增加异步

Point.py

@@ -1,129 +1,130 @@
+from datetime import time
+
 import numpy as np
 from PIL import Image
 import math
 
 import config
-from cat import clip_and_rotate_point_cloud, merge_point_clouds
+import asyncio
-from image import detect_large_holes
-
-
-def tiff_depth_to_point_clouds(tiff_paths, sn, dedup=True):
-    """
-    将多个 TIFF 深度图转换为点云，并拼接在一起后生成 PCD 文件
-
-    :param tiff_paths: list of str, TIFF 深度图路径列表
-    :param sn: str, 设备序列号，用于查找配置参数
-    :param dedup: bool, 是否根据 (x, y) 去重，默认开启
-    :return: list of [x, y, z], 合并后的点云数据
-    """
-    merged_points = []
-
-    # Step 1: 遍历每个 TIFF 文件，生成对应的点云
-    for tiff_path in tiff_paths:
-        points = tiff_depth_to_point_cloud(tiff_path+".tiff", sn, dedup)
-        merged_points.extend([points])
-
-    # Step 2: 可选：使用 merge_point_clouds 对点云进行 x 轴拼接（若需要）
-    # 注意：如果每个点云已经带有偏移，则不需要再次拼接
-    has_voids = True
-    if hasattr(config, 'CUT_CONFIG_MAP') and isinstance(config.CUT_CONFIG_MAP, dict) and (sn in config.CUT_CONFIG_MAP):
-        merged_points, xrange = merge_point_clouds(merged_points, config.CUT_CONFIG_MAP[sn],sn)
-
-        real_holes = detect_large_holes(merged_points, sn, xrange)
-        if real_holes.__len__()==0:
-            has_voids=False
-        # Step 3: 写入最终的 PCD 文件
-        output_pcd_path = config.save_path("pcd", f"{sn}_merged.pcd")
-        write_pcd(output_pcd_path, merged_points)
-    # print(f"Merged and saved to {output_pcd_path}")
-    return has_voids
-
-
-def tiff_depth_to_point_cloud(tiff_path,sn,  dedup=True):
-    """
-    将 TIFF 深度图转换为点云，基于给定的视场角计算相机内参
-
-    :param tiff_path: str, TIFF 深度图路径
-    :param hfov_degrees: float or None, 水平视场角（单位：度）
-    :param vfov_degrees: float or None, 垂直视场角（单位：度）
-    :param output_ply_path: str or None, 输出 Pcd 文件路径（可选）
-    :param dedup: bool, 是否根据 (x, y) 去重，默认开启
-    :return: list of [x, y, z] 点云数据
-    """
-    hfov_degrees = config.CAMERA_CONFIG_MAP[sn].get("x_angle")
-    max_z = config.CAMERA_CONFIG_MAP[sn].get("max_z")
-    vfov_degrees = config.CAMERA_CONFIG_MAP[sn].get("y_angle")
-    # plane_scaling_ratio = config.CAMERA_CONFIG_MAP[sn].get("plane_scaling_ratio")
-    # 加载 TIFF 图像
-    depth_image = Image.open(tiff_path)
-    depth_array = np.array(depth_image, dtype=np.float32)
-
-    height, width = depth_array.shape
-
-    # 根据提供的 FOV 计算 fx/fy
-    if hfov_degrees is not None:
-        hfov_rad = math.radians(hfov_degrees)
-        fx = width / (2 * math.tan(hfov_rad / 2))
-        fy = fx * height / width  # 保持像素方形比例
-    else:
-        vfov_rad = math.radians(vfov_degrees)
-        fy = height / (2 * math.tan(vfov_rad / 2))
-        fx = fy * width / height  # 保持像素方形比例
-
-    cx = width / 2
-    cy = height / 2
 
-    print(f"Calculated intrinsics: fx={fx:.2f}, fy={fy:.2f}, cx={cx:.2f}, cy={cy:.2f}")
 
-    points = []
+import numpy as np
-    seen_xy = set()  # 用于记录已添加的 (x, y)
+from scipy.spatial.transform import Rotation as R
-
-    # 获取裁剪配置
-    clip_config = config.CUT_CONFIG_MAP.get(sn)
-    if clip_config:
-        min_pt = np.array(clip_config.get("min_pt", [-np.inf] * 3))
-        max_pt = np.array(clip_config.get("max_pt", [np.inf] * 3))
-        rotation = np.array(clip_config.get("rotation", [1,0,0,0,1,0,0,0,1])).reshape(3, 3)
-    else:
-        min_pt = max_pt = rotation = None
-
-    for v in range(height):
-        for u in range(width):
-            z = depth_array[v, u]  # mm -> m
-            if z <= 0 or z > max_z:
-                continue
-
-            x = (u - cx) * z / fx
-            y = (v - cy) * z / fy
 
-            # 构造点并旋转（保留浮点精度）
+from logConfig import get_logger
-            point = np.array([x, y, z])
-            if clip_config:
-                rotated_point = (rotation @ point.T).T
-                if not np.all(rotated_point >= min_pt) or not np.all(rotated_point <= max_pt):
-                    continue
-                x_final, y_final, z_final = rotated_point
-            else:
-                x_final, y_final, z_final = point
 
-            # 到这里才进行去重判断
+# 使用示例
-            if dedup:
+logger = get_logger()
-                x_int = int(round(x_final))
-                y_int = int(round(y_final))
-                z_int = int(round(z_final))
-                if (x_int, y_int) in seen_xy:
-                    continue
-                seen_xy.add((x_int, y_int))
-                # 保留浮点精度写入结果
-                points.append([x_int, y_int, z_int])
 
+def apply_rotation(matrix, angle_degrees, axis='x'):
+    """
+    对输入的 3x3 旋转矩阵 应用额外的旋转
 
-    # 可选输出PLY文件用于可视化
+    参数:
-    if config.CAMERA_CONFIG_MAP[sn].get("save_pcd"):
+        matrix (np.ndarray): 原始的 3x3 旋转矩阵
-        output_ply_path = config.save_path("pcd",sn+".pcd")
+        angle_degrees (float): 旋转角度 (单位: 度)
-        write_pcd(output_ply_path, points)
+        axis (str): 旋转轴，支持 'x', 'y', 'z'
 
-    return points
+    返回:
+        np.ndarray: 新的 3x3 旋转矩阵
+    """
+    # 创建绕指定轴旋转的旋转对象
+    rot = R.from_euler(axis, angle_degrees, degrees=True)
+
+    # 将当前旋转转换为矩阵
+    rotation_matrix = rot.as_matrix()
+
+    # 合并旋转：新旋转 × 原始矩阵
+    combined_rotation = np.dot(rotation_matrix, matrix)
+
+    return combined_rotation
+
+#
+# def tiff_depth_to_point_cloud(tiff_path,sn,  dedup=True):
+#     """
+#     将 TIFF 深度图转换为点云，基于给定的视场角计算相机内参
+#
+#     :param tiff_path: str, TIFF 深度图路径
+#     :param hfov_degrees: float or None, 水平视场角（单位：度）
+#     :param vfov_degrees: float or None, 垂直视场角（单位：度）
+#     :param output_ply_path: str or None, 输出 Pcd 文件路径（可选）
+#     :param dedup: bool, 是否根据 (x, y) 去重，默认开启
+#     :return: list of [x, y, z] 点云数据
+#     """
+#     hfov_degrees = config.CAMERA_CONFIG_MAP[sn].get("x_angle")
+#     max_z = config.CAMERA_CONFIG_MAP[sn].get("max_z")
+#     vfov_degrees = config.CAMERA_CONFIG_MAP[sn].get("y_angle")
+#     # plane_scaling_ratio = config.CAMERA_CONFIG_MAP[sn].get("plane_scaling_ratio")
+#     # 加载 TIFF 图像
+#     depth_image = Image.open(tiff_path)
+#     depth_array = np.array(depth_image, dtype=np.float32)
+#
+#     height, width = depth_array.shape
+#
+#     # 根据提供的 FOV 计算 fx/fy
+#     if hfov_degrees is not None:
+#         hfov_rad = math.radians(hfov_degrees)
+#         fx = width / (2 * math.tan(hfov_rad / 2))
+#         fy = fx * height / width  # 保持像素方形比例
+#     else:
+#         vfov_rad = math.radians(vfov_degrees)
+#         fy = height / (2 * math.tan(vfov_rad / 2))
+#         fx = fy * width / height  # 保持像素方形比例
+#
+#     cx = width / 2
+#     cy = height / 2
+#
+#     print(f"Calculated intrinsics: fx={fx:.2f}, fy={fy:.2f}, cx={cx:.2f}, cy={cy:.2f}")
+#
+#     points = []
+#     seen_xy = set()  # 用于记录已添加的 (x, y)
+#
+#     # 获取裁剪配置
+#     clip_config = config.CUT_CONFIG_MAP.get(sn)
+#     if clip_config:
+#         min_pt = np.array(clip_config.get("min_pt", [-np.inf] * 3))
+#         max_pt = np.array(clip_config.get("max_pt", [np.inf] * 3))
+#         rotation = np.array(clip_config.get("rotation", [1,0,0,0,1,0,0,0,1])).reshape(3, 3)
+#     else:
+#         min_pt = max_pt = rotation = None
+#
+#     for v in range(height):
+#         for u in range(width):
+#             z = depth_array[v, u]  # mm -> m
+#             if z <= 0 or z > max_z:
+#                 continue
+#
+#             x = (u - cx) * z / fx
+#             y = (v - cy) * z / fy
+#
+#             # 构造点并旋转（保留浮点精度）
+#             point = np.array([x, y, z])
+#             if clip_config:
+#                 rotated_point = (rotation @ point.T).T
+#                 if not np.all(rotated_point >= min_pt) or not np.all(rotated_point <= max_pt):
+#                     continue
+#                 x_final, y_final, z_final = rotated_point
+#             else:
+#                 x_final, y_final, z_final = point
+#
+#             # 到这里才进行去重判断
+#             if dedup:
+#                 x_int = int(round(x_final))
+#                 y_int = int(round(y_final))
+#                 z_int = int(round(z_final))
+#                 if (x_int, y_int) in seen_xy:
+#                     continue
+#                 seen_xy.add((x_int, y_int))
+#                 # 保留浮点精度写入结果
+#                 points.append([x_int, y_int, z_int])
+#
+#
+#     # 可选输出PLY文件用于可视化
+#     if config.CAMERA_CONFIG_MAP[sn].get("save_pcd"):
+#         output_ply_path = config.save_path("pcd",sn+".pcd")
+#         write_pcd(output_ply_path, points)
+#
+#     return points
 
 
 # 新增点云数据转换函数
@@ -141,67 +142,139 @@ def convert_sdk_points(sValue, width, height):
     mask = np.all(np.isfinite(points), axis=1)
     return points[mask]
 
+# def process_point_cloud_fast(points, sn, type):
+#     points = np.array(points, dtype=np.float32)
+#     # 获取目标高度和容差（来自模板配置）
+#     template_config = config.TEMPLATE_CONFIG_MAP.get(type, {})
+#     target_z = template_config.get("height", 0)  # 假设 height 是期望的中心高度
+#     tolerance = template_config.get("tolerance", 50)  # 默认容差为 50
+#
+#     # 计算上下限
+#     z_min = target_z - tolerance
+#     z_max = target_z + tolerance  # 不超过 200
+#     # Z 值过滤
+#     valid_mask = ((points[:, 2] > 0)
+#                   & (points[:, 2] <= config.CAMERA_CONFIG_MAP[sn].get("max_z", np.inf))
+#                  )
+#
+#     points = points[valid_mask]
+#
+#     if clip_config := config.CUT_CONFIG_MAP.get(sn):
+#         rotation = np.array(clip_config["rotation"], dtype=np.float32).reshape(3, 3)
+#         min_pt = np.array(clip_config["min_pt"], dtype=np.float32)
+#         max_pt = np.array(clip_config["max_pt"], dtype=np.float32)
+#         floorHeight = clip_config.get("floorHeight", 0)
+#
+#         # 批量旋转
+#         rotated = (rotation @ points.T).T
+#
+#         # 裁剪范围过滤
+#         bounds_mask = np.all((rotated >= min_pt) & (rotated <= max_pt), axis=1)
+#         rotated = rotated[bounds_mask]
+#         x_trimmed = np.round(rotated[:, 0], 3)
+#         y_trimmed = np.round(rotated[:, 1], 3)
+#         z_trimmed = np.round(floorHeight - rotated[:, 2], 3)
+#     else:
+#         x_trimmed = np.round(points[:, 0], 3)
+#         y_trimmed = np.round(points[:, 1], 3)
+#         z_trimmed = np.round(points[:, 2], 3)
+#
+#     # 合并为一个数组
+#     trimmed_points = np.column_stack((x_trimmed, y_trimmed, z_trimmed))
+#     # 应用 z 值过滤
+#     final_mask = (
+#         (trimmed_points[:, 2] >= z_min) &
+#         (trimmed_points[:, 2] <= z_max)
+#     )
+#     filtered_points = trimmed_points[final_mask]
+#
+#     # 去重
+#     keys = np.column_stack((filtered_points[:, 0], filtered_points[:, 1]))
+#     _, unique_indices = np.unique(keys, axis=0, return_index=True)
+#
+#     # 返回 list of [x, y, z]
+#     return filtered_points[unique_indices].tolist()
+def process_point_cloud_fast(points, sn, type):
+    points = np.array(points, dtype=np.float32)
+    if not len(points):
+        return []
+
+    # 获取配置
+    template_config = config.TEMPLATE_CONFIG_MAP.get(type, {})
+    target_z = template_config.get("height", 0)
+    tolerance = template_config.get("tolerance", 25)
+    z_min = target_z - tolerance
+    z_max = target_z + tolerance
+
+    # 初始 Z 过滤
+    valid_mask = (points[:, 2] > 0) & (points[:, 2] <= config.CAMERA_CONFIG_MAP[sn].get("max_z", np.inf))
+    points = points[valid_mask]
 
