feat: 压力传感触发判断

.gitattributes

@@ -0,0 +1 @@
+*.sh text eol=lf

.gitignore

@@ -1,3 +1,4 @@
 /cpp_ext/build/
 /.cursor/
 /dist/
+.idea

.idea/.gitignore

@@ -0,0 +1,8 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/archery.iml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="yolov8" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="yolov8" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="yolov8" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/archery.iml" filepath="$PROJECT_DIR$/.idea/archery.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

.vscode/settings.json

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

app.yaml

@@ -1,6 +1,6 @@
 id: t11
 name: t11
-version: 2.14.1
+version: 2.16.1
 author: t11
 icon: ''
 desc: t11
@@ -12,14 +12,15 @@ files:
   - at_client.py
   - camera_manager.py
   - cameraParameters.xml
+  - charging_exit.sh
   - config.py
   - hardware.py
+  - laser_detector.py
   - laser_manager.py
   - logger_manager.py
   - main.py
-  - model_270139.cvimodel
-  - model_270139.mud
   - network.py
+  - ota_curl.sh
   - ota_manager.py
   - power.py
   - server.pem

at_client.py

@@ -76,10 +76,11 @@ class ATClient:
         """
         expect_b = expect.encode() if isinstance(expect, str) else expect
         with self._cmd_lock:
-            # 初始化等待
+            with self._q_lock:
-            self._waiting = True
+                # 初始化等待
-            self._expect = expect_b
+                self._waiting = True
-            self._resp = b""
+                self._expect = expect_b
+                self._resp = b""
 
             # 发送
             if cmd:
@@ -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:]
 
 
 

charging_exit.sh

@@ -0,0 +1,47 @@
+#!/bin/sh
+
+# The application supplies its own PID. Refuse broad or malformed targets.
+TARGET_PID="$1"
+LASER_DEVICE="${2:-/dev/ttyS1}"
+LASER_BAUD="${3:-9600}"
+
+turn_off_laser() {
+    if [ ! -c "$LASER_DEVICE" ]; then
+        echo "[CHARGE] laser serial device not found: $LASER_DEVICE" >&2
+        return 1
+    fi
+
+    stty -F "$LASER_DEVICE" "$LASER_BAUD" raw -echo 2>/dev/null || return 1
+    printf '\252\000\001\276\000\001\000\000\300' > "$LASER_DEVICE"
+}
+
+case "$TARGET_PID" in
+    ''|*[!0-9]*)
+        echo "[CHARGE] invalid application pid: $TARGET_PID" >&2
+        exit 2
+        ;;
+esac
+
+if [ "$TARGET_PID" -le 1 ]; then
+    echo "[CHARGE] refusing to terminate pid: $TARGET_PID" >&2
+    exit 2
+fi
+
+# First request laser-off while the application still owns the initialized UART.
+turn_off_laser || true
+
+kill -TERM "$TARGET_PID" 2>/dev/null || true
+
+# Wait up to two seconds for a graceful exit, then force termination.
+WAIT_COUNT=0
+while kill -0 "$TARGET_PID" 2>/dev/null && [ "$WAIT_COUNT" -lt 20 ]; do
+    sleep 0.1
+    WAIT_COUNT=$((WAIT_COUNT + 1))
+done
+if kill -0 "$TARGET_PID" 2>/dev/null; then
+    kill -KILL "$TARGET_PID" 2>/dev/null || true
+    sleep 0.1
+fi
+
+# Send laser-off again after the application releases the UART.
+turn_off_laser || true

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 = 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
@@ -134,7 +134,7 @@ IMAGE_CENTER_Y = 240  # 图像中心 Y 坐标
 # ==================== 三角形四角标记：单应性偏移 + PnP 估距 ====================
 # 依赖 cameraParameters.xml（相机内参）与 triangle_positions.json（四角物方坐标，厘米或毫米见 JSON 约定）。
 # 部署时请把这两个文件放到 APP_DIR（与 main 同应用目录），或改下面路径为设备上的实际绝对路径。
-USE_TRIANGLE_OFFSET = True  # False 时仅走黄心圆/椭圆 + 半径估距，不使用三角形路径
+USE_TRIANGLE_OFFSET = False  # False 时仅走黄心圆/椭圆 + 半径估距，不使用三角形路径
 CAMERA_CALIB_XML = APP_DIR + "/cameraParameters.xml"
 TRIANGLE_POSITIONS_JSON = APP_DIR + "/triangle_positions.json"
 # 检测到的三角形边长在图像中的像素范围，分辨率或靶纸占比变化时可微调
@@ -260,7 +260,7 @@ TRIANGLE_SAMPLE_RADIUS_CM = 15.0
 TRIANGLE_SAMPLE_ANGLES_DEG = (0, 90, 180, 270)
 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 = True
+TRIANGLE_YOLO_PRELOAD_ON_BOOT = False
 
 # ── 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）：
@@ -308,8 +308,15 @@ 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_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
 # 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
@@ -336,6 +343,15 @@ 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
 
+# 实机数据：正常放电约为正电流，插入充电线后约为负电流。
+CHARGING_SHUTDOWN_ENABLED = True  # True=充电时退出应用，False=关闭充电关机功能
+CHARGING_DIAGNOSTIC_LOG_ENABLED = False
+CHARGING_CHECK_INTERVAL_MS = 5000
+CHARGING_CURRENT_THRESHOLD_MA = 100.0
+CHARGING_CONFIRM_COUNT = 2
+CHARGING_NOTIFY_TIMEOUT_MS = 30000
+CHARGING_EXIT_SCRIPT = APP_DIR + "/charging_exit.sh"
+
 BATTERY_SOC_LPF_ALPHA = 0.5
 BATTERY_SOC_AVG_WINDOW = 5
 

