fix: wifi连接

.idea/.gitignore

@@ -1,8 +0,0 @@
-# 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/.name

@@ -1 +0,0 @@
-network.py

.idea/archery.iml

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module version="4">
-  <component name="PyDocumentationSettings">
-    <option name="format" value="PLAIN" />
-    <option name="myDocStringFormat" value="Plain" />
-  </component>
-</module>

.idea/inspectionProfiles/profiles_settings.xml

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

.idea/misc.xml

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Black">
-    <option name="sdkName" value="Python 3.13 virtualenv at H:\iot\racingiot_v1\.venv" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="maixcam" project-jdk-type="Python SDK" />
-</project>

.idea/vcs.xml

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

Untitled

@@ -1 +0,0 @@
-v1.2.15.1] [ERROR] main.py:416 - [MAIN] 显示异常: 'LaserManager' object has no attribute 'remote_detect_tick'

adc.py

@@ -4,12 +4,12 @@ from maix import time
 a = adc.ADC(0, adc.RES_BIT_12)
 
 while True:
-    raw_data = a.read()
-    print(f"ADC raw data:{raw_data}")
-    if raw_data > 2450:
-        print(f"ADC raw data:{raw_data}")
-    elif raw_data < 2000:
-        print(f"ADC raw data:{raw_data}")
+    # raw_data = a.read()
+    # print(f"ADC raw data:{raw_data}")
+    # if raw_data > 2450:
+    #     print(f"ADC raw data:{raw_data}")
+    # elif raw_data < 2000:
+    #     print(f"ADC raw data:{raw_data}")
     time.sleep_ms(1)
 
     vol = int(a.read_vol() * 10) / 10

app.yaml

@@ -1,6 +1,6 @@
 id: t11
 name: t11
-version: 2.1.1
+version: 2.15.15
 author: t11
 icon: ''
 desc: t11
@@ -14,16 +14,17 @@ 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
-  - set_autostart.py
   - shoot_manager.py
   - shot_id_generator.py
   - target_roi_yolo.py

config.py

@@ -106,14 +106,6 @@ DEFAULT_LASER_POINT =  (320, 245)   # 默认激光中心点
 HARDCODE_LASER_POINT = True  # 是否使用硬编码的激光点（True=使用硬编码值，False=使用校准值）
 HARDCODE_LASER_POINT_VALUE = (320, 296) # 硬编码的激光点坐标(315, 245) #   # 硬编码的激光点坐标 (x, y)
 
-# 远程激光点识别（TCP cmd=200）：画面内找红点，稳定 N 秒且无明显跳动后上报坐标
-LASER_REMOTE_DETECT_STABLE_SEC = 3.0       # 连续稳定时长（秒）
-LASER_REMOTE_DETECT_MAX_MOVE_PX = 12.0     # 窗口内最大位移超过此值视为大幅移动，重新计时
-LASER_REMOTE_DETECT_SAMPLE_MS = 80         # 采样间隔
-LASER_REMOTE_DETECT_MIN_SAMPLES = 8        # 判定稳定前窗口内最少样本数
-LASER_REMOTE_DETECT_WARMUP_MS = 500        # cmd=200 开激光后等待稳定再采样
-# 远程识别会话无总超时：cmd=200 启动后持续检测并上报，直至 cmd=201 停止
-
 # 激光点检测配置
 LASER_DETECTION_THRESHOLD = 140  # 红色通道阈值（默认120，可调整，范围建议：100-150）
 LASER_RED_RATIO = 1.5  # 红色相对于绿色/蓝色的倍数要求（默认1.5，可调整，范围建议：1.3-2.0）
@@ -142,7 +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
@@ -152,13 +144,6 @@ TRIANGLE_SIZE_RANGE = (8, 500)
 # 如果射箭距离很固定，可设具体范围（如 min=2.5, max=6.0）作为额外保险
 TRIANGLE_DISTANCE_MIN_M = 0.0   # 0=不启用下限检查
 TRIANGLE_DISTANCE_MAX_M = 0.0   # 0=不启用上限检查
-# 三角形方向校验：四角黑三角应为 ◤ ◥ / ◣ ◢，即三角形从外角指向靶心；用于过滤相邻靶混入/跨靶组合
-TRIANGLE_DIRECTION_VALIDATE_ENABLE = False
-TRIANGLE_DIRECTION_MIN_PASS = 3  # 至少多少个真实三角方向正确才认为该组有效；3点补全时推荐3，误检多可设2
-TRIANGLE_DIRECTION_DOT_MIN = 0.0  # 方向点积阈值；0=只要求同向半平面，0.35≈夹角<70°，0.5≈夹角<60°
-TRIANGLE_DIRECTION_TO_CENTER_DOT_MIN = 0.35  # 必须指向候选靶心；0.35≈夹角<70°，用于过滤相邻靶混入
-TRIANGLE_CENTER_DISTANCE_VALIDATE_ENABLE = True  # 四角三角到候选靶心距离需近似一致，过滤跨靶组合
-TRIANGLE_CENTER_DISTANCE_RATIO_TOL = 0.45  # (max_dist-min_dist)/mean_dist 最大允许值；越小越严格
 # 三角形检测兜底增强：CLAHE（更鲁棒但更慢）。颜色阈值修复后通常不需要，保持关闭以优先速度。
 TRIANGLE_ENABLE_CLAHE_FALLBACK = False
 # 三角形检测调试：保存 Otsu 二值化图像（临时调试用，定位后关闭）
@@ -184,7 +169,6 @@ TRIANGLE_SHAPE_COS_TOLERANCE = 0.25    # 直角余弦绝对值上限（原 0.20
 # 建议设为实测最坏耗时的 1.2 倍；超时后圆心检测仍会并行跑完，跑完后若三角形已结束则优先用三角形。
 TRIANGLE_TIMEOUT_MS = 1000
 # True=打印各阶段耗时(ms)，用于定位瓶颈；稳定后可 False 减少日志
-ARCHERY_TIMING_ENABLE = False  # 总开关：False 关闭所有算法耗时统计（shoot_manager + triangle_target + vision）
 TRIANGLE_TIMING_LOG = True
 # True=Stage2 每个子框内传统三角失败时打一条统计（Otsu/Adaptive 下轮廓数与各拒绝原因计数）
 TRIANGLE_LOG_STAGE2_PATCH_REJECT = True
@@ -272,15 +256,11 @@ 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_ENABLE = True
-TRIANGLE_SAMPLE_TIMING_ENABLE = True  # 仅统计物方采样耗时（其他 timing 可关）
 TRIANGLE_SAMPLE_RADIUS_CM = 15.0
 TRIANGLE_SAMPLE_ANGLES_DEG = (0, 90, 180, 270)
 TRIANGLE_SAMPLE_PATCH_HALF_PX = 2
-# 物方采样判断黑白阈值（R/G/B 均小于此值视为黑）；40cm 黑靶在靶面位置全黑，20cm 白靶则 R/G/B 偏高
-TRIANGLE_SAMPLE_BLACK_THRESH = 30.0
 # 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）：
@@ -328,16 +308,21 @@ 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_RAW_SHOT_IMAGE_ENABLED = False  # 是否额外保存射箭原图；可通过 TCP cmd=46 动态开关
-VISION_TIMING_ENABLE = True  # 视觉圆检测耗时统计（detect_circle_v3 内部各步骤耗时）
+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
 TRIANGLE_BLACK_YOLO_STAGE2_ROI_MAX_IMAGES = None
 
-SHOW_CAMERA_PHOTO_WHILE_SHOOTING = True  # 是否在拍摄时显示摄像头图像（True=显示，False=不显示），建议在连着USB测试过程中打开
+SHOW_CAMERA_PHOTO_WHILE_SHOOTING = False  # 是否在拍摄时显示摄像头图像（True=显示，False=不显示），建议在连着USB测试过程中打开
 
 # ==================== OTA配置 ====================
 MAX_BACKUPS = 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

@@ -6,7 +6,6 @@
 """
 import _thread
 import json
-import math
 import os
 import binascii
 from maix import time
@@ -35,13 +34,9 @@ class LaserManager:
         self._calibration_active = False
         self._calibration_result = None
         self._calibration_lock = threading.Lock()
-        self._remote_detect_active = False
-        self._remote_detect_lock = threading.Lock()
-        self._remote_detect_result = None
         self._laser_point = None
         self._laser_turned_on = False
         self._last_frame_with_ellipse = None  # 保存绘制了椭圆的图像（用于调试/显示）
-        self._remote_detect_last_pos = None
         self._initialized = True
     
     # ==================== 状态访问（只读属性）====================
@@ -59,8 +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:
-            return config.HARDCODE_LASER_POINT_VALUE
+        # if config.HARDCODE_LASER_POINT:
+        #     return config.HARDCODE_LASER_POINT_VALUE
         return self._laser_point
     
     def get_last_frame_with_ellipse(self):
@@ -107,262 +102,30 @@ 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
-        如果启用硬编码模式，则直接使用硬编码值
-        """
-        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
+        """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"""
+        # 优先：从本地持久化文件加载（由 cmd 201 保存）
         try:
-            if os.path.exists(config.CONFIG_FILE):
+            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:
-                        raise ValueError
-            else:
-                self._laser_point = config.DEFAULT_LASER_POINT
-        except Exception as e:
-            if self.logger:
-                self.logger.warning(f"[LASER] 加载激光点失败，使用默认值: {e}")
-            self._laser_point = config.DEFAULT_LASER_POINT
+        except Exception:
+            pass
 
+        # 其次：硬编码值
+        if config.HARDCODE_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
     
-    @property
-    def remote_detect_active(self):
-        with self._remote_detect_lock:
-            return self._remote_detect_active
-
-    def get_remote_detect_result(self):
-        """获取并清除远程激光识别结果 (x, y) 或 None。"""
-        with self._remote_detect_lock:
-            result = self._remote_detect_result
-            self._remote_detect_result = None
-            return result
-
-    def remote_detect_tick(self, frame):
-        """
-        主循环显示路径调用的轻量 tick。
-        兼容旧调用点：当前远程识别由后台线程处理，这里不做重计算，
-        仅保留接口避免 AttributeError。
-        """
-        return None
-
-    def overlay_remote_detect_preview(self, frame):
-        """
-        在预览画面叠加远程识别点与坐标文本。
-        """
-        try:
-            import cv2
-            from maix import image
-            with self._remote_detect_lock:
-                pos = self._remote_detect_last_pos
-            if not pos:
-                return frame
-
-            img_cv = image.image2cv(frame, False, False)
-            if img_cv is None or img_cv.size == 0:
-                return frame
-
-            x, y = int(pos[0]), int(pos[1])
-            h, w = img_cv.shape[:2]
-            if x < 0 or y < 0 or x >= w or y >= h:
-                return frame
-
-            color = (255, 0, 0)  # RGB
-            cv2.circle(img_cv, (x, y), 8, color, 2)
-            cv2.line(img_cv, (x - 12, y), (x + 12, y), color, 1)
-            cv2.line(img_cv, (x, y - 12), (x, y + 12), color, 1)
-            cv2.putText(img_cv, f"laser=({x},{y})", (max(5, x + 10), max(20, y - 10)),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.55, color, 1, cv2.LINE_AA)
-
-            return image.cv2image(img_cv, False, False)
-        except Exception as e:
-            if self.logger:
-                self.logger.debug(f"[LASER-REMOTE] overlay 绘制失败: {e}")
-            return frame
-
-    def _set_remote_detect_result(self, result):
-        with self._remote_detect_lock:
-            self._remote_detect_result = result
-
-    def set_hardcoded_laser_point(self, x, y):
-        """更新 config.HARDCODE_LASER_POINT_VALUE（TCP cmd=201）。"""
-        try:
-            ix = int(round(float(x)))
-            iy = int(round(float(y)))
-        except (TypeError, ValueError) as e:
-            raise ValueError(f"invalid laser point ({x!r}, {y!r})") from e
-        config.HARDCODE_LASER_POINT = True
-        config.HARDCODE_LASER_POINT_VALUE = (ix, iy)
-        self._laser_point = (ix, iy)
-        try:
-            with open(config.CONFIG_FILE, "w") as f:
-                json.dump([ix, iy], f)
-        except Exception as e:
-            if self.logger:
-                self.logger.warning(f"[LASER] 保存硬编码激光点到本地失败: {e}")
-            raise
-        if self.logger:
-            self.logger.info(
-                f"[LASER] 已设置硬编码激光点 HARDCODE_LASER_POINT_VALUE=({ix}, {iy}) 并已保存到 {config.CONFIG_FILE}"
-            )
-        return ix, iy
-
-    def start_remote_laser_detect(self):
-        """
-        启动远程激光识别会话（TCP cmd=200）：开激光后持续检测。
-        每次稳定 3s 上报一次坐标，外循环直到 cmd=201 调用 stop_remote_laser_detect()。
-        Returns:
-            True 已启动；False 会话已在运行
-        """
-        with self._remote_detect_lock:
-            if self._remote_detect_active:
-                return False
-            self._remote_detect_active = True
-            self._remote_detect_result = None
-            self._remote_detect_last_pos = None
-        _thread.start_new_thread(self._remote_laser_detect_worker, ())
-        if self.logger:
-            self.logger.info("[LASER] 远程激光识别已启动 (cmd=200)")
-        return True
-
-    def stop_remote_laser_detect(self):
-        with self._remote_detect_lock:
-            self._remote_detect_active = False
-
-    def _remote_laser_detect_worker(self):
-        from camera_manager import camera_manager
-
-        stable_sec = float(getattr(config, "LASER_REMOTE_DETECT_STABLE_SEC", 3.0))
-        max_move = float(getattr(config, "LASER_REMOTE_DETECT_MAX_MOVE_PX", 12.0))
-        sample_ms = int(getattr(config, "LASER_REMOTE_DETECT_SAMPLE_MS", 80))
-        min_samples = int(getattr(config, "LASER_REMOTE_DETECT_MIN_SAMPLES", 8))
-        warmup_ms = int(getattr(config, "LASER_REMOTE_DETECT_WARMUP_MS", 500))
-        stable_ms = int(max(500, stable_sec * 1000))
-
-        samples = []
-        miss_count = 0
-        stable_hit_count = 0
-        reported = False
-
-        try:
-            if not self._laser_turned_on:
-                try:
-                    self.turn_on_laser()
-                except Exception as e:
-                    if self.logger:
-                        self.logger.warning(f"[LASER] cmd200 worker 开激光失败: {e}")
-            if warmup_ms > 0:
-                if self.logger:
-                    self.logger.info(f"[LASER] cmd200 激光预热 {warmup_ms}ms …")
-                time.sleep_ms(warmup_ms)
-
-            if self.logger:
-                self.logger.info("[LASER] 远程识别外循环已启动，直至 cmd=201 停止")
-
-            while True:
-                with self._remote_detect_lock:
-                    if not self._remote_detect_active:
-                        if self.logger:
-                            self.logger.info("[LASER] 远程识别会话结束 (cmd=201 或取消)")
-                        return
-
-                try:
-                    frame = camera_manager.read_frame()
-                    pos = self.find_red_laser_remote(frame)
-                except Exception as e:
-                    if self.logger:
-                        self.logger.warning(f"[LASER] 远程识别帧异常: {e}")
-                    pos = None
-                    time.sleep_ms(sample_ms)
-                    continue
-
-                now_ms = time.ticks_ms()
-                if pos is None:
-                    miss_count += 1
-                    samples.clear()
-                    stable_hit_count = 0
-                    if miss_count == 1 or miss_count % 40 == 0:
-                        if self.logger:
-                            self.logger.info(
-                                f"[LASER-REMOTE] 本帧未检出激光点（累计 {miss_count} 帧），"
-                                f"全图多策略搜索中…"
-                            )
-                    time.sleep_ms(sample_ms)
-                    continue
-
-                miss_count = 0
-                x, y = float(pos[0]), float(pos[1])
-                samples.append((now_ms, x, y))
-                cutoff = now_ms - stable_ms
-                samples = [(t, px, py) for t, px, py in samples if t >= cutoff]
-
-                if len(samples) < 2:
-                    time.sleep_ms(sample_ms)
-                    continue
-
-                xs = [s[1] for s in samples]
-                ys = [s[2] for s in samples]
-                span = max(
-                    max(xs) - min(xs),
-                    max(ys) - min(ys),
-                )
-                for i in range(len(samples)):
-                    for j in range(i + 1, len(samples)):
-                        d = math.hypot(
-                            samples[i][1] - samples[j][1],
-                            samples[i][2] - samples[j][2],
-                        )
-                        span = max(span, d)
-
-                if span > max_move:
-                    if self.logger:
-                        self.logger.debug(
-                            f"[LASER] 检测到大幅位移 span={span:.1f}px>{max_move}，重新计时"
-                        )
-                    samples.clear()
-                    stable_hit_count = 0
-                    time.sleep_ms(sample_ms)
-                    continue
-
-                window_ms = samples[-1][0] - samples[0][0]
-                if window_ms >= stable_ms and len(samples) >= min_samples:
-                    fx = int(round(sum(xs) / len(xs)))
-                    fy = int(round(sum(ys) / len(ys)))
-                    stable_hit_count += 1
-                    if self.logger:
-                        self.logger.info(
-                            f"[LASER] 远程识别稳定命中 {stable_hit_count}/3 span={span:.1f}px → ({fx}, {fy})"
-                        )
-                    samples.clear()
-                    if stable_hit_count >= 3 and not reported:
-                        reported = True
-                        self._set_remote_detect_result(
-                            {"result":"laser_detect_ok", "x": fx, "y": fy}
-                        )
-                        if self.logger:
-                            self.logger.info(
-                                f"[LASER] 已连续3次坐标稳定，完成上报，继续等待 cmd=201 关闭会话"
-                            )
-                    time.sleep_ms(sample_ms)
-                    continue
-
-                time.sleep_ms(sample_ms)
-        finally:
-            if self.logger:
-                self.logger.info("[LASER] 远程识别线程退出，等待下一次 cmd=200")
-            with self._remote_detect_lock:
-                self._remote_detect_active = False
-
     def save_laser_point(self, point):
         """保存激光中心点到配置文件
         如果启用硬编码模式，则不保存（直接返回 True）
@@ -1065,172 +828,6 @@ class LaserManager:
             # 使用原来的最亮点方法
             return self._find_red_laser_brightest(frame, threshold, search_radius, ellipse_params)
 