-def process_point_cloud(points, sn, dedup=True):
+    # 获取裁剪配置
-    """
+    clip_config = config.CUT_CONFIG_MAP.get(sn + "_" + type, None) or config.CUT_CONFIG_MAP.get(sn)
-    对原始点云应用旋转、裁剪和去重
 
-    :param points: np.ndarray(shape=(N,3)), 原始点云数据
-    :param sn: str, 设备序列号（用于加载配置）
-    :param dedup: bool, 是否启用去重
-    :return: list of [x, y, z], 处理后的点云列表
-    """
-    # 加载配置参数
-    clip_config = config.CUT_CONFIG_MAP.get(sn)
     if clip_config:
-        min_pt = np.array(clip_config.get("min_pt", [-np.inf] * 3))
+        rotation = np.array(clip_config["rotation"], dtype=np.float32).reshape(3, 3)
-        max_pt = np.array(clip_config.get("max_pt", [np.inf] * 3))
+        min_pt = np.array(clip_config["min_pt"], dtype=np.float32)
-        rotation = np.array(clip_config.get("rotation", [1, 0, 0, 0, 1, 0, 0, 0, 1])).reshape(3, 3)
+        max_pt = np.array(clip_config["max_pt"], dtype=np.float32)
+        floorHeight = clip_config.get("floorHeight", 0)
+
+        # 批量旋转
+        rotated = (rotation @ points.T).T
+
+        # 裁剪范围过滤
+        bounds_mask = np.all((rotated >= min_pt) & (rotated <= max_pt), axis=1)
+        rotated = rotated[bounds_mask]
+
+        # 计算坐标并应用 floorHeight
+        x_trimmed = rotated[:, 0]
+        y_trimmed = rotated[:, 1]
+        z_trimmed = floorHeight - rotated[:, 2]
     else:
-        min_pt = max_pt = rotation = None
+        x_trimmed = points[:, 0]
-
+        y_trimmed = points[:, 1]
-    processed_points = []
+        z_trimmed = points[:, 2]
-    seen_xy = set()
-
-    for point in points:
-        x, y, z = point
 
-        # 无效点过滤（Z ≤ 0 或超出最大距离）
+    # 合并为一个数组
-        if z <= 0 or z > config.CAMERA_CONFIG_MAP[sn].get("max_z", np.inf):
+    trimmed_points = np.column_stack((
-            continue
+        np.round(x_trimmed, 3),
+        np.round(y_trimmed, 3),
+        np.round(z_trimmed, 3)
+    ))
 
-        # 应用旋转矩阵
+    # 应用 z 值过滤
-        if clip_config:
+    final_mask = (trimmed_points[:, 2] >= z_min) & (trimmed_points[:, 2] <= z_max)
-            rotated = rotation @ point
+    filtered_points = trimmed_points[final_mask]
-            if not np.all(rotated >= min_pt) or not np.all(rotated <= max_pt):
-                continue
-            x_final, y_final, z_final = rotated
-        else:
-            x_final, y_final, z_final = x, y, z
-
-        # 去重逻辑（保留浮点精度）
-        if dedup:
-            # 使用浮点哈希避免离散化损失
-            key = (round(x_final, 3), round(y_final, 3))
-            if key in seen_xy:
-                continue
-            seen_xy.add(key)
 
-        processed_points.append([x_final, y_final, z_final])
+    # 去重
+    keys = filtered_points[:, :2]  # 只取 x,y
+    _, unique_indices = np.unique(keys, axis=0, return_index=True)
 
-    return processed_points
+    # 返回 list of [x, y, z]
+    return filtered_points[unique_indices].tolist()
 
 
-def sValue_to_pcd(sValue,stPointCloudImage,sn):
+def sValue_to_pcd(sValue,stPointCloudImage,sn,type):
     # 数据格式转换
     points = convert_sdk_points(sValue, stPointCloudImage.nWidth, stPointCloudImage.nHeight)
 
     # 应用旋转、裁剪、去重（假设设备序列号已知）
-    processed_points = process_point_cloud(points, sn, dedup=True)
+    processed_points = process_point_cloud_fast(points, sn,type)
 
     output_ply_path = config.save_path("pcd", sn + ".pcd")
     # 保存结果
-    write_pcd(output_ply_path, processed_points)
+    # Point.py 修改部分
+    from thread_pool import submit_task
+
+    if config.CAMERA_CONFIG_MAP[sn].get("save_pcd"):
+        output_ply_path = config.save_path("pcd", sn + ".pcd")
+        # 使用线程池异步执行点云文件写入
+        submit_task(write_pcd, output_ply_path, processed_points)
+        if config.CAMERA_CONFIG_MAP[sn].get("save_not_cut", True):
+            output_ply_path = config.save_path("pcd", sn + "_original.pcd")
+            submit_task(write_pcd, output_ply_path, points)
+
+    return output_ply_path,processed_points
 
 
 def write_pcd(filename, points):
@@ -214,23 +287,21 @@ def write_pcd(filename, points):
     with open(filename, 'w') as f:
         # 写入 PCD 文件头
         f.write("# .PCD v0.7 - Point Cloud Data file format\n")
-        f.write(f"VERSION 0.7\n")
+        f.write("VERSION 0.7\n")
         f.write("FIELDS x y z\n")
-        f.write("SIZE 4 4 4\n")  # float 类型的大小是 4 字节
+        f.write("SIZE 4 4 4\n")
-        f.write("TYPE F F F\n")  # 每个字段的数据类型
+        f.write("TYPE F F F\n")
-        f.write(f"COUNT 1 1 1\n")  # 每个字段的数量
+        f.write("COUNT 1 1 1\n")
-        f.write(f"WIDTH {len(points)}\n")  # 点数量
+        f.write(f"WIDTH {len(points)}\n")
-        f.write("HEIGHT 1\n")  # 单行表示无结构点云
+        f.write(f"HEIGHT 1\n")
-        f.write("VIEWPOINT 0 0 0 1 0 0 0\n")  # 默认视角参数
+        f.write("VIEWPOINT 0 0 0 1 0 0 0\n")
-        f.write(f"POINTS {len(points)}\n")  # 总点数
+        f.write(f"POINTS {len(points)}\n")
-        f.write("DATA ascii\n")  # 数据部分以 ASCII 形式存储
+        f.write("DATA ascii\n")
+
 
         # 写入点数据
         for point in points:
             f.write(f"{point[0]} {point[1]} {point[2]}\n")
 
-    print(f"Saved point cloud to {filename}")
+    logger.info(f"Saved point cloud to {filename}")
-
 
-if __name__ == '__main__':
-    tiff_depth_to_point_cloud("D:/git/test/hik3d-python/image/2025-06-26/depth/191330147_-Depth.tiff", "00DA6823936")

SimpleView_SaveImage.py

@@ -5,6 +5,7 @@ import ctypes
 import time
 import os
 import struct
+from thread_pool import submit_task
 from ctypes import *
 from datetime import datetime
 import Point as point
@@ -17,17 +18,47 @@ from Mv3dRgbdImport.Mv3dRgbdDefine import DeviceType_Ethernet, DeviceType_USB, D
 import config as configMap
 # 全局变量
 SN_MAP = {}  # {sn: camera_instance}
+import time
+import random
+from ctypes import *
 
+from logConfig import get_logger
 
-def initialize_devices():
+# 使用示例
-    nDeviceNum = ctypes.c_uint(0)
+logger = get_logger()
+
+def reconnect_device(sn, max_retries=5, base_delay=1.0, max_delay=10.0):
+    """
+    重新连接指定序列号的设备
+
+    Args:
+        sn (str): 设备序列号
+        max_retries (int): 最大重试次数
+        base_delay (float): 初始延迟时间(秒)
+        max_delay (float): 最大延迟时间(秒)
+
+    Returns:
+        bool: 重连是否成功
+    """
+    camera = SN_MAP.get(sn)
+
+
+    # 如果设备已存在，先尝试关闭
+    try:
+        camera.MV3D_RGBD_CloseDevice()
+    except:
+        pass  # 忽略关闭时可能的错误
+
+    # 获取设备信息（需要重新枚举设备）
+    nDeviceNum = c_uint(0)
     ret = Mv3dRgbd.MV3D_RGBD_GetDeviceNumber(
         DeviceType_Ethernet | DeviceType_USB | DeviceType_Ethernet_Vir | DeviceType_USB_Vir,
         byref(nDeviceNum)
     )
+
     if ret != MV3D_RGBD_OK or nDeviceNum.value == 0:
-        print("Failed to get device number or no devices found.")
+        logger.error("Failed to get device number or no devices found.")
-        return
+        return False
 
     stDeviceList = MV3D_RGBD_DEVICE_INFO_LIST()
     Mv3dRgbd.MV3D_RGBD_GetDeviceList(
@@ -35,115 +66,280 @@ def initialize_devices():
         pointer(stDeviceList.DeviceInfo[0]), 20, byref(nDeviceNum)
     )
 
+    # 查找对应序列号的设备
+    target_device_info = None
+    for i in range(nDeviceNum.value):
+        device_sn = ''.join(chr(c) for c in stDeviceList.DeviceInfo[i].chSerialNumber).rstrip('\x00')
+        if device_sn == sn:
+            target_device_info = stDeviceList.DeviceInfo[i]
+            break
+
+    if not target_device_info:
+        logger.error(f"Device with SN: {sn} not found during reconnection attempt")
+        return False
+
+    # 尝试重连
+    for attempt in range(max_retries):
+        try:
+            # 创建新的相机实例
+            new_camera = Mv3dRgbd()
+
+            # 尝试打开设备
+            ret = new_camera.MV3D_RGBD_OpenDevice(pointer(target_device_info))
+            if ret == MV3D_RGBD_OK:
+                # 更新全局映射
+                SN_MAP[sn] = new_camera
+                logger.info(f"Successfully reconnected to device {sn}")
+                return True
+            else:
+                logger.info(f"Failed to reconnect to device {sn}. Error code: {ret:#x}")
+
+        except Exception as e:
+            logger.info(f"Exception during reconnection attempt {attempt + 1}: {e}")
+
+        # 如果不是最后一次尝试，等待后重试
+        if attempt < max_retries - 1:
+            # 指数退避延迟
+            delay = min(base_delay * (2 ** attempt) + random.uniform(0, 1), max_delay)
+            logger.info(f"Retrying reconnection in {delay:.2f} seconds...")
+            time.sleep(delay)
+
+    logger.info(f"Failed to reconnect to device {sn} after {max_retries} attempts")
+    return False
+
+
+def retry_initialize_devices(max_retries=3, base_delay=2.0):
+    """
+    重试初始化设备
+
+    Args:
+        max_retries (int): 最大重试次数
+        base_delay (float): 初始延迟时间(秒)
+
+    Returns:
+        bool: 初始化是否成功
+    """
+    for attempt in range(max_retries):
+        try:
+            # 清空现有设备映射
+            SN_MAP.clear()
+
+            nDeviceNum = c_uint(0)
+            ret = Mv3dRgbd.MV3D_RGBD_GetDeviceNumber(
+                DeviceType_Ethernet | DeviceType_USB | DeviceType_Ethernet_Vir | DeviceType_USB_Vir,
+                byref(nDeviceNum)
+            )
+
+            if ret != MV3D_RGBD_OK or nDeviceNum.value == 0:
+                logger.info("Failed to get device number or no devices found.")
+                if attempt < max_retries - 1:
+                    delay = base_delay * (2 ** attempt)
+                    logger.info(f"Retrying device initialization in {delay} seconds...")
+                    time.sleep(delay)
+                continue
+
+            stDeviceList = MV3D_RGBD_DEVICE_INFO_LIST()
+            Mv3dRgbd.MV3D_RGBD_GetDeviceList(
+                DeviceType_Ethernet | DeviceType_USB | DeviceType_Ethernet_Vir | DeviceType_USB_Vir,
+                pointer(stDeviceList.DeviceInfo[0]), 20, byref(nDeviceNum)
+            )
+
+            success_count = 0
             for i in range(nDeviceNum.value):
                 serial_number = ''.join(chr(c) for c in stDeviceList.DeviceInfo[i].chSerialNumber).rstrip('\x00')
-        print(f"Found device [{i}]: Serial Number: {serial_number}")
+                logger.info(f"Found device [{i}]: Serial Number: {serial_number}")
 
                 camera = Mv3dRgbd()
 
                 # 打开设备
                 ret = camera.MV3D_RGBD_OpenDevice(pointer(stDeviceList.DeviceInfo[i]))
                 if ret != MV3D_RGBD_OK:
-            print(f"Failed to open device with SN: {serial_number}. Error code: {ret:#x}")
+                    logger.info(f"Failed to open device with SN: {serial_number}. Error code: {ret:#x}")
                     continue
 
                 # 存入全局 map
                 SN_MAP[serial_number] = camera
-        print(f"Successfully added device {serial_number} to SN_MAP")
+                logger.info(f"Successfully added device {serial_number} to SN_MAP")
+                success_count += 1
+
+            if success_count > 0:
+                logger.info(f"Successfully initialized {success_count} devices")
+                return True
+            else:
+                logger.info("No devices were successfully initialized")
+
+        except Exception as e:
+            logger.info(f"Exception during device initialization attempt {attempt + 1}: {e}")
+
+        if attempt < max_retries - 1:
+            delay = base_delay * (2 ** attempt)
+            logger.info(f"Retrying device initialization in {delay} seconds...")
+            time.sleep(delay)
+
+    logger.info(f"Failed to initialize devices after {max_retries} attempts")
+    return False
 
