triangle algo refind

config.py

@@ -11,6 +11,15 @@ APP_VERSION = VERSION
 APP_DIR = "/maixapp/apps/t11"
 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 = 0.5
+
 # ==================== 服务器配置 ====================
 # SERVER_IP = "stcp.shelingxingqiu.com"
 SERVER_IP = "www.shelingxingqiu.com"

design_doc/command_record.md

@@ -72,7 +72,7 @@ printf 'AT+MHTTPDLFILE="http://static.shelingxingqiu.com/shoot/v1/main.py","down
    遇到问题：
    ./build_all.sh: line 56: maixtool: command not found
    解决办法：
-   export PATH="/mnt/d/code/MaixCDK/.venv/bin:$PATH" 
+   pip install maixtool
 
     遇到问题：
     ./update_img.sh: line 80: mcopy: command not found

design_doc/solution_record.md

@@ -221,4 +221,56 @@ 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
+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。

main.py

@@ -100,8 +100,7 @@ def cmd_str():
     init_ina226()
 
     # 4. 初始化显示和相机
-    camera_manager.init_camera(640, 480)
+    camera_manager.init_camera(getattr(config, "CAMERA_WIDTH", 640), getattr(config, "CAMERA_HEIGHT", 480))
-    # camera_manager.init_camera(1280,720)
     camera_manager.init_display()
 
     # ==================== 第二阶段：软件初始化 ====================

network.py

@@ -1489,56 +1489,48 @@ class NetworkManager:
         qiniu_key = key.rstrip("/") + "/" + archive_filename
         self.logger.info(f"[LOG_UPLOAD] 日志归档已生成: {archive_path}, qiniu_key: {qiniu_key}")
 
+        # 2) 自动检测网络类型
+        if self._network_type == "wifi" and self.is_wifi_connected():
+            mode = "wifi"
+        else:
+            mode = "4g"
+
+        self.logger.info(f"[LOG_UPLOAD] Using {mode} path, archive: {archive_path}")
+
         try:
-            # 2) WiFi 优先：只要 WiFi 已连接就先尝试 WiFi，失败再回落到 4G
+            if mode == "wifi":
-            wifi_tried = False
+                # ---- WiFi path: 使用 requests 库上传 ----
-            wifi_ok = False
+                import requests
+                import urllib3
+                urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
-            if self.is_wifi_connected():
+                with open(archive_path, 'rb') as f:
-                wifi_tried = True
+                    files = {'file': (archive_filename, f, 'application/octet-stream')}
-                self.logger.info(f"[LOG_UPLOAD] Using wifi path (preferred), archive: {archive_path}")
+                    data = {'token': upload_token, 'key': qiniu_key}
-                try:
+                    wifi_upload_url = upload_url.replace('https://', 'http://', 1)
-                    # ---- WiFi path: 使用 requests 库上传 ----
+                    self.logger.info(f"[LOG_UPLOAD] WiFi upload URL: {wifi_upload_url}")
-                    import requests
+                    response = requests.post(wifi_upload_url, files=files, data=data, timeout=120, verify=False)
-                    import urllib3
+                    response.raise_for_status()
-                    urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+                    result_json = response.json()
+                    uploaded_key = result_json.get('key', qiniu_key)
 
-                    with open(archive_path, 'rb') as f:
+                self.logger.info(f"[LOG_UPLOAD] WiFi upload ok: key={uploaded_key}")
-                        files = {'file': (archive_filename, f, 'application/octet-stream')}
-                        data = {'token': upload_token, 'key': qiniu_key}
-                        wifi_upload_url = upload_url.replace('https://', 'http://', 1)
-                        self.logger.info(f"[LOG_UPLOAD] WiFi upload URL: {wifi_upload_url}")
-                        response = requests.post(wifi_upload_url, files=files, data=data, timeout=120, verify=False)
-                        response.raise_for_status()
-                        result_json = response.json()
-                        uploaded_key = result_json.get('key', qiniu_key)
 