-    def find_red_laser_remote(self, frame):
-        """
-        cmd=200 远程识别专用：全图搜索、多策略、放宽阈值，不限距画面中心距离。
-        常规 find_red_laser 仅搜中心 ±LASER_SEARCH_RADIUS 且距中心 >50px 会丢弃。
-        """
-        import cv2
-        import numpy as np
-        from maix import image
-
-        img_cv = image.image2cv(frame, False, False)
-        if img_cv is None or img_cv.size == 0:
-            return None
-        h, w = img_cv.shape[:2]
-
-        r = img_cv[:, :, 0].astype(np.int32)
-        g = img_cv[:, :, 1].astype(np.int32)
-        b = img_cv[:, :, 2].astype(np.int32)
-        brightness = r + g + b
-        red_ratio = float(getattr(config, "LASER_RED_RATIO", 1.5))
-        ratio_lo = max(1.15, red_ratio - 0.35)
-
-        strategies = []
-        base_th = int(getattr(config, "LASER_DETECTION_THRESHOLD", 140))
-        for th in (base_th, 120, 100, 80, 60):
-            mask = (
-                (r > th)
-                & (r > g * ratio_lo)
-                & (r > b * ratio_lo)
-            )
-            strategies.append(("rgb", th, mask))
-
-        oe_th = int(getattr(config, "LASER_OVEREXPOSED_THRESHOLD", 200))
-        oe_diff = int(getattr(config, "LASER_OVEREXPOSED_DIFF", 10))
-        mask_oe = (
-            (r > oe_th - 30)
-            & (g > oe_th - 40)
-            & (b > oe_th - 40)
-            & (r >= g)
-            & (r >= b)
-            & ((r - g) > max(5, oe_diff - 5))
-            & ((r - b) > max(5, oe_diff - 5))
-        )
-        strategies.append(("overexposed", oe_th, mask_oe))
-
-        mask_bright = (brightness > 380) & (r >= g) & (r >= b) & ((r - g) > 3)
-        strategies.append(("bright", 0, mask_bright))
-
-        hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
-        hc, sc, vc = cv2.split(hsv)
-        mask_hsv = ((hc <= 18) | (hc >= 162)) & (sc >= 60) & (vc >= 60)
-        strategies.append(("hsv", 0, mask_hsv))
-
-        best_pos = None
-        best_score = -1.0
-        best_tag = None
-
-        max_area = float(getattr(config, "LASER_REMOTE_MAX_AREA", 300.0))
-        min_circularity = float(getattr(config, "LASER_REMOTE_MIN_CIRCULARITY", 0.25))
-
-        for name, th, mask in strategies:
-            m = (mask.astype(np.uint8)) * 255
-            if cv2.countNonZero(m) == 0:
-                continue
-            contours, _ = cv2.findContours(m, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-            if not contours:
-                continue
-            for cnt in contours:
-                area = cv2.contourArea(cnt)
-                if area < 1.5 or area > max_area:
-                    continue
-                peri = cv2.arcLength(cnt, True)
-                if peri <= 0:
-                    continue
-                circularity = float(4.0 * math.pi * area / (peri * peri))
-                if circularity < min_circularity:
-                    continue
-                M = cv2.moments(cnt)
-                if M["m00"] <= 0:
-                    continue
-                cx = float(M["m10"] / M["m00"])
-                cy = float(M["m01"] / M["m00"])
-                ix, iy = int(round(cx)), int(round(cy))
-                if ix < 0 or iy < 0 or ix >= w or iy >= h:
-                    continue
-                local_r = float(r[iy, ix])
-                score = area * local_r * (1.0 + local_r / 255.0) * (0.5 + circularity)
-                if score > best_score:
-                    best_score = score
-                    best_pos = (ix, iy)
-                    best_tag = (name, th, area)
-
-        if best_pos is not None:
-            with self._remote_detect_lock:
-                self._remote_detect_last_pos = best_pos
-            self._save_remote_detect_debug_image(frame, best_pos, best_tag)
-            if self.logger:
-                self.logger.info(
-                    f"[LASER-REMOTE] 检测到激光点 {best_pos} "
-                    f"strategy={best_tag[0]} th={best_tag[1]} area={best_tag[2]:.1f}"
-                )
-        elif self.logger:
-            self.logger.debug("[LASER-REMOTE] 未通过面积/圆度过滤")
-        return best_pos
-
-    def _save_remote_detect_debug_image(self, frame, pos, tag=None):
-        """保存远程识别调试图：叠加激光坐标并落盘。"""
-        try:
-            if not bool(getattr(config, "SAVE_IMAGE_ENABLED", True)):
-                return
-            import cv2
-            from maix import image
-
-            img_cv = image.image2cv(frame, False, False)
-            if img_cv is None or img_cv.size == 0:
-                return
-
-            x, y = int(pos[0]), int(pos[1])
-            h, w = img_cv.shape[:2]
-            if x < 0 or y < 0 or x >= w or y >= h:
-                return
-
-            cv2.circle(img_cv, (x, y), 8, (255, 0, 0), 2)
-            cv2.line(img_cv, (x - 12, y), (x + 12, y), (255, 0, 0), 1)
-            cv2.line(img_cv, (x, y - 12), (x, y + 12), (255, 0, 0), 1)
-
-            desc = ""
-            if tag:
-                desc = f" {tag[0]} th={tag[1]} area={tag[2]:.1f}"
-            cv2.putText(
-                img_cv,
-                f"laser=({x},{y}){desc}",
-                (10, 24),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.55,
-                (255, 0, 0),
-                1,
-                cv2.LINE_AA,
-            )
-
-            base_dir = getattr(config, "PHOTO_DIR", "/root/phot")
-            debug_dir = f"{base_dir}/laser_remote"
-            try:
-                if debug_dir not in os.listdir("/root") and "/" not in debug_dir.replace("/root/", ""):
-                    os.mkdir(debug_dir)
-                else:
-                    try:
-                        os.makedirs(debug_dir, exist_ok=True)
-                    except Exception:
-                        pass
-            except Exception:
-                try:
-                    os.makedirs(debug_dir, exist_ok=True)
-                except Exception:
-                    return
-
-            ts = int(time.ticks_ms())
-            filename = f"{debug_dir}/remote_{x}_{y}_{ts}.jpg"
-            out = image.cv2image(img_cv, False, False)
-            out.save(filename)
-
-            if self.logger:
-                self.logger.info(f"[LASER-REMOTE] 调试图已保存: {filename}")
-        except Exception as e:
-            if self.logger:
-                self.logger.warning(f"[LASER-REMOTE] 保存调试图失败: {e}")
-
     def calibrate_laser_position(self, timeout_ms=8000, check_sharpness=True):
         """
         执行激光校准：循环拍照 → 检测靶心 → 检查激光点清晰度 → 找红点 → 保存坐标
@@ -1664,6 +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）
-            self._log_queue = queue.Queue(-1)
+            # 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(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()
-        n = len(pressure_buf)
-        avg = (pressure_sum / n) if n else 0
-        avg_abs = (pressure_abs_sum / n) if n else 0
-        # 一行输出：方便后处理画曲线；同时带上统计信息便于快速看波峰
-        line = (
-            f"[气压批量] reason={reason} "
-            f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
-            f"min={pressure_min} max={pressure_max} avg={avg:.1f} avg_abs={avg_abs:.3f} "
-            f"values={','.join(map(str, pressure_buf))}"
-            f" convert value (kpa): {(max(pressure_buf, key=lambda x: x[1])[1] - last_avg_abs) / (5 - 2.5) * config.AIR_PRESSURE_HARDWARE_MAX:.1f}"
-        )
-        if logger:
-            logger.debug(line)
-        else:
-            print(line)
+        if config.AIR_PRESSURE_lOG:
+            t1_ms = time.ticks_ms()
+            n = len(pressure_buf)
+            avg = (pressure_sum / n) if n else 0
+            avg_abs = (pressure_abs_sum / n) if n else 0
+            line = (
+                f"[气压批量] reason={reason} "
+                f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
+                f"min={pressure_min} max={pressure_max} avg={avg:.1f} avg_abs={avg_abs:.3f} "
+                f"values={','.join(map(str, pressure_buf))}"
+                f" convert value (kpa): {(max(pressure_buf, key=lambda x: x[1])[1] - last_avg_abs) / (5 - 2.5) * config.AIR_PRESSURE_HARDWARE_MAX:.1f}"
+            )
+            if logger:
+                logger.debug(line)
+            else:
+                print(line)
+            last_avg_abs = avg_abs
+        # 无论是否记录日志，都必须清空 buffer，否则内存泄漏
         pressure_buf = []
         pressure_sum = 0
         pressure_abs_sum = 0
         pressure_min = 4095
         pressure_max = 0
         pressure_t0_ms = None
-        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():
@@ -402,14 +401,7 @@ def cmd_str():
             else:
                 if config.SHOW_CAMERA_PHOTO_WHILE_SHOOTING:
                     try:
-                        frame = camera_manager.read_frame()
-                        laser_manager.remote_detect_tick(frame)
-                        if (
-                            laser_manager.remote_detect_active
-                            and getattr(config, "LASER_REMOTE_DETECT_DRAW_PREVIEW", False)
-                        ):
-                            frame = laser_manager.overlay_remote_detect_preview(frame)
-                        camera_manager.show(frame)
+                        camera_manager.show(camera_manager.read_frame())
                     except Exception as e:
                         logger = logger_manager.logger
                         if logger:

model_270820.mud

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

network.py

@@ -8,7 +8,7 @@ import json
 import re
 from math import e
 import struct
-from maix import time
+from maix import time, network, err
 import hmac
 import hashlib
 import ujson
@@ -21,8 +21,7 @@ from hardware import hardware_manager
 from power import get_bus_voltage, voltage_to_percent
 from logger_manager import logger_manager
 from wifi import wifi_manager
-
-
+import subprocess
 
 
 def _wifi_tls_would_block(exc):
@@ -37,8 +36,8 @@ def _wifi_tls_would_block(exc):
     if _ssl is not None and isinstance(exc, _ssl.SSLError):
         err = getattr(exc, "errno", None)
         if err in (
-            getattr(_ssl, "SSL_ERROR_WANT_READ", 2),
-            getattr(_ssl, "SSL_ERROR_WANT_WRITE", 3),
+                getattr(_ssl, "SSL_ERROR_WANT_READ", 2),
+                getattr(_ssl, "SSL_ERROR_WANT_WRITE", 3),
         ):
             return True
     msg = str(exc).lower()
@@ -73,6 +72,10 @@ class NetworkManager:
         self._raw_line_data = []
         self._manual_trigger_flag = False
 
+        # 限制并发命令线程数
+        self._cmd_thread_lock = threading.Lock()
+        self._cmd_thread_count = 0
+
         # 网络类型状态
         self._network_type = None  # "wifi" 或 "4G" 或 None
         # 本次上电曾因 WiFi 质量差切换到 4G 后，直至关机不再改回 WiFi
@@ -84,7 +87,8 @@ class NetworkManager:
         try:
             import archery_netcore as _netcore
             self._netcore = _netcore
-            if hasattr(self._netcore, "parse_packet") and hasattr(self._netcore, "make_packet") and hasattr(self._netcore, "actions_for_inner_cmd"):
+            if hasattr(self._netcore, "parse_packet") and hasattr(self._netcore, "make_packet") and hasattr(
+                    self._netcore, "actions_for_inner_cmd"):
                 print("[NET] archery_netcore found")
             else:
                 print("[NET] archery_netcore not found parse_packet or make_packet")
@@ -147,7 +151,6 @@ class NetworkManager:
 
     # ==================== 内部状态管理方法 ====================
 
-    
     def set_manual_trigger(self, value=True):
         """设置手动触发标志（公共方法）"""
         self._manual_trigger_flag = value
@@ -166,11 +169,15 @@ class NetworkManager:
         self._password = password
 
     def _enqueue(self, item, high=False):
-        """线程安全地加入队列（内部方法）"""
+        """线程安全地加入队列（内部方法），队列满时丢弃最旧消息"""
         with self._queue_lock:
             if high:
+                if len(self._high_send_queue) >= config.MAX_SEND_QUEUE_SIZE:
+                    self._high_send_queue.pop(0)
                 self._high_send_queue.append(item)
             else:
+                if len(self._normal_send_queue) >= config.MAX_SEND_QUEUE_SIZE:
+                    self._normal_send_queue.pop(0)
                 self._normal_send_queue.append(item)
         self._send_event.set()
 
@@ -199,10 +206,34 @@ class NetworkManager:
         """获取队列锁（用于with语句）"""
         return self._queue_lock
 
+    def _spawn_cmd_thread(self, target, args=()):
+        """安全创建命令线程，限制并发数，防止无限创建导致内存耗尽"""
+        with self._cmd_thread_lock:
+            if self._cmd_thread_count >= config.MAX_CMD_THREADS:
+                self.logger.warning(
+                    f"[NET] 并发命令线程已达上限({config.MAX_CMD_THREADS})，跳过: {getattr(target, '__name__', str(target))}"
+                )
+                return False
+            self._cmd_thread_count += 1
+
+        def _wrapper(*a):
+            try:
+                target(*a)
+            except Exception as e:
+                self.logger.error(f"[NET] 命令线程异常: {e}")
+            finally:
+                with self._cmd_thread_lock:
+                    self._cmd_thread_count -= 1
+
+        import _thread
+        _thread.start_new_thread(_wrapper, args)
+        return True
+
     # ==================== 业务方法 ====================
 
     def read_device_id(self):
         """从 /device_key 文件读取设备唯一 ID，失败则使用默认值"""
+
         def _set_password_for_device_id(device_id):
             if getattr(config, "USE_TCP_SSL", False):
                 iccid = self.get_4g_mccid()
@@ -242,6 +273,7 @@ class NetworkManager:
         连接 Wi-Fi：委托 ``wifi_manager.connect_wifi``。
         未指定 ``verify_host``/``verify_port`` 时，可达性校验使用本管理器配置的 ``_server_ip``/``_server_port``。
         """
+
         def _verify(ip: str):
             v_host = verify_host if verify_host is not None else self._server_ip
             v_port = verify_port if verify_port is not None else self._server_port
@@ -299,7 +331,10 @@ class NetworkManager:
             if atc is None:
                 return False
 
-            with self.get_uart_lock():
+            if not self._uart4g_lock.acquire(timeout=3000):
+                self.logger.warning("[4G] 获取 uart4g_lock 超时，跳过 4G 可用性检查")
+                return False
+            try:
                 # 1) SIM 就绪
                 r = atc.send("AT+CPIN?", "READY", 3000)
                 if "READY" not in r:
@@ -340,6 +375,8 @@ class NetworkManager:
                 if ip2:
                     return True
                 return False
+            finally:
+                self._uart4g_lock.release()
         except Exception:
             return False
 
@@ -355,8 +392,13 @@ class NetworkManager:
             atc = hardware_manager.at_client
             if atc is None:
                 return None
-            with self.get_uart_lock():
+            if not self._uart4g_lock.acquire(timeout=3000):
+                self.logger.warning("[4G] get_4g_phone_number 获取锁超时")
+                return None
+            try:
                 resp = atc.send("AT+CNUM", "OK", 3000)
+            finally:
+                self._uart4g_lock.release()
             if not resp:
                 return None
             # 可能多行 +CNUM，取第一个非空号码
@@ -379,8 +421,13 @@ class NetworkManager:
             atc = hardware_manager.at_client
             if atc is None:
                 return None
-            with self.get_uart_lock():
+            if not self._uart4g_lock.acquire(timeout=3000):
+                self.logger.warning("[4G] get_4g_mccid 获取锁超时")
+                return None
+            try:
                 resp = atc.send("AT+MCCID", "OK", 3000)
+            finally:
+                self._uart4g_lock.release()
             if not resp or "ERROR" in resp.upper():
                 return None
             m = re.search(r"\+MCCID:\s*(.+)", resp, re.IGNORECASE)
@@ -537,14 +584,116 @@ class NetworkManager:
             self._session_force_4g = False
             return False
 
+    def _cmd200_detect_laser(self):
+        """后台线程执行 cmd200 激光检测，避免阻塞主循环"""
+        from laser_manager import laser_manager
+        try:
+            laser_manager.turn_on_laser()
+            self.logger.info("[LASER] cmd200 已发送开激光指令")
+        except Exception as e:
+            self.logger.warning(f"[LASER] cmd200 开激光异常: {e}")
+
+        try:
+            from laser_detector import get_stable_laser_point
+            time.sleep_ms(500)
+            result = get_stable_laser_point(timeout_ms=60000)
+            if result:
+                x, y = result
+                self.safe_enqueue({
+                    "cmd": 200,
+                    "result": "laser_detect_ok",
+                    "x": x,
+                    "y": y,
+                }, 2)
+                self.logger.info(f"[LASER] cmd200 检测结果: ({x}, {y})")
+            else:
+                self.safe_enqueue({
+                    "cmd": 200,
+                    "result": "laser_detect_failed",
+                }, 2)
+                self.logger.warning("[LASER] cmd200 检测失败")
+        except Exception as e:
+            self.logger.error(f"[LASER] cmd200 检测异常: {e}")
+
+    def _cmd300_ota(self, data_obj):
+        """后台线程执行 cmd300 OTA，避免阻塞主循环"""
+        hardware_manager.start_idle_timer()
+        inner_data = data_obj.get("data", {}) if isinstance(data_obj, dict) else {}
+        self.logger.info(f"[New Ota] cmd300 , data: {inner_data}")
+        ssid = inner_data.get("ssid")
+        password = inner_data.get("password")
+        ota_res_url = inner_data.get("url")
+        try:
+            w = network.wifi.Wifi()
+            e = w.connect(ssid, password, wait=True, timeout=15)
+            err.check_raise(e, "connect wifi failed")
+            if self.logger:
+                self.logger.info(f"[ota] Connect success, got ip{w.get_ip()}")
+            self.safe_enqueue(
+                {
+                    "cmd": 300,
+                    "result": "ota start...",
+                    "wifi": w.get_ip(),
+                },
+                2,
+            )
+            subprocess.run(
+                ["sh", "/maixapp/apps/t11/ota_curl.sh", ota_res_url])
+            self.safe_enqueue(
+                {
+                    "cmd": 300,
+                    "result": "success",
+                    "wifi": w.get_ip(),
+                },
+                2,
+            )
+        except Exception as e:
+            self.logger.error(f"[ota] cmd300 失败: {e}")
+            self.safe_enqueue(
+                {
+                    "cmd": 300,
+                    "result": "ota fail",
+                    "reason": str(e),
+                },
+                2,
+            )
+
+    def _cmd600_conn_wifi(self, data_obj):
+        hardware_manager.start_idle_timer()
+        inner_data = data_obj.get("data", {}) if isinstance(data_obj, dict) else {}
+        self.logger.info(f"[conn wifi] cmd600 , data: {inner_data}")
+        ssid = inner_data.get("ssid")
+        password = inner_data.get("password")
+        try:
+            w = network.wifi.Wifi()
+            e = w.connect(ssid, password, wait=True, timeout=15)
+            err.check_raise(e, "connect wifi failed")
+            if self.logger:
+                self.logger.info(f"[ota] Connect success, got ip{w.get_ip()}")
+            self.safe_enqueue(
+                {
+                    "cmd": 600,
+                    "result": "success",
+                    "wifi": w.get_ip(),
+                },
+                2,
+            )
+        except Exception as e:
+            self.logger.error(f"cmd600 失败: {e}")
+            self.safe_enqueue(
+                {
+                    "cmd": 600,
+                    "result": "conn fail",
+                    "reason": str(e),
+                },
+                2,
+            )
+            self._switch_to_4g_due_to_poor_wifi()
 
     def safe_enqueue(self, data_dict, msg_type=2, high=False):
         """线程安全地将消息加入队列（公共方法）"""
         self._enqueue((msg_type, data_dict), high)
 
-
-    
-
     def connect_server(self):
         """
         连接到服务器（自动选择WiFi或4G）
@@ -672,7 +821,10 @@ class NetworkManager:
         host = self._server_ip
         port = getattr(config, "TCP_SSL_PORT", 443) if use_ssl else config.SERVER_PORT
         tail = getattr(config, "MIPOPEN_TAIL", "")
-        with self.get_uart_lock():
+        if not self._uart4g_lock.acquire(timeout=15000):
+            self.logger.warning("[4G-TCP] 连接：获取 uart4g_lock 超时")
+            return False
+        try:
             resp = hardware_manager.at_client.send(f"AT+MIPCLOSE={link_id}", "OK", 1000)
             self.logger.info(f"[4G-TCP] AT+MIPCLOSE={link_id} response: {resp}")
 
@@ -686,6 +838,8 @@ class NetworkManager:
             cmd = f'AT+MIPOPEN={link_id},"TCP","{host}",{port}'
             res = hardware_manager.at_client.send(cmd, "+MIPOPEN", 8000)
             self.logger.info(f"[4G-TCP] {cmd} response: {res}")
+        finally:
+            self._uart4g_lock.release()
         if f"+MIPOPEN: {link_id},0" in res:
             self._tcp_connected = True
             return True
@@ -808,9 +962,13 @@ class NetworkManager:
 
     def _disconnect_tcp_via_4g(self):
         link_id = getattr(config, "TCP_LINK_ID", 0)
-        with self.get_uart_lock():
+        if not self._uart4g_lock.acquire(timeout=2000):
+            self.logger.warning("[4G-TCP] 断开连接：获取 uart4g_lock 超时")
+            return
+        try:
             hardware_manager.at_client.send(f"AT+MIPCLOSE={link_id}", "OK", 1000)
-
+        finally:
+            self._uart4g_lock.release()
 
     def tcp_send_raw(self, data: bytes, max_retries=2) -> bool:
         """
@@ -859,7 +1017,7 @@ class NetworkManager:
                             raise
                         if sent == 0:
                             # socket连接已断开
-                            self.logger.warning(f"[WIFI-TCP] 发送失败，socket已断开（尝试 {attempt+1}/{max_retries}）")
+                            self.logger.warning(f"[WIFI-TCP] 发送失败，socket已断开（尝试 {attempt + 1}/{max_retries}）")
                             raise OSError("wifi socket closed (send returned 0)")
                         total_sent += sent
 