-def pic(sn):
+
+def enhanced_pic(sn, type):
+    """
+    增强版的图片采集函数，包含重连机制和点云数据重试机制
+    """
     camera = SN_MAP.get(sn)
 
     if not camera:
-        print(f"No camera found for SN: {sn}")
+        logger.info(f"No camera found for SN: {sn}, attempting reconnection...")
-        return
+        if not reconnect_device(sn):
+            logger.info(f"Failed to reconnect to device {sn}")
+            return None
+        camera = SN_MAP.get(sn)
 
     config = configMap.CAMERA_CONFIG_MAP.get(sn)
     if not config:
-        print(f"No config found for SN: {sn}")
+        logger.info(f"No config found for SN: {sn}")
-        return
+        return None
 
-    time_on = config.get("time_on", 0)  # 延时开始（毫秒）
+    time_on = config.get("time_on", 0)
-    print(f"Delaying start by {time_on}ms...")
+    logger.info(f"Delaying start by {time_on}ms...")
-    time.sleep(time_on / 1000.0)  # 转成秒
+    time.sleep(time_on / 1000.0)
 
     saved_files = {
-        "depth": [],
+        "sn": sn,
-        "color": [],
+        "depth": "",
-        "pcd": []
+        "color": "",
+        "pcd": "",
+        "point": []
     }
 
-    # 开始取流
+    # 尝试开始取流，如果失败则尝试重连
     ret = camera.MV3D_RGBD_Start()
     if ret != MV3D_RGBD_OK:
-        print(f"Failed to start grabbing. Error code: {ret:#x}")
+        logger.info(f"Failed to start grabbing. Error code: {ret:#x}")
-        return
+        logger.info("Attempting reconnection...")
+        if reconnect_device(sn):
+            camera = SN_MAP.get(sn)
+            ret = camera.MV3D_RGBD_Start()
+            if ret != MV3D_RGBD_OK:
+                logger.info(f"Failed to start grabbing after reconnection. Error code: {ret:#x}")
+                return saved_files
+        else:
+            logger.info("Reconnection failed")
+            return saved_files
 
     try:
+        # 最多重试10次获取有效的点云数据
+        max_retries = 10
+        retry_count = 0
+        point_cloud_success = False
+
+        # 用于存储最后一次成功的彩色图数据
+        last_color_image_info = None
+        last_color_file_name = None
+
+        while retry_count < max_retries and not point_cloud_success:
             stFrameData = MV3D_RGBD_FRAME_DATA()
 
             # 获取单帧数据
             ret = camera.MV3D_RGBD_FetchFrame(pointer(stFrameData), 5000)
             if ret == MV3D_RGBD_OK:
+                has_depth_data = False
+                current_color_image_info = None
+                current_color_file_name = None
 
+                flag = True
                 for i in range(stFrameData.nImageCount):
                     image_info = stFrameData.stImageData[i]
 
                     # 保存深度图
                     if image_info.enImageType == ImageType_Depth:
-                    file_name = configMap.save_path("depth", "-Depth")
+                        has_depth_data = True
-                    ret_save = camera.MV3D_RGBD_SaveImage(pointer(image_info), FileType_TIFF, file_name)
-                    print("Saved depth image." if ret_save == MV3D_RGBD_OK else "Failed to save depth image.")
-                    if ret_save == MV3D_RGBD_OK:
-                        saved_files["depth"].append(file_name)
-
-                    # 点云转换与保存
                         stPointCloudImage = MV3D_RGBD_IMAGE_DATA()
-                    ret = camera.MV3D_RGBD_MapDepthToPointCloud(pointer(stFrameData.stImageData[i]), pointer(stPointCloudImage))
+                        ret = camera.MV3D_RGBD_MapDepthToPointCloud(pointer(stFrameData.stImageData[i]),
+                                                                    pointer(stPointCloudImage))
                         if MV3D_RGBD_OK != ret:
-                        print("_MapDepthToPointCloud() Run failed...")
+                            logger.info(f"_MapDepthToPointCloud() Run failed... Retry: {retry_count + 1}/{max_retries}")
                         else:
-                        print(
+                            logger.info(
                                 "_MapDepthToPointCloud() Run Succeed: framenum (%d) height(%d) width(%d)  len (%d)!" % (
                                     stPointCloudImage.nFrameNum,
                                     stPointCloudImage.nHeight, stPointCloudImage.nWidth, stPointCloudImage.nDataLen))
                             strMode = string_at(stPointCloudImage.pData, stPointCloudImage.nDataLen)
                             sValue = struct.unpack('f' * int(stPointCloudImage.nHeight * stPointCloudImage.nWidth * 3),
                                                    strMode)
-                        pcd_file = point.sValue_to_pcd(sValue, stPointCloudImage, sn)
+                            pcd_file, processed_points = point.sValue_to_pcd(sValue, stPointCloudImage, sn, type)
-                        if pcd_file:
+                            if pcd_file and len(processed_points) > 0:
-                            saved_files["pcd"].append(pcd_file)
+                                saved_files["pcd"] = pcd_file
+                                saved_files["point"] = processed_points
+                                point_cloud_success = True  # 成功获取点云数据
+                                flag = False
 
-                # 保存彩色图
+                    # 记录彩色图信息，但不立即保存
                     elif image_info.enImageType in (
                             ImageType_RGB8_Planar, ImageType_YUV420SP_NV12,
                             ImageType_YUV420SP_NV21, ImageType_YUV422
                     ):
-                    file_name = configMap.save_path("color", "-_Color")
+                        file_name = configMap.save_path("color", sn+"_Color")
-                    ret_save = camera.MV3D_RGBD_SaveImage(pointer(image_info), FileType_BMP, file_name)
+                        if config.get("save_image"):
-                    print("Saved color image." if ret_save == MV3D_RGBD_OK else "Failed to save color image.")
+                            submit_task(camera.MV3D_RGBD_SaveImage,pointer(image_info), FileType_BMP, file_name)
-                    if ret_save == MV3D_RGBD_OK:
+                            saved_files["color"] = file_name + ".bmp"
-                        saved_files["color"].append(file_name)
+                            logger.info(f"Color image saved successfully: {saved_files['color']}")
+
+                if flag:break
+                # 如果没有深度数据或点云处理失败，增加重试次数
+                if not has_depth_data:
+                    logger.info(f"No depth data found in frame. Retry: {retry_count + 1}/{max_retries}")
+                    retry_count += 1
+                    time.sleep(0.1)  # 短暂等待后重试
+                elif not point_cloud_success:
+                    retry_count += 1
+                    time.sleep(0.1)  # 短暂等待后重试
 
             else:
-            print("Failed to fetch frame.")
+                logger.info(f"Failed to fetch frame. Error code: {ret:#x}. Retry: {retry_count + 1}/{max_retries}")
+                retry_count += 1
+                time.sleep(0.1)  # 短暂等待后重试
+
+    except Exception as e:
+        logger.error(f"Error during frame capture for device {sn}: {e}")
 
     finally:
         camera.MV3D_RGBD_Stop()
-        print("Single capture completed.")
+        # logger.info(f"Single capture completed. Results: {saved_files}")
-
-    # 输出结果路径
-    print("Saved files:")
-    for key, paths in saved_files.items():
-        if paths:
-            print(f"  {key.upper()}:")
-            for path in paths:
-                print(f"    - {path}")
 
     return saved_files
 
 
-initialize_devices()
+def initialize_devices():
+    nDeviceNum = ctypes.c_uint(0)
+    ret = Mv3dRgbd.MV3D_RGBD_GetDeviceNumber(
+        DeviceType_Ethernet | DeviceType_USB | DeviceType_Ethernet_Vir | DeviceType_USB_Vir,
+        byref(nDeviceNum)
+    )
+    if ret != MV3D_RGBD_OK or nDeviceNum.value == 0:
+        logger.info("Failed to get device number or no devices found.")
+        return
+
+    stDeviceList = MV3D_RGBD_DEVICE_INFO_LIST()
+    Mv3dRgbd.MV3D_RGBD_GetDeviceList(
+        DeviceType_Ethernet | DeviceType_USB | DeviceType_Ethernet_Vir | DeviceType_USB_Vir,
+        pointer(stDeviceList.DeviceInfo[0]), 20, byref(nDeviceNum)
+    )
+
+    for i in range(nDeviceNum.value):
+        serial_number = ''.join(chr(c) for c in stDeviceList.DeviceInfo[i].chSerialNumber).rstrip('\x00')
+        logger.info(f"Found device [{i}]: Serial Number: {serial_number}")
+
+        camera = Mv3dRgbd()
+
+        # 打开设备
+        ret = camera.MV3D_RGBD_OpenDevice(pointer(stDeviceList.DeviceInfo[i]))
+        if ret != MV3D_RGBD_OK:
+            logger.info(f"Failed to open device with SN: {serial_number}. Error code: {ret:#x}")
+            continue
 
-if __name__ == '__main__':
+        # 存入全局 map
-    pic("00DA6823936")
+        SN_MAP[serial_number] = camera
+        logger.info(f"Successfully added device {serial_number} to SN_MAP")
+
+
+
+initialize_devices()

config.py

@@ -6,7 +6,7 @@ from datetime import datetime
 CAMERA_CONFIG_MAP = {}  # {sn: config_dict}
 CUT_CONFIG_MAP = {}     # {filename_without_ext: config_dict}
 DIRECTION_CAMERA = {}
-TEMPLATE_MAP = {}
+TEMPLATE_CONFIG_MAP = {}
 
 def load_camera_configs(config_dir="./config/camera"):
     """
@@ -50,7 +50,7 @@ def load_template_configs(config_dir="./config/template"):
                     config = json.load(f)
                     type = config.get("type")
                     if type:
-                        TEMPLATE_MAP[type] = config
+                        TEMPLATE_CONFIG_MAP[type] = config
                         print(f"Loaded camera config: {type}")
                     else:
                         print(f"[WARN] No 'sn' found in {filename}")

config/camera/00DA6823936.json

@@ -1,13 +1,10 @@
 {
   "sn": "00DA6823936",
   "direction": "1",
-  "x_angle": 125,
-  "y_angle": 75,
   "save_pcd": true,
+  "save_image": true,
   "resolution": 8,
-  "max_z": 1800,
+  "max_z": 2000,
-  "reverse_order": false,
+  "time_on": 0,
-  "time_on": 100,
+  "save_not_cut": true
-  "time_off": 5500,
-  "time_hop": 800
 }

config/camera/00DA6823953.json

@@ -0,0 +1,10 @@
+{
+  "sn": "00DA6823953",
+  "direction": "2",
+  "save_pcd": true,
+  "save_image": true,
+  "resolution": 8,
+  "max_z": 2000,
+  "time_on": 0,
+  "save_not_cut": true
+}

config/cut/00DA6823936.json

@@ -0,0 +1,24 @@
+{
+    "floorHeight": 1920,
+    "max_pt": [
+        550.1860961914062,
+        -325.56182861328125,
+        1564.596435546875
+    ],
+    "min_pt": [
+        -560.0139770507812,
+        -1206.5701904296875,
+        344.9464111328125
+    ],
+    "rotation": [
+        1,
+        0,
+        0,
+        0,
+        0.7197034358978271,
+        -0.6942816376686096,
+        0,
+        0.6942816376686096,
+        0.7197034358978271
+    ]
+}

config/cut/00DA6823936_47.json

@@ -0,0 +1,24 @@
+{
+    "floorHeight": 1920,
+    "max_pt": [
+        400.86029052734375,
+        -500.30865478515625,
+        1564.596435546875
+    ],
+    "min_pt": [
+        -530.2582397460938,
+        -968.01953125,
+        344.9464111328125
+    ],
+    "rotation": [
+        1,
+        0,
+        0,
+        0,
+        0.7197034358978271,
+        -0.6942816376686096,
+        0,
+        0.6942816376686096,
+        0.7197034358978271
+    ]
+}

config/cut/00DA6823936_A.json

@@ -1,24 +0,0 @@
-{
-    "floorHeight": 1,
-    "max_pt": [
-        103.20252990722656,
-        -169.6024932861328,
-        182.84439086914062
-    ],
-    "min_pt": [
-        16.854652404785156,
-        -1234.1793212890625,
-        153.4544677734375
-    ],
-    "rotation": [
-        -0.006754159927368164,
-        -0.9998819828033447,
-        0.013793319463729858,
-        0.43973052501678467,
-        -0.015358209609985352,
-        -0.8979983329772949,
-        0.8981043100357056,
-        0,
-        0.4397825002670288
-    ]
-}

config/cut/00DA6823936_B.json

@@ -1,24 +0,0 @@
-{
-    "floorHeight": 1,
-    "max_pt": [
-        59.17729568481445,
-        -177.37328052520752,
-        823.2836303710938
-    ],
-    "min_pt": [
-        0,
-        -1263.9830322265625,
-        200.47930908203125
-    ],
-    "rotation": [
-        -0.0070345401763916016,
-        -0.9998821020126343,
-        0.013652533292770386,
-        0.4579751193523407,
-        -0.015358328819274902,
-        -0.8888324499130249,
-        0.88893723487854,
-        -2.9802322387695312e-08,
-        0.45802903175354004
-    ]
-}

config/cut/00DA6823953.json

@@ -0,0 +1,24 @@
+{
+    "floorHeight": 1920,
+    "max_pt": [
+        512.0901489257812,
+        -500.2588500976562,
+        1564.596435546875
+    ],
+    "min_pt": [
+        -508.4150390625,
+        -1008.01953125,
+        344.9464111328125
+    ],
+    "rotation": [
+        1,
+        0,
+        0,
+        0,
+        0.7197034358978271,
+        -0.6942816376686096,
+        0,
+        0.6942816376686096,
+        0.7197034358978271
+    ]
+}

