feat: 根据激光找出图片中心点坐标

.gitignore

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+/cpp_ext/build/
+/.cursor/
+/dist/

.idea/.gitignore

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+# 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

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+network.py

.idea/archery.iml

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+<?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

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?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

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

4g.py

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+# from maix import app, key, uart, pinmap, time
+# import hashlib
+# import hmac
+# import ujson
+
+# # 配置 UART2（用于 HTTP 上传）
+# pinmap.set_pin_function("A29", "UART2_RX")
+# pinmap.set_pin_function("A28", "UART2_TX")
+# http_serial = uart.UART("/dev/ttyS2", 115200, uart.BITS.BITS_8,
+#                         uart.PARITY.PARITY_NONE, uart.STOP.STOP_1)
+
+# # 按键初始化
+# key_triggered = False
+# def on_key_event(key_id, state):
+#     global key_triggered
+#     if state == key.State.KEY_PRESSED:
+#         key_triggered = True
+# key_obj = key.Key(on_key_event)
+
+# # Token生成
+# def generate_token(device_id):
+#     SALT = "shootMessageFire"
+#     SALT2 = "shoot"
+#     return "Arrow_" + hmac.new((SALT + device_id).encode(), SALT2.encode(), hashlib.sha256).hexdigest()
+
+# # 发送AT命令
+# http_instance_id = -1
+# def send_cmd(cmd_str, expect_create_id=False):
+#     global http_instance_id
+#     print("[AT指令] =>", cmd_str)
+#     http_serial.write((cmd_str + "\r\n").encode())
+#     buffer = b""
+#     start = time.ticks_ms()
+#     while time.ticks_ms() - start < 3000:
+#         data = http_serial.read(128)
+#         if data:
+#             buffer += data
+#             try:
+#                 decoded = buffer.decode()
+#                 print("返回:", decoded.strip())
+#                 if expect_create_id and "+MHTTPCREATE:" in decoded:
+#                     http_instance_id = int(decoded.split(":")[1].split("\r")[0].strip())
+#                 if "OK" in decoded: return True
+#                 if "+CME ERROR" in decoded or "ERROR" in decoded: return False
+#             except:
+#                 print("解码异常")
+#         time.sleep_ms(10)
+#     return False
+
+# def create_http_instance(url):
+#     return send_cmd(f'AT+MHTTPCREATE="{url}"', True) and http_instance_id != -1
+
+# def send_data(instance_id, token, api_path, json_data):
+#     # 设置Header（使用统一的AT+MHTTPCFG="header"）
+#     send_cmd(f'AT+MHTTPCFG="header",{instance_id},"Content-Type: application/json"')
+#     send_cmd(f'AT+MHTTPCFG="header",{instance_id},"Authorization: {token}"')
+#     send_cmd(f'AT+MHTTPCFG="header",{instance_id},"DeviceId: {device_id}"')
+    
+#     # 发送Body数据
+#     json_str = ujson.dumps(json_data)
+#     send_cmd(f'AT+MHTTPCONTENT={instance_id},0,0,"{json_str}"')
+#     send_cmd(f'AT+MHTTPREQUEST={instance_id},2,0,"{api_path}"')
+
+# def read_response(timeout_ms=5000):
+#     start = time.ticks_ms()
+#     while time.ticks_ms() - start < timeout_ms:
+#         data = http_serial.read(128)
+#         if data:
+#             try:
+#                 print("响应:", data.decode("utf-8").strip())
+#             except:
+#                 print("响应(raw):", data)
+#         time.sleep_ms(50)
+
+# # 参数配置
+# device_id = "wZhC7kAZ" #需要根据实际硬件ID来测试
+# url = "http://ws.shelingxingqiu.com"
+# api_path = "/home/shoot/device_fire/arrow/fire"
+# token = generate_token(device_id)
+# print("生成Token:", token)
+
+# # 主循环：仅监听按键并上传模拟数据
+# while not app.need_exit():
+#     if key_triggered:
+#         key_triggered = False
+#         print("按键按下，准备上传...")
+
+#         # 模拟数据（可替换为实际测量值）
+#         timestamp = int(time.time() * 1000)
+#         json_data = {
+#             "id": timestamp,
+#             "DeviceId": device_id,
+#             "x": 12.34,
+#             "y": -5.67,
+#             "rag": 90.0,
+#             "time": timestamp,
+#             "dst": 234.5,
+#             "battery": 80,
+#             "errorCode": 0
+#         }
+
+#         if http_instance_id == -1 and not create_http_instance(url):
+#             print("创建 HTTP 实例失败")
+#         elif http_instance_id != -1:
+#             send_data(http_instance_id, token, api_path, json_data)
+#             read_response()
+#     else:
+#         time.sleep_ms(100)
+from maix import app, uart, pinmap, time
+import hashlib
+import hmac
+import ujson
+
+# ========== 配置 ==========
+# UART2 for HTTP
+pinmap.set_pin_function("A29", "UART2_RX")
+pinmap.set_pin_function("A28", "UART2_TX")
+http_serial = uart.UART("/dev/ttyS2", 115200, uart.BITS.BITS_8,
+                        uart.PARITY.PARITY_NONE, uart.STOP.STOP_1)
+
+# 设备参数
+device_id = "wZhC7kAZ"
+url = "http://ws.shelingxingqiu.com"
+api_path = "/home/shoot/device_fire/arrow/fire"
+
+# ========== 工具函数 ==========
+def generate_token(device_id):
+    SALT = "shootMessageFire"
+    SALT2 = "shoot"
+    return "Arrow_" + hmac.new((SALT + device_id).encode(), SALT2.encode(), hashlib.sha256).hexdigest()
+
+def send_cmd(cmd_str, timeout_ms=3000):
+    """发送 AT 指令并等待 OK / ERROR"""
+    print("[AT] =>", cmd_str)
+    http_serial.write((cmd_str + "\r\n").encode())
+    buffer = b""
+    start = time.ticks_ms()
+    while time.ticks_ms() - start < timeout_ms:
+        data = http_serial.read(128)
+        if data:
+            buffer += data
+            try:
+                decoded = buffer.decode()
+                print("<= ", decoded.strip())
+                if "OK" in decoded:
+                    return True
+                if "+CME ERROR" in decoded or "ERROR" in decoded:
+                    return False
+            except:
+                pass
+        time.sleep_ms(10)
+    return False
+
+def create_http_instance(url):
+    cmd = f'AT+MHTTPCREATE="{url}"'
+    if send_cmd(cmd):
+        # 尝试提取 instance ID（如果模块返回）
+        # 注意：部分模块不会返回 ID，可忽略，直接用 0 或 1
+        return True
+    return False
+
+def send_http_request(url, api_path, token, device_id, json_data):
+    # 1. 创建 HTTP 实例
+    if not create_http_instance(url):
+        print("❌ 创建 HTTP 实例失败")
+        return False
+
+    # 2. 设置 Headers（假设实例 ID 为 0，或根据模块默认）
+    instance_id = 0  # 大多数模块默认实例为 0；若支持多实例，需解析返回值
+    send_cmd(f'AT+MHTTPCFG="header",{instance_id},"Content-Type: application/json"')
+    send_cmd(f'AT+MHTTPCFG="header",{instance_id},"Authorization: {token}"')
+    send_cmd(f'AT+MHTTPCFG="header",{instance_id},"DeviceId: {device_id}"')
+
+    # 3. 发送 Body
+    json_str = ujson.dumps(json_data)
+    send_cmd(f'AT+MHTTPCONTENT={instance_id},0,0,"{json_str}"')
+
+    # 4. 发起 POST 请求
+    if send_cmd(f'AT+MHTTPREQUEST={instance_id},2,0,"{api_path}"'):
+        print("✅ HTTP 请求已发送")
+        return True
+    else:
+        print("❌ 发送请求失败")
+        return False
+
+def read_response(timeout_ms=5000):
+    print("⏳ 等待响应...")
+    start = time.ticks_ms()
+    while time.ticks_ms() - start < timeout_ms:
+        data = http_serial.read(128)
+        if data:
+            try:
+                print("📡 响应:", data.decode("utf-8").strip())
+            except:
+                print("📡 响应(raw):", data)
+        time.sleep_ms(100)
+
+# ========== 主程序：直接上传 ==========
+print("🚀 启动直接上传流程...")
+
+token = generate_token(device_id)
+print("🔑 Token:", token)
+
+# 构造模拟数据
+timestamp = int(time.time() * 1000)
+json_data = {
+    "id": timestamp,
+    "DeviceId": device_id,
+    "x": 12.34,
+    "y": -5.67,
+    "rag": 90.0,
+    "time": timestamp,
+    "dst": 234.5,
+    "battery": 80,
+    "errorCode": 0
+}
+
+# 执行上传
+if send_http_request(url, api_path, token, device_id, json_data):
+    read_response()
+else:
+    print("💥 上传流程失败")
+
+print("🔚 程序结束")

I2C.py

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+from maix import i2c, pinmap, time
+
+# 配置 I2C1 引脚（请根据实际连接修改）
+pinmap.set_pin_function("P18", "I2C1_SCL")
+pinmap.set_pin_function("P21", "I2C1_SDA")
+
+bus = i2c.I2C(1, i2c.Mode.MASTER)
+
+INA226_ADDR = 0x40
+
+# 寄存器定义
+REG_CONFIGURATION = 0x00
+REG_BUS_VOLTAGE = 0x02
+REG_CALIBRATION = 0x05
+
+# 校准值（不读取电流/功率时也可以省略）
+CALIBRATION_VALUE = 0x1400
+
+def write_register(reg, value):
+    data = [(value >> 8) & 0xFF, value & 0xFF]
+    bus.writeto_mem(INA226_ADDR, reg, bytes(data))
+
+def read_register(reg):
+    data = bus.readfrom_mem(INA226_ADDR, reg, 2)
+    return (data[0] << 8) | data[1]
+
+def init_ina226():
+    write_register(REG_CONFIGURATION, 0x4527)
+    write_register(REG_CALIBRATION, CALIBRATION_VALUE)
+
+def get_bus_voltage():
+    raw = read_register(REG_BUS_VOLTAGE)
+    return raw * 1.25 / 1000  # 单位 V
+
+def voltage_to_percent(voltage):
+    if voltage >= 4.20:
+        return 100
+    elif voltage >= 4.15:
+        return 95
+    elif voltage >= 4.10:
+        return 90
+    elif voltage >= 4.05:
+        return 85
+    elif voltage >= 4.00:
+        return 80
+    elif voltage >= 3.95:
+        return 75
+    elif voltage >= 3.90:
+        return 70
+    elif voltage >= 3.85:
+        return 65
+    elif voltage >= 3.80:
+        return 60
+    elif voltage >= 3.75:
+        return 55
+    elif voltage >= 3.70:
+        return 50
+    elif voltage >= 3.65:
+        return 45
+    elif voltage >= 3.60:
+        return 40
+    elif voltage >= 3.55:
+        return 35
+    elif voltage >= 3.50:
+        return 30
+    elif voltage >= 3.45:
+        return 25
+    elif voltage >= 3.40:
+        return 20
+    elif voltage >= 3.35:
+        return 15
+    elif voltage >= 3.30:
+        return 10
+    elif voltage >= 3.20:
+        return 5
+    else:
+        return 0
+
+# 初始化 INA226
+init_ina226()
+
+# 主循环，只显示电量百分比
+while True:
+    voltage = get_bus_voltage()
+    battery_percent = voltage_to_percent(voltage)
+    print(f"当前电压: {voltage:.3f} V")
+    print(f"估算电池电量: {battery_percent} %\n")
+    time.sleep(2000)

README.md

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+
+# 🎯 激光射击系统（双版本）
+
+适用于 **MaixPy** 平台，支持远程控制、电池监测、Wi-Fi 连接及 OTA 升级。
+
+提供两个独立实现版本，共享相同网络协议与 OTA 机制，便于统一部署管理：
+
+- `main.py`：**视觉测距版**
+- `laser.py`：**激光测距版**
+
+---
+
+## 📁 项目结构
+
+```
+laser_shooting_system/
+├── README.md
+├── main.py       # 视觉测距版主程序
+└── laser.py      # 激光测距版主程序
+```
+
+---
+
+## ⚙️ 硬件依赖
+
+| 版本       | 必需硬件                                   |
+|------------|----------------------------------------|
+| `main.py`  | Maix 系列开发板 + 摄像头 + 其他硬件                |
+| `laser.py` | Maix 系列开发板 + 激光测距模块（I²C） + 摄像头 + 其他硬件） |
+
+> 💡 **注意：引脚复用风险**  
+> Maix 开发板部分 GPIO 兼容多协议（如 Wi-Fi / I²C 复用 A15/A27）。  
+> **Wi-Fi 初始化前禁止提前配置 I²C 引脚！**
+
+### ❗ 关键提示
+
+| 问题场景 | 后果 |
+|---------|------|
+| 提前初始化 I²C | Wi-Fi 初始化失败、OTA 中断、系统重启 |
+
+✅ **正确做法：**
+
+- **`main.py`（视觉版）&`laser.py`（激光版）**  
+  使用wifi时启用下面代码，注释与WiFi复用的：
+  ```python
+  # 以下代码（如有，请启用）：
+  # pinmap.set_pin_function("A15", "I2C5_SCL")
+  # pinmap.set_pin_function("A27", "I2C5_SDA")
+  ```
+
+
+
+
+## 📡 网络通信协议（TCP / JSON）
+
+设备上电后自动连预设服务器，支持以下指令：
+
+```json
+{"data": {"cmd": N, "ssid": "...", "password": "..."}}
+```
+
+| `cmd` | 参数                | 功能说明                     |
+|-------|---------------------|------------------------------|
+| 2     | —                   | 开启激光校准模式             |
+| 3     | —                   | 关闭激光                     |
+| 4     | —                   | 查询电池电量 & 电压          |
+| 5     | `ssid`, `password`  | 配置 Wi-Fi + 触发 OTA 升级   |
+| 6     | —                   | 返回当前 IP 地址             |
+| 7     | —                   | 已联网时，直接执行 OTA 下载  |
+
+### 示例交互
+
+▶️ 下发指令（服务器 → 设备）：
+```json
+{"data": {"cmd": 6}}
+```
+
+◀️ 设备响应（设备 → 服务器）：
+```json
+{"result": "current_ip", "ip": "192.168.1.105"}
+```
+
+---
+
+## 🛠️ 部署步骤
+
+1. **选择版本**
+   - 固定场景 / 低成本 → `main.py`
+   - 高精度需求 → `laser.py`
+
+2. **烧录程序**
+   - 将选定文件重命名为 `main.py`，或通过 MaixPy IDE 直接运行
+
+3. **首次配置 Wi-Fi**
+   - 串口下发，或服务器推送 `cmd=5`：
+     ```json
+     {"data": {"cmd": 5, "ssid": "YourWiFi", "password": "12345678"}}
+     ```
+
+4. **后续 OTA 升级**
+   - 确保设备在线后，下发 `cmd=7` 即可触发 OTA
+
+---
+
+## 📝 注意事项
+
+- 🔗 **OTA 地址**：由全局变量 `url` 定义，部署前务必修改为实际地址  
+- 🧵 **线程安全**：通过 `update_thread_started` 标志防止 OTA 并发下载  
+- ☀️ **视觉版**：光照敏感，建议在均匀光源环境使用  
+- 📏 **激光版**：确认模块 I²C 地址（默认 `0x29`），避免长线干扰  
+- 🌐 **网络操作**：均在子线程执行，主线程保持实时响应
+
+## 🔧 打包步骤（命令行）
+- 以t11的名称打包，或者修改代码升级路径
+---
+> 文档版本：v1.2  
+> 更新时间：2025-11-21  
+> 维护人：ZZH
+```
+

S99archery

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+#!/bin/sh
+# /etc/init.d/S99archery
+# 系统启动时处理致命错误恢复（仅处理无法启动的情况）
+# 注意：应用的启动由系统自动启动机制处理（通过 auto_start.txt）
+# 功能：
+# 1. 处理致命错误（无法启动）- 恢复 main.py
+# 2. 如果重启次数超过阈值，恢复 main.py 并重启系统
+
+APP_DIR="/maixapp/apps/t11"
+MAIN_PY="$APP_DIR/main.py"
+PENDING_FILE="$APP_DIR/ota_pending.json"
+BACKUP_BASE="$APP_DIR/backups"
+
+# 进入应用目录
+cd "$APP_DIR" || exit 0
+
+# 检查 pending 文件，如果存在且超过重启次数，恢复 main.py（处理致命错误）
+if [ -f "$PENDING_FILE" ]; then
+    echo "[S99] 检测到 ota_pending.json，检查重启计数..."
+    
+    # 尝试从JSON中提取重启计数（使用grep简单提取）
+    RESTART_COUNT=$(cat "$PENDING_FILE" 2>/dev/null | grep -o '"restart_count":[0-9]*' | grep -o '[0-9]*' || echo "0")
+    MAX_RESTARTS=$(cat "$PENDING_FILE" 2>/dev/null | grep -o '"max_restarts":[0-9]*' | grep -o '[0-9]*' || echo "3")
+    
+    if [ -n "$RESTART_COUNT" ] && [ "$RESTART_COUNT" -ge "$MAX_RESTARTS" ]; then
+        echo "[S99] 检测到重启次数 ($RESTART_COUNT) 超过阈值 ($MAX_RESTARTS)，恢复 main.py..."
+        
+        # 尝试从JSON中提取备份目录
+        BACKUP_DIR=$(cat "$PENDING_FILE" 2>/dev/null | grep -o '"backup_dir":"[^"]*"' | grep -o '/[^"]*' || echo "")
+        
+        if [ -n "$BACKUP_DIR" ] && [ -f "$BACKUP_DIR/main.py" ]; then
+            # 使用指定的备份目录
+            echo "[S99] 从备份目录恢复: $BACKUP_DIR/main.py"
+            cp "$BACKUP_DIR/main.py" "$MAIN_PY" 2>/dev/null && echo "[S99] 已恢复 main.py"
+        else
+            # 查找最新的备份目录
+            LATEST_BACKUP=$(ls -dt "$BACKUP_BASE"/backup_* 2>/dev/null | head -1)
+            if [ -n "$LATEST_BACKUP" ] && [ -f "$LATEST_BACKUP/main.py" ]; then
+                echo "[S99] 从最新备份恢复: $LATEST_BACKUP/main.py"
+                cp "$LATEST_BACKUP/main.py" "$MAIN_PY" 2>/dev/null && echo "[S99] 已恢复 main.py"
+            else
+                # 如果没有备份目录，尝试使用 main.py.bak
+                if [ -f "$APP_DIR/main.py.bak" ]; then
+                    echo "[S99] 从 main.py.bak 恢复"
+                    cp "$APP_DIR/main.py.bak" "$MAIN_PY" 2>/dev/null && echo "[S99] 已恢复 main.py"
+                fi
+            fi
+        fi
+        
+        # 恢复后重置重启计数，避免循环恢复
+        # 注意：不在这里删除 pending 文件，让 main.py 在心跳成功后删除
+        # 但是重置重启计数，以便恢复后的版本可以重新开始计数
+        python3 -c "
+import json, os
+try:
+    pending_path = '$PENDING_FILE'
+    if os.path.exists(pending_path):
+        with open(pending_path, 'r', encoding='utf-8') as f:
+            d = json.load(f)
+        d['restart_count'] = 0  # 重置重启计数
+        with open(pending_path, 'w', encoding='utf-8') as f:
+            json.dump(d, f)
+        print('[S99] 已重置重启计数为 0')
+except Exception as e:
+    print(f'[S99] 重置重启计数失败: {e}')
+" 2>/dev/null || echo "[S99] 无法重置重启计数（可能需要Python支持）"
+        
+        echo "[S99] 已恢复 main.py，重启系统..."
+        echo "[S99] 注意：pending 文件将在心跳成功后由 main.py 删除"
+        sleep 2
+        reboot
+        exit 0
+    fi
+fi
+
+# 不启动应用，让系统自动启动机制处理
+# 这个脚本只负责处理致命错误恢复
+exit 0
+

Untitled

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

adc.py

@@ -0,0 +1,16 @@
+from maix.peripheral import adc
+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}")
+    time.sleep_ms(1)
+
+    vol = int(a.read_vol() * 10) / 10
+    print(f"ADC vol:{vol:.1f}, {time.time():.4f}")

app.yaml

@@ -1,6 +1,6 @@
 id: t11
 name: t11
-version: 2.15.35
+version: 2.1.1
 author: t11
 icon: ''
 desc: t11
@@ -12,18 +12,18 @@ files:
   - at_client.py
   - camera_manager.py
   - cameraParameters.xml
-  - charging_exit.sh
   - config.py
   - hardware.py
-  - laser_detector.py
   - laser_manager.py
   - logger_manager.py
   - main.py
+  - model_270139.cvimodel
+  - model_270139.mud
   - network.py
-  - ota_curl.sh
   - ota_manager.py
   - power.py
   - server.pem
+  - set_autostart.py
   - shoot_manager.py
   - shot_id_generator.py
   - target_roi_yolo.py