@@ -870,7 +1028,7 @@ class NetworkManager:
                     time.sleep_ms(50)
 
                 except OSError as e:
-                    self.logger.error(f"[WIFI-TCP] 发送异常: {e}（尝试 {attempt+1}/{max_retries}）")
+                    self.logger.error(f"[WIFI-TCP] 发送异常: {e}（尝试 {attempt + 1}/{max_retries}）")
                     # 发送异常通常意味着连接已不可用，主动关闭以触发重连
                     try:
                         wifi_manager.wifi_socket.close()
@@ -880,7 +1038,7 @@ class NetworkManager:
                     self._tcp_connected = False
                     return False
                 except Exception as e:
-                    self.logger.error(f"[WIFI-TCP] 未知错误: {e}（尝试 {attempt+1}/{max_retries}）")
+                    self.logger.error(f"[WIFI-TCP] 未知错误: {e}（尝试 {attempt + 1}/{max_retries}）")
                     try:
                         wifi_manager.wifi_socket.close()
                     except:
@@ -893,7 +1051,10 @@ class NetworkManager:
 
     def _tcp_send_raw_via_4g(self, data: bytes, max_retries=2) -> bool:
         link_id = getattr(config, "TCP_LINK_ID", 0)
-        with self.get_uart_lock():
+        if not self._uart4g_lock.acquire(timeout=2000):
+            self.logger.warning("[4G-TCP] 获取 uart4g_lock 超时（其他线程持有），跳过本次发送")
+            return False
+        try:
             for _ in range(max_retries):
                 cmd = f'AT+MIPSEND={link_id},{len(data)}'
                 if ">" not in hardware_manager.at_client.send(cmd, ">", 2000):
@@ -914,6 +1075,8 @@ class NetworkManager:
                     return True
                 time.sleep_ms(50)
             return False
+        finally:
+            self._uart4g_lock.release()
 
     def _configure_ssl_before_connect(self, link_id: int) -> bool:
         """按手册：MSSLCFG(auth) -> (可选) MSSLCERTWR -> MSSLCFG(cert) -> MIPCFG(ssl)"""
@@ -966,7 +1129,6 @@ class NetworkManager:
             r = hardware_manager.at_client.send(f'AT+MSSLCERTRD="{cert_filename}"', "OK", 3000)
             self.logger.info(f"[4G-TCP] AT+MSSLCERTRD=\"{cert_filename}\" response: {r}")
 
-
             # 3) 引用根证书
             r = hardware_manager.at_client.send(f'AT+MSSLCFG="cert",{ssl_id},"{cert_filename}"', "OK", 3000)
             if "OK" not in r:
@@ -1024,7 +1186,8 @@ class NetworkManager:
             self.logger.error(f"[WIFI-TCP] 接收数据异常: {e}")
             return b""
 
-    def _upload_log_file(self, upload_url, wifi_ssid=None, wifi_password=None, include_rotated=True, max_files=None, archive_format="tgz"):
+    def _upload_log_file(self, upload_url, wifi_ssid=None, wifi_password=None, include_rotated=True, max_files=None,
+                         archive_format="tgz"):
         """上传日志文件到指定URL
         
         Args:
@@ -1157,7 +1320,8 @@ class NetworkManager:
                     staged_paths.append(dst)
             except Exception as e:
                 self.logger.error(f"[LOG_UPLOAD] 复制日志快照失败: {e}")
-                self.safe_enqueue({"result": "log_upload_failed", "reason": "snapshot_failed", "detail": str(e)[:100]}, 2)
+                self.safe_enqueue({"result": "log_upload_failed", "reason": "snapshot_failed", "detail": str(e)[:100]},
+                                  2)
                 try:
                     shutil.rmtree(staging_dir)
                 except:
@@ -1185,7 +1349,8 @@ class NetworkManager:
                 self.logger.info(f"[LOG_UPLOAD] 日志压缩包已生成: {archive_path}")
             except Exception as e:
                 self.logger.error(f"[LOG_UPLOAD] 打包压缩失败: {e}")
-                self.safe_enqueue({"result": "log_upload_failed", "reason": "archive_failed", "detail": str(e)[:100]}, 2)
+                self.safe_enqueue({"result": "log_upload_failed", "reason": "archive_failed", "detail": str(e)[:100]},
+                                  2)
                 try:
                     shutil.rmtree(staging_dir)
                 except:
@@ -1234,7 +1399,8 @@ class NetworkManager:
                         "status_code": response.status_code
                     }, 2)
                 else:
-                    self.logger.error(f"[LOG_UPLOAD] 上传失败! 状态码: {response.status_code}, 响应: {response.text[:200]}")
+                    self.logger.error(
+                        f"[LOG_UPLOAD] 上传失败! 状态码: {response.status_code}, 响应: {response.text[:200]}")
                     self.safe_enqueue({
                         "result": "log_upload_failed",
                         "reason": f"http_{response.status_code}",
@@ -1370,7 +1536,8 @@ class NetworkManager:
         except Exception as e:
             return None, f"prepare_exception: {e}"
 
-    def _upload_log_file_v2(self, upload_url, upload_token, key, outlink="", include_rotated=True, max_files=None, archive_format="tgz"):
+    def _upload_log_file_v2(self, upload_url, upload_token, key, outlink="", include_rotated=True, max_files=None,
+                            archive_format="tgz"):
         """上传日志到 Qiniu（支持 WiFi 和 4G 双路径）
 
         流程：准备日志归档 -> 自动检测网络 -> WiFi(requests) 或 4G(AT命令) 上传
@@ -1592,8 +1759,6 @@ class NetworkManager:
 
     def tcp_main(self):
         """TCP 主通信循环：登录、心跳、处理指令、发送数据"""
-        import _thread
-        
         self.logger.info("[NET] TCP主线程启动")
 
         send_hartbeat_fail_count = 0
@@ -1705,7 +1870,8 @@ class NetworkManager:
 
                             if not logged_in:
                                 try:
-                                    self.logger.debug(f"[TCP] rx link={link_id} len={len(payload)} head={payload[:12].hex()}")
+                                    self.logger.debug(
+                                        f"[TCP] rx link={link_id} len={len(payload)} head={payload[:12].hex()}")
                                 except:
                                     pass
 
@@ -1731,7 +1897,8 @@ class NetworkManager:
                                                     pending_obj = json.load(f)
                                             except:
                                                 pending_obj = {}
-                                            self.safe_enqueue({"result": "ota_ok", "url": pending_obj.get("url", "")}, 2)
+                                            self.safe_enqueue({"result": "ota_ok", "url": pending_obj.get("url", "")},
+                                                              2)
                                             self.logger.info("[OTA] 已上报 ota_ok，等待心跳确认后删除 pending")
                                     except Exception as e:
                                         self.logger.error(f"[OTA] ota_ok 上报失败: {e}")
@@ -1750,7 +1917,8 @@ class NetworkManager:
                                     t = body.get('t', 0)
                                     v = body.get('v')
                                     # 如果是第一个分片，清空之前的缓存
-                                    if len(self._raw_line_data) == 0 or (len(self._raw_line_data) > 0 and self._raw_line_data[0].get('v') != v):
+                                    if len(self._raw_line_data) == 0 or (
+                                            len(self._raw_line_data) > 0 and self._raw_line_data[0].get('v') != v):
                                         self._raw_line_data.clear()
                                     # 或者更简单：每次收到命令40时，如果版本号不同，清空缓存
                                     if len(self._raw_line_data) > 0:
@@ -1767,7 +1935,7 @@ class NetworkManager:
                                             file.write("\n".join(stock_array))
                                         ota_manager.apply_ota_and_reboot(None, local_filename)
                                     else:
-                                        self.safe_enqueue({'data':{'l': len(self._raw_line_data), 'v': v}, 'cmd': 41})
+                                        self.safe_enqueue({'data': {'l': len(self._raw_line_data), 'v': v}, 'cmd': 41})
                                     self.logger.info(f"已下载{len(self._raw_line_data)} 全部：{t} 版本：{v}")
 
                             elif logged_in and msg_type == 100:
@@ -1784,7 +1952,8 @@ class NetworkManager:
                                     # 验证必需字段
                                     if not upload_url or not upload_token or not shoot_id:
                                         self.logger.error("[IMAGE_UPLOAD] 缺少必需参数: uploadUrl, token 或 shootId")
-                                        self.safe_enqueue({"result": "image_upload_failed", "reason": "missing_params"}, 2)
+                                        self.safe_enqueue({"result": "image_upload_failed", "reason": "missing_params"},
+                                                          2)
                                     else:
                                         self.logger.info(f"[IMAGE_UPLOAD] 收到图片上传命令，shootId: {shoot_id}")
                                         # 查找文件名中包含 shoot_id 的图片文件（文件名格式：shot_{shoot_id}_*.bmp）
@@ -1805,15 +1974,19 @@ class NetworkManager:
                                                         reverse=True
                                                     )
                                                     target_image = os.path.join(photo_dir, matched_images[0])
-                                                    self.logger.info(f"[IMAGE_UPLOAD] 找到匹配shootId的图片: {matched_images[0]}")
+                                                    self.logger.info(
+                                                        f"[IMAGE_UPLOAD] 找到匹配shootId的图片: {matched_images[0]}")
                                                 else:
-                                                    self.logger.warning(f"[IMAGE_UPLOAD] 未找到包含shootId={shoot_id}的图片文件")
+                                                    self.logger.warning(
+                                                        f"[IMAGE_UPLOAD] 未找到包含shootId={shoot_id}的图片文件")
                                         except Exception as e:
                                             self.logger.error(f"[IMAGE_UPLOAD] 查找图片失败: {e}")
 
                                         if not target_image:
                                             self.logger.error(f"[IMAGE_UPLOAD] 未找到shootId={shoot_id}对应的图片文件")
-                                            self.safe_enqueue({"result": "image_upload_failed", "reason": "no_image_found", "shootId": shoot_id}, 2)
+                                            self.safe_enqueue(
+                                                {"result": "image_upload_failed", "reason": "no_image_found",
+                                                 "shootId": shoot_id}, 2)
                                         else:
                                             # 构建上传key
                                             ext = os.path.splitext(target_image)[1].lower()
@@ -1821,8 +1994,7 @@ class NetworkManager:
                                             self.logger.info(f"[IMAGE_UPLOAD] 准备上传: {target_image} -> {key}")
 
                                             # 在新线程中执行上传，避免阻塞主循环
-                                            import _thread
-                                            _thread.start_new_thread(
+                                            self._spawn_cmd_thread(
                                                 self._upload_image_file,
                                                 (target_image, upload_url, upload_token, key, shoot_id, outlink)
                                             )
@@ -1845,18 +2017,51 @@ class NetworkManager:
                                     # 验证必需字段
                                     if not upload_url or not upload_token or not key:
                                         self.logger.error("[LOG_UPLOAD] 缺少必需参数: uploadUrl, token 或 key")
-                                        self.safe_enqueue({"result": "log_upload_failed", "reason": "missing_params"}, 2)
+                                        self.safe_enqueue({"result": "log_upload_failed", "reason": "missing_params"},
+                                                          2)
                                     else:
                                         self.logger.info(f"[LOG_UPLOAD] 收到日志上传命令，key: {key}")
                                         # 在新线程中执行上传，避免阻塞主循环
-                                        import _thread
-                                        _thread.start_new_thread(
+                                        self._spawn_cmd_thread(
                                             self._upload_log_file_v2,
-                                            (upload_url, upload_token, key, outlink, include_rotated, max_files, archive_format)
+                                            (upload_url, upload_token, key, outlink, include_rotated, max_files,
+                                             archive_format)
                                         )
                                         # 立即返回已入队确认
                                         self.safe_enqueue({"result": "log_upload_queued"}, 2)
-
+                            elif logged_in and msg_type == 201:
+                                if self.logger:
+                                    self.logger.info(f"[LASER] cmd201:{body}")
+                                raw_x = body.get("x")
+                                raw_y = body.get("y")
+                                try:
+                                    from laser_manager import laser_manager
+                                    ix, iy = laser_manager.set_hardcoded_laser_point(
+                                        raw_x, raw_y
+                                    )
+                                    self.safe_enqueue(
+                                        {
+                                            "cmd": 201,
+                                            "result": "laser_point_set",
+                                            "x": ix,
+                                            "y": iy,
+                                        },
+                                        2,
+                                    )
+                                    self.logger.info(
+                                        f"[LASER] cmd201 硬编码激光点=({ix}, {iy})"
+                                    )
+                                except Exception as e:
+                                    self.logger.error(f"[LASER] cmd201 失败: {e}")
+                                    self.safe_enqueue(
+                                        {
+                                            "cmd": 201,
+                                            "result": "laser_point_set_failed",
+                                            "reason": str(e),
+                                        },
+                                        2,
+                                    )
+                                hardware_manager.start_idle_timer()
                             # 处理业务指令
                             elif logged_in and isinstance(body, dict):
                                 inner_cmd = None
@@ -1868,7 +2073,7 @@ class NetworkManager:
                                         if not laser_manager.calibration_active:
                                             laser_manager.turn_on_laser()
                                             time.sleep_ms(100)
-                                            hardware_manager.stop_idle_timer() # 停表
+                                            hardware_manager.stop_idle_timer()  # 停表
                                             if not config.HARDCODE_LASER_POINT:
                                                 laser_manager.start_calibration()
                                                 self.safe_enqueue({"result": "calibrating"}, 2)
@@ -1879,8 +2084,7 @@ class NetworkManager:
                                         from laser_manager import laser_manager
                                         laser_manager.turn_off_laser()
                                         laser_manager.stop_calibration()
-                                        laser_manager.stop_remote_laser_detect()
-                                        hardware_manager.start_idle_timer() # 开表
+                                        hardware_manager.start_idle_timer()  # 开表
                                         self.safe_enqueue({"result": "laser_off"}, 2)
                                     elif inner_cmd == 4:  # 上报电量
                                         voltage = get_bus_voltage()
@@ -1888,6 +2092,7 @@ class NetworkManager:
                                         battery_data = {
                                             "battery": battery_percent,
                                             "voltage": round(float(voltage), 3),
+                                            "netType": self.network_type,
                                         }
                                         self.safe_enqueue(battery_data, 2)
                                         self.logger.info(f"电量上报: {battery_percent}%")
@@ -1921,17 +2126,19 @@ class NetworkManager:
                                                 # 只有同时满足：WiFi已连接 且 提供了WiFi凭证，才使用WiFi
                                                 if self.is_wifi_connected() and ssid and password:
                                                     mode = "wifi"
-                                                    self.logger.info("ota auto-selected: wifi (WiFi connected and credentials provided)")
+                                                    self.logger.info(
+                                                        "ota auto-selected: wifi (WiFi connected and credentials provided)")
                                                 else:
                                                     mode = "4g"
-                                                    self.logger.info("ota auto-selected: 4g (WiFi not available or no credentials)")
+                                                    self.logger.info(
+                                                        "ota auto-selected: 4g (WiFi not available or no credentials)")
 
                                         hardware_manager.stop_idle_timer()  # 停表，注意OTA停表之后，就没有再开表，因为OTA后面会重启，会重新开表
 
                                         if mode == "4g":
                                             ota_manager._set_ota_url(ota_url)  # 记录 OTA URL，供命令7使用
                                             ota_manager._start_update_thread()
-                                            _thread.start_new_thread(ota_manager.direct_ota_download_via_4g, (ota_url,))
+                                            self._spawn_cmd_thread(ota_manager.direct_ota_download_via_4g, (ota_url,))
                                         else:  # mode == "wifi"
                                             if not ssid or not password:
                                                 self.logger.error("ota wifi mode requires ssid and password")
@@ -1940,10 +2147,12 @@ class NetworkManager:
                                                 self.logger.info(f"ssid: {ssid}")
                                                 self.logger.info(f"password: {password}")
                                                 ota_manager._start_update_thread()
-                                                _thread.start_new_thread(ota_manager.handle_wifi_and_update, (ssid, password, ota_url))
+                                                self._spawn_cmd_thread(ota_manager.handle_wifi_and_update,
+                                                                         (ssid, password, ota_url))
                                     elif inner_cmd == 6:
                                         try:
-                                            ip = os.popen("ifconfig wlan0 2>/dev/null | grep 'inet ' | awk '{print $2}'").read().strip()
+                                            ip = os.popen(
+                                                "ifconfig wlan0 2>/dev/null | grep 'inet ' | awk '{print $2}'").read().strip()
                                             ip = ip if ip else "no_ip"
                                         except:
                                             ip = "error_getting_ip"
@@ -1951,91 +2160,18 @@ class NetworkManager:
                                     elif inner_cmd == 44:  # 读 4G 本机号码（AT+CNUM）
                                         cnum = self.get_4g_phone_number()
                                         self.logger.info(f"4G 本机号码: {cnum}")
-                                        self.safe_enqueue({"result": "cnum", "number": cnum if cnum is not None else ""}, 2)
+                                        self.safe_enqueue(
+                                            {"result": "cnum", "number": cnum if cnum is not None else ""}, 2)
                                     elif inner_cmd == 45:  # 读 MCCID（AT+MCCID）
                                         mccid = self.get_4g_mccid()
                                         self.logger.info(f"4G MCCID: {mccid}")
-                                        self.safe_enqueue({"result": "mccid", "mccid": mccid if mccid is not None else ""}, 2)
-                                    elif inner_cmd == 200:  # 远程激光点识别：稳定 3s 后上报 (x,y)
-                                        from laser_manager import laser_manager
-                                        # 远程激光识别期间不能停掉心跳/空闲计时，否则会影响数据上报与连接保持
-                                        # 这里仅启动远程识别，不停止网络侧心跳。
-                                        try:
-                                            laser_manager.turn_on_laser()
-                                            if self.logger:
-                                                self.logger.info("[LASER] cmd200 已发送开激光指令")
-                                        except Exception as e:
-                                            if self.logger:
-                                                self.logger.warning(
-                                                    f"[LASER] cmd200 开激光异常: {e}"
-                                                )
-                                        if not laser_manager.start_remote_laser_detect():
-                                            self.safe_enqueue(
-                                                {
-                                                    "cmd": 200,
-                                                    "result": "laser_detect_busy",
-                                                },
-                                                2,
-                                            )
-                                        else:
-                                            self.safe_enqueue(
-                                                {
-                                                    "cmd": 200,
-                                                    "result": "laser_detect_started",
-                                                },
-                                                2,
-                                            )
-                                    elif inner_cmd == 201:  # 设置硬编码激光点并结束远程识别会话
-                                        from laser_manager import laser_manager
-                                        laser_manager.stop_remote_laser_detect()
-                                        inner_data = (
-                                            data_obj.get("data", {})
-                                            if isinstance(data_obj.get("data"), dict)
-                                            else {}
-                                        )
-                                        raw_x = data_obj.get("x", inner_data.get("x"))
-                                        raw_y = data_obj.get("y", inner_data.get("y"))
-                                        try:
-                                            ix, iy = laser_manager.set_hardcoded_laser_point(
-                                                raw_x, raw_y
-                                            )
-                                            self.safe_enqueue(
-                                                {
-                                                    "cmd": 201,
-                                                    "result": "laser_point_set",
-                                                    "x": ix,
-                                                    "y": iy,
-                                                },
-                                                2,
-                                            )
-                                            self.logger.info(
-                                                f"[LASER] cmd201 硬编码激光点=({ix}, {iy})"
-                                            )
-                                        except Exception as e:
-                                            self.logger.error(f"[LASER] cmd201 失败: {e}")
-                                            self.safe_enqueue(
-                                                {
-                                                    "cmd": 201,
-                                                    "result": "laser_point_set_failed",
-                                                    "reason": str(e),
-                                                },
-                                                2,
-                                            )
-                                        hardware_manager.start_idle_timer()
-                                    elif inner_cmd == 46:  # 开关射箭原图保存
-                                        inner_data = data_obj.get("data", {}) if isinstance(data_obj, dict) else {}
-                                        enabled = True
-                                        if isinstance(inner_data, dict) and "enable" in inner_data:
-                                            enabled = bool(inner_data.get("enable"))
-                                        config.SAVE_RAW_SHOT_IMAGE_ENABLED = enabled
-                                        self.logger.info(f"[RAW_IMAGE] 射箭原图保存开关: {enabled}")
-                                        self.safe_enqueue({"result": "raw_image_save", "enabled": enabled}, 2)
-                                        hardware_manager.start_idle_timer()  # 重新计时
+                                        self.safe_enqueue(
+                                            {"result": "mccid", "mccid": mccid if mccid is not None else ""}, 2)
                                     elif inner_cmd == 41:
                                         self.logger.info(f"[TEST] 收到TCP射箭触发命令, {time.time()}")
                                         self._manual_trigger_flag = True
                                         self.safe_enqueue({"result": "trigger_ack"}, 2)
-                                        hardware_manager.start_idle_timer() # 重新计时
+                                        hardware_manager.start_idle_timer()  # 重新计时
                                     elif inner_cmd == 42:  # 关机命令
                                         self.logger.info("[SHUTDOWN] 收到TCP关机命令，准备关机...")
                                         self.safe_enqueue({"result": "shutdown_ack"}, 2)
@@ -2067,17 +2203,28 @@ class NetworkManager:
 