config/cut/00DA6823953_47.json

@@ -0,0 +1,24 @@
+{
+    "floorHeight": 1920,
+    "max_pt": [
+        450.0901489257812,
+        -500.2588500976562,
+        1564.596435546875
+    ],
+    "min_pt": [
+        -460.4150390625,
+        -908.01953125,
+        344.9464111328125
+    ],
+    "rotation": [
+        1,
+        0,
+        0,
+        0,
+        0.7197034358978271,
+        -0.6942816376686096,
+        0,
+        0.6942816376686096,
+        0.7197034358978271
+    ]
+}

config/template/45.json

@@ -1,6 +1,8 @@
 {
   "type": "45",
-	"width":"299",
+	"width":299,
-	"length":"355",
+	"length":355,
-	"high":"314"
+	"height": 1540 ,
+	"min_area": 50,
+	"tolerance": 60
 }

config/template/46.json

@@ -0,0 +1,8 @@
+{
+    "type": "46",
+    "width": 140,
+    "length": 138,
+    "height": 1070,
+    "min_area": 120,
+    "tolerance": 150
+}

config/template/47.json

@@ -0,0 +1,8 @@
+{
+    "type": "47",
+    "width": 299,
+    "length": 355,
+    "height": 1370,
+    "min_area": 500,
+    "tolerance": 150
+}

config/接口说明

@@ -0,0 +1,190 @@
+以下是您当前 Flask 应用中已实现的两个主要接口的 **API 接口文档**，采用标准 RESTful 风格描述。
+
+---
+
+## 🌐 接口文档
+
+### 1. 更新配置 `/config/update`
+
+#### ✅ 方法
+- `GET`
+
+#### 📦 描述
+- 重新加载所有配置文件（相机、裁剪、模板等），用于刷新服务端配置。
+
+#### 📤 响应示例
+
+```json
+{
+  "message": "Hello, this service config update!",
+  "status": "OK"
+}
+```
+
+
+---
+
+### 2. 添加/更新模板配置 `/api/addTemplate`
+
+#### ✅ 方法
+- `POST`
+
+#### 📦 描述
+- 接收一个 JSON 对象，以 `"type"` 字段作为文件名，保存为 `./config/template/{type}.json`。
+- 支持覆盖写入已有模板。
+- 写入成功后自动调用 [config.load_configs()](file://D:\git\test\hik3d-python\config.py#L82-L89) 刷新配置。
+
+#### 📥 请求体（JSON）
+
+| 字段名      | 类型   | 必填 | 示例值    | 描述               |
+|-------------|--------|------|-----------|--------------------|
+| type        | string | ✅   | "45"      | 模板名称             |
+| width       | string | ❌   | "299"     | 模板宽度（暂无作用）  |
+| length      | string | ❌   | "355"     | 模板长度（暂无作用）  |
+| height      | number | ✅   | 314       | 最高高度（单位：毫米）|
+| min_area    | number | ✅   | 20        | 空洞最小面积         |
+| tolerance   | number | ✅   | 30        | 容差范围（单位：毫米）|
+
+##### 示例请求体：
+
+```json
+{
+  "type": "45",
+  "width": "299",
+  "length": "355",
+  "height": 314,
+  "min_area": 20,
+  "tolerance": 30
+}
+```
+
+
+#### 📤 成功响应
+
+```json
+{
+  "status": "OK",
+  "message": "Template '45' saved successfully."
+}
+```
+
+
+#### 📤 错误响应
+
+- 缺少数据：
+  ```json
+  {
+    "status": "ERROR",
+    "message": "No data provided"
+  }
+  ```
+
+
+- 缺少 `type`：
+  ```json
+  {
+    "status": "ERROR",
+    "message": "Missing 'type' field"
+  }
+  ```
+
+
+- 写入失败：
+  ```json
+  {
+    "status": "ERROR",
+    "message": "Failed to save template: [错误信息]"
+  }
+  ```
+
+
+---
+
+### 3. 图像处理与空洞检测 `/api/picCompute`
+
+#### ✅ 方法
+- `POST`
+
+#### 📦 描述
+- 根据设备编号和模板类型，获取点云并检测是否存在大空洞。
+- 调用了 [SimpleView_SaveImage.pic()](file://D:\git\test\hik3d-python\SimpleView_SaveImage.py#L53-L137) 和 [image.detect_large_holes()](file://D:\git\test\hik3d-python\image.py#L187-L208)。
+
+#### 📥 请求体（JSON）
+
+| 字段名 | 类型   | 必填 | 示例值     | 描述           |
+|--------|--------|------|------------|----------------|
+| sn     | string | ✅   | "00DA6823936" | 设备序列号       |
+| type   | string | ✅   | "45"       | 模板类型（对应模板配置） |
+
+##### 示例请求体：
+
+```json
+{
+  "sn": "00DA6823936",
+  "type": "45"
+}
+```
+
+
+#### 📤 成功响应
+
+```json
+{
+  "message": true,
+  "status": "OK"
+}
+```
+
+
+> `message` 表示是否检测到空洞：
+> - `true`：有空洞
+> - `false`：无空洞
+
+#### 📤 错误响应
+
+- 缺少参数：
+  ```json
+  {
+    "message": "",
+    "status": "ERROR",
+    "error": "Missing required parameter: 'sn'"
+  }
+  ```
+
+
+- 模板不存在：
+  ```json
+  {
+    "message": "",
+    "status": "ERROR",
+    "error": "Missing required parameter: 'type'"
+  }
+  ```
+
+
+- 其他异常：
+  ```json
+  {
+    "message": "",
+    "status": "ERROR",
+    "error": "Failed to get TIFF paths: [错误信息]"
+  }
+  ```
+
+
+---
+
+## ⚠️ 全局异常处理
+
+所有未捕获的异常都会被全局异常处理器捕获，并返回如下格式：
+
+```json
+{
+  "message": "",
+  "status": "ERROR",
+  "error": "[错误信息]"
+}
+```
+
+
+---

config/配置文件说明.txt

@@ -2,13 +2,26 @@ camera文档
 {
   "sn": "00DA6823936",#sn
   "direction": "1",#方向
-  "x_angle": 55,#相机的水平视场角（相机自身的）
-  "y_angle": 84,#相机的垂直视场角
   "save_pcd": true,#是否保存pcd
-  "resolution": 8,#像素大小（可以调整来区别
+  "resolution": 8,#像素大小（可以调整生成的图片，根据生成图片识别空洞
   "max_z": 2000,#最大深度（裁剪之前的
-  "reverse_order": false,#是否pcd组成的图片是否翻转
   "time_on": 300,#开始时间
-  "time_off": 3500,#结束时间
+  "save_not_cut": true #基础的没有裁剪的pcd是否保存
-  "time_hop": 500#时间间隔
 }
+
+cut 裁剪文档
+名称：sn_template
+当template没有的时候读取sn.json
+
+template 模版文档
+{
+  "type": "45", #模版名称
+	"width":"299",#模版横 暂无作用
+	"length":"355",#模版宽 暂无作用
+	"height": 314 ,#最高高度，从裁剪文档里的floorHeight为0
+	"min_area": 20 ,#空洞的最小面积
+	"tolerance": 30 #冗余，将判断在最高的位置附近，单位毫米
+}
+
+
+先进行最低的位置，写入floorHeight和旋转参数

image.py

@@ -1,12 +1,50 @@
 import numpy as np
 import cv2
-import matplotlib.pyplot as plt
 
+import threading
+from thread_pool import submit_task
 from scipy.ndimage import label
 
 import config
 
-
+from logConfig import get_logger
+
+# 使用示例
+logger = get_logger()
+#
+# def point_cloud_to_2d_image(points, resolution=1.0, x_range=(17, 275), y_range=(-129, -1227)):
+#     """
+#     将点云转换为 2D 图像（X-Y 平面），每个像素值表示对应位置的 Z 值平均值
+#     """
+#     if not isinstance(points, np.ndarray):
+#         points = np.array(points)
+#
+#     if len(points) == 0:
+#         raise ValueError("点云为空")
+#
+#     x_min, x_max = x_range
+#     y_min, y_max = y_range
+#
+#     width = int((x_max - x_min) / resolution) + 1
+#     height = int((y_max - y_min) / resolution) + 1
+#
+#     image = np.zeros((height, width), dtype=np.float32)
+#     count_map = np.zeros((height, width), dtype=np.int32)
+#
+#     for x, y, z in points:
+#         xi = int((x - x_min) / resolution)
+#         yi = int((y - y_min) / resolution)
+#
+#         if 0 <= xi < width and 0 <= yi < height:
+#             image[yi, xi] += z
+#             count_map[yi, xi] += 1
+#
+#     # 防止除零错误
+#     count_map[count_map == 0] = 1
+#     image /= count_map
+#
+#     return image, (x_min, y_min)
+# 优化 point_cloud_to_2d_image 函数
 def point_cloud_to_2d_image(points, resolution=1.0, x_range=(17, 275), y_range=(-129, -1227)):
     """
     将点云转换为 2D 图像（X-Y 平面），每个像素值表示对应位置的 Z 值平均值
@@ -23,16 +61,32 @@ def point_cloud_to_2d_image(points, resolution=1.0, x_range=(17, 275), y_range=(
     width = int((x_max - x_min) / resolution) + 1
     height = int((y_max - y_min) / resolution) + 1
 
+    # 使用向量化操作替代循环
+    x_coords = points[:, 0]
+    y_coords = points[:, 1]
+    z_coords = points[:, 2]
+
+    # 计算像素坐标
+    xi = ((x_coords - x_min) / resolution).astype(int)
+    yi = ((y_coords - y_min) / resolution).astype(int)
+
+    # 筛选有效坐标
+    valid_mask = (xi >= 0) & (xi < width) & (yi >= 0) & (yi < height)
+    xi = xi[valid_mask]
+    yi = yi[valid_mask]
+    z_coords = z_coords[valid_mask]
+
+    # 使用直方图统计替代循环累加
     image = np.zeros((height, width), dtype=np.float32)
     count_map = np.zeros((height, width), dtype=np.int32)
 
-    for x, y, z in points:
+    # 使用 bincount 进行快速统计
-        xi = int((x - x_min) / resolution)
+    indices = yi * width + xi
-        yi = int((y - y_min) / resolution)
+    z_sums = np.bincount(indices, weights=z_coords, minlength=height * width)
+    counts = np.bincount(indices, minlength=height * width)
 
-        if 0 <= xi < width and 0 <= yi < height:
+    image.flat[:] = z_sums
-            image[yi, xi] += z
+    count_map.flat[:] = counts
-            count_map[yi, xi] += 1
 
     # 防止除零错误
     count_map[count_map == 0] = 1
@@ -41,50 +95,30 @@ def point_cloud_to_2d_image(points, resolution=1.0, x_range=(17, 275), y_range=(
     return image, (x_min, y_min)
 
 
-def detect_holes_by_density(density_map, density_threshold_ratio=0.5, min_area=100):
+def stitch(imagePath1, imagePath2):
-    """
+    # 读取两张图像
-    基于点云密度图识别空洞区域
+    image1 = cv2.imread(imagePath1)
-
+    image2 = cv2.imread(imagePath2)
-    :param density_map: 2D numpy array, 每个像素表示该位置点云密度
-    :param density_threshold_ratio: 密度低于均值的 ratio 倍时视为空洞候选
-    :param min_area: 最小空洞面积（像素数）
-    :return: list of ((cx, cy), area)，空洞中心和面积（图像坐标）
-    """
-    # 计算邻域平均密度（3x3窗口）
-    avg_density = np.zeros_like(density_map)
-    for i in range(density_map.shape[0]):
-        for j in range(density_map.shape[1]):
-            # 取 3x3 邻域
-            neighbors = density_map[
-                        max(0, i - 1):min(i + 2, density_map.shape[0]),
-                        max(0, j - 1):min(j + 2, density_map.shape[1])
-                        ]
-            avg_density[i, j] = np.mean(neighbors)
-
-    # 构建空洞候选区：密度低于邻域平均值的 50%
-    binary_map = (density_map < avg_density * density_threshold_ratio).astype(np.uint8)
 
-    # 连通域分析
+    # 创建 Stitcher 对象
-    structure = np.array([[1, 1, 1],
+    stitcher = cv2.Stitcher_create()
-                          [1, 1, 1],
-                          [1, 1, 1]])
-    labeled_map, num_features = label(binary_map, structure=structure)
 
-    holes = []
+    # 拼接图像
-    for label_id in range(1, num_features + 1):
+    (status, stitched) = stitcher.stitch((image1, image2))
-        coords = np.where(labeled_map == label_id)
-        hole_pixel_count = len(coords[0])
 
-        if hole_pixel_count >= min_area:
+    # 检查拼接结果
-            cx = np.mean(coords[1])  # x 坐标（列）
+    if status == cv2.Stitcher_OK:
-            cy = np.mean(coords[0])  # y 坐标（行）
+        cv2.imwrite('stitched_output.jpg', stitched)
-            area = hole_pixel_count
+        print("图像拼接成功！")
-            holes.append(((cx, cy), area))
+    else:
+        print(f"图像拼接失败，错误代码: {status}")
+if __name__ == '__main__':
+    stitch('D:/git/test/hik3d-python/image/2025-07-01/color/105601193_-_Color.bmp'
+           , 'D:/git/test/hik3d-python/image/2025-07-01/color/164646720_-_Color.bmp')
 
-    return holes
 
 
-def detect_black_regions(binary_mask, min_area=10):
+def detect_black_regions(binary_mask, min_area=10,box_area=10):
     """
     检测图像中的黑色连通区域（值为 0 的区域）
 
