fix: 获取电量错误

4g_download_manager.py

@@ -255,6 +255,10 @@ class DownloadManager4G:
         parsed = urlparse(url)
         host = parsed.hostname
         path = parsed.path or "/"
+        if parsed.query:
+            path = f"{path}?{parsed.query}"
+        if parsed.fragment:
+            path = f"{path}#{parsed.fragment}"
         if not host:
             return False, "bad_url (no host)"
 

4g_upload_manager.py

@@ -0,0 +1,450 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+4G Image Upload Manager
+Uploads images to Qiniu cloud via ML307R 4G module TCP socket (MIPOPEN + MIPSEND).
+
+AT Command Sequence (ML307R TCP socket POST):
+  AT+MIPCALL=1,1                                  // Ensure PDP context active
+  AT+MIPCLOSE=<id>                                // Close old socket (ignore error)
+  AT+MIPOPEN=<id>,"TCP","<host>",80              // Open TCP socket
+  // Wait for +MIPOPEN: <id>,0 (success)
+  AT+MIPSEND=<id>,<len>                           // Send data
+  // Wait for ">" prompt, then write raw bytes
+  // Repeat MIPSEND for all chunks
+  // Wait for +MIPURC: "rtcp" response
+  AT+MIPCLOSE=<id>                                // Close socket
+"""
+
+import re
+import os
+import json
+from maix import time
+from urllib.parse import urlparse
+from logger_manager import logger_manager
+from hardware import hardware_manager
+
+# Multipart form boundary (simple alphanumeric to avoid AT command parser issues)
+BOUNDARY = "QiniuFormBoundary" + hex(int(time.time()))[2:]
+# Chunk size for MIPSEND (max 1024 to avoid AT line buffer limits)
+SEND_CHUNK = 1024
+# Socket ID for upload (dedicated to avoid conflict with main app TCP)
+UPLOAD_SOCK_ID = 3
+
+
+class FourGUploadManager:
+    """4G image upload manager using ML307R TCP socket (MIPOPEN + MIPSEND)"""
+
+    def __init__(self, at_client):
+        """Initialize with AT client instance"""
+        self.at = at_client
+        self.logger = logger_manager.logger
+
+    # ------------------------------------------------------------------ logging
+    def _log(self, msg):
+        try:
+            self.logger.debug("[4G-UL] " + msg)
+        except Exception:
+            print("[4G-UL] " + msg)
+
+    def _log_info(self, msg):
+        try:
+            self.logger.info("[4G-UL] " + msg)
+        except Exception:
+            print("[4G-UL] " + msg)
+
+    def _log_error(self, msg):
+        try:
+            self.logger.error("[4G-UL] " + msg)
+        except Exception:
+            print("[4G-UL] " + msg)
+
+    # --------------------------------------------------------------- helpers
+    def _ensure_pdp(self):
+        """Ensure PDP context is active; returns (ok, ip)"""
+        r = self.at.send("AT+CGPADDR=1", "OK", 3000)
+        m = re.search(r'\+CGPADDR:\s*1,"([^"]+)"', r)
+        ip = m.group(1) if m else ""
+        if ip and ip != "0.0.0.0":
+            return True, ip
+        self.at.send("AT+MIPCALL=1,1", "OK", 15000)
+        for _ in range(10):
+            r = self.at.send("AT+CGPADDR=1", "OK", 3000)
+            m = re.search(r'\+CGPADDR:\s*1,"([^"]+)"', r)
+            ip = m.group(1) if m else ""
+            if ip and ip != "0.0.0.0":
+                return True, ip
+            time.sleep(1)
+        return False, ip
+
+    def _is_error(self, resp):
+        """Check AT response for any error indicators"""
+        return "ERROR" in resp or "CME ERROR" in resp
+
+    # --------------------------------------------------------- multipart body
+    def _build_multipart_body(self, image_path, upload_token, key):
+        """
+        Build multipart/form-data body as bytes for Qiniu upload.
+
+        Fields:
+          - token : Qiniu upload token
+          - key   : object key in bucket
+          - file  : binary image data
+        """
+        boundary = BOUNDARY.encode()
+
+        with open(image_path, "rb") as f:
+            file_data = f.read()
+
+        filename = os.path.basename(image_path)
+        ext = os.path.splitext(image_path)[1].lower()
+        ct_map = {
+            ".png": "image/png",
+            ".jpg": "image/jpeg",
+            ".jpeg": "image/jpeg",
+            ".bmp": "image/bmp",
+            ".webp": "image/webp",
+        }
+        content_type = ct_map.get(ext, "application/octet-stream")
+
+        body = bytearray()
+
+        # -- token field --
+        body += b"--" + boundary + b"\r\n"
+        body += b'Content-Disposition: form-data; name="token"\r\n'
+        body += b"\r\n"
+        body += upload_token.encode("utf-8") + b"\r\n"
+
+        # -- key field --
+        body += b"--" + boundary + b"\r\n"
+        body += b'Content-Disposition: form-data; name="key"\r\n'
+        body += b"\r\n"
+        body += key.encode("utf-8") + b"\r\n"
+
+        # -- file field --
+        body += b"--" + boundary + b"\r\n"
+        body += (
+            b'Content-Disposition: form-data; name="file"; filename="'
+            + filename.encode("utf-8")
+            + b'"\r\n'
+        )
+        body += b"Content-Type: " + content_type.encode("utf-8") + b"\r\n"
+        body += b"\r\n"
+        body += file_data + b"\r\n"
+
+        # -- closing boundary --
+        body += b"--" + boundary + b"--\r\n"
+
+        return bytes(body)
+
+    # --------------------------------------------------- TCP socket helpers
+    def _close_socket(self, sock_id):
+        """Close socket, ignore CME ERROR 55 (already closed)"""
+        try:
+            resp = self.at.send("AT+MIPCLOSE=" + str(sock_id), "OK", 5000)
+            self._log("socket " + str(sock_id) + " closed: " + resp)
+        except Exception as e:
+            # Ignore CME ERROR 55 (socket not open)
+            self._log("socket close (may already be closed): " + str(e))
+
+    def _open_socket(self, sock_id, host, port):
+        """
+        Open TCP socket to host:port.
+        Returns (success, error_msg)
+        """
+        cmd = 'AT+MIPOPEN=' + str(sock_id) + ',"TCP","' + host + '",' + str(port)
+        resp = self.at.send(cmd, "OK", 15000)
+
+        if self._is_error(resp):
+            return False, "MIPOPEN failed: " + resp
+
+        # Wait for +MIPOPEN: <id>,0 (success) or +MIPOPEN: <id>,<error_code>
+        # The URC may come in the same response or separately
+        mipopen_pattern = r"\+MIPOPEN:\s*" + str(sock_id) + r",(\d+)"
+        m = re.search(mipopen_pattern, resp)
+
+        if m:
+            result_code = int(m.group(1))
+            if result_code == 0:
+                return True, ""
+            else:
+                return False, "MIPOPEN error code: " + str(result_code)
+
+        # If not in initial response, wait for URC
+        try:
+            urc_resp = self.at.send("", "+MIPOPEN:", 15000)
+            m = re.search(mipopen_pattern, urc_resp)
+            if m:
+                result_code = int(m.group(1))
+                if result_code == 0:
+                    return True, ""
+                else:
+                    return False, "MIPOPEN error code: " + str(result_code)
+        except Exception as e:
+            return False, "MIPOPEN URC timeout: " + str(e)
+
+        return False, "MIPOPEN no response"
+
+    def _send_chunk(self, sock_id, chunk):
+        """
+        Send a single chunk via MIPSEND.
+        Thread safety is provided by the outer network_manager.get_uart_lock().
+        NOTE: Do NOT add self.at._cmd_lock here — self.at.send() already
+        acquires it internally and threading.Lock is not reentrant.
+        Returns (success, error_msg)
+        """
+        chunk_len = len(chunk)
+
+        # Step 1: Send AT+MIPSEND command and wait for ">" prompt
+        cmd = "AT+MIPSEND=" + str(sock_id) + "," + str(chunk_len)
+        try:
+            resp = self.at.send(cmd, ">", 3000)
+            if ">" not in resp:
+                return False, "MIPSEND no > prompt: " + resp
+        except Exception as e:
+            return False, "MIPSEND > prompt error: " + str(e)
+
+        # Step 2: Write raw binary bytes directly to UART
+        # Must be done immediately after ">" prompt, no lock re-acquisition
+        try:
+            self.at.uart.write(chunk)
+        except Exception as e:
+            return False, "MIPSEND write error: " + str(e)
+
+        # Step 3: Wait for OK or SEND OK confirmation
+        try:
+            confirm_resp = self.at.send("", "OK", 8000)
+            if self._is_error(confirm_resp):
+                return False, "MIPSEND confirmation error: " + confirm_resp
+        except Exception as e:
+            return False, "MIPSEND confirmation timeout: " + str(e)
+
+        return True, ""
+
+    def _send_data(self, sock_id, data):
+        """
+        Send data in chunks via MIPSEND.
+        Returns (success, error_msg)
+        """
+        total_len = len(data)
+        offset = 0
+        chunk_num = 0
+
+        while offset < total_len:
+            end = min(offset + SEND_CHUNK, total_len)
+            chunk = data[offset:end]
+
+            ok, err = self._send_chunk(sock_id, chunk)
+            if not ok:
+                return False, "Chunk " + str(chunk_num) + " failed: " + err
+
+            chunk_num += 1
+            offset = end
+
+            if chunk_num % 10 == 0 or offset >= total_len:
+                self._log(
+                    "send progress: "
+                    + str(offset) + "/" + str(total_len)
+                    + " bytes (" + str(chunk_num) + " chunks)"
+                )
+
+        self._log("all data sent: " + str(chunk_num) + " chunks, " + str(total_len) + " bytes")
+        return True, ""
+
+    def _wait_for_response(self, sock_id, timeout_ms=30000):
+        """
+        Wait for +MIPURC: "rtcp" response.
+        Returns (success, status_code, body, error_msg)
+        """
+        pattern = r'\+MIPURC:\s*"rtcp",\s*' + str(sock_id) + r',\s*(\d+),'
+        t0 = time.ticks_ms()
+
+        while time.ticks_diff(time.ticks_ms(), t0) < timeout_ms:
+            try:
+                # Try to get response with short timeout
+                resp = self.at.send("", "+MIPURC:", 1000)
+                m = re.search(pattern, resp)
+                if m:
+                    data_len = int(m.group(1))
+                    # Extract HTTP response data after the URC header
+                    # Format: +MIPURC: "rtcp",<sock_id>,<len>,<data>
+                    urc_end = resp.find("+MIPURC:")
+                    if urc_end >= 0:
+                        # Find the data after the length field
+                        match_end = m.end()
+                        http_data = resp[match_end:match_end + data_len]
+
+                        # Parse HTTP status line
+                        status_match = re.search(r"HTTP/\d\.\d\s+(\d+)", http_data)
+                        status_code = int(status_match.group(1)) if status_match else None
+
+                        # Extract body (after headers)
+                        header_end = http_data.find("\r\n\r\n")
+                        if header_end >= 0:
+                            body = http_data[header_end + 4:]
+                        else:
+                            body = http_data
+
+                        return True, status_code, body, ""
+            except Exception:
+                pass
+
+            time.sleep_ms(100)
+
+        return False, None, "", "Response timeout"
+
+    def _build_http_request(self, host, body_bytes):
+        """
+        Build full HTTP POST request as bytes.
+        """
+        headers = (
+            "POST / HTTP/1.1\r\n"
+            "Host: " + host + "\r\n"
+            "Content-Type: multipart/form-data; boundary=" + BOUNDARY + "\r\n"
+            "Content-Length: " + str(len(body_bytes)) + "\r\n"
+            "Connection: close\r\n"
+            "\r\n"
+        )
+        return headers.encode("utf-8") + body_bytes
+
+    # ============================================================ public API
+    def upload_file(self, file_path, upload_url, upload_token, key):
+        """Generic file upload to Qiniu cloud via 4G TCP socket POST.
+
+        Args:
+            file_path: Local path to any file
+            upload_url: Qiniu upload URL
+            upload_token: Qiniu upload token
+            key: File key in Qiniu bucket
+
+        Returns:
+            dict with 'success' bool and 'key'/'error' fields
+        """
+        return self.upload_image(file_path, upload_url, upload_token, key)
+
+    def upload_image(self, image_path, upload_url, upload_token, key):
+        """
+        Upload image to Qiniu cloud via 4G TCP socket POST.
+
+        Args:
+            image_path: Local path to image file
+            upload_url: Qiniu upload URL (e.g., "https://upload.qiniup.com")
+            upload_token: Qiniu upload token
+            key: File key in Qiniu (e.g., "shootPic/device01/shoot01.png")
+
+        Returns:
+            dict with 'success' bool and 'key'/'error' fields
+        """
+        if not self.at:
+            return {"success": False, "error": "AT client not available"}
+
+        if not os.path.exists(image_path):
+            return {"success": False, "error": "Image file not found: " + image_path}
+
+        # Force HTTP for 4G module (extract hostname, use port 80)
+        parsed = urlparse(upload_url)
+        host = parsed.hostname
+        if not host:
+            return {"success": False, "error": "Invalid upload URL: " + upload_url}
+
+        if upload_url.lower().startswith("https://"):
+            self._log_info("Converted HTTPS->HTTP for 4G module")
+
+        file_size = os.path.getsize(image_path)
+        self._log_info(
+            "upload: " + image_path + " (" + str(file_size) + "B) -> "
+            + host + " key=" + key
+        )
+
+        from network import network_manager
+        with network_manager.get_uart_lock():
+            try:
+                # ---- Step 1: Ensure PDP context ----
+                ok_pdp, ip = self._ensure_pdp()
+                if not ok_pdp:
+                    return {"success": False, "error": "PDP not ready (ip=" + str(ip) + ")"}
+
+                # ---- Step 2: Close old socket ----
+                self._close_socket(UPLOAD_SOCK_ID)
+
+                # ---- Step 3: Open TCP socket ----
+                ok, err = self._open_socket(UPLOAD_SOCK_ID, host, 80)
+                if not ok:
+                    return {"success": False, "error": "Socket open failed: " + err}
+
+                try:
+                    # ---- Step 4: Build multipart body and HTTP request ----
+                    body = self._build_multipart_body(image_path, upload_token, key)
+                    http_request = self._build_http_request(host, body)
+                    self._log("HTTP request size: " + str(len(http_request)) + " bytes")
+
+                    # ---- Step 5: Send data via MIPSEND ----
+                    ok, err = self._send_data(UPLOAD_SOCK_ID, http_request)
+                    if not ok:
+                        return {"success": False, "error": "Send failed: " + err}
+
+                    # ---- Step 6: Wait for response ----
+                    ok, status_code, resp_body, err = self._wait_for_response(UPLOAD_SOCK_ID)
+                    if not ok:
+                        return {"success": False, "error": "Response error: " + err}
+
+                    # ---- Step 7: Parse response ----
+                    if status_code is None:
+                        return {"success": False, "error": "No HTTP status in response"}
+
+                    if 200 <= status_code < 300:
+                        try:
+                            resp_json = json.loads(resp_body)
+                            resp_key = resp_json.get("key", key)
+                            self._log_info("upload success: key=" + resp_key + " code=" + str(status_code))
+                            return {"success": True, "key": resp_key}
+                        except Exception as e:
+                            self._log_error("response parse error: " + str(e))
+                            return {
+                                "success": True,
+                                "key": key,
+                                "raw": resp_body,
+                            }
+                    else:
+                        self._log_error(
+                            "HTTP error: code=" + str(status_code) + " body=" + resp_body[:200]
+                        )
+                        return {
+                            "success": False,
+                            "error": "HTTP " + str(status_code),
+                            "response": resp_body,
+                        }
+
+                finally:
+                    # ---- Step 8: Always close socket ----
+                    self._close_socket(UPLOAD_SOCK_ID)
+
+            except Exception as e:
+                self._log_error("upload exception: " + str(e))
+                return {"success": False, "error": str(e)}
+
+
+# ====================================================================== demo
+if __name__ == "__main__":
+    # Demo usage — requires actual ML307R 4G module hardware to run.
+    print("FourGUploadManager - requires ML307R 4G module hardware")
+    print()
+    print("Usage:")
+    print("  from hardware import hardware_manager")
+    print("  from at_client import ATClient")
+    print("  from maix import uart")
+    print()
+    print("  # Initialize UART and AT client (normally done in hardware init)")
+    print("  uart4g = uart.UART('/dev/ttyS1', 115200, ...)")
+    print("  at_client = ATClient(uart4g)")
+    print("  at_client.start()")
+    print()
+    print("  # Upload image to Qiniu")
+    print("  uploader = FourGUploadManager(at_client)")
+    print("  result = uploader.upload_image(")
+    print("      image_path='/maixapp/apps/t11/shoot.png',")
+    print("      upload_url='https://upload.qiniup.com',")
+    print("      upload_token='<qiniu_upload_token>',")
+    print("      key='shootPic/device01/shoot01.png'")
+    print("  )")
+    print("  print('Upload result:', result)")

adc.py

@@ -4,14 +4,13 @@ from maix import time
 a = adc.ADC(0, adc.RES_BIT_12)
 
 while True:
-    raw_data = a.read()
-    print(f"ADC raw data:{raw_data}")
+    # raw_data = a.read()
+    # print(f"ADC raw data:{raw_data}")
     # if raw_data > 2450:
     #     print(f"ADC raw data:{raw_data}")
     # elif raw_data < 2000:
     #     print(f"ADC raw data:{raw_data}")
-    # time.sleep_ms(50)
+    time.sleep_ms(1)
 
-    # vol = a.read_vol()
-
-    # print(f"ADC vol:{vol}")
+    vol = int(a.read_vol() * 10) / 10
+    print(f"ADC vol:{vol:.1f}, {time.time():.4f}")

app.yaml

@@ -1,25 +1,36 @@
 id: t11
 name: t11
-version: 1.2.10
+version: 2.16.4
 author: t11
 icon: ''
 desc: t11
 files:
+  - 4g_download_manager.py
+  - 4g_upload_manager.py
   - app.yaml
   - archery_netcore.cpython-311-riscv64-linux-gnu.so
   - at_client.py
   - camera_manager.py
+  - cameraParameters.xml
   - config.py
   - hardware.py
+  - laser_detector.py
   - laser_manager.py
   - logger_manager.py
   - main.py
   - network.py
+  - ota_curl.sh
   - ota_manager.py
   - power.py
+  - server.pem
   - shoot_manager.py
   - shot_id_generator.py
+  - target_roi_yolo.py
   - time_sync.py
+  - triangle_positions.json
+  - triangle_target.py
   - version.py
-  - vision.cpython-311-riscv64-linux-gnu.so
+  - vision.py
+  - wifi_config_httpd.py
   - wifi.py
+  - wpa_supplicant_conf.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
-            self._waiting = True
-            self._expect = expect_b
-            self._resp = b""
+            with self._q_lock:
+                # 初始化等待
+                self._waiting = True
+                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:
-                        self._rx = self._rx[-4096:]
+                    if len(self._rx) > 32768:
+                        self._rx = self._rx[-16384:]
 
 
 

cameraParameters.xml

@@ -0,0 +1,33 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<calibrationDate>"Sat Apr 11 12:05:27 2026"</calibrationDate>
+<framesCount>29</framesCount>
+<cameraResolution>
+  640 480</cameraResolution>
+<camera_matrix type_id="opencv-matrix">
+  <rows>3</rows>
+  <cols>3</cols>
+  <dt>d</dt>
+  <data>
+    2207.9058323074869 0. 328.90661220953149 0. 2207.9058323074869
+    205.49515894111076 0. 0. 1.</data></camera_matrix>
+<camera_matrix_std_dev type_id="opencv-matrix">
+  <rows>4</rows>
+  <cols>1</cols>
+  <dt>d</dt>
+  <data>
+    0. 11.687428265309892 3.6908895632668468 3.597571733110271</data></camera_matrix_std_dev>
+<distortion_coefficients type_id="opencv-matrix">
+  <rows>1</rows>
+  <cols>5</cols>
+  <dt>d</dt>
+  <data>
+    -0.63036604771649651 3.3832710000807449 0. 0. -0.45113389267675552</data></distortion_coefficients>
+<distortion_coefficients_std_dev type_id="opencv-matrix">
+  <rows>5</rows>
+  <cols>1</cols>
+  <dt>d</dt>
+  <data>
+    0.025002349846111244 1.0651877135605927 0. 0. 0.04021252864120229</data></distortion_coefficients_std_dev>
+<avg_reprojection_error>0.28992233810828955</avg_reprojection_error>
+</opencv_storage>

config.py

@@ -9,68 +9,77 @@ from version import VERSION
 # ==================== 应用配置 ====================
 APP_VERSION = VERSION
 APP_DIR = "/maixapp/apps/t11"
-LOCAL_FILENAME = "/maixapp/apps/t11/main_tmp.py"
+LOCAL_FILENAME = APP_DIR + "/main_tmp.py"
+
+# ==================== 相机配置 ====================
+# 相机初始化分辨率（CameraManager / main.py 使用）
+CAMERA_WIDTH = 640
+CAMERA_HEIGHT = 480
+
+# 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
+TRIANGLE_DETECT_SCALE = 0.4
 
 # ==================== 服务器配置 ====================
 # 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
 WIFI_QUALITY_RTT_BAD_MS = 600.0  # 中位数超过此值认为延迟过高
 WIFI_QUALITY_RTT_WARN_MS = 350.0  # 与 RSSI 联合：超过此值且信号弱也判为差
 WIFI_QUALITY_RSSI_BAD_DBM = -80.0  # 低于此 dBm（更负更差）视为信号弱
-WIFI_QUALITY_USE_RSSI = True  # 是否把 RSSI 纳入综合判定（False 则仅看 RTT）
+WIFI_QUALITY_USE_RSSI = True  # 是否把 RSSI 纳入综合判定
 
 # WiFi 热点配网（手机连设备 AP，浏览器提交路由器 SSID/密码；仅 GET/POST，标准库 socket）
-WIFI_CONFIG_AP_FALLBACK = True # # WiFi 配网失败时，是否退回热点模式，并等待重新配网
+WIFI_CONFIG_AP_FALLBACK = False # # WiFi 配网失败时，是否退回热点模式，并等待重新配网
 WIFI_AP_FALLBACK_WAIT_SEC = 5 # 等待5秒后再检测STA/4G
 WIFI_CONFIG_AP_TIMEOUT = 5  # 热点模式超时时间（秒）
-WIFI_CONFIG_AP_ENABLED = True  # True=启动时开热点并起迷你 HTTP 配网服务
+WIFI_CONFIG_AP_ENABLED = False  # True=启动时开热点并起迷你 HTTP 配网服务
 WIFI_CONFIG_AP_SSID = "ArcherySetup"       # 设备发出的热点名称
 WIFI_CONFIG_AP_PASSWORD = "12345678"       # 热点密码（WPA2 通常至少 8 位）
 WIFI_CONFIG_HTTP_HOST = "0.0.0.0"          # HTTP 监听地址
 WIFI_CONFIG_HTTP_PORT = 8080               # 默认 8080，避免占用 80 需 root
 WIFI_CONFIG_AP_IP = "192.168.66.1"         # 与 MaixPy Wifi.start_ap 默认一致，手机访问 http://192.168.66.1:8080/
-
+                                           # 这个地址需要和 /boot/wifi.ipv4_prefix 配合，才能正确访问。
+                                           # 比如说 /boot/wifi.ipv4_prefix 需要写成 192.168.66 
 # ===== TCP over SSL(TLS) 配置 =====
-USE_TCP_SSL = False          # True=按手册走 MSSLCFG/MIPCFG 绑定 SSL
+USE_TCP_SSL = True          # True=按手册走 MSSLCFG/MIPCFG 绑定 SSL
 TCP_LINK_ID = 2             # 
-TCP_SSL_PORT = 443          # TLS 端口（不一定必须 443，以服务器为准）
+TCP_SSL_PORT = 50006          # TLS 端口（不一定必须 443，以服务器为准）
 
 # SSL profile
 SSL_ID = 1                  # ssl_id=1
-SSL_AUTH_MODE = 0           # 1=单向认证（验证服务器），2=双向
+SSL_AUTH_MODE = 1           # 1=单向认证（验证服务器），2=双向
 SSL_VERIFY_MODE = 1         # 0=不验（仅测试用）；1=写入并使用 CA 证书
 
-SSL_CERT_FILENAME = "www.shelingxingqiu.com.crt"      # 模组里证书名（MSSLCERTWR / MSSLCFG="cert" 用）
-SSL_CERT_PATH = "/root/www.shelingxingqiu.com.crt"    # 设备文件系统里 CA 证书路径（你自己放进去）
+SSL_CERT_FILENAME = "server.pem"      # 模组里证书名（MSSLCERTWR / MSSLCFG="cert" 用）
+SSL_CERT_PATH = APP_DIR + "/server.pem"    # 设备文件系统里 CA 证书路径（你自己放进去）
 # MIPOPEN 末尾的参数在不同固件里含义可能不同；按你手册例子保留
 MIPOPEN_TAIL = ",,0"
 
 # ==================== 文件路径配置 ====================
 CONFIG_FILE = "/root/laser_config.json"
-LOG_FILE = "/maixapp/apps/t11/app.log"
-BACKUP_BASE = "/maixapp/apps/t11/backups"
+LOG_FILE = APP_DIR + "/app.log"
+BACKUP_BASE = APP_DIR + "/backups"
 
 # ==================== 硬件配置 ====================
-# WiFi模块开关（True=有WiFi模块，False=无WiFi模块）
-HAS_WIFI_MODULE = True  # 根据实际硬件情况设置
-
 # UART配置
 UART4G_DEVICE = "/dev/ttyS2"
 UART4G_BAUDRATE = 115200
 DISTANCE_SERIAL_DEVICE = "/dev/ttyS1"
 DISTANCE_SERIAL_BAUDRATE = 9600
 
-# I2C配置（根据WiFi模块开关自动选择）
-# 无WiFi模块：I2C_BUS_NUM = 1，引脚：P18(I2C1_SCL), P21(I2C1_SDA)
-# 有WiFi模块：I2C_BUS_NUM = 5，引脚：A15(I2C5_SCL), A27(I2C5_SDA)
-I2C_BUS_NUM = 5 if HAS_WIFI_MODULE else 1
+# I2C：板载 WiFi 方案固定 I2C5，引脚 A15(SCL) / A27(SDA)，供 INA226 等
+I2C_BUS_NUM = 5
 
 INA226_ADDR = 0x40
+# False=完全不访问 INA226（无电源计量板或未供电时避免 ~2.5s writeto 重试与底层 write failed 日志）；量产有芯片时设为 True
+INA226_ENABLE = True
+# True=整总线 I2C scan 探测 INA226（在部分平台上极慢，可达 ~90s+）；False=仅对 INA226_ADDR 快速探测（writeto 空写）
+INA226_PROBE_FULL_BUS_SCAN = False
 REG_CONFIGURATION = 0x00
 REG_BUS_VOLTAGE = 0x02
 REG_CURRENT = 0x04  # 电流寄存器
@@ -95,7 +104,7 @@ DEFAULT_LASER_POINT =  (320, 245)   # 默认激光中心点
 
 # 硬编码激光点配置
 HARDCODE_LASER_POINT = True  # 是否使用硬编码的激光点（True=使用硬编码值，False=使用校准值）
-HARDCODE_LASER_POINT_VALUE = (320, 245) # 硬编码的激光点坐标(315, 245) #   # 硬编码的激光点坐标 (x, y)
+HARDCODE_LASER_POINT_VALUE = (320, 296) # 硬编码的激光点坐标(315, 245) #   # 硬编码的激光点坐标 (x, y)
 
 # 激光点检测配置
 LASER_DETECTION_THRESHOLD = 140  # 红色通道阈值（默认120，可调整，范围建议：100-150）
@@ -122,7 +131,176 @@ LASER_CAMERA_OFFSET_CM = 1.4  # 激光在摄像头下方的物理距离（厘米
 IMAGE_CENTER_X = 320  # 图像中心 X 坐标
 IMAGE_CENTER_Y = 240  # 图像中心 Y 坐标
 