laser_detector.py

@@ -0,0 +1,248 @@
+from maix import image, time
+from logger_manager import logger_manager
+from camera_manager import camera_manager
+
+_USE_CV = False
+try:
+    import cv2
+    import numpy as np
+
+    _USE_CV = True
+except ImportError:
+    pass
+
+WIDTH = 640
+HEIGHT = 480
+THRESHOLD = 100
+RED_RATIO = 1.5
+SEARCH_RADIUS = 80
+TRACK_RADIUS = 30
+MIN_PIXELS = 3
+COARSE_STEP = 2
+STABLE_COUNT = 2
+MAX_SKIP_FRAMES = 5
+
+# Temporal smoothing
+_EMA_ALPHA = 0.35
+_GATE_PX = 10
+_FRAME_INTERVAL_MS = 50
+
+_prev_smoothed = None
+
+
+def _red_weighted_centroid(r_ch, g_ch, b_ch, mask, x0, y0):
+    y_ids, x_ids = np.where(mask)
+    if len(y_ids) == 0:
+        return None
+    r_vals = r_ch[y_ids, x_ids].astype(np.float64)
+    g_vals = g_ch[y_ids, x_ids].astype(np.float64)
+    b_vals = b_ch[y_ids, x_ids].astype(np.float64)
+    w = r_vals - np.maximum(g_vals, b_vals)
+    w = np.clip(w, 0, None)
+    w = w * w
+    total_w = w.sum()
+    if total_w < 1e-6:
+        return None
+    cx = (x_ids.astype(np.float64) * w).sum() / total_w + x0
+    cy = (y_ids.astype(np.float64) * w).sum() / total_w + y0
+    return (float(cx), float(cy))
+
+
+def find_ellipse(img_cv, cx, cy, roi_r, th, ratio):
+    x1 = max(0, cx - roi_r)
+    x2 = min(WIDTH, cx + roi_r)
+    y1 = max(0, cy - roi_r)
+    y2 = min(HEIGHT, cy + roi_r)
+    roi = img_cv[y1:y2, x1:x2]
+    if roi.size == 0:
+        return None
+    r = roi[:, :, 0].astype(np.int32)
+    g = roi[:, :, 1].astype(np.int32)
+    b = roi[:, :, 2].astype(np.int32)
+    mask = (r > th) & (r > g * ratio) & (r > b * ratio)
+    oe = (r > 200) & (g > 200) & (b > 200) & (r >= g) & (r >= b) & ((r - g) > 10) & ((r - b) > 10)
+    combined = (mask | oe).astype(np.uint8) * 255
+    contours, _ = cv2.findContours(combined, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return None
+    largest = max(contours, key=cv2.contourArea)
+    if cv2.contourArea(largest) < 5:
+        return None
+    cnt = largest.copy()
+    for pt in cnt:
+        pt[0][0] += x1
+        pt[0][1] += y1
+    ellipse_valid = len(cnt) >= 5
+    if ellipse_valid:
+        (ex, ey), (ew, eh), ang = cv2.fitEllipse(cnt)
+        mask_ellipse = np.zeros((HEIGHT, WIDTH), dtype=np.uint8)
+        cv2.ellipse(mask_ellipse, (int(ex), int(ey)), (int(ew / 2), int(eh / 2)), ang, 0, 360, 255, -1)
+        return _red_weighted_centroid(
+            img_cv[:, :, 0], img_cv[:, :, 1], img_cv[:, :, 2],
+            mask_ellipse > 0, 0, 0
+        )
+    M = cv2.moments(cnt)
+    if M["m00"] > 0:
+        return (float(M["m10"] / M["m00"]), float(M["m01"] / M["m00"]))
+    return None
+
+
+def is_red(r, g, b, th, ratio):
+    if r > th and r > g * ratio and r > b * ratio:
+        return True
+    if (r > 200 and g > 200 and b > 200 and r >= g and r >= b
+            and (r - g) > 10 and (r - b) > 10):
+        return True
+    return False
+
+
+def find_brightest_bytes(frame, cx, cy, roi_r, th, ratio):
+    x1 = max(0, cx - roi_r)
+    x2 = min(WIDTH, cx + roi_r)
+    y1 = max(0, cy - roi_r)
+    y2 = min(HEIGHT, cy + roi_r)
+    data = frame.to_bytes()
+
+    best_score = 0
+    best_x = (x1 + x2) // 2
+    best_y = (y1 + y2) // 2
+    found_any = False
+    for y in range(y1, y2, COARSE_STEP):
+        for x in range(x1, x2, COARSE_STEP):
+            idx = (y * WIDTH + x) * 3
+            r = data[idx]
+            g = data[idx + 1]
+            b = data[idx + 2]
+            if is_red(r, g, b, th, ratio):
+                score = r + g + b
+                dx = x - cx
+                dy = y - cy
+                dist_decay = max(0.5, 1.0 - ((dx * dx + dy * dy) ** 0.5 / roi_r) * 0.5)
+                score *= dist_decay
+                if score > best_score:
+                    best_score = score
+                    best_x = x
+                    best_y = y
+                    found_any = True
+
+    if not found_any:
+        return None
+
+    sf = 4
+    fx1 = max(x1, best_x - sf)
+    fx2 = min(x2, best_x + sf + 1)
+    fy1 = max(y1, best_y - sf)
+    fy2 = min(y2, best_y + sf + 1)
+
+    sum_x = 0.0
+    sum_y = 0.0
+    total_w = 0.0
+    count = 0
+    for y in range(fy1, fy2):
+        for x in range(fx1, fx2):
+            idx = (y * WIDTH + x) * 3
+            r = data[idx]
+            g = data[idx + 1]
+            b = data[idx + 2]
+            if is_red(r, g, b, th, ratio):
+                w = r + g + b
+                sum_x += x * w
+                sum_y += y * w
+                total_w += w
+                count += 1
+
+    if count < MIN_PIXELS:
+        return (float(best_x), float(best_y))
+
+    return (float(sum_x / total_w), float(sum_y / total_w))
+
+
+def _ema_filter(pos, alpha=_EMA_ALPHA):
+    global _prev_smoothed
+    if _prev_smoothed is None:
+        _prev_smoothed = pos
+        return pos
+    sx = alpha * pos[0] + (1 - alpha) * _prev_smoothed[0]
+    sy = alpha * pos[1] + (1 - alpha) * _prev_smoothed[1]
+    _prev_smoothed = (sx, sy)
+    return _prev_smoothed
+
+
+def _gated(pos, gate_px=_GATE_PX):
+    global _prev_smoothed
+    if _prev_smoothed is None:
+        return True
+    dx = pos[0] - _prev_smoothed[0]
+    dy = pos[1] - _prev_smoothed[1]
+    return (dx * dx + dy * dy) <= gate_px * gate_px
+
+
+def get_stable_laser_point(timeout_ms=15000, stable_count=STABLE_COUNT):
+    global _prev_smoothed
+    _prev_smoothed = None
+    try:
+        last_raw = None
+        stable = 0
+        start = time.ticks_ms()
+        cx, cy = WIDTH // 2, HEIGHT // 2
+        track_count = 0
+        skip_count = 0
+        while True:
+            if abs(time.ticks_diff(time.ticks_ms(), start)) > timeout_ms:
+                _prev_smoothed = None
+                return None
+            frame = camera_manager.read_frame()
+            if frame is None:
+                time.sleep_ms(10)
+                continue
+
+            if track_count > 0 and _prev_smoothed is not None:
+                search_cx = int(_prev_smoothed[0])
+                search_cy = int(_prev_smoothed[1])
+                search_r = TRACK_RADIUS
+            else:
+                search_cx = cx
+                search_cy = cy
+                search_r = SEARCH_RADIUS
+
+            pos_bright = find_brightest_bytes(frame, search_cx, search_cy, search_r, THRESHOLD, RED_RATIO)
+            pos = pos_bright
+            if _USE_CV:
+                img_cv = image.image2cv(frame, False, False)
+                pos_ellipse = find_ellipse(img_cv, search_cx, search_cy, search_r, THRESHOLD, RED_RATIO)
+                if pos_ellipse is not None:
+                    pos = pos_ellipse
+
+            if pos is not None:
+                skip_count = 0
+                track_count += 1
+                filtered = _ema_filter(pos)
+                if last_raw is not None:
+                    dx = abs(filtered[0] - last_raw[0])
+                    dy = abs(filtered[1] - last_raw[1])
+                    if dx <= 2 and dy <= 2:
+                        stable += 1
+                    else:
+                        stable = 1
+                else:
+                    stable = 1
+                last_raw = filtered
+                if logger_manager.logger:
+                    logger_manager.logger.info(f"pos:{pos},filtered:{filtered},stable:{stable}")
+                if stable >= stable_count:
+                    result = (int(filtered[0]), int(filtered[1]))
+                    _prev_smoothed = None
+                    return result
+            else:
+                skip_count += 1
+                if logger_manager.logger:
+                    logger_manager.logger.info(f"find_brightest_bytes None, skip={skip_count}, track={track_count}, search_center=({search_cx},{search_cy}), search_r={search_r}")
+                if skip_count > MAX_SKIP_FRAMES:
+                    _prev_smoothed = None
+                    track_count = 0
+                    stable = 0
+                    last_raw = None
+
+            time.sleep_ms(_FRAME_INTERVAL_MS)
+    finally:
+        _prev_smoothed = None