                                         if not upload_url:
                                             self.logger.error("[LOG_UPLOAD] 缺少 url 参数")
-                                            self.safe_enqueue({"result": "log_upload_failed", "reason": "missing_url"}, 2)
+                                            self.safe_enqueue({"result": "log_upload_failed", "reason": "missing_url"},
+                                                              2)
                                         else:
                                             self.logger.info(f"[LOG_UPLOAD] 收到日志上传命令，目标URL: {upload_url}")
-                                            # 在新线程中执行上传，避免阻塞主循环
-                                            import _thread
-                                            _thread.start_new_thread(
-                                                self._upload_log_file,
-                                                (upload_url, wifi_ssid, wifi_password, include_rotated, max_files, archive_format)
-                                            )
-                                    
-                                else: # data的结构不是 dict
+                                        # 在新线程中执行上传，避免阻塞主循环
+                                        self._spawn_cmd_thread(
+                                            self._upload_log_file,
+                                            (upload_url, wifi_ssid, wifi_password, include_rotated, max_files,
+                                             archive_format)
+                                        )
+                                    elif inner_cmd == 200:
+                                        self.logger.info("[LASER] cmd200 在后台线程执行检测")
+                                        self._spawn_cmd_thread(self._cmd200_detect_laser, ())
+                                    elif inner_cmd == 300:
+                                        self.logger.info("[New Ota] cmd300 在后台线程执行OTA")
+                                        self._spawn_cmd_thread(self._cmd300_ota, (data_obj,))
+                                    elif inner_cmd == 600:
+                                        self.logger.info("[conn wifi] cmd600 在后台线程执行连接wifi: {data_obj}")
+                                        self._spawn_cmd_thread(self._cmd600_conn_wifi, (data_obj,))
+                                    elif inner_cmd == 601:
+                                        pass
+                                else:  # data的结构不是 dict
                                     self.logger.info(f"[NET] body={body}, {time.time()}")
                             else:
                                 self.logger.info(f"[NET] 未知数据 {body}, {time.time()}")
@@ -2105,12 +2252,14 @@ class NetworkManager:
                             msg_type, data_dict = item
                             pkt = self._netcore.make_packet(msg_type, data_dict)
                             if not self.tcp_send_raw(pkt):
-                                # 发送失败：将消息放回队首，触发重连（避免丢消息）
+                                # 发送失败：将消息放回队首（队列满则丢弃）
                                 with self.get_queue_lock():
                                     if item_is_high:
-                                        self._high_send_queue.insert(0, item)
+                                        if len(self._high_send_queue) < config.MAX_SEND_QUEUE_SIZE:
+                                            self._high_send_queue.insert(0, item)
                                     else:
-                                        self._normal_send_queue.insert(0, item)
+                                        if len(self._normal_send_queue) < config.MAX_SEND_QUEUE_SIZE:
+                                            self._normal_send_queue.insert(0, item)
                                 self._tcp_connected = False
                                 try:
                                     self.disconnect_server()
@@ -2118,7 +2267,7 @@ class NetworkManager:
                                     pass
                                 break
 
-                    # 发送激光校准结果（cmd=2 等传统校准）
+                    # 发送激光校准结果
                     if logged_in:
                         from laser_manager import laser_manager
                         result = laser_manager.get_calibration_result()
@@ -2126,27 +2275,13 @@ class NetworkManager:
                             x, y = result
                             self.safe_enqueue({"result": "ok", "x": x, "y": y}, 2)
 
-                    # 发送远程激光识别结果（cmd=200，会话持续至 cmd=201）
-                    if logged_in:
-                        from laser_manager import laser_manager
-                        rd = laser_manager.get_remote_detect_result()
-                        if rd and isinstance(rd, dict) and rd.get("status") == "ok":
-                            self.safe_enqueue(
-                                {
-                                    "cmd": 200,
-                                    "result": "laser_detect_ok",
-                                    "x": rd.get("x"),
-                                    "y": rd.get("y"),
-                                },
-                                2,
-                            )
-
                     # 定期发送心跳
                     current_time = time.ticks_ms()
                     if logged_in and current_time - last_heartbeat_send_time > config.HEARTBEAT_INTERVAL * 1000:
                         vol_val = get_bus_voltage()
-                        if not self.tcp_send_raw(self._netcore.make_packet(4, {"vol": vol_val, "vol_per": voltage_to_percent(vol_val)})):
-                        # if not self.tcp_send_raw(self.make_packet(4, {"vol": vol_val, "vol_per": voltage_to_percent(vol_val)})):
+                        if not self.tcp_send_raw(
+                                self._netcore.make_packet(4, {"vol": vol_val, "vol_per": voltage_to_percent(vol_val)})):
+                            # if not self.tcp_send_raw(self.make_packet(4, {"vol": vol_val, "vol_per": voltage_to_percent(vol_val)})):
                             send_hartbeat_fail_count += 1
                             # 短暂波动可能导致一次发送失败：连续失败达到阈值才重连，避免重连风暴
                             self.logger.error(f"心跳发送失败({send_hartbeat_fail_count}/3)，准备重试")
@@ -2191,7 +2326,7 @@ class NetworkManager:
 
                 self._tcp_connected = False
                 self.logger.error("连接异常，2秒后重连...")
-                time.sleep_ms(2000)
+                time.sleep_ms(200)
 
             except Exception as e:
                 # TCP主循环的顶层异常捕获，防止线程静默退出
@@ -2199,44 +2334,50 @@ class NetworkManager:
                 import traceback
                 self.logger.error(traceback.format_exc())
                 self._tcp_connected = False
-                time.sleep_ms(5000)  # 等待5秒后重试连接
+                time.sleep_ms(500)  # 等待5秒后重试连接
 
 
 # 创建全局单例实例
 network_manager = NetworkManager()
 
+
 # ==================== 向后兼容的函数接口 ====================
 
 def tcp_main():
     """TCP主循环（向后兼容接口）"""
     return network_manager.tcp_main()
 
+
 def read_device_id():
     """读取设备ID（向后兼容接口）"""
     return network_manager.read_device_id()
 
+
 def safe_enqueue(data_dict, msg_type=2, high=False):
     """线程安全地加入队列（向后兼容接口）"""
     return network_manager.safe_enqueue(data_dict, msg_type, high)
 
+
 def connect_server():
     """连接服务器（向后兼容接口）"""
     return network_manager.connect_server()
 
+
 def disconnet_server():
     """断开服务器连接（向后兼容接口）"""
     return network_manager.disconnect_server()
 
+
 def is_wifi_connected():
     """检查WiFi是否已连接（向后兼容接口）"""
     return network_manager.is_wifi_connected()
 
+
 def connect_wifi(ssid, password):
     """连接WiFi（向后兼容接口）"""
     return network_manager.connect_wifi(ssid, password)
 
+
 def is_server_reachable(host, port=80, timeout=5):
     """检查服务器是否可达（向后兼容接口）"""
     return network_manager.is_server_reachable(host, port, timeout)
-
-

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

shoot_manager.py

@@ -8,7 +8,7 @@ from laser_manager import laser_manager
 from logger_manager import logger_manager
 from network import network_manager
 from triangle_target import load_camera_from_xml, load_triangle_positions, try_triangle_scoring
-from vision import estimate_distance, detect_circle_v3, enqueue_save_shot, enqueue_save_raw_shot
+from vision import estimate_distance, detect_circle_v3, enqueue_save_shot
 from maix import image, time
 
 # 缓存相机标定与三角形位置，避免每次射箭重复读磁盘
@@ -58,7 +58,6 @@ def analyze_shot(frame, laser_point=None):
     # ── Step 1: 确定激光点 ────────────────────────────────────────────────────
     laser_point_method = None
     distance_m_first = None
-    best_radius1_temp = None
 
     if config.HARDCODE_LASER_POINT:
         laser_point = laser_manager.laser_point
@@ -103,22 +102,9 @@ def analyze_shot(frame, laser_point=None):
         r_img, center, radius, method, best_radius1, ellipse_params = cdata
         dx, dy = None, None
         d_m = distance_m_first
-        tri_h = None
         if center and radius:
             dx, dy = laser_manager.compute_laser_position(center, (x, y), radius, method)
             d_m = estimate_distance(best_radius1) if best_radius1 else distance_m_first
-            try:
-                import numpy as _np
-                px_per_cm = float(radius) / 10.0
-                if px_per_cm > 1e-6:
-                    cxp, cyp = float(center[0]), float(center[1])
-                    tri_h = _np.array([
-                        [1.0 / px_per_cm, 0.0, -cxp / px_per_cm],
-                        [0.0, 1.0 / px_per_cm, -cyp / px_per_cm],
-                        [0.0, 0.0, 1.0],
-                    ], dtype=float)
-            except Exception:
-                tri_h = None
         out = {
             "success": True,
             "result_img": r_img,
@@ -128,7 +114,6 @@ def analyze_shot(frame, laser_point=None):
             "laser_point": laser_point, "laser_point_method": laser_point_method,
             "offset_method": "yellow_ellipse" if ellipse_params else "yellow_circle",
             "distance_method": "yellow_radius",
-            "tri_homography": tri_h,
         }
         if yolo_roi_xyxy is not None:
             out["yolo_roi_xyxy"] = yolo_roi_xyxy
@@ -144,10 +129,8 @@ def analyze_shot(frame, laser_point=None):
     roi_xyxy = None
     yolo_ring_ms = 0.0
     yolo_black_ms = 0.0
-    _timing_on = bool(getattr(config, "ARCHERY_TIMING_ENABLE", True))
-    _sample_on = bool(getattr(config, "TRIANGLE_SAMPLE_ENABLE", False))
     if getattr(config, "TRIANGLE_YOLO_ROI_ENABLE", False):
-        _t_yolo_ring = time_std.perf_counter() if _timing_on else None
+        _t_yolo_ring = time_std.perf_counter()
         try:
             from target_roi_yolo import try_get_triangle_roi_from_yolo
             roi_xyxy = try_get_triangle_roi_from_yolo(
@@ -157,8 +140,7 @@ def analyze_shot(frame, laser_point=None):
             if logger:
                 logger.warning(f"[YOLO-ROI] {e}")
         finally:
-            if _timing_on and _t_yolo_ring is not None:
-                yolo_ring_ms = (time_std.perf_counter() - _t_yolo_ring) * 1000.0
+            yolo_ring_ms = (time_std.perf_counter() - _t_yolo_ring) * 1000.0
 
     _loc_mode = str(
         getattr(config, "TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE", "yolo")
@@ -173,7 +155,7 @@ def analyze_shot(frame, laser_point=None):
         and roi_xyxy is not None
     )
     if _run_stage2_black_yolo:
-        _t_yolo_black = time_std.perf_counter() if _timing_on else None
+        _t_yolo_black = time_std.perf_counter()
         try:
             from target_roi_yolo import try_black_triangle_boxes_work
 
@@ -184,8 +166,7 @@ def analyze_shot(frame, laser_point=None):
             if logger:
                 logger.warning(f"[YOLO-BLACK] {e}")
         finally:
-            if _timing_on and _t_yolo_black is not None:
-                yolo_black_ms = (time_std.perf_counter() - _t_yolo_black) * 1000.0
+            yolo_black_ms = (time_std.perf_counter() - _t_yolo_black) * 1000.0
     elif (
         logger
         and _loc_mode == "traditional"
@@ -203,7 +184,7 @@ def analyze_shot(frame, laser_point=None):
         try:
             logger.info(f"[TRI] begin {datetime.now()}")
             logger.info(f"[TRI] K: {K}, dist: {dist_coef}, pos: {pos},  {datetime.now()}")
-            _t_wall_try = time_std.perf_counter() if _timing_on else None
+            _t_wall_try = time_std.perf_counter()
             tri = try_triangle_scoring(
                 img_cv, (x, y), pos, K, dist_coef,
                 size_range=getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500)),
@@ -212,8 +193,8 @@ def analyze_shot(frame, laser_point=None):
                 yolo_ring_ms=yolo_ring_ms,
                 yolo_black_ms=yolo_black_ms,
             )
-            _wall_try_ms = (time_std.perf_counter() - _t_wall_try) * 1000.0 if _timing_on else 0.0
-            if logger and bool(getattr(config, "TRIANGLE_LOG_E2E_TIMING", True)) and _timing_on:
+            _wall_try_ms = (time_std.perf_counter() - _t_wall_try) * 1000.0
+            if logger and bool(getattr(config, "TRIANGLE_LOG_E2E_TIMING", True)):
                 _e2e = float(yolo_ring_ms) + float(yolo_black_ms) + float(_wall_try_ms)
                 logger.info(
                     f"[TRI] timing_e2e_triangle_ms={_e2e:.1f} "
@@ -299,16 +280,6 @@ def analyze_shot(frame, laser_point=None):
             "tri_markers_completed": tri.get("markers_completed", []),
             "tri_homography": tri.get("homography"),
         }
-        try:
-            import numpy as _np
-            _H = tri.get("homography")
-            if _H is not None and _np.all(_np.isfinite(_H)):
-                _H_inv = _np.linalg.inv(_H)
-                _pt = _np.array([[[0.0, 0.0]]], dtype=_np.float32)
-                _center_pt = cv2.perspectiveTransform(_pt, _H_inv)[0][0]
-                out["tri_center_px"] = [float(_center_pt[0]), float(_center_pt[1])]
-        except Exception:
-            pass
         if yolo_roi_xyxy is not None:
             out["yolo_roi_xyxy"] = yolo_roi_xyxy
         return out
@@ -347,22 +318,11 @@ def process_shot(adc_val):
     :return: None
     """
     logger = logger_manager.logger
-    _timing_on = bool(getattr(config, "ARCHERY_TIMING_ENABLE", True))
 
     try:
         network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True)
         frame = camera_manager.read_frame()
 
-        from shot_id_generator import shot_id_generator
-        shot_id = shot_id_generator.generate_id()
-
-        if getattr(config, "SAVE_RAW_SHOT_IMAGE_ENABLED", False):
-            enqueue_save_raw_shot(
-                frame,
-                shot_id=shot_id,
-                photo_dir=config.PHOTO_DIR if config.SAVE_IMAGE_ENABLED else None,
-            )
-
         # 调用算法分析
         analysis_result = analyze_shot(frame)
 
@@ -396,107 +356,6 @@ def process_shot(adc_val):
         )
         x, y = laser_point
 
-        # 物方采样调试（config.TRIANGLE_SAMPLE_ENABLE）：靶心为原点，取两个对称点判断黑白来区分 40/20 标靶
-        # 逻辑：若两个采样点 RGB 均 < 阈值 → 全黑 → 40cm 标靶；否则 → 20cm 标靶
-        sample_target_type = None
-        _t_sample = time_std.perf_counter() if _timing_on else None
-        _t_sample_ms = 0.0
-        sample_points = []
-        sample_patch_half = 2
-        if bool(getattr(config, "TRIANGLE_SAMPLE_ENABLE", False)):
-            sample_obj_radius_cm = float(getattr(config, "TRIANGLE_SAMPLE_RADIUS_CM", 15.0))
-            sample_obj_angles_deg = (0, 180)  # 只取两个对称点：+X 和 -X
-            sample_patch_half = int(getattr(config, "TRIANGLE_SAMPLE_PATCH_HALF_PX", 2))
-            sample_black_thresh = float(getattr(config, "TRIANGLE_SAMPLE_BLACK_THRESH", 30.0))
-            try:
-                import math as _math
-                import numpy as _np
-                import cv2 as _cv2
-
-                if tri_homography is not None:
-                    _H_inv = _np.linalg.inv(tri_homography)
-                    for _ang in sample_obj_angles_deg:
-                        _rad = _math.radians(float(_ang))
-                        _pt_obj = _np.array([
-                            [[sample_obj_radius_cm * _math.cos(_rad), sample_obj_radius_cm * _math.sin(_rad)]]
-                        ], dtype=_np.float32)
-                        _pt_img = _cv2.perspectiveTransform(_pt_obj, _H_inv)[0][0]
-                        _px, _py = float(_pt_img[0]), float(_pt_img[1])
-                        sample_points.append({
-                            "angle_deg": float(_ang),
-                            "obj_cm": (float(sample_obj_radius_cm * _math.cos(_rad)), float(sample_obj_radius_cm * _math.sin(_rad))),
-                            "img_px": (int(round(_px)), int(round(_py))),
-                        })
-                elif center and radius:
-                    _px_per_cm = float(radius) / 10.0
-                    for _ang in sample_obj_angles_deg:
-                        _rad = _math.radians(float(_ang))
-                        _px = float(center[0]) + sample_obj_radius_cm * _math.cos(_rad) * _px_per_cm
-                        _py = float(center[1]) + sample_obj_radius_cm * _math.sin(_rad) * _px_per_cm
-                        sample_points.append({
-                            "angle_deg": float(_ang),
-                            "obj_cm": (float(sample_obj_radius_cm * _math.cos(_rad)), float(sample_obj_radius_cm * _math.sin(_rad))),
-                            "img_px": (int(round(_px)), int(round(_py))),
-                        })
-
-                # 取样后立即读像素并判断黑白：三角成功用 H_inv；三角失败但圆心成功用 center/radius 近似物方半径
-                _all_black = False
-                _sample_infos = []
-                if sample_points:
-                    _img_cv_for_sample = image.image2cv(result_img, False, False)
-                    _all_black = True
-                    for _sp in sample_points:
-                        _sx, _sy = _sp["img_px"]
-                        _hh = max(1, sample_patch_half)
-                        _patch = []
-                        for _yy in range(_sy - _hh, _sy + _hh + 1):
-                            if _yy < 0 or _yy >= _img_cv_for_sample.shape[0]:
-                                continue
-                            for _xx in range(_sx - _hh, _sx + _hh + 1):
-                                if _xx < 0 or _xx >= _img_cv_for_sample.shape[1]:
-                                    continue
-                                _patch.append(_img_cv_for_sample[_yy, _xx].astype(float))
-                        if _patch:
-                            _mean_rgb = _np.mean(_patch, axis=0)
-                            _is_black = bool(_mean_rgb[0] < sample_black_thresh
-                                           and _mean_rgb[1] < sample_black_thresh
-                                           and _mean_rgb[2] < sample_black_thresh)
-                            if not _is_black:
-                                _all_black = False
-                            _sample_infos.append(
-                                f"{int(_sp['angle_deg'])}°@{_sx},{_sy} rgb=({int(_mean_rgb[0])},{int(_mean_rgb[1])},{int(_mean_rgb[2])})"
-                            )
-                    sample_target_type = "40cm_black" if _all_black else "20cm"
-                if _sample_infos:
-                    logger.info("[采样] " + " | ".join(_sample_infos) + f" → {sample_target_type}")
-            except Exception as _e_sample:
-                sample_points = []
-                if logger:
-                    logger.warning(f"[采样] 标靶类型判断失败: {_e_sample}")
-        if _timing_on and _t_sample is not None:
-            _t_sample_ms = (time_std.perf_counter() - _t_sample) * 1000.0
-
-        # 采样提前完成后，先确定靶型对应的物理半径，供后续距离/偏移/上报使用。
-        # 40cm_black 表示直径40cm，半径20cm；20cm 表示直径20cm，半径10cm。
-        target_radius_cm = 20.0 if sample_target_type == "40cm_black" else (10.0 if sample_target_type == "20cm" else 20.0)
-        target_type_value = 40 if sample_target_type == "40cm_black" else (20 if sample_target_type == "20cm" else None)
-
-        # 圆心分支原算法默认按40cm靶半径20cm换算；若采样判定为20cm靶，在上报前修正距离和偏移。
-        # 三角分支使用 triangle_positions.json 的物方坐标，不在这里二次缩放，避免影响三角单应性结果。
-        if sample_target_type == "20cm" and center and radius and not tri_markers:
-            try:
-                distance_m = (target_radius_cm * config.FOCAL_LENGTH_PIX) / float(radius) / 100.0
-                _scale = target_radius_cm / 20.0
-                if dx is not None:
-                    dx = float(dx) * _scale
-                if dy is not None:
-                    dy = float(dy) * _scale
-                if logger:
-                    logger.info(f"[采样] 20cm靶修正圆心测距/偏移: distance={distance_m:.2f}m scale={_scale:.2f}")
-            except Exception as _e_fix:
-                if logger:
-                    logger.warning(f"[采样] 20cm靶修正失败: {_e_fix}")
-
         # 三角形路径成功时 center/radius 为空是正常的；此时用 triangle 方法名用于保存文件名与上报字段 m
         if (not method) and tri_markers:
             method = "triangle_homography"
@@ -507,6 +366,10 @@ def process_shot(adc_val):
         if dx is None and dy is None and logger:
             logger.warning("[MAIN] 未检测到偏移量（三角形与圆形均失败），但会保存图像")
 