aruco_detector.py

@@ -0,0 +1,420 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+ArUco标记检测模块
+提供基于ArUco标记的靶心标定和激光点定位功能
+"""
+import cv2
+import numpy as np
+import math
+import config
+from logger_manager import logger_manager
+
+
+class ArUcoDetector:
+    """ArUco标记检测器"""
+    
+    def __init__(self):
+        self.logger = logger_manager.logger
+        # 创建ArUco字典和检测器参数
+        self.aruco_dict = cv2.aruco.getPredefinedDictionary(config.ARUCO_DICT_TYPE)
+        self.detector_params = cv2.aruco.DetectorParameters()
+        
+        # 设置检测参数
+        self.detector_params.minMarkerPerimeterRate = config.ARUCO_MIN_MARKER_PERIMETER_RATE
+        self.detector_params.cornerRefinementMethod = config.ARUCO_CORNER_REFINEMENT_METHOD
+        
+        # 创建检测器
+        self.detector = cv2.aruco.ArucoDetector(self.aruco_dict, self.detector_params)
+        
+        # 预定义靶纸上的标记位置（物理坐标，毫米）
+        self.marker_positions_mm = config.ARUCO_MARKER_POSITIONS_MM
+        self.marker_ids = config.ARUCO_MARKER_IDS
+        self.marker_size_mm = config.ARUCO_MARKER_SIZE_MM
+        self.target_paper_size_mm = config.TARGET_PAPER_SIZE_MM
+        
+        # 靶心偏移（相对于靶纸中心）
+        self.target_center_offset_mm = config.TARGET_CENTER_OFFSET_MM
+        
+        if self.logger:
+            self.logger.info(f"[ARUCO] ArUco检测器初始化完成，字典类型: {config.ARUCO_DICT_TYPE}")
+    
+    def detect_markers(self, frame):
+        """
+        检测图像中的ArUco标记
+        
+        Args:
+            frame: MaixPy图像帧对象
+            
+        Returns:
+            (corners, ids, rejected) - 检测到的标记角点、ID列表、被拒绝的候选
+            如果检测失败返回 (None, None, None)
+        """
+        try:
+            # 转换为OpenCV格式
+            from maix import image
+            img_cv = image.image2cv(frame, False, False)
+            
+            # 转换为灰度图（ArUco检测需要）
+            if len(img_cv.shape) == 3:
+                gray = cv2.cvtColor(img_cv, cv2.COLOR_RGB2GRAY)
+            else:
+                gray = img_cv
+            
+            # 检测标记
+            corners, ids, rejected = self.detector.detectMarkers(gray)
+            
+            if self.logger and ids is not None:
+                self.logger.debug(f"[ARUCO] 检测到 {len(ids)} 个标记: {ids.flatten().tolist()}")
+            
+            return corners, ids, rejected
+            
+        except Exception as e:
+            if self.logger:
+                self.logger.error(f"[ARUCO] 标记检测失败: {e}")
+            return None, None, None
+    
+    def get_target_center_from_markers(self, corners, ids):
+        """
+        从检测到的ArUco标记计算靶心位置
+        
+        Args:
+            corners: 标记角点列表
+            ids: 标记ID列表
+            
+        Returns:
+            (center_x, center_y, radius, ellipse_params) 或 (None, None, None, None)
+            center_x, center_y: 靶心像素坐标
+            radius: 估计的靶心半径（像素）
+            ellipse_params: 椭圆参数用于透视校正
+        """
+        if ids is None or len(ids) < 3:
+            if self.logger:
+                self.logger.debug(f"[ARUCO] 检测到的标记数量不足: {len(ids) if ids is not None else 0} < 3")
+            return None, None, None, None
+        
+        try:
+            # 将ID转换为列表便于查找
+            detected_ids = ids.flatten().tolist()
+            
+            # 收集检测到的标记中心点和对应的物理坐标
+            image_points = []  # 图像坐标 (像素)
+            object_points = []  # 物理坐标 (毫米)
+            marker_centers = {}  # 存储每个标记的中心
+            
+            for i, marker_id in enumerate(detected_ids):
+                if marker_id not in self.marker_ids:
+                    continue
+                
+                # 计算标记中心（四个角的平均值）
+                corner = corners[i][0]  # shape: (4, 2)
+                center_x = np.mean(corner[:, 0])
+                center_y = np.mean(corner[:, 1])
+                marker_centers[marker_id] = (center_x, center_y)
+                
+                # 添加到点列表
+                image_points.append([center_x, center_y])
+                object_points.append(self.marker_positions_mm[marker_id])
+            
+            if len(image_points) < 3:
+                if self.logger:
+                    self.logger.debug(f"[ARUCO] 有效标记数量不足: {len(image_points)} < 3")
+                return None, None, None, None
+            
+            # 转换为numpy数组
+            image_points = np.array(image_points, dtype=np.float32)
+            object_points = np.array(object_points, dtype=np.float32)
+            
+            # 计算单应性矩阵（Homography）
+            # 这建立了物理坐标到图像坐标的映射
+            H, status = cv2.findHomography(object_points, image_points, cv2.RANSAC, 5.0)
+            
+            if H is None:
+                if self.logger:
+                    self.logger.warning("[ARUCO] 无法计算单应性矩阵")
+                return None, None, None, None
+            
+            # 计算靶心在图像中的位置
+            # 靶心物理坐标 = 靶纸中心 + 偏移
+            target_center_mm = np.array([[self.target_center_offset_mm[0], 
+                                          self.target_center_offset_mm[1]]], dtype=np.float32)
+            target_center_mm = target_center_mm.reshape(-1, 1, 2)
+            
+            # 使用单应性矩阵投影到图像坐标
+            target_center_img = cv2.perspectiveTransform(target_center_mm, H)
+            center_x = target_center_img[0][0][0]
+            center_y = target_center_img[0][0][1]
+            
+            # 计算靶心半径（像素）
+            # 使用已知物理距离和像素距离的比例
+            # 选择两个标记计算比例尺
+            if len(marker_centers) >= 2:
+                # 使用对角线上的标记计算比例尺
+                if 0 in marker_centers and 2 in marker_centers:
+                    p1_img = np.array(marker_centers[0])
+                    p2_img = np.array(marker_centers[2])
+                    p1_mm = np.array(self.marker_positions_mm[0])
+                    p2_mm = np.array(self.marker_positions_mm[2])
+                elif 1 in marker_centers and 3 in marker_centers:
+                    p1_img = np.array(marker_centers[1])
+                    p2_img = np.array(marker_centers[3])
+                    p1_mm = np.array(self.marker_positions_mm[1])
+                    p2_mm = np.array(self.marker_positions_mm[3])
+                else:
+                    # 使用任意两个标记
+                    keys = list(marker_centers.keys())
+                    p1_img = np.array(marker_centers[keys[0]])
+                    p2_img = np.array(marker_centers[keys[1]])
+                    p1_mm = np.array(self.marker_positions_mm[keys[0]])
+                    p2_mm = np.array(self.marker_positions_mm[keys[1]])
+                
+                pixel_distance = np.linalg.norm(p1_img - p2_img)
+                mm_distance = np.linalg.norm(p1_mm - p2_mm)
+                
+                if mm_distance > 0:
+                    pixels_per_mm = pixel_distance / mm_distance
+                    # 标准靶心半径：10环半径约1.22cm = 12.2mm
+                    # 但这里我们返回一个估计值，实际环数计算在laser_manager中
+                    radius_mm = 122.0  # 整个靶纸的半径约200mm，但靶心区域较小
+                    radius = int(radius_mm * pixels_per_mm)
+                else:
+                    radius = 100  # 默认值
+            else:
+                radius = 100  # 默认值
+            
+            # 计算椭圆参数（用于透视校正）
+            # 从单应性矩阵可以推导出透视变形
+            ellipse_params = self._compute_ellipse_params(H, center_x, center_y)
+            
+            if self.logger:
+                self.logger.info(f"[ARUCO] 靶心计算成功: 中心=({center_x:.1f}, {center_y:.1f}), "
+                               f"半径={radius}px, 检测到{len(marker_centers)}个标记")
+            
+            return (int(center_x), int(center_y)), radius, "aruco", ellipse_params
+            
+        except Exception as e:
+            if self.logger:
+                self.logger.error(f"[ARUCO] 计算靶心失败: {e}")
+                import traceback
+                self.logger.error(traceback.format_exc())
+            return None, None, None, None
+    
+    def _compute_ellipse_params(self, H, center_x, center_y):
+        """
+        从单应性矩阵计算椭圆参数，用于透视校正
+        
+        Args:
+            H: 单应性矩阵 (3x3)
+            center_x, center_y: 靶心图像坐标
+            
+        Returns:
+            ellipse_params: ((center_x, center_y), (width, height), angle)
+        """
+        try:
+            # 在物理坐标系中画一个圆，投影到图像中看变成什么形状
+            # 物理圆：半径10mm
+            r_mm = 10.0
+            angles = np.linspace(0, 2*np.pi, 16)
+            circle_mm = np.array([[self.target_center_offset_mm[0] + r_mm * np.cos(a),
+                                   self.target_center_offset_mm[1] + r_mm * np.sin(a)] 
+                                  for a in angles], dtype=np.float32)
+            circle_mm = circle_mm.reshape(-1, 1, 2)
+            
+            # 投影到图像
+            circle_img = cv2.perspectiveTransform(circle_mm, H)
+            circle_img = circle_img.reshape(-1, 2)
+            
+            # 拟合椭圆
+            if len(circle_img) >= 5:
+                ellipse = cv2.fitEllipse(circle_img.astype(np.float32))
+                return ellipse
+            else:
+                # 从单应性矩阵近似估计
+                # 提取缩放和旋转
+                # H = K * [R|t] 的近似
+                # 这里简化处理：假设没有严重变形
+                scale_x = np.linalg.norm(H[0, :2])
+                scale_y = np.linalg.norm(H[1, :2])
+                avg_scale = (scale_x + scale_y) / 2
+                
+                width = r_mm * 2 * scale_x
+                height = r_mm * 2 * scale_y
+                angle = np.degrees(np.arctan2(H[1, 0], H[0, 0]))
+                
+                return ((center_x, center_y), (width, height), angle)
+                
+        except Exception as e:
+            if self.logger:
+                self.logger.debug(f"[ARUCO] 计算椭圆参数失败: {e}")
+            return None
+    
+    def transform_laser_point(self, laser_point, corners, ids):
+        """
+        将激光点从图像坐标转换到物理坐标（毫米），再计算相对于靶心的偏移
+        
+        Args:
+            laser_point: (x, y) 激光点在图像中的坐标
+            corners: 检测到的标记角点
+            ids: 检测到的标记ID
+            
+        Returns:
+            (dx_mm, dy_mm) 激光点相对于靶心的偏移（毫米），或 (None, None)
+        """
+        if laser_point is None or ids is None or len(ids) < 3:
+            return None, None
+        
+        try:
+            # 重新计算单应性矩阵（可以优化为缓存）
+            detected_ids = ids.flatten().tolist()
+            image_points = []
+            object_points = []
+            
+            for i, marker_id in enumerate(detected_ids):
+                if marker_id not in self.marker_ids:
+                    continue
+                corner = corners[i][0]
+                center_x = np.mean(corner[:, 0])
+                center_y = np.mean(corner[:, 1])
+                image_points.append([center_x, center_y])
+                object_points.append(self.marker_positions_mm[marker_id])
+            
+            if len(image_points) < 3:
+                return None, None
+            
+            image_points = np.array(image_points, dtype=np.float32)
+            object_points = np.array(object_points, dtype=np.float32)
+            
+            H, _ = cv2.findHomography(object_points, image_points, cv2.RANSAC, 5.0)
+            if H is None:
+                return None, None
+            
+            # 求逆矩阵，将图像坐标转换到物理坐标
+            H_inv = np.linalg.inv(H)
+            
+            laser_img = np.array([[laser_point[0], laser_point[1]]], dtype=np.float32)
+            laser_img = laser_img.reshape(-1, 1, 2)
+            
+            laser_mm = cv2.perspectiveTransform(laser_img, H_inv)
+            laser_x_mm = laser_mm[0][0][0]
+            laser_y_mm = laser_mm[0][0][1]
+            
+            # 计算相对于靶心的偏移
+            # 注意：Y轴方向可能需要翻转（图像Y向下，物理Y通常向上）
+            dx_mm = laser_x_mm - self.target_center_offset_mm[0]
+            dy_mm = -(laser_y_mm - self.target_center_offset_mm[1])  # 翻转Y轴
+            
+            if self.logger:
+                self.logger.debug(f"[ARUCO] 激光点转换: 图像({laser_point[0]:.1f}, {laser_point[1]:.1f}) -> "
+                                f"物理({laser_x_mm:.1f}, {laser_y_mm:.1f}) -> "
+                                f"偏移({dx_mm:.1f}, {dy_mm:.1f})mm")
+            
+            return dx_mm, dy_mm
+            
+        except Exception as e:
+            if self.logger:
+                self.logger.error(f"[ARUCO] 激光点转换失败: {e}")
+            return None, None
+    
+    def draw_debug_info(self, frame, corners, ids, target_center=None, laser_point=None):
+        """
+        在图像上绘制调试信息
+        
+        Args:
+            frame: MaixPy图像帧
+            corners: 标记角点
+            ids: 标记ID
+            target_center: 计算的靶心位置
+            laser_point: 激光点位置
+            
+        Returns:
+            绘制后的图像
+        """
+        try:
+            from maix import image
+            img_cv = image.image2cv(frame, False, False).copy()
+            
+            # 绘制检测到的标记
+            if ids is not None:
+                cv2.aruco.drawDetectedMarkers(img_cv, corners, ids)
+                
+                # 绘制标记ID和中心
+                for i, marker_id in enumerate(ids.flatten()):
+                    corner = corners[i][0]
+                    center_x = int(np.mean(corner[:, 0]))
+                    center_y = int(np.mean(corner[:, 1]))
+                    
+                    # 绘制中心点
+                    cv2.circle(img_cv, (center_x, center_y), 5, (0, 255, 0), -1)
+                    
+                    # 绘制ID
+                    cv2.putText(img_cv, f"ID:{marker_id}", 
+                               (center_x + 10, center_y - 10),
+                               cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+            
+            # 绘制靶心
+            if target_center:
+                cv2.circle(img_cv, target_center, 8, (255, 0, 0), -1)
+                cv2.circle(img_cv, target_center, 50, (255, 0, 0), 2)
+                cv2.putText(img_cv, "TARGET", (target_center[0] + 15, target_center[1] - 15),
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
+            
+            # 绘制激光点
+            if laser_point:
+                cv2.circle(img_cv, (int(laser_point[0]), int(laser_point[1])), 6, (0, 0, 255), -1)
+                cv2.putText(img_cv, "LASER", (int(laser_point[0]) + 10, int(laser_point[1]) - 10),
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
+            
+            # 转换回MaixPy图像
+            return image.cv2image(img_cv, False, False)
+            
+        except Exception as e:
+            if self.logger:
+                self.logger.error(f"[ARUCO] 绘制调试信息失败: {e}")
+            return frame
+
+
+# 创建全局单例实例
+aruco_detector = ArUcoDetector()
+
+
+def detect_target_with_aruco(frame, laser_point=None):
+    """
+    使用ArUco标记检测靶心的便捷函数
+    
+    Args:
+        frame: MaixPy图像帧
+        laser_point: 激光点坐标（可选）
+        
+    Returns:
+        (result_img, center, radius, method, best_radius1, ellipse_params)
+        与detect_circle_v3保持相同的返回格式
+    """
+    detector = aruco_detector
+    
+    # 检测ArUco标记
+    corners, ids, rejected = detector.detect_markers(frame)
+    
+    # 计算靶心
+    center, radius, method, ellipse_params = detector.get_target_center_from_markers(corners, ids)
+    
+    # 绘制调试信息
+    result_img = detector.draw_debug_info(frame, corners, ids, center, laser_point)
+    
+    # 返回与detect_circle_v3相同的格式
+    # best_radius1用于距离估算，这里用radius代替
+    return result_img, center, radius, method, radius, ellipse_params
+
+
+def compute_laser_offset_aruco(laser_point, corners, ids):
+    """
+    使用ArUco计算激光点相对于靶心的偏移（毫米）
+    
+    Args:
+        laser_point: (x, y) 激光点图像坐标
+        corners: ArUco标记角点
+        ids: ArUco标记ID
+        
+    Returns:
+        (dx_mm, dy_mm) 偏移量（毫米），或 (None, None)
+    """
+    return aruco_detector.transform_laser_point(laser_point, corners, ids)

at_client.py

@@ -69,14 +69,13 @@ class ATClient:
         # 同上：避免在 _reader_loop 持锁期间二次 acquire
         self._http_events.append(ev)
 
-    def send(self, cmd: str, expect: str = "OK", timeout_ms: int = 2000, abort_event=None):
+    def send(self, cmd: str, expect: str = "OK", timeout_ms: int = 2000):
         """
         发送 AT 命令并等待 expect（子串匹配）。
         注意：expect=">" 用于等待 prompt。
         """
         expect_b = expect.encode() if isinstance(expect, str) else expect
         with self._cmd_lock:
-            with self._q_lock:
             # 初始化等待
             self._waiting = True
             self._expect = expect_b
@@ -90,9 +89,6 @@ class ATClient:
 
             t0 = time.ticks_ms()
             while abs(time.ticks_diff(time.ticks_ms(), t0)) < timeout_ms:
-                if abort_event is not None and abort_event.is_set():
-                    self._waiting = False
-                    break
                 if (not self._waiting) or (self._expect in self._resp):
                     self._waiting = False
                     break
@@ -105,39 +101,6 @@ class ATClient:
             except:
                 return str(self._resp)
 
-    def send_raw_and_wait(self, data: bytes, expect: str = "OK", timeout_ms: int = 1000,
-                          suffix: bytes = b""):
-        """Register the response waiter before writing raw UART data."""
-        expect_b = expect.encode() if isinstance(expect, str) else expect
-        with self._cmd_lock:
-            with self._q_lock:
-                self._waiting = True
-                self._expect = expect_b
-                self._resp = b""
-
-            total = 0
-            while total < len(data):
-                n = self.uart.write(data[total:])
-                if not n or n < 0:
-                    time.sleep_ms(1)
-                    continue
-                total += n
-            if suffix:
-                self.uart.write(suffix)
-
-            t0 = time.ticks_ms()
-            while abs(time.ticks_diff(time.ticks_ms(), t0)) < timeout_ms:
-                if (not self._waiting) or (self._expect in self._resp):
-                    self._waiting = False
-                    break
-                time.sleep_ms(5)
-
-            self._waiting = False
-            try:
-                return self._resp.decode(errors="ignore")
-            except:
-                return str(self._resp)
-
     def _find_urc_tag(self, tag: bytes):
         """
         只在"真正的 URC 边界"查找 tag，避免误命中 HTTP payload 内容。
@@ -337,8 +300,8 @@ class ATClient:
                     if len(self._rx) > 512 * 1024:
                         self._rx = self._rx[-256 * 1024:]
                 else:
-                    if len(self._rx) > 32768:
+                    if len(self._rx) > 16384:
-                        self._rx = self._rx[-16384:]
+                        self._rx = self._rx[-4096:]
 
 
 

charging_exit.sh

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

config.py

@@ -24,7 +24,7 @@ TRIANGLE_DETECT_SCALE = 0.4
 # SERVER_IP = "stcp.shelingxingqiu.com"
 SERVER_IP = "www.shelingxingqiu.com"
 SERVER_PORT = 50005
-HEARTBEAT_INTERVAL = 5  # 心跳间隔（秒）
+HEARTBEAT_INTERVAL = 15  # 心跳间隔（秒）
 
 # WiFi 质量评估（开机先尝试 WiFi；质量差且 4G 可用则切到 4G，本次上电直至关机锁定 4G）
 WIFI_QUALITY_RTT_SAMPLES = 3  # 到业务服务器 TCP 建连耗时采样次数，取中位数
@@ -106,6 +106,14 @@ 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）
@@ -134,7 +142,7 @@ 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 = False  # False 时仅走黄心圆/椭圆 + 半径估距，不使用三角形路径
+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
 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
@@ -144,6 +152,13 @@ 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）
@@ -169,6 +184,7 @@ 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
@@ -256,11 +272,15 @@ 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 = False
+TRIANGLE_YOLO_PRELOAD_ON_BOOT = True
 
 # ── 第二段 YOLO：仅在 Stage1 裁切出的靶环图上推理（与合成 stage2 训练数据一致）→ 子框内传统算法取直角点 ──
 # Stage1 靶环裁切内如何找黑三角标记（对比耗时时可切换）：
@@ -308,21 +328,16 @@ 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 = False  # 是否保存图像（True=保存，False=不保存）
+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）
 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 = False  # 是否在拍摄时显示摄像头图像（True=显示，False=不显示），建议在连着USB测试过程中打开
+SHOW_CAMERA_PHOTO_WHILE_SHOOTING = True  # 是否在拍摄时显示摄像头图像（True=显示，False=不显示），建议在连着USB测试过程中打开
 
 # ==================== OTA配置 ====================
 MAX_BACKUPS = 5
@@ -343,18 +358,6 @@ PIN_MAPPINGS = {
 # ==================== 电源配置 ====================
 AUTO_POWER_OFF_IN_SECONDS = 10 * 60  # 自动关机时间（秒），0表示不自动关机
 
-# 实机数据：正常放电约为正电流，插入充电线后约为负电流。
-CHARGING_SHUTDOWN_ENABLED = True  # True=充电时退出应用，False=关闭充电关机功能
-CHARGING_DIAGNOSTIC_LOG_ENABLED = False
-CHARGING_CHECK_INTERVAL_MS = 3000
-CHARGING_CURRENT_THRESHOLD_MA = 100.0
-CHARGING_CONFIRM_COUNT = 2
-CHARGING_NOTIFY_TIMEOUT_MS = 30000
-CHARGING_4G_UART_LOCK_TIMEOUT_SEC = 2.5
-CHARGING_4G_PROMPT_TIMEOUT_MS = 1500
-CHARGING_4G_CONFIRM_TIMEOUT_MS = 1000
-CHARGING_EXIT_SCRIPT = APP_DIR + "/charging_exit.sh"
-
 BATTERY_SOC_LPF_ALPHA = 0.5
 BATTERY_SOC_AVG_WINDOW = 5
 

cpp_ext/CMakeLists.txt

@@ -0,0 +1,72 @@
+cmake_minimum_required(VERSION 3.16)
+project(archery_netcore CXX)
+
+set(CMAKE_SYSTEM_NAME Linux)
+set(CMAKE_SYSTEM_PROCESSOR riscv64)
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_POSITION_INDEPENDENT_CODE ON)
+
+if(NOT DEFINED PY_INCLUDE_DIR)
+  message(FATAL_ERROR "PY_INCLUDE_DIR not set")
+endif()
+if(NOT DEFINED PY_LIB)
+  message(FATAL_ERROR "PY_LIB not set")
+endif()
+if(NOT DEFINED PY_EXT_SUFFIX)
+  message(FATAL_ERROR "PY_EXT_SUFFIX not set")
+endif()
+if(NOT DEFINED MAIXCDK_PATH)
+  message(FATAL_ERROR "MAIXCDK_PATH not set (need components/3rd_party/pybind11)")
+endif()
+
+add_library(archery_netcore MODULE 
+  archery_netcore.cpp
+  native_logger.cpp
+  utils.cpp
+  decrypt_ota_file.cpp
+  msg_handler.cpp
+  tcp_ssl_password.cpp
+)
+
+target_include_directories(archery_netcore PRIVATE
+  "${PY_INCLUDE_DIR}"
+  "${MAIXCDK_PATH}/components/3rd_party/pybind11/pybind11/include"
+  "${MAIXCDK_PATH}/components/3rd_party/openssl/include"
+  "${CMAKE_CURRENT_SOURCE_DIR}/third_party"  # 添加 nlohmann/json 路径
+)
+
+# 尽量减少 .so 体积并增加逆向成本
+target_compile_options(archery_netcore PRIVATE
+  -Os
+  -ffunction-sections
+  -fdata-sections
+  -fvisibility=hidden
+  -fvisibility-inlines-hidden
+)
+target_link_options(archery_netcore PRIVATE
+  -Wl,--gc-sections
+  -Wl,-s
+)
+
+set_target_properties(archery_netcore PROPERTIES
+  PREFIX ""
+  SUFFIX "${PY_EXT_SUFFIX}"
+)
+
+# OpenSSL (for AES-256-GCM decrypt)
+# 使用 MaixCDK 提供的 OpenSSL 库（在 so/maixcam 目录下）
+set(OPENSSL_LIB_DIR "${MAIXCDK_PATH}/components/3rd_party/openssl/so/maixcam")
+if(EXISTS "${OPENSSL_LIB_DIR}/libcrypto.so")
+    target_link_directories(archery_netcore PRIVATE "${OPENSSL_LIB_DIR}")
+    target_link_libraries(archery_netcore PRIVATE "${PY_LIB}" crypto ssl)
+    message(STATUS "Using OpenSSL from MaixCDK: ${OPENSSL_LIB_DIR}")
+else()
+    # Fallback: 尝试 find_package 或系统库
+    find_package(OpenSSL QUIET)
+    if(OpenSSL_FOUND)
+        target_link_libraries(archery_netcore PRIVATE "${PY_LIB}" OpenSSL::Crypto OpenSSL::SSL)
+    else()
+        message(WARNING "OpenSSL not found in MaixCDK, trying system libraries (may fail)")
+        target_link_libraries(archery_netcore PRIVATE "${PY_LIB}" crypto ssl)
+    endif()
+endif()

cpp_ext/archery_netcore.cpp

@@ -0,0 +1,117 @@
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>  // 支持 std::vector, std::map 等
+#include <nlohmann/json.hpp>
+#include <cstring>
+#include <cstdint>
+#include <vector>
+#include <string>
+#include <fstream>
+#include <array>
+
+#include "msg_handler.hpp"
+#include "native_logger.hpp"
+#include "decrypt_ota_file.hpp"
+#include "utils.hpp"
+#include "tcp_ssl_password.hpp"
+
+namespace py = pybind11;
+using json = nlohmann::json;
+
+namespace {
+    // 配置项
+    const std::string _cfg_server_ip = "www.shelingxingqiu.com";
+    const int _cfg_server_port = 50005;
+    const std::string _cfg_device_id_file = "/device_key";
+
+
+}
+
+// 定义获取配置的函数
+py::dict get_config() {
+    py::dict config;
+    config["SERVER_IP"] = _cfg_server_ip;
+    config["SERVER_PORT"] = _cfg_server_port;
+    return config;
+}
+
+
+
+
+
+PYBIND11_MODULE(archery_netcore, m) {
+    m.doc() = "Archery net core (native, pybind11).";
+
+    // Optional: configure native logger from Python.
+    // Default log file: /maixapp/apps/t11/netcore.log
+    m.def("set_log_file", [](const std::string& path) { netcore::set_log_file(path); }, py::arg("path"));
+    m.def("set_log_level", [](int level) {
+        if (level < 0) level = 0;
+        if (level > 3) level = 3;
+        netcore::set_log_level(static_cast<netcore::LogLevel>(level));
+    }, py::arg("level"));
+    m.def("log_test", [](const std::string& msg) {
+        netcore::log_info(std::string("log_test: ") + msg);
+    }, py::arg("msg"));
+
+    m.def("make_packet", &netcore::make_packet,
+          "Pack TCP packet: header (len+type+checksum) + JSON body",
+          py::arg("msg_type"), py::arg("body_dict"));
+    
+    m.def("parse_packet", &netcore::parse_packet,
+          "Parse TCP packet, return (msg_type, body_dict)");
+
+    m.def("get_config", &get_config, "Get system configuration");
+
+    m.def(
+        "calculate_tcp_ssl_password",
+        &netcore::calculate_tcp_ssl_password,
+        "Calculate TCP SSL password: hex(md5(hex(md5(device_id)) + iccid))",
+        py::arg("device_id"),
+        py::arg("iccid")
+    );
+
+    m.def(
+        "decrypt_ota_file",
+        [](const std::string& input_path, const std::string& output_zip_path) {
+            netcore::log_info(std::string("decrypt_ota_file in=") + input_path + " out=" + output_zip_path);
+            return netcore::decrypt_ota_file_impl(input_path, output_zip_path);
+        },
+        py::arg("input_path"),
+        py::arg("output_zip_path"),
+        "Decrypt OTA encrypted file (MAGIC|nonce|ciphertext|tag) to plaintext zip."
+    );
+
+    // Minimal demo: return actions for inner_cmd=41 (manual trigger + ack)
+    m.def("actions_for_inner_cmd", [](int inner_cmd) {
+        py::list actions;
+
+        if (inner_cmd == 41) {
+            // 1) set manual trigger flag
+            {
+                py::dict a;
+                a["type"] = "SET_FLAG";
+                py::dict args;
+                args["name"] = "manual_trigger_flag";
+                args["value"] = true;
+                a["args"] = args;
+                actions.append(a);
+            }
+
+            // 2) enqueue trigger_ack
+            {
+                py::dict a;
+                a["type"] = "ENQUEUE";
+                py::dict args;
+                args["msg_type"] = 2;
+                args["high"] = false;
+                py::dict body;
+                body["result"] = "trigger_ack";
+                args["body"] = body;
+                a["args"] = args;
+                actions.append(a);
+            }
+        }
+
+        return actions;
+    });
+}

cpp_ext/decrypt_ota_file.cpp

@@ -0,0 +1,155 @@
+#include <cstring>
+#include <cstdint>
+#include <vector>
+#include <string>
+#include <fstream>
+#include <array>
+#include <algorithm>
+#include <openssl/evp.h>
+#include "native_logger.hpp"
+
+namespace netcore{
+
+    // OTA AEAD format: MAGIC(7) | nonce(12) | ciphertext(N) | tag(16)
+    constexpr const char* kOtaMagic = "AROTAE1";
+    constexpr size_t kOtaMagicLen = 7;
+    constexpr size_t kGcmNonceLen = 12;
+    constexpr size_t kGcmTagLen = 16;
+    constexpr size_t kHeaderLen = kOtaMagicLen + kGcmNonceLen;
+    // 分块解密，避免整包读入导致 RAM 峰值约为「文件大小×2」（小内存设备易 OOM）
+    constexpr size_t kDecryptChunk = 65536;
+
+    static std::array<uint8_t, 32> ota_key_bytes() {
+        static const std::array<uint8_t, 32> a = {
+            0x92,0x99,0x4d,0x06,0x6f,0xb6,0xa6,0x3d,0x85,0x08,0xbe,0x73,0x5e,0x73,0x4d,0x8a,
+            0x53,0x88,0xe6,0x99,0xfc,0x10,0x29,0xb9,0x16,0x9b,0xe7,0x0c,0x65,0x21,0x1c,0xce
+        };
+        static const std::array<uint8_t, 32> b = {
+            0xcf,0x60,0xa2,0xc2,0x32,0x7a,0x61,0xb0,0x4c,0x8e,0x8a,0x62,0x31,0xc7,0x82,0xff,
+            0xec,0xac,0xa1,0x04,0x2a,0x4d,0xaa,0xf2,0xb0,0x5b,0x39,0x2b,0xf4,0xb3,0xad,0xad
+        };
+        std::array<uint8_t, 32> k{};
+        for (size_t i = 0; i < k.size(); i++) k[i] = static_cast<uint8_t>(a[i] ^ b[i]);
+        return k;
+    }
+
+    bool decrypt_ota_file_impl(const std::string& input_path, const std::string& output_zip_path) {
+        std::ifstream ifs(input_path, std::ios::binary);
+        if (!ifs) {
+            netcore::log_error(std::string("decrypt_ota_file: open in failed: ") + input_path);
+            return false;
+        }
+        ifs.seekg(0, std::ios::end);
+        const std::streampos szp = ifs.tellg();
+        if (szp <= 0) {
+            netcore::log_error("decrypt_ota_file: empty input");
+            return false;
+        }
+        const uint64_t file_size = static_cast<uint64_t>(szp);
+        const size_t min_len = kHeaderLen + kGcmTagLen + 1;
+        if (file_size < min_len) {
+            netcore::log_error("decrypt_ota_file: too short");
+            return false;
+        }
+        const uint64_t ciphertext_len = file_size - kHeaderLen - kGcmTagLen;
+
+        ifs.seekg(0, std::ios::beg);
+        std::array<uint8_t, kHeaderLen> header{};
+        ifs.read(reinterpret_cast<char*>(header.data()), static_cast<std::streamsize>(kHeaderLen));
+        if (ifs.gcount() != static_cast<std::streamsize>(kHeaderLen)) {
+            netcore::log_error("decrypt_ota_file: read header failed");
+            return false;
+        }
+        if (!std::equal(header.begin(), header.begin() + kOtaMagicLen,
+                         reinterpret_cast<const uint8_t*>(kOtaMagic))) {
+            netcore::log_error("decrypt_ota_file: bad magic");
+            return false;
+        }
+        const uint8_t* nonce = header.data() + kOtaMagicLen;
+
+        std::ofstream ofs(output_zip_path, std::ios::binary | std::ios::trunc);
+        if (!ofs) {
+            netcore::log_error(std::string("decrypt_ota_file: open out failed: ") + output_zip_path);
+            return false;
+        }
+
+        EVP_CIPHER_CTX* ctx = EVP_CIPHER_CTX_new();
+        if (!ctx) {
+            netcore::log_error("decrypt_ota_file: EVP_CIPHER_CTX_new failed");
+            return false;
+        }
+
+        bool ok = false;
+        auto key = ota_key_bytes();
+        std::vector<uint8_t> chunk_in(kDecryptChunk);
+        std::vector<uint8_t> chunk_out(kDecryptChunk + EVP_MAX_BLOCK_LENGTH);
+
+        do {
+            if (1 != EVP_DecryptInit_ex(ctx, EVP_aes_256_gcm(), nullptr, nullptr, nullptr)) {
+                netcore::log_error("decrypt_ota_file: DecryptInit failed");
+                break;
+            }
+            if (1 != EVP_CIPHER_CTX_ctrl(ctx, EVP_CTRL_GCM_SET_IVLEN, static_cast<int>(kGcmNonceLen), nullptr)) {
+                netcore::log_error("decrypt_ota_file: set ivlen failed");
+                break;
+            }
+            if (1 != EVP_DecryptInit_ex(ctx, nullptr, nullptr, key.data(), nonce)) {
+                netcore::log_error("decrypt_ota_file: set key/iv failed");
+                break;
+            }
+
+            uint64_t remaining = ciphertext_len;
+            while (remaining > 0) {
+                const size_t n = static_cast<size_t>(std::min<uint64_t>(remaining, kDecryptChunk));
+                ifs.read(reinterpret_cast<char*>(chunk_in.data()), static_cast<std::streamsize>(n));
+                if (ifs.gcount() != static_cast<std::streamsize>(n)) {
+                    netcore::log_error("decrypt_ota_file: read ciphertext chunk failed");
+                    goto cleanup_ctx;
+                }
+                int outl = 0;
+                if (1 != EVP_DecryptUpdate(ctx, chunk_out.data(), &outl,
+                                          chunk_in.data(), static_cast<int>(n))) {
+                    netcore::log_error("decrypt_ota_file: update failed");
+                    goto cleanup_ctx;
+                }
+                if (outl > 0) {
+                    ofs.write(reinterpret_cast<const char*>(chunk_out.data()), outl);
+                    if (!ofs) {
+                        netcore::log_error("decrypt_ota_file: write plaintext failed");
+                        goto cleanup_ctx;
+                    }
+                }
+                remaining -= n;
+            }
+
+            std::array<uint8_t, kGcmTagLen> tag{};
+            ifs.read(reinterpret_cast<char*>(tag.data()), static_cast<std::streamsize>(kGcmTagLen));
+            if (ifs.gcount() != static_cast<std::streamsize>(kGcmTagLen)) {
+                netcore::log_error("decrypt_ota_file: read tag failed");
+                break;
+            }
+            if (1 != EVP_CIPHER_CTX_ctrl(ctx, EVP_CTRL_GCM_SET_TAG, static_cast<int>(kGcmTagLen), tag.data())) {
+                netcore::log_error("decrypt_ota_file: set tag failed");
+                break;
+            }
+
+            int outl2 = 0;
+            if (1 != EVP_DecryptFinal_ex(ctx, chunk_out.data(), &outl2)) {
+                netcore::log_error("decrypt_ota_file: final failed (auth tag mismatch?)");
+                break;
+            }
+            if (outl2 > 0) {
+                ofs.write(reinterpret_cast<const char*>(chunk_out.data()), outl2);
+                if (!ofs) {
+                    netcore::log_error("decrypt_ota_file: write final failed");
+                    break;
+                }
+            }
+            ok = true;
+        } while (false);
+
+cleanup_ctx:
+        EVP_CIPHER_CTX_free(ctx);
+        return ok;
+    }
+}  // namespace netcore