laser_manager.py

@@ -54,8 +54,8 @@ class LaserManager:
     @property
     def laser_point(self):
         """当前激光点（如果启用硬编码，则返回硬编码值）"""
-        if config.HARDCODE_LASER_POINT:
+        # if config.HARDCODE_LASER_POINT:
-            return config.HARDCODE_LASER_POINT_VALUE
+        #     return config.HARDCODE_LASER_POINT_VALUE
         return self._laser_point
     
     def get_last_frame_with_ellipse(self):
@@ -102,31 +102,28 @@ class LaserManager:
     # ==================== 业务方法 ====================
     
     def load_laser_point(self):
-        """从配置文件加载激光中心点，失败则使用默认值
+        """加载激光中心点：优先使用本地保存的坐标，其次硬编码值，最后默认值"""
-        如果启用硬编码模式，则直接使用硬编码值
+        # 优先：从本地持久化文件加载（由 cmd 201 保存）
-        """
-        if config.HARDCODE_LASER_POINT:
-            # 硬编码模式：直接使用硬编码值
-            self._laser_point = config.HARDCODE_LASER_POINT_VALUE
-            self.logger.info(f"[LASER] 使用硬编码激光点: {self._laser_point}")
-            return self._laser_point
-        
-        # 正常模式：从配置文件加载
         try:
             if "laser_config.json" in os.listdir("/root"):
                 with open(config.CONFIG_FILE, "r") as f:
                     data = json.load(f)
                     if isinstance(data, list) and len(data) == 2:
                         self._laser_point = (int(data[0]), int(data[1]))
-                        self.logger.debug(f"[INFO] 加载激光点: {self._laser_point}")
+                        self.logger.info(f"[LASER] 从本地加载激光点: {self._laser_point}")
                         return self._laser_point
-                    else:
+        except Exception:
-                        raise ValueError
+            pass
-            else:
+
-                self._laser_point = config.DEFAULT_LASER_POINT
+        # 其次：硬编码值
-        except:
+        if config.HARDCODE_LASER_POINT:
-            self._laser_point = config.DEFAULT_LASER_POINT
+            self._laser_point = config.HARDCODE_LASER_POINT_VALUE
-        
+            self.logger.info(f"[LASER] 使用硬编码激光点: {self._laser_point}")
+            return self._laser_point
+
+        # 最后：默认值
+        self._laser_point = config.DEFAULT_LASER_POINT
+        self.logger.info(f"[LASER] 使用默认激光点: {self._laser_point}")
         return self._laser_point
     
     def save_laser_point(self, point):
@@ -1264,6 +1261,28 @@ class LaserManager:
                 except Exception as e:
                     self.logger.error(f"[LASER] 关闭激光失败: {e}")
 
+    def set_hardcoded_laser_point(self, raw_x, raw_y):
+        """
+        设置服务下发的硬编码激光点坐标，并保存到本地持久化文件。
+        下次启动时 load_laser_point() 会优先使用此保存的值。
+
+        Args:
+            raw_x: 服务下发的 x 坐标
+            raw_y: 服务下发的 y 坐标
+
+        Returns:
+            (int_x, int_y) 元组
+        """
+        ix = int(raw_x)
+        iy = int(raw_y)
+        self._laser_point = (ix, iy)
+        try:
+            with open(config.CONFIG_FILE, "w") as f:
+                json.dump([ix, iy], f)
+            self.logger.info(f"[LASER] 设置并持久化激光点: ({ix}, {iy})")
+        except Exception as e:
+            self.logger.error(f"[LASER] 持久化激光点失败: {e}")
+        return ix, iy
 
 # 创建全局单例实例
 laser_manager = LaserManager()

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

@@ -22,7 +22,7 @@ from version import VERSION
 # from logger import init_logging, get_logger, stop_logging
 from logger_manager import logger_manager
 from time_sync import sync_system_time_from_4g
-from power import init_ina226
+from power import charging_shutdown_monitor, init_ina226
 from laser_manager import laser_manager
 from vision import start_save_shot_worker
 from network import network_manager
@@ -122,9 +122,16 @@ def cmd_str():
 
     # 1. 初始化日志系统
     import logging
-    logger_manager.init_logging(log_level=logging.DEBUG)
+    logger_manager.init_logging(log_level=logging.WARNING)
     logger = logger_manager.logger
 
+    # 充电关机独立读取 INA226，不依赖 TCP 连接或心跳流程。
+    try:
+        _thread.start_new_thread(charging_shutdown_monitor, ())
+    except Exception as e:
+        if logger:
+            logger.error(f"[CHARGE] 启动独立监测线程失败: {e}")
+
     # 补充：因为初始化的时候，激光会亮，先关了它
     # laser_manager.turn_off_laser()
 
@@ -283,41 +290,38 @@ def cmd_str():
 
     pressure_buf = []
     pressure_sum = 0
-    pressure_abs_sum = 0
     pressure_min = 4095
     pressure_max = 0
     pressure_t0_ms = None
-    last_avg_abs = 0
+
+    # 压力突变检测（上升沿触发）
+    prev_adc_val = 0            # 上一次ADC值
+    ADC_JUMP_THRESHOLD = 250    # 突变阈值：相邻两次采样差值超过此值视为突变
 
     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
-        t1_ms = time.ticks_ms()
+        if config.AIR_PRESSURE_lOG:
-        n = len(pressure_buf)
+            t1_ms = time.ticks_ms()
-        avg = (pressure_sum / n) if n else 0
+            n = len(pressure_buf)
-        avg_abs = (pressure_abs_sum / n) if n else 0
+            avg = (pressure_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 = (
-        line = (
+                f"[气压批量] reason={reason} "
-            f"[气压批量] reason={reason} "
+                f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
-            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"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)
-        if logger:
+            else:
-            logger.debug(line)
+                print(line)
-        else:
+        # 无论是否记录日志，都必须清空 buffer，否则内存泄漏
-            print(line)
         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():
@@ -336,6 +340,7 @@ def cmd_str():
                 time.sleep_ms(250)
                 continue
 
+            # todo 去除或者不在这里检测
             # 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")
@@ -352,12 +357,10 @@ 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:
@@ -368,27 +371,35 @@ 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:
+
-            # print(f"adc :{adc_val}")
+            # 压力突变检测：当前值与前一次值的差值超过阈值时触发
-            if adc_val >= config.ADC_TRIGGER_THRESHOLD:
+            adc_diff = adc_val - prev_adc_val
+            if adc_diff >= ADC_JUMP_THRESHOLD and prev_adc_val > 0 :
                 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")
+                    prev_adc_val = adc_val
                     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
+                # 触发前先把缓存刷出来
                 _flush_pressure_buf("before_trigger")
 