-FLASH_LASER_WHILE_SHOOTING = True  # 是否在拍摄时闪一下激光（True=闪，False=不闪）
+# ==================== 三角形四角标记：单应性偏移 + PnP 估距 ====================
+# 依赖 cameraParameters.xml（相机内参）与 triangle_positions.json（四角物方坐标，厘米或毫米见 JSON 约定）。
+# 部署时请把这两个文件放到 APP_DIR（与 main 同应用目录），或改下面路径为设备上的实际绝对路径。
+USE_TRIANGLE_OFFSET = False  # False 时仅走黄心圆/椭圆 + 半径估距，不使用三角形路径
+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
+TRIANGLE_SIZE_RANGE = (8, 500)
+# PnP 距离合理性检查（可选）：超出范围时认为本次检测有误，回退圆心算法
+# 设为 0 表示不启用（主要防线是单应矩阵 sx/sy 比值检查，无需提前知道距离）
+# 如果射箭距离很固定，可设具体范围（如 min=2.5, max=6.0）作为额外保险
+TRIANGLE_DISTANCE_MIN_M = 0.0   # 0=不启用下限检查
+TRIANGLE_DISTANCE_MAX_M = 0.0   # 0=不启用上限检查
+# 三角形检测兜底增强：CLAHE（更鲁棒但更慢）。颜色阈值修复后通常不需要，保持关闭以优先速度。
+TRIANGLE_ENABLE_CLAHE_FALLBACK = False
+# 三角形检测调试：保存 Otsu 二值化图像（临时调试用，定位后关闭）
+TRIANGLE_SAVE_DEBUG_IMAGE = False
+# 三角形颜色过滤阈值（三角形内部灰度判定）
+# 如果三角形标记印刷较浅/环境较亮，可放宽：
+#   max_interior_gray: 三角形内部平均灰度上限（越大越宽松，90→130 适应浅色印刷）
+#   dark_pixel_gray: "暗像素"灰度判定阈值（越大越宽松，80→130）
+#   min_dark_ratio: 暗像素占比下限（越小越宽松，0.70→0.30）
+TRIANGLE_MAX_INTERIOR_GRAY = 130
+TRIANGLE_DARK_PIXEL_GRAY = 130
+TRIANGLE_MIN_DARK_RATIO = 0.30
+# 三角形相对对比度阈值：内部比周围暗多少灰度值才认为有效（0=禁用相对对比度）
+TRIANGLE_MIN_CONTRAST_DIFF = 15
+# 三角形形状约束容差（等腰直角判定松紧度）
+# 增大可容忍轮廓轻微变形（印刷不均、阴影局部切角），减少"差一点点就失败"的漏检
+# 建议范围：0.20（原始/严格） ~ 0.30（宽松）；超过 0.35 容易误检非三角形
+TRIANGLE_SHAPE_LEG_TOLERANCE = 0.25    # 两直角边长度比例容差（原 0.20）
+TRIANGLE_SHAPE_HYP_TOLERANCE = 0.25    # 斜边与期望长度比例容差（原 0.20）
+TRIANGLE_SHAPE_COS_TOLERANCE = 0.25    # 直角余弦绝对值上限（原 0.20，越小越严格）
+# 三角形检测主超时（毫秒）：join 等待子线程的最长时间。
+# 整段 try_triangle_scoring 含「多路径二值化 + C(n,4) 四角评分 + 单应性 + PnP」，往往比黄心圆检测慢。
+# 建议设为实测最坏耗时的 1.2 倍；超时后圆心检测仍会并行跑完，跑完后若三角形已结束则优先用三角形。
+TRIANGLE_TIMEOUT_MS = 1000
+# True=打印各阶段耗时(ms)，用于定位瓶颈；稳定后可 False 减少日志
+TRIANGLE_TIMING_LOG = True
+# True=Stage2 每个子框内传统三角失败时打一条统计（Otsu/Adaptive 下轮廓数与各拒绝原因计数）
+TRIANGLE_LOG_STAGE2_PATCH_REJECT = True
+
+# 仅检出 3 个真实三角时：是否在预测位置附近做小 ROI（Otsu/adaptive）再搜第 4 个真实三角。
+# False=跳过该搜索，直接用几何推算的虚拟第 4 点（offset_method=triangle_homography_3pt），省 ~10~120ms；若实测偏移可接受可关。
+TRIANGLE_FOURTH_ROI_SEARCH_ENABLE = False
+
+# ── 轻量锐化（Unsharp Mask）──────────────────────────────────────────────────
+# 目的：轻度/中度模糊时增强边缘，让三角形轮廓更易被 approxPolyDP 检出。
+# 严重运动模糊时反而会放大噪声，建议搭配 sharpness 检测自动触发（见下）。
+# YOLO 裁切后图已较清晰时可 False，省去 Unsharp 开销并减轻振铃。
+TRIANGLE_SHARPEN_ENABLE = False     # False=关闭锐化（彻底跳过计算，最省时）
+# 仅当帧清晰度（Laplacian 方差）低于此值时才锐化；高于此值说明图片本身够清晰，不动
+# 0=总是锐化；建议 50~150；对应日志中 [TRI] sharpness=xxx
+TRIANGLE_SHARPEN_THRESHOLD = 0.0  # 0=总是锐化（不做 Laplacian 判断，省去计算）
+# Unsharp Mask 高斯核 sigma（越大锐化越强，通常 1.0~3.0）
+TRIANGLE_SHARPEN_SIGMA = 2.0
+# Unsharp Mask 强度系数（越大锐化越猛，通常 1.2~2.0；>2 易产生振铃）
+TRIANGLE_SHARPEN_STRENGTH = 1.5
+
+# 三角形检测用灰度来源（ROI 裁切、缩放到 img_det 之后；与 vision 一致按 RGB 输入）
+# rgb — 常规 cv2.cvtColor RGB2GRAY
+# v_suppress — HSV 的 V：亮度 >= TRIANGLE_HSV_V_SUPPRESS_ABOVE 的像素灰度强制为 255，压制黄/红/蓝等亮环后再走原有 Otsu 流水线
+# fallback_v_suppress — 先用 rgb 跑 detect；若检出三角形 <3，再用 v_suppress 重跑一遍（省平均耗时，坏帧可多救一点）
+# try_both — rgb 与 v_suppress 各完整跑一遍 detect_triangle_markers，取检出数更多一侧（平局保留 rgb）；耗时约 2 倍，用于对比效果
+TRIANGLE_GRAY_MODE = "v_suppress"
+TRIANGLE_HSV_V_SUPPRESS_ABOVE = 200  # 0~255；偏高则环残留多，偏低则可能伤到暗三角边缘，建议 180~220 扫一圈
+
+# 三角形检测性能/鲁棒性参数（偏向速度的默认值）
+# 说明：
+# - Otsu 是最快的全局阈值；adaptiveThreshold 更鲁棒但更慢
+# - filtered 候选过多时，枚举 C(n,4) 会变慢，需限幅
+TRIANGLE_EARLY_EXIT_CANDIDATES = 3          # 找到3个候选即停（第4个由几何推算）；原来4需跑完全adaptive
+TRIANGLE_ADAPTIVE_BLOCK_SIZES = (11,)       # 只用1个block_size；原(11,21)跑两遍adaptive
+TRIANGLE_MAX_FILTERED_FOR_COMBO = 10        # 参与四点组合评分的最大候选数（超过则截断到最可能的一部分）
+
+# ROI 局部阈值：四个象限各自 Otsu（+ 可选 ROI 内 adaptive），再合并候选。
+# 顺序：紧接在全局 Otsu 之后、整图 adaptive 之前（见 triangle_target.detect_triangle_markers）。
+# 用途：阴阳脸/大阴影下往往比「先整图 adaptive」更省时间且更稳；整图 adaptive 最慢，作补充。
+#
+# YOLO 已裁到靶区时，整幅小图上单一全局 Otsu 容易把环与四角揉在一个阈值里；可跳过第一轮「全局轮廓提取」，
+# 直接进入下面四象限 ROI Otsu（仍会算全局 b_otsu 供 relaxed approxPolyDP 回退）。整图模式勿开。
+TRIANGLE_SKIP_GLOBAL_OTSU_EXTRACT_ON_YOLO_ROI = True
+
+TRIANGLE_ROI_ENABLED = False
+TRIANGLE_ROI_MIN_CANDIDATES = 3  # 候选数低于此值时启用 ROI 局部阈值（需至少 3 个点才能三角解算）
+TRIANGLE_ROI_OVERLAP_RATIO = 0.08  # 象限 ROI 的重叠比例（避免角标落在分割边界被切断）
+TRIANGLE_ROI_USE_ADAPTIVE = False  # ROI 内关闭 adaptive（只跑ROI Otsu，省去4×adaptive）；遇到阴阳脸再开
+
+# 多路径融合：不同二值化路径若得到相近中心（dedup 格点），累加 path_votes，后续优先参与四点组合。
+TRIANGLE_MULTI_PATH_VOTE = True
+
+# 失败回退（仍不足 TRIANGLE_FALLBACK_MIN_CANDIDATES 时按序尝试，每条仅在前序仍不足时执行）
+TRIANGLE_FALLBACK_MIN_CANDIDATES = 3
+# 对同一幅 Otsu 二值图用更宽松的 approxPolyDP，找回被“切角”的轮廓
+TRIANGLE_FALLBACK_RELAXED_EPS = True
+TRIANGLE_RELAXED_POLY_EPS_SCALE = 1.65
+# Black-hat（顶帽逆）：突出比周围暗的斑块，再 Otsu；对阴影/照度不均往往有效，略慢于纯 Otsu
+TRIANGLE_FALLBACK_BLACKHAT = True
+TRIANGLE_BLACKHAT_KERNEL_FRAC = 0.018  # 核大小 ≈ min(h,w)*frac，取奇数，范围约 [7, 31]
+
+# ── 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"
+# 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_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。
+TRIANGLE_YOLO_RETRY_ON_EMPTY = True
+TRIANGLE_YOLO_RETRY_CONF_TH = 0.5
+TRIANGLE_YOLO_ROI_MARGIN_FRAC = 0.11
+# union: 所有候选框外接矩形（一类多框：环+四角）；largest: 只取面积最大的框
+TRIANGLE_YOLO_ROI_MERGE_MODE = "union"
+# native: Maix 已将框映射到相机分辨率；letterbox: 框在网络输入坐标需逆变换（重复映射会出细条 ROI）
+TRIANGLE_YOLO_COORD_MODE = "native"
+# 参与 ROI 合并前丢弃过小的框（低 conf 时边角 1×1 假阳性）
+TRIANGLE_YOLO_MIN_BOX_SIDE_PX = 8
+TRIANGLE_YOLO_REJECT_BAD_ROI = True
+# try_triangle_scoring 收到 ROI 后裁剪的最小边长（像素），过小则退回整图
+TRIANGLE_CROP_ROI_MIN_SIDE_PX = 64
+# 射箭保存图 / 预览上绘制 YOLO 靶环 ROI 矩形 (x0,y0,x1,y1)，核对是否裁准；不需要时改 False
+TRIANGLE_YOLO_DRAW_ROI_ON_SHOT = True
+# 物方采样调试：以靶心为中心，取半径 15cm 的圆周样本点，用于黑/白颜色对比
+TRIANGLE_SAMPLE_RADIUS_CM = 15.0
+TRIANGLE_SAMPLE_ANGLES_DEG = (0, 90, 180, 270)
+TRIANGLE_SAMPLE_PATCH_HALF_PX = 2
+# 开机阶段预加载 YOLO detector；detect 使用 dual_buff=False，避免返回上一帧结果。
+TRIANGLE_YOLO_PRELOAD_ON_BOOT = False
+
+# ── 第二段 YOLO：仅在 Stage1 裁切出的靶环图上推理（与合成 stage2 训练数据一致）→ 子框内传统算法取直角点 ──
+# Stage1 靶环裁切内如何找黑三角标记（对比耗时时可切换）：
+# "yolo" — 调 Stage2 黑三角模型得子框，再子框内传统提取（需 TRIANGLE_BLACK_YOLO_ENABLE=True）。
+# "traditional" — 不调 Stage2 模型；仅在 Stage1 ROI 整幅上跑传统 detect_triangle_markers（与 yolo 路径对比用）。
+TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE = "traditional"
+# True 时每箭另打一枪端到端耗时：yolo_ring + yolo_black + try_triangle_scoring 墙钟（毫秒）
+TRIANGLE_LOG_E2E_TIMING = True
+TRIANGLE_BLACK_YOLO_ENABLE = True
+TRIANGLE_BLACK_YOLO_MODEL_PATH = APP_DIR + "/model_270820.mud"
+TRIANGLE_BLACK_YOLO_CLASS_IDS = (0,)
+TRIANGLE_BLACK_YOLO_CONF_TH = 0.5
+TRIANGLE_BLACK_YOLO_IOU_TH = 0.45
+# Maix YOLOv5 detect 返回的框已映射到传入的 Stage1 裁切图坐标；contain/letterbox 是模型内部预处理。
+TRIANGLE_BLACK_YOLO_COORD_MODE = "native"
+# 子框相对 YOLO 框的扩展（在靶环裁切图坐标系下），利于传统算法取边
+TRIANGLE_BLACK_YOLO_BOX_MARGIN_FRAC = 0.08
+TRIANGLE_BLACK_YOLO_MIN_BOX_SIDE_PX = 6.0
+# 子框传统检测不足 3 个时是否回退为「整幅靶环 ROI」上的原 detect_triangle_markers
+TRIANGLE_BLACK_YOLO_FALLBACK_ON_PATCH_FAIL = True
+# Stage2 子框内传统提取使用的灰度（有缩略时默认在 Stage1 全分辨率灰度上切片）：
+# "rgb" — 仅用 RGB→灰度（不再做 Unsharp、不做 V 抑制），最省 CPU（推荐子框已对准黑三角时）。
+# "global" — 与整幅 ROI 三角流程同一张 gray（含 TRIANGLE_GRAY_MODE 的 v_suppress 与锐化）；更稳但更耗时。
+TRIANGLE_BLACK_YOLO_PATCH_GRAY_SOURCE = "rgb"
+# Stage2 子框内轮廓→三角形：approxPolyDP 的 ε=周长×FRAC×mult。边模糊时略增大 FRAC 或保留多级 mult。
+TRIANGLE_PATCH_APPROXPOLY_FRAC = 0.055
+TRIANGLE_PATCH_APPROXPOLY_RELAX_MULTS = (1.0, 1.3, 1.65)
+# Otsu/Adaptive 前对子框灰度轻模糊：0=关闭；3 或 5=Gaussian ksize（须为奇数），压锯齿利于收成 3 顶点
+TRIANGLE_PATCH_PRE_BLUR_KSIZE = 0
+TRIANGLE_BLACK_YOLO_PRELOAD_ON_BOOT = True
+# 每箭是否在日志中打印黑三角 detect 统计（raw/类过滤/是否在环内）；调通后可 False 减日志
+TRIANGLE_BLACK_YOLO_LOG_EACH_SHOT = True
+# True=每次射箭将 Stage1 裁切图（黑三角模型输入）存为 JPEG；调试用，量产请 False
+TRIANGLE_BLACK_YOLO_SAVE_ROI_CROP = True
+# 存盘目录；空字符串表示使用 PHOTO_DIR + "/stage2_roi"
+TRIANGLE_BLACK_YOLO_ROI_CROP_DIR = ""
+# 存盘 JPEG 上绘制 Stage2（黑三角 YOLO）最终子框（绿框 + s2_0… 标签）
+TRIANGLE_BLACK_YOLO_SAVE_ROI_DRAW_BOXES = True
+
+FLASH_LASER_WHILE_SHOOTING = False  # 是否在拍摄时闪一下激光（True=闪，False=不闪）
 FLASH_LASER_DURATION_MS = 1000  # 闪一下激光的持续时间（毫秒）
 
 # ==================== 显示配置 ====================
@@ -130,10 +308,20 @@ LASER_COLOR = (0, 255, 0)  # RGB颜色
 LASER_THICKNESS = 1
 LASER_LENGTH = 2
 
+# ==================== 队列大小限制（防止内存泄漏） ====================
+MAX_SEND_QUEUE_SIZE = 500       # 发送队列上限
+MAX_TCP_PAYLOADS = 500          # AT TCP 载荷缓存上限
+MAX_HTTP_EVENTS = 200           # AT HTTP 事件缓存上限
+LOG_QUEUE_MAXSIZE = 10000       # 日志队列上限
+MAX_CMD_THREADS = 10            # 并发命令线程上限（防止服务器下发命令时无限创建线程）
+
 # ==================== 图像保存配置 ====================
-SAVE_IMAGE_ENABLED = True  # 是否保存图像（True=保存，False=不保存）
+SAVE_IMAGE_ENABLED = False  # 是否保存图像（True=保存，False=不保存）
+SAVE_IMAGE_ON_FAILURE = True  # 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
+# 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
 
@@ -142,67 +330,20 @@ MAX_BACKUPS = 5
 LOG_MAX_BYTES = 10 * 1024 * 1024  # 10MB
 LOG_BACKUP_COUNT = 5
 
-# ==================== 引脚映射配置 ====================
-# 无WiFi模块的引脚映射（I2C1）
-PIN_MAPPINGS_NO_WIFI = {
-    "A18": "UART1_RX",
-    "A19": "UART1_TX",
-    "A29": "UART2_RX",
-    "A28": "UART2_TX",
-    "P18": "I2C1_SCL",
-    "P21": "I2C1_SDA",
-}
-
-# 有WiFi模块的引脚映射（I2C5）
-PIN_MAPPINGS_WITH_WIFI = {
+# ==================== 引脚映射配置（板载 WiFi，I2C5）====================
+PIN_MAPPINGS = {
     "A18": "UART1_RX",
     "A19": "UART1_TX",
     "A29": "UART2_RX",
     "A28": "UART2_TX",
     "A15": "I2C5_SCL",
     "A27": "I2C5_SDA",
-    "A24": "GPIOA24", # 电源板的引脚
+    "A24": "GPIOA24",  # 电源板关机控制
 }
 
-# 根据WiFi模块开关选择引脚映射
-PIN_MAPPINGS = PIN_MAPPINGS_WITH_WIFI if HAS_WIFI_MODULE else PIN_MAPPINGS_NO_WIFI
-
-# ==================== ArUco标定配置 ====================
-USE_ARUCO = False  # 是否使用ArUco标定（True=使用ArUco，False=使用传统黄色靶心检测）
-
-# ArUco标记配置
-if USE_ARUCO:
-    import cv2
-    ARUCO_DICT_TYPE = cv2.aruco.DICT_4X4_50  # ArUco字典类型
-    ARUCO_MARKER_SIZE_MM = 40  # ArUco标记边长（毫米）
-    ARUCO_MARKER_IDS = [0, 1, 2, 3]  # 四个角的ArUco标记ID
-
-    # 靶纸物理尺寸（毫米）
-    TARGET_PAPER_SIZE_MM = 400  # 靶纸边长 400mm x 400mm
-
-    # ArUco标记在靶纸上的中心坐标（毫米，以靶纸中心为原点）
-    # 靶纸坐标系：中心(0,0)，X向右，Y向下（图像坐标系）
-    # 四个角位置：(20,20), (20,380), (380,380), (380,20)
-    # 转换为以中心为原点的坐标：
-    # 左上角(0): (-180, -180) -> 实际(20,20)相对于中心(200,200) = (-180,-180)
-    # 右上角(1): (180, -180)  -> 实际(380,20)相对于中心 = (180,-180)
-    # 右下角(2): (180, 180)   -> 实际(380,380)相对于中心 = (180,180)
-    # 左下角(3): (-180, 180)  -> 实际(20,380)相对于中心 = (-180,180)
-    ARUCO_MARKER_POSITIONS_MM = {
-        0: (-180, -180),  # 左上角
-        1: (180, -180),   # 右上角
-        2: (180, 180),    # 右下角
-        3: (-180, 180),   # 左下角
-    }
-
-    # 靶心（黄心）在靶纸上的位置（毫米，相对于靶纸中心）
-    # 标准靶纸靶心就在正中心
-    TARGET_CENTER_OFFSET_MM = (0, 0)
-
-    # ArUco检测参数
-    ARUCO_MIN_MARKER_PERIMETER_RATE = 0.03  # 最小标记周长比例（相对于图像）
-    ARUCO_CORNER_REFINEMENT_METHOD = cv2.aruco.CORNER_REFINE_SUBPIX  # 角点精修方法
-
 # ==================== 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
 
+BATTERY_SOC_LPF_ALPHA = 0.5
+BATTERY_SOC_AVG_WINDOW = 5
+

cpp_ext/CMakeLists.txt

@@ -25,6 +25,7 @@ add_library(archery_netcore MODULE
   utils.cpp
   decrypt_ota_file.cpp
   msg_handler.cpp
+  tcp_ssl_password.cpp
 )
 
 target_include_directories(archery_netcore PRIVATE

cpp_ext/archery_netcore.cpp

@@ -12,6 +12,7 @@
 #include "native_logger.hpp"
 #include "decrypt_ota_file.hpp"
 #include "utils.hpp"
+#include "tcp_ssl_password.hpp"
 
 namespace py = pybind11;
 using json = nlohmann::json;
@@ -61,6 +62,14 @@ PYBIND11_MODULE(archery_netcore, m) {
 
     m.def("get_config", &get_config, "Get system configuration");
 
+    m.def(
+        "calculate_tcp_ssl_password",
+        &netcore::calculate_tcp_ssl_password,
+        "Calculate TCP SSL password: hex(md5(hex(md5(device_id)) + iccid))",
+        py::arg("device_id"),
+        py::arg("iccid")
+    );
+
     m.def(
         "decrypt_ota_file",
         [](const std::string& input_path, const std::string& output_zip_path) {

cpp_ext/decrypt_ota_file.cpp

@@ -1,14 +1,11 @@
-#include <pybind11/pybind11.h>
-#include <pybind11/stl.h>  // 支持 std::vector, std::map 等
-#include <nlohmann/json.hpp>
 #include <cstring>
 #include <cstdint>
 #include <vector>
 #include <string>
 #include <fstream>
 #include <array>
-#include <openssl/evp.h>
 #include <algorithm>
+#include <openssl/evp.h>
 #include "native_logger.hpp"
 
 namespace netcore{
@@ -18,11 +15,11 @@ namespace netcore{
     constexpr size_t kOtaMagicLen = 7;
     constexpr size_t kGcmNonceLen = 12;
     constexpr size_t kGcmTagLen = 16;
+    constexpr size_t kHeaderLen = kOtaMagicLen + kGcmNonceLen;
+    // 分块解密，避免整包读入导致 RAM 峰值约为「文件大小×2」（小内存设备易 OOM）
+    constexpr size_t kDecryptChunk = 65536;
 
-    // 固定 32-byte AES-256-GCM key（提高被直接查看的成本；不是绝对安全）
-    // 注意：需要与打包端传入的 --aead-key-hex 保持一致。
     static std::array<uint8_t, 32> ota_key_bytes() {
-        // 简单拆分混淆：key = a XOR b
         static const std::array<uint8_t, 32> a = {
             0x92,0x99,0x4d,0x06,0x6f,0xb6,0xa6,0x3d,0x85,0x08,0xbe,0x73,0x5e,0x73,0x4d,0x8a,
             0x53,0x88,0xe6,0x99,0xfc,0x10,0x29,0xb9,0x16,0x9b,0xe7,0x0c,0x65,0x21,0x1c,0xce
@@ -36,56 +33,45 @@ namespace netcore{
         return k;
     }
 
-    static bool read_file_all(const std::string& path, std::vector<uint8_t>& out) {
-        std::ifstream ifs(path, std::ios::binary);
-        if (!ifs) return false;
-        ifs.seekg(0, std::ios::end);
-        std::streampos size = ifs.tellg();
-        if (size <= 0) return false;
-        ifs.seekg(0, std::ios::beg);
-        out.resize(static_cast<size_t>(size));
-        if (!ifs.read(reinterpret_cast<char*>(out.data()), size)) return false;
-        return true;
-    }
-
-    static bool write_file_all(const std::string& path, const uint8_t* data, size_t len) {
-        std::ofstream ofs(path, std::ios::binary | std::ios::trunc);
-        if (!ofs) return false;
-        ofs.write(reinterpret_cast<const char*>(data), static_cast<std::streamsize>(len));
-        return static_cast<bool>(ofs);
-    }
-
     bool decrypt_ota_file_impl(const std::string& input_path, const std::string& output_zip_path) {
-        std::vector<uint8_t> in;
-        if (!netcore::read_file_all(input_path, in)) {
-            netcore::log_error(std::string("decrypt_ota_file: read failed: ") + input_path);
+        std::ifstream ifs(input_path, std::ios::binary);
+        if (!ifs) {
+            netcore::log_error(std::string("decrypt_ota_file: open in failed: ") + input_path);
             return false;
         }
-
-        const size_t min_len = kOtaMagicLen + kGcmNonceLen + kGcmTagLen + 1;
-        if (in.size() < min_len) {
+        ifs.seekg(0, std::ios::end);
+        const std::streampos szp = ifs.tellg();
+        if (szp <= 0) {
+            netcore::log_error("decrypt_ota_file: empty input");
+            return false;
+        }
+        const uint64_t file_size = static_cast<uint64_t>(szp);
+        const size_t min_len = kHeaderLen + kGcmTagLen + 1;
+        if (file_size < min_len) {
             netcore::log_error("decrypt_ota_file: too short");
             return false;
         }
-        if (!std::equal(in.begin(), in.begin() + kOtaMagicLen, reinterpret_cast<const uint8_t*>(kOtaMagic))) {
+        const uint64_t ciphertext_len = file_size - kHeaderLen - kGcmTagLen;
+
+        ifs.seekg(0, std::ios::beg);
+        std::array<uint8_t, kHeaderLen> header{};
+        ifs.read(reinterpret_cast<char*>(header.data()), static_cast<std::streamsize>(kHeaderLen));
+        if (ifs.gcount() != static_cast<std::streamsize>(kHeaderLen)) {
+            netcore::log_error("decrypt_ota_file: read header failed");
+            return false;
+        }
+        if (!std::equal(header.begin(), header.begin() + kOtaMagicLen,
+                         reinterpret_cast<const uint8_t*>(kOtaMagic))) {
             netcore::log_error("decrypt_ota_file: bad magic");
             return false;
         }
+        const uint8_t* nonce = header.data() + kOtaMagicLen;
 
-        const uint8_t* nonce = in.data() + kOtaMagicLen;
-        const uint8_t* ct_and_tag = in.data() + kOtaMagicLen + kGcmNonceLen;
-        const size_t ct_and_tag_len = in.size() - (kOtaMagicLen + kGcmNonceLen);
-        if (ct_and_tag_len <= kGcmTagLen) {
-            netcore::log_error("decrypt_ota_file: no ciphertext");
+        std::ofstream ofs(output_zip_path, std::ios::binary | std::ios::trunc);
+        if (!ofs) {
+            netcore::log_error(std::string("decrypt_ota_file: open out failed: ") + output_zip_path);
             return false;
         }
-        const size_t ciphertext_len = ct_and_tag_len - kGcmTagLen;
-        const uint8_t* ciphertext = ct_and_tag;
-        const uint8_t* tag = ct_and_tag + ciphertext_len;
-
-        std::vector<uint8_t> plain(ciphertext_len);
-        int out_len1 = 0;
-        int out_len2 = 0;
 
         EVP_CIPHER_CTX* ctx = EVP_CIPHER_CTX_new();
         if (!ctx) {
@@ -95,6 +81,8 @@ namespace netcore{
 
         bool ok = false;
         auto key = ota_key_bytes();
+        std::vector<uint8_t> chunk_in(kDecryptChunk);
+        std::vector<uint8_t> chunk_out(kDecryptChunk + EVP_MAX_BLOCK_LENGTH);
 
         do {
             if (1 != EVP_DecryptInit_ex(ctx, EVP_aes_256_gcm(), nullptr, nullptr, nullptr)) {
@@ -109,27 +97,59 @@ namespace netcore{
                 netcore::log_error("decrypt_ota_file: set key/iv failed");
                 break;
             }
-            if (1 != EVP_DecryptUpdate(ctx, plain.data(), &out_len1, ciphertext, static_cast<int>(ciphertext_len))) {
-                netcore::log_error("decrypt_ota_file: update failed");
+
+            uint64_t remaining = ciphertext_len;
+            while (remaining > 0) {
+                const size_t n = static_cast<size_t>(std::min<uint64_t>(remaining, kDecryptChunk));
+                ifs.read(reinterpret_cast<char*>(chunk_in.data()), static_cast<std::streamsize>(n));
+                if (ifs.gcount() != static_cast<std::streamsize>(n)) {
+                    netcore::log_error("decrypt_ota_file: read ciphertext chunk failed");
+                    goto cleanup_ctx;
+                }
+                int outl = 0;
+                if (1 != EVP_DecryptUpdate(ctx, chunk_out.data(), &outl,
+                                          chunk_in.data(), static_cast<int>(n))) {
+                    netcore::log_error("decrypt_ota_file: update failed");
+                    goto cleanup_ctx;
+                }
+                if (outl > 0) {
+                    ofs.write(reinterpret_cast<const char*>(chunk_out.data()), outl);
+                    if (!ofs) {
+                        netcore::log_error("decrypt_ota_file: write plaintext failed");
+                        goto cleanup_ctx;
+                    }
+                }
+                remaining -= n;
+            }
+
+            std::array<uint8_t, kGcmTagLen> tag{};
+            ifs.read(reinterpret_cast<char*>(tag.data()), static_cast<std::streamsize>(kGcmTagLen));
+            if (ifs.gcount() != static_cast<std::streamsize>(kGcmTagLen)) {
+                netcore::log_error("decrypt_ota_file: read tag failed");
                 break;
             }
-            if (1 != EVP_CIPHER_CTX_ctrl(ctx, EVP_CTRL_GCM_SET_TAG, static_cast<int>(kGcmTagLen), const_cast<uint8_t*>(tag))) {
+            if (1 != EVP_CIPHER_CTX_ctrl(ctx, EVP_CTRL_GCM_SET_TAG, static_cast<int>(kGcmTagLen), tag.data())) {
                 netcore::log_error("decrypt_ota_file: set tag failed");
                 break;
             }
-            if (1 != EVP_DecryptFinal_ex(ctx, plain.data() + out_len1, &out_len2)) {
+
+            int outl2 = 0;
+            if (1 != EVP_DecryptFinal_ex(ctx, chunk_out.data(), &outl2)) {
                 netcore::log_error("decrypt_ota_file: final failed (auth tag mismatch?)");
                 break;
             }
-            const size_t plain_len = static_cast<size_t>(out_len1 + out_len2);
-            if (!netcore::write_file_all(output_zip_path, plain.data(), plain_len)) {
-                netcore::log_error(std::string("decrypt_ota_file: write failed: ") + output_zip_path);
-                break;
+            if (outl2 > 0) {
+                ofs.write(reinterpret_cast<const char*>(chunk_out.data()), outl2);
+                if (!ofs) {
+                    netcore::log_error("decrypt_ota_file: write final failed");
+                    break;
+                }
             }
             ok = true;
         } while (false);
 