cpp_ext/decrypt_ota_file.hpp

@@ -0,0 +1,7 @@
+#pragma once
+
+#include <string>
+
+namespace netcore{
+    bool decrypt_ota_file_impl(const std::string& input_path, const std::string& output_zip_path);
+}

cpp_ext/msg_handler.cpp

@@ -0,0 +1,113 @@
+#include <nlohmann/json.hpp>
+#include <string>
+#include <cstring>
+#include <cstdint>
+#include <vector>
+#include "native_logger.hpp"
+#include "msg_handler.hpp"
+#include "utils.hpp"
+
+namespace py = pybind11;
+using json = nlohmann::json;
+
+namespace netcore {
+    // 打包 TCP 数据包
+    py::bytes make_packet(int msg_type, py::dict body_dict) {
+        netcore::log_debug(std::string("make_packet msg_type=") + std::to_string(msg_type));
+        // 1) 将 py::dict 转为 JSON 字符串
+        json j = netcore::py_dict_to_json(body_dict);
+        std::string body_str = j.dump();
+
+        // 2) 计算 body_len 和 checksum
+        uint32_t body_len = body_str.size();
+        uint32_t checksum = body_len + msg_type;
+
+        // 3) 打包头部（大端序）
+        std::vector<uint8_t> packet;
+        packet.reserve(12 + body_len);
+
+        // body_len (big-endian, 4 bytes)
+        packet.push_back((body_len >> 24) & 0xFF);
+        packet.push_back((body_len >> 16) & 0xFF);
+        packet.push_back((body_len >> 8) & 0xFF);
+        packet.push_back(body_len & 0xFF);
+
+        // msg_type (big-endian, 4 bytes)
+        packet.push_back((msg_type >> 24) & 0xFF);
+        packet.push_back((msg_type >> 16) & 0xFF);
+        packet.push_back((msg_type >> 8) & 0xFF);
+        packet.push_back(msg_type & 0xFF);
+
+        // checksum (big-endian, 4 bytes)
+        packet.push_back((checksum >> 24) & 0xFF);
+        packet.push_back((checksum >> 16) & 0xFF);
+        packet.push_back((checksum >> 8) & 0xFF);
+        packet.push_back(checksum & 0xFF);
+
+        // 4) 追加 body
+        packet.insert(packet.end(), body_str.begin(), body_str.end());
+
+        netcore::log_debug(std::string("make_packet done bytes=") + std::to_string(packet.size()));
+        return py::bytes(reinterpret_cast<const char*>(packet.data()), packet.size());
+    }
+
+    // 解析 TCP 数据包
+    py::tuple parse_packet(py::bytes data) {
+        // 1) 转换为 bytes view
+        py::buffer_info buf = py::buffer(data).request();
+        if (buf.size < 12) {
+            netcore::log_error(std::string("parse_packet too_short len=") + std::to_string(buf.size));
+            return py::make_tuple(py::none(), py::none());
+        }
+
+        const uint8_t* ptr = static_cast<const uint8_t*>(buf.ptr);
+
+        // 2) 解析头部（大端序）
+        uint32_t body_len = (ptr[0] << 24) | (ptr[1] << 16) | (ptr[2] << 8) | ptr[3];
+        uint32_t msg_type = (ptr[4] << 24) | (ptr[5] << 16) | (ptr[6] << 8) | ptr[7];
+        uint32_t checksum = (ptr[8] << 24) | (ptr[9] << 16) | (ptr[10] << 8) | ptr[11];
+
+        // 3) 校验 checksum（可选，你现有代码不强制校验）
+        // if (checksum != (body_len + msg_type)) {
+        //     return py::make_tuple(py::none(), py::none());
+        // }
+
+        // 4) 检查长度
+        uint32_t expected_len = 12 + body_len;
+        if (buf.size < expected_len) {
+            // 半包
+            netcore::log_warn(std::string("parse_packet incomplete got=") + std::to_string(buf.size) +
+                              " expected=" + std::to_string(expected_len));
+            return py::make_tuple(py::none(), py::none());
+        }
+
+        // 5) 防御性检查：如果 data 比预期长，说明可能有粘包
+        // （只解析第一个包，忽略多余数据）
+        if (buf.size > expected_len) {
+            netcore::log_warn(std::string("parse_packet concat got=") + std::to_string(buf.size) +
+                              " expected=" + std::to_string(expected_len) +
+                              " body_len=" + std::to_string(body_len) +
+                              " msg_type=" + std::to_string(msg_type));
+        }
+
+        // 6) 提取 body 并解析 JSON
+        std::string body_str(reinterpret_cast<const char*>(ptr + 12), body_len);
+
+        try {
+            json j = json::parse(body_str);
+            py::dict body_dict = netcore::json_to_py_dict(j);
+            return py::make_tuple(py::int_(msg_type), body_dict);
+        } catch (const json::parse_error& e) {
+            // JSON 解析失败，返回 raw（兼容你现有的逻辑）
+            netcore::log_error(std::string("parse_packet json_parse_error: ") + e.what());
+            py::dict raw_dict;
+            raw_dict["raw"] = body_str;
+            return py::make_tuple(py::int_(msg_type), raw_dict);
+        } catch (const std::exception& e) {
+            netcore::log_error(std::string("parse_packet json_parse_error: ") + e.what());
+            py::dict raw_dict;
+            raw_dict["raw"] = body_str;
+            return py::make_tuple(py::int_(msg_type), raw_dict);
+        }
+    }
+}

cpp_ext/msg_handler.hpp

@@ -0,0 +1,14 @@
+#pragma once
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>  // 支持 std::vector, std::map 等
+
+namespace py = pybind11;
+
+namespace netcore {
+
+    // 打包 TCP 数据包
+    py::bytes make_packet(int msg_type, py::dict body_dict);
+    // 解包 TCP 数据包
+    py::tuple parse_packet(py::bytes data);
+}

cpp_ext/native_logger.cpp

@@ -0,0 +1,100 @@
+#include "native_logger.hpp"
+
+#include <cerrno>
+#include <cstring>
+#include <mutex>
+#include <string>
+
+#include <fcntl.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <time.h>
+#include <unistd.h>
+
+namespace netcore {
+
+static std::mutex g_mu;
+static int g_fd = -1;
+static std::string g_path = "netcore.log";
+static LogLevel g_level = LogLevel::kDebug; //LogLevel::kInfo;
+
+static const char* level_name(LogLevel lvl) {
+    switch (lvl) {
+        case LogLevel::kError: return "E";
+        case LogLevel::kWarn:  return "W";
+        case LogLevel::kInfo:  return "I";
+        case LogLevel::kDebug: return "D";
+        default: return "?";
+    }
+}
+
+static void ensure_open_locked() {
+    if (g_path.empty()) return;
+    if (g_fd >= 0) return;
+    g_fd = ::open(g_path.c_str(), O_CREAT | O_WRONLY | O_APPEND, 0644);
+}
+
+void set_log_file(const std::string& path) {
+    std::lock_guard<std::mutex> lk(g_mu);
+    g_path = path;
+    if (g_fd >= 0) {
+        ::close(g_fd);
+        g_fd = -1;
+    }
+    ensure_open_locked();
+}
+
+void set_log_level(LogLevel level) {
+    std::lock_guard<std::mutex> lk(g_mu);
+    g_level = level;
+}
+
+void log(LogLevel level, const std::string& msg) {
+    std::lock_guard<std::mutex> lk(g_mu);
+    if (static_cast<int>(level) > static_cast<int>(g_level)) return;
+    if (g_path.empty()) return;
+
+    ensure_open_locked();
+    if (g_fd < 0) {
+        // Last resort: stderr (avoid any Python APIs)
+        ::write(STDERR_FILENO, msg.c_str(), msg.size());
+        ::write(STDERR_FILENO, "\n", 1);
+        return;
+    }
+
+    // Timestamp: epoch milliseconds (simple and cheap)
+    struct timespec ts;
+    clock_gettime(CLOCK_REALTIME, &ts);
+//    long long ms = (long long)ts.tv_sec * 1000LL + ts.tv_nsec / 1000000LL;
+    // 1. 将秒数转换为本地时间结构体 struct tm
+    struct tm *tm_info = localtime(&ts.tv_sec);
+
+    // 2. 准备一个缓冲区来存储时间字符串
+    char buffer[30];
+
+    // 3. 格式化秒的部分
+    // 格式: 年-月-日 时:分:秒
+    strftime(buffer, sizeof(buffer), "%Y-%m-%d %H:%M:%S", tm_info);
+
+    // 4. 计算毫秒部分并追加到字符串中
+    // ts.tv_nsec 是纳秒，除以 1,000,000 得到毫秒
+    char ms_buffer[8];
+    snprintf(ms_buffer, sizeof(ms_buffer), ".%03ld", ts.tv_nsec / 1000000);
+
+    // Build one line to keep writes atomic-ish
+    char head[256];
+    int n = ::snprintf(head, sizeof(head), "[%s%s] [%s] ", buffer, ms_buffer, level_name(level));
+    if (n < 0) n = 0;
+
+    ::write(g_fd, head, (size_t)n);
+    ::write(g_fd, msg.c_str(), msg.size());
+    ::write(g_fd, "\n", 1);
+}
+
+void log_debug(const std::string& msg) { log(LogLevel::kDebug, msg); }
+void log_info (const std::string& msg) { log(LogLevel::kInfo,  msg); }
+void log_warn (const std::string& msg) { log(LogLevel::kWarn,  msg); }
+void log_error(const std::string& msg) { log(LogLevel::kError, msg); }
+
+} // namespace netcore
+

cpp_ext/native_logger.hpp

@@ -0,0 +1,28 @@
+#pragma once
+
+#include <string>
+
+namespace netcore {
+
+enum class LogLevel : int {
+    kError = 0,
+    kWarn  = 1,
+    kInfo  = 2,
+    kDebug = 3,
+};
+
+// Set log file path. If empty, logging is disabled.
+void set_log_file(const std::string& path);
+
+// Set minimum log level to write (default: kInfo).
+void set_log_level(LogLevel level);
+
+// Log helpers (thread-safe, never calls into Python).
+void log(LogLevel level, const std::string& msg);
+void log_debug(const std::string& msg);
+void log_info(const std::string& msg);
+void log_warn(const std::string& msg);
+void log_error(const std::string& msg);
+
+} // namespace netcore
+

cpp_ext/tcp_ssl_password.cpp

@@ -0,0 +1,33 @@
+#include "tcp_ssl_password.hpp"
+
+#include <openssl/md5.h>
+#include <sstream>
+#include <iomanip>
+
+namespace netcore {
+
+static std::string md5_hex(const std::string& input) {
+    MD5_CTX ctx;
+    MD5_Init(&ctx);
+    MD5_Update(&ctx, input.data(), input.size());
+
+    unsigned char digest[MD5_DIGEST_LENGTH];
+    MD5_Final(digest, &ctx);
+
+    std::ostringstream oss;
+    oss << std::hex << std::setfill('0');
+    for (int i = 0; i < MD5_DIGEST_LENGTH; ++i) {
+        oss << std::setw(2) << static_cast<unsigned int>(digest[i]);
+    }
+    return oss.str();
+}
+
+std::string calculate_tcp_ssl_password(const std::string& device_id, const std::string& iccid) {
+    std::string md5_device_hex = md5_hex(device_id);
+    if (!iccid.empty()) {
+        md5_device_hex += iccid;
+    }
+    return md5_hex(md5_device_hex);
+}
+
+} // namespace netcore

cpp_ext/tcp_ssl_password.hpp

@@ -0,0 +1,7 @@
+#pragma once
+
+#include <string>
+
+namespace netcore {
+    std::string calculate_tcp_ssl_password(const std::string& device_id, const std::string& iccid);
+}

cpp_ext/utils.cpp

@@ -0,0 +1,95 @@
+#include <fstream>
+#include <cstring>
+#include <cstdint>
+#include <string>
+#include <fstream>
+#include "utils.hpp"
+
+namespace netcore {
+// 辅助函数：将 py::dict 转为 nlohmann::json
+json py_dict_to_json(py::dict d) {
+    json j;
+    for (auto item : d) {
+        std::string key = py::str(item.first);
+        py::object val = py::reinterpret_borrow<py::object>(item.second);
+
+        if (py::isinstance<py::dict>(val)) {
+            j[key] = py_dict_to_json(py::cast<py::dict>(val));
+        } else if (py::isinstance<py::list>(val)) {
+            py::list py_list = py::cast<py::list>(val);
+            json arr = json::array();
+            for (auto elem : py_list) {
+                py::object elem_obj = py::reinterpret_borrow<py::object>(elem);
+                if (py::isinstance<py::dict>(elem_obj)) {
+                    arr.push_back(py_dict_to_json(py::cast<py::dict>(elem_obj)));
+                } else if (py::isinstance<py::int_>(elem_obj)) {
+                    arr.push_back(py::cast<int64_t>(elem_obj));
+                } else if (py::isinstance<py::float_>(elem_obj)) {
+                    arr.push_back(py::cast<double>(elem_obj));
+                } else {
+                    arr.push_back(py::str(elem_obj));
+                }
+            }
+            j[key] = arr;
+        } else if (py::isinstance<py::int_>(val)) {
+            j[key] = py::cast<int64_t>(val);
+        } else if (py::isinstance<py::float_>(val)) {
+            j[key] = py::cast<double>(val);
+        } else if (py::isinstance<py::bool_>(val)) {
+            j[key] = py::cast<bool>(val);
+        } else if (val.is_none()) {
+            j[key] = nullptr;
+        } else {
+            j[key] = py::str(val);
+        }
+    }
+    return j;
+}
+
+// 辅助函数：将 nlohmann::json 转为 py::dict
+py::dict json_to_py_dict(const json& j) {
+    py::dict d;
+    if (j.is_object()) {
+        for (auto& item : j.items()) {
+            std::string key = item.key();
+            json val = item.value();
+
+            if (val.is_object()) {
+                d[py::str(key)] = json_to_py_dict(val);
+            } else if (val.is_array()) {
+                py::list py_list;
+                for (auto& elem : val) {
+                    if (elem.is_object()) {
+                        py_list.append(json_to_py_dict(elem));
+                    } else if (elem.is_number_integer()) {
+                        py_list.append(py::int_(elem.get<int64_t>()));
+                    } else if (elem.is_number_float()) {
+                        py_list.append(py::float_(elem.get<double>()));
+                    } else if (elem.is_boolean()) {
+                        py_list.append(py::bool_(elem.get<bool>()));
+                    } else if (elem.is_null()) {
+                        py_list.append(py::none());
+                    } else {
+                        py_list.append(py::str(elem.get<std::string>()));
+                    }
+                }
+                d[py::str(key)] = py_list;
+            } else if (val.is_number_integer()) {
+                d[py::str(key)] = py::int_(val.get<int64_t>());
+            } else if (val.is_number_float()) {
+                d[py::str(key)] = py::float_(val.get<double>());
+            } else if (val.is_boolean()) {
+                d[py::str(key)] = py::bool_(val.get<bool>());
+            } else if (val.is_null()) {
+                d[py::str(key)] = py::none();
+            } else {
+                d[py::str(key)] = py::str(val.get<std::string>());
+            }
+        }
+    }
+    return d;
+}
+
+
+
+}

cpp_ext/utils.hpp

@@ -0,0 +1,15 @@
+#pragma once
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>  // 支持 std::vector, std::map 等
+#include <nlohmann/json.hpp>
+#include <string>
+
+namespace py = pybind11;
+using json = nlohmann::json;
+
+namespace netcore {
+
+json py_dict_to_json(py::dict d);
+py::dict json_to_py_dict(const json& j);
+}

cslaser.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+激光模块开关测试脚本
+平台：MaixPy (Sipeed MAIX)
+功能：每2秒循环开启/关闭激光，验证硬件是否正常响应
+作者：ZZH
+"""
+
+from maix import uart, pinmap, time
+
+# === 配置 ===
+UART_PORT = "/dev/ttyS1"   # 激光模块连接的串口（通常是 UART1）
+BAUDRATE = 9600            # 波特率（根据你的模块调整）
+
+# 引脚映射（根据你硬件连接修改）
+pinmap.set_pin_function("A18", "UART1_RX")  # RX
+pinmap.set_pin_function("A19", "UART1_TX")  # TX
+
+# 激光控制指令（根据你的模块协议）
+MODULE_ADDR = 0x00
+LASER_ON_CMD  = bytes([0xAA, MODULE_ADDR, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x01, 0xC1])
+LASER_OFF_CMD = bytes([0xAA, MODULE_ADDR, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x00, 0xC0])
+
+# === 初始化串口 ===
+print("🔧 正在初始化激光串口...")
+laser_uart = uart.UART(UART_PORT, BAUDRATE)
+
+# === 辅助函数 ===
+def send_laser_cmd(cmd, name):
+    """发送激光指令并尝试读取回包"""
+    print(f"➡️ 发送指令: {name}")
+    laser_uart.write(cmd)
+    time.sleep_ms(50)  # 等待模块处理
+    
+    # 尝试读取回包（非必须，部分模块无返回）
+    resp = laser_uart.read(20)
+    if resp:
+        print(f"✅ 收到回包 ({len(resp)}字节): {resp.hex()}")
+    else:
+        print("🔇 无回包（正常或模块不支持）")
+
+# === 主测试循环 ===
+print("\n🚀 开始激光开关测试（按 Ctrl+C 停止）")
+print("周期：开1秒 → 关1秒\n")
+
+try:
+    while True:
+        # 开启激光
+        send_laser_cmd(LASER_ON_CMD, "LASER ON")
+        time.sleep(1.0)  # 持续开启 1 秒
+
+        # 关闭激光
+        send_laser_cmd(LASER_OFF_CMD, "LASER OFF")
+        time.sleep(1.0)  # 关闭 1 秒
+
+except KeyboardInterrupt:
+    print("\n🛑 测试被用户中断")
+    # 最终确保激光关闭
+    laser_uart.write(LASER_OFF_CMD)
+    print("✅ 已发送最终关闭指令")

design_doc/algo.md

@@ -0,0 +1,184 @@
+1. 系统目标
+# 检测靶纸四角的等腰直角三角形标记（每个角一个）
+# 计算激光落点在靶面上的二维偏移（厘米）
+# 通过PnP算法估算靶面到相机的距离（米）
+
+2. 核心算法流程
+2.1 三角形检测 (detect_triangle_markers)
+采用多策略级联保证鲁棒性：
+图像输入 → 多阈值策略 → 候选三角形过滤 → 四点匹配
+检测策略（按优先级）：
+
+1.全局Otsu二值化（最快，~10ms）
+2.自适应阈值（多种block size，光照不均时）
+3.ROI局部阈值（候选不足3个时，分象限独立处理）
+4.Black-Hat形态学增强（仍不足时，突出暗色标记）
+
+三角形几何验证：
+# 必须是直角三角形（检查勾股定理，容差20%）
+# 两直角边长度差<20%
+# 内部像素足够暗（灰度≤130，暗像素比例≥30%）
+# 与周围背景对比度≥15灰度级
+
+四点匹配算法：
+# 从候选三角形中枚举所有4点组合
+# 计算四边形评分：(对角比-1)*3 + (水平比-1) + (垂直比-1) + (边长偏差)*2
+# 选择评分最低的组合作为四角标记
+
+2.2 单应性落点计算 (homography_calibration)
+建立图像坐标系 → 靶面坐标系（二维平面）的透视变换
+
+将激光点像素坐标映射到靶面坐标（厘米）
+
+使用RANSAC提高鲁棒性（阈值1像素）
+
+2.3 PnP距离估计 (pnp_distance_meters)
+已知四个标记点的三维坐标（x,y,z，单位cm）
+
+通过solvePnP求解相机外参（旋转+平移）
+
+距离 = ‖平移向量‖ / 100（转换为米）
+
+3. 关键优化策略
+3.1 多路径投票
+同一图像区域被不同二值化方法检测到时，path_votes++
+
+选择投票数高的候选，提高检测可信度
+
+3.2 早退机制
+候选≥3个 且 覆盖3个以上象限 → 停止更多阈值尝试
+
+大幅降低嵌入式设备计算开销
+
+3.3 3点补全机制
+当只检测到3个角时，通过仿射变换估算第4个角位置
+
+公式：P_missing = M_inv @ [x_target, y_target, 1]
+
+3.4 图像缩放
+默认缩放到0.5倍进行检测（由config控制）
+
+坐标还原时乘以inv_scale，保持与标定矩阵一致
+
+4. 数据流示例
+python
+输入:
+  - img_rgb: H×W×3 图像
+  - laser_xy: (x_px, y_px) 激光点像素坐标
+  - marker_positions: {0:[0,0,0], 1:[0,30,0], 2:[30,30,0], 3:[30,0,0]} # 4角3D坐标(cm)
+
+输出:
+  {
+    "ok": True,
+    "dx_cm": 2.5,        # 靶面X偏移(cm，向右为正)
+    "dy_cm": -3.2,       # 靶面Y偏移(cm，向上为正)
+    "distance_m": 5.43,  # 相机到靶面距离(米)
+    "offset_method": "triangle_homography",
+    "distance_method": "pnp_triangle"
+  }
+5. 鲁棒性设计
+5.1 参数自适应
+从config.py动态读取所有阈值（可在线调整）
+
+三角形边长范围、灰度阈值、对比度要求等均可配置
+
+5.2 异常处理
+角点退化检测（距离<3像素判定为重复）
+
+NaN/Inf校验（单应性矩阵、偏移量、距离）
+
+距离合理性检查（0.3~20米）
+
+5.3 降级策略
+PnP失败 → 只输出偏移，距离置None
+
+4角检测失败 → 尝试3角补全
+
+快速路径失败 → CLAHE增强兜底（可选）
+
+6. 性能特点
+CPU友好：默认Otsu单次处理，多数场景10-30ms完成检测
+
+内存可控：最大候选数截断（默认10个），避免组合爆炸
+
+嵌入式适配：支持图像缩放、早退机制降低计算量
+
+7. 局限性
+依赖四个等腰直角三角形（需靶纸特殊设计）
+
+要求三角形内部足够暗、与背景有对比度
+
+单应性假设靶面为平面（实际靶纸可能有轻微起伏）
+
+这套算法在射击训练系统中作为主要定位手段。
+
+8. 为了加速单应性的计算，引入了yolo模型，一共做了两个模型，一个为靶纸和黑色三角形一体的识别模型，用于做原照片上快速找到靶纸区域。另一个模型是黑色三角形的模型，用于做靶纸区域再找黑色三角形。但是经过对比发现，引入黑色三角形模型反而更慢。入下面的流程A和流程B：
+        yolo靶纸+传统（流程B） 	 yolo靶纸+yolo黑色三角形（流程A）
+平均值   646.08	                916.4457143	
+标准差   94.61300968	          57.40401849	
+
+公共前置（两条路都一样）
+是否用靶环模型裁 Stage1
+
+TRIANGLE_YOLO_ROI_ENABLE=True 时：跑 靶环 YOLO，得到全图上的 roi_xyxy，后面的三角形都在 img_work = 全图[roi] 上做（必要时再缩成 img_det 给整图传统分支用）。
+False 时：roi_xyxy=None，三角形在 整幅相机图 上当 img_work。
+之后都进入 try_triangle_scoring(img_cv, …, roi_xyxy=…, black_yolo_boxes_work=…)
+
+在里面先做灰度、v_suppress、锐化、det_scale 缩略图等 prep（与是否黑三角模型无关）。
+差别从 black_yolo_boxes_work 有没有有效子框列表 开始。
+
+流程 A：用黑色三角形模型（Stage2 黑三角 YOLO）
+配置要点：TRIANGLE_BLACK_YOLO_ENABLE=True，且 TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE="yolo"，并且 已有 Stage1 裁切（roi_xyxy 不能为 None，否则根本不会跑黑三角 YOLO）。
+
+步骤概要：
+
+try_black_triangle_boxes_work
+
+输入：全图 RGB + Stage1 的 ring_roi_xyxy。
+在 Stage1 裁切图（与训练一致的 slab）上跑 黑三角 YOLO，得到若干个 子框（black_boxes_work，坐标在 裁切图/work 系）。
+try_triangle_scoring 内
+
+若 black_yolo_boxes_work 非空：
+按配置在 Stage1 全分辨率灰度（或缩略灰度，视 det_scale / TRIANGLE_BLACK_YOLO_PATCH_GRAY_SOURCE）上，对每个子框裁 patch，跑 _extract_triangle_from_yolo_patch（子框内：Otsu → 失败再单次 Adaptive + 轮廓 + 形状/颜色）。
+median_leg 过滤，再 四点分配 ID。
+若 ≥3 个（通常 4 个）有效：认为 Stage2 成功，跳过 整幅 Stage1 上的 detect_triangle_markers。
+若 不足 3 个 且未关 fallback：在 缩略后的整幅 work 灰度上再走 detect_triangle_markers（整图 Otsu + 整图 Adaptive×block_sizes + 各类 fallback），与「不用黑三角模型时的传统主路径」同类。
+后续
+
+角点从 det 坐标 ×inv_scale 回到 work，再 +roi 原点 回到全图；单应性、补第 4 点、PnP 等与另一条路相同。
+耗时上多出来的部分：黑三角 YOLO 推理 + 每个子框一遍传统小流水线（成功时通常 不再付整图 detect_triangle_markers）。
+
+流程 B：不用黑色三角形模型（纯传统定位三角）
+典型配置（任一即可达到「不用黑三角模型」的效果）：
+
+TRIANGLE_BLACK_YOLO_ENABLE=False，或
+TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE="traditional"（即使模型开关开着也不跑黑三角 YOLO），或
+没有 Stage1 ROI（roi_xyxy is None）时，当前逻辑下 也不会跑 Stage2 黑三角 YOLO。
+此时 black_yolo_boxes_work=None（或不等价于「有子框」）。
+
+步骤概要：
+
+try_triangle_scoring 内
+不跑 子框 _extract_triangle_from_yolo_patch。
+直接在 img_det（缩略后的 work） 上调用 detect_triangle_markers：
+全局 Otsu（若 TRIANGLE_SKIP_GLOBAL_OTSU_EXTRACT_ON_YOLO_ROI 在有 ROI 时可能 不算 Otsu 轮廓，但仍会生成 Otsu 图供后续用）；
+可选 象限 ROI（TRIANGLE_ROI_ENABLED）；
+整图 Adaptive（TRIANGLE_ADAPTIVE_BLOCK_SIZES，例如 (11,)）；
+不足再走 放宽 approxPolyDP、BlackHat 等。
+后面同样是过滤、四点组合/象限分配、单应性、PnP 等。
+特点：没有黑三角 NPU 时间，也 没有「按框重复 4 次子框传统」；但要在 一整张（缩略）ROI 图 上跑一套更重的 整图 pipeline。
+
+对照一句话
+用黑三角 YOLO（流程 A）	不用黑三角 YOLO（流程 B）
+Stage2
+黑三角模型给子框 → 子框内 Otsu + 至多一次 Adaptive
+无 Stage2 模型
+三角角点从哪来
+优先 子框传统；不够再 整图 detect_triangle_markers
+只有 整图 detect_triangle_markers
+和「全图是否只做 Adaptive」
+子框 不是只做 Adaptive；整图回退时也与全图路径一致（先 Otsu 等）
+整图路径 也不是只做 Adaptive
+靶环 YOLO（Stage1 裁切）在 A/B 里都可以开或关，与「黑三角模型」是独立开关。
+
+