+                if logger:
+                    logger.info(
+                        f"[TRIGGER] ▲ 压力突变触发! "
+                        f"adc={adc_val}, prev={prev_adc_val}, "
+                        f"diff=+{adc_diff}, t={current_time}ms"
+                    )
+
                 try:
                     process_shot(adc_val)
                 except Exception as e:
@@ -397,17 +408,19 @@ def cmd_str():
                         logger.error(f"[MAIN] 图像处理异常: {e}")
                         import traceback
                         logger.error(traceback.format_exc())
-                    time.sleep_ms(100)
+
-                    continue
+            prev_adc_val = adc_val
-            else:
+
-                if config.SHOW_CAMERA_PHOTO_WHILE_SHOOTING:
+            # 未触发时的显示逻辑
-                    try:
+            # 未触发时的显示逻辑
-                        camera_manager.show(camera_manager.read_frame())
+            if config.SHOW_CAMERA_PHOTO_WHILE_SHOOTING:
-                    except Exception as e:
+                try:
-                        logger = logger_manager.logger
+                    camera_manager.show(camera_manager.read_frame())
-                        if logger:
+                except Exception as e:
-                            logger.error(f"[MAIN] 显示异常: {e}")
+                    logger = logger_manager.logger
-                time.sleep_ms(5)
+                    if logger:
+                        logger.error(f"[MAIN] 显示异常: {e}")
+            time.sleep_ms(1)
 
         except Exception as e:
             # 主循环的顶层异常捕获，防止程序静默退出

ota_curl.sh

@@ -0,0 +1,57 @@
+#!/bin/sh
+# OTA 更新脚本 - 使用 curl 断点下载
+# 用法: sh ota_curl.sh <下载URL>
+# 示例: sh ota_curl.sh http://example.com/maix-t11-v2.15.1.zip
+
+set -e
+
+APP_DIR="/maixapp/apps/t11"
+BACKUP_BASE="$APP_DIR/backups"
+TMP_DIR="/tmp/ota_curl"
+PENDING_FILE="$APP_DIR/ota_pending.json"
+
+if [ $# -lt 1 ]; then
+    echo "用法: $0 <下载URL>"
+    exit 1
+fi
+
+OTA_URL="$1"
+FILENAME=$(basename "$OTA_URL" | sed 's/?.*//')
+[ -z "$FILENAME" ] && FILENAME="update.zip"
+
+mkdir -p "$TMP_DIR" "$BACKUP_BASE"
+
+# 1. 断点下载
+echo "[OTA] 开始下载: $OTA_URL"
+echo "[OTA] 保存到: $TMP_DIR/$FILENAME"
+curl -C - -L --retry 3 --retry-delay 5 -o "$TMP_DIR/$FILENAME" "$OTA_URL"
+echo "[OTA] 下载完成"
+
+# 2. 备份当前目录
+TIMESTAMP=$(date +%Y%m%d_%H%M%S 2>/dev/null || echo "00000000_000000")
+BACKUP_DIR="$BACKUP_BASE/backup_$TIMESTAMP"
+mkdir -p "$BACKUP_DIR"
+echo "[OTA] 备份到: $BACKUP_DIR"
+for f in "$APP_DIR"/*.py "$APP_DIR"/*.json "$APP_DIR"/*.xml "$APP_DIR"/*.yaml "$APP_DIR"/*.pem "$APP_DIR"/*.mud "$APP_DIR"/*.so "$APP_DIR"/S99archery; do
+    [ -f "$f" ] && cp "$f" "$BACKUP_DIR/"
+done
+echo "[OTA] 备份完成"
+
+# 3. 解压并替换文件
+echo "[OTA] 开始更新..."
+if echo "$FILENAME" | grep -qi '\.zip$'; then
+    unzip -q -o "$TMP_DIR/$FILENAME" -d "$APP_DIR/"
+else
+    cp "$TMP_DIR/$FILENAME" "$APP_DIR/"
+fi
+sync
+
+# 4. 写入 pending 文件（用于崩溃恢复）
+echo '{"ts":0,"url":"'"$OTA_URL"'","backup_dir":"'"$BACKUP_DIR"'","restart_count":0,"max_restarts":3}' > "$PENDING_FILE"
+sync
+
+echo "[OTA] 更新完成，准备重启..."
+
+# 5. 重启
+sleep 1
+reboot

power.py

@@ -5,6 +5,8 @@
 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
 
@@ -85,7 +87,7 @@ 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 +98,13 @@ 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 +131,7 @@ 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
@@ -140,6 +142,115 @@ def is_charging(threshold_ma=10.0):
         return False
 
 
+def charging_shutdown_monitor():
+    """独立监测 INA226；连续确认充电后通知服务器并退出应用。"""
+    logger = logger_manager.logger
+    shutdown_enabled = bool(getattr(config, "CHARGING_SHUTDOWN_ENABLED", False))
+    diagnostic_enabled = bool(getattr(config, "CHARGING_DIAGNOSTIC_LOG_ENABLED", False))
+    if not shutdown_enabled and not diagnostic_enabled:
+        if logger:
+            logger.info("[CHARGE] 充电退出监测已禁用")
+        return
+
+    interval_ms = max(100, int(getattr(config, "CHARGING_CHECK_INTERVAL_MS", 5000)))
+    threshold_ma = float(getattr(config, "CHARGING_CURRENT_THRESHOLD_MA", 10.0))
+    confirm_required = max(1, int(getattr(config, "CHARGING_CONFIRM_COUNT", 2)))
+    confirm_count = 0
+
+    if logger:
+        logger.info(
+            f"[CHARGE] 独立监测线程启动: interval={interval_ms}ms, "
+            f"threshold={threshold_ma:.1f}mA, confirm={confirm_required}, "
+            f"shutdown={'on' if shutdown_enabled else 'off'}"
+        )
+
+    while True:
+        current_ma = get_current()
+        if diagnostic_enabled and logger:
+            voltage = get_bus_voltage()
+            logger.info(
+                f"[CHARGE-DIAG] INA226 voltage={voltage:.3f}V, "
+                f"current={current_ma:.1f}mA"
+            )
+
+        if not shutdown_enabled:
+            maix_time.sleep_ms(interval_ms)
+            continue
+
+        if current_ma < -abs(threshold_ma):
+            confirm_count += 1
+            if logger:
+                logger.info(
+                    f"[CHARGE] INA226 充电电流 {current_ma:.1f}mA "
+                    f"({confirm_count}/{confirm_required})"
+                )
+        else:
+            confirm_count = 0
+
+        if confirm_count >= confirm_required:
+            script_path = getattr(
+                config,
+                "CHARGING_EXIT_SCRIPT",
+                config.APP_DIR + "/charging_exit.sh",
+            )
+            if not os.path.isfile(script_path):
+                if logger:
+                    logger.error(f"[CHARGE] 退出脚本不存在: {script_path}")
+                confirm_count = 0
+            else:
+                if logger:
+                    logger.warning(
+                        f"[CHARGE] 已连续确认充电，通知服务器后退出应用: current={current_ma:.1f}mA"
+                    )
+                try:
+                    from network import network_manager
+
+                    notify_timeout_ms = max(
+                        0,
+                        int(getattr(config, "CHARGING_NOTIFY_TIMEOUT_MS", 30000)),
+                    )
+                    notification_sent = network_manager.safe_enqueue_and_wait(
+                        {"poweroff": "充电中"},
+                        2,
+                        high=True,
+                        timeout_ms=notify_timeout_ms,
+                    )
+                    if notification_sent:
+                        if logger:
+                            logger.info("[CHARGE] 充电状态已发送到服务器")
+                    elif logger:
+                        logger.warning(
+                            f"[CHARGE] 等待服务器发送超时({notify_timeout_ms}ms)，继续执行退出"
+                        )
+                except Exception as e:
+                    if logger:
+                        logger.error(f"[CHARGE] 充电状态上报失败，继续执行退出: {e}")
+                try:
+                    from laser_manager import laser_manager
+
+                    laser_manager.turn_off_laser()
+                    if logger:
+                        logger.info("[CHARGE] 激光关闭命令已发送")
+                except Exception as e:
+                    if logger:
+                        logger.error(f"[CHARGE] Python 关闭激光失败，交由退出脚本兜底: {e}")
+                try:
+                    subprocess.Popen([
+                        "/bin/sh",
+                        script_path,
+                        str(os.getpid()),
+                        str(getattr(config, "DISTANCE_SERIAL_DEVICE", "/dev/ttyS1")),
+                        str(getattr(config, "DISTANCE_SERIAL_BAUDRATE", 9600)),
+                    ])
+                    return
+                except Exception as e:
+                    if logger:
+                        logger.error(f"[CHARGE] 调用退出脚本失败: {e}")
+                    confirm_count = 0
+
+        maix_time.sleep_ms(interval_ms)
+
+
 def voltage_to_percent(voltage):
     """
     根据电压估算电池百分比（高密度查表插值 + 滤波）。

shoot_manager.py

@@ -320,8 +320,8 @@ 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()
+        network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True)
 