+        # 生成射箭ID
+        from shot_id_generator import shot_id_generator
+        shot_id = shot_id_generator.generate_id()
+
         if logger:
             logger.info(f"[MAIN] 射箭ID: {shot_id}")
 
@@ -523,7 +386,7 @@ def process_shot(adc_val):
             "shot_id": shot_id,
             "x": srv_x,
             "y": srv_y,
-            "r": target_radius_cm,  # 物理靶半径 cm：40cm靶=20，20cm靶=10
+            "r": 20.0,  # 保留字段（服务端当前忽略，物理外环半径 cm）
             "d": round((distance_m or 0.0) * 100),
             "d_laser": round((laser_distance_m or 0.0) * 100),
             "d_laser_quality": laser_signal_quality,
@@ -534,7 +397,6 @@ def process_shot(adc_val):
             "target_y": float(y),
             "offset_method": offset_method,
             "distance_method": distance_method,
-            "target_type": target_type_value,
         }
 
         if ellipse_params:
@@ -609,11 +471,6 @@ def process_shot(adc_val):
                     except Exception:
                         pass
 
-                # 物方采样标靶类型判断耗时（合并在上面采样块内，单独统计）
-                if _timing_on and bool(getattr(config, "TRIANGLE_SAMPLE_ENABLE", False)) and sample_target_type is not None:
-                    logger.info(f"[采样] 标靶类型: {sample_target_type}  耗时: {_t_sample_ms:.2f}ms")
-
-
                 # 叠加信息：落点-圆心距离 / 相机-靶距离等
                 try:
                     import math as _math

test/test_algo_preview_live.py

@@ -1,403 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-实时摄像头预览：叠加与射箭存图相同的算法标注（YOLO ROI、三角/圆心、激光十字等），默认不写盘。
-
-在 MaixCAM 上从项目根目录运行：
-  python3 test/test_algo_preview_live.py
-  python3 test/test_algo_preview_live.py --interval 1.5
-  python3 test/test_algo_preview_live.py --every-frame
-
-说明：
-  - 完整算法走 shoot_manager.analyze_shot（与 process_shot 一致，含 YOLO + 三角/圆心）。
-  - 画面标注对齐 process_shot 存图前绘制 + vision._draw_yolo_roi_on_rgb_numpy / 圆心存图线。
-  - 预览模式会关闭 Stage2 裁切 JPEG 落盘，避免写满 /root/phot。
-"""
-
-from __future__ import annotations
-
-import argparse
-import math
-import os
-import sys
-import threading
-import time
-
-_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-if _ROOT not in sys.path:
-    sys.path.insert(0, _ROOT)
-
-import cv2
-import numpy as np
-from maix import image, time as maix_time
-
-import config
-from camera_manager import camera_manager
-from laser_manager import laser_manager
-from shoot_manager import analyze_shot, preload_triangle_calib
-from target_roi_yolo import preload_yolo_detector
-from vision import _draw_yolo_roi_on_rgb_numpy
-
-
-def _copy_maix_frame(frame):
-    """相机下一帧可能复用缓冲区，异步分析前先复制。"""
-    img_cv = image.image2cv(frame, False, False)
-    return image.cv2image(np.ascontiguousarray(img_cv), False, False)
-
-
-def _patch_preview_config():
-    """预览不写调试 JPEG，避免刷屏占存储。"""
-    config.TRIANGLE_BLACK_YOLO_SAVE_ROI_CROP = False
-    config.TRIANGLE_SAVE_DEBUG_IMAGE = False
-
-
-def _annotate_like_saved_shot(analysis: dict):
-    """
-    将 analyze_shot 结果绘制成与 process_shot -> enqueue_save_shot 存盘前一致的 Maix 图。
-    """
-    result_img = analysis.get("result_img")
-    if result_img is None:
-        return None
-
-    center = analysis.get("center")
-    radius = analysis.get("radius")
-    method = analysis.get("method")
-    ellipse_params = analysis.get("ellipse_params")
-    laser_point = analysis.get("laser_point")
-    dx = analysis.get("dx")
-    dy = analysis.get("dy")
-    distance_m = analysis.get("distance_m")
-    offset_method = analysis.get("offset_method", "")
-    distance_method = analysis.get("distance_method", "")
-    tri_markers = analysis.get("tri_markers") or []
-    tri_markers_completed = analysis.get("tri_markers_completed") or []
-    tri_homography = analysis.get("tri_homography")
-    yolo_roi_xyxy = analysis.get("yolo_roi_xyxy")
-
-    if laser_point is None:
-        return result_img
-
-    x, y = laser_point
-    draw_yolo_roi = (
-        yolo_roi_xyxy is not None
-        and getattr(config, "TRIANGLE_YOLO_DRAW_ROI_ON_SHOT", True)
-    )
-
-    if tri_markers:
-        img_cv = image.image2cv(result_img, False, False).copy()
-
-        if draw_yolo_roi:
-            _draw_yolo_roi_on_rgb_numpy(img_cv, yolo_roi_xyxy)
-
-        for m in tri_markers:
-            corners = np.array(m["corners"], dtype=np.int32)
-            cv2.polylines(img_cv, [corners], True, (0, 255, 0), 2)
-            cx, cy = int(m["center"][0]), int(m["center"][1])
-            cv2.circle(img_cv, (cx, cy), 4, (0, 0, 255), -1)
-            cv2.putText(
-                img_cv,
-                f"T{m['id']}",
-                (cx - 18, cy - 12),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.55,
-                (0, 255, 0),
-                1,
-            )
-
-        for m in tri_markers_completed:
-            if not m.get("is_virtual"):
-                continue
-            cx, cy = int(m["center"][0]), int(m["center"][1])
-            cv2.circle(img_cv, (cx, cy), 6, (255, 0, 255), 2)
-            cv2.putText(
-                img_cv,
-                f"VT{m['id']}",
-                (cx - 22, cy - 12),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.55,
-                (255, 0, 255),
-                1,
-            )
-
-        if tri_homography is not None:
-            try:
-                H_inv = np.linalg.inv(tri_homography)
-                c_img = cv2.perspectiveTransform(
-                    np.array([[[0.0, 0.0]]], dtype=np.float32), H_inv
-                )[0][0]
-                ocx, ocy = int(c_img[0]), int(c_img[1])
-                cv2.circle(img_cv, (ocx, ocy), 5, (0, 0, 255), -1)
-                cv2.circle(img_cv, (ocx, ocy), 9, (0, 0, 255), 1)
-            except Exception:
-                pass
-
-        lines = []
-        if dx is not None and dy is not None:
-            r_cm = math.hypot(float(dx), float(dy))
-            lines.append(f"offset=({float(dx):.2f},{float(dy):.2f})cm |r|={r_cm:.2f}cm")
-        if distance_m is not None:
-            lines.append(f"cam_dist={float(distance_m):.2f}m ({distance_method})")
-        if method:
-            lines.append(f"method={method} ({offset_method})")
-        y0 = 22
-        for i, t in enumerate(lines):
-            cv2.putText(
-                img_cv,
-                t,
-                (10, y0 + i * 18),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.5,
-                (0, 255, 0),
-                1,
-            )
-
-        out = image.cv2image(img_cv, False, False)
-    else:
-        img_cv = image.image2cv(result_img, False, False).copy()
-        if draw_yolo_roi:
-            _draw_yolo_roi_on_rgb_numpy(img_cv, yolo_roi_xyxy)
-
-        if center and radius:
-            cx, cy = center
-            if ellipse_params:
-                (ell_center, (width, height), angle) = ellipse_params
-                cx_ell, cy_ell = int(ell_center[0]), int(ell_center[1])
-                cv2.ellipse(
-                    img_cv,
-                    (cx_ell, cy_ell),
-                    (int(width / 2), int(height / 2)),
-                    angle,
-                    0,
-                    360,
-                    (0, 255, 0),
-                    2,
-                )
-                cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1)
-                minor_length = min(width, height) / 2
-                minor_angle = angle + 90 if width >= height else angle
-                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 = (int(cx_ell - dx_minor), int(cy_ell - dy_minor))
-                pt2 = (int(cx_ell + dx_minor), int(cy_ell + dy_minor))
-                cv2.line(img_cv, pt1, pt2, (0, 0, 255), 2)
-            else:
-                cv2.circle(img_cv, (int(cx), int(cy)), int(radius), (0, 0, 255), 2)
-                cv2.circle(img_cv, (int(cx), int(cy)), 2, (0, 0, 255), -1)
-            cv2.line(img_cv, (int(x), int(y)), (int(cx), int(cy)), (255, 255, 0), 1)
-
-        lines = []
-        if dx is not None and dy is not None:
-            lines.append(f"offset=({float(dx):.2f},{float(dy):.2f})cm")
-        if distance_m is not None:
-            lines.append(f"dist={float(distance_m):.2f}m ({distance_method})")
-        if method:
-            lines.append(f"method={method}")
-        for i, t in enumerate(lines):
-            cv2.putText(
-                img_cv,
-                t,
-                (10, 22 + i * 18),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.5,
-                (0, 255, 0),
-                1,
-            )
-        out = image.cv2image(img_cv, False, False)
-
-    lc = image.Color(config.LASER_COLOR[0], config.LASER_COLOR[1], config.LASER_COLOR[2])
-    out.draw_line(
-        int(x - config.LASER_LENGTH),
-        int(y),
-        int(x + config.LASER_LENGTH),
-        int(y),
-        lc,
-        config.LASER_THICKNESS,
-    )
-    out.draw_line(
-        int(x),
-        int(y - config.LASER_LENGTH),
-        int(x),
-        int(y + config.LASER_LENGTH),
-        lc,
-        config.LASER_THICKNESS,
-    )
-    out.draw_circle(int(x), int(y), 1, lc, config.LASER_THICKNESS)
-    return out
-
-
-class _AlgoWorker:
-    def __init__(self):
-        self._lock = threading.Lock()
-        self._busy = False
-        self._latest_preview = None
-        self._latest_meta = ""
-        self._last_ms = 0.0
-
-    @property
-    def busy(self):
-        with self._lock:
-            return self._busy
-
-    @property
-    def last_ms(self):
-        with self._lock:
-            return self._last_ms
-
-    def get_preview(self):
-        with self._lock:
-            return self._latest_preview, self._latest_meta
-
-    def run_async(self, frame):
-        with self._lock:
-            if self._busy:
-                return False
-            self._busy = True
-
-        def _job():
-            t0 = time.perf_counter()
-            meta = ""
-            preview = None
-            try:
-                analysis = analyze_shot(frame)
-                if not analysis.get("success"):
-                    reason = analysis.get("reason", "unknown")
-                    meta = f"fail:{reason}"
-                else:
-                    preview = _annotate_like_saved_shot(analysis)
-                    dx, dy = analysis.get("dx"), analysis.get("dy")
-                    method = analysis.get("method") or "?"
-                    if dx is not None and dy is not None:
-                        meta = f"ok {method} ({dx:.2f},{dy:.2f})cm"
-                    else:
-                        meta = f"ok {method} no_offset"
-            except Exception as e:
-                meta = f"err:{e}"
-            elapsed = (time.perf_counter() - t0) * 1000.0
-            with self._lock:
-                self._latest_preview = preview
-                self._latest_meta = f"{meta} {elapsed:.0f}ms"
-                self._last_ms = elapsed
-                self._busy = False
-
-        threading.Thread(target=_job, daemon=True).start()
-        return True
-
-
-def _draw_status(frame, lines, color=None):
-    if color is None:
-        color = image.COLOR_YELLOW
-    y = 4
-    for line in lines:
-        frame.draw_string(4, y, line, color=color)
-        y += 16
-
-
-def _save_preview_jpeg(maix_img, out_dir):
-    os.makedirs(out_dir, exist_ok=True)
-    fn = os.path.join(out_dir, f"preview_{int(time.time() * 1000)}.jpg")
-    maix_img.save(fn)
-    return fn
-
-
-def main():
-    parser = argparse.ArgumentParser(description="实时预览射箭算法存图效果")
-    parser.add_argument(
-        "--interval",
-        type=float,
-        default=2.0,
-        help="两次完整 analyze_shot 的最小间隔（秒）；--every-frame 时忽略",
-    )
-    parser.add_argument(
-        "--every-frame",
-        action="store_true",
-        help="每帧都触发算法（很慢，仅调试用）",
-    )
-    parser.add_argument(
-        "--width",
-        type=int,
-        default=getattr(config, "CAMERA_WIDTH", 640),
-    )
-    parser.add_argument(
-        "--height",
-        type=int,
-        default=getattr(config, "CAMERA_HEIGHT", 480),
-    )
-    parser.add_argument(
-        "--save-dir",
-        default=config.PHOTO_DIR,
-        help="按板子按键无；用 --save-every N 每 N 次成功分析存一张",
-    )
-    parser.add_argument(
-        "--save-every",
-        type=int,
-        default=0,
-        help="每成功分析 N 次自动存一张到 --save-dir（0=不自动存）",
-    )
-    args = parser.parse_args()
-
-    _patch_preview_config()
-    print("[INFO] 预览模式：已关闭 TRIANGLE_BLACK_YOLO_SAVE_ROI_CROP / TRIANGLE_SAVE_DEBUG_IMAGE")
-
-    laser_manager.load_laser_point()
-    preload_triangle_calib()
-    if getattr(config, "TRIANGLE_YOLO_PRELOAD_ON_BOOT", False) or getattr(
-        config, "TRIANGLE_BLACK_YOLO_PRELOAD_ON_BOOT", False
-    ):
-        print("[INFO] 预加载 YOLO …")
-        preload_yolo_detector()
-
-    camera_manager.init_camera(args.width, args.height)
-    camera_manager.init_display()
-    worker = _AlgoWorker()
-
-    interval_s = 0.0 if args.every_frame else max(0.3, float(args.interval))
-    last_trigger = 0.0
-    ok_count = 0
-    frame_idx = 0
-
-    print(
-        f"[INFO] 摄像头 {args.width}x{args.height} "
-        f"interval={'每帧' if args.every_frame else f'{interval_s}s'}"
-    )
-    print("[INFO] 退出：Ctrl+C")
-
-    try:
-        while True:
-            frame = camera_manager.read_frame()
-            frame_idx += 1
-            now = time.perf_counter()
-
-            due = args.every_frame or (now - last_trigger >= interval_s)
-            if due and not worker.busy:
-                last_trigger = now
-                worker.run_async(_copy_maix_frame(frame))
-
-            preview, meta = worker.get_preview()
-            if preview is not None:
-                show_img = preview
-                status = [f"#{frame_idx}", meta]
-                if args.save_every > 0 and meta.startswith("ok"):
-                    ok_count += 1
-                    if ok_count % args.save_every == 0:
-                        try:
-                            fn = _save_preview_jpeg(preview, args.save_dir)
-                            status.append(f"saved:{fn}")
-                        except Exception as e:
-                            status.append(f"save_err:{e}")
-            else:
-                show_img = frame
-                if worker.busy:
-                    status = [f"#{frame_idx}", "analyzing…"]
-                else:
-                    status = [f"#{frame_idx}", "waiting…"]
-
-            _draw_status(show_img, status)
-            camera_manager.show(show_img)
-            maix_time.sleep_ms(1)
-    except KeyboardInterrupt:
-        print("[INFO] 已退出")
-
-
-if __name__ == "__main__":
-    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_laser_center_point.py