design_doc/command_record.md

@@ -0,0 +1,102 @@
+
+1. CPP构建命令：在docker环境下执行以下命令
+
+cd /data/cpp_ext
+rm -rf build && mkdir build && cd build
+
+TOOLCHAIN_BIN=/data/MaixCDK-main/dl/extracted/toolchains/maixcam/host-tools/gcc/riscv64-linux-musl-x86_64/bin
+PYDEV=/data/python3_lib_maixcam_musl_3.11.6
+MAIXCDK=/data/MaixCDK-main
+
+cmake .. -G Ninja \
+  -DCMAKE_C_COMPILER="${TOOLCHAIN_BIN}/riscv64-unknown-linux-musl-gcc" \
+  -DCMAKE_CXX_COMPILER="${TOOLCHAIN_BIN}/riscv64-unknown-linux-musl-g++" \
+  -DCMAKE_BUILD_TYPE=Release \
+  -DCMAKE_C_FLAGS="-mcpu=c906fdv -march=rv64imafdcv0p7xthead -mcmodel=medany -mabi=lp64d" \
+  -DCMAKE_CXX_FLAGS="-mcpu=c906fdv -march=rv64imafdcv0p7xthead -mcmodel=medany -mabi=lp64d" \
+  -DPY_INCLUDE_DIR="${PYDEV}/include/python3.11" \
+  -DPY_LIB="${PYDEV}/lib/libpython3.11.so" \
+  -DPY_EXT_SUFFIX=".cpython-311-riscv64-linux-gnu.so" \
+  -DMAIXCDK_PATH="${MAIXCDK}"
+
+ninja
+
+
+2. Maixvision 直接跑项目的时候，是复制到板子上的这个目录：/tmp/maixpy_run
+
+3. 4g 模块的终端测试方法：
+    3.1 一个窗口 ssh 到maixcam的板子上之后，通过 printf 输入命令到 /dev/ttyS2, 然后另外一个窗口通过 cat /dev/ttyS2 输出
+# 1. 确保 PDP 激活
+printf 'AT+CGPADDR=1\r\n' > /dev/ttyS2
+# 2. 开启日志监听（另一个 SSH 窗口）
+cat /dev/ttyS2
+# 3. 发送下载命令（原窗口）
+printf 'AT+MHTTPDLFILE="http://static.shelingxingqiu.com/shoot/v1/main.py","downloaded.py",5120\r\n' > /dev/ttyS2
+
+4. wifi的启动条件，在 /boot 目录下，看看是否有 wifi.sta 和 wifi.ssid， wifi.pass 这些文件。其中 wifi.sta 是开关文件。
+   如果没有了它就不会启动wifi流程。具体的wifi流程 由 /etc/init.d/S30wifi 控制。它会判断 wifi.sta 是否存在，然后是否启动wifi，还是启动热点。
+
+5. 给自己的程序打包到基础镜像中，参考：https://wiki.sipeed.com/maixpy/doc/zh/pro/compile_os.html
+    5.1. 按照链接中的步骤，去github上获取了基础镜像，这次使用的是 v4.12.4，把Assets中的下面几样东西下载下来，我是在windows的wsl中执行的，注意，
+         假如是在windows中下载的文件，在wsl中编译会很慢，所以我采用的是直接在wsl中下载，放到wsl的自己的文件系统中。
+         1）maixcam-2025-12-31-maixpy-v4.12.4.img.xz
+         2）maixcam_builtin_files.tar.xz 
+         3）MaixPy-4.12.4-py3-none-any.whl
+         4）Source code(zip)
+    5.2. 把自己的文件放到 buildtin_files中：
+         1）我把项目文件目录 t11 放到了 maixcam_builtin_files\maixapp\apps 这个目录下。
+         2）为了能让它自启动，我把 auto_start.txt 放到了 maixcam_builtin_files\maixapp 这个目录下。
+         
+    5.3. 然后在解压后的源码中找到tools/os目录下 /home/saga/maixcam/MaixPy-4.12.4/tools/os/maixcam
+        执行
+        export MAIXCDK_PATH=/home/saga/maixcam/MaixCDK
+        编译：
+        ./gen_os.sh ../../../../../maixcam/maixcam-2025-12-31-maixpy-v4.12.4.img ../../../../../maixcam/MaixPy-4.12.4-py3-none-any.whl ../../../../../maixcam/maixcam_builtin_files 0 maixcam
+        注意，在编译过程中，也会去 github 下载内容，所以需要打开梯子。
+    5.4. 等待编译完成，会编译成镜像文件，然后根据 https://wiki.sipeed.com/hardware/zh/maixcam/os.html 这个指引来烧录系统。
+    5.5. 烧录完系统后，需要安装 runtime， 可以按照  https://wiki.sipeed.com/maixpy/doc/zh/README_no_screen.html 这个来升级运行库，或者直接在 Maixvision 中链接的时候安装 runtime。
+    5.6. 安装 runtime 之后，重启，我们的系统就会自己启动起来了。
+
+   遇到问题：
+   /mnt/d/code/shooting/compile_maixcam/MaixPy-4.12.4/MaixPy-4.12.4/tools/os/maixcam/fuse2fs: error while loading shared libraries: libfuse.so.2: cannot open shared object file: No such file or directory
+   解决办法：
+    安装 libfuse2
+    sudo apt update
+     sudo apt install libfuse2
+
+   遇到问题：
+    python 缺少  yaml
+    解决办法：
+    pip install pyyaml
+
+   遇到问题：
+   ./build_all.sh: line 56: maixtool: command not found
+   解决办法：
+   pip install maixtool
+
+    遇到问题：
+    ./update_img.sh: line 80: mcopy: command not found
+    解决办法：
+    sudo apt update
+    sudo apt install mtools
+
+6. 相机标定：
+然后在板子上跑 test 目录下的 test_camera_rtsp.py ，让相机启动了一个服务，然后在电脑上接收这个视频流，并且跑opencv 内置的标定程序：
+set OPENCV_FFMPEG_CAPTURE_OPTIONS="rtsp_transport;tcp"
+opencv_interactive-calibration -t=chessboard -w=9 -h=6 -sz=0.025 -v="http://192.168.1.81:8000/stream" 2>nul
+
+
+7. 生成训练图片：在test目录下，执行以下命令。注意，其中 D:\code\shooting\target_photo\write.png 是靶纸的图片。
+ D:\data\test_target_photo 是用来叠加的背景图
+
+ 7.1 生成靶纸及黑色三角形的截图的图片，带动动，但1.12的外框
+bak
+python .\synth_compose_yolo.py  --perspective 0.04 --perspective-prob 0.8 --color-jitter 0.6  --bg-dir D:\data\test_target_photo --fg D:\code\shooting\target_photo\write.png --out ./synth_out --class-name triangle --zip ./maix_dataset.zip --num 60 --triangles-json archery_triangles_default.json --format voc  --stage2-crop  --stage2-pad-min 0.03  --stage2-pad-max 0.18 --motion-prob 0.9 --motion-kernel-max 8 --blur-max 0 --triangle-bbox-pad-frac 0.12
+
+bak_2
+python synth_keypoints_right_angle.py   --bg-dir D:\data\test_target_photo --fg D:\code\shooting\target_photo\write.png  --triangles-json archery_triangles_default.json --out ./synth_out --num 1000  --offscreen-shift-prob 0.3  --offscreen-shift-frac 0.4   --offscreen-min-visible 1  --stage2-crop  --stage2-pad-min 0.03  --stage2-pad-max 0.18 --motion-prob 0.9 --motion-kernel-max 8 --blur-max 0   --perspective-mode planar   --yaw-max-deg 10 --pitch-max-deg 8   --roll-max-deg 4   --planar-focal-frac 1.45   --perspective-prob 0.4
+
+python synth_keypoints_right_angle.py   --bg-dir D:\data\test_target_photo --fg D:\code\shooting\target_photo\write.png  --triangles-json archery_triangles_default.json --out ./synth_out --num 1000  --offscreen-shift-prob 0.3  --offscreen-shift-frac 0.4   --offscreen-min-visible 1  --stage2-crop  --stage2-pad-min 0.03  --stage2-pad-max 0.18 --motion-prob 1.0 --motion-kernel-max 8 --blur-max 0   --perspective-mode planar   --yaw-max-deg 10 --pitch-max-deg 8   --roll-max-deg 4   --planar-focal-frac 1.45   --perspective-prob 0.4
+
+
+ python pose_pixel_metrics.py --model D:\code\archery\runs\pose\runs\pose\target_pose_train\weights\best.pt --data D:\code\archery\datasets\dataset_pose.yaml --imgsz 640

design_doc/debug.md

@@ -0,0 +1,41 @@
+1. 问题描述：开机失败，一直遇到Traceback (most recent call last):
+      File "/tmp/maixpy_run/main.py", line 525, in <module>
+        cmd_str()
+      File "/tmp/maixpy_run/main.py", line 102, in cmd_str
+        camera_manager.init_camera(640, 480)
+      File "/tmp/maixpy_run/camera_manager.py", line 59, in init_camera
+        self._camera = camera.Camera(width, height)
+                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+        RuntimeError: : Runtime error: mmf vi init failed
+    解决方案：
+    根据过往经验，极有可能是摄像头的接线有问题。因为在测试环境，摄像头是通过一个24针转22针的线出来的，然后再通过一个接线中继，连接到一个22针
+    的fpc线到Maixcam。接线中继如果是24针的，多了两针，需要选好一边然后对连。但这里很容易出错或者松动。可以先用摄像头本身的金色接线直接接到
+    Maixcam，然后跑test目录下的test_cammera.py，看看能不能正常启动，如果正常，就确定是中继接线的问题。
+    
+2. 问题描述：202609 批次的拓展版，在连接 202601 批次的电源板，或者不链接电源板的时候，开机后不久，出错，程序退出，日志是：
+ [v1.2.10] [INFO] network.py:1078 - [NET] TCP主线程启动
+ [v1.2.10] [INFO] network.py:406 - [NET] WiFi不可用或无法连接服务器，使用4G网络
+ [v1.2.10] [INFO] network.py:475 - 连接到服务器,使用4G...
+ [v1.2.10] [INFO] network.py:527 - [4G-TCP] AT+MIPCLOSE=2 response:
+ OK
+ +MIPCLOSE: 2
+ -- [E] read failed
+ Trigger signal, code:SIGSEGV(11)!
+ maix multi-media driver released.
+ ISP Vipipe(0) Free pa(0x8a52c000) va(0x0x3fbeb5e000)
+ program exit failed. exit code: 1. 
+
+    解决方案：
+    从日志看，就是开始发送登录信息之后就崩溃了。出发了底层的read failed。经过排查，是一定要插上电源板的数据连线，以及电源板要插上电池。这个应该是登录时需要读电源电压数据。后面我们已经优化了日志，而且增加了对ina226的试探，但发现ina226不存在的时候，就直接返回电压和电流为0.0。而且，一定要注意，在新配套的电源板和核心板上面，才能正常读到电流和电压。
+
+3. a）问题描述：202609 批次的拓展版，有一块maixcam的蓝灯常亮，询问maixcam的人，他们觉得应该是卡没有插好。但是拓展版上的激光口挡住了数据卡的出口，
+    没法拔出检查，
+    解决方案：需要做拓展版的公司（深链鑫创）在做好板子之后，确定系统能正常启动
+
+    b）问题描述：2022609 批次的拓展板，有一次maixcam的蓝灯亮的时候很长，不会闪烁，后面把sd卡插进去一点，又恢复正常了，初步怀疑是射箭时没有缓冲，
+        导致了sd 卡被撞松了
+
+4. 问题描述：4G模块不可用，模块的绿灯没有闪亮
+    解决方案：有这样的一种情况，就是4G模块的天线，触碰到了旁边的电容，导致短路，所以模块启动失败。需要保证电容和天线的金属头不会触碰
+5. 
+    

design_doc/solution_record.md

@@ -0,0 +1,276 @@
+1.  4G OTA 下载的时候，为什么使用十六进制下载，读取 URC 事件？
+    因为使用二进制下载的时候，经常会出现错误，并且会失败？然后最稳定传输的办法，是每次传输的时候，是分块，而且每次分块都要“删/建”http实例。推测原因是因为我们现在是直接传输文件的源代码，代码中含有了一些字符串可能和 AT指令重复，导致了 AT 模块在解释的时候出错。而使用 16 进制的方式，可以避免这个问题。因为十六进制直接把数据先转成了字符串，然后在设备端再把字符串转成数据，这样就不可能出现 AT的指令，从而减少了麻烦。
+2.  4G OTA 下载的时候，为什么不用 AT 模块里 HTTPDLFILE 的指令？
+    因为在测试中发现，使用 HTTPDLFILE，其实是下载到了 4G 模块内部，需要重新从模块内部转到存储卡，而且 4G 模块的存储较小，大概只有 40k，所以还需要分块来下载和转存，比较麻烦，于是最终使用了使用读取串口事件的模式。
+3.  4G OTA 下载的时候，为什么不用 AT 模块里 HTTPREAD 的指令？
+    因为之前测试发现，READ模式其实是需要多步：
+    3.1. AT+MHTTPCREATE
+    3.2. AT+MHTTPCFG
+    3.3. AT+MHTTPREQUEST
+    3.4. AT+MHTTPREAD
+    它其实也是把数据下载到 4g 模块的缓存里，然后再从缓存里读取出来。所以也是比较繁琐的，还不如 HTTPDLFILE 简单。
+4.  WiFi OTA 流程（ota_manager.handle_wifi_and_update()）
+    * 解析 ota_url 得到 host:port
+    * 调用 network_manager.connect_wifi(ssid, password, verify_host=host, verify_port=port, persist=True)
+    * 只有“能连上 WiFi 且能访问 OTA host:port”才会把新凭证保留在 /boot
+    * 连接成功后开始下载 OTA 文件（download_file()）
+    * 下载成功则 apply_ota_and_reboot()
+5.  TCP 通信
+    1) 平时 TCP 通信主流程（network_manager.tcp_main()）
+        外层无限循环：一直尝试保持与服务器的 TCP 会话。
+        每轮开始：
+            如果 OTA 正在进行：暂停（避免抢占资源/串口）。
+            connect_server()：建立 TCP 连接（自动选 WiFi 或 4G）。
+            发送“登录包”（msg_type=1），等待服务器返回“登录成功”。
+        登录成功后进入内层循环：
+            接收数据：
+                WiFi：非阻塞 recv()；没数据返回 b""；有数据进入缓冲区拼包解析。
+                4G：从 ATClient 的队列 pop_tcp_payload() 取数据。
+            处理命令/ACK：
+                登录响应、心跳 ACK、OTA 命令、关机命令、日志上传命令等。
+            发送业务队列：
+                从高优/普通队列取 1 条，发送失败会放回队首，并断线重连（不再丢消息）。
+            发送心跳：
+                按 HEARTBEAT_INTERVAL 发心跳包。
+                心跳失败会计数（当前为连续失败到阈值才重连）。
+        任何发送/接收致命失败：
+    关闭 socket/断开连接 → 跳出内层循环 → 外层等待一会儿后重新 connect_server() → 重新登录。
+6. “WiFi 连接/验证”
+    TCP 连接建立与网络选择（connect_server() / select_network()）
+    * select_network()：WiFi 优先，但要求：
+        is_wifi_connected() 为 True（系统层面有 WiFi IP 或 Maix WLAN connected）
+        且能连到 TCP 服务器 SERVER_IP:SERVER_PORT
+        否则回退到 4G
+    * connect_server()：
+        若已有连接：WiFi 会做 _check_wifi_connection() 轻量检查；4G 直接认为 OK（由 AT 层维护）。
+        否则按网络类型走：
+            WiFi：创建 socket → connect → setblocking(False)（接收用非阻塞）
+            4G：AT+MIPOPEN 建链
+    WiFi 链接（connect_wifi()）
+    当前 connect_wifi() 的关键特点是：必须让 /etc/init.d/S30wifi restart 真正用新 SSID 去连，所以会临时写 /boot/wifi.ssid 和 /boot/wifi.pass，失败自动回滚。
+    流程是：
+        (1) 备份旧配置
+            * /boot/wifi.ssid、/boot/wifi.pass
+            * /etc/wpa_supplicant.conf（尽量备份）
+        (2) 写入新凭证
+            * 把新 ssid/pass 写到 /boot/*
+            -（同时尽量写 /etc/wpa_supplicant.conf，但不强依赖）
+        (3) 重启 WiFi 服务：/etc/init.d/S30wifi restart
+        (4) 等待获取 IP（默认 20 秒，可调）
+        (5) 验证可用性，连到 verify_host:verify_port
+        (6) 成功
+            * persist=True：保留 /boot/*（持久化）
+            * persist=False：回滚 /boot/* 到旧值（不重启，当前连接仍可继续）
+        (7) 失败
+            * 回滚 /boot/* + 回滚 /etc/wpa_supplicant.conf（如果有备份）
+            * 再 S30wifi restart 恢复旧网络
+            * 返回错误
+
+7. 日志上传（inner_cmd == 43），当前只支持 wifi 上传日志
+    命令带 ssid/password/url 时：
+        * 若 WiFi 未连接：先 connect_wifi(..., verify_host=upload_host, verify_port=upload_port, persist=True)
+    上传内容：
+        * sync # 把日志从内存同步到文件
+        * 快照 app.log* 到 /tmp staging
+        * 打包成 tar.gz（默认）或 zip
+        * 以 multipart/form-data 的 file 字段 POST 到 url
+
+8. 自动关机：
+    hardware中设定了开停表，然后再增加了获取idle的时间。
+    自动关机的时机： 超过配置的idle时长，
+    禁止自动关机的情况：1.校准中，2.OTA中
+    重启计时的时机：1.校准完成，2.命令触发射箭，3.真实触发射箭，4.初始化完成
+9. Wifi网络监控：
+有两次发现wifi网络下，有些消息发送很慢，但具体是什么缘故还不清楚，现在增加了wifi网络下的检测，并一旦发现wifi的网络质量差，就会切换到4G。
+WiFi 连接成功
+    ↓
+启动后台监测线程
+    ↓
+每 5 秒循环：
+    测量 RTT (1 样本，600ms timeout)
+    获取 RSSI
+    更新缓存
+    判断是否差：
+        - RTT >= 600ms → 差
+        - RTT >= 350ms 且 RSSI <= -80dBm → 差
+    ↓
+如果质量差：
+    快速重试2次，如果其中任意一次网络恢复了，继续使用wifi。否则，
+    调用 _switch_to_4g_due_to_poor_wifi()
+    关闭 WiFi socket
+    重置连接状态
+    尝试切换到 4G
+    ↓
+上层检测到连接断开：
+    重新 connect_server() → 自动选择 4G
+
+10. 现在使用的相机，其实是支持更大的分辨率的，比如说1920*1280，但是由于我们的图像处理，拍照处理之后很容易触发OOM。
+
+11. 环数计算流程：
+现在设备侧的目标是：算出箭点相对靶心的偏移(dx,dy)，单位是物理厘米（cm），然后把它作为 x,y 上报给后端；后端再去算环。
+设备侧本身不直接算环数，它算的是偏移与距离，并上报。
+
+算法流程（一次射箭从触发到上报）
+1) 触发后取一帧图
+    在 process_shot() 里读取相机帧并调用 analyze_shot(frame)
+2) 确定激光点（laser_point） 
+
+    analyze_shot() 第一步先确定激光点 (x,y)（像素坐标）：
+
+   硬编码：config.HARDCODE_LASER_POINT=True → 用 laser_manager.laser_point
+   已校准：laser_manager.has_calibrated_point() → 用校准值
+   动态模式：先 detect_circle_v3(frame, None) 粗估距离，再根据距离反推激光点
+   代码在：
+
+   if config.HARDCODE_LASER_POINT:
+       ...
+   elif laser_manager.has_calibrated_point():
+       ...
+   else:
+       _, _, _, _, best_radius1_temp, _ = detect_circle_v3(frame, None)
+       distance_m_first = estimate_distance(best_radius1_temp) ...
+       laser_point = laser_manager.calculate_laser_point_from_distance(distance_m_first)
+3) 优先走三角形路径（成功就直接用于上报 x/y）
+如果 config.USE_TRIANGLE_OFFSET=True，先尝试识别靶面四角三角形标记：
+
+if getattr(config, "USE_TRIANGLE_OFFSET", False):
+    K, dist_coef, pos = _get_triangle_calib()
+    img_rgb = image.image2cv(frame, False, False)
+    tri = try_triangle_scoring(img_rgb, (x, y), pos, K, dist_coef, ...)
+    if tri.get("ok"):
+        return {... "dx": tri["dx_cm"], "dy": tri["dy_cm"], "distance_m": tri.get("distance_m"), ...}
+这一步里 try_triangle_scoring() 做了两件事（都在 triangle_target.py）：
+
+单应性（homography）：把激光点从图像坐标映射到靶面坐标系，得到(dx,dy)（cm）
+PnP：用识别到的角点与相机标定，估算 相机到靶的距离 distance_m
+关键代码：
+
+ok_h, tx, ty, _H = homography_calibration(...)
+out["dx_cm"] = tx
+out["dy_cm"] = -ty
+out["distance_m"] = dist_m
+out["distance_method"] = "pnp_triangle"
+注意：这里 dy_cm 取了负号，是为了和现网约定一致（laser_manager.compute_laser_position 的坐标方向）。
+
+4) 三角形失败 → 回退圆形/椭圆靶心检测（兜底）
+如果三角形不可用或识别失败，就走传统靶心检测：
+
+detect_circle_v3(frame, laser_point) 找黄心/红心、半径、椭圆参数
+用 laser_manager.compute_laser_position() 把像素偏移换算成厘米偏移(dx,dy)
+在 shoot_manager.py：
+
+result_img, center, radius, method, best_radius1, ellipse_params = detect_circle_v3(frame, laser_point)
+if center and radius:
+    dx, dy = laser_manager.compute_laser_position(center, (x, y), radius, method)
+    distance_m = estimate_distance(best_radius1) ...
+在 laser_manager.compute_laser_position()（核心换算逻辑）：
+
+r = radius * 5
+target_x = (lx-cx)/r*100
+target_y = (ly-cy)/r*100
+return (target_x, -target_y)
+这里 (像素差)/(radius*5)*100 是你们旧约定下的“像素→厘米”比例模型（并且 y 方向同样取负号）。
+
+5) 上报数据：把(dx,dy) 作为 x/y 发给后端
+最终上报发生在 process_shot()，直接把 dx,dy 填到 inner_data["x"],["y"]：
+
+srv_x = round(float(dx), 4) if dx is not None else 200.0
+srv_y = round(float(dy), 4) if dy is not None else 200.0
+inner_data = {
+    "x": srv_x,
+    "y": srv_y,
+    "d": round((distance_m or 0.0) * 100),
+    "m": method if method else "no_target",
+    "offset_method": offset_method,
+    "distance_method": distance_method,
+    ...
+}
+network_manager.safe_enqueue(...)
+x,y：物理厘米（cm）
+d：相机到靶距离（m→cm，乘 100；三角形成功时来自 PnP）
+m/offset_method/distance_method：标记本次用的算法路径（triangle / yellow / pnp 等）
+后端收到 x,y 后，再用你之前给的 Go 公式 CalculateRingNumber(x,y,tenRingRadius) 计算环数。
+
+你现在的“环数计算”实际依赖关系
+最好路径（快+稳）：三角形 → dx,dy（单应性） + distance_m（PnP）
+兜底路径：圆/椭圆靶心 → dx,dy（基于黄心半径比例/透视校正） + distance_m（黄心半径估距）
+
+12. 4g模块上传文件：
+
+Upload images from MaixCam to Qiniu cloud via ML307R 4G module's AT commands. The HTTP body requires multipart/form-data with real CR/LF bytes (0x0D 0x0A) in boundaries.
+Methods Tried
+#	Method	AT Commands	Result	Root Cause
+1	Raw binary, no encoding	MHTTPCONTENT with raw bytes + length param	ERROR at first chunk	CR/LF in binary data terminates AT command parser
+2	Encoding mode 2 (escape)	MHTTPCFG="encoding",0,2 + \r\n escapes	Server 400 Bad Request	Module sends literal text \r\n to server, NOT actual 0x0D 0x0A bytes. Multipart body is garbled
+3	Encoding mode 1 (hex)	MHTTPCFG="encoding",0,1 + hex-encoded data	CME ERROR: 650/50	Firmware doesn't properly support hex mode for MHTTPCONTENT
+4	No chunked mode	Skip MHTTPCFG="chunked"	CME ERROR: 65	Module requires chunked mode to accept MHTTPCONTENT at all
+5	Single large MHTTPCONTENT	All data in one command (2793 bytes)	+MHTTPURC: "err",0,5 (timeout)	Possible buffer limit; module hangs then times out
+6	Per-chunk HTTP instance (OTA style)	CREATE→POST→DELETE per chunk	Not feasible	Each instance = separate HTTP request; Qiniu needs complete body in single POST
+Conclusion: AT HTTP layer (MHTTPCONTENT) is fundamentally broken for binary uploads.
+The Solution: Raw TCP Socket (MIPOPEN + MIPSEND)
+Bypass the AT HTTP layer entirely. Open a raw TCP connection and send a hand-crafted HTTP POST:
+plaintext
+AT+MIPCLOSE=3                                    // Clean up old socket
+AT+MIPOPEN=3,"TCP","upload.qiniup.com",80        // Raw TCP connection
+AT+MIPSEND=3,1024  →  ">"  →  [raw bytes]  →  OK  // Binary-safe!
+AT+MIPSEND=3,1024  →  ">"  →  [raw bytes]  →  OK
+AT+MIPSEND=3,766   →  ">"  →  [raw bytes]  →  OK
+// Response: +MIPURC: "rtcp",3,<len>,HTTP/1.1 200 OK...
+AT+MIPCLOSE=3
+Why it works:
+MIPSEND enters prompt mode (>) — after the >, the AT parser treats ALL bytes as data, including CR/LF
+We construct the complete HTTP request ourselves (headers + Content-Length + multipart body) with real CRLF bytes
+
+Key bug found during integration: _send_chunk() wrapped calls in self.at._cmd_lock, but self.at.send() also acquires the same lock internally — threading.Lock() is not reentrant, causing deadlock. Fixed by removing the outer lock (the network_manager.get_uart_lock() already provides thread safety).Trade-off: UART is locked during the entire upload, so heartbeats pause. For small JPEG files (~2-80KB), this is 5-20 seconds — acceptable if server heartbeat timeout is generous
+
+
+13. 算环数算法1：「黄心 + 红心」椭圆/圆：主要在 vision.py 的 detect_circle_v3() 里完成：颜色先用 HSV 做掩码，再在轮廓上做面积、圆度筛选，黄圈用椭圆拟合，红圈预先筛成候选，最后用几何关系配对。
+
+    1. 黄色怎么判、范围是什么？
+    图像先转 HSV（cv2.COLOR_RGB2HSV，注意输入是 RGB）。
+    饱和度 S 整体乘 1.1 并限制在 0–255（让黄色更「显」一点）。
+    黄色 inRange（OpenCV HSV，H 多为 0–179）：
+    通道	下限	上限
+    H       7       32
+    S       80      255
+    V       0       255
+    在黄掩码上找轮廓后，还要满足：面积 > 50，圆度 > 0.7（circularity = 4π·面积/周长²），且点数 ≥5 才 fitEllipse 当黄心椭圆。
+
+    2. 红色怎么判、范围是什么？
+    红色在 HSV 里跨 0°，所以用 两段 H 做并集：
+    两段分别是：
+    H 0–10，S 80–255，V 0–255
+    H 170–180，S 80–255，V 0–255
+    红轮廓候选：面积 > 50，圆度 > 0.6（比黄略松），再拟合椭圆或最小外接圆得到圆心和半径。
+
+    3. 「黄心」和「红心」怎样算一对？（几何范围）
+    对每个黄圈，在红色候选里找第一个满足：
+
+    两圆心距离 dist_centers < yellow_radius * 1.5
+    红半径 red_radius > yellow_radius * 0.8（红在外圈、略大）
+                dist_centers = math.hypot(ddx, ddy)
+                if dist_centers < yellow_radius * 1.5 and rc["radius"] > yellow_radius * 0.8:
+    小结：黄色 = HSV H∈[7,32]、S≥80（且 S 放大 1.1）+ 形态学闭运算 + 面积/圆度；红色 = 两段 H（0–10 与 170–180）、S≥80 + 闭运算 + 面积/圆度；配对用 同心/包含 的距离与半径比例阈值。若你还关心 laser_manager.py 里「激光红点」的另一套阈值（LASER_*），那是另一条链路，和靶心黄/红 HSV 可以分开看。
+
+14. 算环数算法2：
+    使用单应性矩阵计算：镜头中心点（照片中心像素）到虚拟平面的转换。它不需要知道相机在 3D 空间中的具体位置，直接通过单应性矩阵 H的逆运算，将 2D 像素“翻译”成虚拟平面上的 2D 坐标。
+
+    一、转换的本质：2D 到 2D 的“查字典”
+    单应性变换（Homography）是平面到平面的映射。它不处理 3D 空间中的“投影线”，而是直接建立图像像素 (u,v)​ 与虚拟平面坐标 (x,y)​ 的一一对应关系。
+    你可以把单应性矩阵 H想象成一本“翻译字典”：
+    正变换 H：已知靶纸上的真实位置 (x,y)，查字典得到它在照片上哪个像素 (u,v)。
+    逆变换 H−1：已知照片上的像素 (u,v)（如镜头中心点），查字典反推它在靶纸上的真实位置 (x,y)。
+    这个“虚拟平面”就是你的靶纸平面（Z=0 的世界坐标系）。算法没有在物理上移动任何点，只是在做坐标系的换算。
+
+    二、详细步骤：镜头中心点如何“落地”
+
+    相机分辨率是 640x480，镜头中心点（光轴与图像的交点）通常是 (u0,v0)=(320,240)。
+    1. 输入：镜头中心点（像素）
+    2. 核心运算：乘以逆矩阵
+    通过 4 个黑色三角形的角点（已知真实坐标）计算出了单应性矩阵 H。现在使用它的逆矩阵 H−1
+    3. 输出：虚拟平面上的落点（物理坐标）
+    计算后，你会得到：(xhit,yhit)
+    这就是镜头中心点对应的靶纸上的真实位置（单位：毫米）。
+    4. 计算环数
+    由于虚拟平面原点 (0,0)就是靶纸圆心，直接计算欧氏距离。​
+    这个 d就是箭着点偏离圆心的真实物理距离，直接用于环数判定。