+cleanup_ctx:
         EVP_CIPHER_CTX_free(ctx);
         return ok;
     }
-}
+}  // namespace netcore

cpp_ext/tcp_ssl_password.cpp

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

cpp_ext/tcp_ssl_password.hpp

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

design_doc/algo.md

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

design_doc/command_record.md

@@ -1,12 +1,12 @@
 
-1. CPP构建命令：
+1. CPP构建命令：在docker环境下执行以下命令
 
-cd /mnt/d/code/archery/cpp_ext
+cd /data/cpp_ext
 rm -rf build && mkdir build && cd build
 
-TOOLCHAIN_BIN=/mnt/d/code/MaixCDK/dl/extracted/toolchains/maixcam/host-tools/gcc/riscv64-linux-musl-x86_64/bin
-PYDEV=/mnt/d/code/shooting/python3_lib_maixcam_musl_3.11.6
-MAIXCDK=/mnt/d/code/MaixCDK
+TOOLCHAIN_BIN=/data/MaixCDK-main/dl/extracted/toolchains/maixcam/host-tools/gcc/riscv64-linux-musl-x86_64/bin
+PYDEV=/data/python3_lib_maixcam_musl_3.11.6
+MAIXCDK=/data/MaixCDK-main
 
 cmake .. -G Ninja \
   -DCMAKE_C_COMPILER="${TOOLCHAIN_BIN}/riscv64-unknown-linux-musl-gcc" \
@@ -36,3 +36,67 @@ printf 'AT+MHTTPDLFILE="http://static.shelingxingqiu.com/shoot/v1/main.py","down
 4. wifi的启动条件，在 /boot 目录下，看看是否有 wifi.sta 和 wifi.ssid， wifi.pass 这些文件。其中 wifi.sta 是开关文件。
    如果没有了它就不会启动wifi流程。具体的wifi流程 由 /etc/init.d/S30wifi 控制。它会判断 wifi.sta 是否存在，然后是否启动wifi，还是启动热点。
 
+5. 给自己的程序打包到基础镜像中，参考：https://wiki.sipeed.com/maixpy/doc/zh/pro/compile_os.html
+    5.1. 按照链接中的步骤，去github上获取了基础镜像，这次使用的是 v4.12.4，把Assets中的下面几样东西下载下来，我是在windows的wsl中执行的，注意，
+         假如是在windows中下载的文件，在wsl中编译会很慢，所以我采用的是直接在wsl中下载，放到wsl的自己的文件系统中。
+         1）maixcam-2025-12-31-maixpy-v4.12.4.img.xz
+         2）maixcam_builtin_files.tar.xz 
+         3）MaixPy-4.12.4-py3-none-any.whl
+         4）Source code(zip)
+    5.2. 把自己的文件放到 buildtin_files中：
+         1）我把项目文件目录 t11 放到了 maixcam_builtin_files\maixapp\apps 这个目录下。
+         2）为了能让它自启动，我把 auto_start.txt 放到了 maixcam_builtin_files\maixapp 这个目录下。
+         
+    5.3. 然后在解压后的源码中找到tools/os目录下 /home/saga/maixcam/MaixPy-4.12.4/tools/os/maixcam
+        执行
+        export MAIXCDK_PATH=/home/saga/maixcam/MaixCDK
+        编译：
+        ./gen_os.sh ../../../../../maixcam/maixcam-2025-12-31-maixpy-v4.12.4.img ../../../../../maixcam/MaixPy-4.12.4-py3-none-any.whl ../../../../../maixcam/maixcam_builtin_files 0 maixcam
+        注意，在编译过程中，也会去 github 下载内容，所以需要打开梯子。
+    5.4. 等待编译完成，会编译成镜像文件，然后根据 https://wiki.sipeed.com/hardware/zh/maixcam/os.html 这个指引来烧录系统。
+    5.5. 烧录完系统后，需要安装 runtime， 可以按照  https://wiki.sipeed.com/maixpy/doc/zh/README_no_screen.html 这个来升级运行库，或者直接在 Maixvision 中链接的时候安装 runtime。
+    5.6. 安装 runtime 之后，重启，我们的系统就会自己启动起来了。
+
+   遇到问题：
+   /mnt/d/code/shooting/compile_maixcam/MaixPy-4.12.4/MaixPy-4.12.4/tools/os/maixcam/fuse2fs: error while loading shared libraries: libfuse.so.2: cannot open shared object file: No such file or directory
+   解决办法：
+    安装 libfuse2
+    sudo apt update
+     sudo apt install libfuse2
+
+   遇到问题：
+    python 缺少  yaml
+    解决办法：
+    pip install pyyaml
+
+   遇到问题：
+   ./build_all.sh: line 56: maixtool: command not found
+   解决办法：
+   pip install maixtool
+
+    遇到问题：
+    ./update_img.sh: line 80: mcopy: command not found
+    解决办法：
+    sudo apt update
+    sudo apt install mtools
+
+6. 相机标定：
+然后在板子上跑 test 目录下的 test_camera_rtsp.py ，让相机启动了一个服务，然后在电脑上接收这个视频流，并且跑opencv 内置的标定程序：
+set OPENCV_FFMPEG_CAPTURE_OPTIONS="rtsp_transport;tcp"
+opencv_interactive-calibration -t=chessboard -w=9 -h=6 -sz=0.025 -v="http://192.168.1.81:8000/stream" 2>nul
+
+
+7. 生成训练图片：在test目录下，执行以下命令。注意，其中 D:\code\shooting\target_photo\write.png 是靶纸的图片。
+ D:\data\test_target_photo 是用来叠加的背景图
+
+ 7.1 生成靶纸及黑色三角形的截图的图片，带动动，但1.12的外框
+bak
+python .\synth_compose_yolo.py  --perspective 0.04 --perspective-prob 0.8 --color-jitter 0.6  --bg-dir D:\data\test_target_photo --fg D:\code\shooting\target_photo\write.png --out ./synth_out --class-name triangle --zip ./maix_dataset.zip --num 60 --triangles-json archery_triangles_default.json --format voc  --stage2-crop  --stage2-pad-min 0.03  --stage2-pad-max 0.18 --motion-prob 0.9 --motion-kernel-max 8 --blur-max 0 --triangle-bbox-pad-frac 0.12
+
+bak_2
+python synth_keypoints_right_angle.py   --bg-dir D:\data\test_target_photo --fg D:\code\shooting\target_photo\write.png  --triangles-json archery_triangles_default.json --out ./synth_out --num 1000  --offscreen-shift-prob 0.3  --offscreen-shift-frac 0.4   --offscreen-min-visible 1  --stage2-crop  --stage2-pad-min 0.03  --stage2-pad-max 0.18 --motion-prob 0.9 --motion-kernel-max 8 --blur-max 0   --perspective-mode planar   --yaw-max-deg 10 --pitch-max-deg 8   --roll-max-deg 4   --planar-focal-frac 1.45   --perspective-prob 0.4
+
+python synth_keypoints_right_angle.py   --bg-dir D:\data\test_target_photo --fg D:\code\shooting\target_photo\write.png  --triangles-json archery_triangles_default.json --out ./synth_out --num 1000  --offscreen-shift-prob 0.3  --offscreen-shift-frac 0.4   --offscreen-min-visible 1  --stage2-crop  --stage2-pad-min 0.03  --stage2-pad-max 0.18 --motion-prob 1.0 --motion-kernel-max 8 --blur-max 0   --perspective-mode planar   --yaw-max-deg 10 --pitch-max-deg 8   --roll-max-deg 4   --planar-focal-frac 1.45   --perspective-prob 0.4
+
+
+ python pose_pixel_metrics.py --model D:\code\archery\runs\pose\runs\pose\target_pose_train\weights\best.pt --data D:\code\archery\datasets\dataset_pose.yaml --imgsz 640

design_doc/debug.md

@@ -26,8 +26,7 @@
  program exit failed. exit code: 1. 
 
     解决方案：
-    从日志看，就是开始发送登录信息之后就崩溃了。出发了底层的read failed。经过排查，是一定要插上电源板的数据连线，以及电源板要插上电池。这个应该是
-    登录时需要读电源电压数据，
+    从日志看，就是开始发送登录信息之后就崩溃了。出发了底层的read failed。经过排查，是一定要插上电源板的数据连线，以及电源板要插上电池。这个应该是登录时需要读电源电压数据。后面我们已经优化了日志，而且增加了对ina226的试探，但发现ina226不存在的时候，就直接返回电压和电流为0.0。而且，一定要注意，在新配套的电源板和核心板上面，才能正常读到电流和电压。
 
 3. a）问题描述：202609 批次的拓展版，有一块maixcam的蓝灯常亮，询问maixcam的人，他们觉得应该是卡没有插好。但是拓展版上的激光口挡住了数据卡的出口，
     没法拔出检查，

design_doc/solution_record.md

@@ -103,3 +103,174 @@ WiFi 连接成功
     ↓
 上层检测到连接断开：
     重新 connect_server() → 自动选择 4G
+
+10. 现在使用的相机，其实是支持更大的分辨率的，比如说1920*1280，但是由于我们的图像处理，拍照处理之后很容易触发OOM。
+
+11. 环数计算流程：
+现在设备侧的目标是：算出箭点相对靶心的偏移(dx,dy)，单位是物理厘米（cm），然后把它作为 x,y 上报给后端；后端再去算环。
+设备侧本身不直接算环数，它算的是偏移与距离，并上报。
+
+算法流程（一次射箭从触发到上报）
+1) 触发后取一帧图
+    在 process_shot() 里读取相机帧并调用 analyze_shot(frame)
+2) 确定激光点（laser_point） 
+
+    analyze_shot() 第一步先确定激光点 (x,y)（像素坐标）：
+
+   硬编码：config.HARDCODE_LASER_POINT=True → 用 laser_manager.laser_point
+   已校准：laser_manager.has_calibrated_point() → 用校准值
+   动态模式：先 detect_circle_v3(frame, None) 粗估距离，再根据距离反推激光点
+   代码在：
+
+   if config.HARDCODE_LASER_POINT:
+       ...
+   elif laser_manager.has_calibrated_point():
+       ...
+   else:
+       _, _, _, _, best_radius1_temp, _ = detect_circle_v3(frame, None)
+       distance_m_first = estimate_distance(best_radius1_temp) ...
+       laser_point = laser_manager.calculate_laser_point_from_distance(distance_m_first)
+3) 优先走三角形路径（成功就直接用于上报 x/y）
+如果 config.USE_TRIANGLE_OFFSET=True，先尝试识别靶面四角三角形标记：
+
+if getattr(config, "USE_TRIANGLE_OFFSET", False):
+    K, dist_coef, pos = _get_triangle_calib()
+    img_rgb = image.image2cv(frame, False, False)
+    tri = try_triangle_scoring(img_rgb, (x, y), pos, K, dist_coef, ...)
+    if tri.get("ok"):
+        return {... "dx": tri["dx_cm"], "dy": tri["dy_cm"], "distance_m": tri.get("distance_m"), ...}
+这一步里 try_triangle_scoring() 做了两件事（都在 triangle_target.py）：
+
+单应性（homography）：把激光点从图像坐标映射到靶面坐标系，得到(dx,dy)（cm）
+PnP：用识别到的角点与相机标定，估算 相机到靶的距离 distance_m
+关键代码：
+
+ok_h, tx, ty, _H = homography_calibration(...)
+out["dx_cm"] = tx
+out["dy_cm"] = -ty
+out["distance_m"] = dist_m
+out["distance_method"] = "pnp_triangle"
+注意：这里 dy_cm 取了负号，是为了和现网约定一致（laser_manager.compute_laser_position 的坐标方向）。
+
+4) 三角形失败 → 回退圆形/椭圆靶心检测（兜底）
+如果三角形不可用或识别失败，就走传统靶心检测：
+
+detect_circle_v3(frame, laser_point) 找黄心/红心、半径、椭圆参数
+用 laser_manager.compute_laser_position() 把像素偏移换算成厘米偏移(dx,dy)
+在 shoot_manager.py：
+
+result_img, center, radius, method, best_radius1, ellipse_params = detect_circle_v3(frame, laser_point)
+if center and radius:
+    dx, dy = laser_manager.compute_laser_position(center, (x, y), radius, method)
+    distance_m = estimate_distance(best_radius1) ...
+在 laser_manager.compute_laser_position()（核心换算逻辑）：
+
+r = radius * 5
+target_x = (lx-cx)/r*100
+target_y = (ly-cy)/r*100
+return (target_x, -target_y)
+这里 (像素差)/(radius*5)*100 是你们旧约定下的“像素→厘米”比例模型（并且 y 方向同样取负号）。
+
+5) 上报数据：把(dx,dy) 作为 x/y 发给后端
+最终上报发生在 process_shot()，直接把 dx,dy 填到 inner_data["x"],["y"]：
+
+srv_x = round(float(dx), 4) if dx is not None else 200.0
+srv_y = round(float(dy), 4) if dy is not None else 200.0
+inner_data = {
+    "x": srv_x,
+    "y": srv_y,
+    "d": round((distance_m or 0.0) * 100),
+    "m": method if method else "no_target",
+    "offset_method": offset_method,
+    "distance_method": distance_method,
+    ...
+}
+network_manager.safe_enqueue(...)
+x,y：物理厘米（cm）
+d：相机到靶距离（m→cm，乘 100；三角形成功时来自 PnP）
+m/offset_method/distance_method：标记本次用的算法路径（triangle / yellow / pnp 等）
+后端收到 x,y 后，再用你之前给的 Go 公式 CalculateRingNumber(x,y,tenRingRadius) 计算环数。
+
+你现在的“环数计算”实际依赖关系
+最好路径（快+稳）：三角形 → dx,dy（单应性） + distance_m（PnP）
+兜底路径：圆/椭圆靶心 → dx,dy（基于黄心半径比例/透视校正） + distance_m（黄心半径估距）
+
+12. 4g模块上传文件：
+
+Upload images from MaixCam to Qiniu cloud via ML307R 4G module's AT commands. The HTTP body requires multipart/form-data with real CR/LF bytes (0x0D 0x0A) in boundaries.
+Methods Tried
+#	Method	AT Commands	Result	Root Cause
+1	Raw binary, no encoding	MHTTPCONTENT with raw bytes + length param	ERROR at first chunk	CR/LF in binary data terminates AT command parser
+2	Encoding mode 2 (escape)	MHTTPCFG="encoding",0,2 + \r\n escapes	Server 400 Bad Request	Module sends literal text \r\n to server, NOT actual 0x0D 0x0A bytes. Multipart body is garbled
+3	Encoding mode 1 (hex)	MHTTPCFG="encoding",0,1 + hex-encoded data	CME ERROR: 650/50	Firmware doesn't properly support hex mode for MHTTPCONTENT
+4	No chunked mode	Skip MHTTPCFG="chunked"	CME ERROR: 65	Module requires chunked mode to accept MHTTPCONTENT at all
+5	Single large MHTTPCONTENT	All data in one command (2793 bytes)	+MHTTPURC: "err",0,5 (timeout)	Possible buffer limit; module hangs then times out
+6	Per-chunk HTTP instance (OTA style)	CREATE→POST→DELETE per chunk	Not feasible	Each instance = separate HTTP request; Qiniu needs complete body in single POST
+Conclusion: AT HTTP layer (MHTTPCONTENT) is fundamentally broken for binary uploads.
+The Solution: Raw TCP Socket (MIPOPEN + MIPSEND)
+Bypass the AT HTTP layer entirely. Open a raw TCP connection and send a hand-crafted HTTP POST:
+plaintext
+AT+MIPCLOSE=3                                    // Clean up old socket
+AT+MIPOPEN=3,"TCP","upload.qiniup.com",80        // Raw TCP connection
+AT+MIPSEND=3,1024  →  ">"  →  [raw bytes]  →  OK  // Binary-safe!
+AT+MIPSEND=3,1024  →  ">"  →  [raw bytes]  →  OK
+AT+MIPSEND=3,766   →  ">"  →  [raw bytes]  →  OK
+// Response: +MIPURC: "rtcp",3,<len>,HTTP/1.1 200 OK...
+AT+MIPCLOSE=3
+Why it works:
+MIPSEND enters prompt mode (>) — after the >, the AT parser treats ALL bytes as data, including CR/LF
+We construct the complete HTTP request ourselves (headers + Content-Length + multipart body) with real CRLF bytes
+
+Key bug found during integration: _send_chunk() wrapped calls in self.at._cmd_lock, but self.at.send() also acquires the same lock internally — threading.Lock() is not reentrant, causing deadlock. Fixed by removing the outer lock (the network_manager.get_uart_lock() already provides thread safety).Trade-off: UART is locked during the entire upload, so heartbeats pause. For small JPEG files (~2-80KB), this is 5-20 seconds — acceptable if server heartbeat timeout is generous
+
+
+13. 算环数算法1：「黄心 + 红心」椭圆/圆：主要在 vision.py 的 detect_circle_v3() 里完成：颜色先用 HSV 做掩码，再在轮廓上做面积、圆度筛选，黄圈用椭圆拟合，红圈预先筛成候选，最后用几何关系配对。
+
+    1. 黄色怎么判、范围是什么？
+    图像先转 HSV（cv2.COLOR_RGB2HSV，注意输入是 RGB）。
+    饱和度 S 整体乘 1.1 并限制在 0–255（让黄色更「显」一点）。
+    黄色 inRange（OpenCV HSV，H 多为 0–179）：
+    通道	下限	上限
+    H       7       32
+    S       80      255
+    V       0       255
+    在黄掩码上找轮廓后，还要满足：面积 > 50，圆度 > 0.7（circularity = 4π·面积/周长²），且点数 ≥5 才 fitEllipse 当黄心椭圆。
+
+    2. 红色怎么判、范围是什么？
+    红色在 HSV 里跨 0°，所以用 两段 H 做并集：
+    两段分别是：
+    H 0–10，S 80–255，V 0–255
+    H 170–180，S 80–255，V 0–255
+    红轮廓候选：面积 > 50，圆度 > 0.6（比黄略松），再拟合椭圆或最小外接圆得到圆心和半径。
+
+    3. 「黄心」和「红心」怎样算一对？（几何范围）
+    对每个黄圈，在红色候选里找第一个满足：
+
+    两圆心距离 dist_centers < yellow_radius * 1.5
+    红半径 red_radius > yellow_radius * 0.8（红在外圈、略大）
+                dist_centers = math.hypot(ddx, ddy)
+                if dist_centers < yellow_radius * 1.5 and rc["radius"] > yellow_radius * 0.8:
+    小结：黄色 = HSV H∈[7,32]、S≥80（且 S 放大 1.1）+ 形态学闭运算 + 面积/圆度；红色 = 两段 H（0–10 与 170–180）、S≥80 + 闭运算 + 面积/圆度；配对用 同心/包含 的距离与半径比例阈值。若你还关心 laser_manager.py 里「激光红点」的另一套阈值（LASER_*），那是另一条链路，和靶心黄/红 HSV 可以分开看。
+
+14. 算环数算法2：
+    使用单应性矩阵计算：镜头中心点（照片中心像素）到虚拟平面的转换。它不需要知道相机在 3D 空间中的具体位置，直接通过单应性矩阵 H的逆运算，将 2D 像素“翻译”成虚拟平面上的 2D 坐标。
+
+    一、转换的本质：2D 到 2D 的“查字典”
+    单应性变换（Homography）是平面到平面的映射。它不处理 3D 空间中的“投影线”，而是直接建立图像像素 (u,v)​ 与虚拟平面坐标 (x,y)​ 的一一对应关系。
+    你可以把单应性矩阵 H想象成一本“翻译字典”：
+    正变换 H：已知靶纸上的真实位置 (x,y)，查字典得到它在照片上哪个像素 (u,v)。
+    逆变换 H−1：已知照片上的像素 (u,v)（如镜头中心点），查字典反推它在靶纸上的真实位置 (x,y)。
+    这个“虚拟平面”就是你的靶纸平面（Z=0 的世界坐标系）。算法没有在物理上移动任何点，只是在做坐标系的换算。
+
+    二、详细步骤：镜头中心点如何“落地”
+
+    相机分辨率是 640x480，镜头中心点（光轴与图像的交点）通常是 (u0,v0)=(320,240)。
+    1. 输入：镜头中心点（像素）
+    2. 核心运算：乘以逆矩阵
+    通过 4 个黑色三角形的角点（已知真实坐标）计算出了单应性矩阵 H。现在使用它的逆矩阵 H−1
+    3. 输出：虚拟平面上的落点（物理坐标）
+    计算后，你会得到：(xhit,yhit)
+    这就是镜头中心点对应的靶纸上的真实位置（单位：毫米）。
+    4. 计算环数
+    由于虚拟平面原点 (0,0)就是靶纸圆心，直接计算欧氏距离。​
+    这个 d就是箭着点偏离圆心的真实物理距离，直接用于环数判定。

design_doc/todo.md

@@ -1,17 +1,6 @@
 你现在要防的是“别人拿到设备/拿到代码包后，能伪造请求、刷接口、下发恶意 OTA、甚至劫持通信”。单靠隐藏 Python 源码只能提高门槛，真正的安全要靠协议和密钥设计。结合你仓库里实际内容，建议你重点隐藏/整改这些点（按风险排序）。
-1. 必须隐藏/必须整改（高风险）
-1.1 登录口令规则太弱（几乎等于明文）
-你现在的登录是 password = device_id + "."（见 network.py 读取设备 ID 后直接拼出来），这意味着只要攻击者知道/猜到 device_id，就能直接登录伪装设备。
-相关位置：
-with open("/device_key", "r") as f:    device_id = f.read().strip()    ...    self._device_id = device_id    self._password = device_id + "."
-1.2 HTTP 鉴权 token 的盐值是硬编码常量（泄露后可离线伪造）
-你 token 是 HMAC-SHA256((SALT+device_id), SALT2)，而 SALT/SALT2 是固定字符串："shootMessageFire" / "shoot"。这类“硬编码盐值 + 可猜/可读的 device_id”意味着：攻击者只要拿到代码包/逆向 .so，就能在自己电脑上批量算 token，伪造 HTTP 请求。
-相关位置：
-SALT = "shootMessageFire"SALT2 = "shoot"return "Arrow_" + hmac.new((SALT + device_id).encode(), SALT2.encode(), hashlib.sha256).hexdigest()
-1.3 TLS 配置目前看起来没有做证书校验（容易被中间人攻击）
-config.py 虽然 USE_TCP_SSL=True，但你在 network.py 里实际把 MSSLCFG="auth" 固定成 0（不验），且写证书分支被 if False 禁用了。这样“看起来是 TLS”，但仍可能被抓包/篡改/假服务器接入。
-相关位置：
-r = hardware_manager.at_client.send(f'AT+MSSLCFG="auth",{ssl_id},0', "OK", 3000)...if False:  # 写证书/校验被禁用    ...r = hardware_manager.at_client.send(f'AT+MIPCFG="ssl",{link_id},{ssl_id},1', "OK", 3000)
+
+
 1.4 OTA 下发“url”如果缺少强校验，就是远程代码执行入口
 你 OTA 逻辑里从服务器指令拿到 url 就去下载并替换文件/重启（这是正常 OTA），但安全性取决于：
 是否只允许白名单域名/路径
@@ -24,18 +13,13 @@ r = hardware_manager.at_client.send(f'AT+MSSLCFG="auth",{ssl_id},0', "OK", 3000)
 这些在 config.py 是明文：
 SERVER_IP = "stcp.shelingxingqiu.com"SERVER_PORT = 50005HTTP_URL = "http://ws.shelingxingqiu.com"HTTP_API_PATH = "/home/shoot/device_fire/arrow/fire"
 注意：即使你把它们藏进 .so，攻击者仍可能通过抓包/观察 DNS/SNI/流量拿到域名或 IP，所以“隐藏”只能降低静态分析风险，不能替代鉴权/签名。
-2.2 WiFi 凭证落盘位置
-你会把 SSID/密码写到 /boot/wifi.ssid 和 /boot/wifi.pass（network.py/wifi.py 都有）。拿到设备存储就能读到明文密码，这属于设备侧安全问题。
-2.3 日志/调试信息泄露
-你仓库里 .cursor/debug.log* 已经记录了完整的 AT+MIPOPEN ... host:port 等信息；如果这些被打进包或被用户导出，也会泄露通信细节。
-你发布包里“真正会被带走的敏感内容”
-你现在 app.yaml 的打包清单只包含核心运行文件（不包含 test/、backup_code/、.cursor/ 等），这一点很好：
-files:  - app.yaml  - at_client.py  - camera_manager.py  - config.py  - hardware.py  - laser_manager.py  - logger_manager.py  - main.py  - network.py  - ota_manager.py  - power.py  - shot_id_generator.py  - time_sync.py  - version.py  - vision.py
-但要注意：你当前最敏感的“盐值/口令规则/HTTP header 逻辑/OTA 策略”都在这些会打包的文件里（尤其是 network.py、ota_manager.py、config.py）。
+
+
 
 3. 最有效的“防黑”措施（比隐藏更关键）
-把“password=device_id+.” 改为真正的 per-device secret（设备出厂烧录，服务端保存；或服务端下发公钥，设备用私钥签名）
+
 所有上报/控制命令加签名 + nonce/timestamp + 服务端防重放（别人抓到一次包也不能复用）
 OTA 包必须做签名校验（设备端内置公钥，下载后验签通过才应用）
-TLS 必须做证书校验/最好做 pinning（至少别用 auth=0）
+
 如果你告诉我：你们服务端目前能不能改协议（例如新增签名字段、下发 challenge、做 OTA 签名），我可以按“最小改动但提升最大安全”的顺序，帮你规划一套从现状平滑升级的方案。
+

laser_detector.py

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

laser_manager.py

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

logger_manager.py

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

main.py

@@ -14,6 +14,7 @@ from maix.peripheral import adc
 import _thread
 import os
 import json
+import time as wall_time
 
 # 导入新模块
 import config
@@ -21,13 +22,15 @@ from version import VERSION
 # from logger import init_logging, get_logger, stop_logging
 from logger_manager import logger_manager
 from time_sync import sync_system_time_from_4g
-from power import init_ina226, get_bus_voltage, voltage_to_percent
+from power import init_ina226
 from laser_manager import laser_manager
-from vision import detect_circle_v3, estimate_distance, enqueue_save_shot, start_save_shot_worker
+from vision import start_save_shot_worker
 from network import network_manager
 from ota_manager import ota_manager
 from hardware import hardware_manager
 from camera_manager import camera_manager
+from shoot_manager import process_shot, preload_triangle_calib
+from target_roi_yolo import preload_yolo_detector
 
 
 def laser_calibration_worker():
@@ -95,18 +98,31 @@ def cmd_str():
     # 3. 初始化激光模块（串口 + 开机关闭激光防误触发）
     laser_manager.init()
 
-    # 3. 初始化 INA226 电量监测芯片
+    # 3. 初始化 INA226 电量监测芯片（与后续相机启动之间的耗时，便于定位启动卡顿）
+    _w_boot = wall_time.time()
+    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")
 
     # 4. 初始化显示和相机
-    camera_manager.init_camera(640, 480)
+    _w_boot = wall_time.time()
+    print(
+        f"[BOOT] init_camera({getattr(config, 'CAMERA_WIDTH', 640)}x{getattr(config, 'CAMERA_HEIGHT', 480)}) "
+        f"开始 wall_s={_w_boot:.3f}"
+    )
+    camera_manager.init_camera(getattr(config, "CAMERA_WIDTH", 640), getattr(config, "CAMERA_HEIGHT", 480))
+    print(f"[BOOT] init_camera 结束 wall +{int(round((wall_time.time() - _w_boot) * 1000))} ms")
+
+    _w_boot = wall_time.time()
+    print(f"[BOOT] init_display 开始 wall_s={_w_boot:.3f}")
     camera_manager.init_display()
+    print(f"[BOOT] init_display 结束 wall +{int(round((wall_time.time() - _w_boot) * 1000))} ms")
 
     # ==================== 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
@@ -127,6 +143,32 @@ def cmd_str():
     # 2.5. 启动存图 worker 线程（队列 + worker，避免主循环阻塞）
     start_save_shot_worker()
 
+    # 2.6 预加载三角形标定/坐标文件（避免首次射箭卡顿）
+    try:
+        preload_triangle_calib()
+    except Exception:
+        pass
+
+    # 2.7 预加载 YOLO（靶环 ROI + 黑三角）；dual_buff=False 时无需 warmup 消除一帧延迟
+    try:
+        _preload_yolo = bool(getattr(config, "TRIANGLE_YOLO_PRELOAD_ON_BOOT", True))
+        _loc_black = str(
+            getattr(config, "TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE", "yolo")
+        ).lower().strip()
+        if _loc_black not in ("yolo", "traditional"):
+            _loc_black = "yolo"
+        _need_black_preload = (
+            bool(getattr(config, "TRIANGLE_BLACK_YOLO_ENABLE", False))
+            and _loc_black == "yolo"
+            and bool(getattr(config, "TRIANGLE_BLACK_YOLO_PRELOAD_ON_BOOT", True))
+        )
+        _preload_yolo = _preload_yolo or _need_black_preload
+        if _preload_yolo:
+            preload_yolo_detector(logger)
+    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}")
+
     # 3. 启动时检查：是否需要恢复备份
     pending_path = f"{config.APP_DIR}/ota_pending.json"
     if os.path.exists(pending_path):
@@ -203,8 +245,8 @@ 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）
-    if config.SAVE_IMAGE_ENABLED:
+    # 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 or getattr(config, "SAVE_IMAGE_ON_FAILURE", False):
         photo_dir = config.PHOTO_DIR
         if photo_dir not in os.listdir("/root"):
             try:
@@ -236,46 +278,45 @@ def cmd_str():
         logger.info("系统准备完成...")
 