@@ -1,541 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-激光中心点检测单元测试（单文件，无项目依赖）
-直接使用 maix 标准库，实现红色激光点坐标检测
-
-运行方式：
-  python3 test/test_laser_center_point.py
-
-Ctrl+C 退出，按 s 保存截图
-"""
-
-from maix import camera, display, image, time, app, uart, pinmap
-import os
-import struct
-import select
-
-_USE_CV = False
-try:
-    import cv2
-    import numpy as np
-    _USE_CV = True
-except ImportError:
-    pass
-
-WIDTH = 640
-HEIGHT = 480
-THRESHOLD = 140
-SEARCH_RADIUS = 50
-
-
-def read_key_ev():
-    """非阻塞读取 /dev/input/event0 按键（返回 key_code 或 -1）"""
-    try:
-        r, _, _ = select.select([_key_fd], [], [], 0)
-        if r:
-            event = _key_fd.read(16)
-            if len(event) == 16:
-                _, _, etype, code, value = struct.unpack("IIHHI", event)
-                if etype == 1 and value == 1:
-                    return code
-    except Exception:
-        pass
-    return -1
-
-
-def find_ellipse(img_cv, cx, cy, roi_r, th):
-    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 * 1.5) & (r > b * 1.5)
-    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
-    if len(cnt) >= 5:
-        (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)
-        brightness = img_cv[:, :, 0].astype(np.int32) + img_cv[:, :, 1].astype(np.int32) + img_cv[:, :, 2].astype(np.int32)
-        masked = np.where(mask_ellipse > 0, brightness, 0)
-        vals = masked[masked > 0]
-        if len(vals) > 0:
-            bth = np.percentile(vals, 90)
-            bmask = (masked >= bth).astype(np.uint8) * 255
-            bcontours, _ = cv2.findContours(bmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-            if bcontours:
-                blargest = max(bcontours, key=cv2.contourArea)
-                if cv2.contourArea(blargest) >= 3 and len(blargest) >= 5:
-                    (ix, iy), _, _ = cv2.fitEllipse(blargest)
-                    return (float(ix), float(iy))
-                M = cv2.moments(blargest)
-                if M["m00"] > 0:
-                    return (float(M["m10"] / M["m00"]), float(M["m01"] / M["m00"]))
-        return (float(ex), float(ey))
-    M = cv2.moments(cnt)
-    if M["m00"] > 0:
-        return (float(M["m10"] / M["m00"]), float(M["m01"] / M["m00"]))
-    return None
-
-
-def find_brightest(img_cv, cx, cy, roi_r, th):
-    x1 = max(0, cx - roi_r)
-    x2 = min(WIDTH, cx + roi_r)
-    y1 = max(0, cy - roi_r)
-    y2 = min(HEIGHT, cy + roi_r)
-    best_score = 0
-    best_pos = None
-    for y in range(y1, y2):
-        for x in range(x1, x2):
-            r, g, b = int(img_cv[y, x, 0]), int(img_cv[y, x, 1]), int(img_cv[y, x, 2])
-            is_red = (r > th and r > g * 1.5 and r > b * 1.5)
-            is_oe = (r > 200 and g > 200 and b > 200 and r >= g and r >= b and (r - g) > 10 and (r - b) > 10)
-            if is_red or is_oe:
-                score = r + g + b
-                dx, dy = x - cx, y - cy
-                dist = (dx * dx + dy * dy) ** 0.5
-                score *= max(0.5, 1.0 - (dist / roi_r) * 0.5)
-                if score > best_score:
-                    best_score = score
-                    best_pos = (float(x), float(y))
-    return best_pos
-
-
-# 打开键盘输入设备
-_key_fd = None
-try:
-    _key_fd = open("/dev/input/event0", "rb")
-except Exception:
-    try:
-        _key_fd = open("/dev/input/event1", "rb")
-    except Exception:
-        _key_fd = None
-
-print("=" * 50)
-print("激光中心点检测单元测试")
-print("=" * 50)
-print()
-
-cam = camera.Camera(WIDTH, HEIGHT)
-disp = display.Display()
-print("[OK] 摄像头和显示初始化完成")
-
-# 初始化激光串口
-_laser_on = False
-_laser_uart = None
-try:
-    pinmap.set_pin_function("A18", "UART1_RX")
-    pinmap.set_pin_function("A19", "UART1_TX")
-    _laser_uart = uart.UART("/dev/ttyS1", 9600)
-    _laser_uart.read(-1)
-    print("[OK] 激光串口初始化完成")
-except Exception as e:
-    print(f"[WARN] 激光串口初始化失败: {e}")
-
-LASER_ON = bytes([0xAA, 0x00, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x01, 0xC1])
-LASER_OFF = bytes([0xAA, 0x00, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x00, 0xC0])
-
-# 默认开启激光
-if _laser_uart:
-    try:
-        _laser_uart.write(LASER_ON)
-        time.sleep_ms(50)
-        _laser_uart.read(-1)
-        _laser_on = True
-        print("[OK] 激光已开启")
-    except Exception as e:
-        print(f"[WARN] 开启激光失败: {e}")
-print()
-
-pos_ellipse = None
-pos_bright = None
-frame_count = 0
-use_ellipse = True
-
-while not app.need_exit():
-    frame = cam.read()
-    if frame is None:
-        time.sleep_ms(10)
-        continue
-
-    frame_count += 1
-
-    if _USE_CV:
-        img_cv = image.image2cv(frame, False, False)
-        cx, cy = WIDTH // 2, HEIGHT // 2
-
-        t0 = time.ticks_ms()
-        pos_ellipse = find_ellipse(img_cv, cx, cy, SEARCH_RADIUS, THRESHOLD)
-        t1 = time.ticks_ms()
-        pos_bright = find_brightest(img_cv, cx, cy, SEARCH_RADIUS, THRESHOLD)
-        t2 = time.ticks_ms()
-
-        dt_e = abs(time.ticks_diff(t0, t1))
-        dt_b = abs(time.ticks_diff(t1, t2))
-
-        if frame_count % 5 == 0:
-            e_str = f"({pos_ellipse[0]:.1f},{pos_ellipse[1]:.1f})" if pos_ellipse else "None"
-            b_str = f"({pos_bright[0]:.1f},{pos_bright[1]:.1f})" if pos_bright else "None"
-            print(f"[LASER] ellipse={e_str} ({dt_e}ms)  brightest={b_str} ({dt_b}ms)  "
-                  f"th={THRESHOLD}  radius={SEARCH_RADIUS}")
-
-        # 叠加显示
-        pos = pos_ellipse if use_ellipse else pos_bright
-        h, w = img_cv.shape[:2]
-        cv2.circle(img_cv, (cx, cy), SEARCH_RADIUS, (0, 255, 0), 1)
-        cv2.circle(img_cv, (cx, cy), 2, (0, 255, 0), -1)
-        if pos:
-            x, y = int(pos[0]), int(pos[1])
-            cv2.circle(img_cv, (x, y), 6, (0, 0, 255), 2)
-            cv2.line(img_cv, (x - 14, y), (x + 14, y), (0, 0, 255), 1)
-            cv2.line(img_cv, (x, y - 14), (x, y + 14), (0, 0, 255), 1)
-            cv2.putText(img_cv, f"({x},{y})", (x + 10, y - 10),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
-        info = [
-            f"pos={pos if pos else 'None'}",
-            f"method={'ellipse' if use_ellipse else 'brightest'}  th={THRESHOLD}",
-            f"laser={'ON' if _laser_on else 'OFF'}",
-        ]
-        for i, line in enumerate(info):
-            cv2.putText(img_cv, line, (8, 20 + i * 22),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
-
-        display_frame = image.cv2image(img_cv, False, False)
-    else:
-        display_frame = frame
-
-    disp.show(display_frame)
-
-    # 按键处理（非阻塞）
-    key = read_key_ev()
-    if key > 0:
-        c = chr(key & 0xFF) if key < 256 else ""
-        if key == 113 or key == 81 or key == 0x1b:  # q/Q/ESC
-            break
-        if c == "e" or key == 18:  # e
-            use_ellipse = not use_ellipse
-            print(f"[KEY] Method: {'ellipse' if use_ellipse else 'brightest'}")
-        if c == "l" or key == 12:  # l
-            _laser_on = not _laser_on
-            if _laser_uart:
-                try:
-                    _laser_uart.write(LASER_ON if _laser_on else LASER_OFF)
-                    time.sleep_ms(30)
-                    _laser_uart.read(-1)
-                    print(f"[KEY] Laser: {'ON' if _laser_on else 'OFF'}")
-                except Exception as e:
-                    print(f"[KEY] Laser error: {e}")
-            else:
-                print("[KEY] Laser UART not available")
-    time.sleep_ms(30)
-
-# 关闭激光
-if _laser_on and _laser_uart:
-    try:
-        _laser_uart.write(LASER_OFF)
-        _laser_uart.read(-1)
-        print("[EXIT] 激光已关闭")
-    except Exception:
-        pass
-print("[EXIT] 测试结束")
-if _key_fd:
-    _key_fd.close()
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-激光中心点检测单元测试（单文件，无项目依赖）
-直接使用 maix 标准库，实现红色激光点坐标检测
-
-运行方式：
-  python3 test/test_laser_center_point.py
-
-Ctrl+C 退出，按 s 保存截图
-"""
-
-from maix import camera, display, image, time, app, uart, pinmap
-import os
-import struct
-import select
-
-_USE_CV = False
-try:
-    import cv2
-    import numpy as np
-    _USE_CV = True
-except ImportError:
-    pass
-
-WIDTH = 640
-HEIGHT = 480
-THRESHOLD = 120
-RED_RATIO = 1.3
-SEARCH_RADIUS = 60
-
-
-def read_key_ev():
-    """非阻塞读取 /dev/input/event0 按键（返回 key_code 或 -1）"""
-    try:
-        r, _, _ = select.select([_key_fd], [], [], 0)
-        if r:
-            event = _key_fd.read(16)
-            if len(event) == 16:
-                _, _, etype, code, value = struct.unpack("IIHHI", event)
-                if etype == 1 and value == 1:
-                    return code
-    except Exception:
-        pass
-    return -1
-
-
-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
-    if len(cnt) >= 5:
-        (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)
-        brightness = img_cv[:, :, 0].astype(np.int32) + img_cv[:, :, 1].astype(np.int32) + img_cv[:, :, 2].astype(np.int32)
-        masked = np.where(mask_ellipse > 0, brightness, 0)
-        vals = masked[masked > 0]
-        if len(vals) > 0:
-            bth = np.percentile(vals, 90)
-            bmask = (masked >= bth).astype(np.uint8) * 255
-            bcontours, _ = cv2.findContours(bmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-            if bcontours:
-                blargest = max(bcontours, key=cv2.contourArea)
-                if cv2.contourArea(blargest) >= 3 and len(blargest) >= 5:
-                    (ix, iy), _, _ = cv2.fitEllipse(blargest)
-                    return (float(ix), float(iy))
-                M = cv2.moments(blargest)
-                if M["m00"] > 0:
-                    return (float(M["m10"] / M["m00"]), float(M["m01"] / M["m00"]))
-        return (float(ex), float(ey))
-    M = cv2.moments(cnt)
-    if M["m00"] > 0:
-        return (float(M["m10"] / M["m00"]), float(M["m01"] / M["m00"]))
-    return None
-
-
-def find_brightest_bytes(frame, cx, cy, roi_r, th, ratio):
-    """使用 frame.to_bytes() 两阶段搜索，避免 cv2 转换"""
-    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_pos = None
-    # 第一阶段：隔点粗搜
-    for y in range(y1, y2, 2):
-        for x in range(x1, x2, 2):
-            idx = (y * WIDTH + x) * 3
-            r = data[idx]; g = data[idx+1]; b = data[idx+2]
-            if (r > th and r > g * ratio and r > b * ratio) or \
-               (r > 200 and g > 200 and b > 200 and r >= g and r >= b and (r - g) > 10 and (r - b) > 10):
-                score = r + g + b
-                dx = x - cx; dy = y - cy
-                score *= max(0.5, 1.0 - ((dx*dx + dy*dy) ** 0.5 / roi_r) * 0.5)
-                if score > best_score:
-                    best_score = score
-                    best_pos = (x, y)
-    if best_pos is None:
-        return None
-    # 第二阶段：候选点 7x7 精细搜索
-    fx, fy = best_pos
-    x1f = max(0, fx - 3); x2f = min(WIDTH, fx + 4)
-    y1f = max(0, fy - 3); y2f = min(HEIGHT, fy + 4)
-    best_bright = 0
-    final_pos = best_pos
-    for y in range(y1f, y2f):
-        for x in range(x1f, x2f):
-            idx = (y * WIDTH + x) * 3
-            r = data[idx]; g = data[idx+1]; b = data[idx+2]
-            if (r > th and r > g * ratio and r > b * ratio) or \
-               (r > 200 and g > 200 and b > 200 and r >= g and r >= b and (r - g) > 10 and (r - b) > 10):
-                rgb_sum = r + g + b
-                if rgb_sum > best_bright:
-                    best_bright = rgb_sum
-                    final_pos = (float(x), float(y))
-    return final_pos
-
-
-# 打开键盘输入设备
-_key_fd = None
-try:
-    _key_fd = open("/dev/input/event0", "rb")
-except Exception:
-    try:
-        _key_fd = open("/dev/input/event1", "rb")
-    except Exception:
-        _key_fd = None
-
-print("=" * 50)
-print("激光中心点检测单元测试")
-print("=" * 50)
-print()
-
-cam = camera.Camera(WIDTH, HEIGHT)
-disp = display.Display()
-print("[OK] 摄像头和显示初始化完成")
-
-# 初始化激光串口
-_laser_on = False
-_laser_uart = None
-try:
-    pinmap.set_pin_function("A18", "UART1_RX")
-    pinmap.set_pin_function("A19", "UART1_TX")
-    _laser_uart = uart.UART("/dev/ttyS1", 9600)
-    _laser_uart.read(-1)
-    print("[OK] 激光串口初始化完成")
-except Exception as e:
-    print(f"[WARN] 激光串口初始化失败: {e}")
-
-LASER_ON = bytes([0xAA, 0x00, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x01, 0xC1])
-LASER_OFF = bytes([0xAA, 0x00, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x00, 0xC0])
-
-# 默认开启激光
-if _laser_uart:
-    try:
-        _laser_uart.write(LASER_ON)
-        time.sleep_ms(50)
-        _laser_uart.read(-1)
-        _laser_on = True
-        print("[OK] 激光已开启")
-    except Exception as e:
-        print(f"[WARN] 开启激光失败: {e}")
-print()
-
-pos_ellipse = None
-pos_bright = None
-frame_count = 0
-use_ellipse = True
-
-while not app.need_exit():
-    frame = cam.read()
-    if frame is None:
-        time.sleep_ms(10)
-        continue
-
-    frame_count += 1
-
-    cx, cy = WIDTH // 2, HEIGHT // 2
-
-    t0 = time.ticks_ms()
-    pos_bright = find_brightest_bytes(frame, cx, cy, SEARCH_RADIUS, THRESHOLD, RED_RATIO)
-    t1 = time.ticks_ms()
-
-    pos_ellipse = None
-    if _USE_CV:
-        img_cv = image.image2cv(frame, False, False)
-        t2 = time.ticks_ms()
-        pos_ellipse = find_ellipse(img_cv, cx, cy, SEARCH_RADIUS, THRESHOLD, RED_RATIO)
-        t3 = time.ticks_ms()
-    else:
-        img_cv = None
-        t3 = t2 = t1
-
-    dt_b = abs(time.ticks_diff(t0, t1))
-    dt_e = abs(time.ticks_diff(t2, t3))
-
-    if frame_count % 5 == 0:
-        e_str = f"({pos_ellipse[0]:.1f},{pos_ellipse[1]:.1f})" if pos_ellipse else "None"
-        b_str = f"({pos_bright[0]:.1f},{pos_bright[1]:.1f})" if pos_bright else "None"
-        print(f"[LASER] ellipse={e_str} ({dt_e}ms)  brightest={b_str} ({dt_b}ms)  "
-              f"th={THRESHOLD}  ratio={RED_RATIO}  radius={SEARCH_RADIUS}")
-
-    pos = pos_ellipse if use_ellipse else pos_bright
-    if img_cv is not None:
-        cv2.circle(img_cv, (cx, cy), SEARCH_RADIUS, (0, 255, 0), 1)
-        cv2.circle(img_cv, (cx, cy), 2, (0, 255, 0), -1)
-        if pos:
-            x, y = int(pos[0]), int(pos[1])
-            cv2.circle(img_cv, (x, y), 6, (0, 0, 255), 2)
-            cv2.line(img_cv, (x - 14, y), (x + 14, y), (0, 0, 255), 1)
-            cv2.line(img_cv, (x, y - 14), (x, y + 14), (0, 0, 255), 1)
-            cv2.putText(img_cv, f"({x},{y})", (x + 10, y - 10),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
-        info = [
-            f"pos={pos if pos else 'None'}",
-            f"method={'ellipse' if use_ellipse else 'brightest'}  th={THRESHOLD}  ratio={RED_RATIO}",
-            f"laser={'ON' if _laser_on else 'OFF'}",
-        ]
-        for i, line in enumerate(info):
-            cv2.putText(img_cv, line, (8, 20 + i * 22),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
-        display_frame = image.cv2image(img_cv, False, False)
-    else:
-        display_frame = frame
-
-    disp.show(display_frame)
-
-    # 按键处理（非阻塞）
-    key = read_key_ev()
-    if key > 0:
-        c = chr(key & 0xFF) if key < 256 else ""
-        if key == 113 or key == 81 or key == 0x1b:  # q/Q/ESC
-            break
-        if c == "e" or key == 18:  # e
-            use_ellipse = not use_ellipse
-            print(f"[KEY] Method: {'ellipse' if use_ellipse else 'brightest'}")
-        if c == "l" or key == 12:  # l
-            _laser_on = not _laser_on
-            if _laser_uart:
-                try:
-                    _laser_uart.write(LASER_ON if _laser_on else LASER_OFF)
-                    time.sleep_ms(30)
-                    _laser_uart.read(-1)
-                    print(f"[KEY] Laser: {'ON' if _laser_on else 'OFF'}")
-                except Exception as e:
-                    print(f"[KEY] Laser error: {e}")
-            else:
-                print("[KEY] Laser UART not available")
-    time.sleep_ms(30)
-
-# 关闭激光
-if _laser_on and _laser_uart:
-    try:
-        _laser_uart.write(LASER_OFF)
-        _laser_uart.read(-1)
-        print("[EXIT] 激光已关闭")
-    except Exception:
-        pass
-print("[EXIT] 测试结束")
-if _key_fd:
-    _key_fd.close()

test/test_yolo26.py

@@ -1,18 +0,0 @@
-from maix import camera, display, image, nn, app
-
-# 1. 初始化模型 (请确保模型文件 .mud 路径正确)
-detector = nn.YOLOv5(model="/root/model_279350.mud", dual_buff=True)
-
-# 2. 初始化摄像头，分辨率与模型输入匹配
-cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
-disp = display.Display()
-
-# 3. 主循环：实时检测与显示
-while not app.need_exit():
-    img = cam.read()                         # 从摄像头读取一帧
-    objs = detector.detect(img, conf_th=0.5, iou_th=0.45) # 执行YOLO11推理
-    for obj in objs:                         # 绘制所有检测到的目标
-        img.draw_rect(obj.x, obj.y, obj.w, obj.h, color=image.COLOR_RED)
-        msg = f'{detector.labels[obj.class_id]}: {obj.score:.2f}'
-        img.draw_string(obj.x, obj.y, msg, color=image.COLOR_RED)
-    disp.show(img)                           # 更新屏幕显示