design_doc/todo.md

@@ -0,0 +1,25 @@
+你现在要防的是“别人拿到设备/拿到代码包后，能伪造请求、刷接口、下发恶意 OTA、甚至劫持通信”。单靠隐藏 Python 源码只能提高门槛，真正的安全要靠协议和密钥设计。结合你仓库里实际内容，建议你重点隐藏/整改这些点（按风险排序）。
+
+
+1.4 OTA 下发“url”如果缺少强校验，就是远程代码执行入口
+你 OTA 逻辑里从服务器指令拿到 url 就去下载并替换文件/重启（这是正常 OTA），但安全性取决于：
+是否只允许白名单域名/路径
+是否强制 https 并校验服务器证书
+是否对 OTA 包做签名校验（最关键）
+你这里能看到固定域名 static.shelingxingqiu.com 的特殊处理（ota_manager.py 里还在纠结 http/https），这块一定要“服务端签名 + 设备端验签”，否则隐藏源码也没用。
+
+2. 建议隐藏（中风险，但很容易被人利用）
+2.1 所有服务器地址/端口/API 路径（可被用于扫描、压测、撞库、协议逆向）
+这些在 config.py 是明文：
+SERVER_IP = "stcp.shelingxingqiu.com"SERVER_PORT = 50005HTTP_URL = "http://ws.shelingxingqiu.com"HTTP_API_PATH = "/home/shoot/device_fire/arrow/fire"
+注意：即使你把它们藏进 .so，攻击者仍可能通过抓包/观察 DNS/SNI/流量拿到域名或 IP，所以“隐藏”只能降低静态分析风险，不能替代鉴权/签名。
+
+
+
+3. 最有效的“防黑”措施（比隐藏更关键）
+
+所有上报/控制命令加签名 + nonce/timestamp + 服务端防重放（别人抓到一次包也不能复用）
+OTA 包必须做签名校验（设备端内置公钥，下载后验签通过才应用）
+
+如果你告诉我：你们服务端目前能不能改协议（例如新增签名字段、下发 challenge、做 OTA 签名），我可以按“最小改动但提升最大安全”的顺序，帮你规划一套从现状平滑升级的方案。
+

keygen.py

@@ -0,0 +1,52 @@
+import os
+
+
+def generate_key_pair():
+    """
+    生成一对新的密钥a和b，使得a XOR b等于原始key
+    :return: (a, b, key) 元组，每个元素都是32字节的字节数组
+    """
+    # 原始key值
+    key = bytes([
+        0x5d, 0xf9, 0xef, 0xc4, 0x5d, 0xcc, 0xc7, 0x8d, 0xc9, 0x86, 0x34, 0x11, 0x6f, 0xb4, 0xcf, 0x75,
+        0xbf, 0x24, 0x47, 0x9d, 0xd6, 0x5d, 0x83, 0x4b, 0xa6, 0xc0, 0xde, 0x27, 0x91, 0x92, 0xb1, 0x63
+    ])
+
+    # 随机生成a
+    a = os.urandom(32)
+
+    # 计算b = key XOR a
+    b = bytes([key[i] ^ a[i] for i in range(32)])
+
+    return a, b, key
+
+
+def format_hex_array(data):
+    """
+    将字节数组格式化为C++风格的十六进制数组
+    :param data: 字节数组
+    :return: 格式化后的字符串
+    """
+    return "{" + ",".join([f"0x{b:02x}" for b in data]) + "}"
+
+
+def generate_new_key_pair():
+    """
+    生成新的密钥对并打印出来
+    """
+    a, b, key = generate_key_pair()
+
+    print("原始key:")
+    print(format_hex_array(key))
+    print("\n新的密钥对:")
+    print("a =", format_hex_array(a))
+    print("b =", format_hex_array(b))
+
+    # 验证a XOR b是否等于key
+    verify_key = bytes([a[i] ^ b[i] for i in range(32)])
+    assert verify_key == key, "验证失败：a XOR b 不等于 key"
+    print("\n验证成功：a XOR b 等于 key")
+
+
+if __name__ == "__main__":
+    generate_new_key_pair()

laser_detector.py

@@ -1,248 +0,0 @@
-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

@@ -6,6 +6,7 @@
 """
 import _thread
 import json
+import math
 import os
 import binascii
 from maix import time
@@ -34,9 +35,13 @@ 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）====================
@@ -54,8 +59,8 @@ class LaserManager:
     @property
     def laser_point(self):
         """当前激光点（如果启用硬编码，则返回硬编码值）"""
-        # if config.HARDCODE_LASER_POINT:
+        if config.HARDCODE_LASER_POINT:
-        #     return config.HARDCODE_LASER_POINT_VALUE
+            return config.HARDCODE_LASER_POINT_VALUE
         return self._laser_point
     
     def get_last_frame_with_ellipse(self):
@@ -102,29 +107,261 @@ class LaserManager:
     # ==================== 业务方法 ====================
     
     def load_laser_point(self):
-        """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"""
+        """从配置文件加载激光中心点，失败则使用默认值
-        # 优先：从本地持久化文件加载（由 cmd 201 保存）
+        如果启用硬编码模式，则直接使用硬编码值
-        try:
+        """
-            if "laser_config.json" in os.listdir("/root"):
-                with open(config.CONFIG_FILE, "r") as f:
-                    data = json.load(f)
-                    if isinstance(data, list) and len(data) == 2:
-                        self._laser_point = (int(data[0]), int(data[1]))
-                        self.logger.info(f"[LASER] 从本地加载激光点: {self._laser_point}")
-                        return self._laser_point
-        except Exception:
-            pass
-
-        # 其次：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
         
-        # 最后：默认值
+        # 正常模式：从配置文件加载
-        self._laser_point = config.DEFAULT_LASER_POINT
+        try:
-        self.logger.info(f"[LASER] 使用默认激光点: {self._laser_point}")
+            if os.path.exists(config.CONFIG_FILE):
+                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}")
                         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
+        
+        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
@@ -828,6 +1065,172 @@ 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
@@ -1261,28 +1664,6 @@ 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（有界队列，防止内存泄漏；满时自动丢弃旧日志）
+            # 创建日志队列（无界队列）
-            self._log_queue = queue.Queue(maxsize=config.LOG_QUEUE_MAXSIZE)
+            self._log_queue = queue.Queue(-1)
             
             # 确保日志文件所在的目录存在
             log_dir = os.path.dirname(log_file)

main.py

@@ -122,7 +122,7 @@ def cmd_str():
 
     # 1. 初始化日志系统
     import logging
-    logger_manager.init_logging(log_level=logging.WARNING)
+    logger_manager.init_logging(log_level=logging.DEBUG)
     logger = logger_manager.logger
 
     # 补充：因为初始化的时候，激光会亮，先关了它
@@ -283,34 +283,41 @@ def cmd_str():
 
     pressure_buf = []
     pressure_sum = 0
+    pressure_abs_sum = 0
     pressure_min = 4095
     pressure_max = 0
     pressure_t0_ms = None
+    last_avg_abs = 0
 
     def _flush_pressure_buf(reason: str):
-        nonlocal pressure_buf, pressure_sum, pressure_min, pressure_max, pressure_t0_ms, logger
+        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
-        if config.AIR_PRESSURE_lOG:
         t1_ms = time.ticks_ms()
         n = len(pressure_buf)
         avg = (pressure_sum / n) if n else 0
+        avg_abs = (pressure_abs_sum / n) if n else 0
+        # 一行输出：方便后处理画曲线；同时带上统计信息便于快速看波峰
         line = (
             f"[气压批量] reason={reason} "
             f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
-                f"min={pressure_min} max={pressure_max} avg={avg:.1f} "
+            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)
-        # 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():
@@ -329,7 +336,6 @@ def cmd_str():
                 time.sleep_ms(250)
                 continue
 
-            # todo 去除或者不在这里检测
             # 不在 OTA 状态下，检测是否空闲足够长，自动关机
             # print(f"[MAIN] 空闲时间: {hardware_manager.get_idle_time_in_sec() }秒")
             # print(f"配置关机时间：{config.AUTO_POWER_OFF_IN_SECONDS} 秒")
@@ -346,10 +352,12 @@ def cmd_str():
                 if network_manager.manual_trigger_flag:
                     network_manager.clear_manual_trigger()
                     adc_val = config.ADC_TRIGGER_THRESHOLD + 1
+                    adc_abs_val = 10
                     if logger:
                         logger.info("[TEST] TCP命令触发射箭")
                 else:
                     adc_val = hardware_manager.adc_obj.read()
+                    adc_abs_val = hardware_manager.adc_obj.read_vol()
             except Exception as e:
                 logger = logger_manager.logger
                 if logger:
@@ -360,8 +368,9 @@ def cmd_str():
             # ====== 气压采样缓存（每次循环都记录，批量输出日志）======
             if pressure_t0_ms is None:
                 pressure_t0_ms = current_time
-            pressure_buf.append(adc_val)
+            pressure_buf.append((adc_val, adc_abs_val))
             pressure_sum += adc_val
+            pressure_abs_sum += adc_abs_val
             if adc_val < pressure_min:
                 pressure_min = adc_val
             if adc_val > pressure_max:
@@ -393,7 +402,14 @@ def cmd_str():
             else:
                 if config.SHOW_CAMERA_PHOTO_WHILE_SHOOTING:
                     try:
-                        camera_manager.show(camera_manager.read_frame())
+                        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)
                     except Exception as e:
                         logger = logger_manager.logger
                         if logger:

model_270139.mud

@@ -0,0 +1,13 @@
+
+[basic]
+type = cvimodel
+model = model_270139.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 = 黑三角和圆环
+

ota_curl.sh

@@ -1,57 +0,0 @@
-#!/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

package.py

@@ -0,0 +1,230 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+应用打包脚本
+根据 app.yaml 中列出的文件，打包成 zip 文件
+版本号从 version.py 中读取
+"""
+import argparse
+import os
+import yaml
+import zipfile
+from datetime import datetime
+import sys
+import secrets
+
+MAGIC = b"AROTAE1"  # 7 bytes: Archery OTA Encrypted v1
+GCM_NONCE_LEN = 12
+GCM_TAG_LEN = 16
+
+# 添加当前目录到路径，以便导入 version 模块
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+
+def load_app_yaml(yaml_path='app.yaml'):
+    """加载 app.yaml 文件"""
+    try:
+        with open(yaml_path, 'r', encoding='utf-8') as f:
+            return yaml.safe_load(f)
+    except Exception as e:
+        print(f"[ERROR] 读取 {yaml_path} 失败: {e}")
+        return None
+
+
+def check_files_exist(files, base_dir='.'):
+    """检查文件是否存在"""
+    missing_files = []
+    existing_files = []
+
+    for file_path in files:
+        full_path = os.path.join(base_dir, file_path)
+        if os.path.exists(full_path):
+            existing_files.append(file_path)
+        else:
+            missing_files.append(file_path)
+
+    return existing_files, missing_files
+
+
+def get_version_from_version_py():
+    """从 version.py 读取版本号"""
+    try:
+        from version import VERSION
+        return VERSION
+    except ImportError:
+        print("[WARNING] 无法导入 version.py，使用默认版本号 1.0.0")
+        return '1.0.0'
+    except Exception as e:
+        print(f"[WARNING] 读取 version.py 失败: {e}，使用默认版本号 1.0.0")
+        return '1.0.0'
+
+
+def create_zip_package(app_info, files, output_dir='.', base_dir='.'):
+    """创建 zip 打包文件"""
+    # 生成输出文件名：{name}_v{version}_{timestamp}.zip
+    # 版本号从 version.py 读取，而不是从 app.yaml
+    app_name = app_info.get('name', 'app')
+    version = get_version_from_version_py()  # 从 version.py 读取版本号
+    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+    zip_filename = f"{app_name}_v{version}_{timestamp}.zip"
+    zip_path = os.path.join(output_dir, zip_filename)
+
+    print(f"[INFO] 开始打包: {zip_filename}")
+    print(f"[INFO] 包含文件数: {len(files)}")
+
+    try:
+        with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
+            for file_path in files:
+                full_path = os.path.join(base_dir, file_path)
+                # 使用相对路径作为 zip 内的路径
+                zipf.write(full_path, file_path)
+                print(f"  ✓ {file_path}")
+
+        # 获取文件大小
+        file_size = os.path.getsize(zip_path)
+        file_size_mb = file_size / (1024 * 1024)
+
+        print(f"\n[SUCCESS] 打包完成!")
+        print(f"  文件名: {zip_filename}")
+        print(f"  文件大小: {file_size_mb:.2f} MB ({file_size:,} 字节)")
+        print(f"  文件路径: {os.path.abspath(zip_path)}")
+
+        return zip_path
+    except Exception as e:
+        print(f"[ERROR] 打包失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return None
+
+
+def _validate_key_hex(key_hex: str) -> bytes:
+    if not isinstance(key_hex, str):
+        raise ValueError("aead key must be hex string")
+    key_hex = key_hex.strip().lower()
+    if key_hex.startswith("0x"):
+        key_hex = key_hex[2:]
+    if len(key_hex) != 64:
+        raise ValueError("aead key must be 64 hex chars (32 bytes)")
+    try:
+        key = bytes.fromhex(key_hex)
+    except Exception as e:
+        raise ValueError(f"invalid hex key: {e}")
+    if len(key) != 32:
+        raise ValueError("aead key must be 32 bytes")
+    return key
+
+
+def encrypt_zip_aead(zip_path: str, key_hex: str, out_ext: str = ".enc") -> str:
+    """
+    Encrypt the whole zip file as one blob:
+    output format: MAGIC(7) | nonce(12) | ciphertext(N) | tag(16)
+    using AES-256-GCM (AEAD).
+    """
+    # Lazy import: packaging-only dependency
+    try:
+        from cryptography.hazmat.primitives.ciphers.aead import AESGCM
+    except Exception as e:
+        raise RuntimeError(
+            "Missing dependency: cryptography. Install with: pip install cryptography. "
+            f"Import error: {e}"
+        )
+
+    key = _validate_key_hex(key_hex)
+    with open(zip_path, "rb") as f:
+        plain = f.read()
+
+    nonce = secrets.token_bytes(GCM_NONCE_LEN)
+    aesgcm = AESGCM(key)
+    ct_and_tag = aesgcm.encrypt(nonce, plain, None)  # ciphertext || tag (16 bytes)
+
+    enc_path = zip_path + out_ext if out_ext else (zip_path + ".enc")
+    with open(enc_path, "wb") as f:
+        f.write(MAGIC)
+        f.write(nonce)
+        f.write(ct_and_tag)
+
+    return enc_path
+
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(description="打包 app.yaml 文件列表到 zip，并可选进行 AES-256-GCM 加密输出 .enc")
+    parser.add_argument("--aead-key-hex", default=None, help="AES-256-GCM key (64 hex chars = 32 bytes). If set, output encrypted file.")
+    parser.add_argument("--keep-zip", action="store_true", help="Keep the plaintext zip when encryption is enabled.")
+    parser.add_argument("--out-ext", default=".enc", help="Encrypted output extension appended to zip path. Default: .enc (produces *.zip.enc)")
+    args = parser.parse_args()
+
+    print("=" * 60)
+    print("应用打包脚本")
+    print("=" * 60)
+
+    # 1. 加载 app.yaml
+    app_info = load_app_yaml('app.yaml')
+    if app_info is None:
+        return
+
+    # 从 version.py 读取版本号
+    version = get_version_from_version_py()
+    
+    print(f"\n[INFO] 应用信息:")
+    print(f"  ID: {app_info.get('id', 'N/A')}")
+    print(f"  名称: {app_info.get('name', 'N/A')}")
+    print(f"  版本: {version} (来自 version.py)")
+    print(f"  作者: {app_info.get('author', 'N/A')}")
+    if app_info.get('version') != version:
+        print(f"  [注意] app.yaml 中的版本 ({app_info.get('version', 'N/A')}) 与 version.py 不一致")
+
+    # 2. 获取文件列表
+    files = app_info.get('files', [])
+    if not files:
+        print("[ERROR] app.yaml 中没有找到 files 列表")
+        return
+
+    print(f"\n[INFO] 文件列表 ({len(files)} 个文件):")
+
+    # 3. 检查文件是否存在
+    existing_files, missing_files = check_files_exist(files)
+
+    if missing_files:
+        print(f"\n[WARNING] 以下文件不存在，将被跳过:")
+        for f in missing_files:
+            print(f"  ✗ {f}")
+
+    if not existing_files:
+        print("\n[ERROR] 没有找到任何有效文件，无法打包")
+        return
+
+    print(f"\n[INFO] 找到 {len(existing_files)} 个有效文件")
+
+    # 4. 创建 zip 包
+    zip_path = create_zip_package(app_info, existing_files)
+
+    if zip_path:
+        enc_path = None
+        if args.aead_key_hex:
+            try:
+                enc_path = encrypt_zip_aead(zip_path, args.aead_key_hex, out_ext=args.out_ext)
+                enc_size = os.path.getsize(enc_path)
+                print(f"\n[SUCCESS] AEAD加密完成: {os.path.basename(enc_path)} ({enc_size:,} bytes)")
+                print(f"  文件路径: {os.path.abspath(enc_path)}")
+                if not args.keep_zip:
+                    try:
+                        os.remove(zip_path)
+                        print(f"[INFO] 已删除明文zip: {os.path.basename(zip_path)}")
+                    except Exception as e:
+                        print(f"[WARNING] 删除明文zip失败（可忽略）: {e}")
+            except Exception as e:
+                print(f"\n[ERROR] AEAD加密失败: {e}")
+                print("[ERROR] 保留明文zip用于排查。")
+
+        print("\n" + "=" * 60)
+        print("打包成功完成!")
+        print("=" * 60)
+    else:
+        print("\n" + "=" * 60)
+        print("打包失败!")
+        print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

power.py

@@ -5,8 +5,6 @@
 提供电压、电流监测和充电状态检测
 """
 import config
-import os
-import subprocess
 from logger_manager import logger_manager
 from maix import time as maix_time
 
@@ -87,7 +85,7 @@ def get_bus_voltage():
 def get_current():
     """
     读取电流（单位：mA）
-    当前电源板实测：正数表示放电，负数表示充电。
+    正数表示充电，负数表示放电
     
     INA226 电流计算公式：
     Current = (Current Register Value) × Current_LSB
@@ -98,13 +96,13 @@ def get_current():
             return 0.0
         raw = read_register(config.REG_CURRENT)
         # INA226 电流寄存器是16位有符号整数
-        # 最高位是符号位；电流方向含义取决于电源板的采样电阻接线方向。
+        # 最高位是符号位：0=正（充电），1=负（放电）
         # 计算 Current_LSB（根据 CALIBRATION_VALUE）
         current_lsb = 0.001 * config.CALIBRATION_VALUE / 4096  # 单位：A
         # 处理有符号数：如果最高位为1，转换为负数
-        if raw & 0x8000:
+        if raw & 0x8000:  # 最高位为1，表示负数（放电）
             signed_raw = raw - 0x10000  # 转换为有符号整数
-        else:
+        else:  # 最高位为0，表示正数（充电）
             signed_raw = raw
         # 转换为毫安
         current_ma = signed_raw * current_lsb * 1000
@@ -131,7 +129,7 @@ def is_charging(threshold_ma=10.0):
     """
     try:
         current = get_current()
-        is_charge = current < -abs(float(threshold_ma))
+        is_charge = current > threshold_ma
         return is_charge
     except Exception as e:
         logger = logger_manager.logger

set_autostart.py

@@ -0,0 +1,57 @@
+import configparser, os
+
+def parse_apps_info():
+    info_path = "/maixapp/apps/app.info"
+    conf = configparser.ConfigParser()
+    conf.read(info_path)
+    version = conf["basic"]["version"]
+    apps = {}
+    for id in list(conf.keys()):
+        if id in ["basic", "DEFAULT"]:
+            continue
+        apps[id] = conf[id]
+    return apps
+
+def list_apps():
+    apps = parse_apps_info()
+    print(f"APP num: {len(apps)}")
+    for i, (id, info) in enumerate(apps.items()):
+        name_zh = info.get("name[zh]", "")
+        print(f"{i + 1}. [{info['name']}] {name_zh}:")
+        print(f"    id: {id}")
+        print(f"    exec: {info['exec']}")
+        print(f"    author: {info['author']}")
+        print(f"    desc: {info['desc']}")
+        print(f"    desc_zh: {info.get('desc', 'None')}")
+        print("")
+
+
+def get_curr_autostart_app():
+    path = "/maixapp/auto_start.txt"
+    if os.path.exists(path):
+        with open(path, "r") as f:
+            app_id = f.readline().strip()
+            return app_id
+    return None
+
+def set_autostart_app(app_id):
+    path = "/maixapp/auto_start.txt"
+    if not app_id:
+        if os.path.exists(path):
+            os.remove(path)
+        return
+    with open(path, "w") as f:
+        f.write(app_id)
+    os.sync()
+
+if __name__ == "__main__":
+    new_autostart_app_id = "t11"   # change to app_id you want to set
+    # new_autostart_app_id = None           # remove autostart
+    # new_autostart_app_id = "z1222"   # change to app_id you want to set
+
+    list_apps()
+    print("Before set autostart appid:", get_curr_autostart_app())
+    set_autostart_app(new_autostart_app_id)
+    print("Current autostart appid:", get_curr_autostart_app())
+
+

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
+from vision import estimate_distance, detect_circle_v3, enqueue_save_shot, enqueue_save_raw_shot
 from maix import image, time
 
 # 缓存相机标定与三角形位置，避免每次射箭重复读磁盘
@@ -58,6 +58,7 @@ 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
@@ -102,9 +103,22 @@ 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,
@@ -114,6 +128,7 @@ 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
@@ -129,8 +144,10 @@ 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()
+        _t_yolo_ring = time_std.perf_counter() if _timing_on else None
         try:
             from target_roi_yolo import try_get_triangle_roi_from_yolo
             roi_xyxy = try_get_triangle_roi_from_yolo(
@@ -140,6 +157,7 @@ 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
 
     _loc_mode = str(
@@ -155,7 +173,7 @@ 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()
+        _t_yolo_black = time_std.perf_counter() if _timing_on else None
         try:
             from target_roi_yolo import try_black_triangle_boxes_work
 
@@ -166,6 +184,7 @@ 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
     elif (
         logger
@@ -184,7 +203,7 @@ 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()
+            _t_wall_try = time_std.perf_counter() if _timing_on else None
             tri = try_triangle_scoring(
                 img_cv, (x, y), pos, K, dist_coef,
                 size_range=getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500)),
@@ -193,8 +212,8 @@ 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
+            _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)):
+            if logger and bool(getattr(config, "TRIANGLE_LOG_E2E_TIMING", True)) and _timing_on:
                 _e2e = float(yolo_ring_ms) + float(yolo_black_ms) + float(_wall_try_ms)
                 logger.info(
                     f"[TRI] timing_e2e_triangle_ms={_e2e:.1f} "
@@ -280,6 +299,16 @@ 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
@@ -318,10 +347,21 @@ def process_shot(adc_val):
     :return: None
     """
     logger = logger_manager.logger
+    _timing_on = bool(getattr(config, "ARCHERY_TIMING_ENABLE", True))
 
     try:
-        frame = camera_manager.read_frame()
         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)
@@ -356,6 +396,107 @@ 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"
@@ -366,10 +507,6 @@ def process_shot(adc_val):
         if dx is None and dy is None and logger:
             logger.warning("[MAIN] 未检测到偏移量（三角形与圆形均失败），但会保存图像")
 
-        # 生成射箭ID
-        from shot_id_generator import shot_id_generator
-        shot_id = shot_id_generator.generate_id()
-
         if logger:
             logger.info(f"[MAIN] 射箭ID: {shot_id}")
 
@@ -386,7 +523,7 @@ def process_shot(adc_val):
             "shot_id": shot_id,
             "x": srv_x,
             "y": srv_y,
-            "r": 20.0,  # 保留字段（服务端当前忽略，物理外环半径 cm）
+            "r": target_radius_cm,  # 物理靶半径 cm：40cm靶=20，20cm靶=10
             "d": round((distance_m or 0.0) * 100),
             "d_laser": round((laser_distance_m or 0.0) * 100),
             "d_laser_quality": laser_signal_quality,
@@ -397,6 +534,7 @@ 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:
@@ -471,6 +609,11 @@ 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

@@ -0,0 +1,403 @@
+#!/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_audio.py