     last_adc_trigger = 0
+    # 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
+    try:
+        last_adc_val = hardware_manager.adc_obj.read()
+    except Exception:
+        last_adc_val = 0
+    peak_adc_val = 0  # 当前周期内的压力峰值
     # 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:
-            return
-        nonlocal pressure_buf, pressure_sum, pressure_min, pressure_max, pressure_t0_ms, logger, pressure_abs_sum, last_avg_abs
+        nonlocal pressure_buf, pressure_sum, pressure_min, pressure_max, pressure_t0_ms, logger
         if not pressure_buf:
             return
-        t1_ms = time.ticks_ms()
-        n = len(pressure_buf)
-        avg = (pressure_sum / n) if n else 0
-        avg_abs = (pressure_abs_sum / n) if n else 0
-        # 一行输出：方便后处理画曲线；同时带上统计信息便于快速看波峰
-        line = (
-            f"[气压批量] reason={reason} "
-            f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
-            f"min={pressure_min} max={pressure_max} avg={avg:.1f} avg_abs={avg_abs:.3f} "
-            f"values={','.join(map(str, pressure_buf))}"
-            f" convert value (kpa): {(max(pressure_buf, key=lambda x: x[1])[1] - last_avg_abs) / (5 - 2.5) * config.AIR_PRESSURE_HARDWARE_MAX:.1f}"
-        )
-        if logger:
-            logger.debug(line)
-        else:
-            print(line)
+        if config.AIR_PRESSURE_lOG:
+            t1_ms = time.ticks_ms()
+            n = len(pressure_buf)
+            avg = (pressure_sum / n) if n else 0
+            line = (
+                f"[气压批量] reason={reason} "
+                f"t0={pressure_t0_ms} t1={t1_ms} n={n} "
+                f"min={pressure_min} max={pressure_max} avg={avg:.1f} "
+                f"values={','.join(map(str, pressure_buf))}"
+            )
+            if logger:
+                logger.debug(line)
+            else:
+                print(line)
+        # 无论是否记录日志，都必须清空 buffer，否则内存泄漏
         pressure_buf = []
         pressure_sum = 0
-        pressure_abs_sum = 0
         pressure_min = 4095
         pressure_max = 0
         pressure_t0_ms = None
-        last_avg_abs = avg_abs
 
     # 主循环：检测扳机触发 → 拍照 → 分析 → 上报
     while not app.need_exit():
@@ -310,12 +351,10 @@ 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:
@@ -326,166 +365,34 @@ def cmd_str():
             # ====== 气压采样缓存（每次循环都记录，批量输出日志）======
             if pressure_t0_ms is None:
                 pressure_t0_ms = current_time
-            pressure_buf.append((adc_val, adc_abs_val))
+            pressure_buf.append(adc_val)
             pressure_sum += adc_val
-            pressure_abs_sum += adc_abs_val
             if adc_val < pressure_min:
                 pressure_min = adc_val
             if adc_val > pressure_max:
                 pressure_max = adc_val
             if len(pressure_buf) >= PRESSURE_BATCH_SIZE:
                 _flush_pressure_buf("batch")
-            # if adc_val >= 2000:
-            # print(f"adc :{adc_val}")
-            if adc_val >= config.ADC_TRIGGER_THRESHOLD:
+            # 峰值检测：压力从峰值下降时触发，确保捕获到最大冲击时刻
+            if adc_val > peak_adc_val:
+                peak_adc_val = adc_val  # 更新峰值
+            if (peak_adc_val >= config.ADC_TRIGGER_THRESHOLD
+                    and adc_val < peak_adc_val
+                    and last_adc_val >= peak_adc_val):
+                # 封顶后下降沿触发：peak是最大值，当前值开始下降，且上次值还在peak位置
                 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")
+                    peak_adc_val = 0  # 去抖期间重置峰值
+                    time.sleep_ms(5)
                     continue
                 last_adc_trigger = current_time
+                peak_adc_val = 0  # 触发后重置峰值
                 # 触发前先把缓存刷出来，避免波形被长耗时处理截断
                 _flush_pressure_buf("before_trigger")
 
                 try:
-                    frame = camera_manager.read_frame()
-                    
-                    laser_point_method = None  # 记录激光点选择方法
-                    if config.HARDCODE_LASER_POINT:
-                        # 硬编码模式：使用硬编码值
-                        laser_point = laser_manager.laser_point
-                        laser_point_method = "hardcode"
-                    elif laser_manager.has_calibrated_point():
-                        # 假如校准过，并且有保存值，使用校准值
-                        laser_point = laser_manager.laser_point
-                        laser_point_method = "calibrated"
-                        logger_manager.logger.info(f"[算法] 使用校准值: {laser_manager.laser_point}")
-                    elif distance_m and distance_m > 0:
-                        # 动态计算模式：根据距离计算激光点
-                        # 先检测靶心以获取距离（用于计算激光点）
-                        # 第一次检测不使用激光点，仅用于获取距离
-                        result_img_temp, center_temp, radius_temp, method_temp, best_radius1_temp, ellipse_params_temp = detect_circle_v3(frame, None)
-                        # 计算距离
-                        distance_m = estimate_distance(best_radius1_temp) if best_radius1_temp else None
-                        laser_point = laser_manager.calculate_laser_point_from_distance(distance_m)
-                        laser_point_method = "dynamic"
-                    if laser_point is None:
-                        logger = logger_manager.logger
-                        if logger:
-                            logger.warning("[MAIN] 激光点未初始化，跳过本次检测")
-                        time.sleep_ms(100)
-                        continue
-                    
-                    x, y = laser_point
-
-                    # 检测靶心
-                    result_img, center, radius, method, best_radius1, ellipse_params = detect_circle_v3(frame, laser_point)
-
-                    if config.SHOW_CAMERA_PHOTO_WHILE_SHOOTING:
-                        camera_manager.show(result_img)
-
-                    # 计算偏移与距离（如果检测到靶心）
-                    if center and radius:
-                        dx, dy = laser_manager.compute_laser_position(center, (x, y), radius, method)
-                        distance_m = estimate_distance(best_radius1)
-                    else:
-                        # 未检测到靶心
-                        dx, dy = None, None
-                        distance_m = None
-                        if logger:
-                            logger.warning("[MAIN] 未检测到靶心，但会保存图像")
-
-                    # 快速激光测距（激光一闪而过，约500-600ms）
-                    laser_distance_m = None
-                    laser_signal_quality = 0
-                    # try:
-                    #     result = laser_manager.quick_measure_distance()
-                    #     if isinstance(result, tuple) and len(result) == 2:
-                    #         laser_distance_m, laser_signal_quality = result
-                    #     else:
-                    #         # 向后兼容：如果返回的是单个值
-                    #         laser_distance_m = result if isinstance(result, (int, float)) else 0.0
-                    #         laser_signal_quality = 0
-                    #     if logger:
-                    #         if laser_distance_m > 0:
-                    #             logger.info(f"[MAIN] 激光测距成功: {laser_distance_m:.3f} m, 信号质量: {laser_signal_quality}")
-                    #         else:
-                    #             logger.warning("[MAIN] 激光测距失败或返回0")
-                    # except Exception as e:
-                    #     if logger:
-                    #         logger.error(f"[MAIN] 激光测距异常: {e}")
-
-                    # 读取电量
-                    voltage = get_bus_voltage()
-                    battery_percent = voltage_to_percent(voltage)
-
-                    # 生成射箭ID
-                    from shot_id_generator import shot_id_generator
-                    shot_id = shot_id_generator.generate_id() # 不需要使用device_id
-                    
-
-
-                    # 构造上报数据
-                    inner_data = {
-                        "shot_id": shot_id,  # 射箭ID，用于关联图片和服务端日志
-                        "x": float(dx) if dx is not None else 200.0,
-                        "y": float(dy) if dy is not None else 200.0,
-                        "r": 90.0,
-                        "d": round((distance_m or 0.0) * 100),  # 视觉测距值（厘米）
-                        "d_laser": round((laser_distance_m or 0.0) * 100),  # 激光测距值（厘米）
-                        "d_laser_quality": laser_signal_quality,  # 激光测距信号质量
-                        "m": method if method else "no_target",
-                        "adc": adc_val,
-                        # 新增字段：激光点选择方法
-                        "laser_method": laser_point_method,  # 激光点选择方法：hardcode/calibrated/dynamic/default
-                        # 激光点坐标（像素）
-                        "target_x": float(x),  # 激光点 X 坐标（像素）
-                        "target_y": float(y),  # 激光点 Y 坐标（像素）
-                    }
-                    
-                    # 添加椭圆参数（如果存在）
-                    if ellipse_params:
-                        (ell_center, (width, height), angle) = ellipse_params
-                        inner_data["ellipse_major_axis"] = float(max(width, height))  # 长轴（像素）
-                        inner_data["ellipse_minor_axis"] = float(min(width, height))  # 短轴（像素）
-                        inner_data["ellipse_angle"] = float(angle)  # 椭圆角度（度）
-                        inner_data["ellipse_center_x"] = float(ell_center[0])  # 椭圆中心 X 坐标（像素）
-                        inner_data["ellipse_center_y"] = float(ell_center[1])  # 椭圆中心 Y 坐标（像素）
-                    else:
-                        inner_data["ellipse_major_axis"] = None
-                        inner_data["ellipse_minor_axis"] = None
-                        inner_data["ellipse_angle"] = None
-                        inner_data["ellipse_center_x"] = None
-                        inner_data["ellipse_center_y"] = None
-                    
-                    report_data = {"cmd": 1, "data": inner_data}
-                    network_manager.safe_enqueue(report_data, msg_type=2, high=True)
-                    # 闪一下激光（射箭反馈）
-                    if config.FLASH_LASER_WHILE_SHOOTING:
-                        laser_manager.flash_laser(config.FLASH_LASER_DURATION_MS)
-
-                    # 保存图像（无论是否检测到靶心都保存）：放入队列由 worker 异步保存，不阻塞主循环
-                    enqueue_save_shot(
-                        result_img,
-                        center,
-                        radius,
-                        method,
-                        ellipse_params,
-                        (x, y),
-                        distance_m,
-                        shot_id=shot_id,
-                        photo_dir=config.PHOTO_DIR if config.SAVE_IMAGE_ENABLED else None,
-                    )
-
-
-                    if center and radius:
-                        logger.info(f"射箭事件已加入发送队列（已检测到靶心），ID: {shot_id}")
-                    else:
-                        logger.info(f"射箭事件已加入发送队列（未检测到靶心，已保存图像），ID: {shot_id}")
-
-
-
-                    time.sleep_ms(100)
+                    process_shot(adc_val)
                 except Exception as e:
                     logger = logger_manager.logger
                     if logger:
@@ -499,10 +406,9 @@ def cmd_str():
                     try:
                         camera_manager.show(camera_manager.read_frame())
                     except Exception as e:
-                        logger = logger_manager.logger
-                        if logger:
-                            logger.error(f"[MAIN] 显示异常: {e}")
+                        pass
                 time.sleep_ms(5)
+            last_adc_val = adc_val
 
         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

@@ -0,0 +1,13 @@
+
+[basic]
+type = cvimodel
+model = model_270139.cvimodel
+
+[extra]
+model_type = yolov5
+input_type = rgb
+mean = 0, 0, 0
+scale = 0.00392156862745098, 0.00392156862745098, 0.00392156862745098
+anchors = 10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326
+labels = 黑三角和圆环
+

model_270820.mud

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

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

ota_manager.py

@@ -758,19 +758,24 @@ class OTAManager:
 
         parsed = urlparse(url)
         host = parsed.hostname
+        # MHTTPREQUEST 的路径必须包含 query（七牛/ OSS 签名、token 多在 ? 后），否则易 403/HTML，header 无 CL → no_header_or_total
         path = parsed.path or "/"
+        if parsed.query:
+            path = f"{path}?{parsed.query}"
+        if parsed.fragment:
+            path = f"{path}#{parsed.fragment}"
         if not host:
             return False, "bad_url (no host)"
 
         # 很多 ML307R 的 MHTTP 对 https 不稳定；对已知域名做降级
 
         if isinstance(url, str) and url.startswith("https://static.shelingxingqiu.com/"):
-            base_url = "https://static.shelingxingqiu.com"
-            # TODO：使用https，看看是否能成功
-            self._is_https = True
+            base_url = "http://static.shelingxingqiu.com"
+            self._is_https = False
         else:
             base_url = f"http://{host}"
             self._is_https = False
+        self.logger.info(f"base_url: {base_url}, self._is_https: {self._is_https}")
         # logger removed - use self.logger instead
 
         def _log(*a):
@@ -1155,8 +1160,8 @@ class OTAManager:
                 self.logger.error(f"[OTA-4G][PWR] before_urc read_failed: {e}")
 
             t_dl0 = time.ticks_ms()
-            success, msg = self.download_file_via_4g(ota_url, downloaded_filename, debug=False)
-            t_dl_cost = time.ticks_diff(t_dl0, time.ticks_ms())
+            success, msg = self.download_file_via_4g(ota_url, downloaded_filename, debug=True)
+            t_dl_cost = time.ticks_diff(time.ticks_ms(), t_dl0)
             self.logger.info(f"[OTA-4G] {msg}")
             self.logger.info(f"[OTA-4G] download_cost_ms={t_dl_cost}")
 

power.py

@@ -5,14 +5,38 @@
 提供电压、电流监测和充电状态检测
 """
 import config
+import os
+import subprocess
 from logger_manager import logger_manager
+from maix import time as maix_time
+
+_INA226_PRESENT = None
+
+
+def _ina226_ready() -> bool:
+    """
+    是否允许访问 INA226。
+
+    重要：
+    - 这里刻意不做任何 I2C 探测/读写。
+    - 经验上，在 INA226 未供电/未响应时，I2C 的 readfrom_mem 可能直接触发底层崩溃（SIGSEGV），try/except 无法拦截。
+    - 因此只在开机 init_ina226() 成功后才允许后续读电压/电流。
+    """
+    return bool(getattr(config, "INA226_ENABLE", True)) and (_INA226_PRESENT is True)
 
 
 def write_register(reg, value):
     """写入INA226寄存器"""
     from hardware import hardware_manager
+    logger = logger_manager.logger
     data = [(value >> 8) & 0xFF, value & 0xFF]
-    hardware_manager.bus.writeto_mem(config.INA226_ADDR, reg, bytes(data))
+    # 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))
+    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}")
+        raise OSError(ret)
 
 
 def read_register(reg):
@@ -24,35 +48,62 @@ def read_register(reg):
 
 def init_ina226():
     """初始化 INA226 芯片：配置模式 + 校准值"""
-    write_register(config.REG_CONFIGURATION, 0x4527)
-    write_register(config.REG_CALIBRATION, config.CALIBRATION_VALUE)
+    global _INA226_PRESENT
+    logger = logger_manager.logger
+    if not getattr(config, "INA226_ENABLE", True):
+        if logger:
+            logger.info("[INA226] INA226_ENABLE=False，跳过初始化与 I2C 探测")
+        # 显式标记不可用，避免后续误读
+        _INA226_PRESENT = False
+        return False
+    try:
+        # 仅通过“写寄存器成功”来判定可用，避免额外的读操作触发底层崩溃
+        write_register(config.REG_CONFIGURATION, 0x4527)
+        write_register(config.REG_CALIBRATION, config.CALIBRATION_VALUE)
+        _INA226_PRESENT = True
+        return True
+    except Exception as e:
+        _INA226_PRESENT = False
+        if logger:
+            logger.error(f"[INA226] 初始化失败：{e}")
+        return False
 
 
 def get_bus_voltage():
-    """读取总线电压（单位：V）"""
-    raw = read_register(config.REG_BUS_VOLTAGE)
-    return raw * 1.25 / 1000
+    """读取总线电压（单位：V）。未探测到 INA226 或读失败时返回 0.0（上报用，避免 null）。"""
+    logger = logger_manager.logger
+    if not _ina226_ready():
+        return 0.0
+    try:
+        raw = read_register(config.REG_BUS_VOLTAGE)
+        return raw * 1.25 / 1000
+    except Exception as e:
+        if logger:
+            logger.error(f"[INA226] 读取电压失败：{e}")
+        return 0.0
 
 
 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
     """
     try:
+        if not _ina226_ready():
+            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
@@ -79,7 +130,7 @@ def is_charging(threshold_ma=10.0):
     """
     try:
         current = get_current()
-        is_charge = current > threshold_ma
+        is_charge = current < -abs(float(threshold_ma))
         return is_charge
     except Exception as e:
         logger = logger_manager.logger
@@ -91,22 +142,114 @@ def is_charging(threshold_ma=10.0):
 
 
 def voltage_to_percent(voltage):
-    """根据电压估算电池百分比（查表插值）"""
-    points = [
-        (4.20, 100), (4.10, 95), (4.05, 85), (4.00, 75), (3.95, 65),
-        (3.90, 55), (3.85, 45), (3.80, 35), (3.75, 25), (3.70, 15),
-        (3.65, 5), (3.60, 0)
-    ]
-    if voltage >= points[0][0]:
-        return 100
-    if voltage <= points[-1][0]:
+    """
+    根据电压估算电池百分比（高密度查表插值 + 滤波）。
+
+    - 电压先做 5 点移动平均（抑制瞬时抖动）
+    - SOC 再做一阶低通（抑制“跳电量”）
+
+    注意：
+    - 该方法仍是“开路电压→SOC”的近似；负载较大/瞬时大电流时电压会下沉，SOC 会偏低。
+    - 滤波会带来滞后：电量变化会更平滑，但更新更慢。
+    """
+    if voltage is None:
         return 0
-    for i in range(len(points) - 1):
-        v1, p1 = points[i]
-        v2, p2 = points[i + 1]
-        if voltage >= v2:
-            ratio = (voltage - v1) / (v2 - v1)
-            percent = p1 + (p2 - p1) * ratio
-            return max(0, min(100, int(round(percent))))
-    return 0
+    try:
+        v = float(voltage)
+    except Exception:
+        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
+
+
+class BatteryMonitor:
+    """
+    电压→SOC 估算器（查表 + 线性插值 + 双重滤波）。
+
+    说明：
+    - 表为单节锂电“静态电压”近似曲线；不同电池/温度/老化会有偏差。
+    - 这里不区分充电/放电曲线（滞后），主要用于“显示电量/粗略判断”。
+    """
+
+    def __init__(self, avg_window: int = 5, alpha: float = 0.2):
+        # 电压-SOC对照表（电压从高到低）
+        self.voltages = [
+            4.20, 4.15, 4.10, 4.05, 4.00,
+            3.95, 3.90, 3.88, 3.85, 3.82,
+            3.80, 3.78, 3.75, 3.72, 3.70,
+            3.65, 3.60, 3.55, 3.50, 3.45,
+            3.40, 3.35, 3.30, 3.20, 2.50,
+        ]
+        self.socs = [
+            100, 98, 95, 90, 85,
+            80, 75, 72, 68, 64,
+            60, 56, 52, 48, 44,
+            38, 32, 26, 20, 14,
+            10, 6, 3, 1, 0,
+        ]
+
+        self.avg_window = max(1, int(avg_window))
+        self.alpha = float(alpha) if alpha is not None else 0.2
+        if not (0.0 < self.alpha <= 1.0):
+            self.alpha = 0.2
+
+        self.voltage_history = []
+        self.last_soc = 50.0
+
+    def _voltage_to_soc_raw(self, voltage: float) -> float:
+        # 越界
+        if voltage >= self.voltages[0]:
+            return 100.0
+        if voltage <= self.voltages[-1]:
+            return 0.0
+
+        # 表是降序，二分查找
+        left, right = 0, len(self.voltages) - 1
+        while left <= right:
+            mid = (left + right) // 2
+            vm = self.voltages[mid]
+            if vm == voltage:
+                return float(self.socs[mid])
+            elif vm < voltage:
+                right = mid - 1
+            else:
+                left = mid + 1
+
+        # 线性插值：right 在高电压侧，left 在低电压侧（降序表）
+        # 例：voltages = [4.2,4.15,...]，则 v_high=voltages[right] >= voltage >= voltages[left]=v_low
+        v_high, v_low = float(self.voltages[right]), float(self.voltages[left])
+        soc_high, soc_low = float(self.socs[right]), float(self.socs[left])
+        if abs(v_high - v_low) < 1e-9:
+            return soc_low
+        soc = soc_low + (voltage - v_low) * (soc_high - soc_low) / (v_high - v_low)
+        return soc
+
+    def get_soc(self, raw_voltage: float) -> float:
+        # 1) 电压滤波（移动平均）
+        self.voltage_history.append(float(raw_voltage))
+        if len(self.voltage_history) > self.avg_window:
+            self.voltage_history.pop(0)
+        voltage = sum(self.voltage_history) / float(len(self.voltage_history))
+
+        # 2) 查表插值
+        raw_soc = self._voltage_to_soc_raw(voltage)
+
+        # 3) SOC 低通滤波
+        a = self.alpha
+        self.last_soc = a * raw_soc + (1.0 - a) * float(self.last_soc)
+
+        # clip
+        if self.last_soc < 0.0:
+            self.last_soc = 0.0
+        if self.last_soc > 100.0:
+            self.last_soc = 100.0
+        return float(self.last_soc)
+
+
+# 模块级单例：保留历史，实现平滑（进程重启会重置）
+_BATTERY_MONITOR = BatteryMonitor(
+    avg_window=int(getattr(config, "BATTERY_SOC_AVG_WINDOW", 5)),
+    alpha=float(getattr(config, "BATTERY_SOC_LPF_ALPHA", 0.2)),
+)
 

server.pem

@@ -0,0 +1,33 @@
+-----BEGIN CERTIFICATE-----
+MIIFwjCCA6qgAwIBAgIUAZIGjFLTekYI+IIquQ/87qLDuNAwDQYJKoZIhvcNAQEL
+BQAwXjELMAkGA1UEBhMCQ04xDjAMBgNVBAgMBUxvY2FsMQ4wDAYDVQQHDAVMb2Nh
+bDEOMAwGA1UECgwFTG9jYWwxHzAdBgNVBAMMFnd3dy5zaGVsaW5neGluZ3FpdS5j
+b20wIBcNMjYwNDA3MDc0NDI2WhgPMjEyNjAzMTQwNzQ0MjZaMF4xCzAJBgNVBAYT
+AkNOMQ4wDAYDVQQIDAVMb2NhbDEOMAwGA1UEBwwFTG9jYWwxDjAMBgNVBAoMBUxv
+Y2FsMR8wHQYDVQQDDBZ3d3cuc2hlbGluZ3hpbmdxaXUuY29tMIICIjANBgkqhkiG
+9w0BAQEFAAOCAg8AMIICCgKCAgEAvKRcWr8QeT1OzhMbWlHmqxmduE+e7r2Oet9I
+mU4O888U1X1YKaIDnq+zqRCNteid3jrOWucDLReZzNnrZ4l3Jq9nbWuTwj9Y9vCq
+ahW3K3BOhnuJ+qvqX2Izn1Z9iNCFhXnUaFy8+iP0nJNNIRXwg7ioKbY6+SaTbBzI
+vfG33MjOmwnQlqZzdGyNpvieO9XzqVyRxeDen/LJf4Z1NocP2rOjqQC3dIDXOfBt
+/ZOZymb4XwQ9b/t+6WJn9Zfycw0tp/7GqI+vqLDUMpipO4ahmybJPO02IhokZ09t
+BnCXe0enLnMAshIipTxSaJEick9HnQVSUzF+9A1F0cCFAhS8cM/04aksfYsJD2xj
+riiVHVoVo6tb0GJSCM+b0j9ObH9bDx3DKfy9EcqP25mJxWQTuT8G0oiyuxE5knjA
+HL7yjwd5gVSuig+ACnxE3vITeVKtvyep7sD4tJqkN93t7OMeBRFMGsYpJ8w+8u6X
++9/RmMcOnuNcT/4HrOuAtlAnM1D44MSI1RLaOCJJ9evqhpWdktfn2Uv4gCnaTjUr
+OiEU/G+lquST2kggjbcReLqkk+7yN3XkaR9dun4iV35WfEo1ENThVhLPGV61LaJq
+PwbjltQlkcAFPJ1GJyE9FVO79bB51d0w/rlI/CcDUpTRMaXR35EmTjxvXOr/a/XI
+56GUNaUCAwEAAaN2MHQwHQYDVR0OBBYEFH1HCDm4N7LMhIX2Fb2FXAfdyhwQMB8G
+A1UdIwQYMBaAFH1HCDm4N7LMhIX2Fb2FXAfdyhwQMA8GA1UdEwEB/wQFMAMBAf8w
+IQYDVR0RBBowGIIWd3d3LnNoZWxpbmd4aW5ncWl1LmNvbTANBgkqhkiG9w0BAQsF
+AAOCAgEAG/PMwXCXJOaqCpU/LaY6w04ue6wk95RbPXf4JH4CrrLUfgyUmFlNNQPA
+LuZSBRI6KUGkTvzuz/3ofZHVEin3CyE5NadB3UItpfA4Wl4r3jMPifIgnA/NT8xo
+GE1gYaDbcfJNE8jy6GebjZekbVrPvCY9YgcUT2AmW5fcbnCTy+/iC7lf9MvvqHTJ
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+nFBDkgzZxr4ZYlhVLzbSoab8D4A/z6riEMqv4S+oF5VkaJLhsN8vgHh9aPspCC3Q
+zhcosH8XmNmJmT/X64FhhRqxAqX65WanVQABtBS/vsC+FAQDGMb3RkZSbLEnIlgj
+bx/6bSkhHl+J2xIqA7tLvYhRSvM3H12X7VSVc+tkVzI5JoUSugZLxxRDGpYgkvRz
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+YNUG9oW2TXAZjSECyTo28NFkFfwBhpHWigFCANNCd8Nrn0k0YMuJOkqW5e4w3/24
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+UlRvtU9mkZPK04GbLsoo+8tZTDRtkuCiC19xk33XiitZrmavc24=
+-----END CERTIFICATE-----

shoot_manager.py

@@ -1,11 +1,45 @@
+import os
+import threading
+import time as time_std
+
 import config
 from camera_manager import camera_manager
 from laser_manager import laser_manager
 from logger_manager import logger_manager
 from network import network_manager
-from power import get_bus_voltage, voltage_to_percent
-from vision import estimate_distance, detect_circle_v3, save_shot_image
-from maix import camera, display, image, app, time, uart, pinmap, i2c
+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 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
+_tri_calib_cache = None
+
+def _get_triangle_calib():
+    """返回 (K, dist, marker_positions)；首次调用时从磁盘加载并缓存。"""
+    global _tri_calib_cache
+    if _tri_calib_cache is not None:
+        return _tri_calib_cache
+    calib_path = getattr(config, "CAMERA_CALIB_XML", "")
+    tri_json = getattr(config, "TRIANGLE_POSITIONS_JSON", "")
+    if not (os.path.isfile(calib_path) and os.path.isfile(tri_json)):
+        _tri_calib_cache = (None, None, None)
+        return _tri_calib_cache
+    K, dist = load_camera_from_xml(calib_path)
+    pos = load_triangle_positions(tri_json)
+    _tri_calib_cache = (K, dist, pos)
+    return _tri_calib_cache
+
+
+def preload_triangle_calib():
+    """
+    启动阶段预加载三角形标定与坐标文件，避免首次射箭触发时的读盘/解析开销。
+    """
+    try:
+        _get_triangle_calib()
+    except Exception:
+        # 预加载失败不影响主流程；射箭时会再次按需尝试
+        pass
+
 
 def analyze_shot(frame, laser_point=None):
     """
@@ -13,18 +47,18 @@ def analyze_shot(frame, laser_point=None):
     :param frame: 图像帧
     :param laser_point: 激光点坐标 (x, y)
     :return: 包含分析结果的字典
+
+    优先级：
+      1. 三角形单应性（USE_TRIANGLE_OFFSET=True 时）— 成功则直接返回，跳过圆形检测
+      2. 圆形检测（三角形不可用或识别失败时兜底）
     """
     logger = logger_manager.logger
+    from datetime import datetime
 