@@ -111,19 +145,18 @@ def detect_black_regions(binary_mask, min_area=10):
     labeled_map, num_features = label(binary, structure=structure)
 
     regions = []
+    count = 0
 
     for label_id in range(1, num_features + 1):
         coords = np.where(labeled_map == label_id)
         area = len(coords[0])
-
         if area >= min_area:
             cx = np.mean(coords[1])  # x 坐标（列）
             cy = np.mean(coords[0])  # y 坐标（行）
-
+            logger.info(f"区域: {label_id} 中心: {cx, cy} 面积: {area}")
             regions.append(((cx, cy),area))
-
+            count += ( area/box_area)
-
+    return regions,count
-    return regions
 def convert_image_holes_to_real(holes, offset, resolution):
     real_holes = []
     x_min, y_min = offset
@@ -138,23 +171,39 @@ def visualize_holes_on_image(image, holes, output_path=None):
     """
     在图像上画出检测到的空洞中心和轮廓
     """
+    try:
+        # 确保图像数据有效
+        if image is None or len(image) == 0:
+            print("Warning: Empty image data for visualization")
+            return
+
         # 彩色化灰度图用于可视化
+        if len(image.shape) == 2:  # 灰度图
             color_image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+        else:
+            color_image = image.copy()
 
+        # 绘制空洞标记
+        if holes:
             for (cx, cy), _ in holes:
-        # 绘制圆形标注空洞中心
+                # 确保坐标有效
+                if 0 <= int(cx) < color_image.shape[1] and 0 <= int(cy) < color_image.shape[0]:
                     cv2.circle(color_image, (int(cx), int(cy)), radius=5, color=(0, 0, 255), thickness=-1)
 
-    # 显示图像
+        # 生成输出路径
-    # plt.figure(figsize=(10, 8))
+        if output_path is None:
-    plt.imshow(cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB))
-    # plt.title("Detected Holes")
-    plt.axis("off")
             output_path = config.save_path("image", "_holes.png")
-    if output_path:
+
-        plt.savefig(output_path, bbox_inches='tight', dpi=200)
+        # 保存图像
+        success = cv2.imwrite(output_path, color_image)
+        if success:
             print(f"Saved visualization to {output_path}")
-    # plt.show()
+        else:
+            print(f"Failed to save visualization to {output_path}")
+
+    except Exception as e:
+        print(f"Error in visualize_holes_on_image: {e}")
+
 
 def read_pcd_points(pcd_path):
     """
@@ -183,30 +232,44 @@ def read_pcd_points(pcd_path):
                         continue
     return points
 
-def detect_large_holes(points,sn, x_max):
+
+def detect_large_holes(points, sn, type):
+    # 获取裁剪配置
+    cat_map = config.CUT_CONFIG_MAP.get(sn + "_" + type, None) or config.CUT_CONFIG_MAP.get(sn)
+    template_map = config.TEMPLATE_CONFIG_MAP[type]
+    camera_map = config.CAMERA_CONFIG_MAP[sn]
 
     # 2. 生成 2D 图像
-    x_range = (config.CUT_CONFIG_MAP[sn]["min_pt"][0],x_max)       # 手动指定 X 范围
+    x_range = (cat_map["min_pt"][0], cat_map["max_pt"][0])
-    y_range = (config.CUT_CONFIG_MAP[sn]["min_pt"][1], config.CUT_CONFIG_MAP[sn]["max_pt"][1])  # 注意：这里要保证 y_min < y_max，否则反转一下
+    y_range = (cat_map["min_pt"][1], cat_map["max_pt"][1])
     resolution = config.CAMERA_CONFIG_MAP[sn].get("resolution")
     image, offset = point_cloud_to_2d_image(points, resolution=resolution, x_range=x_range, y_range=y_range)
 
     # 3. 图像归一化用于可视化
     normalized_image = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
 
+    min_area = min(template_map["min_area"], (template_map["width"] * template_map["height"]) /(2*camera_map["resolution"]*camera_map["resolution"]))
     # 4. 检测空洞
-    holes = detect_black_regions(normalized_image, min_area=20)
+    holes, count = detect_black_regions(normalized_image, min_area, (template_map["width"] * template_map["height"]) / 2)
+
+    if config.CAMERA_CONFIG_MAP[sn].get("save_image"):
+        # 创建数据副本确保异步执行时数据完整性
+        image_copy = normalized_image.copy()
+        holes_copy = list(holes)  # 创建holes的副本
 
-    # 5. 可视化空洞
+        # 预先生成输出路径
-    visualize_holes_on_image(normalized_image, holes)
+        output_path = config.save_path("image", f"_{sn}_holes.png")
+
+        # 使用线程池异步执行可视化操作
+        submit_task(visualize_holes_on_image, image_copy, holes_copy, output_path)
 
     # 6. 输出真实世界坐标
     real_holes = convert_image_holes_to_real(holes, offset, resolution)
-    return real_holes
+    return real_holes, count
 
-if __name__ == '__main__':
+# if __name__ == '__main__':
-    points = read_pcd_points("D:/PycharmProjects/Hik3D/image/2025-06-25/pcd/182109899_00DA6823936_merged.pcd")
+#     points = read_pcd_points("D:/PycharmProjects/Hik3D/image/2025-06-25/pcd/182109899_00DA6823936_merged.pcd")
-    sn = "00DA6823936"
+#     sn = "00DA6823936"
-    x_max = 326
+#     x_max = 326
-    detect_large_holes(points,sn, x_max)
+#     detect_large_holes(points,sn, x_max)
     

logConfig.py

@@ -0,0 +1,64 @@
+import logging
+from logging.handlers import TimedRotatingFileHandler
+import asyncio
+import time
+import os
+
+
+# 确保 logs 目录存在
+def ensure_logs_directory():
+    logs_dir = "logs"
+    if not os.path.exists(logs_dir):
+        os.makedirs(logs_dir)
+    return logs_dir
+
+
+# 配置日志
+def setup_logger(name, log_file, level=logging.INFO):
+    # 确保 logs 目录存在
+    ensure_logs_directory()
+
+    # 构建完整的日志文件路径
+    logs_dir = "logs"
+    full_log_path = os.path.join(logs_dir, log_file)
+
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+
+    # 创建按时间轮转的文件处理器
+    handler = TimedRotatingFileHandler(
+        full_log_path,  # 使用完整路径
+        when="midnight",  # 每天午夜轮转
+        interval=1,  # 间隔1天
+        backupCount=30  # 保留30天的日志
+    )
+    handler.setFormatter(formatter)
+
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    # 避免重复添加 handler
+    if not logger.handlers:
+        logger.addHandler(handler)
+
+    return logger
+
+
+# 创建全局logger实例
+logger = setup_logger('hik3d', 'app.log')
+def get_logger():
+    """
+    获取全局logger实例
+    """
+    return logger
+# 异步写入日志的包装函数
+async def async_log(logger, level, message):
+    # 在线程池中执行日志写入，避免阻塞事件循环
+    loop = asyncio.get_event_loop()
+    await loop.run_in_executor(None, getattr(logger, level), message)
+
+
+# # 使用示例
+# logger = setup_logger('my_app', 'app.log')
+
+
+# 运行
+# asyncio.run(main())