-                    self.logger.info(f"[LOG_UPLOAD] WiFi upload ok: key={uploaded_key}")
+                access_url = None
+                if outlink:
+                    access_url = f"https://{outlink}/{uploaded_key}"
 
-                    access_url = None
+                response_data = {
-                    if outlink:
+                    "result": "log_upload_ok",
-                        access_url = f"https://{outlink}/{uploaded_key}"
+                    "key": uploaded_key,
+                    "via": "wifi",
+                }
+                if access_url:
+                    response_data["url"] = access_url
 
-                    response_data = {
+                self.safe_enqueue(response_data, 2)
-                        "result": "log_upload_ok",
-                        "key": uploaded_key,
-                        "via": "wifi",
-                    }
-                    if access_url:
-                        response_data["url"] = access_url
-
-                    self.safe_enqueue(response_data, 2)
-                    wifi_ok = True
-                except Exception as e:
-                    # WiFi 上传失败不影响主链路：记录原因并回落 4G
-                    self.logger.warning(f"[LOG_UPLOAD] WiFi upload failed, fallback to 4g: {e}")
-
-            if not wifi_ok:
-                if not wifi_tried:
-                    self.logger.info(f"[LOG_UPLOAD] WiFi not connected, using 4g path, archive: {archive_path}")
-                else:
-                    self.logger.info(f"[LOG_UPLOAD] Using 4g fallback path, archive: {archive_path}")
 
+            else:
                 # ---- 4G path: 使用 FourGUploadManager AT命令上传 ----
                 import importlib.util
                 spec = importlib.util.spec_from_file_location(
@@ -1578,8 +1570,11 @@ class NetworkManager:
                     }, 2)
 
         except Exception as e:
-            self.logger.error(f"[LOG_UPLOAD] upload exception: {e}")
+            self.logger.error(f"[LOG_UPLOAD] upload exception ({mode}): {e}")
-            self.safe_enqueue({"result": "log_upload_failed", "reason": str(e)[:100]}, 2)
+            self.safe_enqueue({
+                "result": "log_upload_failed",
+                "reason": str(e)[:100]
+            }, 2)
         finally:
             # 清理临时归档文件
             try:
@@ -1600,57 +1595,51 @@ class NetworkManager:
             shoot_id: 射击ID
             outlink: 外链域名（可选，用于构建访问URL）
         """
-        # WiFi 优先（独立于 TCP 主链路）：只要 WiFi 已连接就先走 WiFi，失败再回落 4G
+        # 自动检测网络类型，选择上传路径
-        mode = "wifi" if self.is_wifi_connected() else "4g"
+        if self._network_type == "wifi" and self.is_wifi_connected():
+            mode = "wifi"
+        else:
+            mode = "4g"
 
         self.logger.info(f"[IMAGE_UPLOAD] Using {mode} path, image: {image_path}")
 
         try:
-            wifi_ok = False
-
             if mode == "wifi":
-                try:
+                # ---- WiFi path: 使用 requests 库上传 ----
-                    # ---- WiFi path: 使用 requests 库上传 ----
+                import requests
-                    import requests
+                import urllib3
-                    import urllib3
+                urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
-                    urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
-                    with open(image_path, 'rb') as f:
+                with open(image_path, 'rb') as f:
-                        files = {'file': (os.path.basename(image_path), f, 'application/octet-stream')}
+                    files = {'file': (os.path.basename(image_path), f, 'application/octet-stream')}
-                        data = {'token': upload_token, 'key': key}
+                    data = {'token': upload_token, 'key': key}
-                        # 将 HTTPS 转为 HTTP（设备端 SSL 兼容性）
+                    # 测试：将HTTPS转为HTTP
-                        wifi_upload_url = upload_url.replace('https://', 'http://', 1)
+                    wifi_upload_url = upload_url.replace('https://', 'http://', 1)
-                        self.logger.info(f"[IMAGE_UPLOAD] WiFi upload URL: {wifi_upload_url}")
+                    self.logger.info(f"[IMAGE_UPLOAD] WiFi upload URL: {wifi_upload_url}")
-                        response = requests.post(wifi_upload_url, files=files, data=data, timeout=120, verify=False)
+                    response = requests.post(wifi_upload_url, files=files, data=data, timeout=120, verify=False)
-                        response.raise_for_status()
+                    response.raise_for_status()
-                        result_json = response.json()
+                    result_json = response.json()
-                        uploaded_key = result_json.get('key', key)
+                    uploaded_key = result_json.get('key', key)
 