-    # 先检测靶心以获取距离（用于计算激光点）
-    result_img_temp, center_temp, radius_temp, method_temp, best_radius1_temp, ellipse_params_temp = detect_circle_v3(
-        frame, None)
-
-    # 计算距离
-    distance_m = estimate_distance(best_radius1_temp) if best_radius1_temp else None
-
-    # 根据距离动态计算激光点坐标
+    # ── Step 1: 确定激光点 ────────────────────────────────────────────────────
     laser_point_method = None
+    distance_m_first = None
+
     if config.HARDCODE_LASER_POINT:
         laser_point = laser_manager.laser_point
         laser_point_method = "hardcode"
@@ -33,65 +67,248 @@ def analyze_shot(frame, laser_point=None):
         laser_point_method = "calibrated"
         if logger:
             logger.info(f"[算法] 使用校准值: {laser_manager.laser_point}")
-    elif distance_m and distance_m > 0:
-        laser_point = laser_manager.calculate_laser_point_from_distance(distance_m)
-        laser_point_method = "dynamic"
-        if logger:
-            logger.info(f"[算法] 使用比例尺: {laser_point}")
     else:
-        laser_point = laser_manager.laser_point
-        laser_point_method = "default"
-        if logger:
-            logger.info(f"[算法] 使用默认值: {laser_point}")
+        # 动态模式：先做一次无激光点检测以估算距离，再推算激光点
+        _, _, _, _, best_radius1_temp, _ = detect_circle_v3(frame, None)
+        distance_m_first = estimate_distance(best_radius1_temp) if best_radius1_temp else None
+        if distance_m_first and distance_m_first > 0:
+            laser_point = laser_manager.calculate_laser_point_from_distance(distance_m_first)
+            laser_point_method = "dynamic"
+            if logger:
+                logger.info(f"[算法] 使用比例尺: {laser_point}")
+        else:
+            laser_point = laser_manager.laser_point
+            laser_point_method = "default"
+            if logger:
+                logger.info(f"[算法] 使用默认值: {laser_point}")
 
     if laser_point is None:
-        return {
-            "success": False,
-            "reason": "laser_point_not_initialized"
-        }
+        return {"success": False, "reason": "laser_point_not_initialized"}
 
     x, y = laser_point
 
-    # 绘制激光十字线
-    color = image.Color(config.LASER_COLOR[0], config.LASER_COLOR[1], config.LASER_COLOR[2])
-    frame.draw_line(
-        int(x - config.LASER_LENGTH), int(y),
-        int(x + config.LASER_LENGTH), int(y),
-        color, config.LASER_THICKNESS
-    )
-    frame.draw_line(
-        int(x), int(y - config.LASER_LENGTH),
-        int(x), int(y + config.LASER_LENGTH),
-        color, config.LASER_THICKNESS
-    )
-    frame.draw_circle(int(x), int(y), 1, color, config.LASER_THICKNESS)
+    # ── Step 2: 提前转换一次图像，两个检测线程共享（只读）────────────────────────
+    img_cv = image.image2cv(frame, False, False)
 
-    # 重新检测靶心（使用计算出的激光点）
-    result_img, center, radius, method, best_radius1, ellipse_params = detect_circle_v3(frame, laser_point)
+    # ── Step 3: 检查三角形是否可用 ────────────────────────────────────────────────
+    use_tri = getattr(config, "USE_TRIANGLE_OFFSET", False)
+    K = dist_coef = pos = None
+    if use_tri:
+        K, dist_coef, pos = _get_triangle_calib()
+        use_tri = K is not None and dist_coef is not None and pos
 
-    # 计算偏移与距离
-    if center and radius:
-        dx, dy = laser_manager.compute_laser_position(center, (x, y), radius, method)
-        distance_m = estimate_distance(best_radius1)
-    else:
+    def _build_circle_result(cdata, yolo_roi_xyxy=None):
+        """从圆形检测结果构建 analyze_shot 返回值。"""
+        r_img, center, radius, method, best_radius1, ellipse_params = cdata
         dx, dy = None, None
-        distance_m = None
+        d_m = distance_m_first
+        if center and radius:
+            dx, dy = laser_manager.compute_laser_position(center, (x, y), radius, method)
+            d_m = estimate_distance(best_radius1) if best_radius1 else distance_m_first
+        out = {
+            "success": True,
+            "result_img": r_img,
+            "center": center, "radius": radius, "method": method,
+            "best_radius1": best_radius1, "ellipse_params": ellipse_params,
+            "dx": dx, "dy": dy, "distance_m": d_m,
+            "laser_point": laser_point, "laser_point_method": laser_point_method,
+            "offset_method": "yellow_ellipse" if ellipse_params else "yellow_circle",
+            "distance_method": "yellow_radius",
+        }
+        if yolo_roi_xyxy is not None:
+            out["yolo_roi_xyxy"] = yolo_roi_xyxy
+        return out
 
-    # 返回分析结果
-    return {
-        "success": True,
-        "result_img": result_img,
-        "center": center,
-        "radius": radius,
-        "method": method,
-        "best_radius1": best_radius1,
-        "ellipse_params": ellipse_params,
-        "dx": dx,
-        "dy": dy,
-        "distance_m": distance_m,
-        "laser_point": laser_point,
-        "laser_point_method": laser_point_method
-    }
+    if not use_tri:
+        # 三角形未配置，直接跑圆形检测
+        return _build_circle_result(
+            detect_circle_v3(frame, laser_point, img_cv=img_cv)
+        )
+
+    # ── Step 4: 先独占跑三角形，超时或失败后再跑圆形（不与圆心并行，避免抢 CPU）──
+    roi_xyxy = None
+    yolo_ring_ms = 0.0
+    yolo_black_ms = 0.0
+    if getattr(config, "TRIANGLE_YOLO_ROI_ENABLE", False):
+        _t_yolo_ring = time_std.perf_counter()
+        try:
+            from target_roi_yolo import try_get_triangle_roi_from_yolo
+            roi_xyxy = try_get_triangle_roi_from_yolo(
+                frame, img_cv.shape[1], img_cv.shape[0], logger
+            )
+        except Exception as e:
+            if logger:
+                logger.warning(f"[YOLO-ROI] {e}")
+        finally:
+            yolo_ring_ms = (time_std.perf_counter() - _t_yolo_ring) * 1000.0
+
+    _loc_mode = str(
+        getattr(config, "TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE", "yolo")
+    ).lower().strip()
+    if _loc_mode not in ("yolo", "traditional"):
+        _loc_mode = "yolo"
+
+    black_boxes_work = None
+    _run_stage2_black_yolo = (
+        _loc_mode == "yolo"
+        and getattr(config, "TRIANGLE_BLACK_YOLO_ENABLE", False)
+        and roi_xyxy is not None
+    )
+    if _run_stage2_black_yolo:
+        _t_yolo_black = time_std.perf_counter()
+        try:
+            from target_roi_yolo import try_black_triangle_boxes_work
+
+            black_boxes_work = try_black_triangle_boxes_work(
+                img_cv, roi_xyxy, logger
+            )
+        except Exception as e:
+            if logger:
+                logger.warning(f"[YOLO-BLACK] {e}")
+        finally:
+            yolo_black_ms = (time_std.perf_counter() - _t_yolo_black) * 1000.0
+    elif (
+        logger
+        and _loc_mode == "traditional"
+        and roi_xyxy is not None
+        and getattr(config, "TRIANGLE_BLACK_YOLO_ENABLE", False)
+    ):
+        logger.info(
+            "[TRI] TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE=traditional：跳过 Stage2 黑三角 YOLO，"
+            "仅在 Stage1 裁切内跑整幅传统三角检测"
+        )
+
+    tri_result = {}
+
+    def _run_triangle():
+        try:
+            logger.info(f"[TRI] begin {datetime.now()}")
+            logger.info(f"[TRI] K: {K}, dist: {dist_coef}, pos: {pos},  {datetime.now()}")
+            _t_wall_try = time_std.perf_counter()
+            tri = try_triangle_scoring(
+                img_cv, (x, y), pos, K, dist_coef,
+                size_range=getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500)),
+                roi_xyxy=roi_xyxy,
+                black_yolo_boxes_work=black_boxes_work,
+                yolo_ring_ms=yolo_ring_ms,
+                yolo_black_ms=yolo_black_ms,
+            )
+            _wall_try_ms = (time_std.perf_counter() - _t_wall_try) * 1000.0
+            if logger and bool(getattr(config, "TRIANGLE_LOG_E2E_TIMING", True)):
+                _e2e = float(yolo_ring_ms) + float(yolo_black_ms) + float(_wall_try_ms)
+                logger.info(
+                    f"[TRI] timing_e2e_triangle_ms={_e2e:.1f} "
+                    f"(yolo_ring={float(yolo_ring_ms):.1f} yolo_black={float(yolo_black_ms):.1f} "
+                    f"try_triangle_wall={_wall_try_ms:.1f} locate_mode={_loc_mode})"
+                )
+            logger.info(f"[TRI] tri: {tri},  {datetime.now()}")
+            tri_result['data'] = tri
+        except Exception as e:
+            logger.error(f"[TRI] 三角形路径异常: {e}")
+            tri_result['data'] = {'ok': False}
+
+    t_tri = threading.Thread(target=_run_triangle, daemon=True)
+    t_tri.start()
+
+    tri_timeout_s = float(getattr(config, "TRIANGLE_TIMEOUT_MS", 2000)) / 1000.0
+
+    t_tri.join(timeout=tri_timeout_s)
+
+    def _tri_ok_validated(tri):
+        try:
+            import numpy as _np
+            ok = bool(tri.get('ok'))
+            if not ok:
+                return False
+
+            dxv = tri.get("dx_cm")
+            dyv = tri.get("dy_cm")
+            H = tri.get("homography")
+            if not _np.isfinite(dxv) or not _np.isfinite(dyv):
+                logger.warning("[TRI] dx/dy 非有限值，判定为误检")
+                return False
+            if H is not None and not _np.all(_np.isfinite(H)):
+                logger.warning("[TRI] 单应矩阵含非有限值，判定为误检")
+                return False
+
+            # ── 检查1：单应矩阵 x/y 缩放比（靶标是正方形，H[0,0]≈H[1,1]）──
+            if H is not None:
+                sx = abs(float(H[0, 0]))
+                sy = abs(float(H[1, 1]))
+                if sy > 1e-6:
+                    hxy_ratio = sx / sy
+                    # 正常拍摄比值在 0.6~1.7 之间；超出则四点严重变形，说明有误检
+                    if not (0.6 <= hxy_ratio <= 1.7):
+                        logger.warning(
+                            f"[TRI] 单应矩阵 sx/sy={hxy_ratio:.2f} 偏差过大，判定为误检，回退圆心"
+                        )
+                        return False
+
+            # ── 检查2：可选配置距离上下限（写 0 表示不启用）──────────────────
+            dist_m = tri.get("distance_m")
+            if dist_m is not None:
+                try:
+                    import config as _vc
+                    d_min = float(getattr(_vc, "TRIANGLE_DISTANCE_MIN_M", 0.0))
+                    d_max = float(getattr(_vc, "TRIANGLE_DISTANCE_MAX_M", 0.0))
+                except Exception:
+                    d_min, d_max = 0.0, 0.0
+                if d_min > 0 and d_max > d_min:
+                    if not (d_min <= dist_m <= d_max):
+                        logger.warning(
+                            f"[TRI] 距离 {dist_m:.2f}m 超出配置范围 [{d_min},{d_max}]，判定为误检，回退圆心"
+                        )
+                        return False
+
+            return True
+        except Exception:
+            return bool(tri.get('ok'))
+
+    def _build_tri_result(tri, yolo_roi_xyxy=None):
+        out = {
+            "success": True,
+            "result_img": frame,
+            "center": None, "radius": None,
+            "method": "triangle_homography",
+            "best_radius1": None, "ellipse_params": None,
+            "dx": tri["dx_cm"], "dy": tri["dy_cm"],
+            "distance_m": tri.get("distance_m") or distance_m_first,
+            "laser_point": laser_point, "laser_point_method": laser_point_method,
+            "offset_method": tri.get("offset_method") or "triangle_homography",
+            "distance_method": tri.get("distance_method") or "pnp_triangle",
+            "tri_markers": tri.get("markers", []),
+            "tri_markers_completed": tri.get("markers_completed", []),
+            "tri_homography": tri.get("homography"),
+        }
+        if yolo_roi_xyxy is not None:
+            out["yolo_roi_xyxy"] = yolo_roi_xyxy
+        return out
+
+    # 三角形在超时内完成
+    if not t_tri.is_alive():
+        tri = tri_result.get('data', {})
+        if _tri_ok_validated(tri):
+            logger.info(f"[TRI] end {datetime.now()} — 使用三角形结果(dx={tri['dx_cm']:.2f},dy={tri['dy_cm']:.2f}cm)")
+            return _build_tri_result(tri, roi_xyxy)
+        logger.info(f"[TRI] end(tri_failed, fallback circle) {datetime.now()}")
+    else:
+        logger.warning(f"[TRI] 超时 {tri_timeout_s:.2f}s 仍未结束，启动圆心算法（三角形仍在后台）")
+
+    # 三角形超时或失败 → 跑圆心；圆心跑完后再检查三角形是否已结束
+    try:
+        cdata = detect_circle_v3(frame, laser_point, img_cv=img_cv)
+    except Exception as e:
+        logger.error(f"[CIRCLE] 圆形检测异常: {e}")
+        cdata = (frame, None, None, None, None, None)
+
+    # 圆心跑完后，若三角形恰好已经结束且结果有效，优先用三角形
+    if not t_tri.is_alive():
+        tri = tri_result.get('data', {})
+        if _tri_ok_validated(tri):
+            logger.info(f"[TRI] 圆心跑完后三角形已就绪 — 优先使用三角形结果(dx={tri['dx_cm']:.2f},dy={tri['dy_cm']:.2f}cm)")
+            return _build_tri_result(tri, roi_xyxy)
+
+    return _build_circle_result(cdata, roi_xyxy)
 
 
 def process_shot(adc_val):
@@ -105,6 +322,9 @@ def process_shot(adc_val):
     try:
         frame = camera_manager.read_frame()
 
+        # 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)
+
         # 调用算法分析
         analysis_result = analyze_shot(frame)
 
@@ -126,16 +346,27 @@ def process_shot(adc_val):
         distance_m = analysis_result["distance_m"]
         laser_point = analysis_result["laser_point"]
         laser_point_method = analysis_result["laser_point_method"]
+        offset_method = analysis_result.get("offset_method", "yellow_circle")
+        distance_method = analysis_result.get("distance_method", "yellow_radius")
+        tri_markers = analysis_result.get("tri_markers", [])
+        tri_markers_completed = analysis_result.get("tri_markers_completed", [])
+        tri_homography = analysis_result.get("tri_homography")
+        yolo_roi_xyxy = analysis_result.get("yolo_roi_xyxy")
+        draw_yolo_roi = (
+            yolo_roi_xyxy is not None
+            and getattr(config, "TRIANGLE_YOLO_DRAW_ROI_ON_SHOT", True)
+        )
         x, y = laser_point
 
-        camera_manager.show(result_img)
+        # 三角形路径成功时 center/radius 为空是正常的；此时用 triangle 方法名用于保存文件名与上报字段 m
+        if (not method) and tri_markers:
+            method = "triangle_homography"
 
-        if not (center and radius) and logger:
-            logger.warning("[MAIN] 未检测到靶心，但会保存图像")
+        if config.SHOW_CAMERA_PHOTO_WHILE_SHOOTING:
+            camera_manager.show(result_img)
 
-        # 读取电量
-        voltage = get_bus_voltage()
-        battery_percent = voltage_to_percent(voltage)
+        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
@@ -144,33 +375,30 @@ def process_shot(adc_val):
         if logger:
             logger.info(f"[MAIN] 射箭ID: {shot_id}")
 
-        # 保存图像
-        save_shot_image(
-            result_img,
-            center,
-            radius,
-            method,
-            ellipse_params,
-            (x, y),
-            distance_m,
-            shot_id=shot_id,
-            photo_dir=config.PHOTO_DIR if config.SAVE_IMAGE_ENABLED else None
-        )
+        laser_distance_m = None
+        laser_signal_quality = 0
+
+        # x,y 单位：物理厘米（compute_laser_position 与三角形单应性均输出物理 cm）
+        # 未检测到靶心时 x/y 用 200.0（脱靶标志）
+        srv_x = round(float(dx), 4) if dx is not None else 200.0
+        srv_y = round(float(dy), 4) if dy is not None else 200.0
 
         # 构造上报数据
         inner_data = {
             "shot_id": shot_id,
-            "x": float(dx) if dx is not None else 200.0,
-            "y": float(dy) if dy is not None else 200.0,
-            "r": 90.0,
+            "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）
             "d": round((distance_m or 0.0) * 100),
-            "d_laser": 0.0,
-            "d_laser_quality": 0,
+            "d_laser": round((laser_distance_m or 0.0) * 100),
+            "d_laser_quality": laser_signal_quality,
             "m": method if method else "no_target",
             "adc": adc_val,
             "laser_method": laser_point_method,
             "target_x": float(x),
             "target_y": float(y),
+            "offset_method": offset_method,
+            "distance_method": distance_method,
         }
 
         if ellipse_params:
@@ -190,14 +418,128 @@ def process_shot(adc_val):
         report_data = {"cmd": 1, "data": inner_data}
         network_manager.safe_enqueue(report_data, msg_type=2, high=True)
 
-        if logger:
-            if center and radius:
-                logger.info(f"射箭事件已加入发送队列（已检测到靶心），ID: {shot_id}")
-            else:
-                logger.info(f"射箭事件已加入发送队列（未检测到靶心，已保存图像），ID: {shot_id}")
+        # 数据上报后再画标注，不干扰检测阶段的原始画面
+        if result_img is not None:
+            # 1. 若有三角形标记，先用 cv2 画轮廓 / 顶点 / ID，再反推靶心位置
+            if tri_markers:
+                import cv2 as _cv2
+                import numpy as _np
+                _img_cv = image.image2cv(result_img, False, False)
+
+                # YOLO 靶环框在 vision.enqueue_save_shot 的 worker 里绘制，避免阻塞主流程
+
+                # 三角形轮廓 + 直角顶点 + ID
+                for _m in tri_markers:
+                    _corners = _np.array(_m["corners"], dtype=_np.int32)
+                    _cv2.polylines(_img_cv, [_corners], True, (0, 255, 0), 2)
+                    _cx, _cy = int(_m["center"][0]), int(_m["center"][1])
+                    _cv2.circle(_img_cv, (_cx, _cy), 4, (0, 0, 255), -1)
+                    _cv2.putText(_img_cv, f"T{_m['id']}",
+                                 (_cx - 18, _cy - 12),
+                                 _cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 1)
+
+                # 3点补全的虚拟角点：只画中心点 + 文本，避免误认为真实检测到的三角形
+                try:
+                    if tri_markers_completed:
+                        for _m in tri_markers_completed:
+                            if not _m.get("is_virtual"):
+                                continue
+                            _cx, _cy = int(_m["center"][0]), int(_m["center"][1])
+                            _cv2.circle(_img_cv, (_cx, _cy), 6, (255, 0, 255), 2)  # 紫色空心圈
+                            _cv2.putText(
+                                _img_cv,
+                                f"VT{_m['id']}",
+                                (_cx - 22, _cy - 12),
+                                _cv2.FONT_HERSHEY_SIMPLEX,
+                                0.55,
+                                (255, 0, 255),
+                                1,
+                            )
+                except Exception:
+                    pass
+
+                # 靶心（H_inv @ [0,0]）：小红圆
+                _center_px = None
+                if tri_homography is not None:
+                    try:
+                        _H_inv = _np.linalg.inv(tri_homography)
+                        _c_img = _cv2.perspectiveTransform(
+                            _np.array([[[0.0, 0.0]]], dtype=_np.float32), _H_inv)[0][0]
+                        _ocx, _ocy = int(_c_img[0]), int(_c_img[1])
+                        _cv2.circle(_img_cv, (_ocx, _ocy), 5, (0, 0, 255), -1)   # 实心
+                        _cv2.circle(_img_cv, (_ocx, _ocy), 9, (0, 0, 255), 1)    # 外框
+                        _center_px = (_ocx, _ocy)
+                        logger.info(f"[算法] 靶心: {_center_px}")
+                    except Exception:
+                        pass
+
+                # 叠加信息：落点-圆心距离 / 相机-靶距离等
+                try:
+                    import math as _math
+                    _lines = []
+                    if dx is not None and dy is not None:
+                        _r_cm = _math.hypot(float(dx), float(dy))
+                        _lines.append(f"offset=({float(dx):.2f},{float(dy):.2f})cm  |r|={_r_cm:.2f}cm")
+                    if distance_m is not None:
+                        _lines.append(f"cam_dist={float(distance_m):.2f}m  ({distance_method})")
+                    if method:
+                        _lines.append(f"method={method}")
+                    if _lines:
+                        _y0 = 22
+                        for i, _t in enumerate(_lines):
+                            _cv2.putText(
+                                _img_cv,
+                                _t,
+                                (10, _y0 + i * 18),
+                                _cv2.FONT_HERSHEY_SIMPLEX,
+                                0.5,
+                                (0, 255, 0),
+                                1,
+                            )
+                except Exception:
+                    pass
+
+                result_img = image.cv2image(_img_cv, False, False)
+
+            elif draw_yolo_roi:
+                # 仅 YOLO 标注时也不在主线程画框，交给存图 worker
+                pass
+
+            # 2. 激光十字线
+            _lc = image.Color(config.LASER_COLOR[0], config.LASER_COLOR[1], config.LASER_COLOR[2])
+            result_img.draw_line(int(x - config.LASER_LENGTH), int(y),
+                                 int(x + config.LASER_LENGTH), int(y),
+                                 _lc, config.LASER_THICKNESS)
+            result_img.draw_line(int(x), int(y - config.LASER_LENGTH),
+                                 int(x), int(y + config.LASER_LENGTH),
+                                 _lc, config.LASER_THICKNESS)
+            result_img.draw_circle(int(x), int(y), 1, _lc, config.LASER_THICKNESS)
 
         # 闪一下激光（射箭反馈）
-        laser_manager.flash_laser(1000)
+        if config.FLASH_LASER_WHILE_SHOOTING:
+            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,
+            radius,
+            method,
+            ellipse_params,
+            (x, y),
+            distance_m,
+            shot_id=shot_id,
+            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:
+            if dx is not None and dy is not None:
+                logger.info(f"射箭事件已加入发送队列（偏移=({dx:.2f},{dy:.2f})cm），ID: {shot_id}")
+            else:
+                logger.info(f"射箭事件已加入发送队列（未检测到偏移，已保存图像），ID: {shot_id}")
 
         time.sleep_ms(100)
     except Exception as e:

