pref: clean code

.vscode/settings.json

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

4g.py

@@ -109,6 +109,7 @@
 from maix import app, uart, pinmap, time
 import hashlib
 import hmac
+import re
 import ujson
 
 # ========== 配置 ==========
@@ -130,53 +131,109 @@ def generate_token(device_id):
     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"""
+    """发送 AT 指令并返回完整响应；超时返回已收到的内容。"""
     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:
+    while time.ticks_diff(time.ticks_ms(), start) < timeout_ms:
         data = http_serial.read(128)
         if data:
             buffer += data
             try:
-                decoded = buffer.decode()
+                decoded = buffer.decode("utf-8", "ignore")
-                print("<= ", decoded.strip())
+                if "OK" in decoded or "+CME ERROR" in decoded or "ERROR" in decoded:
-                if "OK" in decoded:
+                    print("[AT] <=", decoded.strip())
-                    return True
+                    return decoded
-                if "+CME ERROR" in decoded or "ERROR" in decoded:
-                    return False
             except:
                 pass
         time.sleep_ms(10)
+    decoded = buffer.decode("utf-8", "ignore")
+    print("[AT] !! timeout", timeout_ms, "ms, response:", decoded.strip() or "<empty>")
+    return decoded
+
+
+def response_ok(response):
+    return "OK" in response and "ERROR" not in response
+
+
+def wait_modem_ready():
+    """等待模组响应，并确认 PDP 上下文已经获得 IP。"""
+    for attempt in range(15):
+        if response_ok(send_cmd("AT", 1000)):
+            break
+        print("[4G] 等待模组启动", attempt + 1, "/15")
+        time.sleep_ms(1000)
+    else:
+        print("[4G] UART2 无 AT 响应，请检查模组供电、A28/A29 接线和串口占用")
+        return False
+
+    send_cmd("ATE0", 1000)
+    cpin = send_cmd("AT+CPIN?", 3000)
+    if "READY" not in cpin:
+        print("[4G] SIM 卡未就绪:", cpin.strip())
+        return False
+
+    addr = send_cmd("AT+CGPADDR=1", 3000)
+    match = re.search(r'\+CGPADDR:\s*1,"([^\"]+)"', addr)
+    if match and match.group(1) != "0.0.0.0":
+        print("[4G] PDP ready, IP:", match.group(1))
+        return True
+
+    send_cmd("AT+MIPCALL=1,1", 15000)
+    for _ in range(20):
+        addr = send_cmd("AT+CGPADDR=1", 3000)
+        match = re.search(r'\+CGPADDR:\s*1,"([^\"]+)"', addr)
+        if match and match.group(1) != "0.0.0.0":
+            print("[4G] PDP ready, IP:", match.group(1))
+            return True
+        time.sleep_ms(1000)
+
+    print("[4G] PDP 未获得 IP，请检查 SIM 流量、信号和 APN")
     return False
 
+
+def clear_http_instances():
+    for instance_id in range(6):
+        send_cmd(f"AT+MHTTPDEL={instance_id}", 1200)
+
 def create_http_instance(url):
     cmd = f'AT+MHTTPCREATE="{url}"'
-    if send_cmd(cmd):
+    response = send_cmd(cmd, 8000)
-        # 尝试提取 instance ID（如果模块返回）
+    match = re.search(r"\+MHTTPCREATE:\s*(\d+)", response)
-        # 注意：部分模块不会返回 ID，可忽略，直接用 0 或 1
+    if not response_ok(response) or not match:
-        return True
+        print("❌ 创建 HTTP 实例失败，模组响应:", response.strip() or "<empty>")
-    return False
+        return None
+    return int(match.group(1))
 
 def send_http_request(url, api_path, token, device_id, json_data):
     # 1. 创建 HTTP 实例
-    if not create_http_instance(url):
+    instance_id = create_http_instance(url)
-        print("❌ 创建 HTTP 实例失败")
+    if instance_id is None:
         return False
 
-    # 2. 设置 Headers（假设实例 ID 为 0，或根据模块默认）
+    # 2. 设置 Headers
-    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
+    commands = (
-    send_cmd(f'AT+MHTTPCFG="header",{instance_id},"Content-Type: application/json"')
+        f'AT+MHTTPCFG="header",{instance_id},"Content-Type: application/json"',
-    send_cmd(f'AT+MHTTPCFG="header",{instance_id},"Authorization: {token}"')
+        f'AT+MHTTPCFG="header",{instance_id},"Authorization: {token}"',
-    send_cmd(f'AT+MHTTPCFG="header",{instance_id},"DeviceId: {device_id}"')
+        f'AT+MHTTPCFG="header",{instance_id},"DeviceId: {device_id}"',
+    )
+    for command in commands:
+        if not response_ok(send_cmd(command)):
+            print("❌ HTTP Header 配置失败")
+            send_cmd(f"AT+MHTTPDEL={instance_id}", 2000)
+            return False
 
     # 3. 发送 Body
     json_str = ujson.dumps(json_data)
-    send_cmd(f'AT+MHTTPCONTENT={instance_id},0,0,"{json_str}"')
+    at_json = json_str.replace("\\", "\\\\").replace('"', '\\"')
+    if not response_ok(send_cmd(f'AT+MHTTPCONTENT={instance_id},0,0,"{at_json}"', 8000)):
+        print("❌ HTTP Body 配置失败")
+        send_cmd(f"AT+MHTTPDEL={instance_id}", 2000)
+        return False
 
     # 4. 发起 POST 请求
-    if send_cmd(f'AT+MHTTPREQUEST={instance_id},2,0,"{api_path}"'):
+    if response_ok(send_cmd(f'AT+MHTTPREQUEST={instance_id},2,0,"{api_path}"', 15000)):
         print("✅ HTTP 请求已发送")
         return True
     else:
@@ -199,7 +256,7 @@ def read_response(timeout_ms=5000):
 print("🚀 启动直接上传流程...")
 
 token = generate_token(device_id)
-print("🔑 Token:", token)
+print("🔑 Token 已生成:", token[:12] + "...")
 
 # 构造模拟数据
 timestamp = int(time.time() * 1000)
@@ -216,9 +273,16 @@ json_data = {
 }
 
 # 执行上传
-if send_http_request(url, api_path, token, device_id, json_data):
+upload_ok = False
+if not wait_modem_ready():
+    print("💥 4G 模组未就绪")
+else:
+    clear_http_instances()
+    upload_ok = send_http_request(url, api_path, token, device_id, json_data)
+
+if upload_ok:
     read_response()
 else:
     print("💥 上传流程失败")
 
-print("🔚 程序结束")
+print("🔚 程序结束")

app.yaml

@@ -1,6 +1,6 @@
 id: t11
 name: t11
-version: 2.14.1
+version: 2.18.2
 author: t11
 icon: ''
 desc: t11
@@ -14,15 +14,18 @@ files:
   - cameraParameters.xml
   - config.py
   - hardware.py
+  - laser_detector.py
   - laser_manager.py
   - logger_manager.py
   - main.py
-  - model_270139.cvimodel
+  - model_317828.cvimodel
-  - model_270139.mud
+  - model_317828.mud
   - network.py
+  - ota_curl.sh
   - ota_manager.py
   - power.py
   - server.pem
+  - set_autostart.py
   - shoot_manager.py
   - shot_id_generator.py
   - target_roi_yolo.py

at_client.py

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

camera_manager.py

@@ -8,6 +8,15 @@ import threading
 import config
 from logger_manager import logger_manager
 
+_USE_CV = False
+try:
+    import cv2
+    import numpy as np
+    from maix import image as _maix_image
+    _USE_CV = True
+except ImportError:
+    pass
+
 
 class CameraManager:
     """相机管理器（单例）"""
@@ -101,7 +110,23 @@ class CameraManager:
         with self._camera_lock:
             if self._camera is None:
                 self.init_camera()
-            return self._camera.read()
+            frame = self._camera.read()
+            if frame is not None and _USE_CV:
+                try:
+                    v_flip = getattr(config, 'CAMERA_V_FLIP', False)
+                    h_mirror = getattr(config, 'CAMERA_H_MIRROR', False)
+                    if v_flip or h_mirror:
+                        img_cv = _maix_image.image2cv(frame, False, False)
+                        if v_flip and h_mirror:
+                            img_cv = cv2.flip(img_cv, -1)
+                        elif v_flip:
+                            img_cv = cv2.flip(img_cv, 0)
+                        elif h_mirror:
+                            img_cv = cv2.flip(img_cv, 1)
+                        frame = _maix_image.cv2image(img_cv, False, False)
+                except Exception:
+                    pass
+            return frame
     
     def show(self, image):
         """

config.py

@@ -15,6 +15,8 @@ LOCAL_FILENAME = APP_DIR + "/main_tmp.py"
 # 相机初始化分辨率（CameraManager / main.py 使用）
 CAMERA_WIDTH = 640
 CAMERA_HEIGHT = 480
+CAMERA_V_FLIP = True   # 摄像头垂直翻转（上下颠倒时设为 True）
+CAMERA_H_MIRROR = True  # 摄像头水平镜像（左右反了时设为 True）
 
 # 三角形检测缩图比例：默认按相机最长边缩到 1/2（性能更稳；可按需调整）
 # 取值范围建议 (0.25 ~ 1.0]；1.0 表示不缩图
@@ -24,7 +26,7 @@ TRIANGLE_DETECT_SCALE = 0.4
 # SERVER_IP = "stcp.shelingxingqiu.com"
 SERVER_IP = "www.shelingxingqiu.com"
 SERVER_PORT = 50005
-HEARTBEAT_INTERVAL = 15  # 心跳间隔（秒）
+HEARTBEAT_INTERVAL = 5  # 心跳间隔（秒）
 
 # WiFi 质量评估（开机先尝试 WiFi；质量差且 4G 可用则切到 4G，本次上电直至关机锁定 4G）
 WIFI_QUALITY_RTT_SAMPLES = 3  # 到业务服务器 TCP 建连耗时采样次数，取中位数
@@ -96,6 +98,11 @@ ADC_LASER_THRESHOLD = 3000
 
 # ==================== 激光配置 ====================
 MODULE_ADDR = 0x00
+# 激光开关改由 A14 GPIO 控制：低电平开启，高电平关闭。
+LASER_CONTROL_PIN = "A14"
+LASER_CONTROL_GPIO = "GPIOA14"
+LASER_CONTROL_ON_LEVEL = 0
+LASER_CONTROL_OFF_LEVEL = 1
 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])
 DISTANCE_QUERY_CMD = bytes([0xAA, MODULE_ADDR, 0x00, 0x20, 0x00, 0x01, 0x00, 0x00, 0x21])  # 激光测距查询命令
@@ -134,7 +141,7 @@ IMAGE_CENTER_Y = 240  # 图像中心 Y 坐标
 # ==================== 三角形四角标记：单应性偏移 + PnP 估距 ====================
 # 依赖 cameraParameters.xml（相机内参）与 triangle_positions.json（四角物方坐标，厘米或毫米见 JSON 约定）。
 # 部署时请把这两个文件放到 APP_DIR（与 main 同应用目录），或改下面路径为设备上的实际绝对路径。
-USE_TRIANGLE_OFFSET = True  # False 时仅走黄心圆/椭圆 + 半径估距，不使用三角形路径
+USE_TRIANGLE_OFFSET = False  # False 时仅走黄心圆/椭圆 + 半径估距，不使用三角形路径
 CAMERA_CALIB_XML = APP_DIR + "/cameraParameters.xml"
 TRIANGLE_POSITIONS_JSON = APP_DIR + "/triangle_positions.json"
 # 检测到的三角形边长在图像中的像素范围，分辨率或靶纸占比变化时可微调
@@ -234,10 +241,10 @@ TRIANGLE_BLACKHAT_KERNEL_FRAC = 0.018  # 核大小 ≈ min(h,w)*frac，取奇数
 # ── YOLO(NPU) 靶环 ROI → 裁剪后再跑三角形（减小 CPU 处理面积）──────────────────
 # 日志里 net_in=W×H 来自 .mud 模型（det.input_width/height），不是这里配置的。
 TRIANGLE_YOLO_ROI_ENABLE = True
-TRIANGLE_YOLO_MODEL_PATH = APP_DIR + "/model_270139.mud"
+TRIANGLE_YOLO_MODEL_PATH = APP_DIR + "/model_317828.mud"
 # 参与 ROI 的类别：多类时只填「整靶/靶环」的 id；不要填角标类，否则 union 仍可对，但 largest 会偏小。
 TRIANGLE_YOLO_RING_CLASS_IDS = (0,)
-TRIANGLE_YOLO_CONF_TH = 0.7
+TRIANGLE_YOLO_CONF_TH = 0.9
 TRIANGLE_YOLO_IOU_TH = 0.45
 # YOLO 首次/临界帧可能在高阈值下 0 框；启用后仅在 0 候选时用较低阈值重试一次。
 # 后续仍会经过 min_box_side、ROI aspect、三角形几何校验，避免直接放大假阳性。
@@ -260,7 +267,17 @@ TRIANGLE_SAMPLE_RADIUS_CM = 15.0
 TRIANGLE_SAMPLE_ANGLES_DEG = (0, 90, 180, 270)
 TRIANGLE_SAMPLE_PATCH_HALF_PX = 2
 # 开机阶段预加载 YOLO detector；detect 使用 dual_buff=False，避免返回上一帧结果。
-TRIANGLE_YOLO_PRELOAD_ON_BOOT = True
+TRIANGLE_YOLO_PRELOAD_ON_BOOT = False
+
+# YOLO target size classification: class 0=20cm, class 1=40cm.
+TARGET_CLASS_YOLO_ENABLE = True
+TARGET_CLASS_YOLO_MODEL_PATH = APP_DIR + "/model_317828.mud"
+TARGET_CLASS_YOLO_LABELS = (20, 40)
+TARGET_CLASS_YOLO_CONF_TH = 0.66
+TARGET_CLASS_YOLO_IOU_TH = 0.45
+TARGET_CLASS_YOLO_RETRY_ON_EMPTY = False
+TARGET_CLASS_YOLO_RETRY_CONF_TH = 0.25
+TARGET_CLASS_YOLO_PRELOAD_ON_BOOT = True
 
 # ── 第二段 YOLO：仅在 Stage1 裁切出的靶环图上推理（与合成 stage2 训练数据一致）→ 子框内传统算法取直角点 ──
 # Stage1 靶环裁切内如何找黑三角标记（对比耗时时可切换）：
@@ -308,14 +325,25 @@ LASER_COLOR = (0, 255, 0)  # RGB颜色
 LASER_THICKNESS = 1
 LASER_LENGTH = 2
 
+# ==================== 队列大小限制（防止内存泄漏） ====================
+MAX_SEND_QUEUE_SIZE = 500       # 发送队列上限
+MAX_TCP_PAYLOADS = 500          # AT TCP 载荷缓存上限
+MAX_HTTP_EVENTS = 200           # AT HTTP 事件缓存上限
+LOG_QUEUE_MAXSIZE = 10000       # 日志队列上限
+MAX_CMD_THREADS = 10            # 并发命令线程上限（防止服务器下发命令时无限创建线程）
+
 # ==================== 图像保存配置 ====================
 SAVE_IMAGE_ENABLED = True  # 是否保存图像（True=保存，False=不保存）
+SAVE_IMAGE_ON_FAILURE = False  # 检测失败时是否强制保存图像（供调试测试用）
 PHOTO_DIR = "/root/phot"    # 照片存储目录
 MAX_IMAGES = 1000
+SAVE_RAW_IMAGE_ENABLED = False  # 原图保存功能保留，但当前关闭
+RAW_IMAGE_DIR = PHOTO_DIR + "/raw"
+RAW_IMAGE_MAX_IMAGES = MAX_IMAGES
 # Stage2 调试目录（默认 PHOTO_DIR/stage2_roi）内 JPEG 最多保留张数；None 表示与 MAX_IMAGES 相同
 TRIANGLE_BLACK_YOLO_STAGE2_ROI_MAX_IMAGES = None
 
-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 = False  # 关闭拍摄实时显示
 
 # ==================== OTA配置 ====================
 MAX_BACKUPS = 5
@@ -330,11 +358,29 @@ PIN_MAPPINGS = {
     "A28": "UART2_TX",
     "A15": "I2C5_SCL",
     "A27": "I2C5_SDA",
+    "A14": "GPIOA14",  # 激光开关：低开、高关
     "A24": "GPIOA24",  # 电源板关机控制
+    "A25": "GPIOA25",  # 电源状态绿灯
+    "A23": "GPIOA23",  # 电源状态红灯
 }
 
 # ==================== 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
+# 充电时自动关机暂时禁用；需要恢复时改为 True。
+CHARGING_AUTO_POWER_OFF_ENABLED = False
+
+# 一代电源控制：A24 由电源板负责按键/关机信号，软件关机时输出高电平。
+
+# 电源状态指示灯
+STATUS_LED_GREEN_GPIO = "GPIOA25"
+STATUS_LED_RED_GPIO = "GPIOA23"
+STATUS_LED_GREEN_ENABLED = True
+STATUS_LED_RED_ENABLED = True
+STATUS_LED_ACTIVE_LEVEL = 1
+STATUS_LED_LOW_BATTERY_PERCENT = 10
+STATUS_LED_FULL_BATTERY_PERCENT = 90
+STATUS_LED_CHARGING_BLINK_MS = 500
+STATUS_LED_POLL_MS = 1000
 
 BATTERY_SOC_LPF_ALPHA = 0.5
 BATTERY_SOC_AVG_WINDOW = 5

gpio_high.py

@@ -0,0 +1,27 @@
+"""Run independently and keep A24 at a high logic level."""
+
+from maix import app, gpio, pinmap, time
+
+
+PIN = "A17"
+GPIO_NAME = "GPIOA17"
+
+
+def main():
+    pinmap.set_pin_function(PIN, GPIO_NAME)
+    output = gpio.GPIO(GPIO_NAME, gpio.Mode.OUT)
+    output.value(1)
+    print(f"{PIN} is HIGH. Stop the script to set it LOW.")
+
+    try:
+        while not app.need_exit():
+            # Refresh the output in case another component changes its state.
+            output.value(1)
+            time.sleep_ms(100)
+    finally:
+        output.value(0)
+        print(f"{PIN} is LOW.")
+
+
+if __name__ == "__main__":
+    main()

hardware.py

@@ -5,6 +5,7 @@
 提供硬件对象的统一管理和访问
 """
 from maix import time
+import _thread
 import config
 from at_client import ATClient
 