         # 调用算法分析
         analysis_result = analyze_shot(frame)

test/test_charging_shutdown.py

@@ -0,0 +1,144 @@
+import importlib.util
+from pathlib import Path
+import sys
+import types
+import unittest
+from unittest import mock
+
+
+class _StopMonitor(Exception):
+    pass
+
+
+class _FakeTime:
+    now_ms = 0
+    stop_at_ms = None
+
+    @classmethod
+    def reset(cls, stop_at_ms=None):
+        cls.now_ms = 0
+        cls.stop_at_ms = stop_at_ms
+
+    @classmethod
+    def ticks_ms(cls):
+        return cls.now_ms
+
+    @classmethod
+    def sleep_ms(cls, milliseconds):
+        cls.now_ms += milliseconds
+        if cls.stop_at_ms is not None and cls.now_ms >= cls.stop_at_ms:
+            raise _StopMonitor()
+
+
+def _load_power_module():
+    module_path = Path(__file__).resolve().parents[1] / "power.py"
+    module_name = "power_charging_shutdown_test"
+    maix_module = types.ModuleType("maix")
+    maix_module.time = _FakeTime
+
+    previous_maix = sys.modules.get("maix")
+    sys.modules["maix"] = maix_module
+    try:
+        spec = importlib.util.spec_from_file_location(module_name, module_path)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return module
+    finally:
+        if previous_maix is None:
+            sys.modules.pop("maix", None)
+        else:
+            sys.modules["maix"] = previous_maix
+
+
+power = _load_power_module()
+
+
+class ChargingShutdownTests(unittest.TestCase):
+    def setUp(self):
+        self.config_patch = mock.patch.multiple(
+            power.config,
+            CHARGING_SHUTDOWN_ENABLED=True,
+            CHARGING_DIAGNOSTIC_LOG_ENABLED=False,
+            CHARGING_CHECK_INTERVAL_MS=5000,
+            CHARGING_CURRENT_THRESHOLD_MA=100.0,
+            CHARGING_CONFIRM_COUNT=2,
+            CHARGING_NOTIFY_TIMEOUT_MS=30000,
+            CHARGING_EXIT_SCRIPT="/tmp/charging_exit.sh",
+        )
+        self.config_patch.start()
+        self.network_manager = mock.Mock()
+        self.network_manager.safe_enqueue_and_wait.return_value = True
+        network_module = types.ModuleType("network")
+        network_module.network_manager = self.network_manager
+        self.network_module_patch = mock.patch.dict(
+            sys.modules,
+            {"network": network_module},
+        )
+        self.network_module_patch.start()
+        _FakeTime.reset()
+
+    def tearDown(self):
+        self.network_module_patch.stop()
+        self.config_patch.stop()
+
+    def test_two_charging_samples_notify_server_and_exit(self):
+        popen_calls = []
+        with (
+            mock.patch.object(power, "get_current", return_value=-200.0),
+            mock.patch.object(power.os.path, "isfile", return_value=True),
+            mock.patch.object(
+                power.subprocess,
+                "Popen",
+                side_effect=lambda args: popen_calls.append(args),
+            ),
+        ):
+            power.charging_shutdown_monitor()
+
+        self.assertEqual(_FakeTime.now_ms, 5000)
+        self.assertEqual(len(popen_calls), 1)
+        self.network_manager.safe_enqueue_and_wait.assert_called_once_with(
+            {"poweroff": "充电中"}, 2, high=True, timeout_ms=30000
+        )
+
+    def test_discharging_does_not_notify_or_exit(self):
+        _FakeTime.reset(stop_at_ms=10000)
+        popen_calls = []
+
+        with (
+            mock.patch.object(power, "get_current", return_value=200.0),
+            mock.patch.object(power.os.path, "isfile", return_value=True),
+            mock.patch.object(
+                power.subprocess,
+                "Popen",
+                side_effect=lambda args: popen_calls.append(args),
+            ),
+            self.assertRaises(_StopMonitor),
+        ):
+            power.charging_shutdown_monitor()
+
+        self.assertEqual(popen_calls, [])
+        self.network_manager.safe_enqueue_and_wait.assert_not_called()
+
+    def test_failed_sample_resets_confirmation_count(self):
+        popen_calls = []
+        currents = iter((-200.0, 0.0, -200.0, -200.0))
+        with (
+            mock.patch.object(power, "get_current", side_effect=lambda: next(currents)),
+            mock.patch.object(power.os.path, "isfile", return_value=True),
+            mock.patch.object(
+                power.subprocess,
+                "Popen",
+                side_effect=lambda args: popen_calls.append(args),
+            ),
+        ):
+            power.charging_shutdown_monitor()
+
+        self.assertEqual(_FakeTime.now_ms, 15000)
+        self.assertEqual(len(popen_calls), 1)
+        self.network_manager.safe_enqueue_and_wait.assert_called_once_with(
+            {"poweroff": "充电中"}, 2, high=True, timeout_ms=30000
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()