target_roi_yolo.py

@@ -0,0 +1,668 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+MaixCAM NPU YOLOv5：先检靶环/整靶区域并裁切 ROI；黑三角 Stage2 在裁切图上推理（与训练一致），
+再在各子框上跑传统直角点算法。
+
+- 相机全分辨率（如 640×480）与模型输入（如 320×320）不一致时，需把检测框从
+  「网络输入坐标系」映回全图，或直接使用 Maix 已映射到源图坐标的模式（见 config）。
+
+依赖：maix.nn.YOLOv5；靶环模型 config.TRIANGLE_YOLO_MODEL_PATH；黑三角模型
+config.TRIANGLE_BLACK_YOLO_MODEL_PATH（可多实例缓存，按路径区分）。
+
+224×224、320×320 等「网络输入尺寸」由导出的 .mud 决定，运行时打印为 net_in=，无需在业务 config 里写死。
+
+返回 (x0, y0, x1, y1) 为整幅 img_cv 上的轴对齐矩形，半开区间按三角形裁剪习惯：
+实际裁剪为 img[y0:y1, x0:x1]。
+"""
+
+from __future__ import annotations
+
+import os
+import threading
+
+import numpy as np
+
+
+def _stage2_roi_crop_save_worker(
+    slab_rgb,
+    out_local_boxes,
+    rx0,
+    ry0,
+    rw,
+    rh,
+    base_dir,
+    draw_boxes,
+    jpeg_quality,
+    roi_max_images,
+    logger_ref,
+):
+    """后台写 Stage2 裁切 JPEG，避免阻塞 NPU 后续流程。"""
+    try:
+        import time
+
+        import cv2
+
+        os.makedirs(base_dir, exist_ok=True)
+        fn = os.path.join(
+            base_dir,
+            f"stage2_roi_{rx0}_{ry0}_{rw}x{rh}_{int(time.time() * 1000)}.jpg",
+        )
+        bgr = cv2.cvtColor(slab_rgb, cv2.COLOR_RGB2BGR)
+        if draw_boxes and out_local_boxes:
+            for i, (bx0, by0, bx1, by1) in enumerate(out_local_boxes):
+                x0, y0 = int(bx0), int(by0)
+                x1, y1 = int(bx1) - 1, int(by1) - 1
+                x1 = max(x0, min(x1, rw - 1))
+                y1 = max(y0, min(y1, rh - 1))
+                cv2.rectangle(bgr, (x0, y0), (x1, y1), (0, 255, 0), 2)
+                cv2.putText(
+                    bgr,
+                    f"s2_{i}",
+                    (x0, max(0, y0 - 4)),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.5,
+                    (0, 255, 0),
+                    1,
+                    cv2.LINE_AA,
+                )
+        cv2.imwrite(fn, bgr, [int(cv2.IMWRITE_JPEG_QUALITY), int(jpeg_quality)])
+        try:
+            from vision import prune_old_images_in_dir
+
+            prune_old_images_in_dir(
+                base_dir, roi_max_images, logger_ref, "[YOLO-BLACK]"
+            )
+        except Exception:
+            pass
+        if logger_ref:
+            extra = (
+                f"，已绘 Stage2 框×{len(out_local_boxes)}"
+                if (draw_boxes and out_local_boxes)
+                else ""
+            )
+            logger_ref.info(f"[YOLO-BLACK] 已保存 Stage1 裁切图(异步): {fn}{extra}")
+    except Exception as e:
+        if logger_ref:
+            logger_ref.warning(f"[YOLO-BLACK] 异步保存裁切图失败: {e}")
+
+_detector_by_path = {}
+
+
+def reset_yolo_detector_cache():
+    """切换模型路径时可调用（通常不必）。"""
+    global _detector_by_path
+    _detector_by_path.clear()
+
+
+def _get_detector(model_path: str):
+    global _detector_by_path
+    if not model_path or not os.path.isfile(model_path):
+        return None
+    if model_path in _detector_by_path:
+        return _detector_by_path[model_path]
+    try:
+        from maix import nn
+    except ImportError:
+        return None
+    _detector_by_path[model_path] = nn.YOLOv5(model=model_path, dual_buff=False)
+    return _detector_by_path[model_path]
+
+
+def preload_yolo_detector(logger=None):
+    """
+    启动阶段预加载 YOLO detector，避免第一次真实射箭承担模型加载开销。
+    detect 使用 dual_buff=False，不再需要用首帧 warmup 抵消双缓冲的一帧延迟。
+    """
+    try:
+        import config as cfg
+    except Exception as e:
+        if logger:
+            logger.warning(f"[YOLO-ROI] 预加载失败：无法读取 config: {e}")
+        return False
+
+    ok = False
+
+    if bool(getattr(cfg, "TRIANGLE_YOLO_ROI_ENABLE", False)):
+        model_path = getattr(cfg, "TRIANGLE_YOLO_MODEL_PATH", "") or ""
+        det = _get_detector(model_path)
+        if det is None:
+            if logger:
+                logger.warning(f"[YOLO-ROI] 预加载失败：无法加载模型 {model_path}")
+        else:
+            ok = True
+            try:
+                net_w = int(det.input_width())
+                net_h = int(det.input_height())
+            except Exception:
+                net_w = net_h = -1
+            if logger:
+                logger.info(
+                    f"[YOLO-ROI] 靶环模型已预加载: {model_path}, net_in={net_w}×{net_h}"
+                )
+
+    _loc_black = str(
+        getattr(cfg, "TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE", "yolo")
+    ).lower().strip()
+    if _loc_black not in ("yolo", "traditional"):
+        _loc_black = "yolo"
+    _preload_black = (
+        bool(getattr(cfg, "TRIANGLE_BLACK_YOLO_ENABLE", False))
+        and _loc_black == "yolo"
+        and bool(getattr(cfg, "TRIANGLE_BLACK_YOLO_PRELOAD_ON_BOOT", True))
+    )
+    if _preload_black:
+        bp = getattr(cfg, "TRIANGLE_BLACK_YOLO_MODEL_PATH", "") or ""
+        d2 = _get_detector(bp)
+        if d2 is None:
+            if logger:
+                logger.warning(f"[YOLO-BLACK] 预加载失败：无法加载模型 {bp}")
+        else:
+            ok = True
+            try:
+                nw2 = int(d2.input_width())
+                nh2 = int(d2.input_height())
+            except Exception:
+                nw2 = nh2 = -1
+            if logger:
+                logger.info(
+                    f"[YOLO-BLACK] 黑三角模型已预加载: {bp}, net_in={nw2}×{nh2}"
+                )
+    elif logger and bool(getattr(cfg, "TRIANGLE_BLACK_YOLO_ENABLE", False)):
+        if _loc_black != "yolo":
+            logger.info(
+                "[YOLO-BLACK] TRIANGLE_BLACK_TRIANGLE_LOCATE_MODE=%s：跳过黑三角模型预加载"
+                % (_loc_black,)
+            )
+
+    return ok
+
+
+def _letterbox_net_to_src_xyxy(
+    x: float, y: float, w: float, h: float,
+    src_w: int, src_h: int, net_w: int, net_h: int,
+):
+    """
+    检测框在网络输入图上（含 letterbox 填充），映回到 src_w×src_h 原图。
+    x,y,w,h 为网络坐标系下的左上角与宽高。
+    """
+    scale = min(net_w / float(src_w), net_h / float(src_h))
+    nw = src_w * scale
+    nh = src_h * scale
+    pad_x = (net_w - nw) * 0.5
+    pad_y = (net_h - nh) * 0.5
+    x0 = (x - pad_x) / scale
+    y0 = (y - pad_y) / scale
+    x1 = (x + w - pad_x) / scale
+    y1 = (y + h - pad_y) / scale
+    return x0, y0, x1, y1
+
+
+def _det_obj_class_id(o):
+    """Maix / 不同版本可能用 class_id、cls、label 等字段。"""
+    for key in ("class_id", "cls", "label", "category", "cat_id", "id"):
+        if hasattr(o, key):
+            v = getattr(o, key)
+            if v is None:
+                continue
+            try:
+                return int(float(v))
+            except (TypeError, ValueError):
+                continue
+    return None
+
+
+def _det_obj_from_seq(t):
+    """若 detect 返回 list/tuple：[x,y,w,h,score,cls]（Maix 常用 xywh），包装成属性对象。"""
+    if not isinstance(t, (list, tuple)) or len(t) < 6:
+        return None
+
+    class _Box:
+        __slots__ = ("x", "y", "w", "h", "score", "class_id")
+
+    b = _Box()
+    b.x = float(t[0])
+    b.y = float(t[1])
+    b.w = float(t[2])
+    b.h = float(t[3])
+    b.score = float(t[4])
+    b.class_id = int(float(t[5]))
+    return b
+
+
+def _normalize_objs(objs):
+    out = []
+    for o in objs or []:
+        if isinstance(o, (list, tuple)):
+            m = _det_obj_from_seq(o)
+            if m is not None:
+                out.append(m)
+        else:
+            out.append(o)
+    return out
+
+
+def _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)
+    if coord_mode in ("native", "source", "camera", "full"):
+        return x, y, x + w, y + h
+    return _letterbox_net_to_src_xyxy(x, y, w, h, src_w, src_h, net_w, net_h)
+
+
+def _merge_roi_xyxy(xy_list, merge_mode: str):
+    """
+    merge_mode:
+      union — 所有框的外接矩形（适合「整靶+多角标」同属一类、多框场景）
+      largest — 取面积最大的单个框（适合只有一个大框代表整靶）
+    """
+    if not xy_list:
+        return None
+    if merge_mode in ("union", "merge", "all"):
+        x0 = min(a[0] for a in xy_list)
+        y0 = min(a[1] for a in xy_list)
+        x1 = max(a[2] for a in xy_list)
+        y1 = max(a[3] for a in xy_list)
+        return x0, y0, x1, y1
+    # largest
+    def _area(t):
+        return max(0.0, t[2] - t[0]) * max(0.0, t[3] - t[1])
+
+    best = max(xy_list, key=_area)
+    return best[0], best[1], best[2], best[3]
+
+
+def _roi_aspect_sane(x0, y0, x1, y1, src_w: int, src_h: int) -> bool:
+    """过滤 letterbox 重复映射等导致的扁条/细条 ROI。"""
+    bw = x1 - x0
+    bh = y1 - y0
+    if bw < 8 or bh < 8:
+        return False
+    area_frac = (bw * bh) / float(max(1, src_w * src_h))
+    if area_frac < 0.015:  # 小于全图约 1.5% 认为不可信
+        return False
+    ar = bw / max(bh, 1e-6)
+    if ar > 5.5 or ar < 1.0 / 5.5:
+        return False
+    return True
+
+
+def _expand_xyxy(x0, y0, x1, y1, src_w, src_h, margin_frac: float):
+    bw = max(x1 - x0, 1e-6)
+    bh = max(y1 - y0, 1e-6)
+    mx = bw * margin_frac
+    my = bh * margin_frac
+    x0 -= mx
+    y0 -= my
+    x1 += mx
+    y1 += my
+    x0 = max(0, min(int(round(x0)), src_w - 1))
+    y0 = max(0, min(int(round(y0)), src_h - 1))
+    x1 = max(x0 + 1, min(int(round(x1)), src_w))
+    y1 = max(y0 + 1, min(int(round(y1)), src_h))
+    return x0, y0, x1, y1
+
+
+def try_get_triangle_roi_from_yolo(maix_frame, src_w: int, src_h: int, logger=None):
+    """
+    用 YOLO 在 maix_frame 上检测靶环类，返回整图上的裁剪框 (x0,y0,x1,y1)；失败返回 None。
+
+    :param maix_frame: camera.read() 返回的 Maix 图像（与 nn.YOLOv5.detect 一致）
+    :param src_w, src_h: 与 img_cv / 标定一致的分辨率（通常与 camera 一致）
+    """
+    try:
+        import config as cfg
+    except Exception:
+        return None
+
+    if not bool(getattr(cfg, "TRIANGLE_YOLO_ROI_ENABLE", False)):
+        return None
+
+    model_path = getattr(cfg, "TRIANGLE_YOLO_MODEL_PATH", "") or ""
+    if not os.path.isfile(model_path):
+        if logger:
+            logger.warning(f"[YOLO-ROI] 模型文件不存在: {model_path}")
+        return None
+
+    det = _get_detector(model_path)
+    if det is None:
+        if logger:
+            logger.warning("[YOLO-ROI] 无法加载 nn.YOLOv5（非 Maix 环境或导入失败）")
+        return None
+
+    conf_th = float(getattr(cfg, "TRIANGLE_YOLO_CONF_TH", 0.5))
+    iou_th = float(getattr(cfg, "TRIANGLE_YOLO_IOU_TH", 0.45))
+    class_ids = getattr(cfg, "TRIANGLE_YOLO_RING_CLASS_IDS", (0,))
+    if isinstance(class_ids, int):
+        class_ids = (class_ids,)
+    margin_frac = float(getattr(cfg, "TRIANGLE_YOLO_ROI_MARGIN_FRAC", 0.12))
+    coord_mode = str(getattr(cfg, "TRIANGLE_YOLO_COORD_MODE", "native")).lower()
+    merge_mode = str(getattr(cfg, "TRIANGLE_YOLO_ROI_MERGE_MODE", "union")).lower()
+    reject_bad = bool(getattr(cfg, "TRIANGLE_YOLO_REJECT_BAD_ROI", True))
+
+    try:
+        raw = det.detect(maix_frame, conf_th=conf_th, iou_th=iou_th)
+    except Exception as e:
+        if logger:
+            logger.warning(f"[YOLO-ROI] detect 异常: {e}")
+        return None
+
+    objs = _normalize_objs(raw if raw is not None else [])
+
+    candidates = []
+    for o in objs:
+        cid = _det_obj_class_id(o)
+        if cid is not None and cid in class_ids:
+            candidates.append(o)
+
+    if not candidates and bool(getattr(cfg, "TRIANGLE_YOLO_RETRY_ON_EMPTY", False)):
+        retry_conf = float(getattr(cfg, "TRIANGLE_YOLO_RETRY_CONF_TH", conf_th))
+        if retry_conf > 0 and retry_conf < conf_th:
+            try:
+                raw_retry = det.detect(maix_frame, conf_th=retry_conf, iou_th=iou_th)
+                objs_retry = _normalize_objs(raw_retry if raw_retry is not None else [])
+                candidates_retry = []
+                for o in objs_retry:
+                    cid = _det_obj_class_id(o)
+                    if cid is not None and cid in class_ids:
+                        candidates_retry.append(o)
+                if candidates_retry:
+                    if logger:
+                        logger.info(
+                            f"[YOLO-ROI] conf={conf_th} 下 0 候选，"
+                            f"用 retry_conf={retry_conf} 重试得到 {len(candidates_retry)} 个候选"
+                        )
+                    objs = objs_retry
+                    candidates = candidates_retry
+                    conf_th = retry_conf
+                elif logger:
+                    logger.info(
+                        f"[YOLO-ROI] conf={conf_th} 下 0 候选；"
+                        f"retry_conf={retry_conf} 仍为 0 候选"
+                    )
+            except Exception as e:
+                if logger:
+                    logger.warning(f"[YOLO-ROI] 低阈值重试异常: {e}")
+
+    if not candidates:
+        if logger:
+            n = len(objs)
+            if n == 0:
+                logger.info(
+                    f"[YOLO-ROI] detect 返回 0 个框（conf≥{conf_th}）。"
+                    f"可尝试 config 里降低 TRIANGLE_YOLO_CONF_TH（如 0.25~0.35），"
+                    f"或确认射箭帧与训练图光照/构图接近。"
+                )
+            else:
+                seen = []
+                for o in objs[:8]:
+                    cid = _det_obj_class_id(o)
+                    sc = getattr(o, "score", None)
+                    try:
+                        sc_f = float(sc) if sc is not None else None
+                    except Exception:
+                        sc_f = None
+                    seen.append(f"cls={cid},score={sc_f}")
+                logger.info(
+                    f"[YOLO-ROI] 有 {n} 个框但类别不在 {class_ids} 内；"
+                    f"前几条: {seen}。请核对 TRIANGLE_YOLO_RING_CLASS_IDS，"
+                    f"或查看 Maix 文档中检测结果的类别字段名。"
+                )
+        return None
+
+    net_w = int(det.input_width())
+    net_h = int(det.input_height())
+
+    min_side = float(getattr(cfg, "TRIANGLE_YOLO_MIN_BOX_SIDE_PX", 8.0))
+    xy_list = []
+    for o in candidates:
+        x0n, y0n, x1n, y1n = _det_to_src_xyxy(o, coord_mode, src_w, src_h, net_w, net_h)
+        bw, bh = x1n - x0n, y1n - y0n
+        if bw >= min_side and bh >= min_side:
+            xy_list.append((x0n, y0n, x1n, y1n))
+
+    if not xy_list:
+        if logger:
+            logger.info(
+                f"[YOLO-ROI] {len(candidates)} 个候选经 min_side={min_side} 过滤后为空，放弃 ROI"
+            )
+        return None
+
+    merged = _merge_roi_xyxy(xy_list, merge_mode)
+    if merged is None:
+        return None
+    x0, y0, x1, y1 = merged
+
+    # clip 到画布（合并前框可能略越界）
+    x0 = max(0, min(x0, src_w - 1))
+    y0 = max(0, min(y0, src_h - 1))
+    x1 = max(x0 + 1, min(x1, src_w))
+    y1 = max(y0 + 1, min(y1, src_h))
+
+    x0, y0, x1, y1 = _expand_xyxy(x0, y0, x1, y1, src_w, src_h, margin_frac)
+
+    if reject_bad and not _roi_aspect_sane(x0, y0, x1, y1, src_w, src_h):
+        if logger:
+            logger.warning(
+                f"[YOLO-ROI] 裁剪框异常（过小或过扁）mode={coord_mode} merge={merge_mode} "
+                f"→ [{x0},{y0},{x1},{y1}]，放弃 ROI、三角形改用整图。"
+                f"若持续出现可尝试 coord_mode=letterbox/native 切换。"
+            )
+        return None
+
+    if logger:
+        nbox = len(candidates)
+        logger.info(
+            f"[YOLO-ROI] boxes={nbox} merge={merge_mode} coord={coord_mode} "
+            f"net_in={net_w}×{net_h}(来自模型) → crop=[{x0},{y0},{x1},{y1}] "
+            f"({x1-x0}×{y1-y0}px)"
+        )
+
+    return (x0, y0, x1, y1)
+
+
+def _expand_xyxy_local(x0, y0, x1, y1, w_lim, h_lim, margin_frac: float):
+    """在宽 w_lim、高 h_lim 的局部坐标系内扩展框。"""
+    bw = max(x1 - x0, 1e-6)
+    bh = max(y1 - y0, 1e-6)
+    mx = bw * margin_frac
+    my = bh * margin_frac
+    x0 -= mx
+    y0 -= my
+    x1 += mx
+    y1 += my
+    x0 = max(0, min(int(round(x0)), w_lim - 1))
+    y0 = max(0, min(int(round(y0)), h_lim - 1))
+    x1 = max(x0 + 1, min(int(round(x1)), w_lim))
+    y1 = max(y0 + 1, min(int(round(y1)), h_lim))
+    return x0, y0, x1, y1
+
+
+def try_black_triangle_boxes_work(img_rgb, ring_roi_xyxy, logger=None):
+    """
+    Stage2：在 **Stage1 靶环 ROI 裁切图** 上跑黑三角 YOLO（与训练时 stage2 构图一致），
+    检测框坐标已落在 **靶环裁切图**（与 try_triangle_scoring 中 img_work）同一坐标系，
+    返回 (x0,y0,x1,y1) 整数元组列表。
+
+    img_rgb: 与 try_triangle_scoring 相同的全图 RGB（numpy，H×W×3）。
+    ring_roi_xyxy: 全图上的 (rx0, ry0, rx1, ry1)，与 try_get_triangle_roi_from_yolo 一致。
+    """
+    if ring_roi_xyxy is None:
+        return []
+    if img_rgb is None or getattr(img_rgb, "size", 0) == 0:
+        return []
+    try:
+        import config as cfg
+    except Exception:
+        return []
+
+    if not bool(getattr(cfg, "TRIANGLE_BLACK_YOLO_ENABLE", False)):
+        return []
+
+    model_path = getattr(cfg, "TRIANGLE_BLACK_YOLO_MODEL_PATH", "") or ""
+    if not os.path.isfile(model_path):
+        if logger:
+            logger.warning(f"[YOLO-BLACK] 模型文件不存在: {model_path}")
+        return []
+
+    det = _get_detector(model_path)
+    if det is None:
+        if logger:
+            logger.warning("[YOLO-BLACK] 无法加载 nn.YOLOv5")
+        return []
+
+    conf_th = float(getattr(cfg, "TRIANGLE_BLACK_YOLO_CONF_TH", 0.5))
+    iou_th = float(getattr(cfg, "TRIANGLE_BLACK_YOLO_IOU_TH", 0.45))
+    class_ids = getattr(cfg, "TRIANGLE_BLACK_YOLO_CLASS_IDS", (0,))
+    if isinstance(class_ids, int):
+        class_ids = (class_ids,)
+    coord_mode = str(getattr(cfg, "TRIANGLE_BLACK_YOLO_COORD_MODE", "native")).lower()
+    margin_frac = float(getattr(cfg, "TRIANGLE_BLACK_YOLO_BOX_MARGIN_FRAC", 0.08))
+    min_side = float(getattr(cfg, "TRIANGLE_BLACK_YOLO_MIN_BOX_SIDE_PX", 6.0))
+    crop_min = int(getattr(cfg, "TRIANGLE_CROP_ROI_MIN_SIDE_PX", 64))
+
+    h_full, w_full = int(img_rgb.shape[0]), int(img_rgb.shape[1])
+    rx0, ry0, rx1, ry1 = [int(round(float(v))) for v in ring_roi_xyxy]
+    rx0 = max(0, min(rx0, w_full - 1))
+    ry0 = max(0, min(ry0, h_full - 1))
+    rx1 = max(rx0 + 1, min(rx1, w_full))
+    ry1 = max(ry0 + 1, min(ry1, h_full))
+    rw, rh = rx1 - rx0, ry1 - ry0
+
+    if rw < crop_min or rh < crop_min:
+        if logger:
+            logger.warning(
+                f"[YOLO-BLACK] Stage1 ROI 过小 {rw}×{rh} < {crop_min}，跳过黑三角检测"
+            )
+        return []
+
+    # 必须与相机帧缓冲区脱钩：切片常为非连续视图，直接喂 cv2image/NPU 易 SIGSEGV
+    slab = np.ascontiguousarray(
+        img_rgb[ry0:ry1, rx0:rx1], dtype=np.uint8
+    ).copy()
+    if slab.size == 0:
+        return []
+
+    _save_roi = bool(getattr(cfg, "TRIANGLE_BLACK_YOLO_SAVE_ROI_CROP", False))
+
+    try:
+        from maix import image as maix_image
+
+        # copy=True：零拷贝时 detect 内 OpenCV 可能对底层 Mat release 触发 !fixedSize() 断言。
+        roi_maix = maix_image.cv2image(slab, False, True)
+    except Exception as e:
+        if logger:
+            logger.warning(f"[YOLO-BLACK] 裁切图转 Maix image 失败: {e}")
+        return []
+
+    try:
+        raw = det.detect(roi_maix, conf_th=conf_th, iou_th=iou_th)
+    except Exception as e:
+        if logger:
+            logger.warning(f"[YOLO-BLACK] detect 异常: {e}")
+        return []
+
+    objs = _normalize_objs(raw if raw is not None else [])
+    net_w = int(det.input_width())
+    net_h = int(det.input_height())
+
+    n_raw = len(objs)
+    n_cls_ok = 0
+    n_too_small = 0
+
+    out_local = []
+    for o in objs:
+        cid = _det_obj_class_id(o)
+        if cid is None or cid not in class_ids:
+            continue
+        n_cls_ok += 1
+        x0f, y0f, x1f, y1f = _det_to_src_xyxy(o, coord_mode, rw, rh, net_w, net_h)
+        lx0 = max(0, min(float(x0f), rw - 1))
+        ly0 = max(0, min(float(y0f), rh - 1))
+        lx1 = max(lx0 + 1, min(float(x1f), rw))
+        ly1 = max(ly0 + 1, min(float(y1f), rh))
+        lx0, ly0, lx1, ly1 = int(round(lx0)), int(round(ly0)), int(round(lx1)), int(round(ly1))
+        if (lx1 - lx0) < min_side or (ly1 - ly0) < min_side:
+            n_too_small += 1
+            continue
+        lx0, ly0, lx1, ly1 = _expand_xyxy_local(
+            lx0, ly0, lx1, ly1, rw, rh, margin_frac
+        )
+        out_local.append((lx0, ly0, lx1, ly1))
+
+    out_local.sort(key=lambda t: ((t[1] + t[3]) * 0.5, (t[0] + t[2]) * 0.5))
+
+    if logger and bool(
+        getattr(cfg, "TRIANGLE_BLACK_YOLO_LOG_EACH_SHOT", True)
+    ):
+        msg = (
+            f"[YOLO-BLACK] Stage1裁切{rw}×{rh}上推理: raw={n_raw} 类∈{class_ids}→{n_cls_ok} "
+            f"过小丢弃→{n_too_small} 最终子框={len(out_local)} "
+            f"(conf={conf_th}, coord={coord_mode}, net={net_w}×{net_h}, "
+            f"ring全图=[{rx0},{ry0},{rx1},{ry1}])"
+        )
+        logger.info(msg)
+        if n_raw > 0 and n_cls_ok == 0:
+            seen = []
+            for o in objs[:8]:
+                cid = _det_obj_class_id(o)
+                sc = getattr(o, "score", None)
+                try:
+                    sc_f = float(sc) if sc is not None else None
+                except Exception:
+                    sc_f = None
+                seen.append(f"cls={cid},score={sc_f}")
+            logger.info(
+                f"[YOLO-BLACK] 有框但类别不在 {class_ids} 内；前几条: {seen}。"
+                f"请核对 TRIANGLE_BLACK_YOLO_CLASS_IDS。"
+            )
+        elif n_cls_ok > 0 and len(out_local) == 0:
+            logger.info(
+                f"[YOLO-BLACK] {n_cls_ok} 个目标类框但边长均 < min_side={min_side}，已全部丢弃。"
+            )
+
+    if _save_roi:
+        try:
+            base = (getattr(cfg, "TRIANGLE_BLACK_YOLO_ROI_CROP_DIR", "") or "").strip()
+            if not base:
+                base = os.path.join(
+                    getattr(cfg, "PHOTO_DIR", "/tmp") or "/tmp", "stage2_roi"
+                )
+            _draw = bool(
+                getattr(cfg, "TRIANGLE_BLACK_YOLO_SAVE_ROI_DRAW_BOXES", True)
+            )
+            _roi_max_raw = getattr(
+                cfg, "TRIANGLE_BLACK_YOLO_STAGE2_ROI_MAX_IMAGES", None
+            )
+            try:
+                _roi_max = (
+                    int(_roi_max_raw)
+                    if _roi_max_raw is not None
+                    else int(getattr(cfg, "MAX_IMAGES", 1000))
+                )
+            except (TypeError, ValueError):
+                _roi_max = int(getattr(cfg, "MAX_IMAGES", 1000))
+            slab_copy = np.ascontiguousarray(slab, dtype=np.uint8).copy()
+            boxes_copy = [tuple(t) for t in out_local]
+            threading.Thread(
+                target=_stage2_roi_crop_save_worker,
+                args=(
+                    slab_copy,
+                    boxes_copy,
+                    rx0,
+                    ry0,
+                    rw,
+                    rh,
+                    base,
+                    _draw,
+                    92,
+                    _roi_max,
+                    logger,
+                ),
+                daemon=True,
+            ).start()
+        except Exception as e:
+            if logger:
+                logger.warning(f"[YOLO-BLACK] 提交异步保存裁切图失败: {e}")
+
+    return out_local

test/test_audio.py

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

test/test_button.py

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

test/test_camera_rtsp.py

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

test/test_cammera.py

@@ -3,7 +3,10 @@ from maix import camera, display, time
 
 try:
     print("Initializing camera...")
-    cam = camera.Camera(640, 480)
+    cam = camera.Camera(640,480)
+    # cam = camera.Camera(1280,720)
+    # cam.get_exposure_us()
+    # print("Camera exposure: ", cam.get_exposure_us())
     print("Camera initialized successfully!")
     
     disp = display.Display()

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_laser.py

@@ -1,172 +1,246 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
-激光模块测试脚本
-用于诊断激光开关问题
-
-使用方法：
-python test_laser.py
-
-功能：
-1. 初始化串口
-2. 循环测试激光开/关
-3. 打印详细调试信息
+M01激光测距模块测试脚本 - 修正版
+基于文档中的完整命令示例
 """
 
 from maix import uart, pinmap, time
+import binascii
 
 # ==================== 配置 ====================
-UART_PORT = "/dev/ttyS1"   # 激光模块连接的串口（UART1）
-BAUDRATE = 9600            # 波特率
-
-# 引脚映射（确保与硬件连接一致）
-print("=" * 50)
-print("🔧 步骤1: 配置引脚映射")
-print("=" * 50)
+UART_PORT = "/dev/ttyS1"
+BAUDRATE = 9600
 
+# 初始化串口
 try:
     pinmap.set_pin_function("A18", "UART1_RX")
-    print("✅ A18 -> UART1_RX")
-except Exception as e:
-    print(f"❌ A18 配置失败: {e}")
-
-try:
     pinmap.set_pin_function("A19", "UART1_TX")
-    print("✅ A19 -> UART1_TX")
-except Exception as e:
-    print(f"❌ A19 配置失败: {e}")
-
-# ==================== 激光控制指令 ====================
-MODULE_ADDR = 0x00
-
-# 原始命令
-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])
-
-# 备用命令格式（如果原始命令不工作，可以尝试这些）
-# 格式1: 简化命令
-LASER_ON_CMD_ALT1 = bytes([0xAA, 0x01, 0x01])
-LASER_OFF_CMD_ALT1 = bytes([0xAA, 0x01, 0x00])
-
-# 格式2: 不同的协议头
-LASER_ON_CMD_ALT2 = bytes([0x55, 0xAA, 0x01])
-LASER_OFF_CMD_ALT2 = bytes([0x55, 0xAA, 0x00])
-
-print("\n" + "=" * 50)
-print("🔧 步骤2: 初始化串口")
-print("=" * 50)
-print(f"设备: {UART_PORT}")
-print(f"波特率: {BAUDRATE}")
-
-try:
     laser_uart = uart.UART(UART_PORT, BAUDRATE)
-    print(f"✅ 串口初始化成功: {laser_uart}")
+    print("✅ 硬件初始化完成")
 except Exception as e:
-    print(f"❌ 串口初始化失败: {e}")
+    print(f"❌ 初始化失败: {e}")
     exit(1)
 
-# ==================== 测试函数 ====================
-def send_and_check(cmd, name):
-    """发送命令并检查回包"""
-    print(f"\n📤 发送: {name}")
-    print(f"   命令字节: {cmd.hex()}")
-    print(f"   命令长度: {len(cmd)} 字节")
+# ==================== 根据文档的完整命令集 ====================
+# 1. 激光开关（文档2.3.10，已验证可用）
+LASER_ON_CMD = bytes([0xAA, 0x00, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x01, 0xC1])
+LASER_OFF_CMD = bytes([0xAA, 0x00, 0x01, 0xBE, 0x00, 0x01, 0x00, 0x00, 0xC0])
 
-    # 清空接收缓冲区
+# 2. 尝试不同的测距命令格式
+TEST_COMMANDS = [
+    # 格式1：文档2.3.12的单次测量（您测试失败的）
+    {
+        "name": "单次测量 (0x0020)",
+        "cmd": bytes([0xAA, 0x00, 0x00, 0x20, 0x00, 0x01, 0x00, 0x00, 0x21]),
+        "desc": "文档2.3.12 示例命令"
+    },
+    # 格式2：文档2.3.7的读取测量结果
+    {
+        "name": "读取测量结果 (0x0022)",
+        "cmd": bytes([0xAA, 0x80, 0x00, 0x22, 0xA2]),
+        "desc": "文档2.3.7 读取测量结果"
+    },
+    # 格式3：文档2.3.13的快速测量
+    {
+        "name": "快速测量 (0x0022带数据)",
+        "cmd": bytes([0xAA, 0x00, 0x00, 0x22, 0x00, 0x01, 0x00, 0x00, 0x23]),
+        "desc": "文档2.3.13 快速测量"
+    },
+    # 格式4：连续测量模式
+    {
+        "name": "连续测量模式 (0x0021)",
+        "cmd": bytes([0xAA, 0x00, 0x00, 0x21, 0x00, 0x01, 0x00, 0x00, 0x22]),
+        "desc": "文档2.3.14 连续测量"
+    }
+]
+
+def clear_buffer():
+    """清空串口缓冲区"""
     try:
-        old_data = laser_uart.read(-1)
-        if old_data:
-            print(f"   清空缓冲区: {len(old_data)} 字节")
+        data = laser_uart.read(-1)
+        if data:
+            print(f"清空: {len(data)}字节")
     except:
         pass
 
-    # 发送命令
+def send_and_wait(cmd, name, wait_time=2000):
+    """发送命令并等待响应"""
+    print(f"\n📤 发送: {name}")
+    print(f"   命令: {cmd.hex()}")
+    
+    clear_buffer()
+    
     try:
-        written = laser_uart.write(cmd)
-        print(f"   写入字节数: {written}")
+        laser_uart.write(cmd)
+        print(f"   已发送 {len(cmd)} 字节")
     except Exception as e:
-        print(f"   ❌ 写入失败: {e}")
+        print(f"   ❌ 发送失败: {e}")
         return None
     
     # 等待响应
-    time.sleep_ms(100)
+    start_time = time.ticks_ms()
+    response = b""
     
-    # 读取回包
-    try:
-        resp = laser_uart.read(50)
-        if resp:
-            print(f"   📥 收到回包: {resp.hex()} ({len(resp)} 字节)")
-            return resp
-        else:
-            print(f"   ⚠️ 无回包")
-            return None
-    except Exception as e:
-        print(f"   ❌ 读取失败: {e}")
+    while time.ticks_ms() - start_time < wait_time:
+        try:
+            chunk = laser_uart.read(1)
+            if chunk:
+                response += chunk
+                # 完整响应通常是9或13字节
+                if len(response) >= 9:
+                    # 检查是否完整帧
+                    if response[0] in [0xAA, 0xEE]:
+                        if len(response) >= 13:  # 测距完整响应
+                            break
+                        elif response[0] == 0xEE:  # 错误响应
+                            break
+        except:
+            break
+        
+        time.sleep_ms(10)
+    
+    if response:
+        print(f"   📥 响应: {response.hex()}")
+        print(f"   长度: {len(response)} 字节")
+        
+        # 解析错误码
+        if response[0] == 0xEE and len(response) >= 9:
+            err_code = (response[7] << 8) | response[8]
+            error_mapping = {
+                0x0000: "无错误",
+                0x0001: "硬件错误",
+                0x0002: "无输出数据", 
+                0x0003: "反射信号太弱",
+                0x0004: "反射信号太强",
+                0x0005: "温度太高(>40℃)",
+                0x0006: "温度太低(<-10℃)",
+                0x0007: "电源电压低(<2.5V)",
+                0x0008: "超出量程",
+                0x0009: "读通讯错误",
+                0x000A: "写通讯错误",
+                0x000B: "地址错误"
+            }
+            err_msg = error_mapping.get(err_code, f"未知错误: 0x{err_code:04X}")
+            print(f"   ❌ 模块错误: {err_msg}")
+    else:
+        print("   ⚠️ 无响应")
+    
+    return response
+
+def parse_distance_data(response):
+    """解析距离数据"""
+    if not response or len(response) < 13:
         return None
     
-def test_laser_cycle(on_cmd, off_cmd, cmd_name="标准命令"):
-    """测试一个开关周期"""
-    print(f"\n{'='*50}")
-    print(f"🧪 测试 {cmd_name}")
-    print(f"{'='*50}")
+    if response[0] != 0xAA or response[3] not in [0x20, 0x21, 0x22]:
+        return None
     
-    print("\n>>> 测试开启激光")
-    send_and_check(on_cmd, f"{cmd_name} - 开启")
-    print("   ⏱️ 等待 2 秒观察激光是否亮起...")
-    time.sleep(2)
+    # 解析4字节BCD码
+    bcd_bytes = response[6:10]
+    distance_int = 0
     
-    print("\n>>> 测试关闭激光")
-    send_and_check(off_cmd, f"{cmd_name} - 关闭")
-    print("   ⏱️ 等待 2 秒观察激光是否熄灭...")
-    time.sleep(2)
+    for byte in bcd_bytes:
+        high = (byte >> 4) & 0x0F
+        low = byte & 0x0F
+        
+        if high > 9 or low > 9:
+            return None
+        
+        distance_int = distance_int * 100 + high * 10 + low
+    
+    distance_m = distance_int / 1000.0
+    
+    # 信号质量
+    signal = 0
+    if len(response) >= 12:
+        signal = (response[10] << 8) | response[11]
+    
+    return {
+        'meters': distance_m,
+        'millimeters': distance_m * 1000,
+        'signal': signal,
+        'raw': response.hex()
+    }
 
 # ==================== 主测试 ====================
-print("\n" + "=" * 50)
-print("🚀 开始激光测试")
-print("=" * 50)
-print("\n请观察激光模块的状态变化...")
-print("测试将依次尝试不同的命令格式\n")
+print("\n" + "="*50)
+print("M01激光测距模块详细测试")
+print("="*50)
 
 try:
-    # 测试1: 标准命令
-    test_laser_cycle(LASER_ON_CMD, LASER_OFF_CMD, "标准命令")
+    # 1. 测试基本连接
+    print("\n1. 测试模块连接...")
+    version_cmd = bytes([0xAA, 0x80, 0x00, 0x0A, 0x8A])
+    resp = send_and_wait(version_cmd, "读取硬件版本")
     
-    input("\n按回车继续测试备用命令1...")
+    if resp and resp[0] == 0xAA and resp[3] == 0x0A:
+        print(f"✅ 模块正常，版本: {resp[6]:02X}{resp[7]:02X}")
+    else:
+        print("❌ 模块连接测试失败")
+        exit(1)
     
-    # 测试2: 备用命令格式1
-    test_laser_cycle(LASER_ON_CMD_ALT1, LASER_OFF_CMD_ALT1, "备用命令1 (简化)")
+    # 2. 开启激光
+    print("\n2. 开启激光...")
+    resp = send_and_wait(LASER_ON_CMD, "开启激光", 1000)
+    if resp and resp.hex() == "aa0001be00010001c1":
+        print("✅ 激光已开启")
     
-    input("\n按回车继续测试备用命令2...")
+    print("   等待激光稳定...")
+    time.sleep(2)  # 重要等待时间
     
-    # 测试3: 备用命令格式2
-    test_laser_cycle(LASER_ON_CMD_ALT2, LASER_OFF_CMD_ALT2, "备用命令2 (0x55AA头)")
+    # 3. 尝试不同的测距命令
+    print("\n3. 测试不同测距命令...")
     