test/test_yolo_camera_simple.py

@@ -1,209 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-摄像头实时 YOLOv5 简易测试脚本。
-
-特点：
-- 完全独立脚本，直接 python test/test_yolo_camera_simple.py 运行，不需要传参。
-- 不 import config，不依赖项目模块。
-- 直接调用 maix.nn.YOLOv5(model=..., dual_buff=False)。
-- camera.read() 得到的 Maix image 直接送 det.detect()。
-- 在画面上画检测框、类别、置信度，并显示到屏幕。
-
-运行环境：MaixCAM / MaixPy。
-"""
-
-import os
-
-
-CAMERA_WIDTH = 640
-CAMERA_HEIGHT = 480
-# 默认与主项目 config.TRIANGLE_YOLO_MODEL_PATH 一致（勿用 /root/yolo26_int8.mud，那是占位路径）
-_MODEL_DEFAULT = "/maixapp/apps/t11/model_270139.mud"
-try:
-    import config as _cfg
-
-    MODEL_PATH = getattr(_cfg, "TRIANGLE_YOLO_MODEL_PATH", _MODEL_DEFAULT) or _MODEL_DEFAULT
-except Exception:
-    MODEL_PATH = _MODEL_DEFAULT
-CONF_TH = 0.7
-IOU_TH = 0.45
-# native: Maix detect 返回框已映射到 camera.read() 图像坐标；letterbox: 需要从网络输入坐标反算
-COORD_MODE = "native"
-# 只用于 DRAW_ONLY_CLASS_IDS=True 时过滤显示；默认画所有框
-CLASS_IDS = (0,)
-DRAW_ONLY_CLASS_IDS = False  # True=只画 CLASS_IDS 里的类别；False=画所有 YOLO 返回框
-
-
-def _det_obj_class_id(o):
-    for key in ("class_id", "cls", "label", "category", "cat_id", "id"):
-        if hasattr(o, key):
-            v = getattr(o, key)
-            if v is None:
-                continue
-            try:
-                return int(float(v))
-            except (TypeError, ValueError):
-                continue
-    return None
-
-
-def _det_obj_from_seq(t):
-    if not isinstance(t, (list, tuple)) or len(t) < 6:
-        return None
-
-    class Box:
-        pass
-
-    b = Box()
-    b.x = float(t[0])
-    b.y = float(t[1])
-    b.w = float(t[2])
-    b.h = float(t[3])
-    b.score = float(t[4])
-    b.class_id = int(float(t[5]))
-    return b
-
-
-def _normalize_objs(objs):
-    out = []
-    for o in objs or []:
-        if isinstance(o, (list, tuple)):
-            m = _det_obj_from_seq(o)
-            if m is not None:
-                out.append(m)
-        else:
-            out.append(o)
-    return out
-
-
-def _letterbox_net_to_src_xyxy(x, y, w, h, src_w, src_h, net_w, net_h):
-    scale = min(net_w / float(src_w), net_h / float(src_h))
-    new_w = src_w * scale
-    new_h = src_h * scale
-    pad_x = (net_w - new_w) * 0.5
-    pad_y = (net_h - new_h) * 0.5
-    x0 = (x - pad_x) / scale
-    y0 = (y - pad_y) / scale
-    x1 = (x + w - pad_x) / scale
-    y1 = (y + h - pad_y) / scale
-    return x0, y0, x1, y1
-
-
-def _det_to_src_xyxy(o, coord_mode, src_w, src_h, net_w, net_h):
-    x = float(getattr(o, "x", 0.0))
-    y = float(getattr(o, "y", 0.0))
-    w = float(getattr(o, "w", 0.0))
-    h = float(getattr(o, "h", 0.0))
-    if coord_mode in ("native", "source", "camera", "full"):
-        return x, y, x + w, y + h
-    return _letterbox_net_to_src_xyxy(x, y, w, h, src_w, src_h, net_w, net_h)
-
-
-def _clip_xywh(x0, y0, x1, y1, src_w, src_h):
-    x0 = max(0, min(int(round(x0)), src_w - 1))
-    y0 = max(0, min(int(round(y0)), src_h - 1))
-    x1 = max(x0 + 1, min(int(round(x1)), src_w))
-    y1 = max(y0 + 1, min(int(round(y1)), src_h))
-    return x0, y0, x1 - x0, y1 - y0
-
-
-def _label(det, cid):
-    labels = getattr(det, "labels", None)
-    if labels is None:
-        return str(cid)
-    try:
-        return str(labels[int(cid)])
-    except Exception:
-        return str(cid)
-
-
-def main():
-    from maix import camera, display, nn, time, image
-
-    if not MODEL_PATH or not os.path.isfile(MODEL_PATH):
-        print("[ERR] 模型文件不存在:", MODEL_PATH)
-        return
-
-    print("[INFO] 初始化 YOLO 模型:", MODEL_PATH)
-    det = nn.YOLOv26(model=MODEL_PATH, dual_buff=False)
-    net_w = int(det.input_width())
-    net_h = int(det.input_height())
-    print(
-        "[INFO] net_in=%dx%d conf=%.2f iou=%.2f coord=%s class_ids=%s"
-        % (net_w, net_h, CONF_TH, IOU_TH, COORD_MODE, str(CLASS_IDS))
-    )
-
-    print("[INFO] 初始化摄像头: %dx%d" % (CAMERA_WIDTH, CAMERA_HEIGHT))
-    cam = camera.Camera(CAMERA_WIDTH, CAMERA_HEIGHT)
-    disp = display.Display()
-
-    color_cycle = []
-    for name in ("RED", "GREEN", "BLUE", "ORANGE", "YELLOW", "CYAN", "MAGENTA"):
-        c = getattr(image, "COLOR_" + name, None)
-        if c is not None:
-            color_cycle.append(c)
-    if not color_cycle:
-        color_cycle = [getattr(image, "COLOR_RED", 0)]
-
-    frame_idx = 0
-    last_log_ms = time.ticks_ms()
-    fps_count = 0
-
-    while True:
-        frame = cam.read()
-        src_w = frame.width()
-        src_h = frame.height()
-
-        t0 = time.ticks_ms()
-        raw = det.detect(frame, conf_th=CONF_TH, iou_th=IOU_TH)
-        detect_ms = time.ticks_ms() - t0
-        objs = _normalize_objs(raw if raw is not None else [])
-
-        draw_count = 0
-        for i, o in enumerate(objs):
-            cid = _det_obj_class_id(o)
-            if cid is None:
-                cid = -1
-            if DRAW_ONLY_CLASS_IDS and cid not in CLASS_IDS:
-                continue
-            try:
-                score = float(getattr(o, "score", 0.0))
-            except Exception:
-                score = 0.0
-
-            x0, y0, x1, y1 = _det_to_src_xyxy(o, COORD_MODE, src_w, src_h, net_w, net_h)
-            ix, iy, iw, ih = _clip_xywh(x0, y0, x1, y1, src_w, src_h)
-            col = color_cycle[cid % len(color_cycle)] if cid >= 0 else color_cycle[0]
-            frame.draw_rect(ix, iy, iw, ih, color=col)
-            frame.draw_string(ix, max(0, iy - 16), "%s %.2f" % (_label(det, cid), score), color=col)
-            draw_count += 1
-
-        frame.draw_string(4, 4, "YOLO boxes:%d draw:%d %dms" % (len(objs), draw_count, detect_ms), color=color_cycle[0])
-        disp.show(frame)
-
-        frame_idx += 1
-        fps_count += 1
-        now = time.ticks_ms()
-        if now - last_log_ms >= 1000:
-            print(
-                "[INFO] frame=%d fps=%d raw_boxes=%d draw_boxes=%d detect_ms=%d"
-                % (frame_idx, fps_count, len(objs), draw_count, detect_ms)
-            )
-            fps_count = 0
-            last_log_ms = now
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    except KeyboardInterrupt:
-        print("[INFO] exit")
-    except Exception as e:
-        print("[ERR]", e)
-        try:
-            import traceback
-
-            traceback.print_exc()
-        except Exception:
-            pass

test/test_yolov8.py

@@ -1,29 +0,0 @@
-from maix import image, nn, display
-
-# 1. 加载模型
-detector = nn.YOLOv8(model="/root/279350.mud", dual_buff=False)
-# 2. 加载指定图片（根据模型输入尺寸自动缩放宽高）
-img = image.load("/root/tes.jpg")
-if img is None:
-    raise FileNotFoundError("图片加载失败，请检查路径")
-
-# 3. 调整图片尺寸到模型输入要求（可选，detect内部会处理，但提前缩放可提高速度）
-# img = img.resize(detector.input_width(), detector.input_height())
-
-# 4. 检测
-objs = detector.detect(img, conf_th=0.5, iou_th=0.45)
-
-# 5. 在图片上绘制结果
-for obj in objs:
-    img.draw_rect(obj.x, obj.y, obj.w, obj.h, color=image.COLOR_RED)
-    msg = f'{detector.labels[obj.class_id]}: {obj.score:.2f}'
-    img.draw_string(obj.x, obj.y, msg, color=image.COLOR_RED)
-
-# 6. 显示结果（如果设备有屏幕）
-disp = display.Display()
-disp.show(img)
-
-# 7. 保存结果（可选）
-img.save("/root/result.jpg")
-
-print("识别完成，结果已显示并保存为 result.jpg")

triangle_target.py

@@ -22,143 +22,6 @@ def _log(msg):
         pass
 
 
-def _read_triangle_direction_cfg():
-    """读取 config 中三角形方向/中心距校验参数。"""
-    try:
-        import config as cfg
-        return {
-            "enable": bool(getattr(cfg, "TRIANGLE_DIRECTION_VALIDATE_ENABLE", True)),
-            "min_pass": int(getattr(cfg, "TRIANGLE_DIRECTION_MIN_PASS", 3)),
-            "dot_min": float(getattr(cfg, "TRIANGLE_DIRECTION_DOT_MIN", 0.0)),
-            "to_center_dot_min": float(
-                getattr(cfg, "TRIANGLE_DIRECTION_TO_CENTER_DOT_MIN", 0.35)
-            ),
-            "center_dist_enable": bool(
-                getattr(cfg, "TRIANGLE_CENTER_DISTANCE_VALIDATE_ENABLE", True)
-            ),
-            "center_dist_tol": float(
-                getattr(cfg, "TRIANGLE_CENTER_DISTANCE_RATIO_TOL", 0.45)
-            ),
-        }
-    except Exception:
-        return {
-            "enable": True,
-            "min_pass": 3,
-            "dot_min": 0.0,
-            "to_center_dot_min": 0.35,
-            "center_dist_enable": True,
-            "center_dist_tol": 0.45,
-        }
-
-
-def _quad_combo_orient_penalty(cands_4):
-    """
-    四点组合评分用的方向惩罚（原 _score_quad 内 orient_pen 逻辑）。
-    TRIANGLE_DIRECTION_VALIDATE_ENABLE=False 时调用方应跳过（不加罚）。
-    """
-    orient_pen = 0.0
-    orient_vote = []
-    for c in cands_4:
-        cen = np.array(c["center_px"], dtype=np.float32)
-        rpt = np.array(c["right_pt"], dtype=np.float32)
-        vx = float(cen[0] - rpt[0])
-        vy = float(cen[1] - rpt[1])
-        if abs(vx) < 1e-6 or abs(vy) < 1e-6:
-            orient_pen += 1.0
-            orient_vote.append(None)
-            continue
-        if abs(vx) < abs(vy) * 0.15 or abs(vy) < abs(vx) * 0.15:
-            orient_pen += 0.5
-        if vx > 0 and vy > 0:
-            orient_vote.append(0)
-        elif vx < 0 and vy > 0:
-            orient_vote.append(1)
-        elif vx > 0 and vy < 0:
-            orient_vote.append(2)
-        else:
-            orient_vote.append(3)
-    valid_votes = [v for v in orient_vote if v is not None]
-    if valid_votes:
-        from collections import Counter
-        vc = Counter(valid_votes)
-        orient_pen += max(0, max(vc.values()) - 1) * 0.8
-    return orient_pen
-
-
-def _marker_inward_unit(marker):
-    """从直角顶点指向三角内部的单位向量；marker['center'] 为直角顶点。"""
-    right = np.array(marker["center"], dtype=np.float64)
-    corners = marker.get("corners")
-    if not corners or len(corners) < 3:
-        return None
-    cen = np.mean(np.array(corners, dtype=np.float64), axis=0)
-    inv = cen - right
-    n = float(np.linalg.norm(inv))
-    if n < 1e-6:
-        return None
-    return inv / n
-
-
-def _validate_triangle_direction(marker_centers, tri_markers, cfg):
-    """
-    校验：四角到候选靶心距离近似一致；各真实黑三角朝向靶心。
-    仅统计 tri_markers 中真实检出的角（不含几何补全的虚拟点）。
-    Returns:
-        (ok: bool, reason: str)
-    """
-    if not cfg.get("enable", True):
-        return True, ""
-
-    pts = np.array(marker_centers, dtype=np.float64).reshape(-1, 2)
-    if len(pts) < 3:
-        return True, ""
-
-    quad_center = np.mean(pts, axis=0)
-
-    if cfg.get("center_dist_enable", True) and len(pts) >= 3:
-        dists = np.linalg.norm(pts - quad_center, axis=1)
-        mean_d = float(np.mean(dists))
-        if mean_d > 1e-6:
-            ratio = (float(np.max(dists)) - float(np.min(dists))) / mean_d
-            tol = float(cfg.get("center_dist_tol", 0.45))
-            if ratio > tol:
-                return False, f"center_dist_ratio={ratio:.2f}>{tol:.2f}"
-
-    dot_need = max(
-        float(cfg.get("dot_min", 0.0)),
-        float(cfg.get("to_center_dot_min", 0.35)),
-    )
-    pass_n = 0
-    check_n = 0
-    for m in tri_markers or []:
-        if m.get("center") is None:
-            continue
-        check_n += 1
-        right = np.array(m["center"], dtype=np.float64)
-        to_center = quad_center - right
-        nc = float(np.linalg.norm(to_center))
-        if nc < 1e-6:
-            continue
-        inward = _marker_inward_unit(m)
-        if inward is None:
-            continue
-        dot_tc = float(np.dot(inward, to_center / nc))
-        if dot_tc >= dot_need:
-            pass_n += 1
-
-    if check_n == 0:
-        return True, ""
-
-    min_pass = int(cfg.get("min_pass", 3))
-    min_pass = max(1, min(min_pass, check_n))
-    if pass_n < min_pass:
-        return False, (
-            f"direction_pass={pass_n}/{check_n} need>={min_pass} "
-            f"(dot>={dot_need:.2f})"
-        )
-    return True, ""
-
-
 def _gray_suppress_bright_by_v(img_rgb, v_above: int):
     """
     RGB 输入：在 HSV 的 V 上，将亮度 >= v_above 的像素灰度置为 255。
@@ -361,7 +224,7 @@ def detect_triangle_markers(
         blackhat_kernel_frac = 0.018
     try:
         import config as _tcfg
-        _timing_log = bool(getattr(_tcfg, "ARCHERY_TIMING_ENABLE", True)) and bool(getattr(_tcfg, "TRIANGLE_TIMING_LOG", True))
+        _timing_log = bool(getattr(_tcfg, "TRIANGLE_TIMING_LOG", True))
     except Exception:
         _timing_log = True
 
@@ -759,8 +622,6 @@ def detect_triangle_markers(
         bot_pair = sorted(by_y[2:], key=lambda i: pts_4[i][0])
         return top_pair[0], bot_pair[0], bot_pair[1], top_pair[1]
 
-    _dir_cfg_combo = _read_triangle_direction_cfg()
-
     def _score_quad(cands_4):
         pts = [np.array(c["center_px"]) for c in cands_4]
         legs = [c["avg_leg"] for c in cands_4]
@@ -780,13 +641,7 @@ def detect_triangle_markers(
         med_l = float(np.median(legs))
         leg_dev = max(abs(l - med_l) / (med_l + 1e-6) for l in legs)
 
-        orient_pen = (
-            _quad_combo_orient_penalty(cands_4)
-            if _dir_cfg_combo.get("enable", True)
-            else 0.0
-        )
-
-        score = (diag_ratio - 1.0) * 3.0 + (h_ratio - 1.0) + (v_ratio - 1.0) + leg_dev * 2.0 + orient_pen
+        score = (diag_ratio - 1.0) * 3.0 + (h_ratio - 1.0) + (v_ratio - 1.0) + leg_dev * 2.0
         return score, (tl, bl, br, tr)
 
     assigned = None
@@ -1077,8 +932,6 @@ def _assign_marker_ids_from_filtered(filtered, verbose=True):
         bot_pair = sorted(by_y[2:], key=lambda i: pts_4[i][0])
         return top_pair[0], bot_pair[0], bot_pair[1], top_pair[1]
 
-    _dir_cfg_combo = _read_triangle_direction_cfg()
-
     def _score_quad(cands_4):
         pts = [np.array(c["center_px"]) for c in cands_4]
         legs = [c["avg_leg"] for c in cands_4]
@@ -1094,12 +947,7 @@ def _assign_marker_ids_from_filtered(filtered, verbose=True):
         v_ratio = max(s_left, s_right) / (min(s_left, s_right) + 1e-6)
         med_l = float(np.median(legs))
         leg_dev = max(abs(l - med_l) / (med_l + 1e-6) for l in legs)
-        orient_pen = (
-            _quad_combo_orient_penalty(cands_4)
-            if _dir_cfg_combo.get("enable", True)
-            else 0.0
-        )
-        score = (diag_ratio - 1.0) * 3.0 + (h_ratio - 1.0) + (v_ratio - 1.0) + leg_dev * 2.0 + orient_pen
+        score = (diag_ratio - 1.0) * 3.0 + (h_ratio - 1.0) + (v_ratio - 1.0) + leg_dev * 2.0
         return score, (tl, bl, br, tr)
 
     assigned = None
@@ -1265,7 +1113,7 @@ def try_triangle_scoring(
 
     try:
         import config as _cfg_tl
-        _try_timing_log = bool(getattr(_cfg_tl, "ARCHERY_TIMING_ENABLE", True)) and bool(getattr(_cfg_tl, "TRIANGLE_TIMING_LOG", True))
+        _try_timing_log = bool(getattr(_cfg_tl, "TRIANGLE_TIMING_LOG", True))
         _crop_min_side = int(getattr(_cfg_tl, "TRIANGLE_CROP_ROI_MIN_SIDE_PX", 64))
     except Exception:
         _try_timing_log = True
@@ -1885,21 +1733,6 @@ def try_triangle_scoring(
             "is_virtual": bool(_is_virtual),
         })
 
-    # ---------- 方向 / 中心距校验（config.TRIANGLE_DIRECTION_*） ----------
-    _dir_cfg = _read_triangle_direction_cfg()
-    _dir_ok, _dir_reason = _validate_triangle_direction(
-        marker_centers, tri_markers, _dir_cfg
-    )
-    if not _dir_ok:
-        _log(f"[TRI] 方向校验失败: {_dir_reason}")
-        if _try_timing_log:
-            _log(
-                f"[TRI] timing_ms(try_triangle): {_tri_yolo_part} "
-                f"geometry={(time.perf_counter() - _t_seg) * 1000:.1f} "
-                f"total_try={(time.perf_counter() - _t_try0) * 1000:.1f} (方向校验失败)"
-            )
-        return out
-
     # ---------- 结果有效性校验（防 nan/inf 与退化角点） ----------
     try:
         import config as _cfg

version.md

@@ -0,0 +1,28 @@
+# 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

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 = '1.2.15.1'
-
-
-# 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
-# 1.2.15.1 增加了标靶判断 20 40 
-# 1.2.16.1 增加激光校准，三角形方向判断，时间开关
-
-
-
+VERSION = '2.15.15'
 
 

vision.py

@@ -10,7 +10,6 @@ import os
 import math
 import threading
 import queue
-import time
 from maix import image
 import config
 from logger_manager import logger_manager
@@ -532,14 +531,11 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
     if img_cv is None:
         img_cv = image.image2cv(frame, False, False)
     logger = logger_manager.logger
-    _timing_on = bool(getattr(config, "VISION_TIMING_ENABLE", True))
-    _t0 = time.perf_counter() if _timing_on else None
-    _t1 = _t2 = _t3 = _t4 = _t5 = None
     from datetime import datetime
     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
@@ -558,8 +554,6 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
     ellipse_params = None
     
     logger.debug(f"[detect_circle_v3] step 1 fin {datetime.now()}")
-    if _timing_on:
-        _t1 = time.perf_counter()
 
     # -- 2. HSV + 黄色掩码
     hsv = cv2.cvtColor(img_det, cv2.COLOR_RGB2HSV)
@@ -573,26 +567,25 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
     mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
 
     logger.debug(f"[detect_circle_v3] step 2 fin {datetime.now()}")
-    if _timing_on:
-        _t2 = time.perf_counter()
-        _t3 = time.perf_counter()
 
     # -- 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([170, 80, 0]), np.array([180, 255, 255])),
+        cv2.inRange(hsv, np.array([0, 30, 20]),   np.array([12,  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)
@@ -602,22 +595,19 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
             red_candidates.append({"center": (int(xr), int(yr)), "radius": int(rr)})
 
     logger.debug(f"[detect_circle_v3] step 3 fin {datetime.now()}")
-    if _timing_on:
-        _t3 = time.perf_counter()
-        _t4 = time.perf_counter()
 
     # -- 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 <= 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}")
@@ -637,7 +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}, "
@@ -650,13 +644,19 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
                 })
                 matched = True
                 break
-        if not matched and logger:
-            logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
+        if not matched:
+            # 黄圈高置信度兜底：大且圆时跳过红圈验证
+            if area > 30 and circularity > 0.8:
+                valid_targets.append({
+                    "center": yellow_center,
+                    "radius": yellow_radius,
+                    "ellipse": yellow_ellipse,
+                    "area": area,
+                })
+            elif logger:
+                logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
 
     logger.debug(f"[detect_circle_v3] step 4 fin {datetime.now()}")
-    if _timing_on:
-        _t4 = time.perf_counter()
-        _t5 = time.perf_counter()
 