@@ -0,0 +1,50 @@
+# test_audio.pyx
+from maix import audio, time, app, gpio
+
+def run_player_loop():
+    """
+    播放控制主循环函数
+    """
+    # 初始化音频播放器
+    p = audio.Player("/root/gun.wav")
+    p.volume(40)
+    
+    # 初始化 GPIO 引脚为输出
+    led = gpio.GPIO("A25", gpio.Mode.OUT)
+    # 设置低电平
+    led.value(0)
+    
+    # 主循环
+    while not app.need_exit():
+        led.value(1)           # 点亮 LED
+        time.sleep_ms(200)     # 保持 200ms
+        led.value(0)           # 熄灭 LED
+        p.play()               # 播放音频
+        time.sleep_ms(1000)    # 等待 1 秒
+    
+    print("play finish!")
+
+
+# 可选：添加一个简单的测试函数
+def hello():
+    return "Hello from test_audio!"
+
+
+# 可选：添加一个初始化函数
+def init_led():
+    """单独测试 GPIO"""
+    led = gpio.GPIO("A25", gpio.Mode.OUT)
+    led.value(0)
+    return "LED initialized"
+
+
+# 可选：添加一个播放测试函数
+def test_play():
+    """单独测试音频播放"""
+    p = audio.Player("/root/gun.wav")
+    p.volume(50)
+    p.play()
+    return "Playing..."
+
+
+run_player_loop()

test/test_button.py

@@ -0,0 +1,25 @@
+from maix import audio, time, app,gpio
+
+
+# button1 = gpio.GPIO("ADC", gpio.Mode.IN)
+button3 = gpio.GPIO("A26", gpio.Mode.IN) # 可用
+button2 = gpio.GPIO("A16", gpio.Mode.IN)
+#设置低电平
+from maix.peripheral import adc
+channel = 0
+res_bit = adc.RES_BIT_12
+_adc_obj = adc.ADC(channel, res_bit)
+
+
+while not app.need_exit():
+    # print(f"b1: {button1.value()}")
+
+    print(f"b2: {button2.value()}")
+
+    # print(_adc_obj.read_vol())
+    print(f"b3: {button3.value()}")
+    time.sleep_ms(50)
+
+    # time.sleep_ms(1000)
+
+

test/test_camera_rtsp.py

@@ -0,0 +1,36 @@
+# from maix import time, rtsp, camera, image
+
+# # 1. 初始化摄像头（注意：RTSP需要NV21格式）
+# # 分辨率可以根据需要调整，如 640x480 或 1280x720
+# cam = camera.Camera(640, 480, image.Format.FMT_YVU420SP)
+
+# # 2. 创建并启动RTSP服务器
+# server = rtsp.Rtsp()
+# server.bind_camera(cam)
+# server.start()
+
+# # 3. 打印出访问地址，例如： rtsp://192.168.xxx.xxx:8554/live
+# print("RTSP 流地址:", server.get_url())
+
+# # 4. 保持服务运行
+# while True:
+#     time.sleep(1)
+
+
+
+from maix import camera, time, app, http, image
+
+# 初始化相机，注意格式要用 FMT_RGB888（JPEG 编码需要 RGB 输入）
+cam = camera.Camera(640, 480, image.Format.FMT_RGB888)
+
+# 创建 JPEG 流服务器
+stream = http.JpegStreamer()
+stream.start()
+
+print("RTSP 替代方案 - HTTP JPEG 流地址: http://{}:{}".format(stream.host(), stream.port()))
+print("请在浏览器或 OpenCV 中访问: http://<MaixCAM_IP>:8000/stream")
+
+while not app.need_exit():
+    img = cam.read()
+    jpg = img.to_jpeg()      # 将 RGB 图像编码为 JPEG
+    stream.write(jpg)        # 推送到 HTTP 客户端

test/test_cammera.py

@@ -0,0 +1,20 @@
+# test_camera.py
+from maix import camera, display, time
+
+try:
+    print("Initializing camera...")
+    cam = camera.Camera(640,480)
+    # cam = camera.Camera(1280,720)
+    # cam.get_exposure_us()
+    # print("Camera exposure: ", cam.get_exposure_us())
+    print("Camera initialized successfully!")
+    
+    disp = display.Display()
+    
+    while True:
+        frame = cam.read()
+        disp.show(frame)
+        time.sleep_ms(50)
+        
+except Exception as e:
+    print(f"Error: {e}")

test/test_decect_circle.py

@@ -0,0 +1,620 @@
+#!/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
+
+            logger = get_logger()
+            if area > 50 and circularity > 0.7:
+                if logger:
+                    logger.info(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: 0-10度（接近0度的红色）
+                    lower_red1 = np.array([0, 80, 0])
+                    upper_red1 = np.array([10, 255, 255])
+                    mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
+                    
+                    # 红色范围2: 170-180度（接近180度的红色）
+                    lower_red2 = np.array([170, 80, 0])
+                    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
+                    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)
+                    
+                    found_valid_red = False
+                    
+                    if contours_red:
+                        # 找到所有符合条件的红色圆圈
+                        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
+                            
+                            # 红色圆圈也应该有一定的圆度
+                            if area_red > 50 and circularity_red > 0.6:
+                                # 计算红色圆圈的中心和半径
+                                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)
+                                
+                                # 计算黄色和红色圆心的距离
+                                if red_center:
+                                    dx = yellow_center[0] - red_center[0]
+                                    dy = yellow_center[1] - red_center[1]
+                                    distance = np.sqrt(dx*dx + dy*dy)
+                                    
+                                    # 圆心距离阈值：应该小于黄色半径的某个倍数（比如1.5倍）
+                                    max_distance = yellow_radius * 1.5
+                                    
+                                    # 红色圆圈应该比黄色圆圈大（外圈）
+                                    if distance < max_distance and red_radius > yellow_radius * 0.8:
+                                        found_valid_red = True
+                                        logger = get_logger()
+                                        if logger:
+                                            logger.info(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}")
+                                        
+                                        # 记录这个有效目标
+                                        valid_targets.append({
+                                            'center': yellow_center,
+                                            'radius': yellow_radius,
+                                            'ellipse': yellow_ellipse,
+                                            'area': area
+                                        })
+                                        break
+                    
+                    if not found_valid_red:
+                        logger = get_logger()
+                        if logger:
+                            logger.debug("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_test2"  # 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_i2c.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+# test_i2c_devices.py
+
+import os
+from maix import i2c
+
+def list_i2c_devices():
+    """List available I2C device nodes"""
+    print("Available I2C devices:")
+    
+    # Check /dev directory
+    try:
+        dev_files = os.listdir("/dev")
+        i2c_devices = [f for f in dev_files if "i2c" in f]
+        if i2c_devices:
+            for dev in sorted(i2c_devices):
+                print(f"  /dev/{dev}")
+        else:
+            print("  No /dev/i2c-* devices found!")
+    except Exception as e:
+        print(f"  Error listing /dev: {e}")
+
+def try_i2c_bus(bus_num):
+    """Try to initialize an I2C bus"""
+    try:
+        bus = i2c.I2C(bus_num, i2c.Mode.MASTER)
+        print(f"  I2C bus {bus_num}: OK")
+        return True
+    except RuntimeError as e:
+        print(f"  I2C bus {bus_num}: {e}")
+        return False
+    except Exception as e:
+        print(f"  I2C bus {bus_num}: Unexpected error: {e}")
+        return False
+
+def main():
+    print("=" * 60)
+    print("I2C Device Diagnostic")
+    print("=" * 60)
+    
+    # List kernel devices
+    list_i2c_devices()
+    
+    # Try common bus numbers
+    print("\nTesting I2C buses:")
+    working_buses = []
+    for bus_num in range(10):
+        if try_i2c_bus(bus_num):
+            working_buses.append(bus_num)
+    
+    print(f"\nWorking buses: {working_buses}")
+    
+    if not working_buses:
+        print("\nERROR: No I2C buses available!")
+        print("Possible causes:")
+        print("  1. I2C kernel driver not loaded")
+        print("  2. Device tree doesn't enable I2C")
+        print("  3. Different kernel version with different device naming")
+
+if __name__ == "__main__":
+    main()

test/test_laser.py

@@ -0,0 +1,246 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+M01激光测距模块测试脚本 - 修正版
+基于文档中的完整命令示例
+"""
+
+from maix import uart, pinmap, time
+import binascii
+
+# ==================== 配置 ====================
+UART_PORT = "/dev/ttyS1"
+BAUDRATE = 9600
+
+# 初始化串口
+try:
+    pinmap.set_pin_function("A18", "UART1_RX")
+    pinmap.set_pin_function("A19", "UART1_TX")
+    laser_uart = uart.UART(UART_PORT, BAUDRATE)
+    print("✅ 硬件初始化完成")
+except Exception as e:
+    print(f"❌ 初始化失败: {e}")
+    exit(1)
+
+# ==================== 根据文档的完整命令集 ====================
+# 1. 激光开关（文档2.3.10，已验证可用）
+LASER_ON_CMD = bytes([0xAA, 0x00, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x01, 0xC1])
+LASER_OFF_CMD = bytes([0xAA, 0x00, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x00, 0xC0])
+
+# 2. 尝试不同的测距命令格式
+TEST_COMMANDS = [
+    # 格式1：文档2.3.12的单次测量（您测试失败的）
+    {
+        "name": "单次测量 (0x0020)",
+        "cmd": bytes([0xAA, 0x00, 0x00, 0x20, 0x00, 0x01, 0x00, 0x00, 0x21]),
+        "desc": "文档2.3.12 示例命令"
+    },
+    # 格式2：文档2.3.7的读取测量结果
+    {
+        "name": "读取测量结果 (0x0022)",
+        "cmd": bytes([0xAA, 0x80, 0x00, 0x22, 0xA2]),
+        "desc": "文档2.3.7 读取测量结果"
+    },
+    # 格式3：文档2.3.13的快速测量
+    {
+        "name": "快速测量 (0x0022带数据)",
+        "cmd": bytes([0xAA, 0x00, 0x00, 0x22, 0x00, 0x01, 0x00, 0x00, 0x23]),
+        "desc": "文档2.3.13 快速测量"
+    },
+    # 格式4：连续测量模式
+    {
+        "name": "连续测量模式 (0x0021)",
+        "cmd": bytes([0xAA, 0x00, 0x00, 0x21, 0x00, 0x01, 0x00, 0x00, 0x22]),
+        "desc": "文档2.3.14 连续测量"
+    }
+]
+
+def clear_buffer():
+    """清空串口缓冲区"""
+    try:
+        data = laser_uart.read(-1)
+        if data:
+            print(f"清空: {len(data)}字节")
+    except:
+        pass
+
+def send_and_wait(cmd, name, wait_time=2000):
+    """发送命令并等待响应"""
+    print(f"\n📤 发送: {name}")
+    print(f"   命令: {cmd.hex()}")
+    
+    clear_buffer()
+    
+    try:
+        laser_uart.write(cmd)
+        print(f"   已发送 {len(cmd)} 字节")
+    except Exception as e:
+        print(f"   ❌ 发送失败: {e}")
+        return None
+    
+    # 等待响应
+    start_time = time.ticks_ms()
+    response = b""
+    
+    while time.ticks_ms() - start_time < wait_time:
+        try:
+            chunk = laser_uart.read(1)
+            if chunk:
+                response += chunk
+                # 完整响应通常是9或13字节
+                if len(response) >= 9:
+                    # 检查是否完整帧
+                    if response[0] in [0xAA, 0xEE]:
+                        if len(response) >= 13:  # 测距完整响应
+                            break
+                        elif response[0] == 0xEE:  # 错误响应
+                            break
+        except:
+            break
+        
+        time.sleep_ms(10)
+    
+    if response:
+        print(f"   📥 响应: {response.hex()}")
+        print(f"   长度: {len(response)} 字节")
+        
+        # 解析错误码
+        if response[0] == 0xEE and len(response) >= 9:
+            err_code = (response[7] << 8) | response[8]
+            error_mapping = {
+                0x0000: "无错误",
+                0x0001: "硬件错误",
+                0x0002: "无输出数据", 
+                0x0003: "反射信号太弱",
+                0x0004: "反射信号太强",
+                0x0005: "温度太高(>40℃)",
+                0x0006: "温度太低(<-10℃)",
+                0x0007: "电源电压低(<2.5V)",
+                0x0008: "超出量程",
+                0x0009: "读通讯错误",
+                0x000A: "写通讯错误",
+                0x000B: "地址错误"
+            }
+            err_msg = error_mapping.get(err_code, f"未知错误: 0x{err_code:04X}")
+            print(f"   ❌ 模块错误: {err_msg}")
+    else:
+        print("   ⚠️ 无响应")
+    
+    return response
+
+def parse_distance_data(response):
+    """解析距离数据"""
+    if not response or len(response) < 13:
+        return None
+    
+    if response[0] != 0xAA or response[3] not in [0x20, 0x21, 0x22]:
+        return None
+    
+    # 解析4字节BCD码
+    bcd_bytes = response[6:10]
+    distance_int = 0
+    
+    for byte in bcd_bytes:
+        high = (byte >> 4) & 0x0F
+        low = byte & 0x0F
+        
+        if high > 9 or low > 9:
+            return None
+        
+        distance_int = distance_int * 100 + high * 10 + low
+    
+    distance_m = distance_int / 1000.0
+    
+    # 信号质量
+    signal = 0
+    if len(response) >= 12:
+        signal = (response[10] << 8) | response[11]
+    
+    return {
+        'meters': distance_m,
+        'millimeters': distance_m * 1000,
+        'signal': signal,
+        'raw': response.hex()
+    }
+
+# ==================== 主测试 ====================
+print("\n" + "="*50)
+print("M01激光测距模块详细测试")
+print("="*50)
+
+try:
+    # 1. 测试基本连接
+    print("\n1. 测试模块连接...")
+    version_cmd = bytes([0xAA, 0x80, 0x00, 0x0A, 0x8A])
+    resp = send_and_wait(version_cmd, "读取硬件版本")
+    
+    if resp and resp[0] == 0xAA and resp[3] == 0x0A:
+        print(f"✅ 模块正常，版本: {resp[6]:02X}{resp[7]:02X}")
+    else:
+        print("❌ 模块连接测试失败")
+        exit(1)
+    
+    # 2. 开启激光
+    print("\n2. 开启激光...")
+    resp = send_and_wait(LASER_ON_CMD, "开启激光", 1000)
+    if resp and resp.hex() == "aa0001be00010001c1":
+        print("✅ 激光已开启")
+    
+    print("   等待激光稳定...")
+    time.sleep(2)  # 重要等待时间
+    
+    # 3. 尝试不同的测距命令
+    print("\n3. 测试不同测距命令...")
+    
+    for i, test_cmd in enumerate(TEST_COMMANDS):
+        print(f"\n{'='*30}")
+        print(f"测试 {i+1}: {test_cmd['name']}")
+        print(f"{test_cmd['desc']}")
+        print(f"{'='*30}")
+        
+        resp = send_and_wait(test_cmd['cmd'], test_cmd['name'], 3000)
+        
+        if resp:
+            if resp[0] == 0xAA and len(resp) >= 13:
+                result = parse_distance_data(resp)
+                if result:
+                    print(f"✅ 测距成功!")
+                    print(f"   距离: {result['meters']:.3f} m")
+                    print(f"   距离: {result['millimeters']:.1f} mm")
+                    print(f"   信号质量: {result['signal']}")
+                    break
+                else:
+                    print("❌ 无法解析距离数据")
+            elif resp[0] == 0xEE:
+                print("❌ 命令执行错误")
+            else:
+                print("❌ 无效响应格式")
+        else:
+            print("❌ 无响应")
+        
+        time.sleep(1)  # 命令间间隔
+    
+    # 4. 关闭激光
+    print("\n4. 关闭激光...")
+    send_and_wait(LASER_OFF_CMD, "关闭激光", 1000)
+    
+    print("\n" + "="*50)
+    print("🏁 测试完成")
+    print("="*50)
+    
+    print("\n📋 测试总结：")
+    print("1. 模块通信: ✅ 正常")
+    print("2. 激光控制: ✅ 正常")
+    print("3. 测距功能: ❌ 有问题")
+    print("\n建议：")
+    print("1. 检查激光是否实际发光（在暗处观察红点）")
+    print("2. 确保测量目标在有效范围内（0.2-60米）")
+    print("3. 确保目标有足够反射率（白色平面最佳）")
+    print("4. 如果所有测距命令都返回ERR_ADDR，可能是固件版本问题")
+    
+except KeyboardInterrupt:
+    print("\n\n🛑 用户中断")
+    laser_uart.write(LASER_OFF_CMD)
+    print("✅ 已发送关闭指令")
+except Exception as e:
+    print(f"\n❌ 测试出错: {e}")

test/test_laser_center_point.py

@@ -0,0 +1,541 @@
+#!/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_motor.py

@@ -0,0 +1,16 @@
+from maix import gpio, pinmap, time
+ 
+
+#设置引脚为输出
+led = gpio.GPIO("A25", gpio.Mode.OUT)
+#设置低电平
+led.value(0)
+ 
+while 1:
+    # time.sleep_ms(1000)
+    #对该引脚的电平进行取反（原高-》现低）
+    # led.toggle()
+    led.value(1)
+    #延时
+    time.sleep_ms(5000)
+    led.value(0)

test/test_power.py

@@ -0,0 +1,130 @@
+#!/usr/bin/env python3
+# test_power_with_init.py
+
+from maix import i2c, time
+import sys
+
+# INA226 register addresses
+INA226_ADDR = 0x40
+REG_CONFIGURATION = 0x00
+REG_BUS_VOLTAGE = 0x02
+REG_CURRENT = 0x04
+REG_CALIBRATION = 0x05
+
+# Configuration values
+CONFIG_VALUE = 0x4527      # Configuration: 16 averages, 1.1ms conversion time, continuous mode
+CALIBRATION_VALUE = 0x1400  # Calibration value
+
+def write_register(bus, reg, value):
+    """Write to INA226 register"""
+    data = [(value >> 8) & 0xFF, value & 0xFF]
+    bus.writeto_mem(INA226_ADDR, reg, bytes(data))
+
+def read_register(bus, reg):
+    """Read from INA226 register"""
+    data = bus.readfrom_mem(INA226_ADDR, reg, 2)
+    return (data[0] << 8) | data[1]
+
+def init_ina226(bus):
+    """Initialize INA226 chip"""
+    try:
+        # Write configuration register
+        write_register(bus, REG_CONFIGURATION, CONFIG_VALUE)
+        time.sleep_ms(10)
+        
+        # Write calibration register
+        write_register(bus, REG_CALIBRATION, CALIBRATION_VALUE)
+        time.sleep_ms(10)
+        
+        # Verify configuration by reading it back
+        config_read = read_register(bus, REG_CONFIGURATION)
+        if config_read != CONFIG_VALUE:
+            print(f"  Warning: Config readback mismatch: 0x{config_read:04X} != 0x{CONFIG_VALUE:04X}")
+        
+        return True
+    except Exception as e:
+        print(f"  Init failed: {e}")
+        return False
+
+def read_voltage(bus):
+    """Read bus voltage"""
+    raw = read_register(bus, REG_BUS_VOLTAGE)
+    voltage = raw * 1.25 / 1000
+    return voltage
+
+def read_current(bus):
+    """Read current"""
+    raw = read_register(bus, REG_CURRENT)
+    # Handle signed value
+    if raw & 0x8000:
+        raw = raw - 0x10000
+    current_lsb = 0.001 * CALIBRATION_VALUE / 4096
+    current = raw * current_lsb * 1000  # mA
+    return current
+
+def test_i2c_bus(bus_num):
+    """Test a single I2C bus with full initialization"""
+    print(f"\n{'='*60}")
+    print(f"Testing I2C Bus {bus_num}")
+    print(f"{'='*60}")
+    
+    try:
+        # Step 1: Initialize I2C bus
+        print(f"  1. Initializing I2C bus...")
+        bus = i2c.I2C(bus_num, i2c.Mode.MASTER)
+        print(f"     OK")
+        
+        # Step 2: Initialize INA226
+        print(f"  2. Initializing INA226...")
+        if not init_ina226(bus):
+            print(f"     FAILED")
+            return False
+        print(f"     OK")
+        
+        # Step 3: Read voltage multiple times
+        print(f"  3. Reading voltage...")
+        for i in range(5):
+            try:
+                voltage = read_voltage(bus)
+                current = read_current(bus)
+                print(f"     Read {i+1}: {voltage:.3f}V, {current:.1f}mA")
+                time.sleep_ms(100)
+            except Exception as e:
+                print(f"     Read {i+1} failed: {e}")
+        
+        print(f"  SUCCESS")
+        return True
+        
+    except Exception as e:
+        print(f"  FAILED: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def main():
+    """Test all I2C buses"""
+    print("INA226 Test with Proper Initialization")
+    print("=" * 60)
+    
+    # Test buses in order of likelihood
+    test_order = [5, 1, 3, 4, 0, 2]
+    
+    success_buses = []
+    
+    for bus_num in test_order:
+        if test_i2c_bus(bus_num):
+            success_buses.append(bus_num)
+            # If we found a working bus, stop testing others
+            break
+    
+    print(f"\n{'='*60}")
+    print(f"Summary:")
+    print(f"  Working buses: {success_buses}")
+    if not success_buses:
+        print(f"  ERROR: No working I2C bus found!")
+        return 1
+    
+    return 0
+
+if __name__ == "__main__":
+    sys.exit(main())

test/test_yolo26.py

@@ -0,0 +1,18 @@
+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

@@ -0,0 +1,209 @@
+#!/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

@@ -0,0 +1,29 @@
+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")

train_yolo/test_stage2_black_yolo_device.py

@@ -0,0 +1,343 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Stage2 黑三角 YOLO —— 在 Maix 设备上用本地图片测试（与线上 target_roi_yolo.try_black_triangle_boxes_work 完全一致）。
+
+不在 PC 上跑 NPU；需把脚本与 config / target_roi_yolo.py 同步到设备，并在设备上执行。
+
+典型用法
+--------
+  # 输入已是 Stage1 裁切（与你保存的 stage2_roi_*.jpg 一致）
+  python test/test_stage2_black_yolo_device.py /root/phot/stage2_roi_xxx.jpg
+
+  # 输入为整幅相机图，手动给出 Stage1 环靶 ROI（与线上日志 ring全图=[rx0,ry0,rx1,ry1] 一致）
+  python test/test_stage2_black_yolo_device.py /root/phot/full.jpg --roi 197,196,507,461
+
+  # 对比 native / letterbox 坐标映射（排查 contain 训练与推理对齐）
+  python test/test_stage2_black_yolo_device.py ./crop.jpg --compare-coord
+
+  # 覆盖置信度、模型路径（仍读其余项自 config）
+  python test/test_stage2_black_yolo_device.py ./crop.jpg --conf 0.25 -m /maixapp/apps/t11/model_270648.mud
+
+  # 只看 NPU 原始框（映射前）：判断坐标是 ~224 网络空间还是归一化 0~1
+  python test/test_stage2_black_yolo_device.py ./crop.jpg --conf 0.05 --dump-raw 15
+
+依赖：MaixPy（maix.nn）、OpenCV（cv2）、numpy；项目根须在 sys.path（本脚本已插入上级目录）。
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+
+_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+if _ROOT not in sys.path:
+    sys.path.insert(0, _ROOT)
+
+
+def _parse_roi(s: str) -> tuple[int, int, int, int]:
+    parts = [p.strip() for p in s.replace(" ", "").split(",")]
+    if len(parts) != 4:
+        raise ValueError("ROI 需要 4 个整数：x0,y0,x1,y1")
+    return tuple(int(x) for x in parts)  # type: ignore[return-value]
+
+
+def _load_rgb_numpy(path: str) -> "object":
+    import cv2
+    import numpy as np
+
+    bgr = cv2.imread(path, cv2.IMREAD_COLOR)
+    if bgr is None:
+        raise FileNotFoundError(f"cv2.imread 失败: {path}")
+    rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
+    return np.ascontiguousarray(rgb, dtype=np.uint8)
+
+
+def _draw_boxes_on_crop(
+    slab_rgb,
+    boxes: list[tuple[int, int, int, int]],
+    labels: list[str] | None = None,
+):
+    """slab_rgb: H×W×3 RGB uint8；boxes 为扩 margin 后的 Stage2 子框（与线上绿框一致）。"""
+    import cv2
+
+    vis = slab_rgb.copy()
+    bgr = cv2.cvtColor(vis, cv2.COLOR_RGB2BGR)
+    rh, rw = bgr.shape[:2]
+    for i, (bx0, by0, bx1, by1) in enumerate(boxes):
+        x0, y0 = int(bx0), int(by0)
+        x1, y1 = int(bx1) - 1, int(by1) - 1
+        x1 = max(x0, min(x1, rw - 1))
+        y1 = max(y0, min(y1, rh - 1))
+        cv2.rectangle(bgr, (x0, y0), (x1, y1), (0, 255, 0), 2)
+        tag = labels[i] if labels and i < len(labels) else f"s2_{i}"
+        cv2.putText(
+            bgr,
+            tag,
+            (x0, max(0, y0 - 4)),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (0, 255, 0),
+            1,
+            cv2.LINE_AA,
+        )
+    return bgr
+
+
+class _PrintLogger:
+    def info(self, msg):
+        print(msg)
+
+    def warning(self, msg):
+        print(msg)
+
+    def error(self, msg):
+        print(msg)
+
+
+def _run_once(yroi_mod, img_rgb, roi_xyxy, logger):
+    boxes = yroi_mod.try_black_triangle_boxes_work(img_rgb, roi_xyxy, logger)
+    rx0, ry0, rx1, ry1 = roi_xyxy
+    slab = img_rgb[ry0:ry1, rx0:rx1].copy()
+    return boxes, slab
+
+
+def _copy_dump_raw_rows(yroi_mod, objs):
+    """把 Maix detect 返回对象拷贝成基础类型，避免 native 对象跨下一次 detect 存活。"""
+    rows = []
+    for o in objs:
+        cid = yroi_mod._det_obj_class_id(o)
+        try:
+            sc = float(getattr(o, "score", 0.0))
+        except (TypeError, ValueError):
+            sc = 0.0
+        rows.append((cid, sc, float(o.x), float(o.y), float(o.w), float(o.h)))
+    return rows
+
+
+def _dump_raw_and_hard_exit(det, yroi_mod, slab_for_det, rw_s, rh_s, net_w, net_h, conf_th, iou_th, limit):
+    """
+    MaixPy 某些版本在 YOLO detect 返回对象正常析构时会 SIGSEGV/pure virtual。
+    raw dump 是诊断路径，打印完成后硬退出，绕过 Python/native 析构链。
+    """
+    from maix import image as maix_image
+
+    roi_maix = maix_image.cv2image(slab_for_det, False, False)
+    raw = det.detect(roi_maix, conf_th=conf_th, iou_th=iou_th)
+    objs = yroi_mod._normalize_objs(raw if raw is not None else [])
+    dump_rows = _copy_dump_raw_rows(yroi_mod, objs)
+    raw_count = len(dump_rows)
+    print(
+        f"[DUMP-RAW] slab={rw_s}×{rh_s} net={net_w}×{net_h} "
+        f"conf={conf_th} iou={iou_th} → NMS 后 raw 框数={raw_count}（与 coord_mode 无关）"
+    )
+    npr = min(int(limit), raw_count)
+    for i in range(npr):
+        cid, sc, x, y, ww, hh = dump_rows[i]
+        print(f"  #{i} cls={cid} score={sc:.4f} xywh=({x:.3f},{y:.3f},{ww:.3f},{hh:.3f})")
+    if dump_rows:
+        xs = [r[2] for r in dump_rows]
+        ws = [r[4] for r in dump_rows]
+        print(
+            f"[DUMP-RAW]  hint: x 范围≈[{min(xs):.2f},{max(xs):.2f}] "
+            f"w 范围≈[{min(ws):.2f},{max(ws):.2f}] — "
+            f"若整体在 0~{net_w} 量级多为网络画布坐标→应用 letterbox；"
+            f"若 x,w 多在 0~1→可能是归一化，需在代码里乘 net 尺寸"
+        )
+    print("[INFO] --dump-raw 已完成；为规避 MaixPy YOLO native 析构崩溃，测试进程将直接退出。")
+    sys.stdout.flush()
+    sys.stderr.flush()
+    os._exit(0)
+
+
+def main():
+    ap = argparse.ArgumentParser(
+        description="Stage2 黑三角 YOLO 设备本地图测试",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    ap.add_argument("image", help="本地图片路径（设备上的路径）")
+    ap.add_argument(
+        "--roi",
+        default="",
+        metavar="x0,y0,x1,y1",
+        help="可选。若填写：image 为整幅图，在此图上取 Stage1 ROI 再跑 Stage2；"
+        "留空：image 本身就是 Stage1 裁切图（默认）",
+    )
+    ap.add_argument("-o", "--output", default="", help="输出可视化路径；默认 原名_stage2_vis.jpg")
+    ap.add_argument("-m", "--model", default="", help="覆盖 config.TRIANGLE_BLACK_YOLO_MODEL_PATH")
+    ap.add_argument("--conf", type=float, default=None, help="覆盖 TRIANGLE_BLACK_YOLO_CONF_TH")
+    ap.add_argument("--iou", type=float, default=None, help="覆盖 TRIANGLE_BLACK_YOLO_IOU_TH")
+    ap.add_argument(
+        "--coord",
+        choices=["native", "letterbox"],
+        default="",
+        help="覆盖 TRIANGLE_BLACK_YOLO_COORD_MODE；默认用 config",
+    )
+    ap.add_argument(
+        "--compare-coord",
+        action="store_true",
+        help="各跑一次 native 与 letterbox，输出两张图 *_stage2_native.jpg / *_stage2_letterbox.jpg",
+    )
+    ap.add_argument(
+        "--fresh-detector",
+        action="store_true",
+        help="清掉 YOLO 缓存再测（换模型或排查缓存时用）",
+    )
+    ap.add_argument(
+        "--allow-save-roi",
+        action="store_true",
+        help="不强制关闭 TRIANGLE_BLACK_YOLO_SAVE_ROI_CROP（默认测试时会关掉以免写满相册目录）",
+    )
+    ap.add_argument(
+        "--dump-raw",
+        type=int,
+        default=0,
+        metavar="N",
+        help="打印前 N 个 detect 原始框 x,y,w,h,score,cls（coord 映射前；native/letterbox 共用同一批 raw）",
+    )
+    args = ap.parse_args()
+
+    img_path = os.path.abspath(args.image)
+    if not os.path.isfile(img_path):
+        print(f"[ERR] 找不到图片: {img_path}")
+        sys.exit(1)
+
+    try:
+        import config as cfg
+        import target_roi_yolo as yroi
+    except ImportError as e:
+        print(f"[ERR] 无法导入 config / target_roi_yolo: {e}")
+        sys.exit(1)
+
+    if args.fresh_detector:
+        yroi.reset_yolo_detector_cache()
+
+    # 备份并临时覆盖 config（单进程顺序跑）
+    bak: dict[str, object] = {}
+
+    def _patch(key: str, val: object):
+        if key not in bak:
+            bak[key] = getattr(cfg, key, None)
+        setattr(cfg, key, val)
+
+    def _restore():
+        for k, v in bak.items():
+            setattr(cfg, k, v)
+
+    try:
+        _patch("TRIANGLE_BLACK_YOLO_ENABLE", True)
+        if not args.allow_save_roi:
+            _patch("TRIANGLE_BLACK_YOLO_SAVE_ROI_CROP", False)
+        if args.model.strip():
+            _patch("TRIANGLE_BLACK_YOLO_MODEL_PATH", args.model.strip())
+        if args.conf is not None:
+            _patch("TRIANGLE_BLACK_YOLO_CONF_TH", float(args.conf))
+        if args.iou is not None:
+            _patch("TRIANGLE_BLACK_YOLO_IOU_TH", float(args.iou))
+        if args.coord and not args.compare_coord:
+            _patch("TRIANGLE_BLACK_YOLO_COORD_MODE", args.coord)
+
+        mp = getattr(cfg, "TRIANGLE_BLACK_YOLO_MODEL_PATH", "") or ""
+        if not os.path.isfile(mp):
+            print(f"[ERR] 模型文件不存在: {mp}")
+            sys.exit(1)
+
+        img_rgb = _load_rgb_numpy(img_path)
+        h, w = int(img_rgb.shape[0]), int(img_rgb.shape[1])
+
+        if args.roi.strip():
+            roi_xyxy = _parse_roi(args.roi.strip())
+            rx0, ry0, rx1, ry1 = [int(round(float(v))) for v in roi_xyxy]
+            if rx1 <= rx0 or ry1 <= ry0:
+                print("[ERR] ROI 无效：需满足 x1>x0 且 y1>y0")
+                sys.exit(1)
+            # 与 target_roi_yolo.try_black_triangle_boxes_work 相同的 clip
+            rx0 = max(0, min(rx0, w - 1))
+            ry0 = max(0, min(ry0, h - 1))
+            rx1 = max(rx0 + 1, min(rx1, w))
+            ry1 = max(ry0 + 1, min(ry1, h))
+            ring_roi = (rx0, ry0, rx1, ry1)
+            print(f"[INFO] 模式=整图+ROI ring={ring_roi} image={w}×{h}")
+        else:
+            ring_roi = (0, 0, w, h)
+            print(f"[INFO] 模式=已是 Stage1 裁切 crop={w}×{h}")
+
+        logger = _PrintLogger()
+        det = yroi._get_detector(mp)
+        if det is None:
+            print("[ERR] 无法加载 nn.YOLOv5（检查模型路径与 Maix 环境）")
+            sys.exit(1)
+        net_w = int(det.input_width())
+        net_h = int(det.input_height())
+        print(f"[INFO] model={mp} net_in={net_w}×{net_h}")
+
+        rx0, ry0, rx1, ry1 = ring_roi
+        import numpy as np
+
+        slab_for_det = np.ascontiguousarray(img_rgb[ry0:ry1, rx0:rx1], dtype=np.uint8).copy()
+        rh_s, rw_s = int(slab_for_det.shape[0]), int(slab_for_det.shape[1])
+
+        modes = ["native", "letterbox"] if args.compare_coord else [
+            (args.coord or getattr(cfg, "TRIANGLE_BLACK_YOLO_COORD_MODE", "native"))
+        ]
+
+        base, ext = os.path.splitext(img_path)
+        ext = ext if ext else ".jpg"
+
+        for mode in modes:
+            _patch("TRIANGLE_BLACK_YOLO_COORD_MODE", mode)
+            cur_coord = getattr(cfg, "TRIANGLE_BLACK_YOLO_COORD_MODE", mode)
+            print(f"[INFO] --- TRIANGLE_BLACK_YOLO_COORD_MODE={cur_coord} ---")
+
+            boxes, slab = _run_once(yroi, img_rgb, ring_roi, logger)
+            print(
+                f"[INFO] 子框数量={len(boxes)} conf={getattr(cfg, 'TRIANGLE_BLACK_YOLO_CONF_TH', '?')} "
+                f"coord={cur_coord}"
+            )
+            for i, b in enumerate(boxes):
+                print(f"  s2_{i}: {b}")
+
+            if args.compare_coord:
+                out_path = f"{base}_stage2_{mode}{ext}"
+            elif args.output.strip():
+                out_path = args.output.strip()
+            else:
+                out_path = base + "_stage2_vis" + ext
+
+            import cv2
+
+            bgr = _draw_boxes_on_crop(slab, boxes)
+            cv2.imwrite(out_path, bgr, [int(cv2.IMWRITE_JPEG_QUALITY), 92])
+            print(f"[OK] saved: {out_path}")
+
+        if args.compare_coord:
+            print(
+                "[HINT] contain 训练时若 letterbox 对齐更好，请将 config 里 "
+                "TRIANGLE_BLACK_YOLO_COORD_MODE 设为 letterbox"
+            )
+
+        if args.dump_raw > 0:
+            conf_th = float(getattr(cfg, "TRIANGLE_BLACK_YOLO_CONF_TH", 0.5))
+            iou_th = float(getattr(cfg, "TRIANGLE_BLACK_YOLO_IOU_TH", 0.45))
+            print("\n[INFO] --dump-raw 放在最后执行，避免 raw native 对象影响 compare-coord 流程。")
+            _dump_raw_and_hard_exit(
+                det,
+                yroi,
+                slab_for_det,
+                rw_s,
+                rh_s,
+                net_w,
+                net_h,
+                conf_th,
+                iou_th,
+                args.dump_raw,
+            )
+
+    finally:
+        _restore()
+
+
+if __name__ == "__main__":
+    main()