logs/app.log

@@ -0,0 +1,374 @@
+2025-08-15 14:58:42,508 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 14:58:43,733 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 14:58:43,733 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 14:58:44,957 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 14:58:45,411 - hik3d - INFO - 服务启动中...
+2025-08-15 14:58:45,412 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 15:36:29,232 - hik3d - INFO - Failed to get device number or no devices found.
+2025-08-15 15:36:29,679 - hik3d - INFO - 服务启动中...
+2025-08-15 15:36:29,680 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 15:37:16,115 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 15:37:17,490 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 15:37:17,490 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 15:37:18,713 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 15:37:19,071 - hik3d - INFO - 服务启动中...
+2025-08-15 15:37:19,072 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 15:37:32,450 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 15:37:32,453 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 15:37:32,454 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:37:32,454 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 15:37:32,455 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:37:32,754 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:37:32,991 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:37:34,600 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 2.146 秒，发现空洞: False
+2025-08-15 15:37:35,121 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 2.668 秒，发现空洞: False
+2025-08-15 15:37:35,198 - hik3d - INFO - 全设备图片计算完成，总耗时 2.747 秒，结果: False
+2025-08-15 15:37:36,739 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153733795_00DA6823953.pcd
+2025-08-15 15:37:39,054 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153734206_00DA6823936.pcd
+2025-08-15 15:37:41,353 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153734217_00DA6823936_original.pcd
+2025-08-15 15:37:41,502 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153733799_00DA6823953_original.pcd
+2025-08-15 15:37:49,321 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 15:37:49,322 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 15:37:49,322 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:37:49,322 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 15:37:49,322 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:37:49,540 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:37:49,703 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:37:50,722 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.401 秒，发现空洞: False
+2025-08-15 15:37:51,015 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.692 秒，发现空洞: False
+2025-08-15 15:37:51,023 - hik3d - INFO - 全设备图片计算完成，总耗时 1.702 秒，结果: False
+2025-08-15 15:37:52,630 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153750100_00DA6823953.pcd
+2025-08-15 15:37:54,952 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153750465_00DA6823936.pcd
+2025-08-15 15:37:55,908 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 15:37:55,913 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 15:37:55,927 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:37:55,928 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 15:37:55,937 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:37:56,225 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:37:56,430 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:37:58,020 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 2.093 秒，发现空洞: False
+2025-08-15 15:37:58,468 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 2.555 秒，发现空洞: False
+2025-08-15 15:37:58,482 - hik3d - INFO - 全设备图片计算完成，总耗时 2.571 秒，结果: False
+2025-08-15 15:37:59,779 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 15:37:59,790 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 15:37:59,794 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:37:59,799 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 15:37:59,810 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:38:00,140 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:38:00,354 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:38:00,621 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153757193_00DA6823953.pcd
+2025-08-15 15:38:02,657 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 2.858 秒，发现空洞: True
+2025-08-15 15:38:02,670 - hik3d - INFO - 设备 00DA6823953 发现空洞，停止其他任务
+2025-08-15 15:38:02,686 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 2.896 秒，发现空洞: False
+2025-08-15 15:38:02,695 - hik3d - INFO - 全设备图片计算完成，总耗时 2.91 秒，结果: True
+2025-08-15 15:38:03,478 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153750469_00DA6823936_original.pcd
+2025-08-15 15:38:04,460 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153750102_00DA6823953_original.pcd
+2025-08-15 15:38:06,804 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153801589_00DA6823953.pcd
+2025-08-15 15:38:07,354 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153757607_00DA6823936.pcd
+2025-08-15 15:38:11,996 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153757201_00DA6823953_original.pcd
+2025-08-15 15:38:12,311 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153801613_00DA6823936.pcd
+2025-08-15 15:38:13,728 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153757647_00DA6823936_original.pcd
+2025-08-15 15:38:15,130 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153801599_00DA6823953_original.pcd
+2025-08-15 15:38:15,862 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153801622_00DA6823936_original.pcd
+2025-08-15 15:39:37,573 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 15:39:37,574 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 15:39:37,574 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:39:37,575 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 15:39:37,575 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:39:37,812 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:39:37,996 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:39:39,036 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.461 秒，发现空洞: True
+2025-08-15 15:39:39,039 - hik3d - INFO - 设备 00DA6823953 发现空洞，停止其他任务
+2025-08-15 15:39:39,232 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.658 秒，发现空洞: False
+2025-08-15 15:39:39,234 - hik3d - INFO - 全设备图片计算完成，总耗时 1.662 秒，结果: True
+2025-08-15 15:39:40,263 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153938358_00DA6823953.pcd
+2025-08-15 15:39:42,346 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153938671_00DA6823936.pcd
+2025-08-15 15:39:46,206 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153938676_00DA6823936_original.pcd
+2025-08-15 15:39:46,463 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\153938359_00DA6823953_original.pcd
+2025-08-15 15:42:02,249 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 15:42:03,473 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 15:42:03,474 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 15:42:04,712 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 15:42:05,112 - hik3d - INFO - 服务启动中...
+2025-08-15 15:42:05,112 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 15:42:06,533 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 15:42:06,534 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 15:42:06,535 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:42:06,535 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 15:42:06,536 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:42:06,771 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:42:06,922 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:42:08,164 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.629 秒，发现空洞: False
+2025-08-15 15:42:08,582 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 2.048 秒，发现空洞: False
+2025-08-15 15:42:08,669 - hik3d - INFO - 全设备图片计算完成，总耗时 2.135 秒，结果: False
+2025-08-15 15:42:10,387 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\154207390_00DA6823953.pcd
+2025-08-15 15:42:11,069 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 15:42:11,072 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 15:42:11,077 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:42:11,077 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 15:42:11,086 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 15:42:11,340 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:42:11,560 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 15:42:13,340 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 2.263 秒，发现空洞: False
+2025-08-15 15:42:13,597 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 2.525 秒，发现空洞: False
+2025-08-15 15:42:13,616 - hik3d - INFO - 全设备图片计算完成，总耗时 2.547 秒，结果: False
+2025-08-15 15:42:14,486 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\154207737_00DA6823936.pcd
+2025-08-15 15:42:15,688 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\154212842_00DA6823953.pcd
+2025-08-15 15:42:20,353 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\154212845_00DA6823936.pcd
+2025-08-15 15:42:21,444 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\154207750_00DA6823936_original.pcd
+2025-08-15 15:42:21,700 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\154207485_00DA6823953_original.pcd
+2025-08-15 15:42:25,242 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\154212848_00DA6823953_original.pcd
+2025-08-15 15:42:25,928 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\154212853_00DA6823936_original.pcd
+2025-08-15 17:46:24,253 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 17:46:25,489 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 17:46:25,489 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 17:46:26,713 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 17:46:27,162 - hik3d - INFO - 服务启动中...
+2025-08-15 17:46:27,163 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 17:50:05,524 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 17:50:06,758 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 17:50:06,758 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 17:50:07,990 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 17:50:08,348 - hik3d - INFO - 服务启动中...
+2025-08-15 17:50:08,349 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 17:50:33,128 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 17:50:33,129 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 17:50:33,129 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 17:50:37,567 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (90) height(720) width(1280)  len (11059200)!
+2025-08-15 17:50:40,334 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\175038509_00DA6823936.pcd
+2025-08-15 17:50:41,451 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\175038513_00DA6823936_original.pcd
+2025-08-15 17:50:42,486 - hik3d - INFO - Point cloud data acquired but no color image found in frames
+2025-08-15 17:50:42,578 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 9.449 秒，发现空洞: False
+2025-08-15 17:50:42,579 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 17:50:42,579 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 17:50:42,861 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 17:50:43,284 - hik3d - INFO - Point cloud data acquired but no color image found in frames
+2025-08-15 17:50:43,403 - hik3d - INFO - 区域: 2 中心: (np.float64(3.015873015873016), np.float64(18.261904761904763)) 面积: 126
+2025-08-15 17:50:43,436 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 0.857 秒，发现空洞: True
+2025-08-15 17:50:43,439 - hik3d - INFO - 设备 00DA6823953 发现空洞，停止其他任务
+2025-08-15 17:50:43,440 - hik3d - INFO - 全设备图片计算完成，总耗时 10.311 秒，结果: True
+2025-08-15 17:50:44,119 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\175043283_00DA6823953.pcd
+2025-08-15 17:50:45,877 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\175043283_00DA6823953_original.pcd
+2025-08-15 17:52:55,797 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 17:52:57,003 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 17:52:57,004 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 17:52:58,218 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 17:52:58,595 - hik3d - INFO - 服务启动中...
+2025-08-15 17:52:58,595 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 17:53:48,204 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 17:53:48,205 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 17:53:48,205 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 17:53:48,467 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 17:53:50,800 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\175349071_00DA6823936.pcd
+2025-08-15 17:53:52,037 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\175349073_00DA6823936_original.pcd
+2025-08-15 17:55:39,217 - hik3d - ERROR - Error during frame capture for device 00DA6823936: _type_ must have storage info
+2025-08-15 17:55:39,420 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 111.215 秒，发现空洞: False
+2025-08-15 17:55:39,421 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 17:55:39,421 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 17:55:39,681 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 17:55:41,181 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\175540199_00DA6823953.pcd
+2025-08-15 17:55:43,090 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\175540200_00DA6823953_original.pcd
+2025-08-15 17:57:37,611 - hik3d - ERROR - Error during frame capture for device 00DA6823953: _type_ must have storage info
+2025-08-15 17:57:37,683 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 118.262 秒，发现空洞: False
+2025-08-15 17:57:37,683 - hik3d - INFO - 全设备图片计算完成，总耗时 229.479 秒，结果: False
+2025-08-15 18:00:03,885 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 18:00:05,105 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 18:00:05,105 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 18:00:06,337 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 18:00:06,699 - hik3d - INFO - 服务启动中...
+2025-08-15 18:00:06,699 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 18:03:31,258 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:03:31,258 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:03:31,258 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:03:31,515 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:03:33,852 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\180332128_00DA6823936.pcd
+2025-08-15 18:03:36,009 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\180332130_00DA6823936_original.pcd
+2025-08-15 18:04:15,069 - hik3d - ERROR - Error during frame capture for device 00DA6823936: 'NoneType' object has no attribute 'encode'
+2025-08-15 18:04:15,199 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 43.94 秒，发现空洞: False
+2025-08-15 18:04:15,200 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:04:15,200 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:04:15,445 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:04:15,844 - hik3d - ERROR - Error during frame capture for device 00DA6823953: 'NoneType' object has no attribute 'encode'
+2025-08-15 18:04:15,982 - hik3d - INFO - 区域: 2 中心: (np.float64(3.015748031496063), np.float64(17.755905511811022)) 面积: 127
+2025-08-15 18:04:16,016 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 0.817 秒，发现空洞: True
+2025-08-15 18:04:16,018 - hik3d - INFO - 设备 00DA6823953 发现空洞，停止其他任务
+2025-08-15 18:04:16,021 - hik3d - INFO - 全设备图片计算完成，总耗时 44.763 秒，结果: True
+2025-08-15 18:04:16,688 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\180415840_00DA6823953.pcd
+2025-08-15 18:04:18,438 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\180415841_00DA6823953_original.pcd
+2025-08-15 18:04:19,070 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:04:19,070 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:04:19,070 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:04:19,284 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:04:19,748 - hik3d - ERROR - Error during frame capture for device 00DA6823936: 'NoneType' object has no attribute 'encode'
+2025-08-15 18:04:20,001 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 0.931 秒，发现空洞: False
+2025-08-15 18:04:20,004 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:04:20,006 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:04:20,404 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:04:20,888 - hik3d - ERROR - Error during frame capture for device 00DA6823953: 'NoneType' object has no attribute 'encode'
+2025-08-15 18:04:21,096 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.092 秒，发现空洞: False
+2025-08-15 18:04:21,102 - hik3d - INFO - 全设备图片计算完成，总耗时 2.032 秒，结果: False
+2025-08-15 18:04:22,477 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\180420876_00DA6823953.pcd
+2025-08-15 18:04:22,775 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\180419746_00DA6823936.pcd
+2025-08-15 18:04:26,212 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\180419747_00DA6823936_original.pcd
+2025-08-15 18:04:27,027 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\180420879_00DA6823953_original.pcd
+2025-08-15 18:10:39,234 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 18:10:40,467 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 18:10:40,468 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 18:10:41,697 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 18:10:42,091 - hik3d - INFO - 服务启动中...
+2025-08-15 18:10:42,091 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 18:10:44,554 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:10:44,554 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:10:44,555 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:10:44,831 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:10:45,486 - hik3d - ERROR - Error during frame capture for device 00DA6823936: unsupported operand type(s) for +: 'NoneType' and 'str'
+2025-08-15 18:10:45,762 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.208 秒，发现空洞: False
+2025-08-15 18:10:45,764 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:10:45,766 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:10:46,024 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:10:46,428 - hik3d - ERROR - Error during frame capture for device 00DA6823953: unsupported operand type(s) for +: 'NoneType' and 'str'
+2025-08-15 18:10:46,650 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 0.886 秒，发现空洞: False
+2025-08-15 18:10:46,651 - hik3d - INFO - 全设备图片计算完成，总耗时 2.097 秒，结果: False
+2025-08-15 18:10:48,189 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181046397_00DA6823953.pcd
+2025-08-15 18:10:48,746 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181045458_00DA6823936.pcd
+2025-08-15 18:10:50,855 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181045461_00DA6823936_original.pcd
+2025-08-15 18:10:51,277 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181046399_00DA6823953_original.pcd
+2025-08-15 18:16:38,887 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 18:16:40,103 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 18:16:40,103 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 18:16:41,320 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 18:16:41,661 - hik3d - INFO - 服务启动中...
+2025-08-15 18:16:41,661 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 18:16:45,462 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:16:45,463 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:16:45,463 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:16:45,701 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:16:46,339 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181646336_00DA6823936_Color.bmp
+2025-08-15 18:16:46,627 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.163 秒，发现空洞: False
+2025-08-15 18:16:46,629 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:16:46,631 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:16:46,903 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:16:47,323 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181647323_00DA6823953_Color.bmp
+2025-08-15 18:16:47,482 - hik3d - INFO - 区域: 1 中心: (np.float64(3.1548387096774193), np.float64(16.929032258064517)) 面积: 155
+2025-08-15 18:16:47,547 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 0.918 秒，发现空洞: True
+2025-08-15 18:16:47,551 - hik3d - INFO - 设备 00DA6823953 发现空洞，停止其他任务
+2025-08-15 18:16:47,555 - hik3d - INFO - 全设备图片计算完成，总耗时 2.092 秒，结果: True
+2025-08-15 18:16:48,974 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181647310_00DA6823953.pcd
+2025-08-15 18:16:49,964 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181646329_00DA6823936.pcd
+2025-08-15 18:16:54,723 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181646331_00DA6823936_original.pcd
+2025-08-15 18:16:54,887 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181647312_00DA6823953_original.pcd
+2025-08-15 18:18:58,924 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-15 18:19:00,158 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-15 18:19:00,158 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-15 18:19:01,382 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-15 18:19:01,782 - hik3d - INFO - 服务启动中...
+2025-08-15 18:19:01,782 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-15 18:19:23,167 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:19:23,168 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:19:23,168 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:19:23,433 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:19:24,043 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181924040_00DA6823936_Color.bmp
+2025-08-15 18:19:24,313 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.145 秒，发现空洞: False
+2025-08-15 18:19:24,314 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:19:24,318 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:19:24,592 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:19:25,026 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181925024_00DA6823953_Color.bmp
+2025-08-15 18:19:25,237 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 0.923 秒，发现空洞: False
+2025-08-15 18:19:25,241 - hik3d - INFO - 全设备图片计算完成，总耗时 2.073 秒，结果: False
+2025-08-15 18:19:26,553 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:19:26,559 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:19:26,566 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:19:26,987 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:19:27,267 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181925011_00DA6823953.pcd
+2025-08-15 18:19:28,068 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181928068_00DA6823936_Color.bmp
+2025-08-15 18:19:28,673 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 2.114 秒，发现空洞: False
+2025-08-15 18:19:28,674 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:19:28,679 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:19:28,939 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181924033_00DA6823936.pcd
+2025-08-15 18:19:28,971 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:19:29,794 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181929794_00DA6823953_Color.bmp
+2025-08-15 18:19:30,064 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.39 秒，发现空洞: False
+2025-08-15 18:19:30,069 - hik3d - INFO - 全设备图片计算完成，总耗时 3.51 秒，结果: False
+2025-08-15 18:19:31,271 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:19:31,276 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:19:31,276 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:19:31,542 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:19:32,607 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181932607_00DA6823936_Color.bmp
+2025-08-15 18:19:33,200 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.924 秒，发现空洞: False
+2025-08-15 18:19:33,205 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:19:33,210 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:19:33,482 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:19:34,339 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181934339_00DA6823953_Color.bmp
+2025-08-15 18:19:34,610 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.404 秒，发现空洞: False
+2025-08-15 18:19:34,613 - hik3d - INFO - 全设备图片计算完成，总耗时 3.338 秒，结果: False
+2025-08-15 18:19:35,691 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181928053_00DA6823936.pcd
+2025-08-15 18:19:36,188 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:19:36,197 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:19:36,206 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:19:36,503 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:19:37,495 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181937495_00DA6823936_Color.bmp
+2025-08-15 18:19:38,204 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 2.007 秒，发现空洞: False
+2025-08-15 18:19:38,213 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:19:38,219 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:19:38,702 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:19:39,359 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\181939359_00DA6823953_Color.bmp
+2025-08-15 18:19:39,387 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181924035_00DA6823936_original.pcd
+2025-08-15 18:19:39,577 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181929774_00DA6823953.pcd
+2025-08-15 18:19:39,684 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.472 秒，发现空洞: False
+2025-08-15 18:19:39,694 - hik3d - INFO - 全设备图片计算完成，总耗时 3.497 秒，结果: False
+2025-08-15 18:19:41,579 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181925016_00DA6823953_original.pcd
+2025-08-15 18:19:45,468 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181928060_00DA6823936_original.pcd
+2025-08-15 18:19:45,525 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181932590_00DA6823936.pcd
+2025-08-15 18:19:47,666 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181934323_00DA6823953.pcd
+2025-08-15 18:19:50,543 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181929785_00DA6823953_original.pcd
+2025-08-15 18:19:50,887 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181937478_00DA6823936.pcd
+2025-08-15 18:19:51,124 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181932598_00DA6823936_original.pcd
+2025-08-15 18:19:52,495 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181939341_00DA6823953.pcd
+2025-08-15 18:19:53,789 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181934331_00DA6823953_original.pcd
+2025-08-15 18:19:55,718 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181937487_00DA6823936_original.pcd
+2025-08-15 18:19:55,969 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\181939350_00DA6823953_original.pcd
+2025-08-15 18:20:28,544 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:20:28,544 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:20:28,544 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:20:28,763 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:20:29,247 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\182029247_00DA6823936_Color.bmp
+2025-08-15 18:20:29,515 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 0.971 秒，发现空洞: False
+2025-08-15 18:20:29,519 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:20:29,521 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:20:29,756 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:20:30,185 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\182030185_00DA6823953_Color.bmp
+2025-08-15 18:20:30,393 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 0.874 秒，发现空洞: False
+2025-08-15 18:20:30,397 - hik3d - INFO - 全设备图片计算完成，总耗时 1.852 秒，结果: False
+2025-08-15 18:20:31,892 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182030178_00DA6823953.pcd
+2025-08-15 18:20:32,965 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182029244_00DA6823936.pcd
+2025-08-15 18:20:37,227 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:20:37,229 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:20:37,231 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:20:37,494 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:20:37,787 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182029245_00DA6823936_original.pcd
+2025-08-15 18:20:38,279 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\182038279_00DA6823936_Color.bmp
+2025-08-15 18:20:38,473 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182030181_00DA6823953_original.pcd
+2025-08-15 18:20:38,666 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.437 秒，发现空洞: False
+2025-08-15 18:20:38,666 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-15 18:20:38,667 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:20:39,120 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:20:39,788 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\182039788_00DA6823953_Color.bmp
+2025-08-15 18:20:40,061 - hik3d - INFO - 区域: 1 中心: (np.float64(6.953488372093023), np.float64(5.005813953488372)) 面积: 172
+2025-08-15 18:20:40,124 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.458 秒，发现空洞: True
+2025-08-15 18:20:40,130 - hik3d - INFO - 设备 00DA6823953 发现空洞，停止其他任务
+2025-08-15 18:20:40,139 - hik3d - INFO - 全设备图片计算完成，总耗时 2.91 秒，结果: True
+2025-08-15 18:20:42,058 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182039776_00DA6823953.pcd
+2025-08-15 18:20:43,158 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182038265_00DA6823936.pcd
+2025-08-15 18:20:47,244 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182038269_00DA6823936_original.pcd
+2025-08-15 18:20:47,958 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182039781_00DA6823953_original.pcd
+2025-08-15 18:20:54,497 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-15 18:20:54,498 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-15 18:20:54,498 - hik3d - INFO - Delaying start by 0ms...
+2025-08-15 18:20:54,745 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-15 18:20:55,503 - hik3d - INFO - Color image saved successfully: .\image\2025-08-15\color\182055502_00DA6823936_Color.bmp
+2025-08-15 18:20:55,764 - hik3d - INFO - 区域: 4 中心: (np.float64(130.0084985835694), np.float64(10.51841359773371)) 面积: 353
+2025-08-15 18:20:55,852 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.354 秒，发现空洞: True
+2025-08-15 18:20:55,852 - hik3d - INFO - 设备 00DA6823936 发现空洞，停止其他任务
+2025-08-15 18:20:55,860 - hik3d - INFO - 全设备图片计算完成，总耗时 1.361 秒，结果: True
+2025-08-15 18:20:58,111 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182055494_00DA6823936.pcd
+2025-08-15 18:21:00,396 - hik3d - INFO - Saved point cloud to .\image\2025-08-15\pcd\182055498_00DA6823936_original.pcd