-    print("\n" + "=" * 50)
+    for i, test_cmd in enumerate(TEST_COMMANDS):
+        print(f"\n{'='*30}")
+        print(f"测试 {i+1}: {test_cmd['name']}")
+        print(f"{test_cmd['desc']}")
+        print(f"{'='*30}")
+        
+        resp = send_and_wait(test_cmd['cmd'], test_cmd['name'], 3000)
+        
+        if resp:
+            if resp[0] == 0xAA and len(resp) >= 13:
+                result = parse_distance_data(resp)
+                if result:
+                    print(f"✅ 测距成功!")
+                    print(f"   距离: {result['meters']:.3f} m")
+                    print(f"   距离: {result['millimeters']:.1f} mm")
+                    print(f"   信号质量: {result['signal']}")
+                    break
+                else:
+                    print("❌ 无法解析距离数据")
+            elif resp[0] == 0xEE:
+                print("❌ 命令执行错误")
+            else:
+                print("❌ 无效响应格式")
+        else:
+            print("❌ 无响应")
+        
+        time.sleep(1)  # 命令间间隔
+    
+    # 4. 关闭激光
+    print("\n4. 关闭激光...")
+    send_and_wait(LASER_OFF_CMD, "关闭激光", 1000)
+    
+    print("\n" + "="*50)
     print("🏁 测试完成")
-    print("=" * 50)
-    print("\n诊断建议：")
-    print("1. 如果激光始终不亮/始终亮：")
-    print("   - 检查激光模块的电源连接")
-    print("   - 检查串口TX/RX是否接反")
-    print("   - 尝试不同的波特率 (4800/19200)")
-    print("")
-    print("2. 如果有回包但激光无反应：")
-    print("   - 命令格式可能正确但激光硬件问题")
-    print("")
-    print("3. 如果某个备用命令有效：")
-    print("   - 需要更新 config.py 中的命令格式")
+    print("="*50)
+    
+    print("\n📋 测试总结：")
+    print("1. 模块通信: ✅ 正常")
+    print("2. 激光控制: ✅ 正常")
+    print("3. 测距功能: ❌ 有问题")
+    print("\n建议：")
+    print("1. 检查激光是否实际发光（在暗处观察红点）")
+    print("2. 确保测量目标在有效范围内（0.2-60米）")
+    print("3. 确保目标有足够反射率（白色平面最佳）")
+    print("4. 如果所有测距命令都返回ERR_ADDR，可能是固件版本问题")
     
 except KeyboardInterrupt:
-    print("\n\n🛑 测试被中断")
-    # 确保激光关闭
+    print("\n\n🛑 用户中断")
     laser_uart.write(LASER_OFF_CMD)
     print("✅ 已发送关闭指令")
 except Exception as e:
     print(f"\n❌ 测试出错: {e}")
-    import traceback
-    traceback.print_exc()
-
-
-
-
-

test/test_motor.py

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

train_yolo/test_stage2_black_yolo_device.py

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

train_yolo/test_triangle_one_image.py

@@ -0,0 +1,242 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+单张图片快速测试：三角形四角标记识别 + 单应性落点 + PnP 估距
+
+用法（在板子上）：
+  python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --out /root/phot/tri_out.jpg
+
+调参对比（不改代码，临时覆盖 config.TRIANGLE_*）：
+  python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --preset shadow
+  python3 test/test_triangle_one_image.py --image /root/phot/xxx.jpg --max-interior-gray 160 --min-dark-ratio 0.20
+"""
+
+import argparse
+import json
+import os
+import time
+from typing import Any, Dict, Tuple
+
+import cv2
+import numpy as np
+
+import config
+import triangle_target as tri_mod
+from triangle_target import (
+    detect_triangle_markers,
+    load_camera_from_xml,
+    load_triangle_positions,
+    try_triangle_scoring,
+)
+
+
+def _apply_overrides(args) -> None:
+    # 预设：阴影/低对比度场景更宽松（尽量保持速度：不启 CLAHE）
+    if args.preset == "shadow":
+        setattr(config, "TRIANGLE_ENABLE_CLAHE_FALLBACK", False)
+        setattr(config, "TRIANGLE_MIN_CONTRAST_DIFF", 0)
+        setattr(config, "TRIANGLE_MAX_INTERIOR_GRAY", 160)
+        setattr(config, "TRIANGLE_DARK_PIXEL_GRAY", 160)
+        setattr(config, "TRIANGLE_MIN_DARK_RATIO", 0.20)
+        # adaptive 只在 Otsu 失败时尝试，保持尝试次数很少
+        setattr(config, "TRIANGLE_ADAPTIVE_BLOCK_SIZES", (21,))
+
+    # 手动覆盖（优先级高于 preset）
+    if args.max_interior_gray is not None:
+        setattr(config, "TRIANGLE_MAX_INTERIOR_GRAY", int(args.max_interior_gray))
+    if args.dark_pixel_gray is not None:
+        setattr(config, "TRIANGLE_DARK_PIXEL_GRAY", int(args.dark_pixel_gray))
+    if args.min_dark_ratio is not None:
+        setattr(config, "TRIANGLE_MIN_DARK_RATIO", float(args.min_dark_ratio))
+    if args.min_contrast_diff is not None:
+        setattr(config, "TRIANGLE_MIN_CONTRAST_DIFF", int(args.min_contrast_diff))
+    if args.detect_scale is not None:
+        setattr(config, "TRIANGLE_DETECT_SCALE", float(args.detect_scale))
+    if args.adaptive_blocks is not None:
+        bs = tuple(int(x) for x in args.adaptive_blocks.split(",") if x.strip())
+        setattr(config, "TRIANGLE_ADAPTIVE_BLOCK_SIZES", bs)
+
+
+def _dump_config() -> Dict[str, Any]:
+    keys = [
+        "TRIANGLE_DETECT_SCALE",
+        "TRIANGLE_SIZE_RANGE",
+        "TRIANGLE_MAX_INTERIOR_GRAY",
+        "TRIANGLE_DARK_PIXEL_GRAY",
+        "TRIANGLE_MIN_DARK_RATIO",
+        "TRIANGLE_MIN_CONTRAST_DIFF",
+        "TRIANGLE_ADAPTIVE_BLOCK_SIZES",
+        "TRIANGLE_MAX_FILTERED_FOR_COMBO",
+        "TRIANGLE_EARLY_EXIT_CANDIDATES",
+        "TRIANGLE_ENABLE_CLAHE_FALLBACK",
+    ]
+    out = {}
+    for k in keys:
+        out[k] = getattr(config, k, None)
+    return out
+
+
+def _draw_tri_debug(img_bgr: np.ndarray, tri: Dict[str, Any]) -> np.ndarray:
+    out = img_bgr.copy()
+    markers = tri.get("markers") or []
+
+    # 画三角形轮廓 + center + id
+    for m in markers:
+        corners = np.array(m.get("corners", []), dtype=np.int32)
+        if corners.size == 0:
+            continue
+        cv2.polylines(out, [corners], True, (0, 255, 0), 2)
+        c = m.get("center") or (corners[:, 0].mean(), corners[:, 1].mean())
+        cx, cy = int(c[0]), int(c[1])
+        cv2.circle(out, (cx, cy), 4, (0, 0, 255), -1)
+        mid = m.get("id", "?")
+        cv2.putText(out, f"T{mid}", (cx - 18, cy - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 1)
+
+    # 若有 homography，画靶心（把 (0,0) 反投影到图像）
+    H = tri.get("homography")
+    if H is not None:
+        try:
+            H = np.array(H, dtype=np.float64)
+            H_inv = np.linalg.inv(H)
+            c_img = cv2.perspectiveTransform(np.array([[[0.0, 0.0]]], dtype=np.float32), H_inv)[0][0]
+            ocx, ocy = int(c_img[0]), int(c_img[1])
+            cv2.circle(out, (ocx, ocy), 5, (0, 0, 255), -1)
+            cv2.circle(out, (ocx, ocy), 10, (0, 0, 255), 1)
+        except Exception:
+            pass
+
+    # 叠加结果信息
+    lines = []
+    if tri.get("ok"):
+        lines.append("tri_ok=True")
+        if tri.get("dx_cm") is not None and tri.get("dy_cm") is not None:
+            lines.append(f"dx,dy=({tri['dx_cm']:.2f},{tri['dy_cm']:.2f})cm")
+        if tri.get("distance_m") is not None:
+            lines.append(f"dist={float(tri['distance_m']):.2f}m")
+    else:
+        lines.append("tri_ok=False")
+
+    y0 = 22
+    for i, t in enumerate(lines):
+        cv2.putText(out, t, (10, y0 + i * 18), cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 255, 0), 1)
+    return out
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--image", required=True, help="输入图片路径（jpg/png）")
+    ap.add_argument("--out", default="", help="输出标注图片路径（可选）")
+    ap.add_argument("--laser-x", type=int, default=-1, help="激光点 x（像素），默认用图像中心")
+    ap.add_argument("--laser-y", type=int, default=-1, help="激光点 y（像素），默认用图像中心")
+    ap.add_argument("--preset", choices=["", "shadow"], default="", help="调参预设（shadow=阴影更鲁棒，不启 CLAHE）")
+    ap.add_argument("--max-interior-gray", type=int, default=None)
+    ap.add_argument("--dark-pixel-gray", type=int, default=None)
+    ap.add_argument("--min-dark-ratio", type=float, default=None)
+    ap.add_argument("--min-contrast-diff", type=int, default=None)
+    ap.add_argument("--detect-scale", type=float, default=None)
+    ap.add_argument("--adaptive-blocks", default=None, help="例如: 11,21 ；为空表示不改")
+    ap.add_argument("--verbose", action="store_true", help="输出更多检测阶段信息")
+    args = ap.parse_args()
+
+    _apply_overrides(args)
+    # triangle_target.py 的日志默认写到 logger_manager；在离线脚本里 logger 可能未初始化。
+    # verbose 模式下把 _log 重定向为 print，方便直接看到诊断信息。
+    if args.verbose:
+        try:
+            tri_mod._log = lambda msg: print(str(msg))
+        except Exception:
+            pass
+
+    img_bgr = cv2.imread(args.image, cv2.IMREAD_COLOR)
+    if img_bgr is None:
+        raise SystemExit(f"读图失败：{args.image}")
+    # triangle_target.try_triangle_scoring 约定输入为 RGB；OpenCV imread 返回 BGR
+    img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
+
+    h, w = img_bgr.shape[:2]
+    if args.laser_x >= 0 and args.laser_y >= 0:
+        laser_point = (int(args.laser_x), int(args.laser_y))
+    else:
+        laser_point = (w // 2, h // 2)
+
+    K, dist = load_camera_from_xml(getattr(config, "CAMERA_CALIB_XML", ""))
+    pos = load_triangle_positions(getattr(config, "TRIANGLE_POSITIONS_JSON", ""))
+
+    print("[tri-test] image:", args.image, "shape:", (h, w))
+    print("[tri-test] laser_point:", laser_point)
+    print("[tri-test] calib_ok:", bool(K is not None and dist is not None), "pos_ok:", bool(pos))
+    print("[tri-test] config:", json.dumps(_dump_config(), ensure_ascii=False))
+
+    # 先单独跑一次三角形候选检测，便于区分“没找到候选” vs “找到候选但评分/单应性失败”
+    scale = float(getattr(config, "TRIANGLE_DETECT_SCALE", 0.5) or 0.5)
+    if not (0.05 <= scale <= 1.0):
+        scale = 0.5
+    long_side = max(h, w)
+    max_dim = max(64, int(long_side * scale))
+    if long_side > max_dim:
+        det_scale = max_dim / long_side
+        det_w = int(w * det_scale)
+        det_h = int(h * det_scale)
+        img_det = cv2.resize(img_bgr, (det_w, det_h), interpolation=cv2.INTER_LINEAR)
+        inv_scale = 1.0 / det_scale
+        size_range_det = (
+            max(4, int(getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))[0] * det_scale)),
+            max(8, int(getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))[1] * det_scale)),
+        )
+    else:
+        img_det = img_bgr
+        inv_scale = 1.0
+        size_range_det = getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500))
+
+    gray = cv2.cvtColor(img_det, cv2.COLOR_BGR2GRAY)
+    markers_det = detect_triangle_markers(
+        gray,
+        orig_gray=gray,
+        size_range=size_range_det,
+        verbose=bool(args.verbose),
+    )
+    if inv_scale != 1.0 and markers_det:
+        for m in markers_det:
+            m["center"] = [m["center"][0] * inv_scale, m["center"][1] * inv_scale]
+            m["corners"] = [[c[0] * inv_scale, c[1] * inv_scale] for c in m["corners"]]
+
+    print("[tri-test] markers_found:", len(markers_det), "ids:", [m.get("id") for m in markers_det])
+
+    t0 = time.time()
+    tri = try_triangle_scoring(
+        img_rgb,  # try_triangle_scoring 期望 RGB
+        laser_point,
+        pos,
+        K,
+        dist,
+        size_range=getattr(config, "TRIANGLE_SIZE_RANGE", (8, 500)),
+    )
+    dt_ms = int(round((time.time() - t0) * 1000))
+
+    print("[tri-test] elapsed_ms:", dt_ms)
+    print(json.dumps(tri, ensure_ascii=False, indent=2))
+
+    if args.out:
+        out_path = args.out
+        # 允许传目录（如 ./），自动生成文件名；未带扩展名时默认 .jpg
+        if out_path.endswith("/") or out_path.endswith("\\") or os.path.isdir(out_path):
+            out_path = os.path.join(out_path, "tri_out.jpg")
+        root, ext = os.path.splitext(out_path)
+        if not ext:
+            out_path = root + ".jpg"
+
+        # 若 try_triangle_scoring 失败且没带回 markers，至少把候选 markers 画出来，方便肉眼判断
+        tri_for_draw = tri if isinstance(tri, dict) else {"ok": False}
+        if not tri_for_draw.get("markers") and markers_det:
+            tri_for_draw = dict(tri_for_draw)
+            tri_for_draw["markers"] = markers_det
+        out_img = _draw_tri_debug(img_bgr, tri_for_draw)
+        ok = cv2.imwrite(out_path, out_img)
+        if not ok:
+            raise SystemExit(f"写图失败（可能是不支持的扩展名）：{out_path}")
+        print("[tri-test] wrote:", out_path)
+
+
+if __name__ == "__main__":
+    main()
+

train_yolo/test_yolo_draw_boxes.py

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

train_yolo/train_black_triangle_pos.py

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

triangle_positions.json

@@ -0,0 +1,6 @@
+{
+    "0": [-20.0, -20.0, 0.0],
+    "1": [-20.0,  20.0, 0.0],
+    "2": [ 20.0,  20.0, 0.0],
+    "3": [ 20.0, -20.0, 0.0]
+}

version.md

@@ -0,0 +1,31 @@
+# 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

version.py

@@ -4,25 +4,6 @@
 应用版本号
 每次 OTA 更新时，只需要更新这个文件中的版本号
 """
-VERSION = '1.2.10'
-
-# 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
-
-
-
-
-
-
-
+VERSION = '2.16.4'
 
 

vision.py

@@ -14,10 +14,6 @@ from maix import image
 import config
 from logger_manager import logger_manager
 
-# 导入ArUco检测器（如果启用）
-if config.USE_ARUCO:
-    from aruco_detector import detect_target_with_aruco, aruco_detector
-
 # 存图队列 + worker
 _save_queue = queue.Queue(maxsize=16)
 _save_worker_started = False
@@ -217,7 +213,7 @@ def check_image_sharpness(frame, threshold=100.0, save_debug_images=False):
                 
                 # 保存原始图像
                 img_orig = image.cv2image(img_cv, False, False)
-                orig_filename = f"{debug_dir}/sharpness_debug_orig_{img_count:04d}.bmp"
+                orig_filename = f"{debug_dir}/sharpness_debug_orig_{img_count:04d}.jpg"
                 img_orig.save(orig_filename)
                 
                 # # 保存边缘检测结果（可视化）
@@ -294,7 +290,7 @@ def save_calibration_image(frame, laser_pos, photo_dir=None):
             img_count = 0
         
         x, y = laser_pos
-        filename = f"{photo_dir}/calibration_{int(x)}_{int(y)}_{img_count:04d}.bmp"
+        filename = f"{photo_dir}/calibration_{int(x)}_{int(y)}_{img_count:04d}.jpg"
         
         logger = logger_manager.logger
         if logger:
@@ -334,196 +330,454 @@ def save_calibration_image(frame, laser_pos, photo_dir=None):
             logger.error(traceback.format_exc())
         return None
 
-def detect_circle_v3(frame, laser_point=None):
+# def detect_circle_v3(frame, laser_point=None):
+#     """检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本
+#     增加红色圆圈检测，验证黄色圆圈是否为真正的靶心
+#     如果提供 laser_point，会选择最接近激光点的目标
+    
+#     Args:
+#         frame: 图像帧
+#         laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择
+    
+#     Returns:
+#         (result_img, best_center, best_radius, method, best_radius1, ellipse_params)
+#     """
+#     img_cv = image.image2cv(frame, False, False)
+    
+#     best_center = best_radius = best_radius1 = method = None
+#     ellipse_params = None
+    
+#     # HSV 黄色掩码检测（模糊靶心）
+#     hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+#     h, s, v = cv2.split(hsv)
+
+#     # 调整饱和度策略：稍微增强，不要过度
+#     s = np.clip(s * 1.1, 0, 255).astype(np.uint8) 
+
+#     hsv = cv2.merge((h, s, v))
+
+#     # 放宽 HSV 阈值范围（针对模糊图像的关键调整）
+#     lower_yellow = np.array([7, 80, 0])   # 饱和度下限降低，捕捉淡黄色
+#     upper_yellow = np.array([32, 255, 255]) # 亮度上限拉满
+
+#     mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
+
+#     # 调整形态学操作
+#     kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+#     mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel) 
+
+#     contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+#     # 存储所有有效的黄色-红色组合
+#     valid_targets = []
+    
+#     if contours_yellow:
+#         for cnt_yellow in contours_yellow:
+#             area = cv2.contourArea(cnt_yellow)
+#             perimeter = cv2.arcLength(cnt_yellow, True)
+
+#             # 计算圆度
+#             if perimeter > 0:
+#                 circularity = (4 * np.pi * area) / (perimeter * perimeter)
+#             else:
+#                 circularity = 0
+
+#             logger = logger_manager.logger
+#             if area > 50 and circularity > 0.7:
+#                 if logger:
+#                     logger.info(f"[target] -> 面积:{area}, 圆度:{circularity:.2f}")
+#                 # 尝试拟合椭圆
+#                 yellow_center = None
+#                 yellow_radius = None
+#                 yellow_ellipse = None
+                
+#                 if len(cnt_yellow) >= 5:
+#                     (x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
+#                     yellow_ellipse = ((x, y), (width, height), angle)
+#                     axes_minor = min(width, height)
+#                     radius = axes_minor / 2
+#                     yellow_center = (int(x), int(y))
+#                     yellow_radius = int(radius)
+#                 else:
+#                     (x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
+#                     yellow_center = (int(x), int(y))
+#                     yellow_radius = int(radius)
+#                     yellow_ellipse = None
+
+#                 # 如果检测到黄色圆圈，再检测红色圆圈进行验证
+#                 if yellow_center and yellow_radius:
+#                     # HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围）
+#                     # 红色范围1: 0-10度（接近0度的红色）
+#                     lower_red1 = np.array([0, 80, 0])
+#                     upper_red1 = np.array([10, 255, 255])
+#                     mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
+                    
+#                     # 红色范围2: 170-180度（接近180度的红色）
+#                     lower_red2 = np.array([170, 80, 0])
+#                     upper_red2 = np.array([180, 255, 255])
+#                     mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2)
+                    
+#                     # 合并两个红色掩码
+#                     mask_red = cv2.bitwise_or(mask_red1, mask_red2)
+                    
+#                     # 形态学操作
+#                     kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+#                     mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
+                    
+#                     contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                    
+#                     found_valid_red = False
+                    
+#                     if contours_red:
+#                         # 找到所有符合条件的红色圆圈
+#                         for cnt_red in contours_red:
+#                             area_red = cv2.contourArea(cnt_red)
+#                             perimeter_red = cv2.arcLength(cnt_red, True)
+                            
+#                             if perimeter_red > 0:
+#                                 circularity_red = (4 * np.pi * area_red) / (perimeter_red * perimeter_red)
+#                             else:
+#                                 circularity_red = 0
+                            
+#                             # 红色圆圈也应该有一定的圆度
+#                             if area_red > 50 and circularity_red > 0.6:
+#                                 # 计算红色圆圈的中心和半径
+#                                 if len(cnt_red) >= 5:
+#                                     (x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red)
+#                                     radius_red = min(w_red, h_red) / 2
+#                                     red_center = (int(x_red), int(y_red))
+#                                     red_radius = int(radius_red)
+#                                 else:
+#                                     (x_red, y_red), radius_red = cv2.minEnclosingCircle(cnt_red)
+#                                     red_center = (int(x_red), int(y_red))
+#                                     red_radius = int(radius_red)
+                                
+#                                 # 计算黄色和红色圆心的距离
+#                                 if red_center:
+#                                     dx = yellow_center[0] - red_center[0]
+#                                     dy = yellow_center[1] - red_center[1]
+#                                     distance = np.sqrt(dx*dx + dy*dy)
+                                    
+#                                     # 圆心距离阈值：应该小于黄色半径的某个倍数（比如1.5倍）
+#                                     max_distance = yellow_radius * 1.5
+                                    
+#                                     # 红色圆圈应该比黄色圆圈大（外圈）
+#                                     if distance < max_distance and red_radius > yellow_radius * 0.8:
+#                                         found_valid_red = True
+#                                         logger = logger_manager.logger
+#                                         if logger:
+#                                             logger.info(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), 红心({red_center}), 距离:{distance:.1f}, 黄半径:{yellow_radius}, 红半径:{red_radius}")
+                                        
+#                                         # 记录这个有效目标
+#                                         valid_targets.append({
+#                                             'center': yellow_center,
+#                                             'radius': yellow_radius,
+#                                             'ellipse': yellow_ellipse,
+#                                             'area': area
+#                                         })
+#                                         break
+                    
+#                     if not found_valid_red:
+#                         logger = logger_manager.logger
+#                         if logger:
+#                             logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
+    
+#     # 从所有有效目标中选择最佳目标
+#     if valid_targets:
+#         if laser_point:
+#             # 如果有激光点，选择最接近激光点的目标
+#             best_target = None
+#             min_distance = float('inf')
+#             for target in valid_targets:
+#                 dx = target['center'][0] - laser_point[0]
+#                 dy = target['center'][1] - laser_point[1]
+#                 distance = np.sqrt(dx*dx + dy*dy)
+#                 if distance < min_distance:
+#                     min_distance = distance
+#                     best_target = target
+#             if best_target:
+#                 best_center = best_target['center']
+#                 best_radius = best_target['radius']
+#                 ellipse_params = best_target['ellipse']
+#                 method = "v3_ellipse_red_validated_laser_selected"
+#                 best_radius1 = best_radius * 5
+#         else:
+#             # 如果没有激光点，选择面积最大的目标
+#             best_target = max(valid_targets, key=lambda t: t['area'])
+#             best_center = best_target['center']
+#             best_radius = best_target['radius']
+#             ellipse_params = best_target['ellipse']
+#             method = "v3_ellipse_red_validated"
+#             best_radius1 = best_radius * 5
+
+#     result_img = image.cv2image(img_cv, False, False)
+#     return result_img, best_center, best_radius, method, best_radius1, ellipse_params
+
+def detect_circle_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
+        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)
     """
-    img_cv = image.image2cv(frame, False, False)
+    if img_cv is None:
+        img_cv = image.image2cv(frame, False, False)
+    logger = logger_manager.logger
+    from datetime import datetime
+    logger.debug(f"[detect_circle_v3] begin {datetime.now()}")
+    # -- 1. 缩图加速（与三角形路径保持一致）
+    h_orig, w_orig = img_cv.shape[:2]
+    MAX_DET_DIM = 480
+    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
     
-    # HSV 黄色掩码检测（模糊靶心）
-    hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
+    logger.debug(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)
-
-    # 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
-
+    lower_yellow = np.array([7, 80, 0])
+    upper_yellow = np.array([32, 255, 255])
     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)
+    logger.debug(f"[detect_circle_v3] step 2 fin {datetime.now()}")
 
-    # 存储所有有效的黄色-红色组合
-    valid_targets = []
-    
-    if contours_yellow:
-        for cnt_yellow in contours_yellow:
-            area = cv2.contourArea(cnt_yellow)
-            perimeter = cv2.arcLength(cnt_yellow, True)
-
-            # 计算圆度
-            if perimeter > 0:
-                circularity = (4 * np.pi * area) / (perimeter * perimeter)
-            else:
-                circularity = 0
-
-            logger = logger_manager.logger
-            if area > 50 and circularity > 0.7:
-                if logger:
-                    logger.info(f"[target] -> 面积:{area}, 圆度:{circularity:.2f}")
-                # 尝试拟合椭圆
-                yellow_center = None
-                yellow_radius = None
-                yellow_ellipse = None
-                
-                if len(cnt_yellow) >= 5:
-                    (x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
-                    yellow_ellipse = ((x, y), (width, height), angle)
-                    axes_minor = min(width, height)
-                    radius = axes_minor / 2
-                    yellow_center = (int(x), int(y))
-                    yellow_radius = int(radius)
-                else:
-                    (x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
-                    yellow_center = (int(x), int(y))
-                    yellow_radius = int(radius)
-                    yellow_ellipse = None
-
-                # 如果检测到黄色圆圈，再检测红色圆圈进行验证
-                if yellow_center and yellow_radius:
-                    # HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围）
-                    # 红色范围1: 0-10度（接近0度的红色）
-                    lower_red1 = np.array([0, 80, 0])
-                    upper_red1 = np.array([10, 255, 255])
-                    mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
-                    
-                    # 红色范围2: 170-180度（接近180度的红色）
-                    lower_red2 = np.array([170, 80, 0])
-                    upper_red2 = np.array([180, 255, 255])
-                    mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2)
-                    
-                    # 合并两个红色掩码
-                    mask_red = cv2.bitwise_or(mask_red1, mask_red2)
-                    
-                    # 形态学操作
-                    kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
-                    mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
-                    
-                    contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-                    
-                    found_valid_red = False
-                    
-                    if contours_red:
-                        # 找到所有符合条件的红色圆圈
-                        for cnt_red in contours_red:
-                            area_red = cv2.contourArea(cnt_red)
-                            perimeter_red = cv2.arcLength(cnt_red, True)
-                            
-                            if perimeter_red > 0:
-                                circularity_red = (4 * np.pi * area_red) / (perimeter_red * perimeter_red)
-                            else:
-                                circularity_red = 0
-                            
-                            # 红色圆圈也应该有一定的圆度
-                            if area_red > 50 and circularity_red > 0.6:
-                                # 计算红色圆圈的中心和半径
-                                if len(cnt_red) >= 5:
-                                    (x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red)
-                                    radius_red = min(w_red, h_red) / 2
-                                    red_center = (int(x_red), int(y_red))
-                                    red_radius = int(radius_red)
-                                else:
-                                    (x_red, y_red), radius_red = cv2.minEnclosingCircle(cnt_red)
-                                    red_center = (int(x_red), int(y_red))
-                                    red_radius = int(radius_red)
-                                
-                                # 计算黄色和红色圆心的距离
-                                if red_center:
-                                    dx = yellow_center[0] - red_center[0]
-                                    dy = yellow_center[1] - red_center[1]
-                                    distance = np.sqrt(dx*dx + dy*dy)
-                                    
-                                    # 圆心距离阈值：应该小于黄色半径的某个倍数（比如1.5倍）
-                                    max_distance = yellow_radius * 1.5
-                                    
-                                    # 红色圆圈应该比黄色圆圈大（外圈）
-                                    if distance < max_distance and red_radius > yellow_radius * 0.8:
-                                        found_valid_red = True
-                                        logger = logger_manager.logger
-                                        if logger:
-                                            logger.info(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), 红心({red_center}), 距离:{distance:.1f}, 黄半径:{yellow_radius}, 红半径:{red_radius}")
-                                        
-                                        # 记录这个有效目标
-                                        valid_targets.append({
-                                            'center': yellow_center,
-                                            'radius': yellow_radius,
-                                            'ellipse': yellow_ellipse,
-                                            'area': area
-                                        })
-                                        break
-                    
-                    if not found_valid_red:
-                        logger = logger_manager.logger
-                        if logger:
-                            logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
-    
-    # 从所有有效目标中选择最佳目标
-    if valid_targets:
-        if laser_point:
-            # 如果有激光点，选择最接近激光点的目标
-            best_target = None
-            min_distance = float('inf')
-            for target in valid_targets:
-                dx = target['center'][0] - laser_point[0]
-                dy = target['center'][1] - laser_point[1]
-                distance = np.sqrt(dx*dx + dy*dy)
-                if distance < min_distance:
-                    min_distance = distance
-                    best_target = target
-            if best_target:
-                best_center = best_target['center']
-                best_radius = best_target['radius']
-                ellipse_params = best_target['ellipse']
-                method = "v3_ellipse_red_validated_laser_selected"
-                best_radius1 = best_radius * 5
+    # -- 3. 红色掩码：在循环外只算一次
+    mask_red = cv2.bitwise_or(
+        cv2.inRange(hsv, np.array([0, 30, 20]),   np.array([12,  255, 255])),
+        cv2.inRange(hsv, np.array([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 <= 10:
+            continue
+        pr = cv2.arcLength(cnt_r, True)
+        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)
+            red_candidates.append({"center": (int(xr), int(yr)), "radius": int(min(wr, hr) / 2)})
         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']
+            (xr, yr), rr = cv2.minEnclosingCircle(cnt_r)
+            red_candidates.append({"center": (int(xr), int(yr)), "radius": int(rr)})
+
+    logger.debug(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
+        if logger:
+            logger.info(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)
+            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}, "
+                                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:
+            # 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()}")
+
+    # -- 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"
-            best_radius1 = best_radius * 5
-
+        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)
+    logger.debug(f"[detect_circle_v3] step 5 fin {datetime.now()}")
     return result_img, best_center, best_radius, method, best_radius1, ellipse_params
 