train_yolo/test_triangle_one_image.py

@@ -0,0 +1,242 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+单张图片快速测试：三角形四角标记识别 + 单应性落点 + PnP 估距
+
+用法（在板子上）：
+  python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --out /root/phot/tri_out.jpg
+
+调参对比（不改代码，临时覆盖 config.TRIANGLE_*）：
+  python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --preset shadow
+  python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --max-interior-gray 160 --min-dark-ratio 0.20
+"""
+
+import argparse
+import json
+import os
+import time
+from typing import Any, Dict, Tuple
+
+import cv2
+import numpy as np
+
+import config
+import triangle_target as tri_mod
+from triangle_target import (
+    detect_triangle_markers,
+    load_camera_from_xml,
+    load_triangle_positions,
+    try_triangle_scoring,
+)
+
+
+def _apply_overrides(args) -> None:
+    # 预设：阴影/低对比度场景更宽松（尽量保持速度：不启 CLAHE）
+    if args.preset == "shadow":
+        setattr(config, "TRIANGLE_ENABLE_CLAHE_FALLBACK", False)
+        setattr(config, "TRIANGLE_MIN_CONTRAST_DIFF", 0)
+        setattr(config, "TRIANGLE_MAX_INTERIOR_GRAY", 160)
+        setattr(config, "TRIANGLE_DARK_PIXEL_GRAY", 160)
+        setattr(config, "TRIANGLE_MIN_DARK_RATIO", 0.20)
+        # adaptive 只在 Otsu 失败时尝试，保持尝试次数很少
+        setattr(config, "TRIANGLE_ADAPTIVE_BLOCK_SIZES", (21,))
+
+    # 手动覆盖（优先级高于 preset）
+    if args.max_interior_gray is not None:
+        setattr(config, "TRIANGLE_MAX_INTERIOR_GRAY", int(args.max_interior_gray))
+    if args.dark_pixel_gray is not None:
+        setattr(config, "TRIANGLE_DARK_PIXEL_GRAY", int(args.dark_pixel_gray))
+    if args.min_dark_ratio is not None:
+        setattr(config, "TRIANGLE_MIN_DARK_RATIO", float(args.min_dark_ratio))
+    if args.min_contrast_diff is not None:
+        setattr(config, "TRIANGLE_MIN_CONTRAST_DIFF", int(args.min_contrast_diff))
+    if args.detect_scale is not None:
+        setattr(config, "TRIANGLE_DETECT_SCALE", float(args.detect_scale))
+    if args.adaptive_blocks is not None:
+        bs = tuple(int(x) for x in args.adaptive_blocks.split(",") if x.strip())
+        setattr(config, "TRIANGLE_ADAPTIVE_BLOCK_SIZES", bs)
+
+
+def _dump_config() -> Dict[str, Any]:
+    keys = [
+        "TRIANGLE_DETECT_SCALE",
+        "TRIANGLE_SIZE_RANGE",
+        "TRIANGLE_MAX_INTERIOR_GRAY",
+        "TRIANGLE_DARK_PIXEL_GRAY",
+        "TRIANGLE_MIN_DARK_RATIO",
+        "TRIANGLE_MIN_CONTRAST_DIFF",
+        "TRIANGLE_ADAPTIVE_BLOCK_SIZES",
+        "TRIANGLE_MAX_FILTERED_FOR_COMBO",
+        "TRIANGLE_EARLY_EXIT_CANDIDATES",
+        "TRIANGLE_ENABLE_CLAHE_FALLBACK",
+    ]
+    out = {}
+    for k in keys:
+        out[k] = getattr(config, k, None)
+    return out
+
+
+def _draw_tri_debug(img_bgr: np.ndarray, tri: Dict[str, Any]) -> np.ndarray:
+    out = img_bgr.copy()
+    markers = tri.get("markers") or []
+
+    # 画三角形轮廓 + center + id
+    for m in markers:
+        corners = np.array(m.get("corners", []), dtype=np.int32)
+        if corners.size == 0:
+            continue
+        cv2.polylines(out, [corners], True, (0, 255, 0), 2)
+        c = m.get("center") or (corners[:, 0].mean(), corners[:, 1].mean())
+        cx, cy = int(c[0]), int(c[1])
+        cv2.circle(out, (cx, cy), 4, (0, 0, 255), -1)
+        mid = m.get("id", "?")
+        cv2.putText(out, f"T{mid}", (cx - 18, cy - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 1)
+
+    # 若有 homography，画靶心（把 (0,0) 反投影到图像）
+    H = tri.get("homography")
+    if H is not None:
+        try:
+            H = np.array(H, dtype=np.float64)
+            H_inv = np.linalg.inv(H)
+            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(out, (ocx, ocy), 5, (0, 0, 255), -1)
+            cv2.circle(out, (ocx, ocy), 10, (0, 0, 255), 1)
+        except Exception:
+            pass
+
+    # 叠加结果信息
+    lines = []
+    if tri.get("ok"):
+        lines.append("tri_ok=True")
+        if tri.get("dx_cm") is not None and tri.get("dy_cm") is not None:
+            lines.append(f"dx,dy=({tri['dx_cm']:.2f},{tri['dy_cm']:.2f})cm")
+        if tri.get("distance_m") is not None:
+            lines.append(f"dist={float(tri['distance_m']):.2f}m")
+    else:
+        lines.append("tri_ok=False")
+
+    y0 = 22
+    for i, t in enumerate(lines):
+        cv2.putText(out, t, (10, y0 + i * 18), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 1)
+    return out
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--image", required=True, help="输入图片路径（jpg/png）")
+    ap.add_argument("--out", default="", help="输出标注图片路径（可选）")
+    ap.add_argument("--laser-x", type=int, default=-1, help="激光点 x（像素），默认用图像中心")
+    ap.add_argument("--laser-y", type=int, default=-1, help="激光点 y（像素），默认用图像中心")
+    ap.add_argument("--preset", choices=["", "shadow"], default="", help="调参预设（shadow=阴影更鲁棒，不启 CLAHE）")
+    ap.add_argument("--max-interior-gray", type=int, default=None)
+    ap.add_argument("--dark-pixel-gray", type=int, default=None)
+    ap.add_argument("--min-dark-ratio", type=float, default=None)
+    ap.add_argument("--min-contrast-diff", type=int, default=None)
+    ap.add_argument("--detect-scale", type=float, default=None)
+    ap.add_argument("--adaptive-blocks", default=None, help="例如: 11,21 ；为空表示不改")
+    ap.add_argument("--verbose", action="store_true", help="输出更多检测阶段信息")
+    args = ap.parse_args()
+
+    _apply_overrides(args)
+    # triangle_target.py 的日志默认写到 logger_manager；在离线脚本里 logger 可能未初始化。
+    # verbose 模式下把 _log 重定向为 print，方便直接看到诊断信息。
+    if args.verbose:
+        try:
+            tri_mod._log = lambda msg: print(str(msg))
+        except Exception:
+            pass
+
+    img_bgr = cv2.imread(args.image, cv2.IMREAD_COLOR)
+    if img_bgr is None:
+        raise SystemExit(f"读图失败：{args.image}")
+    # triangle_target.try_triangle_scoring 约定输入为 RGB；OpenCV imread 返回 BGR
+    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+
+    h, w = img_bgr.shape[:2]
+    if args.laser_x >= 0 and args.laser_y >= 0:
+        laser_point = (int(args.laser_x), int(args.laser_y))
+    else:
+        laser_point = (w // 2, h // 2)
+
+    K, dist = load_camera_from_xml(getattr(config, "CAMERA_CALIB_XML", ""))
+    pos = load_triangle_positions(getattr(config, "TRIANGLE_POSITIONS_JSON", ""))
+
+    print("[tri-test] image:", args.image, "shape:", (h, w))
+    print("[tri-test] laser_point:", laser_point)
+    print("[tri-test] calib_ok:", bool(K is not None and dist is not None), "pos_ok:", bool(pos))
+    print("[tri-test] config:", json.dumps(_dump_config(), ensure_ascii=False))
+
+    # 先单独跑一次三角形候选检测，便于区分“没找到候选” vs “找到候选但评分/单应性失败”
+    scale = float(getattr(config, "TRIANGLE_DETECT_SCALE", 0.5) or 0.5)
+    if not (0.05 <= scale <= 1.0):
+        scale = 0.5
+    long_side = max(h, w)
+    max_dim = max(64, int(long_side * scale))
+    if long_side > max_dim:
+        det_scale = max_dim / long_side
+        det_w = int(w * det_scale)
+        det_h = int(h * det_scale)
+        img_det = cv2.resize(img_bgr, (det_w, det_h), interpolation=cv2.INTER_LINEAR)
+        inv_scale = 1.0 / det_scale
+        size_range_det = (
+            max(4, int(getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))[0] * det_scale)),
+            max(8, int(getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))[1] * det_scale)),
+        )
+    else:
+        img_det = img_bgr
+        inv_scale = 1.0
+        size_range_det = getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))
+
+    gray = cv2.cvtColor(img_det, cv2.COLOR_BGR2GRAY)
+    markers_det = detect_triangle_markers(
+        gray,
+        orig_gray=gray,
+        size_range=size_range_det,
+        verbose=bool(args.verbose),
+    )
+    if inv_scale != 1.0 and markers_det:
+        for m in markers_det:
+            m["center"] = [m["center"][0] * inv_scale, m["center"][1] * inv_scale]
+            m["corners"] = [[c[0] * inv_scale, c[1] * inv_scale] for c in m["corners"]]
+
+    print("[tri-test] markers_found:", len(markers_det), "ids:", [m.get("id") for m in markers_det])
+
+    t0 = time.time()
+    tri = try_triangle_scoring(
+        img_rgb,  # try_triangle_scoring 期望 RGB
+        laser_point,
+        pos,
+        K,
+        dist,
+        size_range=getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500)),
+    )
+    dt_ms = int(round((time.time() - t0) * 1000))
+
+    print("[tri-test] elapsed_ms:", dt_ms)
+    print(json.dumps(tri, ensure_ascii=False, indent=2))
+
+    if args.out:
+        out_path = args.out
+        # 允许传目录（如 ./），自动生成文件名；未带扩展名时默认 .jpg
+        if out_path.endswith("/") or out_path.endswith("\\") or os.path.isdir(out_path):
+            out_path = os.path.join(out_path, "tri_out.jpg")
+        root, ext = os.path.splitext(out_path)
+        if not ext:
+            out_path = root + ".jpg"
+
+        # 若 try_triangle_scoring 失败且没带回 markers，至少把候选 markers 画出来，方便肉眼判断
+        tri_for_draw = tri if isinstance(tri, dict) else {"ok": False}
+        if not tri_for_draw.get("markers") and markers_det:
+            tri_for_draw = dict(tri_for_draw)
+            tri_for_draw["markers"] = markers_det
+        out_img = _draw_tri_debug(img_bgr, tri_for_draw)
+        ok = cv2.imwrite(out_path, out_img)
+        if not ok:
+            raise SystemExit(f"写图失败（可能是不支持的扩展名）：{out_path}")
+        print("[tri-test] wrote:", out_path)
+
+
+if __name__ == "__main__":
+    main()
+

train_yolo/test_yolo_draw_boxes.py

@@ -0,0 +1,257 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+本地图片 → Maix YOLOv5 检测 → 画框保存（用于核对坐标 mode / 多框 union）。
+
+运行环境：MaixCAM / MaixPy（需 maix.image / maix.nn），在项目根或任意目录执行均可。
+
+示例：
+  python test/test_yolo_draw_boxes.py /root/phot/shot_xxx.jpg
+  python test/test_yolo_draw_boxes.py shot.jpg --loader cv2_rgb --conf 0.25
+  python test/test_yolo_draw_boxes.py shot.jpg --debug
+  python -h  # 查看 --loader / --debug / --union 等全部参数
+
+脚本版本（与设备同步用）：20260206-yolo-vis
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+
+_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+if _ROOT not in sys.path:
+    sys.path.insert(0, _ROOT)
+
+
+def _load_maix_image(path: str, image_mod):
+    """maix.image.load（部分 JPEG 解码后与 camera.read() 像素布局不一致，可能导致 NPU 全空）。"""
+    return image_mod.load(path)
+
+
+def _load_cv2_rgb_as_maix(path: str, image_mod):
+    """
+    OpenCV 读盘为 BGR → 转 RGB → 与 shoot_manager 里 image2cv 逆过程一致，供 YOLO input type: rgb。
+    """
+    import cv2
+
+    arr = cv2.imread(path, cv2.IMREAD_COLOR)
+    if arr is None:
+        raise FileNotFoundError(f"cv2.imread 失败: {path}")
+    arr = cv2.cvtColor(arr, cv2.COLOR_BGR2RGB)
+    return image_mod.cv2image(arr, False, False)
+
+
+def main():
+    ap = argparse.ArgumentParser(
+        description="YOLO 画框测试（Maix）",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="若提示 unrecognized arguments: --debug，说明设备上脚本未更新，请同步仓库中的 test/test_yolo_draw_boxes.py",
+    )
+    ap.add_argument("image", help="输入图片路径")
+    ap.add_argument("-o", "--output", default="", help="输出图片路径；默认 原名_yolo_vis.jpg")
+    ap.add_argument("-m", "--model", default="", help="覆盖 config.TRIANGLE_YOLO_MODEL_PATH")
+    ap.add_argument("--conf", type=float, default=None, help="置信度阈值")
+    ap.add_argument("--iou", type=float, default=None, help="NMS IoU")
+    ap.add_argument(
+        "--coord",
+        choices=["native", "letterbox"],
+        default="",
+        help="坐标映射；默认读 config.TRIANGLE_YOLO_COORD_MODE",
+    )
+    ap.add_argument(
+        "--union",
+        action="store_true",
+        help="按 TRIANGLE_YOLO_RING_CLASS_IDS 过滤后画合并外接矩形（与线上 ROI merge=union 一致）",
+    )
+    ap.add_argument(
+        "--loader",
+        choices=["auto", "maix", "cv2_rgb"],
+        default="auto",
+        help="auto: 先 maix.load，0 框则改用 cv2 RGB（推荐排查「有图但始终 0 框」）",
+    )
+    ap.add_argument(
+        "--debug",
+        action="store_true",
+        help="打印 detect 原始返回类型与 repr（截断）",
+    )
+    args = ap.parse_args()
+
+    try:
+        from maix import image, nn
+    except ImportError:
+        print("[ERR] 需要 MaixPy（maix.image / maix.nn），请在 MaixCAM 上运行。")
+        sys.exit(1)
+
+    import config as cfg
+    import target_roi_yolo as yroi
+
+    img_path = os.path.abspath(args.image)
+    if not os.path.isfile(img_path):
+        print(f"[ERR] 找不到图片: {img_path}")
+        sys.exit(1)
+
+    model_path = (args.model or getattr(cfg, "TRIANGLE_YOLO_MODEL_PATH", "") or "").strip()
+    if not os.path.isfile(model_path):
+        print(f"[ERR] 模型文件不存在: {model_path}")
+        sys.exit(1)
+
+    conf_th = (
+        float(args.conf)
+        if args.conf is not None
+        else float(getattr(cfg, "TRIANGLE_YOLO_CONF_TH", 0.5))
+    )
+    iou_th = (
+        float(args.iou)
+        if args.iou is not None
+        else float(getattr(cfg, "TRIANGLE_YOLO_IOU_TH", 0.45))
+    )
+    coord_mode = (args.coord or getattr(cfg, "TRIANGLE_YOLO_COORD_MODE", "native")).lower()
+
+    out_path = args.output.strip()
+    if not out_path:
+        base, ext = os.path.splitext(img_path)
+        ext = ext if ext else ".jpg"
+        out_path = base + "_yolo_vis" + ext
+
+    det = nn.YOLOv5(model=model_path, dual_buff=False)
+    net_w = int(det.input_width())
+    net_h = int(det.input_height())
+
+    def _run_detect(maix_img, tag: str):
+        r = det.detect(maix_img, conf_th=conf_th, iou_th=iou_th)
+        if args.debug:
+            rlen = len(r) if r is not None and hasattr(r, "__len__") else "n/a"
+            rrepr = repr(r)
+            if len(rrepr) > 300:
+                rrepr = rrepr[:300] + "..."
+            print(f"[DEBUG] loader={tag} raw_type={type(r)} len={rlen} repr={rrepr}")
+        return yroi._normalize_objs(r if r is not None else []), maix_img, tag
+
+    img = None
+    load_tag = ""
+    objs = []
+
+    if args.loader == "cv2_rgb":
+        img = _load_cv2_rgb_as_maix(img_path, image)
+        load_tag = "cv2_rgb"
+        objs, img, load_tag = _run_detect(img, load_tag)
+    elif args.loader == "maix":
+        img = _load_maix_image(img_path, image)
+        load_tag = "maix_load"
+        objs, img, load_tag = _run_detect(img, load_tag)
+    else:
+        # auto
+        img = _load_maix_image(img_path, image)
+        load_tag = "maix_load"
+        objs, img, load_tag = _run_detect(img, load_tag)
+        if len(objs) == 0:
+            print(
+                "[WARN] maix.image.load 在 conf_th=%s 下仍为 0 框，改用 cv2 BGR→RGB→cv2image 重试（常见可恢复）"
+                % conf_th
+            )
+            img2 = _load_cv2_rgb_as_maix(img_path, image)
+            objs, img, load_tag = _run_detect(img2, "cv2_rgb_retry")
+
+    src_w, src_h = img.width(), img.height()
+
+    labels = getattr(det, "labels", None)
+
+    def _label(cid: int) -> str:
+        if labels is None:
+            return str(cid)
+        try:
+            return str(labels[int(cid)])
+        except Exception:
+            return str(cid)
+
+    print(
+        f"[INFO] loader={load_tag} image={src_w}×{src_h}, net_in={net_w}×{net_h}, "
+        f"coord={coord_mode}, conf_th={conf_th}, iou_th={iou_th}"
+    )
+    print(f"[INFO] NMS 后检测框数量={len(objs)} → {out_path}")
+    if len(objs) == 0:
+        print(
+            "[HINT] 仍为 0 框时常见原因：\n"
+            "  1) 强制 cv2 路径: --loader cv2_rgb\n"
+            "  2) NMS 过严: --iou 0.95\n"
+            "  3) 图与训练分布差太大 / 模型未见过该场景\n"
+            "  4) 用 camera.read() 一帧存盘再测，对比 file 与实时是否一致"
+        )
+
+    # 颜色：按类别轮换（仅有 COLOR_* 时常量时用）
+    color_cycle = []
+    for name in ("RED", "GREEN", "BLUE", "ORANGE", "YELLOW", "CYAN", "MAGENTA"):
+        c = getattr(image, f"COLOR_{name}", None)
+        if c is not None:
+            color_cycle.append(c)
+    if not color_cycle:
+        color_cycle = [getattr(image, "COLOR_RED", 0)]
+
+    for i, o in enumerate(objs):
+        cid = yroi._det_obj_class_id(o)
+        if cid is None:
+            cid = -1
+        try:
+            sc = float(o.score)
+        except Exception:
+            sc = 0.0
+        x0, y0, x1, y1 = yroi._det_to_src_xyxy(o, coord_mode, src_w, src_h, net_w, net_h)
+        ix = int(max(0, min(x0, src_w - 1)))
+        iy = int(max(0, min(y0, src_h - 1)))
+        iw = int(max(1, min(x1 - x0, src_w - ix)))
+        ih = int(max(1, min(y1 - y0, src_h - iy)))
+        col = color_cycle[cid % len(color_cycle)] if cid >= 0 else color_cycle[0]
+        img.draw_rect(ix, iy, iw, ih, color=col)
+        ty = max(0, iy - 14)
+        msg = f"{_label(cid)} {sc:.2f}"
+        img.draw_string(ix, ty, msg, color=col)
+        print(f"  #{i} cls={cid} {_label(cid)} score={sc:.3f} xywh=({ix},{iy},{iw},{ih})")
+
+    if args.union:
+        class_ids = getattr(cfg, "TRIANGLE_YOLO_RING_CLASS_IDS", (0,))
+        if isinstance(class_ids, int):
+            class_ids = (class_ids,)
+        cand = [o for o in objs if yroi._det_obj_class_id(o) in class_ids]
+        if cand:
+            xy_list = [
+                yroi._det_to_src_xyxy(o, coord_mode, src_w, src_h, net_w, net_h) for o in cand
+            ]
+            merged = yroi._merge_roi_xyxy(xy_list, "union")
+            if merged:
+                mx0, my0, mx1, my1 = merged
+                mx0 = max(0, min(mx0, src_w - 1))
+                my0 = max(0, min(my0, src_h - 1))
+                mx1 = max(mx0 + 1, min(mx1, src_w))
+                my1 = max(my0 + 1, min(my1, src_h))
+                uw, uh = int(mx1 - mx0), int(my1 - my0)
+                ucol = getattr(image, "COLOR_GREEN", color_cycle[0])
+                # 画粗一点的 union：描两遍错位矩形简易模拟加粗
+                for d in (0, 2):
+                    img.draw_rect(
+                        int(mx0) - d,
+                        int(my0) - d,
+                        uw + 2 * d,
+                        uh + 2 * d,
+                        color=ucol,
+                    )
+                img.draw_string(
+                    int(mx0),
+                    max(0, int(my0) - 28),
+                    f"UNION ({len(cand)} boxes)",
+                    color=ucol,
+                )
+                print(f"[INFO] UNION [{int(mx0)},{int(my0)},{int(mx1)},{int(my1)}] from {len(cand)} boxes")
+        else:
+            print("[WARN] --union 但 RING_CLASS_IDS 过滤后无框")
+
+    try:
+        img.save(out_path, quality=95)
+    except TypeError:
+        img.save(out_path)
+    print(f"[OK] saved: {out_path}")
+
+
+if __name__ == "__main__":
+    main()