@@ -28,6 +29,7 @@ class HardwareManager:
         self._bus = None                 # I2C总线
         self._adc_obj = None             # ADC对象
         self._at_client = None            # AT客户端
+        self._status_led_monitor_started = False
 
         self._last_active_time = 0       # 用于记录用户的最后一次活跃的时间
         self._stop_timer = False         # 用于停止定时器的标志
@@ -104,11 +106,87 @@ class HardwareManager:
             # 物理引脚是 A24，对应 GPIO 功能是 GPIOA24
             # 注意：这里需要先在 config.PIN_MAPPINGS 中配置好 "A24": "GPIOA24"
             from maix import gpio
-            # 输出高电平关闭
+            # 一代电源板关机信号为高电平
             gpio.GPIO("GPIOA24", gpio.Mode.OUT).value(1)
         except Exception as e:
             print(f"关机失败: {e}")
 
+    def start_status_led_monitor(self):
+        """后台更新状态灯：正常/充满绿常亮、充电绿闪烁、低电量红常亮。"""
+        if self._status_led_monitor_started:
+            return
+        self._status_led_monitor_started = True
+        _thread.start_new_thread(self._status_led_loop, ())
+
+    def _status_led_loop(self):
+        from maix import gpio
+        from power import get_bus_voltage, is_charging, voltage_to_percent
+
+        try:
+            green = None
+            if getattr(config, "STATUS_LED_GREEN_ENABLED", True):
+                green = gpio.GPIO(config.STATUS_LED_GREEN_GPIO, gpio.Mode.OUT)
+            red = None
+            if getattr(config, "STATUS_LED_RED_ENABLED", True):
+                red = gpio.GPIO(config.STATUS_LED_RED_GPIO, gpio.Mode.OUT)
+            active = int(config.STATUS_LED_ACTIVE_LEVEL)
+            inactive = 0 if active else 1
+            if green is not None:
+                green.value(inactive)
+            if red is not None:
+                red.value(inactive)
+            last_state = None
+            blink_on = False
+            blink_period = max(100, int(config.STATUS_LED_CHARGING_BLINK_MS))
+            poll_ms = max(100, int(config.STATUS_LED_POLL_MS))
+            tick_ms = min(blink_period, poll_ms)
+            sensor_elapsed = poll_ms
+            blink_elapsed = blink_period
+            state = "normal"
+            percent = None
+            charging = False
+
+            while self._status_led_monitor_started:
+                if sensor_elapsed >= poll_ms:
+                    voltage = get_bus_voltage()
+                    percent = voltage_to_percent(voltage) if voltage > 0 else None
+                    charging = is_charging()
+                    low = percent is not None and percent <= int(config.STATUS_LED_LOW_BATTERY_PERCENT)
+                    full = percent is not None and percent >= int(config.STATUS_LED_FULL_BATTERY_PERCENT)
+                    if charging:
+                        state = "full" if full else "charging"
+                    else:
+                        state = "low" if low else "normal"
+                    sensor_elapsed = 0
+
+                if state == "low":
+                    if green is not None:
+                        green.value(inactive)
+                    if red is not None:
+                        red.value(active)
+                elif state == "charging":
+                    if blink_elapsed >= blink_period:
+                        blink_on = not blink_on
+                        blink_elapsed = 0
+                    if green is not None:
+                        green.value(active if blink_on else inactive)
+                    if red is not None:
+                        red.value(inactive)
+                else:  # normal or full
+                    if green is not None:
+                        green.value(active)
+                    if red is not None:
+                        red.value(inactive)
+                if state != last_state:
+                    print(f"[STATUS_LED] state={state} percent={percent} charging={charging}")
+                    last_state = state
+                time.sleep_ms(tick_ms)
+                sensor_elapsed += tick_ms
+                blink_elapsed += tick_ms
+        except Exception as e:
+            self._status_led_monitor_started = False
+            print(f"[STATUS_LED] monitor failed: {e}")
+
     def start_idle_timer(self):
         self._stop_timer = False
         self._last_active_time = time.time()

laser_detector.py

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

laser_manager.py

@@ -31,6 +31,7 @@ class LaserManager:
         
         # 私有状态
         self._serial = None  # 激光串口，由 laser_manager 自己持有
+        self._laser_gpio = None  # A14 激光开关，低电平开启、高电平关闭
         self._calibration_active = False
         self._calibration_result = None
         self._calibration_lock = threading.Lock()
@@ -54,8 +55,8 @@ 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):
@@ -69,10 +70,21 @@ class LaserManager:
     
     # ==================== 初始化方法 ====================
 
+    def init_control_gpio(self):
+        """尽早初始化 A14，并拉高确保激光关闭。"""
+        from maix import gpio, pinmap
+
+        pinmap.set_pin_function(config.LASER_CONTROL_PIN, config.LASER_CONTROL_GPIO)
+        if self._laser_gpio is None:
+            self._laser_gpio = gpio.GPIO(config.LASER_CONTROL_GPIO, gpio.Mode.OUT)
+        self._laser_gpio.value(config.LASER_CONTROL_OFF_LEVEL)
+        self._laser_turned_on = False
+        print(f"[LASER] {config.LASER_CONTROL_PIN}=HIGH，激光已关闭")
+
     def init(self, serial_device=None, baudrate=None):
         """
-        初始化激光模块（包括串口）
+        初始化激光模块（A14 开关 + 测距串口）
-        初始化完成后主动发送关闭命令，防止 UART 初始化噪声误触发激光
+        初始化时先将 A14 拉高关闭激光，防止开机误触发
         
         Args:
             serial_device: 串口设备路径，默认使用 config.DISTANCE_SERIAL_DEVICE
@@ -82,51 +94,36 @@ class LaserManager:
         device = serial_device or config.DISTANCE_SERIAL_DEVICE
         baud = baudrate or config.DISTANCE_SERIAL_BAUDRATE
 
+        self.init_control_gpio()
+
         self._serial = uart.UART(device, baud)
         print(f"[LASER] 激光串口初始化完成: device={device}, baudrate={baud}")
 
-        # 等待串口稳定后主动关闭激光，防止初始化噪声误触发
-        time.sleep_ms(100)
-        try:
-            self._serial.read(-1)  # 清空接收缓冲区
-        except Exception:
-            pass
-        self._serial.write(config.LASER_OFF_CMD)
-        time.sleep_ms(60)
-        try:
-            self._serial.read(-1)  # 清空回包
-        except Exception:
-            pass
-        print("[LASER] 已发送关闭命令（防止开机误触发）")
-
     # ==================== 业务方法 ====================
     
     def load_laser_point(self):
-        """从配置文件加载激光中心点，失败则使用默认值
+        """加载激光中心点：优先使用本地保存的坐标，其次硬编码值，最后默认值"""
-        如果启用硬编码模式，则直接使用硬编码值
+        # 优先：从本地持久化文件加载（由 cmd 201 保存）
-        """
-        if config.HARDCODE_LASER_POINT:
-            # 硬编码模式：直接使用硬编码值
-            self._laser_point = config.HARDCODE_LASER_POINT_VALUE
-            self.logger.info(f"[LASER] 使用硬编码激光点: {self._laser_point}")
-            return self._laser_point
-        
-        # 正常模式：从配置文件加载
         try:
             if "laser_config.json" in os.listdir("/root"):
                 with open(config.CONFIG_FILE, "r") as f:
                     data = json.load(f)
                     if isinstance(data, list) and len(data) == 2:
                         self._laser_point = (int(data[0]), int(data[1]))
-                        self.logger.debug(f"[INFO] 加载激光点: {self._laser_point}")
+                        self.logger.info(f"[LASER] 从本地加载激光点: {self._laser_point}")
                         return self._laser_point
-                    else:
+        except Exception:
-                        raise ValueError
+            pass
-            else:
+
-                self._laser_point = config.DEFAULT_LASER_POINT
+        # 其次：硬编码值
-        except:
+        if config.HARDCODE_LASER_POINT:
-            self._laser_point = config.DEFAULT_LASER_POINT
+            self._laser_point = config.HARDCODE_LASER_POINT_VALUE
-        
+            self.logger.info(f"[LASER] 使用硬编码激光点: {self._laser_point}")
+            return self._laser_point
+
+        # 最后：默认值
+        self._laser_point = config.DEFAULT_LASER_POINT
+        self.logger.info(f"[LASER] 使用默认激光点: {self._laser_point}")
         return self._laser_point
     
     def save_laser_point(self, point):
@@ -150,66 +147,38 @@ class LaserManager:
             return False
     
     def turn_on_laser(self):
-        """发送指令开启激光，并读取回包（部分模块支持）"""
+        """A14 输出低电平，开启激光。"""
-        if self._serial is None:
+        if self._laser_gpio is None:
-            self.logger.error("[LASER] 激光串口未初始化，请先调用 init()")
+            if self.logger:
-            return None
+                self.logger.error("[LASER] A14 GPIO 未初始化，请先调用 init()")
-        
+            return False
-        # 打印调试信息
-        self.logger.info(f"[LASER] 发送开启命令: {config.LASER_ON_CMD.hex()}")
-        
-        # 清空接收缓冲区
         try:
-            self._serial.read(-1)  # 清空缓冲区
+            self._laser_gpio.value(config.LASER_CONTROL_ON_LEVEL)
-        except:
+            self._laser_turned_on = True
-            pass
+            if self.logger:
-        
+                self.logger.info("[LASER] A14=LOW，激光开启")
-        # 发送命令
+            return True
-        written = self._serial.write(config.LASER_ON_CMD)
+        except Exception as e:
-        self.logger.info(f"[LASER] 写入字节数: {written}")
+            if self.logger:
-        
+                self.logger.error(f"[LASER] A14 开启激光失败: {e}")
-        time.sleep_ms(60)
+            return False
-
-        # 读取回包
-        resp = self._serial.read(len=20, timeout=10)
-        if resp:
-            self.logger.info(f"[LASER] 收到回包 ({len(resp)}字节): {resp.hex()}")
-            if resp == config.LASER_ON_CMD:
-                self.logger.info("✅ 激光开启指令已确认")
-        else:
-            self.logger.warning("🔇 无回包（可能正常或模块不支持回包）")
-        self._laser_turned_on = True
-        return resp
     
     def turn_off_laser(self):
-        """发送指令关闭激光"""
+        """A14 输出高电平，关闭激光。"""
-        if self._serial is None:
+        if self._laser_gpio is None:
-            self.logger.error("[LASER] 激光串口未初始化，请先调用 init()")
+            if self.logger:
-            return None
+                self.logger.error("[LASER] A14 GPIO 未初始化，请先调用 init()")
-        
+            return False
-        # 打印调试信息
-        self.logger.info(f"[LASER] 发送关闭命令: {config.LASER_OFF_CMD.hex()}")
-        
-        # 清空接收缓冲区
         try:
-            self._serial.read(-1)
+            self._laser_gpio.value(config.LASER_CONTROL_OFF_LEVEL)
-        except:
+            self._laser_turned_on = False
-            pass
+            if self.logger:
-        
+                self.logger.info("[LASER] A14=HIGH，激光关闭")
-        # 发送命令
+            return True
-        written = self._serial.write(config.LASER_OFF_CMD)
+        except Exception as e:
-        self.logger.info(f"[LASER] 写入字节数: {written}")
+            if self.logger:
-
+                self.logger.error(f"[LASER] A14 关闭激光失败: {e}")
-        time.sleep_ms(60)
+            return False
-        
-        # 读取回包
-        resp = self._serial.read(20)
-        if resp:
-            self.logger.info(f"[LASER] 收到回包 ({len(resp)}字节): {resp.hex()}")
-        else:
-            self.logger.warning("🔇 无回包")
-        self._laser_turned_on = False
-        return resp
 
     def flash_laser(self, duration_ms=1000):
         """闪一下激光（非阻塞版本）"""
@@ -1264,6 +1233,28 @@ class LaserManager:
                 except Exception as e:
                     self.logger.error(f"[LASER] 关闭激光失败: {e}")
 
+    def set_hardcoded_laser_point(self, raw_x, raw_y):
+        """
+        设置服务下发的硬编码激光点坐标，并保存到本地持久化文件。
+        下次启动时 load_laser_point() 会优先使用此保存的值。
+
+        Args:
+            raw_x: 服务下发的 x 坐标
+            raw_y: 服务下发的 y 坐标
+
+        Returns:
+            (int_x, int_y) 元组
+        """
+        ix = int(raw_x)
+        iy = int(raw_y)
+        self._laser_point = (ix, iy)
+        try:
+            with open(config.CONFIG_FILE, "w") as f:
+                json.dump([ix, iy], f)
+            self.logger.info(f"[LASER] 设置并持久化激光点: ({ix}, {iy})")
+        except Exception as e:
+            self.logger.error(f"[LASER] 持久化激光点失败: {e}")
+        return ix, iy
 
 # 创建全局单例实例
 laser_manager = LaserManager()

logger_manager.py

@@ -65,8 +65,8 @@ class LoggerManager:
             backup_count = config.LOG_BACKUP_COUNT
         
         try:
-            # 创建日志队列（无界队列）
+            # 创建日志队列（有界队列，防止内存泄漏；满时自动丢弃旧日志）
-            self._log_queue = queue.Queue(-1)
+            self._log_queue = queue.Queue(maxsize=config.LOG_QUEUE_MAXSIZE)
             
             # 确保日志文件所在的目录存在
             log_dir = os.path.dirname(log_file)

main.py

@@ -76,12 +76,14 @@ def laser_calibration_worker():
                 import traceback
                 traceback.print_exc()
             time.sleep_ms(1000)  # 等待1秒后继续
-
 def cmd_str():
     """主程序入口"""
     # ==================== 第一阶段：硬件初始化 ====================
     # 按照 main104.py 的顺序，先完成所有硬件初始化
 
+    # 开机第一步先拉高 A14 关闭激光，避免其他硬件初始化期间误亮。
+    laser_manager.init_control_gpio()
+
     # 1. 引脚功能映射
     for pin, func in config.PIN_MAPPINGS.items():
         try:
@@ -103,6 +105,8 @@ def cmd_str():
     print(f"[BOOT] init_ina226 开始 wall_s={_w_boot:.3f}")
     init_ina226()
     print(f"[BOOT] init_ina226 结束 wall +{int(round((wall_time.time() - _w_boot) * 1000))} ms")
+    # 启动 A25 绿灯和 A23 红灯状态指示。
+    hardware_manager.start_status_led_monitor()
 
     # 4. 初始化显示和相机
     _w_boot = wall_time.time()
@@ -120,9 +124,9 @@ def cmd_str():
 
     # ==================== 第二阶段：软件初始化 ====================
 
-    # 1. 初始化日志系统
+    # 1. 初始化日志系统（WARNING级别，不打印/写入INFO和DEBUG日志，提高执行流畅度）
     import logging
-    logger_manager.init_logging(log_level=logging.DEBUG)
+    logger_manager.init_logging(log_level=logging.WARNING)
     logger = logger_manager.logger
 
     # 补充：因为初始化的时候，激光会亮，先关了它
@@ -132,6 +136,7 @@ def cmd_str():
     sync_system_time_from_4g()
 
     # 2.1 WiFi 热点配网兜底：仅当 STA 与 4G 均不可用时起 AP + HTTP；提交后删 /boot/wifi.ap、建 wifi.sta 并 reboot
+    _ota_pending_path = f"{config.APP_DIR}/ota_pending.json"
     try:
         from wifi_config_httpd import maybe_start_wifi_ap_fallback
 
@@ -162,9 +167,15 @@ def cmd_str():
             and _loc_black == "yolo"
             and bool(getattr(config, "TRIANGLE_BLACK_YOLO_PRELOAD_ON_BOOT", True))
         )
-        _preload_yolo = _preload_yolo or _need_black_preload
+        _need_target_preload = (
-        if _preload_yolo:
+            bool(getattr(config, "TARGET_CLASS_YOLO_ENABLE", False))
+            and bool(getattr(config, "TARGET_CLASS_YOLO_PRELOAD_ON_BOOT", True))
+        )
+        _preload_yolo = _preload_yolo or _need_black_preload or _need_target_preload
+        if _preload_yolo and not os.path.exists(f"{config.APP_DIR}/ota_pending.json"):
             preload_yolo_detector(logger)
+        elif _preload_yolo and logger:
+            logger.warning("[YOLO] ota_pending.json found; skip model preload until rollback check")
     except Exception as e:
         if logger:
             logger.warning(f"[YOLO-ROI] 启动预加载异常（不影响后续射箭）: {e}")
@@ -245,8 +256,12 @@ def cmd_str():
     # 4. 初始化设备ID（network_manager 内部会自动设置 device_id 和 password）
     network_manager.read_device_id()
 
-    # 5. 创建照片存储目录（如果启用图像保存）
+    # 5. 创建照片存储目录（如果启用图像保存或检测失败时强制保存）
-    if config.SAVE_IMAGE_ENABLED:
+    if (
+        config.SAVE_IMAGE_ENABLED
+        or getattr(config, "SAVE_IMAGE_ON_FAILURE", False)
+        or getattr(config, "SAVE_RAW_IMAGE_ENABLED", False)
+    ):
         photo_dir = config.PHOTO_DIR
         if photo_dir not in os.listdir("/root"):
             try:
@@ -278,46 +293,46 @@ def cmd_str():
         logger.info("系统准备完成...")
 