-                    self.logger.info(f"[IMAGE_UPLOAD] WiFi upload ok: key={uploaded_key}")
+                self.logger.info(f"[IMAGE_UPLOAD] WiFi upload ok: key={uploaded_key}")
 
-                    access_url = None
+                access_url = None
-                    if outlink:
+                if outlink:
-                        access_url = f"https://{outlink}/{uploaded_key}"
+                    access_url = f"https://{outlink}/{uploaded_key}"
 
-                    response_data = {
+                response_data = {
-                        "result": "image_upload_ok",
+                    "result": "image_upload_ok",
-                        "shootId": shoot_id,
+                    "shootId": shoot_id,
-                        "key": uploaded_key,
+                    "key": uploaded_key,
-                        "via": "wifi",
+                    "via": "wifi",
-                    }
+                }
-                    if access_url:
+                if access_url:
-                        response_data["url"] = access_url
+                    response_data["url"] = access_url
 
-                    self.safe_enqueue(response_data, 2)
+                self.safe_enqueue(response_data, 2)
-                    wifi_ok = True
-                except Exception as e:
-                    self.logger.warning(f"[IMAGE_UPLOAD] WiFi upload failed, fallback to 4g: {e}")
 
-            if not wifi_ok:
+            else:
                 # ---- 4G path: 使用 FourGUploadManager AT命令上传 ----
-                if mode != "4g":
-                    self.logger.info(f"[IMAGE_UPLOAD] Using 4g fallback path, image: {image_path}")
-
                 import importlib.util
                 spec = importlib.util.spec_from_file_location(
                     "four_g_upload_manager",

shoot_manager.py

@@ -165,13 +165,28 @@ def analyze_shot(frame, laser_point=None):
 
     tri = tri_result.get('data', {})
 
-    if tri.get('ok'):
+    # 保险校验：避免三角形返回 nan/inf 或退化点仍被上报
+    try:
+        import numpy as _np
+        tri_ok = bool(tri.get('ok'))
+        if tri_ok:
+            dxv = tri.get("dx_cm")
+            dyv = tri.get("dy_cm")
+            H = tri.get("homography")
+            if not _np.isfinite(dxv) or not _np.isfinite(dyv):
+                tri_ok = False
+            elif H is not None and not _np.all(_np.isfinite(H)):
+                tri_ok = False
+    except Exception:
+        tri_ok = bool(tri.get('ok'))
+
+    if tri_ok:
         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 {
             "success": True,
             "result_img": frame,
             "center": None, "radius": None,
-            "method": tri.get("offset_method") or "triangle_homography",
+            "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,
@@ -231,7 +246,7 @@ def process_shot(adc_val):
 
         # 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 = offset_method or "triangle_homography"
+            method = "triangle_homography"
 
         if config.SHOW_CAMERA_PHOTO_WHILE_SHOOTING:
             camera_manager.show(result_img)

triangle_target.py

@@ -491,9 +491,17 @@ def try_triangle_scoring(
 
     h_orig, w_orig = img_rgb.shape[:2]
 