train_yolo/train_black_triangle_pos.py

@@ -0,0 +1,506 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+YOLO11 关键点检测训练脚本（靶纸四角）。
+
+设备优先级（--device auto）：Intel XPU > NVIDIA CUDA > CPU。
+默认 imgsz=960；批大小默认 4（大图显存紧张时可再降）。
+
+关于「业务像素误差」：
+  Ultralytics 没有在 yaml 里设定「像素阈值」的选项；反向传播仍由 pose/kobj/box 等内部 loss 驱动。
+  - 监控：--pixel-metrics-every N（每 N 个 epoch 打印 mean/p95，并合并进 runs/.../results.csv，见 pose_pixel_metrics.py）。
+  - 选 best.pt / early stopping：加 --best-by-pixel，用验证集 mean 像素误差（与 pose_pixel_metrics
+    同一口径）代替 mAP 合成 fitness（fitness = -mean_px，越小越好）。
+    多卡 DDP（world_size>1）时会自动退回默认 mAP fitness。
+
+  XPU：Ultralytics BaseTrainer._get_memory / _clear_memory 把非 MPS、非 CPU 一律当 CUDA，
+  会在验证前调用 torch.cuda 而报错；本脚本在选用 XPU 时自动打补丁（见 _patch_ultralytics_trainer_for_xpu）。
+
+  务必使用 pose 任务：YOLO(...) 与 model.train(...) 均指定 task='pose'。若误用默认 detect，
+  会把 17 列 Pose 标注当成检测/分割解析，校验时出现「coordinates > 1」或 [2.] 等假象。
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import gc
+import glob
+import os
+import tempfile
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+from ultralytics import YOLO
+
+from pose_pixel_metrics import eval_val_pixel_error
+import warnings
+warnings.filterwarnings('ignore', 
+    message=".*scatter_add_kernel does not have a deterministic implementation.*")
+
+
+def _clear_ultralytics_label_caches(data_yaml_path: str) -> int:
+    """删除 data.yaml 的 path 下 labels/*.cache。
+
+    Ultralytics 的校验缓存 hash 仅依赖「标签/图片路径字符串 + 各文件 size 之和」，不含文件内容；
+    修正 *.txt 后若总和巧合不变，可能继续加载旧 cache 并重播旧的 corrupt 日志，训练前应删掉。"""
+    from ultralytics.utils import YAML
+
+    try:
+        cfg = YAML.load(data_yaml_path)
+    except Exception:
+        return 0
+    root = cfg.get("path")
+    if not root:
+        return 0
+    root = os.path.abspath(os.path.expanduser(str(root)))
+    pattern = os.path.join(root, "labels", "*.cache")
+    n = 0
+    for p in glob.glob(pattern):
+        try:
+            os.unlink(p)
+            n += 1
+        except OSError:
+            pass
+    return n
+
+
+def _pick_device(explicit: str | None):
+    """返回 ultralytics train/predict 可用的 device。"""
+    if explicit and explicit != "auto":
+        e = explicit.lower()
+        if e == "xpu":
+            if getattr(torch, "xpu", None) is None or not torch.xpu.is_available():
+                raise RuntimeError("指定了 --device xpu 但当前环境不可用")
+            return torch.device("xpu")
+        if e in ("0", "cuda", "gpu"):
+            if not torch.cuda.is_available():
+                raise RuntimeError("指定了 CUDA 但不可用")
+            return 0
+        if e == "cpu":
+            return "cpu"
+        return explicit
+    if getattr(torch, "xpu", None) is not None and torch.xpu.is_available():
+        return torch.device("xpu")
+    if torch.cuda.is_available():
+        return 0
+    return "cpu"
+
+
+def _default_amp(device) -> bool:
+    if isinstance(device, torch.device) and device.type == "xpu":
+        return False
+    if device == "cpu":
+        return False
+    return True
+
+
+def _patch_ultralytics_for_xpu():
+    """为 Ultralytics 打补丁，使其能在 XPU 环境下正常训练和验证。"""
+    import ultralytics.engine.trainer as ut_trainer
+    import ultralytics.engine.validator as ut_validator
+    from ultralytics.utils.torch_utils import select_device as _original_select_device
+
+    # 1. 覆盖 select_device：Trainer 初始化传入 torch.device("xpu") 会走原版早返回；
+    #    初始化后 args.device 会变成字符串 "xpu"，中期 val 用 trainer.device，不调用 select_device；
+    #    训练结束 final_eval 里 Validator 会 select_device("xpu")，且 validator 在 import 时已绑定原函数，
+    #    只改 torch_utils 无效，必须同时修补 trainer/validator 模块内的引用。
+    def _patched_select_device(device="", *args, **kwargs):
+        # Ultralytics 8.4.x: select_device(device="", newline=False, verbose=True)
+        # Older forks sometimes passed extra positional args; forward everything.
+        if isinstance(device, str):
+            d = device.strip().lower()
+            if d == "xpu" or d.startswith("xpu:"):
+                return torch.device(device.strip())
+        return _original_select_device(device, *args, **kwargs)
+
+    import ultralytics.utils.torch_utils
+
+    ultralytics.utils.torch_utils.select_device = _patched_select_device
+    ut_trainer.select_device = _patched_select_device
+    ut_validator.select_device = _patched_select_device
+
+    # 2. 修补 Trainer 的内存函数
+    BT = ut_trainer.BaseTrainer
+    if not getattr(BT, "_archery_xpu_memory_patched", False):
+        _orig_get_memory = BT._get_memory
+        _orig_clear_memory = BT._clear_memory
+
+        def _get_memory(self, fraction=False):
+            if self.device.type != "xpu":
+                return _orig_get_memory(self, fraction)
+            # ... (原有的 XPU 内存获取逻辑保持不变) ...
+            memory, total = 0, 0
+            try:
+                idx = self.device.index
+                if idx is None:
+                    idx = torch.xpu.current_device()
+                memory = int(torch.xpu.memory_allocated(idx))
+                if fraction:
+                    total = int(torch.xpu.get_device_properties(idx).total_memory)
+            except Exception:
+                pass
+            return (memory / total) if fraction and total > 0 else (memory / 2**30)
+
+        def _clear_memory(self, threshold=None):
+            if self.device.type != "xpu":
+                return _orig_clear_memory(self, threshold)
+            if threshold is not None:
+                assert 0 <= threshold <= 1, "Threshold must be between 0 and 1."
+                if self._get_memory(fraction=True) <= threshold:
+                    return
+            gc.collect()
+            if hasattr(torch.xpu, "empty_cache"):
+                torch.xpu.empty_cache()
+
+        BT._get_memory = _get_memory
+        BT._clear_memory = _clear_memory
+        BT._archery_xpu_memory_patched = True
+
+    # 3. 修补 Validator 的内存函数 (关键是添加这部分)
+    BV = ut_validator.BaseValidator
+    if not getattr(BV, "_archery_xpu_memory_patched", False):
+        # 为 Validator 添加同样的内存处理方法
+        BV._get_memory = _get_memory
+        BV._clear_memory = _clear_memory
+        BV._archery_xpu_memory_patched = True
+
+
+def _install_best_by_pixel_validate(data_yaml: str, imgsz: int, conf: float) -> None:
+    """用验证集关键点像素 mean 替代 mAP fitness，驱动 best.pt 与 patience early stopping。"""
+    import ultralytics.engine.trainer as ut
+    from ultralytics.utils import RANK
+
+    BT = ut.BaseTrainer
+    if getattr(BT, "_archery_best_by_pixel_installed", False):
+        return
+
+    _orig_validate = BT.validate
+
+    def validate(self):
+        import torch.distributed as dist
+
+        if self.ema and self.world_size > 1:
+            for buffer in self.ema.ema.buffers():
+                dist.broadcast(buffer, src=0)
+        metrics = self.validator(self)
+        if metrics is None:
+            return None, None
+        orig_fitness = metrics.pop("fitness", -self.loss.detach().cpu().numpy())
+
+        use_pixel = self.world_size <= 1 and RANK in {-1, 0}
+        mean_px: float | None = None
+        if use_pixel:
+            tmp_path: str | None = None
+            try:
+                fd, tmp_path = tempfile.mkstemp(suffix=".pt", prefix="archery_pxfit_")
+                os.close(fd)
+                from ultralytics.utils.torch_utils import unwrap_model
+
+                core = unwrap_model(self.ema.ema if self.ema else self.model)
+                torch.save({"ema": deepcopy(core).half(), "train_args": vars(self.args)}, tmp_path)
+                probe = YOLO(tmp_path)
+                stats = eval_val_pixel_error(
+                    probe,
+                    data_yaml,
+                    device=self.device,
+                    imgsz=imgsz,
+                    conf=conf,
+                )
+                mean_px = stats.get("mean_px")
+                if mean_px is None:
+                    raise RuntimeError("无有效 mean_px（检查 val 标签与检测是否为空）")
+            except Exception as exc:
+                print(f"\n⚠️ [best-by-pixel] 像素探针失败，本 epoch 仍用 mAP fitness: {exc}\n")
+                mean_px = None
+            finally:
+                if tmp_path:
+                    try:
+                        os.unlink(tmp_path)
+                    except OSError:
+                        pass
+
+        if mean_px is not None:
+            fitness = -float(mean_px)
+            metrics["metrics/mean_px(val)"] = float(mean_px)
+        else:
+            fitness = float(orig_fitness)
+
+        if not self.best_fitness or self.best_fitness < fitness:
+            self.best_fitness = fitness
+        return metrics, fitness
+
+    BT.validate = validate
+    BT._archery_best_by_pixel_installed = True
+
+
+def _fmt_csv_metric(v: float | int | None) -> str:
+    if v is None:
+        return ""
+    if isinstance(v, float):
+        return f"{v:.6g}"
+    return str(v)
+
+
+# 写入 results.csv 的列名（与 --best-by-pixel 的 metrics/mean_px(val) 区分，避免被 last.pt 回调覆盖 EMA 行）
+_PIXEL_METRIC_COLUMNS: tuple[tuple[str, str], ...] = (
+    ("pixel_error/mean_px", "mean_px"),
+    ("pixel_error/median_px", "median_px"),
+    ("pixel_error/p95_px", "p95_px"),
+    ("pixel_error/max_px", "max_px"),
+    ("pixel_error/n_points", "n_points"),
+    ("pixel_error/n_images", "n_images"),
+    ("pixel_error/skip_no_det", "skip_no_det"),
+    ("pixel_error/skip_no_gt", "skip_no_gt"),
+    ("pixel_error/skip_kpt_mismatch", "skip_kpt_mismatch"),
+)
+
+
+def _merge_pixel_metrics_into_results_csv(save_dir: str | Path, epoch_1based: int, stats: dict) -> None:
+    """在 Ultralytics 写完本 epoch 行之后，把像素指标列合并进 results.csv（扩展表头、补空列）。"""
+    csv_path = Path(save_dir) / "results.csv"
+    if not csv_path.is_file():
+        return
+    try:
+        with open(csv_path, newline="", encoding="utf-8") as f:
+            rows = list(csv.reader(f))
+    except OSError:
+        return
+    if len(rows) < 2:
+        return
+    header = list(rows[0])
+    for col_name, _ in _PIXEL_METRIC_COLUMNS:
+        if col_name not in header:
+            header.append(col_name)
+            for ri in range(1, len(rows)):
+                rows[ri].append("")
+    col_ix = {name: i for i, name in enumerate(header)}
+    rows[0] = header
+    target_ri: int | None = None
+    for ri in range(1, len(rows)):
+        row = rows[ri]
+        while len(row) < len(header):
+            row.append("")
+        try:
+            if int(float(row[0].strip())) == int(epoch_1based):
+                target_ri = ri
+        except (ValueError, IndexError):
+            continue
+    if target_ri is None:
+        return
+    row = rows[target_ri]
+    while len(row) < len(header):
+        row.append("")
+    for col_name, sk in _PIXEL_METRIC_COLUMNS:
+        row[col_ix[col_name]] = _fmt_csv_metric(stats.get(sk))
+    try:
+        with open(csv_path, "w", newline="", encoding="utf-8") as f:
+            w = csv.writer(f)
+            w.writerows(rows)
+    except OSError:
+        pass
+
+
+def _make_pixel_metrics_callback(data_yaml: str, every: int, imgsz: int, conf: float = 0.25):
+    def on_fit_epoch_end(trainer):
+        from ultralytics.utils import RANK
+
+        if RANK not in {-1, 0}:
+            return
+        if every <= 0:
+            return
+        ep = int(getattr(trainer, "epoch", -1))
+        if (ep + 1) % every != 0:
+            return
+        w = Path(trainer.save_dir) / "weights" / "last.pt"
+        if not w.is_file():
+            return
+        m = YOLO(str(w))
+        stats = eval_val_pixel_error(
+            m,
+            data_yaml,
+            device=trainer.device,
+            imgsz=imgsz,
+            conf=conf,
+        )
+        mean_px = stats.get("mean_px")
+        p95_px = stats.get("p95_px")
+        mean_s = f"{mean_px:.3f}" if mean_px is not None else "n/a"
+        p95_s = f"{p95_px:.3f}" if p95_px is not None else "n/a"
+        print(
+            f"\n[pixel-metrics] epoch {ep + 1}: mean_px={mean_s} p95_px={p95_s} "
+            f"n_points={stats.get('n_points', 0)} "
+            f"skip(det/gt/k)={stats['skip_no_det']}/{stats['skip_no_gt']}/{stats['skip_kpt_mismatch']}\n"
+        )
+        _merge_pixel_metrics_into_results_csv(trainer.save_dir, ep + 1, stats)
+
+    return on_fit_epoch_end
+
+
+def main():
+    ap = argparse.ArgumentParser(description="YOLO Pose 训练（XPU/CUDA/CPU）")
+    ap.add_argument("--data", default="datasets/dataset_pose.yaml", help="data.yaml")
+    ap.add_argument("--model", default="yolo11x-pose.pt", help="预训练权重")
+    ap.add_argument("--epochs", type=int, default=100)
+    ap.add_argument("--imgsz", type=int, default=960, help="训练输入边长（默认 960）")
+    ap.add_argument("--batch", type=int, default=4, help="批大小；OOM 时减小")
+    ap.add_argument(
+        "--device",
+        default="auto",
+        help="auto | xpu | 0 | cuda | cpu（auto：XPU 优先）",
+    )
+    ap.add_argument(
+        "--no-amp",
+        action="store_true",
+        help="关闭混合精度（默认：CUDA 开启，XPU/CPU 关闭）",
+    )
+    ap.add_argument("--project", default="runs/pose")
+    ap.add_argument("--name", default="target_pose_train")
+    ap.add_argument("--workers", type=int, default=4)
+    ap.add_argument(
+        "--pixel-metrics-every",
+        type=int,
+        default=0,
+        help="每 N 个 epoch 在 val 上打印像素误差并写入 results.csv 对应 epoch 行（0=关闭）；需 labels 与 data.yaml 布局一致",
+    )
+    ap.add_argument(
+        "--pixel-metrics-conf",
+        type=float,
+        default=0.25,
+        help="--pixel-metrics-every 时 predict 置信度阈值（默认 0.25）",
+    )
+    ap.add_argument(
+        "--best-by-pixel",
+        action="store_true",
+        help="best.pt 与 early stopping 按验证集 mean 像素误差（同 pose_pixel_metrics），fitness=-mean_px；单卡有效，DDP 自动退回 mAP",
+    )
+    ap.add_argument(
+        "--pixel-fitness-conf",
+        type=float,
+        default=0.25,
+        help="--best-by-pixel 时 predict 置信度阈值（默认与 pixel-metrics 一致）",
+    )
+    ap.add_argument(
+        "--export-onnx",
+        action="store_true",
+        help="训练结束后导出 ONNX（需再设 --onnx-imgsz）",
+    )
+    ap.add_argument(
+        "--onnx-imgsz",
+        type=int,
+        nargs=2,
+        metavar=("H", "W"),
+        default=[224, 320],
+        help="导出 ONNX 的 [高, 宽]，默认 224 320（Maix 常用）",
+    )
+    ap.add_argument(
+        "--clear-label-cache",
+        action="store_true",
+        help="启动训练前删除 data.yaml 中 path 下的 labels/*.cache（修正标注后仍报 corrupt 时用）",
+    )
+    args = ap.parse_args()
+
+    device = _pick_device(None if args.device == "auto" else args.device)
+    use_amp = False if args.no_amp else _default_amp(device)
+
+    if isinstance(device, torch.device) and device.type == "xpu":
+        print(f"✅ 使用 Intel XPU: {device}")
+    elif device == 0 or device == "0":
+        print(f"✅ 使用 CUDA: {torch.cuda.get_device_name(0)}")
+    else:
+        print("⚠️ 使用 CPU，训练会较慢")
+
+    if isinstance(device, torch.device) and device.type == "xpu":
+        _patch_ultralytics_for_xpu()
+
+    data_yaml = args.data
+    if not os.path.isabs(data_yaml):
+        data_yaml = os.path.join(os.path.dirname(os.path.abspath(__file__)), data_yaml)
+    if not os.path.exists(data_yaml):
+        print(f"❌ 数据集配置不存在: {data_yaml}")
+        return
+
+    if args.clear_label_cache:
+        n_rm = _clear_ultralytics_label_caches(data_yaml)
+        print(f"🗑️ 已删除标签目录缓存 {n_rm} 个（labels/*.cache），将强制重新扫描标注。")
+
+    print(f"📦 加载模型: {args.model}（固定 task=pose）")
+    model = YOLO(args.model, task="pose")
+
+    if args.best_by_pixel:
+        _install_best_by_pixel_validate(data_yaml, args.imgsz, args.pixel_fitness_conf)
+        print(
+            "📌 已启用 --best-by-pixel：best.pt / patience 按验证集 mean 像素误差（fitness=-mean_px）；"
+            "反向传播仍为 Ultralytics 默认 pose/box loss。"
+        )
+
+    if args.pixel_metrics_every > 0:
+        model.add_callback(
+            "on_fit_epoch_end",
+            _make_pixel_metrics_callback(
+                data_yaml, args.pixel_metrics_every, args.imgsz, conf=args.pixel_metrics_conf
+            ),
+        )
+
+    model.train(
+        task="pose",
+        data=data_yaml,
+        epochs=args.epochs,
+        imgsz=args.imgsz,
+        batch=args.batch,
+        name=args.name,
+        project=args.project,
+        exist_ok=True,
+        save=True,
+        save_period=5,
+        device=device,
+        workers=args.workers,
+        lr0=0.0001,
+        lrf=0.01,
+        optimizer="AdamW",
+        momentum=0.937,
+        weight_decay=0.001,
+        warmup_epochs=0,
+        warmup_momentum=0.8,
+        warmup_bias_lr=0.1,
+        hsv_h=0.015,
+        hsv_s=0.7,
+        hsv_v=0.4,
+        degrees=5.0,
+        translate=0.0,
+        scale=0.2,
+        shear=0.0,
+        perspective=0.0000,
+        flipud=0.0,
+        fliplr=0.5,
+        mosaic=0.0,
+        mixup=0.0,
+        copy_paste=0.0,
+        box=6,
+        cls=0.5,
+        dfl=1.5,
+        pose=18.0,
+        kobj=0.5,
+        freeze=0,
+        seed=42,
+        verbose=True,
+        amp=use_amp,
+        patience=100,
+        cos_lr=True,
+    )
+
+    print("\n✅ 训练完成！")
+    print(f"📁 best: {args.project}/{args.name}/weights/best.pt")
+    print(f"📁 last: {args.project}/{args.name}/weights/last.pt")
+    print("📊 仅看像素误差可运行: python pose_pixel_metrics.py --model <best.pt> --data <yaml> --imgsz", args.imgsz)
+
+    if args.export_onnx:
+        h, w = args.onnx_imgsz
+        print(f"📦 导出 ONNX imgsz=[{h}, {w}] ...")
+        model.export(format="onnx", imgsz=[h, w], simplify=True, opset=17, dynamic=False)
+        print("✅ ONNX 完成")
+
+
+if __name__ == "__main__":
+    main()

triangle_target.py

@@ -22,6 +22,143 @@ 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。
@@ -224,7 +361,7 @@ def detect_triangle_markers(
         blackhat_kernel_frac = 0.018
     try:
         import config as _tcfg
-        _timing_log = bool(getattr(_tcfg, "TRIANGLE_TIMING_LOG", True))
+        _timing_log = bool(getattr(_tcfg, "ARCHERY_TIMING_ENABLE", True)) and bool(getattr(_tcfg, "TRIANGLE_TIMING_LOG", True))
     except Exception:
         _timing_log = True
 
@@ -622,6 +759,8 @@ 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]
@@ -641,7 +780,13 @@ 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)
 
-        score = (diag_ratio - 1.0) * 3.0 + (h_ratio - 1.0) + (v_ratio - 1.0) + leg_dev * 2.0
+        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
         return score, (tl, bl, br, tr)
 
     assigned = None
@@ -932,6 +1077,8 @@ 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]
@@ -947,7 +1094,12 @@ 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)
-        score = (diag_ratio - 1.0) * 3.0 + (h_ratio - 1.0) + (v_ratio - 1.0) + leg_dev * 2.0
+        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
         return score, (tl, bl, br, tr)
 
     assigned = None
@@ -1113,7 +1265,7 @@ def try_triangle_scoring(
 
     try:
         import config as _cfg_tl
-        _try_timing_log = bool(getattr(_cfg_tl, "TRIANGLE_TIMING_LOG", True))
+        _try_timing_log = bool(getattr(_cfg_tl, "ARCHERY_TIMING_ENABLE", True)) and 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
@@ -1733,6 +1885,21 @@ 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.py

@@ -4,6 +4,28 @@
 应用版本号
 每次 OTA 更新时，只需要更新这个文件中的版本号
 """
-VERSION = '2.15.35'
+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
+
+
+
 
 

vision.py

@@ -10,6 +10,7 @@ import os
 import math
 import threading
 import queue
+import time
 from maix import image
 import config
 from logger_manager import logger_manager
@@ -531,11 +532,14 @@ 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 = 480
+    MAX_DET_DIM = 320
     long_side = max(h_orig, w_orig)
     if long_side > MAX_DET_DIM:
         det_scale = MAX_DET_DIM / long_side
@@ -554,6 +558,8 @@ 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)
@@ -567,25 +573,26 @@ 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, 30, 20]),   np.array([12,  255, 255])),
+        cv2.inRange(hsv, np.array([0, 80, 0]),   np.array([10,  255, 255])),
-        cv2.inRange(hsv, np.array([168, 30, 20]), np.array([180, 255, 255])),
+        cv2.inRange(hsv, np.array([170, 80, 0]), 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 <= 10:
+        if ar <= 50:
             continue
         pr = cv2.arcLength(cnt_r, True)
-        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.2:
+        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.6:
             continue
         if len(cnt_r) >= 5:
             (xr, yr), (wr, hr), _ = cv2.fitEllipse(cnt_r)
@@ -595,19 +602,22 @@ 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 <= 15:
+        if area <= 50:
             continue
         perimeter = cv2.arcLength(cnt_yellow, True)
         if perimeter <= 0:
             continue
         circularity = (4 * np.pi * area) / (perimeter * perimeter)
-        if circularity <= 0.5:
+        if circularity <= 0.7:
             continue
         if logger:
             logger.info(f"[target] -> 面积:{area:.1f}, 圆度:{circularity:.2f}")
@@ -627,11 +637,7 @@ 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)
-            max_dist = yellow_radius * 2.0
+            if dist_centers < yellow_radius * 1.5 and rc["radius"] > yellow_radius * 0.8:
-            min_r = min(rc["radius"], yellow_radius)
-            max_r = max(rc["radius"], yellow_radius)
-            size_ratio = min_r / max_r if max_r > 0 else 0
-            if dist_centers < max_dist and size_ratio >= 0.3:
                 if logger:
                     logger.info(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), "
                                 f"红心({rc['center']}), 距离:{dist_centers:.1f}, "
@@ -644,19 +650,13 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
                 })
                 matched = True
                 break
-        if not matched:
+        if not matched and logger:
-            # 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:
@@ -684,7 +684,20 @@ 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)
-    logger.debug(f"[detect_circle_v3] step 5 fin {datetime.now()}")
+    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}"
+        )
     return result_img, best_center, best_radius, method, best_radius1, ellipse_params
 
 def estimate_distance(pixel_radius):
@@ -936,6 +949,51 @@ 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):
@@ -1025,3 +1083,63 @@ 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

@@ -41,7 +41,6 @@ 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
         
@@ -239,6 +238,7 @@ 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:
@@ -250,13 +250,9 @@ class WiFiManager:
                 _write_text(conf_path, full_conf)
             except Exception:
                 pass
-            # 删除 wpa_supplicant.conf，让 S30wifi 回退读 ssid/pass
+            _write_text(boot_wpa_path, full_conf)
-            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())
 
@@ -296,6 +292,7 @@ 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）")
@@ -309,6 +306,7 @@ 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)
@@ -353,11 +351,7 @@ class WiFiManager:
             else:
                 full_conf = build_sta_conf_open(ssid)
             _write_text(conf_path, full_conf)
-            try:
+            _write_text(boot_wpa_path, full_conf)
-                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:
@@ -541,28 +535,22 @@ class WiFiManager:
     
     def start_quality_monitor(self, network_type_callback, on_poor_quality_callback):
         """
-        启动 WiFi 质量后台监测线程（每 5 秒检查 STA 关联状态和 RSSI）
+        启动 WiFi 质量后台监测线程（每 5 秒测量一次 RTT 和 RSSI）
         只在 WiFi 连接时运行，不影响业务发送性能
         
         Args:
             network_type_callback: 获取当前网络类型的回调函数
             on_poor_quality_callback: WiFi质量差时的回调函数
         """
-        with self._wifi_quality_lock:
+        if self._wifi_quality_monitor_thread is not None:
-            current_thread = self._wifi_quality_monitor_thread
-            current_stop_event = self._wifi_quality_stop_event
-            if (current_thread is not None and current_thread.is_alive()
-                    and not current_stop_event.is_set()):
             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
-            stop_event = threading.Event()
+        self._wifi_quality_stop_event.clear()
-            self._wifi_quality_stop_event = stop_event
         self._wifi_quality_monitor_thread = threading.Thread(
             target=self._quality_monitor_loop,
-                args=(stop_event,),
             daemon=True,
             name="wifi_quality_monitor"
         )
@@ -571,64 +559,49 @@ class WiFiManager:
     
     def stop_quality_monitor(self):
         """停止 WiFi 质量监测线程"""
-        with self._wifi_quality_lock:
+        if self._wifi_quality_monitor_thread is None:
-            t = self._wifi_quality_monitor_thread
-            stop_event = self._wifi_quality_stop_event
-            if t is None:
-                return
-            if not t.is_alive():
-                self._wifi_quality_monitor_thread = None
             return
         
-        stop_event.set()
+        self._wifi_quality_stop_event.set()
         try:
-            t.join(timeout=2.0)
+            self._wifi_quality_monitor_thread.join(timeout=2.0)
         except Exception as e:
             self.logger.error(f"[WiFi Monitor] 停止线程失败：{e}")
-        
+        finally:
-        with self._wifi_quality_lock:
-            if t is self._wifi_quality_monitor_thread:
-                if t.is_alive():
-                    self.logger.warning("[WiFi Monitor] 线程未在超时内退出，保留引用防止重复创建")
-                else:
             self._wifi_quality_monitor_thread = None
             self.logger.info("[WiFi Monitor] 已停止后台监测线程")
     
-    def _quality_monitor_loop(self, stop_event):
+    def _quality_monitor_loop(self):
         """
         WiFi 质量监测循环（后台线程）
-        每 5 秒检查 STA 关联状态和 RSSI，发现断链或质量差则触发切换
+        每 5 秒测量一次 RTT 和 RSSI，发现质量差则触发切换
         """
-        while not stop_event.is_set():
+        while not self._wifi_quality_stop_event.is_set():
             try:
                 # 只在 WiFi 连接时才测量
                 network_type = self._network_type_callback()
                 if network_type == "wifi" and self._wifi_socket:
-                    # RTT 测量当前禁用；STA 关联状态用于判断物理 WiFi 链路是否仍存在。
+                    # # 测量 RTT（1 个样本，快速测量）
-                    # 不能把禁用的 RTT 伪装成 0ms，否则关闭热点后会一直被判为正常。
+                    # rtt_ms, reachable = self._measure_wifi_tcp_rtt_ms(
-                    reachable = self.is_sta_associated()
+                    #     self._server_ip, self._server_port,
-                    rtt_ms = None
+                    #     samples=1, per_sample_timeout_ms=600
+                    # )
                     
                     # 获取 RSSI
                     rssi_dbm = self._get_wifi_rssi_dbm()
                     
                     # 更新缓存
-                    self._last_wifi_rtt_ms = rtt_ms
+                    # 不使用 RTT 测量
+                    rtt_ms = 0
+                    reachable = True
+                    self._last_wifi_rtt_ms = rtt_ms if reachable else None
                     self._last_wifi_rssi_dbm = rssi_dbm
-                    _rtt_s = f"{rtt_ms:.0f}ms" if rtt_ms is not None else "n/a"
                     _rssi_s = f"{rssi_dbm:.0f}" if rssi_dbm is not None else "n/a"
-                    self.logger.debug(
+                    self.logger.debug(f"[WiFi Monitor] - RTT={rtt_ms:.0f}ms, RSSI={_rssi_s}dBm")
-                        f"[WiFi Monitor] - associated={reachable}, RTT={_rtt_s}, RSSI={_rssi_s}dBm"
-                    )
                     
                     # 判断质量是否差（切换前做 2 次快速复测，防止瞬时抖动）
                     def _is_bad_now(_reachable, _rtt, _rssi):
-                        if not _reachable:
+                        if (not _reachable) or (_rtt is None) or (_rtt == float("inf")):
-                            return True
-                        # RTT 未启用时不参与质量判断；链路状态仍由 STA 关联保证。
-                        if _rtt is None:
-                            return False
-                        if _rtt == float("inf"):
                             return True
                         return self._is_wifi_quality_bad(_rtt, _rssi)
 
@@ -637,10 +610,14 @@ class WiFiManager:
                         self.logger.warning("[WiFi Monitor] 质量差,切换前快速重试 2 次（每次间隔1秒）")
 
                         for retry_idx in range(2):
-                            if stop_event.wait(1.0):
+                            time.sleep_ms(1000)
-                                return
+                            # 不使用 RTT 测量
-                            reachable2 = self.is_sta_associated()
+                            rtt2 = 0
-                            rtt2 = None
+                            reachable2 = True
+                            # rtt2, reachable2 = self._measure_wifi_tcp_rtt_ms(
+                            #     self._server_ip, self._server_port,
+                            #     samples=1, per_sample_timeout_ms=600
+                            # )
                             rssi2 = self._get_wifi_rssi_dbm()
 
                             # 更新缓存，便于外部查看最新状态
@@ -649,10 +626,14 @@ class WiFiManager:
 
                             bad2 = _is_bad_now(reachable2, rtt2, rssi2)
                             try:
-                                _rtt_disp = f"{rtt2:.0f}ms" if rtt2 is not None else "n/a"
+                                _rtt_disp = (
+                                    rtt2
+                                    if rtt2 is not None and rtt2 != float("inf")
+                                    else -1
+                                )
                                 self.logger.info(
                                     f"[WiFi Monitor] 复测{retry_idx+1}/2: reachable={reachable2}, "
-                                    f"rtt={_rtt_disp}, rssi={rssi2}, bad={bad2}"
+                                    f"rtt={_rtt_disp:.0f}ms, rssi={rssi2}, bad={bad2}"
                                 )
                             except Exception:
                                 pass
@@ -667,7 +648,7 @@ class WiFiManager:
                             self._on_poor_quality_callback()
                 
                 # 休眠 5 秒
-                stop_event.wait(5.0)
+                time.sleep(5)
                 
             except Exception as e:
                 self.logger.error(f"[WiFi Monitor] 监测异常：{e}")

yolo_te.py

@@ -0,0 +1,267 @@
+#!/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()