     last_adc_trigger = 0
+    trigger_adc_val = 0  # 触发时的气压值，气压需降回此值以下才能再次触发
+    # 读取一次ADC初始值，防止开机时传感器已有压力导致误触发
+    enable_check = True
+    try:
+        last_adc_val = hardware_manager.adc_obj.read()
+    except Exception:
+        last_adc_val = 0
     # 气压采样：减少日志频率（每 N 个点输出一条），避免 logger.debug 拖慢采样
     PRESSURE_BATCH_SIZE = 100
 
     pressure_buf = []
     pressure_sum = 0
-    pressure_abs_sum = 0
     pressure_min = 4095
     pressure_max = 0
     pressure_t0_ms = None
-    last_avg_abs = 0
 
     def _flush_pressure_buf(reason: str):
-        if not config.AIR_PRESSURE_lOG:
+        nonlocal pressure_buf, pressure_sum, pressure_min, pressure_max, pressure_t0_ms, logger
-            return
-        nonlocal pressure_buf, pressure_sum, pressure_min, pressure_max, pressure_t0_ms, logger, pressure_abs_sum, last_avg_abs
         if not pressure_buf:
             return
-        t1_ms = time.ticks_ms()
+        if config.AIR_PRESSURE_lOG:
-        n = len(pressure_buf)
+            t1_ms = time.ticks_ms()
-        avg = (pressure_sum / n) if n else 0
+            n = len(pressure_buf)
-        avg_abs = (pressure_abs_sum / n) if n else 0
+            avg = (pressure_sum / n) if n else 0
-        # 一行输出：方便后处理画曲线；同时带上统计信息便于快速看波峰
+            line = (
-        line = (
+                f"[气压批量] reason={reason} "
-            f"[气压批量] reason={reason} "
+                f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
-            f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
+                f"min={pressure_min} max={pressure_max} avg={avg:.1f} "
-            f"min={pressure_min} max={pressure_max} avg={avg:.1f} avg_abs={avg_abs:.3f} "
+                f"values={','.join(map(str, pressure_buf))}"
-            f"values={','.join(map(str, pressure_buf))}"
+            )
-            f" convert value (kpa): {(max(pressure_buf, key=lambda x: x[1])[1] - last_avg_abs) / (5 - 2.5) * config.AIR_PRESSURE_HARDWARE_MAX:.1f}"
+            if logger:
-        )
+                logger.debug(line)
-        if logger:
+            else:
-            logger.debug(line)
+                print(line)
-        else:
+        # 无论是否记录日志，都必须清空 buffer，否则内存泄漏
-            print(line)
         pressure_buf = []
         pressure_sum = 0
-        pressure_abs_sum = 0
         pressure_min = 4095
         pressure_max = 0
         pressure_t0_ms = None
-        last_avg_abs = avg_abs
 
     # 主循环：检测扳机触发 → 拍照 → 分析 → 上报
     while not app.need_exit():
@@ -352,12 +367,10 @@ def cmd_str():
                 if network_manager.manual_trigger_flag:
                     network_manager.clear_manual_trigger()
                     adc_val = config.ADC_TRIGGER_THRESHOLD + 1
-                    adc_abs_val = 10
                     if logger:
                         logger.info("[TEST] TCP命令触发射箭")
                 else:
                     adc_val = hardware_manager.adc_obj.read()
-                    adc_abs_val = hardware_manager.adc_obj.read_vol()
             except Exception as e:
                 logger = logger_manager.logger
                 if logger:
@@ -368,25 +381,24 @@ def cmd_str():
             # ====== 气压采样缓存（每次循环都记录，批量输出日志）======
             if pressure_t0_ms is None:
                 pressure_t0_ms = current_time
-            pressure_buf.append((adc_val, adc_abs_val))
+            pressure_buf.append(adc_val)
             pressure_sum += adc_val
-            pressure_abs_sum += adc_abs_val
             if adc_val < pressure_min:
                 pressure_min = adc_val
             if adc_val > pressure_max:
                 pressure_max = adc_val
             if len(pressure_buf) >= PRESSURE_BATCH_SIZE:
                 _flush_pressure_buf("batch")
-            # if adc_val >= 2000:
+            # 突变增量检测：压力增量大于300时触发
-            # print(f"adc :{adc_val}")
+            # 触发后需等气压降到触发值以下才重新检测增量
-            if adc_val >= config.ADC_TRIGGER_THRESHOLD:
+            if adc_val < trigger_adc_val :
+                enable_check = True
+            if (adc_val - last_adc_val) > 500 and enable_check:
                 hardware_manager.start_idle_timer()  # 重新计时
-                diff_ms = current_time - last_adc_trigger
-                if diff_ms < 3000:
-                    logger.info(f"[MAIN] 扳机触发过于频繁, {diff_ms}ms")
-                    continue
                 last_adc_trigger = current_time
-                # 触发前先把缓存刷出来，避免波形被长耗时处理截断
+                trigger_adc_val = adc_val  # 记录触发时的气压值
+                last_adc_val = adc_val  # 更新基准值，防止连续增量误触发
+                enable_check = False
                 _flush_pressure_buf("before_trigger")
 
                 try:
@@ -404,10 +416,9 @@ def cmd_str():
                     try:
                         camera_manager.show(camera_manager.read_frame())
                     except Exception as e:
-                        logger = logger_manager.logger
+                        pass
-                        if logger:
+                time.sleep_ms(1)
-                            logger.error(f"[MAIN] 显示异常: {e}")
+            last_adc_val = adc_val
-                time.sleep_ms(5)
 
         except Exception as e:
             # 主循环的顶层异常捕获，防止程序静默退出

model_270139.mud

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

model_317828.mud

@@ -1,7 +1,7 @@
 
 [basic]
 type = cvimodel
-model = model_270820.cvimodel
+model = model_317828.cvimodel
 
 [extra]
 model_type = yolov5
@@ -9,5 +9,5 @@ input_type = rgb
 mean = 0, 0, 0
 scale = 0.00392156862745098, 0.00392156862745098, 0.00392156862745098
 anchors = 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326
-labels = triangle
+labels = 20, 40
 