test/test_decect.py

@@ -0,0 +1,330 @@
+#!/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
+import math
+
+# ==================== 全局配置 (与 test_main.py 保持一致) ====================
+REAL_RADIUS_CM = 20  # 靶心实际半径（厘米）
+
+def detect_circle_v3(frame, laser_point=None, img_cv=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
+    优化：
+      1. 缩图到 MAX_DET_DIM 后再做 HSV/形态学，最长边 640->320 可获得 ~4x 加速
+      2. 红色掩码在黄色轮廓循环外只计算一次，避免 N 次重复计算
+      3. img_cv 可由外部传入（与其他线程共享转换结果），为 None 时自动转换
+    Args:
+        frame: 图像帧（img_cv 为 None 时使用）
+        laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择
+        img_cv: 已转换的 numpy BGR/RGB 图像；不为 None 时跳过 image2cv 转换
+    Returns:
+        (result_img, best_center, best_radius, method, best_radius1, ellipse_params)
+    """
+    if img_cv is None:
+        img_cv = image.image2cv(frame, False, False)
+    from datetime import datetime
+    print(f"[detect_circle_v3] begin {datetime.now()}")
+    # -- 1. 缩图加速（与三角形路径保持一致）
+    h_orig, w_orig = img_cv.shape[:2]
+    MAX_DET_DIM = 480
+    long_side = max(h_orig, w_orig)
+    if long_side > MAX_DET_DIM:
+        det_scale = MAX_DET_DIM / long_side
+        img_det = cv2.resize(img_cv, (int(w_orig * det_scale), int(h_orig * det_scale)),
+                             interpolation=cv2.INTER_LINEAR)
+        inv_scale = 1.0 / det_scale  # 检测坐标 -> 原始坐标的倍率
+    else:
+        img_det = img_cv
+        inv_scale = 1.0
+
+    # 激光点映射到检测分辨率
+    lp_det = None
+    if laser_point is not None:
+        lp_det = (laser_point[0] / inv_scale, laser_point[1] / inv_scale)
+    best_center = best_radius = best_radius1 = method = None
+    ellipse_params = None
+
+    print(f"[detect_circle_v3] step 1 fin {datetime.now()}")
+
+    # -- 2. HSV + 黄色掩码
+    hsv = cv2.cvtColor(img_det, cv2.COLOR_RGB2HSV)
+    h, s, v = cv2.split(hsv)
+    s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
+    hsv = cv2.merge((h, s, v))
+    lower_yellow = np.array([7, 80, 0])
+    upper_yellow = np.array([32, 255, 255])
+    mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
+
+    print(f"[detect_circle_v3] step 2 fin {datetime.now()}")
+
+    # -- 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([170, 50, 40]), 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)
+    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 = []
+    for cnt_r in contours_red:
+        ar = cv2.contourArea(cnt_r)
+        if ar <= 10:
+            continue
+        pr = cv2.arcLength(cnt_r, True)
+        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.3:
+            continue
+        if len(cnt_r) >= 5:
+            (xr, yr), (wr, hr), _ = cv2.fitEllipse(cnt_r)
+            red_candidates.append({"center": (int(xr), int(yr)), "radius": int(min(wr, hr) / 2)})
+        else:
+            (xr, yr), rr = cv2.minEnclosingCircle(cnt_r)
+            red_candidates.append({"center": (int(xr), int(yr)), "radius": int(rr)})
+
+    print(f"[detect_circle_v3] step 3 fin {datetime.now()}")
+
+    # -- 4. 黄色轮廓循环（复用上面的红色候选列表）
+    contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    valid_targets = []
+    for cnt_yellow in contours_yellow:
+        area = cv2.contourArea(cnt_yellow)
+        if area <= 15:
+            continue
+        perimeter = cv2.arcLength(cnt_yellow, True)
+        if perimeter <= 0:
+            continue
+        circularity = (4 * np.pi * area) / (perimeter * perimeter)
+        if circularity <= 0.5:
+            continue
+        print(f"[target] -> 面积:{area:.1f}, 圆度:{circularity:.2f}")
+        if len(cnt_yellow) >= 5:
+            (x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
+            yellow_ellipse = ((x, y), (width, height), angle)
+            yellow_center = (int(x), int(y))
+            yellow_radius = int(min(width, height) / 2)
+        else:
+            (x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
+            yellow_center = (int(x), int(y))
+            yellow_radius = int(radius)
+            yellow_ellipse = None
+        # 在预筛好的红色候选中匹配
+        matched = False
+        for rc in red_candidates:
+            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:
+                print(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), "
+                                f"红心({rc['center']}), 距离:{dist_centers:.1f}, "
+                                f"黄半径:{yellow_radius}, 红半径:{rc['radius']}")
+                valid_targets.append({
+                    "center": yellow_center,
+                    "radius": yellow_radius,
+                    "ellipse": yellow_ellipse,
+                    "area": area,
+                })
+                matched = True
+                break
+        if not matched :
+            print("Debug -> 未找到匹配的红色圆圈，可能是误识别")
+
+    print(f"[detect_circle_v3] step 4 fin {datetime.now()}")
+
+    # -- 5. 选最佳目标，坐标还原到原始分辨率
+    if valid_targets:
+        if lp_det:
+            best_target = min(valid_targets,
+                              key=lambda t: (t["center"][0] - lp_det[0]) ** 2
+                                            + (t["center"][1] - lp_det[1]) ** 2)
+            method = "v3_ellipse_red_validated_laser_selected"
+        else:
+            best_target = max(valid_targets, key=lambda t: t["area"])
+            method = "v3_ellipse_red_validated"
+        bc = best_target["center"]
+        br = best_target["radius"]
+        be = best_target["ellipse"]
+        if inv_scale != 1.0:
+            best_center = (int(bc[0] * inv_scale), int(bc[1] * inv_scale))
+            best_radius = int(br * inv_scale)
+            if be is not None:
+                (ex, ey), (ew, eh), ea = be
+                be = ((ex * inv_scale, ey * inv_scale),
+                      (ew * inv_scale, eh * inv_scale), ea)
+        else:
+            best_center = bc
+            best_radius = br
+        ellipse_params = be
+        best_radius1 = best_radius * 5
+    result_img = image.cv2image(img_cv, False, False)
+    print(f"[detect_circle_v3] step 5 fin {datetime.now()}")
+    return result_img, best_center, best_radius, method, best_radius1, ellipse_params
+
+
+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_v3 函数
+    print("[INFO] 正在调用 detect_circle_v3 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
+    base, ext = os.path.splitext(image_path)
+    output_path = f"{base}_result{ext}"
+    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/None_314_258_0_0041.bmp"
+
+    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 not filename.endswith('_result.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_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=0.5), "
+                                          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 > 0.5:
+                                        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}")
+
+                                        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/None_314_258_0_0041.bmp"
+
+    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_p21_voltage.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Read the digital voltage level on the MaixCAM P21 pin.
+
+P21 is a digital GPIO pin, not the MaixCAM analog ADC input. Therefore this
+script can only distinguish LOW and HIGH. For a continuous voltage value,
+connect the signal to the board's B3/ADC pin and use ADC channel 0 instead.
+
+Do not apply more than 3.3 V to P21. Always connect the signal ground to the
+MaixCAM ground.
+"""
+
+from maix import app, gpio, pinmap, time
+
+
+PIN = "P21"
+IO_HIGH_VOLTAGE = 3.3
+SAMPLE_INTERVAL_MS = 200
+
+
+def find_gpio_function(pin):
+    """Return the GPIO function supported by the requested physical pin."""
+    functions = pinmap.get_pin_functions(pin)
+    gpio_functions = [name for name in functions if name.startswith("GPIO")]
+
+    print(f"{pin} supported functions: {', '.join(functions)}")
+    if not gpio_functions:
+        raise RuntimeError(f"{pin} does not provide a GPIO input function")
+
+    return gpio_functions[0]
+
+
+def main():
+    gpio_function = find_gpio_function(PIN)
+    pinmap.set_pin_function(PIN, gpio_function)
+    voltage_input = gpio.GPIO(gpio_function, gpio.Mode.IN)
+
+    print(f"Reading {PIN} through {gpio_function}")
+    print("P21 only reports LOW/HIGH; displayed voltage is an estimate.")
+    print("Press the MaixCAM exit key to stop.")
+
+    while not app.need_exit():
+        level = voltage_input.value()
+        estimated_voltage = IO_HIGH_VOLTAGE if level else 0.0
+        state = "HIGH" if level else "LOW"
+        print(
+            f"{PIN}: level={level}, state={state}, "
+            f"estimated_voltage={estimated_voltage:.1f} V"
+        )
+        time.sleep_ms(SAMPLE_INTERVAL_MS)
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as error:
+        print(f"P21 voltage detection failed: {error}")
+        print("Check that this MaixCAM model exposes P21 as a GPIO pin.")
+        raise