-    # 缩图加速：嵌入式 CPU 上图像处理耗时与面积成正比，缩到最长边 320px 可获得 ~4× 加速
+    # 缩图加速：嵌入式 CPU 上图像处理耗时与面积成正比。
+    # 不再写死 320/640：默认按相机最长边缩到 1/2（由 config.TRIANGLE_DETECT_SCALE 控制）。
     # 检测完后把像素坐标乘以 inv_scale 还原到原始分辨率，再送入单应性/PnP（与 K 标定分辨率一致）
-    MAX_DETECT_DIM = 640
+    try:
+        import config as _cfg
+        scale = float(getattr(_cfg, "TRIANGLE_DETECT_SCALE", 0.5))
+    except Exception:
+        scale = 0.5
+    if not (0.05 <= scale <= 1.0):
+        scale = 0.5
+    MAX_DETECT_DIM = max(64, int(max(h_orig, w_orig) * scale))
     long_side = max(h_orig, w_orig)
     if long_side > MAX_DETECT_DIM:
         det_scale = MAX_DETECT_DIM / long_side
@@ -564,6 +572,40 @@ def try_triangle_scoring(
         marker_centers = [[float(c[0]), float(c[1])] for c in marker_centers]
         offset_tag = "triangle_homography_3pt"
 
+    # ---------- 结果有效性校验（防 nan/inf 与退化角点） ----------
+    try:
+        import config as _cfg
+        min_center_dist_px = float(getattr(_cfg, "TRIANGLE_MIN_CENTER_DIST_PX", 3.0))
+        max_dist_m = float(getattr(_cfg, "TRIANGLE_MAX_DISTANCE_M", 20.0))
+    except Exception:
+        min_center_dist_px = 3.0
+        max_dist_m = 20.0
+
+    def _all_finite(v) -> bool:
+        try:
+            return bool(np.all(np.isfinite(v)))
+        except Exception:
+            return False
+
+    # 1) 4 个角点中心不能退化/重复（两两距离要大于阈值）
+    try:
+        pts = np.array(marker_centers, dtype=np.float64).reshape(-1, 2)
+        ok_centers = True
+        for i in range(len(pts)):
+            for j in range(i + 1, len(pts)):
+                if float(np.linalg.norm(pts[i] - pts[j])) <= min_center_dist_px:
+                    ok_centers = False
+                    break
+            if not ok_centers:
+                break
+        if not ok_centers:
+            _log(f"[TRI] 角点退化/重复：center_dist <= {min_center_dist_px:.1f}px，判定三角形失败")
+            return out
+    except Exception:
+        # 校验异常时不信任结果，直接回退
+        _log("[TRI] 角点校验异常，判定三角形失败")
+        return out
+
     ok_h, tx, ty, _H = homography_calibration(
         marker_centers, marker_ids, marker_positions, [lx, ly]
     )
@@ -571,6 +613,16 @@ def try_triangle_scoring(
         _log("[TRI] 单应性失败")
         return out
 
+    # 2) 单应性矩阵必须是有限数
+    if (not _all_finite(_H)):
+        _log("[TRI] 单应性出现 nan/inf，判定三角形失败")
+        return out
+
+    # 3) dx/dy 必须是有限数
+    if (not _all_finite([tx, ty])):
+        _log("[TRI] 偏移出现 nan/inf，判定三角形失败")
+        return out
+
     # 与 laser_manager.compute_laser_position 现网约定一致：(x_cm, -y_cm_target)
     out["dx_cm"] = tx
     out["dy_cm"] = -ty
@@ -580,7 +632,8 @@ def try_triangle_scoring(
     out["homography"] = _H         # 供上层反推靶心像素位置用
 
     dist_m = pnp_distance_meters(marker_ids, marker_centers, marker_positions, camera_matrix, dist_coeffs)
-    if dist_m is not None and 0.3 < dist_m < 50.0:
+    # 4) distance_m 若存在也必须是有限数且在合理范围（默认 <20m）
+    if dist_m is not None and _all_finite([dist_m]) and 0.3 < dist_m < max_dist_m:
         out["distance_m"] = dist_m
         out["distance_method"] = "pnp_triangle"
         _log(f"[TRI] PnP 距离={dist_m:.2f}m, 偏移=({out['dx_cm']:.2f},{out['dy_cm']:.2f})cm")