logs/app.log.2025-08-13

@@ -0,0 +1,31 @@
+2025-08-13 16:12:20,821 - my_app - INFO - ??????????...
+2025-08-13 16:12:20,822 - my_app - INFO - ????????????????????: http://0.0.0.0:5000
+2025-08-13 16:15:30,880 - my_app - INFO - ???????????????????
+2025-08-13 16:15:30,880 - my_app - INFO - ????????? 00DA6823953 ???? 46 ????????
+2025-08-13 16:15:32,037 - my_app - INFO - ?? 00DA6823953 ????????????????: True
+2025-08-13 16:16:18,432 - my_app - INFO - ???????????????????
+2025-08-13 16:16:18,432 - my_app - INFO - ????????? 00DA6823953 ???? 46 ????????
+2025-08-13 16:18:41,293 - my_app - INFO - ?? 00DA6823953 ????????????????: True
+2025-08-13 16:19:50,738 - my_app - INFO - ???????????????????
+2025-08-13 16:19:50,738 - my_app - INFO - ????????? 00DA6823953 ???? 46 ????????
+2025-08-13 16:22:36,790 - my_app - INFO - ?? 00DA6823953 ????????????????: True
+2025-08-13 16:22:43,332 - my_app - INFO - ???????????????????
+2025-08-13 16:22:43,333 - my_app - INFO - ????????? 00DA6823953 ???? 46 ????????
+2025-08-13 16:32:29,290 - my_app - INFO - ?? 00DA6823953 ????????????????: True
+2025-08-13 16:34:45,693 - my_app - INFO - ??????????...
+2025-08-13 16:34:45,693 - my_app - INFO - ????????????????????: http://0.0.0.0:5000
+2025-08-13 16:34:48,018 - my_app - INFO - ???????????????????
+2025-08-13 16:34:48,019 - my_app - INFO - ????????? 00DA6823953 ???? 46 ????????
+2025-08-13 16:37:21,400 - my_app - INFO - ??????????...
+2025-08-13 16:37:21,400 - my_app - INFO - ????????????????????: http://0.0.0.0:5000
+2025-08-13 16:37:25,853 - my_app - INFO - ???????????????????
+2025-08-13 16:37:25,853 - my_app - INFO - ????????? 00DA6823953 ???? 46 ????????
+2025-08-13 16:39:18,660 - my_app - INFO - ??????????...
+2025-08-13 16:39:18,660 - my_app - INFO - ????????????????????: http://0.0.0.0:5000
+2025-08-13 16:39:20,967 - my_app - INFO - ???????????????????
+2025-08-13 16:39:20,967 - my_app - INFO - ????????? 00DA6823953 ???? 46 ????????
+2025-08-13 16:42:12,161 - my_app - INFO - ??????????...
+2025-08-13 16:42:12,161 - my_app - INFO - ????????????????????: http://0.0.0.0:5000
+2025-08-13 16:42:12,686 - my_app - INFO - ???????????????????
+2025-08-13 16:42:12,686 - my_app - INFO - ????????? 00DA6823953 ???? 46 ????????
+2025-08-13 16:43:58,412 - my_app - INFO - ?? 00DA6823953 ????????????????: True

logs/app.log.2025-08-14

@@ -0,0 +1,129 @@
+2025-08-14 10:53:41,032 - my_app - INFO - 接收到全设备图片计算请求
+2025-08-14 10:53:41,063 - my_app - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-14 10:53:42,203 - my_app - ERROR - 未捕获的异常: 'NoneType' object has no attribute 'get'
+2025-08-14 10:53:58,406 - my_app - ERROR - 未捕获的异常: 405 Method Not Allowed: The method is not allowed for the requested URL.
+2025-08-14 10:54:02,996 - my_app - INFO - 接收到全设备图片计算请求
+2025-08-14 10:54:02,997 - my_app - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-14 10:54:04,023 - my_app - ERROR - 未捕获的异常: 'NoneType' object has no attribute 'get'
+2025-08-14 11:08:48,337 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-14 11:08:49,581 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-14 11:08:49,582 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-14 11:08:50,815 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-14 11:08:51,290 - hik3d - INFO - 服务启动中...
+2025-08-14 11:08:51,290 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-14 11:09:14,203 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-14 11:09:15,432 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-14 11:09:15,432 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-14 11:09:16,673 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-14 11:09:17,005 - hik3d - INFO - 服务启动中...
+2025-08-14 11:09:17,005 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-14 11:09:30,806 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-14 11:09:32,021 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-14 11:09:32,021 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-14 11:09:33,242 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-14 11:09:33,563 - hik3d - INFO - 服务启动中...
+2025-08-14 11:09:33,563 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-14 11:11:28,300 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-14 11:11:28,303 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-14 11:11:28,304 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:11:28,304 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-14 11:11:28,304 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:11:28,604 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:11:28,955 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:11:29,844 - hik3d - INFO - Single capture completed.
+2025-08-14 11:11:29,890 - hik3d - INFO - Single capture completed.
+2025-08-14 11:11:32,596 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111129675_00DA6823953.pcd
+2025-08-14 11:11:32,999 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111129813_00DA6823936.pcd
+2025-08-14 11:11:37,728 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111129826_00DA6823936_original.pcd
+2025-08-14 11:11:37,912 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111129679_00DA6823953_original.pcd
+2025-08-14 11:11:41,891 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 13.588 秒，发现空洞: False
+2025-08-14 11:11:41,891 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 13.587 秒，发现空洞: False
+2025-08-14 11:11:41,901 - hik3d - INFO - 全设备图片计算完成，总耗时 13.601 秒，结果: False
+2025-08-14 11:11:48,424 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-14 11:11:48,425 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-14 11:11:48,425 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:11:48,425 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-14 11:11:48,425 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:11:48,680 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:11:48,738 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:11:49,370 - hik3d - INFO - Single capture completed.
+2025-08-14 11:11:49,373 - hik3d - INFO - Single capture completed.
+2025-08-14 11:11:49,679 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.254 秒，发现空洞: False
+2025-08-14 11:11:49,684 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.259 秒，发现空洞: False
+2025-08-14 11:11:49,693 - hik3d - INFO - 全设备图片计算完成，总耗时 1.269 秒，结果: False
+2025-08-14 11:11:51,210 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111149339_00DA6823953.pcd
+2025-08-14 11:11:51,587 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111149339_00DA6823936.pcd
+2025-08-14 11:11:57,141 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111149341_00DA6823936_original.pcd
+2025-08-14 11:11:57,405 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111149340_00DA6823953_original.pcd
+2025-08-14 11:12:13,342 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-14 11:12:13,343 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-14 11:12:13,343 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:12:13,344 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-14 11:12:13,344 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:12:13,565 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:12:13,702 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:12:14,209 - hik3d - INFO - Single capture completed.
+2025-08-14 11:12:14,244 - hik3d - INFO - Single capture completed.
+2025-08-14 11:12:14,491 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.148 秒，发现空洞: False
+2025-08-14 11:12:14,687 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.343 秒，发现空洞: True
+2025-08-14 11:12:14,691 - hik3d - INFO - 设备 00DA6823953 发现空洞，停止其他任务
+2025-08-14 11:12:14,698 - hik3d - INFO - 全设备图片计算完成，总耗时 1.355 秒，结果: True
+2025-08-14 11:12:15,847 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111214178_00DA6823953.pcd
+2025-08-14 11:12:16,160 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111214178_00DA6823936.pcd
+2025-08-14 11:12:21,519 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111214180_00DA6823936_original.pcd
+2025-08-14 11:12:21,798 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111214182_00DA6823953_original.pcd
+2025-08-14 11:13:01,133 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-14 11:13:01,134 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-14 11:13:01,135 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:13:01,135 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-14 11:13:01,135 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:13:01,356 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:13:01,508 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:13:02,036 - hik3d - INFO - Single capture completed.
+2025-08-14 11:13:02,143 - hik3d - INFO - Single capture completed.
+2025-08-14 11:13:02,325 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.19 秒，发现空洞: False
+2025-08-14 11:13:02,376 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.242 秒，发现空洞: False
+2025-08-14 11:13:02,390 - hik3d - INFO - 全设备图片计算完成，总耗时 1.257 秒，结果: False
+2025-08-14 11:13:03,712 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111301877_00DA6823953.pcd
+2025-08-14 11:13:04,157 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111302097_00DA6823936.pcd
+2025-08-14 11:13:09,521 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111302102_00DA6823936_original.pcd
+2025-08-14 11:13:09,693 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\111301877_00DA6823953_original.pcd
+2025-08-14 11:21:14,864 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-14 11:21:14,865 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-14 11:21:14,865 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:21:14,865 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-14 11:21:14,866 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:21:15,111 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:21:15,278 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:21:15,884 - hik3d - INFO - Single capture completed.
+2025-08-14 11:21:16,011 - hik3d - INFO - Single capture completed.
+2025-08-14 11:21:16,345 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.48 秒，发现空洞: False
+2025-08-14 11:21:16,358 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.493 秒，发现空洞: False
+2025-08-14 11:21:16,370 - hik3d - INFO - 全设备图片计算完成，总耗时 1.506 秒，结果: False
+2025-08-14 11:21:17,908 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\112115786_00DA6823953.pcd
+2025-08-14 11:21:18,291 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\112115958_00DA6823936.pcd
+2025-08-14 11:21:24,472 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\112115965_00DA6823936_original.pcd
+2025-08-14 11:21:24,647 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\112115786_00DA6823953_original.pcd
+2025-08-14 11:25:19,160 - hik3d - INFO - Found device [0]: Serial Number: 00DA6823936
+2025-08-14 11:25:20,425 - hik3d - INFO - Successfully added device 00DA6823936 to SN_MAP
+2025-08-14 11:25:20,425 - hik3d - INFO - Found device [1]: Serial Number: 00DA6823953
+2025-08-14 11:25:21,666 - hik3d - INFO - Successfully added device 00DA6823953 to SN_MAP
+2025-08-14 11:25:22,165 - hik3d - INFO - 服务启动中...
+2025-08-14 11:25:22,165 - hik3d - INFO - 服务启动成功，监听地址: http://0.0.0.0:5000
+2025-08-14 11:25:43,026 - hik3d - INFO - 接收到全设备图片计算请求
+2025-08-14 11:25:43,028 - hik3d - INFO - 开始处理设备 00DA6823936 的图片计算
+2025-08-14 11:25:43,028 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:25:43,029 - hik3d - INFO - 开始处理设备 00DA6823953 的图片计算
+2025-08-14 11:25:43,029 - hik3d - INFO - Delaying start by 0ms...
+2025-08-14 11:25:43,265 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:25:43,531 - hik3d - INFO - _MapDepthToPointCloud() Run Succeed: framenum (1) height(720) width(1280)  len (11059200)!
+2025-08-14 11:25:44,154 - hik3d - INFO - Single capture completed.
+2025-08-14 11:25:44,277 - hik3d - INFO - Single capture completed.
+2025-08-14 11:25:44,474 - hik3d - INFO - 设备 00DA6823953 处理完成，耗时 1.445 秒，发现空洞: False
+2025-08-14 11:25:44,991 - hik3d - INFO - 设备 00DA6823936 处理完成，耗时 1.963 秒，发现空洞: True
+2025-08-14 11:25:44,995 - hik3d - INFO - 设备 00DA6823936 发现空洞，停止其他任务
+2025-08-14 11:25:45,035 - hik3d - INFO - 全设备图片计算完成，总耗时 2.009 秒，结果: True
+2025-08-14 11:25:45,852 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\112543933_00DA6823953.pcd
+2025-08-14 11:25:47,209 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\112544229_00DA6823936.pcd
+2025-08-14 11:25:49,519 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\112544234_00DA6823936_original.pcd
+2025-08-14 11:25:49,681 - hik3d - INFO - Saved point cloud to .\image\2025-08-14\pcd\112544008_00DA6823953_original.pcd

main.py

@@ -1,52 +1,316 @@
 # 这是一个示例 Python 脚本。
-
+import json
+import os
 # 按 Shift+F10 执行或将其替换为您的代码。
 # 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。
 import SimpleView_SaveImage as simple
 import config
-from Point import tiff_depth_to_point_clouds
+import image
+import time  # 在文件顶部导入 time 模块
 
 from flask import Flask, jsonify, request
 
+from logConfig import get_logger
+
 app = Flask(__name__)
+import concurrent.futures
+from functools import partial
 