test/test_wifi_failover.py

@@ -0,0 +1,139 @@
+import json
+import sys
+import types
+import unittest
+
+
+class _FakeTime:
+    @staticmethod
+    def sleep(_seconds):
+        pass
+
+    @staticmethod
+    def sleep_ms(_milliseconds):
+        pass
+
+    @staticmethod
+    def ticks_ms():
+        return 0
+
+    @staticmethod
+    def ticks_diff(left, right):
+        return left - right
+
+
+class _FakeLogger:
+    def debug(self, *_args, **_kwargs):
+        pass
+
+    def info(self, *_args, **_kwargs):
+        pass
+
+    def warning(self, *_args, **_kwargs):
+        pass
+
+    def error(self, *_args, **_kwargs):
+        pass
+
+
+class _FakeSocket:
+    def __init__(self, recv_data=b""):
+        self.recv_data = recv_data
+        self.closed = False
+
+    def close(self):
+        self.closed = True
+
+    def recv(self, _size, *_flags):
+        return self.recv_data
+
+
+class _StopAfterCallback:
+    def __init__(self):
+        self.stopped = False
+
+    def is_set(self):
+        return self.stopped
+
+
+maix_module = types.ModuleType("maix")
+maix_module.time = _FakeTime
+maix_module.network = types.SimpleNamespace()
+maix_module.err = types.SimpleNamespace()
+sys.modules.setdefault("maix", maix_module)
+sys.modules.setdefault("ujson", json)
+
+netcore_module = types.ModuleType("archery_netcore")
+netcore_module.get_config = lambda: {"SERVER_IP": "127.0.0.1", "SERVER_PORT": 1234}
+netcore_module.parse_packet = lambda _packet: (0, {})
+netcore_module.make_packet = lambda *_args, **_kwargs: b""
+netcore_module.actions_for_inner_cmd = lambda *_args, **_kwargs: []
+sys.modules["archery_netcore"] = netcore_module
+
+hardware_module = types.ModuleType("hardware")
+hardware_module.hardware_manager = types.SimpleNamespace()
+sys.modules["hardware"] = hardware_module
+
+power_module = types.ModuleType("power")
+power_module.get_bus_voltage = lambda: 0
+power_module.voltage_to_percent = lambda _voltage: 0
+sys.modules["power"] = power_module
+
+import logger_manager
+import wifi
+import network
+
+
+class WiFiFailoverTests(unittest.TestCase):
+    def setUp(self):
+        logger_manager.logger_manager._logger = _FakeLogger()
+
+    def test_monitor_switches_when_sta_association_is_lost(self):
+        manager = wifi.wifi_manager
+        stop_event = _StopAfterCallback()
+        callbacks = []
+
+        manager._wifi_socket = _FakeSocket()
+        manager._wifi_quality_stop_event = stop_event
+        manager._network_type_callback = lambda: "wifi"
+        manager.is_sta_associated = lambda: False
+        manager._get_wifi_rssi_dbm = lambda: None
+
+        def on_poor_quality():
+            callbacks.append(True)
+            stop_event.stopped = True
+
+        manager._on_poor_quality_callback = on_poor_quality
+        manager._quality_monitor_loop()
+
+        self.assertEqual(callbacks, [True])
+        self.assertIsNone(manager.last_wifi_rtt_ms)
+
+    def test_tls_connection_check_rejects_lost_sta_association(self):
+        manager = network.network_manager
+        sock = _FakeSocket()
+        wifi.wifi_manager._wifi_socket = sock
+        wifi.wifi_manager._wifi_connected = True
+        wifi.wifi_manager._wifi_ip = "192.168.1.2"
+        wifi.wifi_manager.is_sta_associated = lambda: False
+        manager._tcp_connected = True
+
+        self.assertFalse(manager._check_wifi_connection())
+        self.assertTrue(sock.closed)
+        self.assertIsNone(wifi.wifi_manager.wifi_socket)
+        self.assertFalse(manager.tcp_connected)
+
+    def test_receive_eof_marks_wifi_tcp_disconnected(self):
+        manager = network.network_manager
+        sock = _FakeSocket(recv_data=b"")
+        wifi.wifi_manager._wifi_socket = sock
+        manager._tcp_connected = True
+
+        self.assertEqual(manager.receive_tcp_data_via_wifi(), b"")
+        self.assertTrue(sock.closed)
+        self.assertIsNone(wifi.wifi_manager.wifi_socket)
+        self.assertFalse(manager.tcp_connected)
+
+
+if __name__ == "__main__":
+    unittest.main()

version.md

@@ -0,0 +1,39 @@
+# 1.2.0 开始使用C++编译成.so，替换部分代码
+# 1.2.1 ota使用加密包
+# 1.2.2 支持wifi ota，并且设定时区，并使用单独线程保存图片
+# 1.2.3 修改ADC_TRIGGER_THRESHOLD 为2300，支持上传日志到服务器
+# 1.2.4 修改ADC_TRIGGER_THRESHOLD 为3000，并默认关闭摄像头的显示，并把ADC的采样间隔从50ms降低到10ms
+# 1.2.5 支持空气传感器采样，并默认关闭日志。优化断网时的发送队列丢消息问题，解决 WiFi 断线检测不可靠问题。
+# 1.2.6 在链接 wifi 前先判断 wifi 的可用性，假如不可用，则不落盘。增加日志批量压缩上传功能
+# 1.2.7 修复OTA失败的bug, 空气压力传感器的阈值是2500
+# 1.2.8 （1） 加快 wifi 下数据传输的速度。（2） 调整射箭时处理的逻辑，优先上报数据，再存照片之类的操作。（3）假如是用户打开激光的，射箭触发后不再关闭激光，因为是调瞄阶段
+# 1.2.9 增加电源板的控制和自动关机的功能
+# 1.2.10 config formal
+# 1.2.11 增加三角形的单应性算法，适配对应的靶纸
+# 1.2.110 关掉了黑色三角形算法，只用于测试
+# 1.2.13 修改wifi连接
+# 1.2.14 修改了icc登录部分
+# 2.15.3 新版本ota，去除ai算环数方法
+# 2.15.4 更新版本号
+# 2.15.5 打印ota进度
+# 2.15.6 更新版本号
+# 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
+# 2.15.20 加了充电关机，激光也同时关闭
+# 2.15.21 测试4g 扩大了缓存池和改了心跳时间
+# 2.15.22 修复了4g网络和wifi切换问题
+# 2.15.23 合并充电关机与稳定版网络修复
+# 2.15.24 空改测试
+# 2.15.25 修复整合后关机失败和ota格式更新问题
+# 2.15.26 
+# 2.16.1  修复压力传感触发判断按照增量

version.py

@@ -4,28 +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 = '2.14.1'
+VERSION = '2.16.1'
-
-
-# 1.2.0 开始使用C++编译成.so，替换部分代码
-# 1.2.1 ota使用加密包
-# 1.2.2 支持wifi ota，并且设定时区，并使用单独线程保存图片
-# 1.2.3 修改ADC_TRIGGER_THRESHOLD 为2300，支持上传日志到服务器
-# 1.2.4 修改ADC_TRIGGER_THRESHOLD 为3000，并默认关闭摄像头的显示，并把ADC的采样间隔从50ms降低到10ms
-# 1.2.5 支持空气传感器采样，并默认关闭日志。优化断网时的发送队列丢消息问题，解决 WiFi 断线检测不可靠问题。
-# 1.2.6 在链接 wifi 前先判断 wifi 的可用性，假如不可用，则不落盘。增加日志批量压缩上传功能
-# 1.2.7 修复OTA失败的bug, 空气压力传感器的阈值是2500
-# 1.2.8 （1） 加快 wifi 下数据传输的速度。（2） 调整射箭时处理的逻辑，优先上报数据，再存照片之类的操作。（3）假如是用户打开激光的，射箭触发后不再关闭激光，因为是调瞄阶段
-# 1.2.9 增加电源板的控制和自动关机的功能
-# 1.2.10 config formal
-# 1.2.11 增加三角形的单应性算法，适配对应的靶纸
-# 1.2.110 关掉了黑色三角形算法，只用于测试
-# 1.2.13 修改wifi连接
-# 1.2.14 修改了icc登录部分
-
-
-
-
-
 