official_pack_fip.py

@@ -0,0 +1,603 @@
+#!/usr/bin/env python3
+# PYTHON_ARGCOMPLETE_OK
+
+import sys
+import logging
+import os
+import re
+import os.path
+import collections
+import uuid
+import argparse
+import tarfile
+import io
+from struct import pack, unpack
+
+
+PYTHON_MIN_VERSION = (3, 5, 2)  # Ubuntu 16.04 LTS contains Python v3.5.2 by default
+
+
+if sys.version_info < PYTHON_MIN_VERSION:
+    print("Python >= %r is required" % (PYTHON_MIN_VERSION,))
+    sys.exit(-1)
+
+
+try:
+    import coloredlogs
+except ImportError:
+    coloredlogs = None
+
+try:
+    import argcomplete
+except ImportError:
+    argcomplete = None
+
+TOC_HEADER_NAME = 0xAA640001
+FIP_MAX_SIZE = 0xA0000
+FIP_ALIGN_SIZE = 2 * 1024
+ENTRY_SIZE = 0x28
+
+IV_ZERO = b"\0" * 16
+
+
+class FIP_HEADER_FLAG:
+    BitRange = collections.namedtuple("BitRange", "shift, bits")
+
+    REE_SCS = BitRange(0, 2)
+    REE_ENCRYPTION = BitRange(2, 2)
+
+    @classmethod
+    def test(cls, value, flag):
+        v = value >> flag.shift
+        v &= (1 << flag.bits) - 1
+        return v
+
+    @classmethod
+    def value(cls, flag):
+        v = (1 << flag.bits) - 1
+        v <<= flag.shift
+        return v
+
+
+class FIP_UUID:
+    # from arm-trusted-firmware/include/tools_share/firmware_image_package.h
+    uuid_c_define = """
+/* ToC Entry UUIDs */
+#define UUID_LICENSE_FILE \
+    {0x25360c62, 0x5151, 0x48ad, 0xb5, 0x91, {0x2d, 0x35, 0x67, 0x26, 0x85, 0xa5} }
+#define UUID_TRUSTED_UPDATE_FIRMWARE_SCP_BL2U \
+    {0x03279265, 0x742f, 0x44e6, 0x8d, 0xff, {0x57, 0x9a, 0xc1, 0xff, 0x06, 0x10} }
+#define UUID_TRUSTED_UPDATE_FIRMWARE_BL2U \
+    {0x37ebb360, 0xe5c1, 0x41ea, 0x9d, 0xf3, {0x19, 0xed, 0xa1, 0x1f, 0x68, 0x01} }
+#define UUID_TRUSTED_UPDATE_FIRMWARE_NS_BL2U \
+    {0x111d514f, 0xe52b, 0x494e, 0xb4, 0xc5, {0x83, 0xc2, 0xf7, 0x15, 0x84, 0x0a} }
+#define UUID_TRUSTED_FWU_CERT \
+    {0xb28a4071, 0xd618, 0x4c87, 0x8b, 0x2e, {0xc6, 0xdc, 0xcd, 0x50, 0xf0, 0x96} }
+#define UUID_TRUSTED_BOOT_FIRMWARE_BL2 \
+    {0x0becf95f, 0x224d, 0x4d3e, 0xa5, 0x44, {0xc3, 0x9d, 0x81, 0xc7, 0x3f, 0x0a} }
+#define UUID_BLD \
+    {0x3dfd6697, 0xbe89, 0x49e8, 0xae, 0x5d, {0x78, 0xa1, 0x40, 0x60, 0x82, 0x13} }
+#define UUID_EL3_RUNTIME_FIRMWARE_BL31 \
+    {0x6d08d447, 0xfe4c, 0x4698, 0x9b, 0x95, {0x29, 0x50, 0xcb, 0xbd, 0x5a, 0x00} }
+#define UUID_SECURE_PAYLOAD_BL32 \
+    {0x89e1d005, 0xdc53, 0x4713, 0x8d, 0x2b, {0x50, 0x0a, 0x4b, 0x7a, 0x3e, 0x38} }
+#define UUID_NON_TRUSTED_FIRMWARE_BL33 \
+    {0xa7eed0d6, 0xeafc, 0x4bd5, 0x97, 0x82, {0x99, 0x34, 0xf2, 0x34, 0xb6, 0xe4} }
+/* Key certificates */
+#define UUID_ROT_KEY_CERT \
+    {0x721d2d86, 0x60f8, 0x11e4, 0x92, 0x0b, {0x8b, 0xe7, 0x62, 0x16, 0x0f, 0x24} }
+#define UUID_BLD1_KEY_CERT \
+    {0x90e87e82, 0x60f8, 0x11e4, 0xa1, 0xb4, {0x77, 0x7a, 0x21, 0xb4, 0xf9, 0x4c} }
+#define UUID_BLD2_KEY_CERT \
+    {0xa1214202, 0x60f8, 0x11e4, 0x8d, 0x9b, {0xf3, 0x3c, 0x0e, 0x15, 0xa0, 0x14} }
+#define UUID_SOC_FW_KEY_CERT \
+    {0xccbeb88a, 0x60f9, 0x11e4, 0x9a, 0xd0, {0xeb, 0x48, 0x22, 0xd8, 0xdc, 0xf8} }
+#define UUID_TRUSTED_OS_FW_KEY_CERT \
+    {0x03d67794, 0x60fb, 0x11e4, 0x85, 0xdd, {0xb7, 0x10, 0x5b, 0x8c, 0xee, 0x04} }
+#define UUID_BL33_KEY_CERT \
+    {0x2a83d58a, 0x60fb, 0x11e4, 0x8a, 0xaf, {0xdf, 0x30, 0xbb, 0xc4, 0x98, 0x59} }
+/* Content certificates */
+#define UUID_TRUSTED_BOOT_FW_CERT \
+    {0xea69e2d6, 0x635d, 0x11e4, 0x8d, 0x8c, {0x9f, 0xba, 0xbe, 0x99, 0x56, 0xa5} }
+#define UUID_BLD_CONTENT_CERT \
+    {0x046fbe44, 0x635e, 0x11e4, 0xb2, 0x8b, {0x73, 0xd8, 0xea, 0xae, 0x96, 0x56} }
+#define UUID_SOC_FW_CONTENT_CERT \
+    {0x200cb2e2, 0x635e, 0x11e4, 0x9c, 0xe8, {0xab, 0xcc, 0xf9, 0x2b, 0xb6, 0x66} }
+#define UUID_TRUSTED_OS_FW_CONTENT_CERT \
+    {0x11449fa4, 0x635e, 0x11e4, 0x87, 0x28, {0x3f, 0x05, 0x72, 0x2a, 0xf3, 0x3d} }
+#define UUID_BL33_CONTENT_CERT \
+    {0xf3c1c48e, 0x635d, 0x11e4, 0xa7, 0xa9, {0x87, 0xee, 0x40, 0xb2, 0x3f, 0xa7} }
+/* CV keys */
+#define UUID_CV_TRUSTED_KEY_CERT \
+    {0x64fbfc49, 0x4b8c, 0x4ad3, 0xb9, 0x92, {0x93, 0x55, 0x89, 0xee, 0xf0, 0x12} }
+#define UUID_CV_NON_TRUSTED_KEY_CERT \
+    {0xcb48bf0d, 0x7012, 0x4201, 0xbc, 0x35, {0x8a, 0x51, 0xc4, 0x90, 0x90, 0x94} }
+
+/* DDR init*/
+#define UUID_CV_DDRINIT_KEY_CERT \
+    {0xa61c53c9, 0x886c, 0x484f, 0x96, 0x5d, {0xd2, 0xda, 0xd7, 0xc3, 0xeb, 0x13} }
+#define UUID_CV_DDRINIT_CONTENT_CERT \
+    {0x9dfaabd2, 0x7f1b, 0x47e6, 0xa8, 0xa6, {0x6a, 0xc3, 0x10, 0xcc, 0xac, 0x91} }
+#define UUID_CV_DDRINIT \
+    {0x5888a5cd, 0x38fc, 0x4f66, 0xae, 0x3d, {0x2e, 0x18, 0x6d, 0x69, 0x41, 0xfb} }
+
+/* Fast boot */
+#define UUID_CV_FASTBOOT_KEY_CERT \
+    {0x285df54e, 0x7b50, 0x4309, 0x9b, 0x52, {0x4b, 0xc4, 0x92, 0x82, 0x60, 0xdd} }
+#define UUID_CV_FASTBOOT_CONTENT_CERT \
+    {0x61f7595b, 0x8d77, 0x4e13, 0x91, 0x2a, {0x63, 0x6e, 0x58, 0xda, 0x5b, 0x69} }
+#define UUID_CV_FASTBOOT \
+    {0x43766198, 0xc363, 0x48db, 0xa9, 0x97, {0xf1, 0x0e, 0x93, 0x80, 0x4f, 0xea} }
+"""
+
+    @classmethod
+    def cls_init(cls):
+        txt = cls.uuid_c_define
+        txt = txt.replace("\r\n", "\n")
+        txt = txt.replace("\\\n", "\n")
+        rx = r"""
+            \#define\s+
+            (?P<name>\S+)\s+
+            {
+                \s*(?P<u0>0x\S+)\s*,\s*
+                \s*(?P<u1>0x\S+)\s*,\s*
+                \s*(?P<u2>0x\S+)\s*,\s*
+                \s*(?P<u3>0x\S+)\s*,\s*
+                \s*(?P<u4>0x\S+)\s*,\s*
+                {
+                    \s*(?P<u5>0x\S+)\s*,\s*
+                    \s*(?P<u6>0x\S+)\s*,\s*
+                    \s*(?P<u7>0x\S+)\s*,\s*
+                    \s*(?P<u8>0x\S+)\s*,\s*
+                    \s*(?P<u9>0x\S+)\s*,\s*
+                    \s*(?P<u10>0x\S+)\s*
+                }\s*,?\s*
+            }
+        """
+        for m in re.finditer(rx, txt, flags=re.X):
+            name = m.group("name")
+            u = m.group(*["u%d" % i for i in range(11)])
+            u = [int(i, 0) for i in u]
+            u = pack("<IHHBBBBBBBB", *u)
+            u = uuid.UUID(bytes=u)
+            setattr(cls, name, u)
+
+
+class Entry:
+    __slots__ = ["name", "loc", "uuid", "address", "flag", "content"]
+
+    def __init__(self):
+        self.loc = 0
+        self.uuid = uuid.UUID(int=0)
+        self.address = 0
+        self.flag = 0
+        self.content = b""
+
+    @classmethod
+    def make(cls, uuid, content):
+        entry = cls()
+        entry.uuid = uuid
+        entry.content = content
+        return entry
+
+    @classmethod
+    def from_fip(cls, name, loc, fip_bin):
+        data = fip_bin[loc : loc + ENTRY_SIZE]
+        uuid_bytes, address, size, flag = unpack("<16sQQQ", data)
+        content = fip_bin[address : address + size]
+
+        entry = cls()
+        entry.name = name
+        entry.loc = loc
+        entry.uuid = uuid.UUID(bytes=uuid_bytes)
+        entry.address = address
+        entry.flag = flag
+        entry.content = content
+        return entry
+
+    def to_bytes(self):
+        return pack("<16sQQQ", self.uuid.bytes, self.address, self.size, self.flag)
+
+    @property
+    def size(self):
+        return len(self.content)
+
+    @property
+    def end(self):
+        return self.address + self.size
+
+    def __str__(self):
+        return "<%-31s loc=0x%03x U=%s a=0x%05x,0x%05x,0x%05x f=0x%x>" % (
+            self.name,
+            self.loc,
+            self.uuid.hex[:8],
+            self.address,
+            self.end,
+            self.size,
+            self.flag,
+        )
+
+
+class FIP:
+    ENTRY_NAMES = collections.OrderedDict(
+        [
+            ("LICENSE_FILE", "UUID_LICENSE_FILE"),
+            ("BL2", "UUID_TRUSTED_BOOT_FIRMWARE_BL2"),
+            ("BLD", "UUID_BLD"),
+            ("BL31", "UUID_EL3_RUNTIME_FIRMWARE_BL31"),
+            ("BL32", "UUID_SECURE_PAYLOAD_BL32"),
+            ("BL33", "UUID_NON_TRUSTED_FIRMWARE_BL33"),
+            ("BLD1_KEY_CERT", "UUID_BLD1_KEY_CERT"),
+            ("BLD2_KEY_CERT", "UUID_BLD2_KEY_CERT"),
+            ("CV_TRUSTED_KEY_CERT", "UUID_CV_TRUSTED_KEY_CERT"),
+            ("SOC_FW_KEY_CERT", "UUID_SOC_FW_KEY_CERT"),
+            ("TRUSTED_OS_FW_KEY_CERT", "UUID_TRUSTED_OS_FW_KEY_CERT"),
+            ("CV_NON_TRUSTED_KEY_CERT", "UUID_CV_NON_TRUSTED_KEY_CERT"),
+            ("BL33_KEY_CERT", "UUID_BL33_KEY_CERT"),
+            ("TRUSTED_BOOT_FW_CERT", "UUID_TRUSTED_BOOT_FW_CERT"),
+            ("BLD_CONTENT_CERT", "UUID_BLD_CONTENT_CERT"),
+            ("SOC_FW_CONTENT_CERT", "UUID_SOC_FW_CONTENT_CERT"),
+            ("TRUSTED_OS_FW_CONTENT_CERT", "UUID_TRUSTED_OS_FW_CONTENT_CERT"),
+            ("BL33_CONTENT_CERT", "UUID_BL33_CONTENT_CERT"),
+            ("CV_DDRINIT", "UUID_CV_DDRINIT"),
+            ("CV_FASTBOOT", "UUID_CV_FASTBOOT"),
+        ]
+    )
+
+    TOC_Header = collections.namedtuple(
+        "TOC_Header", "name, serial, flag_res, flag_plat, flag_res2"
+    )
+
+    def __init__(self, path):
+        logging.info("FIP_BIN: %s", path)
+        self.path = path
+
+    def load(self):
+        with open(self.path, "rb") as fp:
+            self.binary = fp.read(FIP_MAX_SIZE)
+        logging.info("%s is %d bytes", self.path, len(self.binary))
+
+        self.header = self.TOC_Header(*unpack("<IIIHH", self.binary[0x00:0x10]))
+        if self.header.name != TOC_HEADER_NAME:
+            raise ValueError(
+                "FIP header is 0x%08x but should be 0x%08x"
+                % (self.header[0], TOC_HEADER_NAME)
+            )
+
+        logging.info("TOC header: flag_plat=0x%04x", self.header.flag_plat)
+        logging.info(
+            "  REE_SCS: %r",
+            FIP_HEADER_FLAG.test(self.header.flag_plat, FIP_HEADER_FLAG.REE_SCS),
+        )
+        logging.info(
+            "  REE_ENCRYPTION: %r",
+            FIP_HEADER_FLAG.test(self.header.flag_plat, FIP_HEADER_FLAG.REE_ENCRYPTION),
+        )
+
+        ents = []
+        for k, v in self.ENTRY_NAMES.items():
+            try:
+                ents.append((k, self.find_entry(v)))
+            except ValueError as err:
+                logging.warning("%s", err)
+
+        ents.sort(key=lambda x: x[1].address)
+        for n, (k, v) in enumerate(ents):
+            logging.debug("%s", v)
+            if n > 0:
+                pk, pv = ents[n - 1]
+                if v.loc != pv.loc + ENTRY_SIZE or v.address != pv.address + pv.size:
+                    raise Exception("Invalid FIP")
+
+        rest = self.binary[ents[-1][1].end :]
+        loc = rest.find(b"APLB")
+        if loc < 0:
+            raise Exception("No BLD/DDRC")
+        self.blp_ddrc_binary = rest[loc:]
+        logging.debug("blp_ddrc: 0x%04x at 0x%08x", len(self.blp_ddrc_binary), loc)
+
+        self.ents = collections.OrderedDict(ents)
+
+    def make_fip(self, output_path=None):
+        logging.info("New TOC header: flag_plat=0x%04x", self.header.flag_plat)
+        header_bin = pack("<IIIHH", *self.header)
+        fip_bin = header_bin
+
+        # Sort self.ents by the order of FIP.ENTRY_NAMES
+        sorted_ents = collections.OrderedDict()
+        for name in self.ENTRY_NAMES:
+            try:
+                sorted_ents[name] = self.ents[name]
+            except KeyError:
+                pass
+        self.ents = sorted_ents
+
+        offset = (len(self.ents) + 1) * ENTRY_SIZE + 0x10
+
+        for k, v in self.ents.items():
+            v.address = offset
+            fip_bin += v.to_bytes()
+            offset += v.size
+
+        null_entry = Entry()
+        null_entry.address = offset
+        fip_bin += null_entry.to_bytes()
+
+        for k, v in self.ents.items():
+            fip_bin += v.content
+
+        if (len(fip_bin) % FIP_ALIGN_SIZE) > 0:
