fix:

app.yaml

@@ -1,6 +1,6 @@
 id: t11
 name: t11
-version: 1.2.13.1
+version: 2.15.14
 author: t11
 icon: ''
 desc: t11
@@ -14,12 +14,14 @@ files:
   - cameraParameters.xml
   - 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

config.py

@@ -34,10 +34,10 @@ WIFI_QUALITY_RSSI_BAD_DBM = -80.0  # 低于此 dBm（更负更差）视为信号
 WIFI_QUALITY_USE_RSSI = True  # 是否把 RSSI 纳入综合判定
 
 # WiFi 热点配网（手机连设备 AP，浏览器提交路由器 SSID/密码；仅 GET/POST，标准库 socket）
-WIFI_CONFIG_AP_FALLBACK = True # # WiFi 配网失败时，是否退回热点模式，并等待重新配网
+WIFI_CONFIG_AP_FALLBACK = False # # WiFi 配网失败时，是否退回热点模式，并等待重新配网
 WIFI_AP_FALLBACK_WAIT_SEC = 5 # 等待5秒后再检测STA/4G
 WIFI_CONFIG_AP_TIMEOUT = 5  # 热点模式超时时间（秒）
-WIFI_CONFIG_AP_ENABLED = True  # True=启动时开热点并起迷你 HTTP 配网服务
+WIFI_CONFIG_AP_ENABLED = False  # True=启动时开热点并起迷你 HTTP 配网服务
 WIFI_CONFIG_AP_SSID = "ArcherySetup"       # 设备发出的热点名称
 WIFI_CONFIG_AP_PASSWORD = "12345678"       # 热点密码（WPA2 通常至少 8 位）
 WIFI_CONFIG_HTTP_HOST = "0.0.0.0"          # HTTP 监听地址
@@ -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
@@ -255,8 +255,12 @@ TRIANGLE_YOLO_REJECT_BAD_ROI = True
 TRIANGLE_CROP_ROI_MIN_SIDE_PX = 64
 # 射箭保存图 / 预览上绘制 YOLO 靶环 ROI 矩形 (x0,y0,x1,y1)，核对是否裁准；不需要时改 False
 TRIANGLE_YOLO_DRAW_ROI_ON_SHOT = True
+# 物方采样调试：以靶心为中心，取半径 15cm 的圆周样本点，用于黑/白颜色对比
+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）：
@@ -304,8 +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

design_doc/command_record.md

@@ -1,12 +1,12 @@
 
-1. CPP构建命令：
+1. CPP构建命令：在docker环境下执行以下命令
 
-cd /mnt/d/code/archery/cpp_ext
+cd /data/cpp_ext
 rm -rf build && mkdir build && cd build
 
-TOOLCHAIN_BIN=/mnt/d/code/MaixCDK/dl/extracted/toolchains/maixcam/host-tools/gcc/riscv64-linux-musl-x86_64/bin
+TOOLCHAIN_BIN=/data/MaixCDK-main/dl/extracted/toolchains/maixcam/host-tools/gcc/riscv64-linux-musl-x86_64/bin
-PYDEV=/mnt/d/code/shooting/python3_lib_maixcam_musl_3.11.6
+PYDEV=/data/python3_lib_maixcam_musl_3.11.6
-MAIXCDK=/mnt/d/code/MaixCDK
+MAIXCDK=/data/MaixCDK-main
 
 cmake .. -G Ninja \
   -DCMAKE_C_COMPILER="${TOOLCHAIN_BIN}/riscv64-unknown-linux-musl-gcc" \

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

@@ -290,34 +290,33 @@ def cmd_str():
     last_avg_abs = 0
 
     def _flush_pressure_buf(reason: str):
-        if not config.AIR_PRESSURE_lOG:
-            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
+            avg_abs = (pressure_abs_sum / n) if n else 0
-        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} avg_abs={avg_abs:.3f} "
+                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}"
+                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:
+            if logger:
-            logger.debug(line)
+                logger.debug(line)
-        else:
+            else:
-            print(line)
+                print(line)
+            last_avg_abs = avg_abs
+        # 无论是否记录日志，都必须清空 buffer，否则内存泄漏
         pressure_buf = []
         pressure_sum = 0
         pressure_abs_sum = 0
         pressure_min = 4095
         pressure_max = 0
         pressure_t0_ms = None
-        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():

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

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)

version.md

@@ -0,0 +1,27 @@
+# 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

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 = '1.2.14.1'
+VERSION = '2.15.14'
-
-
-# 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 > 0.5:
                 if logger:
                     logger.info(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), "
                                 f"红心({rc['center']}), 距离:{dist_centers:.1f}, "
@@ -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
         
@@ -542,34 +543,45 @@ class WiFiManager:
             network_type_callback: 获取当前网络类型的回调函数
             on_poor_quality_callback: WiFi质量差时的回调函数
         """
-        if self._wifi_quality_monitor_thread is not None:
+        with self._wifi_quality_lock:
-            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):
         """