 

vision.py

@@ -535,7 +535,7 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
     logger.debug(f"[detect_circle_v3] begin {datetime.now()}")
     # -- 1. 缩图加速（与三角形路径保持一致）
     h_orig, w_orig = img_cv.shape[:2]
-    MAX_DET_DIM = 320
+    MAX_DET_DIM = 480
     long_side = max(h_orig, w_orig)
     if long_side > MAX_DET_DIM:
         det_scale = MAX_DET_DIM / long_side
@@ -570,20 +570,22 @@ 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, 80, 0]),   np.array([10,  255, 255])),
+        cv2.inRange(hsv, np.array([0, 30, 20]),   np.array([12,  255, 255])),
-        cv2.inRange(hsv, np.array([170, 80, 0]), 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 = []
     for cnt_r in contours_red:
         ar = cv2.contourArea(cnt_r)
-        if ar <= 50:
+        if ar <= 10:
             continue
         pr = cv2.arcLength(cnt_r, True)
-        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.6:
+        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)
@@ -599,13 +601,13 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
     valid_targets = []
     for cnt_yellow in contours_yellow:
         area = cv2.contourArea(cnt_yellow)
-        if area <= 50:
+        if area <= 15:
             continue
         perimeter = cv2.arcLength(cnt_yellow, True)
         if perimeter <= 0:
             continue
         circularity = (4 * np.pi * area) / (perimeter * perimeter)
-        if circularity <= 0.7:
+        if circularity <= 0.5:
             continue
         if logger:
             logger.info(f"[target] -> 面积:{area:.1f}, 圆度:{circularity:.2f}")
@@ -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 * 0.8:
+            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 >= 0.3:
                 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}, "
@@ -638,8 +644,17 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
                 })
                 matched = True
                 break
-        if not matched and logger:
+        if not matched:
-            logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
+            # 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()}")
 

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:
@@ -535,73 +541,88 @@ class WiFiManager:
     
     def start_quality_monitor(self, network_type_callback, on_poor_quality_callback):
         """
-        启动 WiFi 质量后台监测线程（每 5 秒测量一次 RTT 和 RSSI）
+        启动 WiFi 质量后台监测线程（每 5 秒检查 STA 关联状态和 RSSI）
         只在 WiFi 连接时运行，不影响业务发送性能
         
         Args:
             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:
-            self.logger.warning("[WiFi Monitor] 监测线程已在运行")
+            if self._wifi_quality_monitor_thread is not None and self._wifi_quality_monitor_thread.is_alive():
-            return
+                self.logger.warning("[WiFi Monitor] 监测线程已在运行")
-        
+                return
-        self._network_type_callback = network_type_callback
+            
-        self._on_poor_quality_callback = on_poor_quality_callback
+            self._network_type_callback = network_type_callback
-        self._wifi_quality_stop_event.clear()
+            self._on_poor_quality_callback = on_poor_quality_callback
-        self._wifi_quality_monitor_thread = threading.Thread(
+            self._wifi_quality_stop_event.clear()
-            target=self._quality_monitor_loop,
+            self._wifi_quality_monitor_thread = threading.Thread(
-            daemon=True,
+                target=self._quality_monitor_loop,
-            name="wifi_quality_monitor"
+                daemon=True,
-        )
+                name="wifi_quality_monitor"
-        self._wifi_quality_monitor_thread.start()
+            )
-        self.logger.info("[WiFi Monitor] 已启动后台监测线程")
+            self._wifi_quality_monitor_thread.start()
+            self.logger.info("[WiFi Monitor] 已启动后台监测线程")
     
     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:
-            return
+            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:
+        
-            self._wifi_quality_monitor_thread = None
+        with self._wifi_quality_lock:
-            self.logger.info("[WiFi Monitor] 已停止后台监测线程")
+            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")
     
     def _quality_monitor_loop(self):
         """
         WiFi 质量监测循环（后台线程）
-        每 5 秒测量一次 RTT 和 RSSI，发现质量差则触发切换
+        每 5 秒检查 STA 关联状态和 RSSI，发现断链或质量差则触发切换
         """
         while not self._wifi_quality_stop_event.is_set():
             try:
                 # 只在 WiFi 连接时才测量
                 network_type = self._network_type_callback()
                 if network_type == "wifi" and self._wifi_socket:
-                    # # 测量 RTT（1 个样本，快速测量）
+                    # RTT 测量当前禁用；STA 关联状态用于判断物理 WiFi 链路是否仍存在。
-                    # rtt_ms, reachable = self._measure_wifi_tcp_rtt_ms(
+                    # 不能把禁用的 RTT 伪装成 0ms，否则关闭热点后会一直被判为正常。
-                    #     self._server_ip, self._server_port,
+                    reachable = self.is_sta_associated()
-                    #     samples=1, per_sample_timeout_ms=600
+                    rtt_ms = None
-                    # )
+
-                    
                     # 获取 RSSI
                     rssi_dbm = self._get_wifi_rssi_dbm()
-                    
+
                     # 更新缓存
-                    # 不使用 RTT 测量
+                    self._last_wifi_rtt_ms = rtt_ms
-                    rtt_ms = 0
-                    reachable = True
-                    self._last_wifi_rtt_ms = rtt_ms if reachable else None
                     self._last_wifi_rssi_dbm = rssi_dbm
+                    _rtt_s = f"{rtt_ms:.0f}ms" if rtt_ms is not None else "n/a"
                     _rssi_s = f"{rssi_dbm:.0f}" if rssi_dbm is not None else "n/a"
-                    self.logger.debug(f"[WiFi Monitor] - RTT={rtt_ms:.0f}ms, RSSI={_rssi_s}dBm")
+                    self.logger.debug(
+                        f"[WiFi Monitor] - associated={reachable}, RTT={_rtt_s}, RSSI={_rssi_s}dBm"
+                    )
                     
                     # 判断质量是否差（切换前做 2 次快速复测，防止瞬时抖动）
                     def _is_bad_now(_reachable, _rtt, _rssi):
-                        if (not _reachable) or (_rtt is None) or (_rtt == float("inf")):
+                        if not _reachable:
+                            return True
+                        # RTT 未启用时不参与质量判断；链路状态仍由 STA 关联保证。
+                        if _rtt is None:
+                            return False
+                        if _rtt == float("inf"):
                             return True
                         return self._is_wifi_quality_bad(_rtt, _rssi)
 
@@ -611,13 +632,8 @@ class WiFiManager:
 
                         for retry_idx in range(2):
                             time.sleep_ms(1000)
-                            # 不使用 RTT 测量
+                            reachable2 = self.is_sta_associated()
-                            rtt2 = 0
+                            rtt2 = None
-                            reachable2 = True
-                            # rtt2, reachable2 = self._measure_wifi_tcp_rtt_ms(
-                            #     self._server_ip, self._server_port,
-                            #     samples=1, per_sample_timeout_ms=600
-                            # )
                             rssi2 = self._get_wifi_rssi_dbm()
 
                             # 更新缓存，便于外部查看最新状态
@@ -626,14 +642,10 @@ class WiFiManager:
 
                             bad2 = _is_bad_now(reachable2, rtt2, rssi2)
                             try:
-                                _rtt_disp = (
+                                _rtt_disp = f"{rtt2:.0f}ms" if rtt2 is not None else "n/a"
-                                    rtt2
-                                    if rtt2 is not None and rtt2 != float("inf")
-                                    else -1
-                                )
                                 self.logger.info(
                                     f"[WiFi Monitor] 复测{retry_idx+1}/2: reachable={reachable2}, "
-                                    f"rtt={_rtt_disp:.0f}ms, rssi={rssi2}, bad={bad2}"
+                                    f"rtt={_rtt_disp}, rssi={rssi2}, bad={bad2}"
                                 )
                             except Exception:
                                 pass