+            fip_bin += b"\x00" * (FIP_ALIGN_SIZE - len(fip_bin) % FIP_ALIGN_SIZE)
+        fip_bin += self.blp_ddrc_binary
+
+        if output_path:
+            path = output_path
+        else:
+            path = os.path.splitext(self.path)
+            path = path[0] + "_signed_encrypted" + path[1]
+        logging.info("Save new FIP image to %s", path)
+        with open(path, "wb") as fp:
+            fp.write(fip_bin)
+
+    def dump_uuids(self):
+        for k, v in vars(FIP_UUID).items():
+            if k.startswith("UUID_"):
+                print("%-38s" % k, v.hex)
+
+    def find_entry(self, name):
+        # UUID=0, offset=any, size=0, flags=0
+        nullm = re.search(rb"\0{16}.{8}\0{16}", self.binary, flags=re.DOTALL)
+        if nullm is None:
+            raise Exception("NULL TOC entry is not found")
+
+        max_toc_size = nullm.start(0)
+        uuid = getattr(FIP_UUID, name)
+        loc = self.binary.find(uuid.bytes, 0, max_toc_size)
+        if loc < 0:
+            raise ValueError("%s is not found" % name)
+        return Entry.from_fip(name, loc, self.binary)
+
+
+def entry(args):
+    logging.debug("cmd_fip")
+
+
+def init_logging(log_file=None, file_level="DEBUG", stdout_level="WARNING"):
+
+    root_logger = logging.getLogger()
+    root_logger.setLevel(logging.NOTSET)
+
+    fmt = "%(asctime)s %(levelname)8s:%(name)s:%(message)s"
+
+    if log_file is not None:
+        file_handler = logging.FileHandler(log_file, encoding="utf-8")
+        file_handler.setFormatter(logging.Formatter(fmt))
+        file_handler.setLevel(file_level)
+        root_logger.addHandler(file_handler)
+
+    if coloredlogs:
+        os.environ["COLOREDLOGS_DATE_FORMAT"] = "%H:%M:%S"
+
+        field_styles = {
+            "asctime": {"color": "green"},
+            "hostname": {"color": "magenta"},
+            "levelname": {"color": "black", "bold": True},
+            "name": {"color": "blue"},
+            "programname": {"color": "cyan"},
+        }
+
+        level_styles = coloredlogs.DEFAULT_LEVEL_STYLES
+        level_styles["debug"]["color"] = "cyan"
+
+        coloredlogs.install(
+            level=stdout_level,
+            fmt=fmt,
+            field_styles=field_styles,
+            level_styles=level_styles,
+            milliseconds=True,
+        )
+
+
+def parse_fip(fip_path):
+    logging.debug("parse_fip: %s", fip_path)
+    fip = FIP(fip_path)
+    fip.load()
+
+
+def unpack_fip(fip_path):
+    logging.debug("unpack_fip: %s", fip_path)
+    fip = FIP(fip_path)
+    fip.load()
+
+    def save(name, content):
+        fn = os.path.splitext(fip_path)
+        fn = "%s_%s%s" % (fn[0], name, fn[1])
+        logging.info("Save %s", fn)
+        with open(fn, "wb") as fp:
+            fp.write(content)
+
+    for k, v in fip.ents.items():
+        save(k, v.content)
+
+    save("BLP_DDRC", fip.blp_ddrc_binary)
+
+
+def tar_bld(fip_path, output_path, multibin):
+    logging.debug("tar_bld: %s multibin=%r", fip_path, multibin)
+    fip = FIP(fip_path)
+    fip.load()
+
+    members = [
+        "BLD_CONTENT_CERT",
+        "BLD2_KEY_CERT",
+        "BLD1_KEY_CERT",
+        "CV_DDRINIT" if multibin else "BLD",
+    ]
+
+    if not output_path:
+        output_path = os.path.join(os.path.dirname(fip_path), "bld.tar")
+
+    logging.info("bld_tar_path=%s", output_path)
+
+    with tarfile.open(output_path, "w") as tf:
+        for m in members:
+            logging.debug("Tar %s", m)
+            try:
+                fp = io.BytesIO(fip.ents[m].content)
+            except KeyError:
+                logging.warning("%s doesn't exist", m)
+                continue
+            info = tarfile.TarInfo(name=m + ".bin")
+            info.size = len(fp.getbuffer())
+            tf.addfile(tarinfo=info, fileobj=fp)
+
+
+def merge_fip(fip_path, inputs, output_path):
+    logging.debug("merge_fip: %s", fip_path)
+    fip = FIP(fip_path)
+    fip.load()
+
+    for name in FIP.ENTRY_NAMES:
+        binary = inputs.get(name)
+        if not binary:
+            continue
+
+        logging.debug("merge %s", name)
+        ent = fip.ents.get(name)
+        if ent:
+            ent.content = binary
+        else:
+            ent = Entry.make(getattr(FIP_UUID, "UUID_" + name), binary)
+            fip.ents[name] = ent
+
+    binary = inputs.get("BLP_DDRC")
+    if binary:
+        fip.blp_ddrc_binary = binary
+
+    if not output_path:
+        fn = os.path.splitext(fip_path)
+        fn = "%s_%s%s" % (fn[0], "merged", fn[1])
+        output_path = fn
+
+    fip.make_fip(output_path)
+
+
+def round_up(n, k):
+    return (n + k - 1) // k * k
+
+
+def read_blp_and_ddrc(inputs, blp_path, ddrc_path):
+    logging.info("Open %s and %s", blp_path, ddrc_path)
+    with open(blp_path, "rb") as fp:
+        blp_bin = fp.read()
+
+    logging.info("Open %s", ddrc_path)
+    with open(ddrc_path, "rb") as fp:
+        ddrc_bin = fp.read()
+
+    blp_bin += b"\0" * (round_up(len(blp_bin), FIP_ALIGN_SIZE) - len(blp_bin))
+    ddrc_bin += b"\0" * (round_up(len(ddrc_bin), FIP_ALIGN_SIZE) - len(ddrc_bin))
+
+    inputs["BLP_DDRC"] = blp_bin + ddrc_bin
+
+
+def read_bld_tar(inputs, bld_tar_path, multibin):
+    logging.info("Open %s multibin=%r", bld_tar_path, multibin)
+    members = [
+        "BLD_CONTENT_CERT.bin",
+        "BLD2_KEY_CERT.bin",
+        "BLD1_KEY_CERT.bin",
+        "CV_DDRINIT.bin" if multibin else "BLD.bin",
+    ]
+
+    with tarfile.open(bld_tar_path, "r") as tf:
+        for member in members:
+            try:
+                fp = tf.extractfile(member)
+                inputs[os.path.splitext(member)[0]] = fp.read()
+            except KeyError:
+                logging.warning("%s does not exist", member)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="FIP packer")
+
+    for name in FIP.ENTRY_NAMES:
+        parser.add_argument(
+            "--add-%s" % name.lower(),
+            dest=name,
+            type=str,
+            help="Merge %s into FIP" % name,
+        )
+    parser.add_argument(
+        "--add-blp-ddrc", dest="BLP_DDRC", type=str, help="Merge BLP+DDRC into FIP"
+    )
+    parser.add_argument("--add-blp", dest="BLP", type=str, help="Merge BLP into FIP")
+    parser.add_argument("--add-ddrc", dest="DDRC", type=str, help="Merge DDRC into FIP")
+    parser.add_argument(
+        "--add-bld-tar", dest="BLD_TAR", type=str, help="Merge BLD.tar into FIP"
+    )
+
+    parser.add_argument("--multibin", action="store_true", help="Use multibin")
+
+    parser.add_argument("FIP_BIN", type=str, nargs=1, help="Input FIP binary")
+    parser.add_argument("--output", type=str, help="Output filename")
+
+    parser.add_argument(
+        "--version", action="store_true", help="Output version information and exit"
+    )
+    parser.add_argument(
+        "--verbose",
+        help="Increase output verbosity",
+        action="store_const",
+        const=logging.DEBUG,
+        default=logging.DEBUG,
+    )
+
+    parser.add_argument("--unpack", action="store_true", help="Unpack FIP.bin")
+    parser.add_argument("--parse", action="store_true", help="Parse FIP.bin")
+    parser.add_argument(
+        "--tar-bld", action="store_true", help="Extrace BLD.bin and tar"
+    )
+
+    if argcomplete:
+        argcomplete.autocomplete(parser)
+
+    args = parser.parse_args()
+
+    init_logging(stdout_level=args.verbose)
+
+    logging.debug("args=%r", args)
+
+    FIP_UUID.cls_init()
+
+    if args.parse:
+        parse_fip(args.FIP_BIN[0])
+
+    if args.unpack:
+        unpack_fip(args.FIP_BIN[0])
+
+    if args.tar_bld:
+        tar_bld(args.FIP_BIN[0], args.output, args.multibin)
+
+    inputs = collections.OrderedDict()
+    for name in list(FIP.ENTRY_NAMES) + ["BLP_DDRC"]:
+        fn = getattr(args, name)
+        if not fn:
+            continue
+        logging.info("Open %s", fn)
+        with open(fn, "rb") as fp:
+            inputs[name] = fp.read()
+
+    if args.BLP or args.DDRC:
+        read_blp_and_ddrc(inputs, args.BLP, args.DDRC)
+
+    if args.BLD_TAR:
+        read_bld_tar(inputs, args.BLD_TAR, args.multibin)
+
+    if len(inputs):
+        merge_fip(args.FIP_BIN[0], inputs, args.output)
+
+
+if __name__ == "__main__":
+    main()

ota_curl.sh

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

power.py

@@ -5,11 +5,14 @@
 提供电压、电流监测和充电状态检测
 """
 import config
+import os
+import subprocess
+import _thread
 from logger_manager import logger_manager
 from maix import time as maix_time
 
-
 _INA226_PRESENT = None
+_INA226_LOCK = _thread.allocate_lock()
 
 
 def _ina226_ready() -> bool:
@@ -31,7 +34,11 @@ def write_register(reg, value):
     data = [(value >> 8) & 0xFF, value & 0xFF]
     # 某些底层驱动在失败时只打印 “write failed” 并返回 -1，而不是抛异常；
     # 为避免误判“初始化成功”导致后续 readfrom_mem SIGSEGV，这里把失败显式转成异常。
-    ret = hardware_manager.bus.writeto_mem(config.INA226_ADDR, reg, bytes(data))
+    _INA226_LOCK.acquire()
+    try:
+        ret = hardware_manager.bus.writeto_mem(config.INA226_ADDR, reg, bytes(data))
+    finally:
+        _INA226_LOCK.release()
     if isinstance(ret, int) and ret < 0:
         if logger:
             logger.error(f"[INA226] writeto_mem 失败: addr=0x{config.INA226_ADDR:02X} reg=0x{reg:02X} ret={ret}")
@@ -41,7 +48,11 @@ def write_register(reg, value):
 def read_register(reg):
     """读取INA226寄存器"""
     from hardware import hardware_manager
-    data = hardware_manager.bus.readfrom_mem(config.INA226_ADDR, reg, 2)
+    _INA226_LOCK.acquire()
+    try:
+        data = hardware_manager.bus.readfrom_mem(config.INA226_ADDR, reg, 2)
+    finally:
+        _INA226_LOCK.release()
     return (data[0] << 8) | data[1]
 
 
@@ -85,8 +96,8 @@ def get_bus_voltage():
 def get_current():
     """
     读取电流（单位：mA）
-    正数表示充电，负数表示放电
+    当前电源板实测：正数表示放电，负数表示充电。
-    
+
     INA226 电流计算公式：
     Current = (Current Register Value) × Current_LSB
     Current_LSB = 0.001 × CALIBRATION_VALUE / 4096
@@ -96,13 +107,13 @@ def get_current():
             return 0.0
         raw = read_register(config.REG_CURRENT)
         # INA226 电流寄存器是16位有符号整数
-        # 最高位是符号位：0=正（充电），1=负（放电）
+        # 最高位是符号位；电流方向含义取决于电源板的采样电阻接线方向。
         # 计算 Current_LSB（根据 CALIBRATION_VALUE）
         current_lsb = 0.001 * config.CALIBRATION_VALUE / 4096  # 单位：A
         # 处理有符号数：如果最高位为1，转换为负数
-        if raw & 0x8000:  # 最高位为1，表示负数（放电）
+        if raw & 0x8000:
             signed_raw = raw - 0x10000  # 转换为有符号整数
-        else:  # 最高位为0，表示正数（充电）
+        else:
             signed_raw = raw
         # 转换为毫安
         current_ma = signed_raw * current_lsb * 1000
@@ -119,17 +130,17 @@ def get_current():
 def is_charging(threshold_ma=10.0):
     """
     检测是否在充电（通过电流方向判断）
-    
+
     Args:
         threshold_ma: 电流阈值（毫安），超过此值认为在充电，默认10mA
-    
+
     Returns:
         True: 正在充电
         False: 未充电或读取失败
     """
     try:
         current = get_current()
-        is_charge = current > threshold_ma
+        is_charge = current < -abs(float(threshold_ma))
         return is_charge
     except Exception as e:
         logger = logger_manager.logger
@@ -159,7 +170,7 @@ def voltage_to_percent(voltage):
         return 0
     if v <= 0:
         return 0
-    return int(int(_BATTERY_MONITOR.get_soc(v) * 10) / 10) # 截断而不是四舍五入
+    return int(int(_BATTERY_MONITOR.get_soc(v) * 10) / 10)  # 截断而不是四舍五入
 
 
 class BatteryMonitor:

shoot_manager.py

@@ -8,7 +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
@@ -320,9 +320,28 @@ def process_shot(adc_val):
     logger = logger_manager.logger
 
     try:
-        network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True)
         frame = camera_manager.read_frame()
 
