择WiFi或4G）
-
+        
         Returns:
             bool: 是否连接成功
         """
@@ -723,7 +706,7 @@ class NetworkManager:
             if self._network_type == "wifi":
                 return self._check_wifi_connection()
             elif self._network_type == "4g":
-                return self._check_4g_connection()
+                return True  # 4G连接状态由AT命令维护
             return False
 
         # 自动选择网络
@@ -740,37 +723,6 @@ 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）。
@@ -1021,11 +973,11 @@ class NetworkManager:
     def tcp_send_raw(self, data: bytes, max_retries=2) -> bool:
         """
         统一的TCP发送接口（自动选择WiFi或4G）
-
+        
         Args:
             data: 要发送的数据
             max_retries: 最大重试次数
-
+        
         Returns:
             bool: 是否发送成功
         """
@@ -1118,8 +1070,6 @@ 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
@@ -1198,10 +1148,10 @@ class NetworkManager:
     def receive_tcp_data_via_wifi(self, timeout_ms=100):
         """
         通过WiFi接收TCP数据
-
+            
         Args:
             timeout_ms: 超时时间（毫秒）
-
+            
         Returns:
             bytes: 接收到的数据，如果没有数据则返回 b""
         """
@@ -1239,7 +1189,7 @@ class NetworkManager:
     def _upload_log_file(self, upload_url, wifi_ssid=None, wifi_password=None, include_rotated=True, max_files=None,
                          archive_format="tgz"):
         """上传日志文件到指定URL
-
+        
         Args:
             upload_url: 上传目标URL，例如 "https://example.com/upload/"
             wifi_ssid: WiFi SSID（可选，如果未连接WiFi则尝试连接）
@@ -1247,7 +1197,7 @@ class NetworkManager:
             include_rotated: 是否包含轮转日志（app.log.1 等）
             max_files: 最多打包多少个日志文件（包含 app.log 本身），None=按 backupCount 自动推断
             archive_format: 打包格式：tgz 或 zip
-
+        
         Note:
             该功能仅在 WiFi 连接时可用，4G 网络暂不支持文件上传
         """
@@ -1834,7 +1784,7 @@ class NetworkManager:
                     continue
 
                 if not self.connect_server():
-                    time.sleep_ms(1000)
+                    time.sleep_ms(5000)
                     continue
 
                 # 发送登录包
@@ -1855,7 +1805,7 @@ class NetworkManager:
                         self.disconnect_server()
                     except:
                         pass
-                    time.sleep_ms(500)
+                    time.sleep_ms(2000)
                     continue
 
                 self.logger.info("➡️ 登录包已发送，等待确认...")
@@ -2145,17 +2095,7 @@ class NetworkManager:
                                             "netType": self.network_type,
                                         }
                                         self.safe_enqueue(battery_data, 2)
-                                        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)
+                                        self.logger.info(f"电量上报: {battery_percent}%")
                                     elif inner_cmd == 5:  # OTA 升级
                                         inner_data = data_obj.get("data", {}) if isinstance(data_obj, dict) else {}
                                         ssid = inner_data.get("ssid")
@@ -2208,7 +2148,7 @@ class NetworkManager:
                                                 self.logger.info(f"password: {password}")
                                                 ota_manager._start_update_thread()
                                                 self._spawn_cmd_thread(ota_manager.handle_wifi_and_update,
-                                                                       (ssid, password, ota_url))
+                                                                         (ssid, password, ota_url))
                                     elif inner_cmd == 6:
                                         try:
                                             ip = os.popen(
@@ -2354,8 +2294,8 @@ class NetworkManager:
                                     pass
                                 break
                             else:
-                                # 不立即断开，让下一轮心跳再试
-                                time.sleep_ms(50)
+                                # 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
+                                time.sleep_ms(200)
                                 continue
                         else:
                             send_hartbeat_fail_count = 0
@@ -2385,8 +2325,8 @@ class NetworkManager:
                     self._send_event.clear()
 
                 self._tcp_connected = False
-                self.logger.error("连接异常，50ms后重连...")
-                time.sleep_ms(50)
+                self.logger.error("连接异常，2秒后重连...")
+                time.sleep_ms(200)
 