-
 def estimate_distance(pixel_radius):
     """根据像素半径估算实际距离（单位：米）"""
     if not pixel_radius:
         return 0.0
     return (config.REAL_RADIUS_CM * config.FOCAL_LENGTH_PIX) / pixel_radius / 100.0
 
+def _draw_yolo_roi_on_rgb_numpy(img_cv, yolo_roi_xyxy):
+    """
+    在 RGB numpy 图像上绘制靶环 YOLO ROI（与原先 shoot_manager 主线程绘制语义一致）。
+    供存图 worker 异步调用，不阻塞射箭主流程。
+    """
+    if yolo_roi_xyxy is None:
+        return
+    if not getattr(config, "TRIANGLE_YOLO_DRAW_ROI_ON_SHOT", True):
+        return
+    try:
+        rx0, ry0, rx1, ry1 = (int(round(float(v))) for v in yolo_roi_xyxy)
+        ih, iw = img_cv.shape[:2]
+        rx0 = max(0, min(rx0, iw - 1))
+        ry0 = max(0, min(ry0, ih - 1))
+        rx1 = max(rx0 + 1, min(rx1, iw))
+        ry1 = max(ry0 + 1, min(ry1, ih))
+        cv2.rectangle(
+            img_cv,
+            (rx0, ry0),
+            (rx1 - 1, ry1 - 1),
+            (0, 255, 255),
+            2,
+        )
+        cv2.putText(
+            img_cv,
+            "YOLO ROI",
+            (max(0, rx0), max(16, ry0 - 4)),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.55,
+            (0, 255, 255),
+            1,
+        )
+    except Exception:
+        pass
+
+
+def prune_old_images_in_dir(photo_dir, max_images, logger=None, log_prefix="[VISION]"):
+    """
+    若目录内 bmp/jpg/jpeg 超过 max_images，按 mtime 从最旧开始删，直到数量 ≤ max_images。
+    与射箭主图目录清理规则一致，供 PHOTO_DIR、stage2_roi 等共用。
+    """
+    try:
+        max_images = int(max_images)
+    except (TypeError, ValueError):
+        return
+    if max_images <= 0 or not photo_dir:
+        return
+    if logger is None:
+        logger = logger_manager.logger
+    try:
+        if not os.path.isdir(photo_dir):
+            return
+        image_files = []
+        for f in os.listdir(photo_dir):
+            if f.endswith((".bmp", ".jpg", ".jpeg")):
+                filepath = os.path.join(photo_dir, f)
+                try:
+                    mtime = os.path.getmtime(filepath)
+                    image_files.append((mtime, filepath, f))
+                except Exception:
+                    pass
+        if len(image_files) <= max_images:
+            return
+        image_files.sort(key=lambda x: x[0])
+        to_delete = len(image_files) - max_images
+        deleted_count = 0
+        for _, filepath, fname in image_files[:to_delete]:
+            try:
+                os.remove(filepath)
+                deleted_count += 1
+                if logger:
+                    logger.debug(f"{log_prefix} 删除旧图片: {fname}")
+            except Exception as e:
+                if logger:
+                    logger.warning(f"{log_prefix} 删除旧图片失败 {fname}: {e}")
+        if logger and deleted_count > 0:
+            logger.info(
+                f"{log_prefix} 已清理 {deleted_count} 张旧图，"
+                f"目录保留至多 {max_images} 张: {photo_dir}"
+            )
+    except Exception as e:
+        if logger:
+            logger.warning(f"{log_prefix} 清理旧图片时出错（可忽略）: {e}")
+
+
 def estimate_pixel(physical_distance_cm, target_distance_m):
     """
     根据物理距离和目标距离计算对应的像素偏移
@@ -542,12 +796,13 @@ 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):
+                          laser_point, distance_m, shot_id=None, photo_dir=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
@@ -560,11 +815,13 @@ def _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
 
         x, y = laser_point
         if shot_id:
-            if center is None or radius is None:
-                filename = f"{photo_dir}/shot_{shot_id}_no_target.bmp"
+            # 之前是用 center/radius 判定 no_target；但三角形路径会返回 center=None（正常）
+            # 这里改为：只要 method 有值，就按 method 命名；否则才回退 no_target
+            method_str = (method or "").strip()
+            if method_str:
+                filename = f"{photo_dir}/shot_{shot_id}_{method_str}.jpg"
             else:
-                method_str = method or "unknown"
-                filename = f"{photo_dir}/shot_{shot_id}_{method_str}.bmp"
+                filename = f"{photo_dir}/shot_{shot_id}_no_target.jpg"
         else:
             try:
                 all_images = [f for f in os.listdir(photo_dir) if f.endswith(('.bmp', '.jpg', '.jpeg'))]
@@ -577,7 +834,9 @@ def _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
             else:
                 method_str = method or "unknown"
                 distance_str = str(round((distance_m or 0.0) * 100))
-            filename = f"{photo_dir}/{method_str}_{int(x)}_{int(y)}_{distance_str}_{img_count:04d}.bmp"
+            filename = f"{photo_dir}/{method_str}_{int(x)}_{int(y)}_{distance_str}_{img_count:04d}.jpg"
+
+        _draw_yolo_roi_on_rgb_numpy(img_cv, yolo_roi_xyxy)
 
         logger = logger_manager.logger
         if logger:
@@ -591,16 +850,16 @@ def _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
             else:
                 logger.info(f"结果 -> 未检测到靶心，保存原始图像（激光点: ({x}, {y})）")
 
-        laser_color = (config.LASER_COLOR[0], config.LASER_COLOR[1], config.LASER_COLOR[2])
-        cross_thickness = int(max(getattr(config, "LASER_THICKNESS", 1), 1))
-        cross_length = int(max(getattr(config, "LASER_LENGTH", 10), 10))
-        cv2.line(img_cv, (int(x - cross_length), int(y)), (int(x + cross_length), int(y)), laser_color, cross_thickness)
-        cv2.line(img_cv, (int(x), int(y - cross_length)), (int(x), int(y + cross_length)), laser_color, cross_thickness)
-        cv2.circle(img_cv, (int(x), int(y)), 1, laser_color, cross_thickness)
-        ring_thickness = 1
-        cv2.circle(img_cv, (int(x), int(y)), 10, laser_color, ring_thickness)
-        cv2.circle(img_cv, (int(x), int(y)), 5, laser_color, ring_thickness)
-        cv2.circle(img_cv, (int(x), int(y)), 2, laser_color, -1)
+        # laser_color = (config.LASER_COLOR[0], config.LASER_COLOR[1], config.LASER_COLOR[2])
+        # cross_thickness = int(max(getattr(config, "LASER_THICKNESS", 1), 1))
+        # cross_length = int(max(getattr(config, "LASER_LENGTH", 10), 10))
+        # cv2.line(img_cv, (int(x - cross_length), int(y)), (int(x + cross_length), int(y)), laser_color, cross_thickness)
+        # cv2.line(img_cv, (int(x), int(y - cross_length)), (int(x), int(y + cross_length)), laser_color, cross_thickness)
+        # cv2.circle(img_cv, (int(x), int(y)), 1, laser_color, cross_thickness)
+        # ring_thickness = 1
+        # cv2.circle(img_cv, (int(x), int(y)), 10, laser_color, ring_thickness)
+        # cv2.circle(img_cv, (int(x), int(y)), 5, laser_color, ring_thickness)
+        # cv2.circle(img_cv, (int(x), int(y)), 2, laser_color, -1)
 
         if center and radius:
             cx, cy = center
@@ -630,37 +889,7 @@ def _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
             else:
                 logger.debug(f"图像已保存（无靶心，含激光十字线）: {filename}")
 
-        # 清理旧图片：如果目录下图片超过100张，删除最老的
-        try:
-            image_files = []
-            for f in os.listdir(photo_dir):
-                if f.endswith(('.bmp', '.jpg', '.jpeg')):
-                    filepath = os.path.join(photo_dir, f)
-                    try:
-                        mtime = os.path.getmtime(filepath)
-                        image_files.append((mtime, filepath, f))
-                    except Exception:
-                        pass
-
-            from config import MAX_IMAGES
-            if len(image_files) > MAX_IMAGES:
-                image_files.sort(key=lambda x: x[0])
-                to_delete = len(image_files) - MAX_IMAGES
-                deleted_count = 0
-                for _, filepath, fname in image_files[:to_delete]:
-                    try:
-                        os.remove(filepath)
-                        deleted_count += 1
-                        if logger:
-                            logger.debug(f"[VISION] 删除旧图片: {fname}")
-                    except Exception as e:
-                        if logger:
-                            logger.warning(f"[VISION] 删除旧图片失败 {fname}: {e}")
-                if logger and deleted_count > 0:
-                    logger.info(f"[VISION] 已清理 {deleted_count} 张旧图片，当前剩余 {MAX_IMAGES} 张")
-        except Exception as e:
-            if logger:
-                logger.warning(f"[VISION] 清理旧图片时出错（可忽略）: {e}")
+        prune_old_images_in_dir(photo_dir, config.MAX_IMAGES, logger, "[VISION]")
 
         return filename
     except Exception as e:
@@ -708,11 +937,13 @@ def start_save_shot_worker():
 
 
 def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
-                      laser_point, distance_m, shot_id=None, photo_dir=None):
+                      laser_point, distance_m, shot_id=None, photo_dir=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
@@ -724,7 +955,19 @@ def enqueue_save_shot(result_img, center, radius, method, ellipse_params,
         if logger:
             logger.error(f"[VISION] enqueue_save_shot 复制图像失败: {e}")
         return
-    task = (img_copy, center, radius, method, ellipse_params, laser_point, distance_m, shot_id, photo_dir)
+    task = (
+        img_copy,
+        center,
+        radius,
+        method,
+        ellipse_params,
+        laser_point,
+        distance_m,
+        shot_id,
+        photo_dir,
+        yolo_roi_xyxy,
+        force_save,
+    )
     try:
         _save_queue.put_nowait(task)
     except queue.Full:
@@ -734,19 +977,31 @@ 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):
+                    laser_point, distance_m, shot_id=None, photo_dir=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
     try:
         img_cv = image.image2cv(result_img, False, False)
-        return _save_shot_image_impl(img_cv, center, radius, method, ellipse_params,
-                                     laser_point, distance_m, shot_id, photo_dir)
+        return _save_shot_image_impl(
+            img_cv,
+            center,
+            radius,
+            method,
+            ellipse_params,
+            laser_point,
+            distance_m,
+            shot_id,
+            photo_dir,
+            yolo_roi_xyxy,
+            force_save,
+        )
     except Exception as e:
         logger = logger_manager.logger
         if logger:
@@ -768,17 +1023,8 @@ def detect_target(frame, laser_point=None):
     """
     logger = logger_manager.logger
 
-    if config.USE_ARUCO:
-        # 使用ArUco检测
-        if logger:
-            logger.debug("[VISION] 使用ArUco标记检测靶心")
-        
-        # 延迟导入以避免循环依赖
-        from aruco_detector import detect_target_with_aruco
-        return detect_target_with_aruco(frame, laser_point)
-    else:
-        # 使用传统黄色靶心检测
-        if logger:
-            logger.debug("[VISION] 使用传统黄色靶心检测")
-        return detect_circle_v3(frame, laser_point)
+    # 项目当前统一使用黄色靶心检测（圆/椭圆），不再保留 ArUco 路径
+    if logger:
+        logger.debug("[VISION] 使用传统黄色靶心检测")
+    return detect_circle_v3(frame, laser_point)
 

wifi.py

@@ -13,6 +13,7 @@ from maix import time
 
 import config
 from logger_manager import logger_manager
+from wpa_supplicant_conf import build_sta_conf_open, build_sta_conf_psk
 
 
 class WiFiManager:
@@ -40,6 +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
         
@@ -144,6 +146,13 @@ class WiFiManager:
             wifi = network.wifi.Wifi()
             if wifi.is_connected():
                 self._wifi_connected = True
+                # MaixPy 的 is_connected 可能不会同步填充 IP，这里用系统命令补齐一次
+                try:
+                    ip = os.popen("ifconfig wlan0 2>/dev/null | grep 'inet ' | awk '{print $2}'").read().strip()
+                    if ip:
+                        self._wifi_ip = ip
+                except Exception:
+                    pass
                 return True
         except:
             self.logger.warning("Failed to check WiFi connection using MaixPy network", exc_info=True)
@@ -163,23 +172,25 @@ class WiFiManager:
     
     def connect_wifi(self, ssid, password, verify_callback=None, persist=True, timeout_s=20):
         """
-        连接 Wi-Fi（先用新凭证尝试连接并验证可用性；失败自动回滚；成功后再决定是否落盘）
+        连接 Wi-Fi（唯一实现：写 wpa_supplicant + /boot 凭证，MaixPy Wifi.connect，再等 IP 与可选校验）。
 
-        重要：系统的 /etc/init.d/S30wifi 通常会读取 /boot/wifi.ssid 与 /boot/wifi.pass 来连接 WiFi。
-        因此要"真正尝试连接新 WiFi"，必须临时写入 /boot/ 触发重启；若失败则把旧值写回去（回滚）。
+        ``NetworkManager.connect_wifi`` 仅封装本方法（通过 ``verify_callback`` 传入 host/port 校验）。
+
+        重要：``/boot/wpa_supplicant.conf`` 存在时 S30wifi 会优先 cp，避免 shell 传中文 SSID。
 
         Args:
             ssid: WiFi SSID
             password: WiFi密码
-            verify_callback: 验证回调函数，接收 (ip) 参数，返回 (success: bool, error: str)
-            persist: 是否持久化保存凭证
-            timeout_s: 连接超时时间（秒）
+            verify_callback: 可选；``(ip) -> (success: bool, error: str)``，在拿到 IP 后调用
+            persist: 是否持久化保存凭证（False 时成功后回滚 /boot 与 /etc 中的本次写入）
+            timeout_s: 等待 DHCP / 轮询 IP 的超时基数（秒）；Maix 连接超时亦据此推导
 
         Returns:
-            (ip, error): IP地址和错误信息（成功时error为None）
+            (ip, error): IP地址和错误信息（成功时 error 为 None）
         """
         # 配置文件路径定义
         conf_path = "/etc/wpa_supplicant.conf"
+        boot_wpa_path = "/boot/wpa_supplicant.conf"
         ssid_file = "/boot/wifi.ssid"
         pass_file = "/boot/wifi.pass"
 
@@ -215,33 +226,54 @@ class WiFiManager:
             except Exception:
                 pass
 
+        def _restore_boot_wpa(old_wpa: str | None):
+            try:
+                if old_wpa is None:
+                    if os.path.exists(boot_wpa_path):
+                        os.remove(boot_wpa_path)
+                else:
+                    _write_text(boot_wpa_path, old_wpa)
+            except Exception:
+                pass
+
         old_conf = _read_text(conf_path)
         old_boot_ssid = _read_text(ssid_file)
         old_boot_pass = _read_text(pass_file)
 
         try:
-            # 生成 wpa_supplicant 配置（写 /etc 作为辅助，具体是否生效取决于 S30wifi 脚本）
-            net_conf = os.popen(f'wpa_passphrase "{ssid}" "{password}"').read()
-            if "network={" not in net_conf:
-                raise RuntimeError("Failed to generate wpa config")
+            try:
+                full_conf = build_sta_conf_psk(ssid.strip(), password.strip())
+            except ValueError as ve:
+                raise RuntimeError(str(ve)) from ve
 
             try:
-                _write_text(
-                    conf_path,
-                    "ctrl_interface=/var/run/wpa_supplicant\n"
-                    "update_config=1\n\n"
-                    + net_conf,
-                )
+                _write_text(conf_path, full_conf)
+            except Exception:
+                pass
+            # 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:
-                # 不强制要求写 /etc 成功（某些系统只用 /boot）
                 pass
 
-            # ====== 临时写入 /boot 凭证，触发 WiFi 服务真正尝试连接新 SSID ======
             _write_text(ssid_file, ssid.strip())
             _write_text(pass_file, password.strip())
 
-            # 重启 Wi-Fi 服务
-            os.system("/etc/init.d/S30wifi restart")
+            from maix import err as maix_err
+            from maix import network as maix_net
+
+            self.logger.info(f"[WIFI] Maix connect start ssid={ssid!r}")
+            w = maix_net.wifi.Wifi()
+            connect_timeout_s = int(timeout_s) if timeout_s and timeout_s > 0 else 60
+            connect_timeout_s = max(10, min(connect_timeout_s, 120))
+            e = w.connect(ssid, password, wait=True, timeout=connect_timeout_s)
+            maix_err.check_raise(e, "connect wifi failed")
+            try:
+                maix_ip = w.get_ip()
+            except Exception:
+                maix_ip = None
+            self.logger.info(f"[WIFI] Maix connect ok ip={maix_ip!r}")
 
             # 等待获取 IP
             wait_s = int(timeout_s) if timeout_s and timeout_s > 0 else 20
@@ -294,7 +326,7 @@ class WiFiManager:
 
     def persist_sta_credentials(self, ssid: str, password: str, restart_service: bool = True):
         """
-        仅写入 STA 凭证（/etc/wpa_supplicant.conf + /boot/wifi.ssid|pass），
+        仅写入 STA 凭证（/etc/wpa_supplicant.conf、/boot/wpa_supplicant.conf、/boot/wifi.ssid|pass），
         可选是否立即 /etc/init.d/S30wifi restart。
         不做可达性验证。用于热点配网页提交后切换到连接指定路由器。
         password 为空时按开放网络（key_mgmt=NONE）写入。
@@ -307,6 +339,7 @@ class WiFiManager:
             return False, "SSID 为空"
 
         conf_path = "/etc/wpa_supplicant.conf"
+        boot_wpa_path = "/boot/wpa_supplicant.conf"
         ssid_file = "/boot/wifi.ssid"
         pass_file = "/boot/wifi.pass"
 
@@ -316,23 +349,17 @@ class WiFiManager:
 
         try:
             if password:
-                net_conf = os.popen(f'wpa_passphrase "{ssid}" "{password}"').read()
-                if "network={" not in net_conf:
-                    return False, "wpa_passphrase 失败"
+                full_conf = build_sta_conf_psk(ssid, password)
             else:
-                esc = ssid.replace("\\", "\\\\").replace('"', '\\"')
-                net_conf = (
-                    "network={\n"
-                    f'    ssid="{esc}"\n'
-                    "    key_mgmt=NONE\n"
-                    "}\n"
-                )
-            _write_text(
-                conf_path,
-                "ctrl_interface=/var/run/wpa_supplicant\n"
-                "update_config=1\n\n"
-                + net_conf,
-            )
+                full_conf = build_sta_conf_open(ssid)
+            _write_text(conf_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:
             return False, str(e)
 
@@ -521,34 +548,45 @@ class WiFiManager:
             network_type_callback: 获取当前网络类型的回调函数
             on_poor_quality_callback: WiFi质量差时的回调函数
         """
-        if self._wifi_quality_monitor_thread is not None:
-            self.logger.warning("[WiFi Monitor] 监测线程已在运行")
-            return
+        with self._wifi_quality_lock:
+            if self._wifi_quality_monitor_thread is not None and self._wifi_quality_monitor_thread.is_alive():
+                self.logger.warning("[WiFi Monitor] 监测线程已在运行")
+                return
             
-        self._network_type_callback = network_type_callback
-        self._on_poor_quality_callback = on_poor_quality_callback
-        self._wifi_quality_stop_event.clear()
-        self._wifi_quality_monitor_thread = threading.Thread(
-            target=self._quality_monitor_loop,
-            daemon=True,
-            name="wifi_quality_monitor"
-        )
-        self._wifi_quality_monitor_thread.start()
-        self.logger.info("[WiFi Monitor] 已启动后台监测线程")
+            self._network_type_callback = network_type_callback
+            self._on_poor_quality_callback = on_poor_quality_callback
+            self._wifi_quality_stop_event.clear()
+            self._wifi_quality_monitor_thread = threading.Thread(
+                target=self._quality_monitor_loop,
+                daemon=True,
+                name="wifi_quality_monitor"
+            )
+            self._wifi_quality_monitor_thread.start()
+            self.logger.info("[WiFi Monitor] 已启动后台监测线程")
     
     def stop_quality_monitor(self):
         """停止 WiFi 质量监测线程"""
-        if self._wifi_quality_monitor_thread is None:
-            return
+        with self._wifi_quality_lock:
+            t = self._wifi_quality_monitor_thread
+            if t is None:
+                return
+            if not t.is_alive():
+                self._wifi_quality_monitor_thread = None
+                return
         
         self._wifi_quality_stop_event.set()
         try:
-            self._wifi_quality_monitor_thread.join(timeout=2.0)
+            t.join(timeout=2.0)
         except Exception as e:
             self.logger.error(f"[WiFi Monitor] 停止线程失败：{e}")
-        finally:
-            self._wifi_quality_monitor_thread = None
-            self.logger.info("[WiFi Monitor] 已停止后台监测线程")
+        
+        with self._wifi_quality_lock:
+            if t is self._wifi_quality_monitor_thread:
+                if t.is_alive():
+                    self.logger.warning("[WiFi Monitor] 线程未在超时内退出，保留引用防止重复创建")
+                else:
+                    self._wifi_quality_monitor_thread = None
+                    self.logger.info("[WiFi Monitor] 已停止后台监测线程")
     
     def _quality_monitor_loop(self):
         """
@@ -575,7 +613,8 @@ class WiFiManager:
                     reachable = True
                     self._last_wifi_rtt_ms = rtt_ms if reachable else None
                     self._last_wifi_rssi_dbm = rssi_dbm
-                    self.logger.debug(f"[WiFi Monitor] - RTT={rtt_ms:.0f}ms, RSSI={rssi_dbm:.0f}dBm")
+                    _rssi_s = f"{rssi_dbm:.0f}" if rssi_dbm is not None else "n/a"
+                    self.logger.debug(f"[WiFi Monitor] - RTT={rtt_ms:.0f}ms, RSSI={_rssi_s}dBm")
                     
                     # 判断质量是否差（切换前做 2 次快速复测，防止瞬时抖动）
                     def _is_bad_now(_reachable, _rtt, _rssi):
@@ -604,9 +643,14 @@ class WiFiManager:
 
                             bad2 = _is_bad_now(reachable2, rtt2, rssi2)
                             try:
+                                _rtt_disp = (
+                                    rtt2
+                                    if rtt2 is not None and rtt2 != float("inf")
+                                    else -1
+                                )
                                 self.logger.info(
                                     f"[WiFi Monitor] 复测{retry_idx+1}/2: reachable={reachable2}, "
-                                    f"rtt={rtt2 if rtt2 != float('inf') else -1:.0f}ms, rssi={rssi2}, bad={bad2}"
+                                    f"rtt={_rtt_disp:.0f}ms, rssi={rssi2}, bad={bad2}"
                                 )
                             except Exception:
                                 pass

wifi_config_httpd.py

@@ -383,6 +383,27 @@ def _ensure_hostapd_modern_security(logger=None) -> bool:
         return False
 
 
+def _cleanup_ap_flag_if_needed(logger):
+    """若 /boot/wifi.ap 残留，删除它并恢复 /boot/wifi.sta，避免 main.py 误判为 AP 配网模式。"""
+    ap_flag = "/boot/wifi.ap"
+    sta_flag = "/boot/wifi.sta"
+    if not os.path.exists(ap_flag):
+        return
+    try:
+        os.remove(ap_flag)
+        logger.info(f"[WIFI-AP] 已清理残留标记 {ap_flag}")
+    except Exception as e:
+        logger.warning(f"[WIFI-AP] 清理 {ap_flag} 失败: {e}")
+        return
+    if not os.path.exists(sta_flag):
+        try:
+            with open(sta_flag, "w", encoding="utf-8") as f:
+                f.write("")
+            logger.info(f"[WIFI-AP] 已恢复 {sta_flag}")
+        except Exception as e:
+            logger.warning(f"[WIFI-AP] 恢复 {sta_flag} 失败: {e}")
+
+
 def _switch_boot_to_ap_mode(logger):
     """
     去掉 STA 标志、建立 AP 标志，由 S30wifi 起 hostapd（与 Maix start_ap 二选一，以系统脚本为准）。
@@ -449,6 +470,8 @@ def maybe_start_wifi_ap_fallback(logger=None):
     logger.info(f"[WIFI-AP] 兜底检测(quick)：sta关联={wifi_ok}, 4g={g4_ok}")
     if wifi_ok or g4_ok:
         logger.info("[WIFI-AP] STA 或 4G 可用，不启动热点配网")
+        # 清理上次开机可能残留的 /boot/wifi.ap 标记，避免 main.py 误判为 AP 配网模式
+        _cleanup_ap_flag_if_needed(logger)
         return
 
     # 两者均不可用：再按配置等待一段时间后复检，避免开机瞬态误判
@@ -466,6 +489,7 @@ def maybe_start_wifi_ap_fallback(logger=None):
 
     if wifi_ok or g4_ok:
         logger.info("[WIFI-AP] STA 或 4G 可用，不启动热点配网")
+        _cleanup_ap_flag_if_needed(logger)
         return
 
     logger.warning("[WIFI-AP] STA 与 4G 均不可用，启动热点配网（/boot/wifi.ap + HTTP）")

wpa_supplicant_conf.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+生成 wpa_supplicant STA 配置（不经过 shell / wpa_passphrase），避免中文 SSID 在 /bin/sh 传参时被破坏。
+
+与 wpa_passphrase 一致：PMK = PBKDF2-SHA1(password_utf8, ssid_utf8, 4096, 32)，
+ssid 行使用 UTF-8 字节的十六进制（无引号），与 wpa_supplicant 文档一致。
+"""
+from __future__ import annotations
+
+import hashlib
+
+_CTRL_HEADER = (
+    "ctrl_interface=/var/run/wpa_supplicant\n"
+    "update_config=1\n\n"
+)
+
+
+def _ssid_utf8_bytes(ssid: str) -> bytes:
+    b = (ssid or "").encode("utf-8")
+    if not b:
+        raise ValueError("SSID 为空")
+    if len(b) > 32:
+        raise ValueError("SSID UTF-8 超过 32 字节")
+    return b
+
+
+def build_sta_conf_psk(ssid: str, password: str) -> str:
+    """WPA2-PSK STA：完整 wpa_supplicant.conf 文本。"""
+    ssid_b = _ssid_utf8_bytes(ssid)
+    pw = (password or "").encode("utf-8")
+    if len(pw) < 8 or len(pw) > 63:
+        raise ValueError("WPA2-PSK 密码长度应为 8–63 字节（UTF-8）")
+    pmk = hashlib.pbkdf2_hmac("sha1", pw, ssid_b, 4096, 32)
+    net = (
+        "network={\n"
+        f"\tssid={ssid_b.hex()}\n"
+        f"\tpsk={pmk.hex()}\n"
+        "}\n"
+    )
+    return _CTRL_HEADER + net
+
+
+def build_sta_conf_open(ssid: str) -> str:
+    """开放网络 STA：完整 wpa_supplicant.conf 文本。"""
+    ssid_b = _ssid_utf8_bytes(ssid)
+    net = (
+        "network={\n"
+        f"\tssid={ssid_b.hex()}\n"
+        "\tkey_mgmt=NONE\n"
+        "}\n"
+    )
+    return _CTRL_HEADER + net