+# 使用示例
+logger = get_logger()
 # 示例接口：GET 请求
 @app.route("/config/update")
 def home():
+    logger.info("配置更新请求")
     config.load_configs()
-    return "Hello, this service config update!"
+    logger.info("配置更新完成")
-
+    return {"message": "Hello, this service config update!", "status": "OK"}
-# 示例接口：返回 JSON 数据
+
-@app.route("/api/data")
+
-def get_data():
+@app.route("/api/addTemplate", methods=["POST"])
-    data = {"message": "This is some data", "status": "OK"}
+def save_template():
-    return jsonify(data)
+    logger.info("接收到添加模板请求")
-
+    data = request.get_json()
-# 示例接口：接收参数
+
-# 示例接口：接收参数
+    # 验证必要字段
-@app.route("/api/compute/<direction>")
+    if not data:
-def compute(direction):
+        logger.error("添加模板失败：未提供数据")
+        return jsonify({"status": "ERROR", "message": "No data provided"}), 400
+
+    template_type = data.get("type")
+    if not template_type:
+        logger.error("添加模板失败：缺少'type'字段")
+        return jsonify({"status": "ERROR", "message": "Missing 'type' field"}), 400
+    CONFIG_DIR = "./config/template"
+    # 构造文件路径
+    filename = f"{template_type}.json"
+    file_path = os.path.join(CONFIG_DIR, filename)
+
+    # 写入文件
     try:
-        sn = config.DIRECTION_CAMERA[direction]
+        with open(file_path, "w", encoding="utf-8") as f:
-    except KeyError:
+            json.dump(data, f, ensure_ascii=False, indent=4)
-        return jsonify({"message": "", "status": "ERROR", "error": f"Direction '{direction}' not found in DIRECTION_CAMERA"}), 400
+        config.load_configs()
+        logger.info(f"模板 '{template_type}' 保存成功")
+        return jsonify({"status": "OK", "message": f"Template '{template_type}' saved successfully."}), 200
+    except Exception as e:
+        logger.error(f"保存模板失败: {str(e)}")
+        return jsonify({"status": "ERROR", "message": f"Failed to save template: {str(e)}"}), 500
+
+
+@app.route("/api/initDevice", methods=['POST'])
+def initDevice():
+    logger.info("设备初始化请求")
+    simple.initialize_devices()
+    logger.info("设备初始化完成")
+
+#
+# @app.route("/api/picComputeAll", methods=['POST'])
+# def picComputeAll():
+#     logger.info("接收到全设备图片计算请求")
+#     start_time_total = time.time()
+#
+#     # 输入参数
+#     # sn:设备编号
+#     # type:类型
+#
+#     # 获取 JSON 数据
+#     data = request.get_json()
+#     type = data['type']
+#     template = config.TEMPLATE_CONFIG_MAP.get(type)
+#     if not template:
+#         logger.error("缺少模板配置: 'type'")
+#         return jsonify({
+#             "message": "",
+#             "status": "ERROR",
+#             "error": "Missing required parameter: 'type'"
+#         }), 400
+#     all_sn_list = list(config.CAMERA_CONFIG_MAP.keys())
+#
+#     # 遍历每个 sn 并判断是否都有 hole
+#     all_have_holes = False
+#     color_list = []  # 新增：用于保存所有 sn 的 color
+#
+#     count_assemble = 0
+#     for sn in all_sn_list:
+#         start_time = time.time()  # 开始计时
+#         logger.info(f"开始处理设备 {sn} 的图片计算")
+#         pic_info = simple.enhanced_pic(sn, type)
+#         points = pic_info.get("point")
+#         if points is None:
+#             logger.warning(f"设备 {sn} 未返回点云数据")
+#             all_have_holes = True
+#             break
+#
+#         # 提取 color 并加入列表
+#         color = pic_info.get("color")
+#         if color is not None:
+#             color_list.append(color)
+#
+#         try:
+#             real_holes, count = image.detect_large_holes(points, sn, type)
+#             # count_assemble += count
+#
+#             if len(real_holes) != 0:
+#                 all_have_holes = True
+#                 # 计算耗时
+#                 elapsed_time = round(time.time() - start_time, 3)
+#                 # 输出日志
+#                 logger.info(f"[INFO] picComputeAll executed in {elapsed_time} seconds {sn}")
+#                 break  # 判断到空洞就直接退出
+#         except Exception as e:
+#             all_have_holes = True
+#             logger.error(f"Failed to process request: {str(e)} sn：{sn}")
+#
+#     # 计算总耗时
+#     total_elapsed_time = round(time.time() - start_time_total, 3)
+#     logger.info(f"全设备图片计算完成，总耗时 {total_elapsed_time} 秒，结果: {all_have_holes}")
+#
+#     # 返回结果中包含 color 列表
+#     return jsonify({
+#         "message": all_have_holes,
+#         "lack": all_have_holes,
+#         "type": type,
+#         "count": count_assemble,
+#         "colors": color_list,  # 添加 color 列表
+#         "status": "OK"
+#     })
+#
+@app.route("/api/picComputeAll", methods=['POST'])
+def picComputeAll():
+    logger.info("接收到全设备图片计算请求")
+    start_time_total = time.time()
+
+    # 获取 JSON 数据
+    data = request.get_json()
+    type = data['type']
+    template = config.TEMPLATE_CONFIG_MAP.get(type)
+    if not template:
+        logger.error("缺少模板配置: 'type'")
+        return jsonify({
+            "message": "",
+            "status": "ERROR",
+            "error": "Missing required parameter: 'type'"
+        }), 400
+    all_sn_list = list(config.CAMERA_CONFIG_MAP.keys())
 
+    # 同步处理每个设备
+    all_have_holes = False
+    color_list = []
+    count_assemble = 0
+
+    # 逐个同步处理设备
+    for sn in all_sn_list:
         try:
-        tiff_paths = simple.pic(sn)
+            start_time = time.time()
+            logger.info(f"开始处理设备 {sn} 的图片计算")
+
+            # 同步采集图片信息
+            pic_info = simple.enhanced_pic(sn, type)
+            points = pic_info.get("point")
+
+            if points is None:
+                logger.warning(f"设备 {sn} 未返回点云数据")
+                all_have_holes = True
+                break
+
+            # 提取 color 并加入列表
+            if pic_info.get("color") is not None:
+                color_list.append(pic_info["color"])
+
+            # 检测空洞
+            real_holes, count = image.detect_large_holes(points, sn, type)
+
+            elapsed_time = round(time.time() - start_time, 3)
+            logger.info(f"设备 {sn} 处理完成，耗时 {elapsed_time} 秒，发现空洞: {len(real_holes) != 0}")
+
+            # 检查是否发现空洞
+            if len(real_holes) != 0:
+                all_have_holes = True
+                count_assemble += count
+                logger.info(f"设备 {sn} 发现空洞，停止其他任务")
+                break  # 发现空洞就直接退出
+
         except Exception as e:
-        return jsonify({"message": "", "status": "ERROR", "error": f"Failed to get TIFF paths: {str(e)}"}), 500
+            logger.error(f"处理设备 {sn} 时发生异常: {str(e)}")
+            all_have_holes = True
+            break
+
+    # 计算总耗时
+    total_elapsed_time = round(time.time() - start_time_total, 3)
+    logger.info(f"全设备图片计算完成，总耗时 {total_elapsed_time} 秒，结果: {all_have_holes}")
+
+    # 返回结果中包含 color 列表
+    return jsonify({
+        "message": all_have_holes,
+        "lack": all_have_holes,
+        "type": type,
+        "count": count_assemble,
+        "colors": color_list,
+        "status": "OK"
+    })
 
+
+
+@app.route("/api/picBitCloudy", methods=['POST'])
+def picBitCloudy():
+    logger.info("接收到picBitCloudy请求")
+
+    # 输入参数
+    # sn:设备编号
+    # type:类型
+
+    # 获取 JSON 数据
+    data = request.get_json()
+    direction = data.get('direction')
+    type = data.get('type')
+    all_sn_list = list(config.CAMERA_CONFIG_MAP.keys())
+
+    # 遍历每个 sn 并判断是否都有 hole
+    all_have_holes = False
+    color_list = []  # 新增：用于保存所有 sn 的 color
+
+    count_assemble = 0
+    for sn in all_sn_list:
+        if direction and config.CAMERA_CONFIG_MAP[sn].get("direction") != direction:
+            logger.debug(f"设备 {sn} 方向不匹配，跳过")
+            continue
+
+        start_time = time.time()  # 开始计时
+        logger.info(f"开始处理设备 {sn} 的图片计算")
+        pic_info = simple.enhanced_pic(sn, type)
+        points = pic_info.get("point")
+        if points is None or len(points) < 300:
+            logger.warning(f"设备 {sn} 点云数据不足")
+            all_have_holes = True
+            break
+
+        # 提取 color 并加入列表
+        color = pic_info.get("color")
+        if color is not None:
+            color_list.append(color)
+
+    logger.info(f"picBitCloudy处理完成，结果: {all_have_holes}")
+    # 返回结果中包含 color 列表
+    return jsonify({
+        "message": all_have_holes,
+        "lack": all_have_holes,
+        "colors": color_list,  # 添加 color 列表
+        "status": "OK"
+    })
+
+
+@app.route("/api/picCompute", methods=['POST'])
+def picCompute():
+    logger.info("接收到单设备图片计算请求")
+
+    # 输入参数
+    # sn:设备编号
+    # type:类型
     try:
-        rest = tiff_depth_to_point_clouds(tiff_paths, sn, dedup=True)
+        # 获取 JSON 数据
+        data = request.get_json()
+        if not data or 'sn' not in data:
+            logger.error("缺少必需参数: 'sn'")
+            return jsonify({
+                "message": "",
+                "status": "ERROR",
+                "error": "Missing required parameter: 'sn'"
+            }), 400
+
+        sn = data['sn']
+        type = data['type']
+        logger.info(f"开始处理设备 {sn} 类型 {type} 的图片计算")
+
+        template = config.TEMPLATE_CONFIG_MAP.get(type)
+        if not template:
+            logger.error(f"缺少模板配置: {type}")
+            return jsonify({
+                "message": "",
+                "status": "ERROR",
+                "error": "Missing required parameter: 'type'"
+            }), 400
+
+        pic_info = simple.enhanced_pic(sn, type)
+        real_holes, count = image.detect_large_holes(pic_info["point"], sn, type)
+
+        result = len(real_holes) != 0
+        logger.info(f"设备 {sn} 图片计算完成，检测到空洞: {result}")
+
+        return jsonify({"message": result,
+                        "lack": result, "status": "OK"})
+
     except Exception as e:
-        return jsonify({"message": "", "status": "ERROR", "error": f"Point cloud processing failed: {str(e)}"}), 500
+        logger.error(f"图片计算失败: {str(e)}")
+        return jsonify({"message": "", "status": "ERROR", "error": f"Failed to get TIFF paths: {str(e)}"}), 500
+
+    return jsonify({"message": True, "lack": True, "status": "OK"})
 
-    print(rest)
-    return jsonify({"message": rest, "status": "OK"})
 
 @app.errorhandler(Exception)
 def handle_exception(e):
     # 处理所有未被捕获的异常
+    logger.error(f"未捕获的异常: {str(e)}")
     return jsonify({
         "message": "",
         "status": "ERROR",
@@ -57,6 +321,7 @@ def handle_exception(e):
 if __name__ == "__main__":
     from waitress import serve
 
+    logger.info("服务启动中...")
     print("Serving on http://0.0.0.0:5000")
+    logger.info("服务启动成功，监听地址: http://0.0.0.0:5000")
     serve(app, host='0.0.0.0', port=5000)
-

thread_pool.py

@@ -0,0 +1,74 @@
+# thread_pool.py
+import concurrent.futures
+import threading
+from typing import Callable, Any
+
+
+class ThreadPoolManager:
+    _instance = None
+    _lock = threading.Lock()
+
+    def __new__(cls):
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    cls._instance = super(ThreadPoolManager, cls).__new__(cls)
+                    cls._instance._initialized = False
+        return cls._instance
+
+    def __init__(self):
+        if not self._initialized:
+            # 创建线程池，最大4个线程
+            self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=4)
+            self._initialized = True
+
+    def submit(self, fn: Callable, *args, **kwargs) -> concurrent.futures.Future:
+        """
+        提交任务到线程池执行
+
+        :param fn: 要执行的函数
+        :param args: 函数的位置参数
+        :param kwargs: 函数的关键字参数
+        :return: Future对象
+        """
+        if kwargs:
+            # 如果有关键字参数，需要包装函数
+            def wrapper():
+                return fn(*args, **kwargs)
+
+            return self.executor.submit(wrapper)
+        else:
+            return self.executor.submit(fn, *args)
+
+    def shutdown(self, wait=True):
+        """
+        关闭线程池
+
+        :param wait: 是否等待所有任务完成
+        """
+        self.executor.shutdown(wait=wait)
+
+
+# 创建全局线程池管理器实例
+_thread_pool_manager = ThreadPoolManager()
+
+
+def get_thread_pool() -> ThreadPoolManager:
+    """
+    获取线程池管理器实例
+
+    :return: ThreadPoolManager实例
+    """
+    return _thread_pool_manager
+
+
+def submit_task(fn: Callable, *args, **kwargs) -> concurrent.futures.Future:
+    """
+    提交任务到线程池执行的便捷函数
+
+    :param fn: 要执行的函数
+    :param args: 函数的位置参数
+    :param kwargs: 函数的关键字参数
+    :return: Future对象
+    """
+    return _thread_pool_manager.submit(fn, *args, **kwargs)