+        # 在任何检测和绘图之前复制原始帧；默认由配置关闭，不增加量产开销。
+        from shot_id_generator import shot_id_generator
+        shot_id = shot_id_generator.generate_id()
+        enqueue_save_raw_shot(frame, shot_id)
+
+        # 网络事件移到拍照之后，避免阻塞拍照
+        network_manager.safe_enqueue({"shoot_event": "start"}, msg_type=2, high=True)
+
+        # Classify only the current shot frame; never reuse a previous result.
+        target_class_result = None
+        try:
+            from target_roi_yolo import try_get_target_class_from_yolo
+
+            target_class_result = try_get_target_class_from_yolo(frame, logger=logger)
+            if logger:
+                logger.info(f"[YOLO-TARGET] 当前箭业务结果: {target_class_result}")
+        except Exception as exc:
+            if logger:
+                logger.warning(f"[YOLO-TARGET] 当前箭分类失败，按未知处理: {exc}")
+
         # 调用算法分析
         analysis_result = analyze_shot(frame)
 
@@ -366,10 +385,6 @@ 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}")
 
@@ -382,11 +397,25 @@ def process_shot(adc_val):
         srv_y = round(float(dy), 4) if dy is not None else 200.0
 
         # 构造上报数据
+        target_label = (
+            target_class_result.get("label")
+            if isinstance(target_class_result, dict)
+            else None
+        )
+        target_confidence = (
+            target_class_result.get("confidence")
+            if isinstance(target_class_result, dict)
+            else None
+        )
         inner_data = {
             "shot_id": shot_id,
             "x": srv_x,
             "y": srv_y,
             "r": 20.0,  # 保留字段（服务端当前忽略，物理外环半径 cm）
+            "target_class": target_label,
+            "target_class_confidence": (
+                float(target_confidence) if target_confidence is not None else None
+            ),
             "d": round((distance_m or 0.0) * 100),
             "d_laser": round((laser_distance_m or 0.0) * 100),
             "d_laser_quality": laser_signal_quality,
@@ -413,7 +442,19 @@ def process_shot(adc_val):
             inner_data["ellipse_center_x"] = None
             inner_data["ellipse_center_y"] = None
 
+        upload_time_ms = int(time_std.time() * 1000)
+        upload_time_sec, upload_time_millis = divmod(upload_time_ms, 1000)
+        inner_data["upload_time"] = (
+            time_std.strftime("%Y-%m-%d %H:%M:%S", time_std.localtime(upload_time_sec))
+            + f".{upload_time_millis:03d}"
+        )
+
         report_data = {"cmd": 1, "data": inner_data}
+        if logger:
+            logger.info(
+                f"[REPORT-TARGET] enqueue shot_id={shot_id}, "
+                f"target_class={target_label}, confidence={target_confidence}"
+            )
         network_manager.safe_enqueue(report_data, msg_type=2, high=True)
 
         # 数据上报后再画标注，不干扰检测阶段的原始画面
@@ -518,6 +559,7 @@ def process_shot(adc_val):
             laser_manager.flash_laser(config.FLASH_LASER_DURATION_MS)
 
         # 保存图像（异步队列，与 main.py 一致）
+        _force_save = (dx is None and dy is None) and getattr(config, "SAVE_IMAGE_ON_FAILURE", False)
         enqueue_save_shot(
             result_img,
             center,
@@ -527,8 +569,9 @@ def process_shot(adc_val):
             (x, y),
             distance_m,
             shot_id=shot_id,
-            photo_dir=config.PHOTO_DIR if config.SAVE_IMAGE_ENABLED else None,
+            photo_dir=config.PHOTO_DIR if (config.SAVE_IMAGE_ENABLED or _force_save) else None,
             yolo_roi_xyxy=yolo_roi_xyxy if draw_yolo_roi else None,
+            force_save=_force_save,
         )
 
         if logger:

target_roi_yolo.py

@@ -89,6 +89,29 @@ def _stage2_roi_crop_save_worker(
 _detector_by_path = {}
 
 
+def _resolve_model_path(model_path: str):
+    """Resolve a model in either the installed app or MaixVision run directory."""
+    model_path = (model_path or "").strip()
+    if model_path and os.path.isfile(model_path):
+        return model_path
+    if not model_path:
+        return ""
+    name = os.path.basename(model_path)
+    module_dir = os.path.dirname(os.path.abspath(__file__))
+    candidates = (
+        os.path.join(module_dir, name),
+        os.path.join(module_dir, "test", name),
+        os.path.join("/tmp/maixpy_run", name),
+        os.path.join("/tmp/maixpy_run", "test", name),
+        os.path.join(os.getcwd(), name),
+        os.path.join(os.getcwd(), "test", name),
+    )
+    for candidate in candidates:
+        if os.path.isfile(candidate):
+            return candidate
+    return model_path
+
+
 def reset_yolo_detector_cache():
     """切换模型路径时可调用（通常不必）。"""
     global _detector_by_path
@@ -175,6 +198,23 @@ def preload_yolo_detector(logger=None):
                 % (_loc_black,)
             )
 
+    if bool(getattr(cfg, "TARGET_CLASS_YOLO_ENABLE", False)) and bool(
+        getattr(cfg, "TARGET_CLASS_YOLO_PRELOAD_ON_BOOT", True)
+    ):
+        class_model_path = _resolve_model_path(
+            getattr(cfg, "TARGET_CLASS_YOLO_MODEL_PATH", "") or ""
+        )
+        class_detector = _get_detector(class_model_path)
+        if class_detector is None:
+            if logger:
+                logger.warning(
+                    f"[YOLO-TARGET] 预加载失败：无法加载模型 {class_model_path}"
+                )
+        else:
+            ok = True
+            if logger:
+                logger.info(f"[YOLO-TARGET] 靶规格模型已预加载: {class_model_path}")
+
     return ok
 
 
@@ -206,8 +246,10 @@ def _det_obj_class_id(o):
             if v is None:
                 continue
             try:
+                if callable(v):
+                    v = v()
                 return int(float(v))
-            except (TypeError, ValueError):
+            except (TypeError, ValueError, AttributeError):
                 continue
     return None
 
@@ -242,6 +284,106 @@ def _normalize_objs(objs):
     return out
 
 
+def _det_obj_score(o):
+    """Return confidence across supported Maix YOLO result formats."""
+    for key in ("score", "confidence", "conf", "prob"):
+        if hasattr(o, key):
+            try:
+                value = getattr(o, key)
+                if callable(value):
+                    value = value()
+                value = float(value)
+                if value == value:
+                    return value
+            except (TypeError, ValueError, AttributeError):
+                pass
+    return 0.0
+
+
+def try_get_target_class_from_yolo(maix_frame, logger=None):
+    """Classify the current target as 20cm or 40cm; return None if unknown."""
+    try:
+        import config as cfg
+    except Exception:
+        return None
+
+    if not bool(getattr(cfg, "TARGET_CLASS_YOLO_ENABLE", False)):
+        return None
+    model_path = _resolve_model_path(
+        getattr(cfg, "TARGET_CLASS_YOLO_MODEL_PATH", "") or ""
+    )
+    if not os.path.isfile(model_path):
+        if logger:
+            logger.warning(f"[YOLO-TARGET] 模型文件不存在: {model_path}")
+        return None
+    detector = _get_detector(model_path)
+    if detector is None:
+        if logger:
+            logger.warning("[YOLO-TARGET] 无法加载 nn.YOLOv5")
+        return None
+
+    conf_th = float(getattr(cfg, "TARGET_CLASS_YOLO_CONF_TH", 0.5))
+    iou_th = float(getattr(cfg, "TARGET_CLASS_YOLO_IOU_TH", 0.45))
+    labels = getattr(cfg, "TARGET_CLASS_YOLO_LABELS", (20, 40))
+    if isinstance(labels, str):
+        labels = tuple(x.strip() for x in labels.split(",") if x.strip())
+    labels = tuple(labels)
+
+    def _detect(threshold):
+        try:
+            raw = detector.detect(maix_frame, conf_th=threshold, iou_th=iou_th)
+        except Exception as exc:
+            if logger:
+                logger.warning(f"[YOLO-TARGET] detect 异常: {exc}")
+            return []
+        return _normalize_objs(raw if raw is not None else [])
+
+    def _candidates(objs):
+        found = []
+        for obj in objs:
+            class_id = _det_obj_class_id(obj)
+            if class_id is None or class_id < 0 or class_id >= len(labels):
+                continue
+            try:
+                label = int(float(labels[class_id]))
+            except (TypeError, ValueError):
+                continue
+            if label in (20, 40):
+                found.append((label, class_id, _det_obj_score(obj)))
+        return found
+
+    objects = _detect(conf_th)
+    candidates = _candidates(objects)
+    if logger and objects:
+        logger.info(
+            "[YOLO-TARGET] 原始框=%d, 解析类别=%s"
+            % (
+                len(objects),
+                [(_det_obj_class_id(o), _det_obj_score(o)) for o in objects[:8]],
+            )
+        )
+    if not candidates and bool(
+        getattr(cfg, "TARGET_CLASS_YOLO_RETRY_ON_EMPTY", False)
+    ):
+        retry_th = float(getattr(cfg, "TARGET_CLASS_YOLO_RETRY_CONF_TH", conf_th))
+        if 0 < retry_th < conf_th:
+            candidates = _candidates(_detect(retry_th))
+
+    if not candidates:
+        if logger:
+            logger.warning("[YOLO-TARGET] 当前帧未识别到 20/40，按未知处理")
+        return None
+
+    label, class_id, confidence = max(candidates, key=lambda item: item[2])
+    result = {"label": label, "class_id": class_id, "confidence": confidence}
+    if logger:
+        logger.info(
+            f"[YOLO-TARGET] 当前帧分类={label}, class_id={class_id}, "
+            f"conf={confidence:.3f}"
+        )
+    return result
+
+
 def _det_to_src_xyxy(o, coord_mode: str, src_w: int, src_h: int, net_w: int, net_h: int):
     """把单个检测框转为全图坐标系下的 xyxy（半开区间语义与后续 clip 一致）。"""
     x, y, w, h = float(o.x), float(o.y), float(o.w), float(o.h)

test/test_decect.py

@@ -0,0 +1,330 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+离线测试脚本：直接复用 detect_circle 逻辑进行测试
+运行环境：MaixPy (Sipeed MAIX)
+"""
+import sys
+import os
+# import time
+from maix import image, time
+import cv2
+import numpy as np
+import math
+
+# ==================== 全局配置 (与 test_main.py 保持一致) ====================
+REAL_RADIUS_CM = 20  # 靶心实际半径（厘米）
+
+def detect_circle_v3(frame, laser_point=None, img_cv=None):
+    """检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本
+    增加红色圆圈检测，验证黄色圆圈是否为真正的靶心
+    如果提供 laser_point，会选择最接近激光点的目标
+    优化：
+      1. 缩图到 MAX_DET_DIM 后再做 HSV/形态学，最长边 640->320 可获得 ~4x 加速
+      2. 红色掩码在黄色轮廓循环外只计算一次，避免 N 次重复计算
+      3. img_cv 可由外部传入（与其他线程共享转换结果），为 None 时自动转换
+    Args:
+        frame: 图像帧（img_cv 为 None 时使用）
+        laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择
+        img_cv: 已转换的 numpy BGR/RGB 图像；不为 None 时跳过 image2cv 转换
+    Returns:
+        (result_img, best_center, best_radius, method, best_radius1, ellipse_params)
+    """
+    if img_cv is None:
+        img_cv = image.image2cv(frame, False, False)
+    from datetime import datetime
+    print(f"[detect_circle_v3] begin {datetime.now()}")
+    # -- 1. 缩图加速（与三角形路径保持一致）
+    h_orig, w_orig = img_cv.shape[:2]
+    MAX_DET_DIM = 480
+    long_side = max(h_orig, w_orig)
+    if long_side > MAX_DET_DIM:
+        det_scale = MAX_DET_DIM / long_side
+        img_det = cv2.resize(img_cv, (int(w_orig * det_scale), int(h_orig * det_scale)),
+                             interpolation=cv2.INTER_LINEAR)
+        inv_scale = 1.0 / det_scale  # 检测坐标 -> 原始坐标的倍率
+    else:
+        img_det = img_cv
+        inv_scale = 1.0
+
+    # 激光点映射到检测分辨率
+    lp_det = None
+    if laser_point is not None:
+        lp_det = (laser_point[0] / inv_scale, laser_point[1] / inv_scale)
+    best_center = best_radius = best_radius1 = method = None
+    ellipse_params = None
+
+    print(f"[detect_circle_v3] step 1 fin {datetime.now()}")
+
+    # -- 2. HSV + 黄色掩码
+    hsv = cv2.cvtColor(img_det, cv2.COLOR_RGB2HSV)
+    h, s, v = cv2.split(hsv)
+    s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
+    hsv = cv2.merge((h, s, v))
+    lower_yellow = np.array([7, 80, 0])
+    upper_yellow = np.array([32, 255, 255])
+    mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
+
+    print(f"[detect_circle_v3] step 2 fin {datetime.now()}")
+
+    # -- 3. 红色掩码：在循环外只算一次
+    mask_red = cv2.bitwise_or(
+        cv2.inRange(hsv, np.array([0, 50, 40]), np.array([10, 255, 255])),
+        cv2.inRange(hsv, np.array([170, 50, 40]), np.array([180, 255, 255])),
+    )
+    kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
+    contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # 预先把红色轮廓筛选成 (center, radius) 列表，后续直接查表
+    red_candidates = []
+    for cnt_r in contours_red:
+        ar = cv2.contourArea(cnt_r)
+        if ar <= 10:
+            continue
+        pr = cv2.arcLength(cnt_r, True)
+        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.3:
+            continue
+        if len(cnt_r) >= 5:
+            (xr, yr), (wr, hr), _ = cv2.fitEllipse(cnt_r)
+            red_candidates.append({"center": (int(xr), int(yr)), "radius": int(min(wr, hr) / 2)})
+        else:
+            (xr, yr), rr = cv2.minEnclosingCircle(cnt_r)
+            red_candidates.append({"center": (int(xr), int(yr)), "radius": int(rr)})
+
+    print(f"[detect_circle_v3] step 3 fin {datetime.now()}")
+
+    # -- 4. 黄色轮廓循环（复用上面的红色候选列表）
+    contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    valid_targets = []
+    for cnt_yellow in contours_yellow:
+        area = cv2.contourArea(cnt_yellow)
+        if area <= 15:
+            continue
+        perimeter = cv2.arcLength(cnt_yellow, True)
+        if perimeter <= 0:
+            continue
+        circularity = (4 * np.pi * area) / (perimeter * perimeter)
+        if circularity <= 0.5:
+            continue
+        print(f"[target] -> 面积:{area:.1f}, 圆度:{circularity:.2f}")
+        if len(cnt_yellow) >= 5:
+            (x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
+            yellow_ellipse = ((x, y), (width, height), angle)
+            yellow_center = (int(x), int(y))
+            yellow_radius = int(min(width, height) / 2)
+        else:
+            (x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
+            yellow_center = (int(x), int(y))
+            yellow_radius = int(radius)
+            yellow_ellipse = None
+        # 在预筛好的红色候选中匹配
+        matched = False
+        for rc in red_candidates:
+            ddx = yellow_center[0] - rc["center"][0]
+            ddy = yellow_center[1] - rc["center"][1]
+            dist_centers = math.hypot(ddx, ddy)
+            if dist_centers < yellow_radius * 1.5 and rc["radius"] > yellow_radius * 0.7:
+                print(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), "
+                                f"红心({rc['center']}), 距离:{dist_centers:.1f}, "
+                                f"黄半径:{yellow_radius}, 红半径:{rc['radius']}")
+                valid_targets.append({
+                    "center": yellow_center,
+                    "radius": yellow_radius,
+                    "ellipse": yellow_ellipse,
+                    "area": area,
+                })
+                matched = True
+                break
+        if not matched :
+            print("Debug -> 未找到匹配的红色圆圈，可能是误识别")
+
+    print(f"[detect_circle_v3] step 4 fin {datetime.now()}")
+
+    # -- 5. 选最佳目标，坐标还原到原始分辨率
+    if valid_targets:
+        if lp_det:
+            best_target = min(valid_targets,
+                              key=lambda t: (t["center"][0] - lp_det[0]) ** 2
+                                            + (t["center"][1] - lp_det[1]) ** 2)
+            method = "v3_ellipse_red_validated_laser_selected"
+        else:
+            best_target = max(valid_targets, key=lambda t: t["area"])
+            method = "v3_ellipse_red_validated"
+        bc = best_target["center"]
+        br = best_target["radius"]
+        be = best_target["ellipse"]
+        if inv_scale != 1.0:
+            best_center = (int(bc[0] * inv_scale), int(bc[1] * inv_scale))
+            best_radius = int(br * inv_scale)
+            if be is not None:
+                (ex, ey), (ew, eh), ea = be
+                be = ((ex * inv_scale, ey * inv_scale),
+                      (ew * inv_scale, eh * inv_scale), ea)
+        else:
+            best_center = bc
+            best_radius = br
+        ellipse_params = be
+        best_radius1 = best_radius * 5
+    result_img = image.cv2image(img_cv, False, False)
+    print(f"[detect_circle_v3] step 5 fin {datetime.now()}")
+    return result_img, best_center, best_radius, method, best_radius1, ellipse_params
+
+
+def run_offline_test(image_path):
+    """读取图片，检测圆，绘制结果，保存图片"""
+
+    # 1. 检查文件是否存在
+    if not os.path.exists(image_path):
+        print(f"[ERROR] 找不到图片文件: {image_path}")
+        return
+
+    # 2. 使用 maix.image 读取图片 (适配 MaixPy v4)
+    try:
+        # 使用 image.load 读取文件，返回 Image 对象
+        img = image.load(image_path)
+        print(f"[INFO] 成功读取图片: {image_path} (尺寸: {img.width()}x{img.height()})")
+    except Exception as e:
+        print(f"[ERROR] 读取图片失败: {e}")
+        print("提示：请确认 MaixPy 版本是否为 v4，且图片路径正确。")
+        return
+
+    # 3. 调用 detect_circle_v3 函数
+    print("[INFO] 正在调用 detect_circle_v3 进行检测...")
+    start_time = time.ticks_ms()
+
+    result_img, center, radius, method, radius1, ellipse_params = detect_circle_v3(img)
+
+    cost_time = time.ticks_ms() - start_time
+    print(f"[INFO] 检测完成，耗时: {cost_time}ms")
+    print(f"       结果 -> 圆心: {center}, 半径: {radius}, 方法: {method}")
+    if ellipse_params:
+        (ell_center, (width, height), angle) = ellipse_params
+        print(
+            f"       椭圆 -> 中心: ({ell_center[0]:.1f}, {ell_center[1]:.1f}), 长轴: {max(width, height):.1f}, 短轴: {min(width, height):.1f}, 角度: {angle:.1f}°")
+
+    # 4. 绘制辅助线（可选，用于调试）
+    if center and radius:
+        # 为了绘制椭圆，需要转换回 cv2 图像
+        img_cv = image.image2cv(result_img, False, False)
+
+        cx, cy = center
+
+        # 如果有椭圆参数，绘制椭圆
+        if ellipse_params:
+            (ell_center, (width, height), angle) = ellipse_params
+            cx_ell, cy_ell = int(ell_center[0]), int(ell_center[1])
+
+            # 确定长轴和短轴
+            if width >= height:
+                # width 是长轴，height 是短轴
+                axes_major = width
+                axes_minor = height
+                major_angle = angle  # 长轴角度就是 angle
+                minor_angle = angle + 90  # 短轴角度 = 长轴角度 + 90度
+            else:
+                # height 是长轴，width 是短轴
+                axes_major = height
+                axes_minor = width
+                major_angle = angle + 90  # 长轴角度 = width角度 + 90度
+                minor_angle = angle  # 短轴角度就是 angle
+
+            # 使用 OpenCV 绘制椭圆（绿色，线宽2）
+            cv2.ellipse(img_cv,
+                        (cx_ell, cy_ell),  # 中心点
+                        (int(width / 2), int(height / 2)),  # 半宽、半高
+                        angle,  # 旋转角度（OpenCV需要原始angle）
+                        0, 360,  # 起始和结束角度
+                        (0, 255, 0),  # 绿色 (RGB格式)
+                        2)  # 线宽
+
+            # 绘制椭圆中心点（红色）
+            cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1)
+
+            import math
+            # 绘制短轴（蓝色线条）
+            minor_length = axes_minor / 2
+            minor_angle_rad = math.radians(minor_angle)
+            dx_minor = minor_length * math.cos(minor_angle_rad)
+            dy_minor = minor_length * math.sin(minor_angle_rad)
+            pt1_minor = (int(cx_ell - dx_minor), int(cy_ell - dy_minor))
+            pt2_minor = (int(cx_ell + dx_minor), int(cy_ell + dy_minor))
+            cv2.line(img_cv, pt1_minor, pt2_minor, (0, 0, 255), 2)  # 蓝色 (RGB格式)
+        else:
+            # 如果没有椭圆参数，绘制圆形（红色）
+            cv2.circle(img_cv, (cx, cy), radius, (0, 0, 255), 2)
+            cv2.circle(img_cv, (cx, cy), 2, (0, 0, 255), -1)
+
+        # 转换回 maix image
+        result_img = image.cv2image(img_cv, False, False)
+
+        # 定义颜色对象用于文字
+        try:
+            color_black = image.Color.from_rgb(0, 0, 0)
+        except AttributeError:
+            color_black = image.Color(0, 0, 0)
+
+        # D. 添加文字信息
+        FOCAL_LENGTH_PIX = 1900
+        d = (REAL_RADIUS_CM * FOCAL_LENGTH_PIX) / radius1 / 100.0
+        info_str = f"R:{radius} M:{method} D:{d:.2f}"
+        print(info_str)
+
+        # 计算文字位置，防止超出图片边界
+        r_outer = int(radius * 11.0) if radius else 100
+        text_y = cy - r_outer - 20 if cy > r_outer + 20 else cy + r_outer + 20
+
+        # 调用 draw_string
+        result_img.draw_string(0, 0, info_str, color=color_black, scale=1.0)
+
+    # 5. 保存结果图片
+    base, ext = os.path.splitext(image_path)
+    output_path = f"{base}_result{ext}"
+    try:
+        result_img.save(output_path, quality=100)
+        print(f"[SUCCESS] 结果已保存至: {output_path}")
+    except Exception as e:
+        print(f"[ERROR] 保存图片失败: {e}")
+
+
+if __name__ == "__main__":
+    # ================= 配置区域 =================
+
+    # 1. 设置要测试的图片路径
+    # 建议将图片放在与脚本同级目录，或者使用绝对路径
+    TARGET_IMAGE = "/root/phot/None_314_258_0_0041.bmp"
+
+    TARGET_DIR = "/root/phot"  # 修改为你想要读取的目录路径
+
+    # 支持的图片格式
+    IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.bmp']
+
+    # ================= 执行区域 =================
+    if 'TARGET_DIR' in locals():
+        # 读取目录下所有图片文件，过滤掉 _result.jpg 后缀的文件
+        image_files = []
+        if os.path.exists(TARGET_DIR) and os.path.isdir(TARGET_DIR):
+            for filename in os.listdir(TARGET_DIR):
+                # 检查文件扩展名
+                if any(filename.lower().endswith(ext) for ext in IMAGE_EXTENSIONS):
+                    # 过滤掉 _result.jpg 后缀的文件
+                    if not filename.endswith('_result.jpg'):
+                        filepath = os.path.join(TARGET_DIR, filename)
+                        if os.path.isfile(filepath):
+                            image_files.append(filepath)
+
+            # 按文件名排序（可选）
+            image_files.sort()
+
+            print(f"[INFO] 在目录 {TARGET_DIR} 中找到 {len(image_files)} 张图片")
+
+            # 处理每张图片
+            for img_path in image_files:
+                print(f"\n{'=' * 10} 开始处理: {img_path} {'=' * 10}")
+                run_offline_test(img_path)
+        else:
+            print(f"[ERROR] 目录不存在或不是有效目录: {TARGET_DIR}")
+
+    else:
+        run_offline_test(TARGET_IMAGE)