             except Exception as e:
                 # TCP主循环的顶层异常捕获，防止线程静默退出

power.py

@@ -5,11 +5,10 @@
 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
 
 
@@ -86,8 +85,8 @@ def get_bus_voltage():
 def get_current():
     """
     读取电流（单位：mA）
-    当前电源板实测：正数表示放电，负数表示充电。
-
+    正数表示充电，负数表示放电
+    
     INA226 电流计算公式：
     Current = (Current Register Value) × Current_LSB
     Current_LSB = 0.001 × CALIBRATION_VALUE / 4096
@@ -97,13 +96,13 @@ 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；电流方向含义取决于电源板的采样电阻接线方向。
+        # 最高位是符号位：0=正（充电），1=负（放电）
         # 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:
+        if raw & 0x8000:  # 最高位为1，表示负数（放电）
             signed_raw = raw - 0x10000  # 转换为有符号整数
-        else:
+        else:  # 最高位为0，表示正数（充电）
             signed_raw = raw
         # 转换为毫安
         current_ma = signed_raw * current_lsb * 1000
@@ -120,17 +119,17 @@ def get_current():
 def is_charging(threshold_ma=10.0):
     """
     检测是否在充电（通过电流方向判断）
-
+    
     Args:
         threshold_ma: 电流阈值（毫安），超过此值认为在充电，默认10mA
-
+    
     Returns:
         True: 正在充电
         False: 未充电或读取失败
     """
     try:
         current = get_current()
-        is_charge = current < -abs(float(threshold_ma))
+        is_charge = current > threshold_ma
         return is_charge
     except Exception as e:
         logger = logger_manager.logger
@@ -160,7 +159,7 @@ def voltage_to_percent(voltage):
         return 0
     if v <= 0:
         return 0
-    return int(int(_BATTERY_MONITOR.get_soc(v) * 10) / 10)  # 截断而不是四舍五入
+    return int(int(_BATTERY_MONITOR.get_soc(v) * 10) / 10) # 截断而不是四舍五入
 
 
 class BatteryMonitor:

shoot_manager.py

@@ -8,12 +8,7 @@ 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,
-    enqueue_save_raw_shot,
-)
+from vision import estimate_distance, detect_circle_v3, enqueue_save_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
@@ -59,7 +54,6 @@ def analyze_shot(frame, laser_point=None):
     """
     logger = logger_manager.logger
     from datetime import datetime
-    yellow_algorithm_ms = 0.0
 
     # ── Step 1: 确定激光点 ────────────────────────────────────────────────────
     laser_point_method = None
@@ -75,11 +69,7 @@ def analyze_shot(frame, laser_point=None):
             logger.info(f"[算法] 使用校准值: {laser_manager.laser_point}")
     else:
         # 动态模式：先做一次无激光点检测以估算距离，再推算激光点
-        _t_yellow = time_std.perf_counter()
-        try:
-            _, _, _, _, best_radius1_temp, _ = detect_circle_v3(frame, None)
-        finally:
-            yellow_algorithm_ms += (time_std.perf_counter() - _t_yellow) * 1000.0
+        _, _, _, _, best_radius1_temp, _ = detect_circle_v3(frame, None)
         distance_m_first = estimate_distance(best_radius1_temp) if best_radius1_temp else None
         if distance_m_first and distance_m_first > 0:
             laser_point = laser_manager.calculate_laser_point_from_distance(distance_m_first)
@@ -124,7 +114,6 @@ def analyze_shot(frame, laser_point=None):
             "laser_point": laser_point, "laser_point_method": laser_point_method,
             "offset_method": "yellow_ellipse" if ellipse_params else "yellow_circle",
             "distance_method": "yellow_radius",
-            "yellow_algorithm_ms": float(yellow_algorithm_ms),
         }
         if yolo_roi_xyxy is not None:
             out["yolo_roi_xyxy"] = yolo_roi_xyxy
@@ -132,12 +121,9 @@ def analyze_shot(frame, laser_point=None):
 
     if not use_tri:
         # 三角形未配置，直接跑圆形检测
-        _t_yellow = time_std.perf_counter()
-        try:
-            cdata = detect_circle_v3(frame, laser_point, img_cv=img_cv)
-        finally:
-            yellow_algorithm_ms += (time_std.perf_counter() - _t_yellow) * 1000.0
-        return _build_circle_result(cdata)
+        return _build_circle_result(
+            detect_circle_v3(frame, laser_point, img_cv=img_cv)
+        )
 
     # ── Step 4: 先独占跑三角形，超时或失败后再跑圆形（不与圆心并行，避免抢 CPU）──
     roi_xyxy = None
@@ -290,7 +276,6 @@ def analyze_shot(frame, laser_point=None):
             "laser_point": laser_point, "laser_point_method": laser_point_method,
             "offset_method": tri.get("offset_method") or "triangle_homography",
             "distance_method": tri.get("distance_method") or "pnp_triangle",
-            "yellow_algorithm_ms": float(yellow_algorithm_ms),
             "tri_markers": tri.get("markers", []),
             "tri_markers_completed": tri.get("markers_completed", []),
             "tri_homography": tri.get("homography"),
@@ -311,11 +296,7 @@ def analyze_shot(frame, laser_point=None):
 
     # 三角形超时或失败 → 跑圆心；圆心跑完后再检查三角形是否已结束
     try:
-        _t_yellow = time_std.perf_counter()
-        try:
-            cdata = detect_circle_v3(frame, laser_point, img_cv=img_cv)
-        finally:
-            yellow_algorithm_ms += (time_std.perf_counter() - _t_yellow) * 1000.0
+        cdata = detect_circle_v3(frame, laser_point, img_cv=img_cv)
     except Exception as e:
         logger.error(f"[CIRCLE] 圆形检测异常: {e}")
         cdata = (frame, None, None, None, None, None)
@@ -339,32 +320,8 @@ def process_shot(adc_val):
     logger = logger_manager.logger
 
     try:
-        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
-        yolo_target_ms = 0.0
-        try:
-            from target_roi_yolo import try_get_target_class_from_yolo
-
-            _t_yolo_target = time_std.perf_counter()
-            try:
-                target_class_result = try_get_target_class_from_yolo(frame, logger=logger)
-            finally:
-                yolo_target_ms = (time_std.perf_counter() - _t_yolo_target) * 1000.0
-            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}")
+        frame = camera_manager.read_frame()
 