     # -- 5. 选最佳目标，坐标还原到原始分辨率
     if valid_targets:
@@ -684,20 +684,7 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
         ellipse_params = be
         best_radius1 = best_radius * 5
     result_img = image.cv2image(img_cv, False, False)
-    if _timing_on:
-        _t5 = time.perf_counter()
-        _t_all = (_t5 - _t0) * 1000
-        _ms1 = (_t1 - _t0) * 1000
-        _ms2 = (_t2 - _t1) * 1000
-        _ms3 = (_t3 - _t2) * 1000
-        _ms4 = (_t4 - _t3) * 1000
-        _ms5 = (_t5 - _t4) * 1000
-        logger.info(
-            f"[VISION timing] total={_t_all:.1f}ms  "
-            f"resize={_ms1:.1f}  hsv_yellow={_ms2:.1f}  "
-            f"red_mask={_ms3:.1f}  yellow_loop={_ms4:.1f}  "
-            f"select_cv2img={_ms5:.1f}"
-        )
+    logger.debug(f"[detect_circle_v3] step 5 fin {datetime.now()}")
     return result_img, best_center, best_radius, method, best_radius1, ellipse_params
 
 def estimate_distance(pixel_radius):
@@ -949,51 +936,6 @@ def start_save_shot_worker():
         logger.info("[VISION] 存图 worker 线程已启动")
 
 
-def enqueue_save_raw_shot(frame, shot_id=None, photo_dir=None):
-    """
-    异步保存射箭原图（无算法标注）。需 SAVE_IMAGE_ENABLED 且 SAVE_RAW_SHOT_IMAGE_ENABLED。
-    文件名：{photo_dir}/shot_{shot_id}_raw.jpg
-    """
-    if not getattr(config, "SAVE_RAW_SHOT_IMAGE_ENABLED", False):
-        return
-    if not getattr(config, "SAVE_IMAGE_ENABLED", True):
-        return
-    if not shot_id:
-        return
-    if photo_dir is None:
-        photo_dir = config.PHOTO_DIR
-
-    try:
-        img_cv = image.image2cv(frame, False, False)
-        img_copy = np.copy(img_cv)
-    except Exception as e:
-        logger = logger_manager.logger
-        if logger:
-            logger.error(f"[VISION] enqueue_save_raw_shot 复制图像失败: {e}")
-        return
-
-    def _job():
-        try:
-            try:
-                if photo_dir not in os.listdir("/root"):
-                    os.mkdir(photo_dir)
-            except Exception:
-                pass
-            filename = f"{photo_dir}/shot_{shot_id}_raw.jpg"
-            out = image.cv2image(img_copy, False, False)
-            out.save(filename)
-            logger = logger_manager.logger
-            if logger:
-                logger.info(f"[VISION] 已保存射箭原图: {filename}")
-            prune_old_images_in_dir(photo_dir, config.MAX_IMAGES, logger, "[VISION]")
-        except Exception as e:
-            logger = logger_manager.logger
-            if logger:
-                logger.error(f"[VISION] 保存射箭原图失败: {e}")
-
-    threading.Thread(target=_job, daemon=True).start()
-
-
 def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
                       laser_point, distance_m, shot_id=None, photo_dir=None,
                       yolo_roi_xyxy=None):
@@ -1083,63 +1025,3 @@ def detect_target(frame, laser_point=None):
         logger.debug("[VISION] 使用传统黄色靶心检测")
     return detect_circle_v3(frame, laser_point)
 
-
-def sample_target_rgb_at_physical_radius(frame, target_center, target_radius_px, radius_cm=None, angles_deg=None, patch_half_px=None, black_thresh=None, timing=False):
-    """
-    在物方半径位置采样 RGB，判断黑/白靶。
-    返回: dict {ok, is_black, mean_rgb, samples, black_ratio, elapsed_ms}
-    """
-    logger = logger_manager.logger
-    if target_center is None or target_radius_px is None:
-        return {"ok": False, "reason": "no_target", "is_black": None, "elapsed_ms": 0.0}
-
-    radius_cm = float(radius_cm if radius_cm is not None else getattr(config, "TRIANGLE_SAMPLE_RADIUS_CM", 15.0))
-    angles_deg = tuple(angles_deg if angles_deg is not None else getattr(config, "TRIANGLE_SAMPLE_ANGLES_DEG", (0, 90, 180, 270)))
-    patch_half_px = int(patch_half_px if patch_half_px is not None else getattr(config, "TRIANGLE_SAMPLE_PATCH_HALF_PX", 2))
-    black_thresh = float(black_thresh if black_thresh is not None else getattr(config, "TRIANGLE_SAMPLE_BLACK_THRESH", 30.0))
-    timing_on = bool(timing) and bool(getattr(config, "TRIANGLE_SAMPLE_TIMING_ENABLE", True))
-    t0 = time.perf_counter() if timing_on else None
-
-    try:
-        img_cv = image.image2cv(frame, False, False)
-        h, w = img_cv.shape[:2]
-        cx, cy = float(target_center[0]), float(target_center[1])
-        scale = float(target_radius_px) / max(radius_cm, 1e-6)
-        samples = []
-        black_count = 0
-        for ang in angles_deg:
-            rad = math.radians(float(ang))
-            sx = int(round(cx + math.cos(rad) * radius_cm * scale))
-            sy = int(round(cy + math.sin(rad) * radius_cm * scale))
-            x0 = max(0, sx - patch_half_px)
-            y0 = max(0, sy - patch_half_px)
-            x1 = min(w, sx + patch_half_px + 1)
-            y1 = min(h, sy + patch_half_px + 1)
-            if x1 <= x0 or y1 <= y0:
-                continue
-            patch = img_cv[y0:y1, x0:x1]
-            mean_rgb = patch.reshape(-1, 3).mean(axis=0)
-            is_black = bool(np.all(mean_rgb < black_thresh))
-            black_count += 1 if is_black else 0
-            samples.append({"angle": float(ang), "xy": (sx, sy), "mean_rgb": tuple(float(v) for v in mean_rgb), "is_black": is_black})
-        black_ratio = float(black_count) / float(len(samples) or 1)
-        out = {
-            "ok": len(samples) > 0,
-            "is_black": black_ratio >= 0.5,
-            "mean_rgb": tuple(float(v) for v in (np.mean([s["mean_rgb"] for s in samples], axis=0) if samples else (0, 0, 0))),
-            "samples": samples,
-            "black_ratio": black_ratio,
-            "elapsed_ms": (time.perf_counter() - t0) * 1000.0 if timing_on else 0.0,
-        }
-        if logger:
-            logger.info(
-                f"[TRI-SAMPLE] radius_cm={radius_cm:.1f} black_thresh={black_thresh:.1f} "
-                f"black_ratio={black_ratio:.2f} is_black={out['is_black']} "
-                f"elapsed_ms={out['elapsed_ms']:.1f} samples={len(samples)}"
-            )
-        return out
-    except Exception as e:
-        if logger:
-            logger.error(f"[TRI-SAMPLE] 采样失败: {e}")
-        return {"ok": False, "reason": str(e), "is_black": None, "elapsed_ms": 0.0}
-

wifi.py

@@ -41,6 +41,7 @@ class WiFiManager:
         # WiFi 质量监测（后台线程）
         self._wifi_quality_monitor_thread = None
         self._wifi_quality_stop_event = threading.Event()
+        self._wifi_quality_lock = threading.Lock()
         self._last_wifi_rtt_ms = None  # 最近一次测量的 RTT
         self._last_wifi_rssi_dbm = None  # 最近一次测量的 RSSI
         
@@ -238,7 +239,6 @@ class WiFiManager:
         old_conf = _read_text(conf_path)
         old_boot_ssid = _read_text(ssid_file)
         old_boot_pass = _read_text(pass_file)
-        old_boot_wpa = _read_text(boot_wpa_path) if os.path.exists(boot_wpa_path) else None
 
         try:
             try:
@@ -250,9 +250,13 @@ class WiFiManager:
                 _write_text(conf_path, full_conf)
             except Exception:
                 pass
-            _write_text(boot_wpa_path, full_conf)
+            # 删除 wpa_supplicant.conf，让 S30wifi 回退读 ssid/pass
+            try:
+                if os.path.exists(boot_wpa_path):
+                    os.remove(boot_wpa_path)
+            except Exception:
+                pass
 
-            # 仍写入 ssid/pass，便于其它脚本/人工查看；S30wifi 优先使用 wpa_supplicant.conf
             _write_text(ssid_file, ssid.strip())
             _write_text(pass_file, password.strip())
 
@@ -292,7 +296,6 @@ class WiFiManager:
                     if not persist:
                         # 不持久化：把 /boot 恢复成旧值（不重启，当前连接保持不变）
                         _restore_boot(old_boot_ssid, old_boot_pass)
-                        _restore_boot_wpa(old_boot_wpa)
                         self.logger.info("[WIFI] 网络验证通过，但按 persist=False 回滚 /boot 凭证（不重启）")
                     else:
                         self.logger.info("[WIFI] 网络验证通过，/boot 凭证已保留（持久化）")
@@ -306,7 +309,6 @@ class WiFiManager:
         except Exception as e:
             # 失败：回滚 /boot 和 /etc，重启 WiFi 恢复旧网络
             _restore_boot(old_boot_ssid, old_boot_pass)
-            _restore_boot_wpa(old_boot_wpa)
             try:
                 if old_conf is not None:
                     _write_text(conf_path, old_conf)
@@ -351,7 +353,11 @@ class WiFiManager:
             else:
                 full_conf = build_sta_conf_open(ssid)
             _write_text(conf_path, full_conf)
-            _write_text(boot_wpa_path, full_conf)
+            try:
+                if os.path.exists(boot_wpa_path):
+                    os.remove(boot_wpa_path)
+            except Exception:
+                pass
         except ValueError as e:
             return False, str(e)
         except Exception as e:
@@ -542,34 +548,45 @@ 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:
-            self.logger.warning("[WiFi Monitor] 监测线程已在运行")
-            return
+        with self._wifi_quality_lock:
+            if self._wifi_quality_monitor_thread is not None and self._wifi_quality_monitor_thread.is_alive():
+                self.logger.warning("[WiFi Monitor] 监测线程已在运行")
+                return
             
-        self._network_type_callback = network_type_callback
-        self._on_poor_quality_callback = on_poor_quality_callback
-        self._wifi_quality_stop_event.clear()
-        self._wifi_quality_monitor_thread = threading.Thread(
-            target=self._quality_monitor_loop,
-            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._network_type_callback = network_type_callback
+            self._on_poor_quality_callback = on_poor_quality_callback
+            self._wifi_quality_stop_event.clear()
+            self._wifi_quality_monitor_thread = threading.Thread(
+                target=self._quality_monitor_loop,
+                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")
     
     def stop_quality_monitor(self):
         """停止 WiFi 质量监测线程"""
-        if self._wifi_quality_monitor_thread is None:
-            return
+        with self._wifi_quality_lock:
+            t = self._wifi_quality_monitor_thread
+            if t is None:
+                return
+            if not t.is_alive():
+                self._wifi_quality_monitor_thread = None
+                return
         
         self._wifi_quality_stop_event.set()
         try:
-            self._wifi_quality_monitor_thread.join(timeout=2.0)
+            t.join(timeout=2.0)
         except Exception as e:
             self.logger.error(f"[WiFi Monitor] 停止线程失败：{e}")
-        finally:
-            self._wifi_quality_monitor_thread = None
-            self.logger.info("[WiFi Monitor] 已停止后台监测线程")
+        
+        with self._wifi_quality_lock:
+            if t is self._wifi_quality_monitor_thread:
+                if t.is_alive():
+                    self.logger.warning("[WiFi Monitor] 线程未在超时内退出，保留引用防止重复创建")
+                else:
+                    self._wifi_quality_monitor_thread = None
+                    self.logger.info("[WiFi Monitor] 已停止后台监测线程")
     
     def _quality_monitor_loop(self):
         """

yolo_te.py

@@ -1,267 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""Standalone live camera + single YOLO runner.
-
-不复用项目内的 `camera_manager` / `target_roi_yolo` / `config` / `logger_manager`。
-
-功能：
-- 独立初始化摄像头
-- 实时读取帧
-- 独立加载单个 YOLO 模型并推理
-- 画出检测框、ROI、FPS
-
-适用场景：
-- 单独验证一个模型是否能跑
-- 验证实时帧率
-- 验证 ROI 是否裁对
-- 不进入主业务射箭流程
-"""
-
-from __future__ import annotations
-
-import os
-import time
-from dataclasses import dataclass
-
-
-@dataclass
-class RunnerConfig:
-    camera_width: int = 640
-    camera_height: int = 480
-    model_path: str = "/root/model_278702.mud"
-    conf_th: float = 0.7
-    retry_conf_th: float = 0.5
-    class_ids: tuple = (0,)
-    merge_mode: str = "union"
-    coord_mode: str = "native"
-    roi_margin_frac: float = 0.11
-    min_box_side_px: int = 8
-
-
-def log(msg: str):
-    print(msg)
-
-
-class DummyLogger:
-    def info(self, msg):
-        log(msg)
-
-    def warning(self, msg):
-        log(msg)
-
-    def error(self, msg):
-        log(msg)
-
-
-class StandaloneYOLORunner:
-    def __init__(self, cfg: RunnerConfig):
-        self.cfg = cfg
-        self.logger = DummyLogger()
-        self._last_fps_t = time.perf_counter()
-        self._frames = 0
-        self._fps = 0.0
-        self._camera = None
-        self._det = None
-
-    def _import_maix(self):
-        try:
-            from maix import camera, image, nn
-            return camera, image, nn
-        except Exception as e:
-            raise RuntimeError(f"maix import failed: {e}")
-
-    def _init_camera(self):
-        camera, _, _ = self._import_maix()
-        if self._camera is not None:
-            return self._camera
-        try:
-            self._camera = camera.Camera(
-                width=self.cfg.camera_width,
-                height=self.cfg.camera_height,
-                format=camera.RGB888,
-            )
-        except Exception:
-            self._camera = camera.Camera(width=self.cfg.camera_width, height=self.cfg.camera_height)
-        return self._camera
-
-    def _load_detector(self, model_path: str):
-        _, _, nn = self._import_maix()
-        if not model_path or not os.path.isfile(model_path):
-            return None
-        return nn.YOLOv5(model=model_path, dual_buff=False)
-
-    @staticmethod
-    def _get_class_id(obj):
-        for key in ("class_id", "cls", "label", "category", "cat_id", "id"):
-            if hasattr(obj, key):
-                v = getattr(obj, key)
-                if v is None:
-                    continue
-                try:
-                    return int(float(v))
-                except Exception:
-                    pass
-        return None
-
-    @staticmethod
-    def _normalize_boxes(raw):
-        out = []
-        for o in raw or []:
-            if isinstance(o, (list, tuple)) and len(o) >= 6:
-                class Box:
-                    pass
-                b = Box()
-                b.x, b.y, b.w, b.h, b.score, b.class_id = map(float, o[:6])
-                out.append(b)
-            else:
-                out.append(o)
-        return out
-
-    def _det_to_xyxy(self, det, obj):
-        x = float(getattr(obj, "x", 0.0))
-        y = float(getattr(obj, "y", 0.0))
-        w = float(getattr(obj, "w", 0.0))
-        h = float(getattr(obj, "h", 0.0))
-        return x, y, x + w, y + h
-
-    def _run_detector(self, det, img, conf_th, class_ids):
-        if det is None:
-            return []
-        raw = det.detect(img, conf_th=conf_th)
-        objs = self._normalize_boxes(raw if raw is not None else [])
-        out = []
-        for o in objs:
-            cid = self._get_class_id(o)
-            if cid is not None and cid not in class_ids:
-                continue
-            out.append(o)
-        return out
-
-    def _calc_fps(self):
-        self._frames += 1
-        now = time.perf_counter()
-        dt = now - self._last_fps_t
-        if dt >= 1.0:
-            self._fps = self._frames / dt
-            self._frames = 0
-            self._last_fps_t = now
-        return self._fps
-
-    def _draw_text(self, img, lines):
-        try:
-            import cv2
-            y = 24
-            for line in lines:
-                cv2.putText(img, line, (10, y), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 1, cv2.LINE_AA)
-                y += 20
-        except Exception:
-            pass
-
-    def _clip_roi(self, x0, y0, x1, y1, w, h):
-        x0 = max(0, min(int(x0), w - 1))
-        y0 = max(0, min(int(y0), h - 1))
-        x1 = max(x0 + 1, min(int(x1), w))
-        y1 = max(y0 + 1, min(int(y1), h))
-        return x0, y0, x1, y1
-
-    def _merge_boxes(self, boxes):
-        if not boxes:
-            return None
-        x0 = min(b[0] for b in boxes)
-        y0 = min(b[1] for b in boxes)
-        x1 = max(b[2] for b in boxes)
-        y1 = max(b[3] for b in boxes)
-        return x0, y0, x1, y1
-
-    def _run_single_yolo(self, frame, img_cv):
-        h, w = int(img_cv.shape[0]), int(img_cv.shape[1])
-        if self._det is None:
-            self._det = self._load_detector(self.cfg.model_path)
-        det = self._det
-        if det is None:
-            return []
-
-        boxes = self._run_detector(det, frame, self.cfg.conf_th, self.cfg.class_ids)
-        if not boxes and self.cfg.retry_conf_th < self.cfg.conf_th:
-            boxes = self._run_detector(det, frame, self.cfg.retry_conf_th, self.cfg.class_ids)
-
-        xyxy = []
-        for obj in boxes:
-            x0, y0, x1, y1 = self._det_to_xyxy(det, obj)
-            if (x1 - x0) < self.cfg.min_box_side_px or (y1 - y0) < self.cfg.min_box_side_px:
-                continue
-            if self.cfg.coord_mode == "native":
-                x0, y0, x1, y1 = self._clip_roi(x0, y0, x1, y1, w, h)
-            xyxy.append((x0, y0, x1, y1))
-        return xyxy
-
-    def run(self):
-        _, image, _ = self._import_maix()
-        cam = self._init_camera()
-        log("[YOLOTE] standalone runner started")
-
-        while True:
-            try:
-                frame = cam.read()
-            except Exception as e:
-                log(f"[YOLOTE] camera read failed: {e}")
-                time.sleep(0.02)
-                continue
-
-            if frame is None:
-                time.sleep(0.01)
-                continue
-
-            try:
-                img_cv = image.image2cv(frame, False, False)
-            except Exception as e:
-                log(f"[YOLOTE] image2cv failed: {e}")
-                time.sleep(0.01)
-                continue
-
-            import cv2
-            t0 = time.perf_counter()
-            boxes = self._run_single_yolo(frame, img_cv)
-            t1 = time.perf_counter()
-
-            for i, (bx0, by0, bx1, by1) in enumerate(boxes):
-                cv2.rectangle(img_cv, (int(bx0), int(by0)), (int(bx1) - 1, int(by1) - 1), (0, 255, 0), 2)
-                cv2.putText(img_cv, f"B{i}", (int(bx0), max(0, int(by0) - 4)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, cv2.LINE_AA)
-
-            fps = self._calc_fps()
-            self._draw_text(
-                img_cv,
-                [
-                    f"FPS: {fps:.1f}",
-                    f"YOLO: {(t1 - t0)*1000.0:.1f} ms",
-                    f"Boxes: {len(boxes)}",
-                    "Ctrl+C to exit",
-                ],
-            )
-
-            try:
-                frame_out = image.cv2image(img_cv, False, False)
-                if hasattr(cam, "show"):
-                    cam.show(frame_out)
-                else:
-                    try:
-                        frame_out.show()
-                    except Exception:
-                        pass
-            except Exception as e:
-                log(f"[YOLOTE] show failed: {e}")
-
-            time.sleep(0.001)
-
-
-def main():
-    cfg = RunnerConfig()
-    runner = StandaloneYOLORunner(cfg)
-    try:
-        runner.run()
-    except KeyboardInterrupt:
-        log("[YOLOTE] interrupted")
-
-
-if __name__ == "__main__":
-    main()