test/test_decect_circle_v4.py

@@ -0,0 +1,635 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+离线测试脚本：直接复用 detect_circle 逻辑进行测试
+运行环境：MaixPy (Sipeed MAIX)
+"""
+import sys
+import os
+# import time
+from maix import image, time
+import cv2
+import numpy as np
+
+# ==================== 全局配置 (与 test_main.py 保持一致) ====================
+REAL_RADIUS_CM = 20  # 靶心实际半径（厘米）
+
+
+# ==================== 复制的核心算法 ====================
+# 注意：这里直接复制了 detect_circle 的逻辑，避免 import main 导致的冲突
+
+
+def detect_circle_v3(frame, laser_point=None):
+    """检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本
+    增加红色圆圈检测，验证黄色圆圈是否为真正的靶心
+    如果提供 laser_point，会选择最接近激光点的目标
+    
+    Args:
+        frame: 图像帧
+        laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择
+    
+    Returns:
+        (result_img, best_center, best_radius, method, best_radius1, ellipse_params)
+    """
+    img_cv = image.image2cv(frame, False, False)
+
+    best_center = best_radius = best_radius1 = method = None
+    ellipse_params = None
+
+    # HSV 黄色掩码检测（模糊靶心）
+    hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    h, s, v = cv2.split(hsv)
+
+    # 调整饱和度策略：稍微增强，不要过度
+    s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
+
+    hsv = cv2.merge((h, s, v))
+
+    # 放宽 HSV 阈值范围（针对模糊图像的关键调整）
+    lower_yellow = np.array([7, 80, 0])  # 饱和度下限降低，捕捉淡黄色
+    upper_yellow = np.array([32, 255, 255])  # 亮度上限拉满
+
+    mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # 调整形态学操作
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
+
+    contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # 存储所有有效的黄色-红色组合
+    valid_targets = []
+
+    if contours_yellow:
+        for cnt_yellow in contours_yellow:
+            area = cv2.contourArea(cnt_yellow)
+            perimeter = cv2.arcLength(cnt_yellow, True)
+
+            # 计算圆度
+            if perimeter > 0:
+                circularity = (4 * np.pi * area) / (perimeter * perimeter)
+            else:
+                circularity = 0
+
+            if area > 50 and circularity > 0.7:
+                print(f"[target] -> 面积:{area}, 圆度:{circularity:.2f}")
+                # 尝试拟合椭圆
+                yellow_center = None
+                yellow_radius = None
+                yellow_ellipse = None
+
+                if len(cnt_yellow) >= 5:
+                    (x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
+                    yellow_ellipse = ((x, y), (width, height), angle)
+                    axes_minor = min(width, height)
+                    radius = axes_minor / 2
+                    yellow_center = (int(x), int(y))
+                    yellow_radius = int(radius)
+                else:
+                    (x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
+                    yellow_center = (int(x), int(y))
+                    yellow_radius = int(radius)
+                    yellow_ellipse = None
+
+                # 如果检测到黄色圆圈，再检测红色圆圈进行验证
+                if yellow_center and yellow_radius:
+                    # HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围）
+                    # 红色范围1: 0-12度（接近0度的红色）
+                    # 放宽S/V阈值：S>=30, V>=20 以捕获淡红/暗红
+                    lower_red1 = np.array([0, 30, 20])
+                    upper_red1 = np.array([12, 255, 255])
+                    mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
+
+                    # 红色范围2: 168-180度（接近180度的红色）
+                    lower_red2 = np.array([168, 30, 20])
+                    upper_red2 = np.array([180, 255, 255])
+                    mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2)
+
+                    # 合并两个红色掩码
+                    mask_red = cv2.bitwise_or(mask_red1, mask_red2)
+
+                    # 形态学操作：先CLOSE填充空洞，再DILATE加厚环状区域
+                    kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+                    mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
+                    mask_red = cv2.dilate(mask_red, kernel_red, iterations=1)
+
+                    contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+                    red_pixel_count = np.sum(mask_red > 0)
+                    print(f"Debug -> 红色掩码: {red_pixel_count} 像素, {len(contours_red)} 个轮廓")
+
+                    found_valid_red = False
+
+                    if contours_red:
+                        for cnt_red in contours_red:
+                            area_red = cv2.contourArea(cnt_red)
+                            perimeter_red = cv2.arcLength(cnt_red, True)
+
+                            if perimeter_red > 0:
+                                circularity_red = (4 * np.pi * area_red) / (perimeter_red * perimeter_red)
+                            else:
+                                circularity_red = 0
+
+                            # 环状轮廓圆度可能偏低，放宽到0.2
+                            print(f"Debug -> 红轮廓: 面积={area_red:.1f}, 圆度={circularity_red:.2f}" +
+                                  f" (面积>15={area_red > 15}, 圆度>0.2={circularity_red > 0.2})")
+                            if area_red > 15 and circularity_red > 0.2:
+                                if len(cnt_red) >= 5:
+                                    (x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red)
+                                    radius_red = min(w_red, h_red) / 2
+                                    red_center = (int(x_red), int(y_red))
+                                    red_radius = int(radius_red)
+                                else:
+                                    (x_red, y_red), radius_red = cv2.minEnclosingCircle(cnt_red)
+                                    red_center = (int(x_red), int(y_red))
+                                    red_radius = int(radius_red)
+
+                                if red_center:
+                                    dx = yellow_center[0] - red_center[0]
+                                    dy = yellow_center[1] - red_center[1]
+                                    distance = np.sqrt(dx * dx + dy * dy)
+
+                                    max_distance = yellow_radius * 2.0
+                                    min_r = min(red_radius, yellow_radius)
+                                    max_r = max(red_radius, yellow_radius)
+                                    size_ratio = min_r / max_r if max_r > 0 else 0
+                                    print(f"Debug -> 圆心距={distance:.1f}(阈值={max_distance:.1f}), "
+                                          f"大小比={size_ratio:.2f}(阈值=0.4), "
+                                          f"距离OK={distance < max_distance}, 大小OK={size_ratio >= 0.4}")
+
+                                    # 允许红圈在黄圈外侧或内侧，只要大小相近（较小/较大 >= 0.5）
+                                    if distance < max_distance and size_ratio >= 0.4:
+                                        found_valid_red = True
+                                        print(
+                                            f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), 红心({red_center}), 距离:{distance:.1f}, 黄半径:{yellow_radius}, 红半径:{red_radius}")
+
+                                        valid_targets.append({
+                                            'center': yellow_center,
+                                            'radius': yellow_radius,
+                                            'ellipse': yellow_ellipse,
+                                            'area': area
+                                        })
+                                        break
+
+                    if not found_valid_red:
+                        # 如果黄圈非常可靠（大且圆），在没有红圈验证时仍接受
+                        if area > 30 and circularity > 0.85:
+                            print(f"[target] -> 黄圈高置信度(面积:{area:.0f}, 圆度:{circularity:.2f})，跳过红圈验证直接接受")
+                            valid_targets.append({
+                                'center': yellow_center,
+                                'radius': yellow_radius,
+                                'ellipse': yellow_ellipse,
+                                'area': area
+                            })
+                        else:
+                            print("Debug -> 未找到匹配的红色圆圈，可能是误识别")
+
+    # 从所有有效目标中选择最佳目标
+    if valid_targets:
+        if laser_point:
+            # 如果有激光点，选择最接近激光点的目标
+            best_target = None
+            min_distance = float('inf')
+            for target in valid_targets:
+                dx = target['center'][0] - laser_point[0]
+                dy = target['center'][1] - laser_point[1]
+                distance = np.sqrt(dx * dx + dy * dy)
+                if distance < min_distance:
+                    min_distance = distance
+                    best_target = target
+            if best_target:
+                best_center = best_target['center']
+                best_radius = best_target['radius']
+                ellipse_params = best_target['ellipse']
+                method = "v3_ellipse_red_validated_laser_selected"
+                best_radius1 = best_radius * 5
+        else:
+            # 如果没有激光点，选择面积最大的目标
+            best_target = max(valid_targets, key=lambda t: t['area'])
+            best_center = best_target['center']
+            best_radius = best_target['radius']
+            ellipse_params = best_target['ellipse']
+            method = "v3_ellipse_red_validated"
+            best_radius1 = best_radius * 5
+
+    result_img = image.cv2image(img_cv, False, False)
+    return result_img, best_center, best_radius, method, best_radius1, ellipse_params
+
+
+def detect_circle(frame):
+    """检测图像中的靶心（优先清晰轮廓，其次黄色区域）"""
+    img_cv = image.image2cv(frame, False, False)
+    # gray = cv2.cvtColor(img_cv, cv2.COLOR_RGB2GRAY)
+    # blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    # edged = cv2.Canny(blurred, 50, 150)
+    # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    # ceroded = cv2.erode(cv2.dilate(edged, kernel), kernel)
+
+    # contours, _ = cv2.findContours(ceroded, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    # best_center = best_radius = best_radius1 = method = None
+
+    # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    # h, s, v = cv2.split(hsv)
+    # s = np.clip(s * 2, 0, 255).astype(np.uint8)
+    # hsv = cv2.merge((h, s, v))
+    # lower_yellow = np.array([7, 80, 0])
+    # upper_yellow = np.array([32, 255, 182])
+    # mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+    # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)
+    # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # if contours:
+    #     largest = max(contours, key=cv2.contourArea)
+    #     if cv2.contourArea(largest) > 50:
+    #         (x, y), radius = cv2.minEnclosingCircle(largest)
+    #         best_center = (int(x), int(y))
+    #         best_radius = int(radius)
+    #         best_radius1 = radius * 5
+    #         method = "v2"
+
+    # auto
+    # R:31 M:v2 D:2.410110127692767
+    # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    # h, s, v = cv2.split(hsv)
+
+    # # 1. 增强饱和度（模糊照片需要更强的增强）
+    # s = np.clip(s * 2.5, 0, 255).astype(np.uint8)  # 从2.0改为2.5
+
+    # # 2. 增强亮度（模糊照片可能偏暗）
+    # v = np.clip(v * 1.2, 0, 255).astype(np.uint8)  # 新增：提升亮度
+
+    # hsv = cv2.merge((h, s, v))
+
+    # # 3. 放宽HSV颜色范围（特别是模糊照片）
+    # # 降低饱和度下限，提高亮度上限
+    # lower_yellow = np.array([5, 50, 30])   # H:5-35, S:50-255, V:30-255
+    # upper_yellow = np.array([35, 255, 255])
+
+    # mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # # 4. 增强形态学操作（连接被分割的区域）
+    # kernel_small = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    # kernel_large = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 9))  # 更大的核
+
+    # # 先开运算去除噪声
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel_small)
+    # # 多次膨胀连接区域（模糊照片需要更多膨胀）
+    # mask = cv2.dilate(mask, kernel_large, iterations=2)  # 增加迭代次数
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel_large)  # 闭运算填充空洞
+
+    # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # if contours:
+    #     largest = max(contours, key=cv2.contourArea)
+    #     area = cv2.contourArea(largest)
+    #     if area > 50:
+    #         # 5. 使用面积计算等效半径（更准确）
+    #         equivalent_radius = np.sqrt(area / np.pi)
+
+    #         # 6. 同时使用minEnclosingCircle作为备选（取较大值）
+    #         (x, y), enclosing_radius = cv2.minEnclosingCircle(largest)
+
+    #         # 取两者中的较大值，确保不遗漏
+    #         radius = max(equivalent_radius, enclosing_radius)
+
+    #         best_center = (int(x), int(y))
+    #         best_radius = int(radius)
+    #         best_radius1 = radius * 5
+    #         method = "v2"
+
+    # codegee
+    # R:24 M:v2 D:3.061493895819174
+    # R:22 M:v2 D:3.3644971681267077  np.clip(s * 1.1, 0, 255)
+    hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    h, s, v = cv2.split(hsv)
+
+    # 2. 调整饱和度策略：
+    # 不要暴力翻倍，可以尝试稍微增强，或者使用 CLAHE 增强亮度/对比度
+    # 这里我们稍微增加一点饱和度，并确保不溢出
+    s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
+    # 对亮度通道 v 也可以做一点 CLAHE 处理来增强对比度（可选）
+    # clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    # v = clahe.apply(v)
+
+    hsv = cv2.merge((h, s, v))
+
+    # 3. 放宽 HSV 阈值范围（针对模糊图像的关键调整）
+    # 降低 S 的下限 (80 -> 35)，提高 V 的上限 (182 -> 255)
+    lower_yellow = np.array([7, 80, 0])  # 饱和度下限降低，捕捉淡黄色
+    upper_yellow = np.array([32, 255, 255])  # 亮度上限拉满
+
+    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # 4. 调整形态学操作
+    # 去掉 MORPH_OPEN，因为它会减小面积。
+    # 使用 MORPH_CLOSE (先膨胀后腐蚀) 来填充内部小黑洞，连接近邻区域
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    # 再进行一次膨胀，确保边缘被包含进来
+    # mask = cv2.dilate(mask, kernel, iterations=1) 
+
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    if contours:
+        largest = max(contours, key=cv2.contourArea)
+
+        # 这里可以适当降低面积阈值，或者保持不变
+        if cv2.contourArea(largest) > 50:
+            # (x, y), radius = cv2.minEnclosingCircle(largest)
+            # best_center = (int(x), int(y))
+            # best_radius = int(radius)
+
+            # --- 核心修改开始 ---
+            # 1. 尝试拟合椭圆 (需要轮廓点至少为5个)
+            if len(largest) >= 5:
+                # 返回值: ((中心x, 中心y), (长轴, 短轴), 旋转角度)
+                (x, y), (axes_major, axes_minor), angle = cv2.fitEllipse(largest)
+
+                # 2. 计算半径
+                # 选项A：取长短轴的平均值 (比较稳健)
+                # radius = (axes_major + axes_minor) / 4
+
+                # 选项B：直接取短轴的一半 (抗模糊最强，推荐)
+                radius = axes_minor / 2
+
+                best_center = (int(x), int(y))
+                best_radius = int(radius)
+                method = "v2_ellipse"
+            else:
+                # 如果点太少无法拟合椭圆，降级回 minEnclosingCircle
+                (x, y), radius = cv2.minEnclosingCircle(largest)
+                best_center = (int(x), int(y))
+                best_radius = int(radius)
+                method = "v2"
+            # --- 核心修改结束 ---
+
+            # 你的后续逻辑
+            best_radius1 = radius * 5
+
+    # operas 4.5
+    # R:25 M:v2 D:2.9554872521538527
+    # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    # h, s, v = cv2.split(hsv)
+
+    # # 1. 适度增强饱和度（不要过度，否则噪声也会增强）
+    # s = np.clip(s * 1.5, 0, 255).astype(np.uint8)
+    # hsv = cv2.merge((h, s, v))
+
+    # # 2. 放宽 HSV 阈值范围（关键改动）
+    # # - 饱和度下限从 80 降到 40（捕捉淡黄色）
+    # # - 亮度上限从 182 提高到 255（允许更亮的黄色）
+    # lower_yellow = np.array([7, 40, 30])
+    # upper_yellow = np.array([35, 255, 255])
+
+    # mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # # 3. 调整形态学操作：用 CLOSE 替代 OPEN
+    # # CLOSE（先膨胀后腐蚀）：填充内部空洞，连接相邻区域
+    # # OPEN（先腐蚀后膨胀）：会缩小区域，不适合模糊图像
+    # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))  # 稍大的核
+    # mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    # mask = cv2.dilate(mask, kernel, iterations=1)  # 额外膨胀，确保边缘被包含
+
+    # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    # if contours:
+    #     largest = max(contours, key=cv2.contourArea)
+    #     if cv2.contourArea(largest) > 50:
+    #         (x, y), radius = cv2.minEnclosingCircle(largest)
+    #         best_center = (int(x), int(y))
+    #         best_radius = int(radius)
+    #         best_radius1 = radius * 5
+    #         method = "v2"
+
+    # # --- 新增：将 Mask 叠加到原图上用于调试 ---
+    # # 创建一个彩色掩码（红色通道为255，其他为0）
+    # mask_overlay = np.zeros_like(img_cv)
+    # mask_overlay[:, :, 2] = mask  # 将掩码放在红色通道 (BGR中的R)
+    #
+    # cv2.addWeighted(img_cv, 0.6, mask_overlay, 0.4, 0, img_cv)
+
+    result_img = image.cv2image(img_cv, False, False)
+    return result_img, best_center, best_radius, method, best_radius1
+
+
+def detect_circle_v2(frame):
+    """检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本"""
+    global REAL_RADIUS_CM
+    img_cv = image.image2cv(frame, False, False)
+
+    best_center = best_radius = best_radius1 = method = None
+    ellipse_params = None  # 存储椭圆参数 ((x, y), (axes_major, axes_minor), angle)
+
+    # HSV 黄色掩码检测（模糊靶心）
+    hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    h, s, v = cv2.split(hsv)
+
+    # 调整饱和度策略：稍微增强，不要过度
+    s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
+
+    hsv = cv2.merge((h, s, v))
+
+    # 放宽 HSV 阈值范围（针对模糊图像的关键调整）
+    lower_yellow = np.array([7, 80, 0])  # 饱和度下限降低，捕捉淡黄色
+    upper_yellow = np.array([32, 255, 255])  # 亮度上限拉满
+
+    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+    # 调整形态学操作
+    # 使用 MORPH_CLOSE (先膨胀后腐蚀) 来填充内部小黑洞，连接近邻区域
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    if contours:
+        largest = max(contours, key=cv2.contourArea)
+
+        if cv2.contourArea(largest) > 50:
+            # 尝试拟合椭圆 (需要轮廓点至少为5个)
+            if len(largest) >= 5:
+                # 返回值: ((中心x, 中心y), (width, height), 旋转角度)
+                # 注意：width 和 height 是外接矩形的尺寸，不是长轴和短轴
+                (x, y), (width, height), angle = cv2.fitEllipse(largest)
+
+                # 保存椭圆参数（保持原始顺序，用于绘制）
+                ellipse_params = ((x, y), (width, height), angle)
+
+                # 计算半径：使用较小的尺寸作为短轴
+                axes_minor = min(width, height)
+                radius = axes_minor / 2
+
+                best_center = (int(x), int(y))
+                best_radius = int(radius)
+                method = "v2_ellipse"
+            else:
+                # 如果点太少无法拟合椭圆，降级回 minEnclosingCircle
+                (x, y), radius = cv2.minEnclosingCircle(largest)
+                best_center = (int(x), int(y))
+                best_radius = int(radius)
+                method = "v2"
+                ellipse_params = None  # 圆形，没有椭圆参数
+
+            best_radius1 = radius * 5
+
+    result_img = image.cv2image(img_cv, False, False)
+    return result_img, best_center, best_radius, method, best_radius1, ellipse_params
+
+
+# ==================== 测试逻辑 ====================
+
+def run_offline_test(image_path):
+    """读取图片，检测圆，绘制结果，保存图片"""
+
+    # 1. 检查文件是否存在
+    if not os.path.exists(image_path):
+        print(f"[ERROR] 找不到图片文件: {image_path}")
+        return
+
+    # 2. 使用 maix.image 读取图片 (适配 MaixPy v4)
+    try:
+        # 使用 image.load 读取文件，返回 Image 对象
+        img = image.load(image_path)
+        print(f"[INFO] 成功读取图片: {image_path} (尺寸: {img.width()}x{img.height()})")
+    except Exception as e:
+        print(f"[ERROR] 读取图片失败: {e}")
+        print("提示：请确认 MaixPy 版本是否为 v4，且图片路径正确。")
+        return
+
+    # 3. 调用 detect_circle_v2 函数
+    print("[INFO] 正在调用 detect_circle_v2 进行检测...")
+    start_time = time.ticks_ms()
+
+    result_img, center, radius, method, radius1, ellipse_params = detect_circle_v3(img)
+
+    cost_time = time.ticks_ms() - start_time
+    print(f"[INFO] 检测完成，耗时: {cost_time}ms")
+    print(f"       结果 -> 圆心: {center}, 半径: {radius}, 方法: {method}")
+    if ellipse_params:
+        (ell_center, (width, height), angle) = ellipse_params
+        print(
+            f"       椭圆 -> 中心: ({ell_center[0]:.1f}, {ell_center[1]:.1f}), 长轴: {max(width, height):.1f}, 短轴: {min(width, height):.1f}, 角度: {angle:.1f}°")
+
+    # 4. 绘制辅助线（可选，用于调试）
+    if center and radius:
+        # 为了绘制椭圆，需要转换回 cv2 图像
+        img_cv = image.image2cv(result_img, False, False)
+
+        cx, cy = center
+
+        # 如果有椭圆参数，绘制椭圆
+        if ellipse_params:
+            (ell_center, (width, height), angle) = ellipse_params
+            cx_ell, cy_ell = int(ell_center[0]), int(ell_center[1])
+
+            # 确定长轴和短轴
+            if width >= height:
+                # width 是长轴，height 是短轴
+                axes_major = width
+                axes_minor = height
+                major_angle = angle  # 长轴角度就是 angle
+                minor_angle = angle + 90  # 短轴角度 = 长轴角度 + 90度
+            else:
+                # height 是长轴，width 是短轴
+                axes_major = height
+                axes_minor = width
+                major_angle = angle + 90  # 长轴角度 = width角度 + 90度
+                minor_angle = angle  # 短轴角度就是 angle
+
+            # 使用 OpenCV 绘制椭圆（绿色，线宽2）
+            cv2.ellipse(img_cv,
+                        (cx_ell, cy_ell),  # 中心点
+                        (int(width / 2), int(height / 2)),  # 半宽、半高
+                        angle,  # 旋转角度（OpenCV需要原始angle）
+                        0, 360,  # 起始和结束角度
+                        (0, 255, 0),  # 绿色 (RGB格式)
+                        2)  # 线宽
+
+            # 绘制椭圆中心点（红色）
+            cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1)
+
+            import math
+            # 绘制短轴（蓝色线条）
+            minor_length = axes_minor / 2
+            minor_angle_rad = math.radians(minor_angle)
+            dx_minor = minor_length * math.cos(minor_angle_rad)
+            dy_minor = minor_length * math.sin(minor_angle_rad)
+            pt1_minor = (int(cx_ell - dx_minor), int(cy_ell - dy_minor))
+            pt2_minor = (int(cx_ell + dx_minor), int(cy_ell + dy_minor))
+            cv2.line(img_cv, pt1_minor, pt2_minor, (0, 0, 255), 2)  # 蓝色 (RGB格式)
+        else:
+            # 如果没有椭圆参数，绘制圆形（红色）
+            cv2.circle(img_cv, (cx, cy), radius, (0, 0, 255), 2)
+            cv2.circle(img_cv, (cx, cy), 2, (0, 0, 255), -1)
+
+        # 转换回 maix image
+        result_img = image.cv2image(img_cv, False, False)
+
+        # 定义颜色对象用于文字
+        try:
+            color_black = image.Color.from_rgb(0, 0, 0)
+        except AttributeError:
+            color_black = image.Color(0, 0, 0)
+
+        # D. 添加文字信息
+        FOCAL_LENGTH_PIX = 1900
+        d = (REAL_RADIUS_CM * FOCAL_LENGTH_PIX) / radius1 / 100.0
+        info_str = f"R:{radius} M:{method} D:{d:.2f}"
+        print(info_str)
+
+        # 计算文字位置，防止超出图片边界
+        r_outer = int(radius * 11.0) if radius else 100
+        text_y = cy - r_outer - 20 if cy > r_outer + 20 else cy + r_outer + 20
+
+        # 调用 draw_string
+        result_img.draw_string(0, 0, info_str, color=color_black, scale=1.0)
+
+    # 5. 保存结果图片
+    output_path = image_path.replace(".bmp", "_result.bmp")
+    output_path = image_path.replace(".jpg", "_result.jpg")
+    try:
+        result_img.save(output_path, quality=100)
+        print(f"[SUCCESS] 结果已保存至: {output_path}")
+    except Exception as e:
+        print(f"[ERROR] 保存图片失败: {e}")
+
+
+if __name__ == "__main__":
+    # ================= 配置区域 =================
+
+    # 1. 设置要测试的图片路径
+    # 建议将图片放在与脚本同级目录，或者使用绝对路径
+    TARGET_IMAGE = "/root/phot/shot_1830921_0_no_target.jpg"
+
+    TARGET_DIR = "/root/phot"  # 修改为你想要读取的目录路径
+
+    # 支持的图片格式
+    IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.bmp']
+
+    # ================= 执行区域 =================
+    if 'TARGET_DIR' in locals():
+        # 读取目录下所有图片文件，过滤掉 _result.jpg 后缀的文件
+        image_files = []
+        if os.path.exists(TARGET_DIR) and os.path.isdir(TARGET_DIR):
+            for filename in os.listdir(TARGET_DIR):
+                # 检查文件扩展名
+                if any(filename.lower().endswith(ext) for ext in IMAGE_EXTENSIONS):
+                    # 过滤掉 _result.jpg 后缀的文件
+                    if filename.endswith('no_target.jpg'):
+                        filepath = os.path.join(TARGET_DIR, filename)
+                        if os.path.isfile(filepath):
+                            image_files.append(filepath)
+
+            # 按文件名排序（可选）
+            image_files.sort()
+
+            print(f"[INFO] 在目录 {TARGET_DIR} 中找到 {len(image_files)} 张图片")
+
+            # 处理每张图片
+            for img_path in image_files:
+                print(f"\n{'=' * 10} 开始处理: {img_path} {'=' * 10}")
+                run_offline_test(img_path)
+        else:
+            print(f"[ERROR] 目录不存在或不是有效目录: {TARGET_DIR}")
+
+    else:
+        run_offline_test(TARGET_IMAGE)

test/test_gpio_a14.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Interactive GPIO test for physical pin A14."""
+
+import sys
+
+from maix import gpio, pinmap
+
+
+PIN = "A14"
+GPIO_NAME = "GPIOA14"
+
+
+def set_level(output, command):
+    if command == "1":
+        output.value(1)
+        print("A14 = HIGH, laser OFF")
+        return True
+    if command == "0":
+        output.value(0)
+        print("A14 = LOW, laser ON")
+        return True
+    return False
+
+
+def main():
+    pinmap.set_pin_function(PIN, GPIO_NAME)
+    output = gpio.GPIO(GPIO_NAME, gpio.Mode.OUT)
+
+    # One-shot mode for SSH/serial shells: python3 test_gpio_a14.py 1|0
+    if len(sys.argv) > 1:
+        command = sys.argv[1].strip()
+        if not set_level(output, command):
+            print("Invalid argument. Use 1 or 0.")
+            return
+        return
+
+    output.value(1)
+    print("A14 laser test: input 0 for ON, 1 for OFF, q to quit.")
+
+    try:
+        while True:
+            try:
+                command = input("A14> ").strip().lower()
+            except EOFError:
+                print("This runner has no stdin. Run from an SSH/serial shell with argument 1 or 0.")
+                return
+            if set_level(output, command):
+                continue
+            elif command in ("q", "quit", "exit"):
+                break
+            elif command:
+                print("Invalid input. Use 1, 0, or q.")
+    except KeyboardInterrupt:
+        print()
+    finally:
+        output.value(1)
+        print("A14 = HIGH, laser OFF, test stopped.")
+
+
+if __name__ == "__main__":
+    main()