         # 调用算法分析
         analysis_result = analyze_shot(frame)
@@ -389,7 +346,6 @@ def process_shot(adc_val):
         laser_point_method = analysis_result["laser_point_method"]
         offset_method = analysis_result.get("offset_method", "yellow_circle")
         distance_method = analysis_result.get("distance_method", "yellow_radius")
-        yellow_algorithm_ms = float(analysis_result.get("yellow_algorithm_ms", 0.0) or 0.0)
         tri_markers = analysis_result.get("tri_markers", [])
         tri_markers_completed = analysis_result.get("tri_markers_completed", [])
         tri_homography = analysis_result.get("tri_homography")
@@ -410,6 +366,10 @@ 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}")
 
@@ -422,27 +382,11 @@ 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": (
-                round(float(target_confidence), 2)
-                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,
@@ -453,8 +397,6 @@ def process_shot(adc_val):
             "target_y": float(y),
             "offset_method": offset_method,
             "distance_method": distance_method,
-            "yellow_algorithm_ms": round(yellow_algorithm_ms, 2),
-            "yolo_target_ms": round(float(yolo_target_ms), 2),
         }
 
         if ellipse_params:
@@ -471,19 +413,7 @@ 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
@@ -588,7 +518,6 @@ 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,
@@ -598,9 +527,8 @@ def process_shot(adc_val):
             (x, y),
             distance_m,
             shot_id=shot_id,
-            photo_dir=config.PHOTO_DIR if (config.SAVE_IMAGE_ENABLED or _force_save) else None,
+            photo_dir=config.PHOTO_DIR if config.SAVE_IMAGE_ENABLED else None,
             yolo_roi_xyxy=yolo_roi_xyxy if draw_yolo_roi else None,
-            force_save=_force_save,
         )
 
         if logger:

target_roi_yolo.py

@@ -89,29 +89,6 @@ 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
@@ -126,19 +103,10 @@ def _get_detector(model_path: str):
         return _detector_by_path[model_path]
     try:
         from maix import nn
-    except Exception:
+    except ImportError:
         return None
-    # YOLO is an optional capability. A broken/incompatible model must not
-    # 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
+    _detector_by_path[model_path] = nn.YOLOv5(model=model_path, dual_buff=False)
+    return _detector_by_path[model_path]
 
 
 def preload_yolo_detector(logger=None):
@@ -207,23 +175,6 @@ 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
 
 
@@ -255,10 +206,8 @@ def _det_obj_class_id(o):
             if v is None:
                 continue
             try:
-                if callable(v):
-                    v = v()
                 return int(float(v))
-            except (TypeError, ValueError, AttributeError):
+            except (TypeError, ValueError):
                 continue
     return None
 
@@ -293,106 +242,6 @@ 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

@@ -154,11 +154,11 @@ def detect_circle_v3(frame, laser_point=None):
                                     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}")
+                                          f"大小比={size_ratio:.2f}(阈值=0.5), "
+                                          f"距离OK={distance < max_distance}, 大小OK={size_ratio > 0.5}")
 
                                     # 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:
+                                    if distance < max_distance and size_ratio > 0.5:
                                         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}")
@@ -598,7 +598,7 @@ if __name__ == "__main__":
 
     # 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_IMAGE = "/root/phot/None_314_258_0_0041.bmp"
 
     TARGET_DIR = "/root/phot"  # 修改为你想要读取的目录路径
 

test/test_target_yolo_maixvision.py

@@ -1,108 +0,0 @@
-#!/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

@@ -1,184 +0,0 @@
-#!/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

@@ -26,15 +26,4 @@
 # 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
+# 2.15.16 修复不关机，空闲计时改用 ticks_ms（不受校时影响），启动时打印自动关机配置

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 = '2.17.18'
+VERSION = '2.15.16'
 
 

vision.py

@@ -631,7 +631,7 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
             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 dist_centers < max_dist and size_ratio > 0.5:
                 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}, "
@@ -797,12 +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, force_save=False):
+                          yolo_roi_xyxy=None):
     """
     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 and not force_save:
+    if not config.SAVE_IMAGE_ENABLED:
         return None
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -908,12 +908,7 @@ 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)
+            _save_shot_image_impl(*item)
         except Exception as e:
             logger = logger_manager.logger
             if logger:
@@ -941,64 +936,13 @@ 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, force_save=False):
+                      yolo_roi_xyxy=None):
     """
     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 and not force_save:
+    if not config.SAVE_IMAGE_ENABLED:
         return
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -1021,7 +965,6 @@ 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)
@@ -1033,12 +976,12 @@ 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, force_save=False):
+                    yolo_roi_xyxy=None):
     """
     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 and not force_save:
+    if not config.SAVE_IMAGE_ENABLED:
         return None
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -1055,7 +998,6 @@ 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