test/wifi_gpio_ina_isolation.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Standalone WiFi/GPIO/INA226 isolation test for the official MaixPy tool.
+
+This file intentionally does not import project modules or start project
+threads. Select TEST_MODE below, then run the file directly.
+"""
+
+import time
+
+from maix import gpio, i2c, network, pinmap
+
+
+# Change only this value before each run.
+# wifi       WiFi only
+# a25        A25 only
+# a23        A23 only
+# gpio       A23/A26 only
+# ina        INA226 only
+# gpio_ina   GPIOs, then INA226
+# a25_wifi   A25, then WiFi
+# a23_wifi   A23, then WiFi
+# all        GPIOs, INA226, then WiFi
+TEST_MODE = "a25_wifi"
+
+WIFI_SSID = "sheling4b02-5G"
+WIFI_PASSWORD = "Aa12345678"
+WIFI_TIMEOUT_S = 20
+I2C_BUS_NUM = 5
+INA226_ADDR = 0x40
+
+
+def init_leds():
+    return init_selected_leds(True, True)
+
+
+def init_selected_leds(use_a26, use_a23):
+    outputs = []
+    if use_a26:
+        print("Initializing A25 -> GPIOA25")
+        pinmap.set_pin_function("A25", "GPIOA25")
+        green = gpio.GPIO("GPIOA25", gpio.Mode.OUT)
+        green.value(0)
+        outputs.append(("GPIOA25", green))
+        print("GPIOA25 initialized LOW")
+    if use_a23:
+        print("Initializing A23 -> GPIOA23")
+        pinmap.set_pin_function("A23", "GPIOA23")
+        red = gpio.GPIO("GPIOA23", gpio.Mode.OUT)
+        red.value(0)
+        outputs.append(("GPIOA23", red))
+        print("GPIOA23 initialized LOW")
+    return outputs
+
+
+def test_ina226():
+    # Match the board mapping used by the application before opening I2C5.
+    pinmap.set_pin_function("A15", "I2C5_SCL")
+    pinmap.set_pin_function("A27", "I2C5_SDA")
+    print("A15/A27 configured for I2C5")
+    print("Initializing I2C bus", I2C_BUS_NUM)
+    bus = i2c.I2C(I2C_BUS_NUM, i2c.Mode.MASTER)
+    print("Reading INA226 at 0x%02X" % INA226_ADDR)
+    config = bus.readfrom_mem(INA226_ADDR, 0x00, 2)
+    voltage_raw = bus.readfrom_mem(INA226_ADDR, 0x02, 2)
+    voltage = ((voltage_raw[0] << 8) | voltage_raw[1]) * 1.25 / 1000
+    print("INA226 config=0x%02X%02X voltage=%.3fV" % (config[0], config[1], voltage))
+    return bus
+
+
+def test_wifi():
+    print("Starting MaixPy WiFi connection...")
+    wifi = network.wifi.Wifi()
+    result = wifi.connect(WIFI_SSID, WIFI_PASSWORD, wait=True, timeout=WIFI_TIMEOUT_S)
+    print("WiFi connect result:", result)
+    print("WiFi connected:", wifi.is_connected())
+    try:
+        print("WiFi IP:", wifi.get_ip())
+    except Exception as exc:
+        print("WiFi status query failed:", exc)
+
+
+def main():
+    valid = ("wifi", "a25", "a23", "gpio", "ina", "gpio_ina", "a25_wifi", "a23_wifi", "all")
+    mode = TEST_MODE.lower()
+    if mode not in valid:
+        print("TEST_MODE must be one of:", ", ".join(valid))
+        return 1
+
+    leds = []
+    try:
+        print("=== Standalone WiFi/GPIO/INA226 isolation ===")
+        print("mode:", mode)
+        if mode in ("a25", "a25_wifi"):
+            leds = init_selected_leds(True, False)
+            time.sleep(1)
+        elif mode in ("a23", "a23_wifi"):
+            leds = init_selected_leds(False, True)
+            time.sleep(1)
+        elif mode in ("gpio", "gpio_ina", "all"):
+            leds = init_leds()
+            time.sleep(1)
+        if mode in ("ina", "gpio_ina", "all"):
+            test_ina226()
+            time.sleep(1)
+        if mode in ("wifi", "a25_wifi", "a23_wifi", "all"):
+            test_wifi()
+        print("TEST COMPLETE")
+        return 0
+    except Exception as exc:
+        print("TEST FAILED:", repr(exc))
+        return 1
+    finally:
+        for name, led in leds:
+            try:
+                led.value(0)
+                print(name, "LOW")
+            except Exception as exc:
+                print(name, "cleanup failed:", exc)
+
+
+main()

test/wifi_gpio_isolation.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Standalone WiFi/GPIO isolation test.
+
+This script intentionally does not import any project module. It only tests
+MaixPy WiFi startup with optional A23/A26 GPIO initialization.
+"""
+
+import time
+
+from maix import gpio, network, pinmap
+
+
+GREEN_PIN = "A26"
+GREEN_GPIO = "GPIOA26"
+RED_PIN = "A23"
+RED_GPIO = "GPIOA23"
+
+# Run this file directly from the official MaixPy tool.
+# Change only TEST_MODE between runs: none -> a26 -> a23 -> both.
+TEST_MODE = "none"
+WIFI_SSID = "sheling4b02-5G"
+WIFI_PASSWORD = "Aa12345678"
+WIFI_TIMEOUT_S = 20
+
+
+def init_gpio(mode):
+    outputs = []
+    if mode in ("a26", "both"):
+        pinmap.set_pin_function(GREEN_PIN, GREEN_GPIO)
+        green = gpio.GPIO(GREEN_GPIO, gpio.Mode.OUT)
+        green.value(1)
+        outputs.append((GREEN_GPIO, green))
+        print("GPIOA26 initialized HIGH")
+    if mode in ("a23", "both"):
+        pinmap.set_pin_function(RED_PIN, RED_GPIO)
+        red = gpio.GPIO(RED_GPIO, gpio.Mode.OUT)
+        red.value(1)
+        outputs.append((RED_GPIO, red))
+        print("GPIOA23 initialized HIGH")
+    return outputs
+
+
+def connect_wifi(ssid, password, timeout_s):
+    print("Starting MaixPy WiFi connection...")
+    wifi = network.wifi.Wifi()
+    result = wifi.connect(ssid, password, wait=True, timeout=timeout_s)
+    print("WiFi connect result:", result)
+    try:
+        print("WiFi connected:", wifi.is_connected())
+        print("WiFi IP:", wifi.get_ip())
+    except Exception as exc:
+        print("WiFi status query failed:", exc)
+    return result
+
+
+def main():
+    mode = TEST_MODE.lower()
+    if mode not in ("none", "a26", "a23", "both"):
+        print("TEST_MODE must be none, a26, a23, or both")
+        return 1
+    ssid = WIFI_SSID
+    password = WIFI_PASSWORD
+    timeout_s = WIFI_TIMEOUT_S
+
+    print("=== Standalone WiFi/GPIO isolation ===")
+    print("mode:", mode)
+    print("ssid:", ssid)
+    outputs = []
+    try:
+        outputs = init_gpio(mode)
+        time.sleep(1)
+        connect_wifi(ssid, password, timeout_s)
+        return 0
+    except Exception as exc:
+        print("TEST FAILED:", repr(exc))
+        return 1
+    finally:
+        for gpio_name, output in outputs:
+            try:
+                output.value(0)
+                print(gpio_name, "LOW")
+            except Exception as exc:
+                print(gpio_name, "cleanup failed:", exc)
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

version.md

@@ -0,0 +1,37 @@
+# 1.2.0 开始使用C++编译成.so，替换部分代码
+# 1.2.1 ota使用加密包
+# 1.2.2 支持wifi ota，并且设定时区，并使用单独线程保存图片
+# 1.2.3 修改ADC_TRIGGER_THRESHOLD 为2300，支持上传日志到服务器
+# 1.2.4 修改ADC_TRIGGER_THRESHOLD 为3000，并默认关闭摄像头的显示，并把ADC的采样间隔从50ms降低到10ms
+# 1.2.5 支持空气传感器采样，并默认关闭日志。优化断网时的发送队列丢消息问题，解决 WiFi 断线检测不可靠问题。
+# 1.2.6 在链接 wifi 前先判断 wifi 的可用性，假如不可用，则不落盘。增加日志批量压缩上传功能
+# 1.2.7 修复OTA失败的bug, 空气压力传感器的阈值是2500
+# 1.2.8 （1） 加快 wifi 下数据传输的速度。（2） 调整射箭时处理的逻辑，优先上报数据，再存照片之类的操作。（3）假如是用户打开激光的，射箭触发后不再关闭激光，因为是调瞄阶段
+# 1.2.9 增加电源板的控制和自动关机的功能
+# 1.2.10 config formal
+# 1.2.11 增加三角形的单应性算法，适配对应的靶纸
+# 1.2.110 关掉了黑色三角形算法，只用于测试
+# 1.2.13 修改wifi连接
+# 1.2.14 修改了icc登录部分
+# 2.15.3 新版本ota，去除ai算环数方法
+# 2.15.4 更新版本号
+# 2.15.5 打印ota进度
+# 2.15.6 更新版本号
+# 2.15.7 更新版本号
+# 2.15.8 启动不加载预加载yolo
+# 2.15.9 20cm
+# 2.15.10 不保存图片
+# 2.15.11 优化内存
+# 2.15.12 优化算法
+# 2.15.13 优化算法
+# 2.15.14 优化算法
+# 2.15.15 优化wifi连接
+# 2.15.16 修复wifi连接问题
+# 2.15.17 修复wifi连接问题
+# 2.15.18 wifi连接成功重新登录
+# 2.16.4  优化射箭延迟
+# 2.17.0  yolo标靶类别识别
+# 2.17.1  26-08-19 17：39 压力传感修改 增量方式
+# 2.17.2 26-08-24 17：56 靶纸识别模型更替
+# 2.17.3 26-08-25 9：57 原图拍摄开关
+# 2.17.4 26-08-25 14：57 模型修改

version.py

@@ -4,28 +4,6 @@
 应用版本号
 每次 OTA 更新时，只需要更新这个文件中的版本号
 """
-VERSION = '2.14.1'
+VERSION = '2.18.2'
-
-
-# 1.2.0 开始使用C++编译成.so，替换部分代码
-# 1.2.1 ota使用加密包
-# 1.2.2 支持wifi ota，并且设定时区，并使用单独线程保存图片
-# 1.2.3 修改ADC_TRIGGER_THRESHOLD 为2300，支持上传日志到服务器
-# 1.2.4 修改ADC_TRIGGER_THRESHOLD 为3000，并默认关闭摄像头的显示，并把ADC的采样间隔从50ms降低到10ms
-# 1.2.5 支持空气传感器采样，并默认关闭日志。优化断网时的发送队列丢消息问题，解决 WiFi 断线检测不可靠问题。
-# 1.2.6 在链接 wifi 前先判断 wifi 的可用性，假如不可用，则不落盘。增加日志批量压缩上传功能
-# 1.2.7 修复OTA失败的bug, 空气压力传感器的阈值是2500
-# 1.2.8 （1） 加快 wifi 下数据传输的速度。（2） 调整射箭时处理的逻辑，优先上报数据，再存照片之类的操作。（3）假如是用户打开激光的，射箭触发后不再关闭激光，因为是调瞄阶段
-# 1.2.9 增加电源板的控制和自动关机的功能
-# 1.2.10 config formal
-# 1.2.11 增加三角形的单应性算法，适配对应的靶纸
-# 1.2.110 关掉了黑色三角形算法，只用于测试
-# 1.2.13 修改wifi连接
-# 1.2.14 修改了icc登录部分
-
-
-
-
-
 
 

vision.py

@@ -535,7 +535,7 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
     logger.debug(f"[detect_circle_v3] begin {datetime.now()}")
     # -- 1. 缩图加速（与三角形路径保持一致）
     h_orig, w_orig = img_cv.shape[:2]
-    MAX_DET_DIM = 320
+    MAX_DET_DIM = 480
     long_side = max(h_orig, w_orig)
     if long_side > MAX_DET_DIM:
         det_scale = MAX_DET_DIM / long_side
@@ -570,20 +570,22 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
 
     # -- 3. 红色掩码：在循环外只算一次
     mask_red = cv2.bitwise_or(
-        cv2.inRange(hsv, np.array([0, 80, 0]),   np.array([10,  255, 255])),
+        cv2.inRange(hsv, np.array([0, 30, 20]),   np.array([12,  255, 255])),
-        cv2.inRange(hsv, np.array([170, 80, 0]), np.array([180, 255, 255])),
+        cv2.inRange(hsv, np.array([168, 30, 20]), np.array([180, 255, 255])),
     )
     kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
     mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
+    # 再加一次膨胀，加厚环状区域避免碎片化
+    mask_red = cv2.dilate(mask_red, kernel_red, iterations=1)
     contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     # 预先把红色轮廓筛选成 (center, radius) 列表，后续直接查表
     red_candidates = []
     for cnt_r in contours_red:
         ar = cv2.contourArea(cnt_r)
-        if ar <= 50:
+        if ar <= 10:
             continue
         pr = cv2.arcLength(cnt_r, True)
-        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.6:
+        if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.2:
             continue
         if len(cnt_r) >= 5:
             (xr, yr), (wr, hr), _ = cv2.fitEllipse(cnt_r)
@@ -599,13 +601,13 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
     valid_targets = []
     for cnt_yellow in contours_yellow:
         area = cv2.contourArea(cnt_yellow)
-        if area <= 50:
+        if area <= 15:
             continue
         perimeter = cv2.arcLength(cnt_yellow, True)
         if perimeter <= 0:
             continue
         circularity = (4 * np.pi * area) / (perimeter * perimeter)
-        if circularity <= 0.7:
+        if circularity <= 0.5:
             continue
         if logger:
             logger.info(f"[target] -> 面积:{area:.1f}, 圆度:{circularity:.2f}")
@@ -625,7 +627,11 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
             ddx = yellow_center[0] - rc["center"][0]
             ddy = yellow_center[1] - rc["center"][1]
             dist_centers = math.hypot(ddx, ddy)
-            if dist_centers < yellow_radius * 1.5 and rc["radius"] > yellow_radius * 0.8:
+            max_dist = yellow_radius * 2.0
+            min_r = min(rc["radius"], yellow_radius)
+            max_r = max(rc["radius"], yellow_radius)
+            size_ratio = min_r / max_r if max_r > 0 else 0
+            if dist_centers < max_dist and size_ratio >= 0.4:
                 if logger:
                     logger.info(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), "
                                 f"红心({rc['center']}), 距离:{dist_centers:.1f}, "
@@ -638,8 +644,17 @@ def detect_circle_v3(frame, laser_point=None, img_cv=None):
                 })
                 matched = True
                 break
-        if not matched and logger:
+        if not matched:
-            logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
+            # 黄圈高置信度兜底：大且圆时跳过红圈验证
+            if area > 30 and circularity > 0.8:
+                valid_targets.append({
+                    "center": yellow_center,
+                    "radius": yellow_radius,
+                    "ellipse": yellow_ellipse,
+                    "area": area,
+                })
+            elif logger:
+                logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
 
     logger.debug(f"[detect_circle_v3] step 4 fin {datetime.now()}")
 
@@ -782,12 +797,12 @@ def estimate_pixel(physical_distance_cm, target_distance_m):
 
 def _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
                           laser_point, distance_m, shot_id=None, photo_dir=None,
-                          yolo_roi_xyxy=None):
+                          yolo_roi_xyxy=None, force_save=False):
     """
     内部实现：在 img_cv (numpy HWC RGB) 上绘制标注并保存。
     由 save_shot_image（同步）和存图 worker（异步）调用。
     """
-    if not config.SAVE_IMAGE_ENABLED:
+    if not config.SAVE_IMAGE_ENABLED and not force_save:
         return None
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -887,13 +902,16 @@ def _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
 
 
 def _save_worker_loop():
-    """存图 worker：从队列取任务并调用 _save_shot_image_impl。"""
+    """存图 worker：处理标注图和可选的纯原图任务。"""
     while True:
         try:
             item = _save_queue.get()
             if item is None:
                 break
-            _save_shot_image_impl(*item)
+            if isinstance(item, dict) and item.get("kind") == "raw":
+                _save_raw_image_impl(item["img_cv"], item["shot_id"], item["photo_dir"])
+            else:
+                _save_shot_image_impl(*item)
         except Exception as e:
             logger = logger_manager.logger
             if logger:
@@ -921,13 +939,60 @@ def start_save_shot_worker():
         logger.info("[VISION] 存图 worker 线程已启动")
 
 
+def _save_raw_image_impl(img_cv, shot_id, photo_dir):
+    """保存未标注、未裁剪的完整原始帧。"""
+    logger = logger_manager.logger
+    try:
+        os.makedirs(photo_dir, exist_ok=True)
+        filename = os.path.join(photo_dir, f"shot_{shot_id}_raw.jpg")
+        image.cv2image(img_cv, False, False).save(filename)
+        prune_old_images_in_dir(
+            photo_dir,
+            getattr(config, "RAW_IMAGE_MAX_IMAGES", config.MAX_IMAGES),
+            logger,
+            "[VISION-RAW]",
+        )
+        if logger:
+            logger.info(f"[VISION-RAW] 已保存纯原图: {filename}")
+        return filename
+    except Exception as e:
+        if logger:
+            logger.error(f"[VISION-RAW] 保存纯原图失败: {e}")
+        return None
+
+
+def enqueue_save_raw_shot(frame, shot_id, photo_dir=None):
+    """复制并异步保存原始帧；由 SAVE_RAW_IMAGE_ENABLED 控制是否启用。"""
+    if not getattr(config, "SAVE_RAW_IMAGE_ENABLED", False):
+        return
+    if photo_dir is None:
+        photo_dir = getattr(config, "RAW_IMAGE_DIR", os.path.join(config.PHOTO_DIR, "raw"))
+    try:
+        img_copy = np.copy(image.image2cv(frame, False, False))
+        _save_queue.put_nowait({
+            "kind": "raw",
+            "img_cv": img_copy,
+            "shot_id": shot_id,
+            "photo_dir": photo_dir,
+        })
+    except queue.Full:
+        logger = logger_manager.logger
+        if logger:
+            logger.warning("[VISION-RAW] 存图队列已满，跳过本次纯原图保存")
+    except Exception as e:
+        logger = logger_manager.logger
+        if logger:
+            logger.error(f"[VISION-RAW] 复制纯原图失败: {e}")
+
+
 def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
                       laser_point, distance_m, shot_id=None, photo_dir=None,
-                      yolo_roi_xyxy=None):
+                      yolo_roi_xyxy=None, force_save=False):
     """
     将存图任务放入队列，由 worker 异步保存。主线程传入 result_img 的复制，不阻塞。
+    force_save=True 时，忽略 SAVE_IMAGE_ENABLED 配置强制保存（用于检测失败时的调试图像）。
     """
-    if not config.SAVE_IMAGE_ENABLED:
+    if not config.SAVE_IMAGE_ENABLED and not force_save:
         return
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -950,6 +1015,7 @@ def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
         shot_id,
         photo_dir,
         yolo_roi_xyxy,
+        force_save,
     )
     try:
         _save_queue.put_nowait(task)
@@ -961,12 +1027,12 @@ def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
 
 def save_shot_image(result_img, center, radius, method, ellipse_params,
                     laser_point, distance_m, shot_id=None, photo_dir=None,
-                    yolo_roi_xyxy=None):
+                    yolo_roi_xyxy=None, force_save=False):
     """
     保存射击图像（带标注）。同步调用，会阻塞。
     主流程建议使用 enqueue_save_shot；此处保留供校准、测试等场景使用。
     """
-    if not config.SAVE_IMAGE_ENABLED:
+    if not config.SAVE_IMAGE_ENABLED and not force_save:
         return None
     if photo_dir is None:
         photo_dir = config.PHOTO_DIR
@@ -983,6 +1049,7 @@ def save_shot_image(result_img, center, radius, method, ellipse_params,
             shot_id,
             photo_dir,
             yolo_roi_xyxy,
+            force_save,
         )
     except Exception as e:
         logger = logger_manager.logger

wifi.py

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