799 lines
37 KiB
C#
799 lines
37 KiB
C#
using CCWin.Win32.Const;
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using GeBoShi.SysCtrl;
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using HZH_Controls.Controls;
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using MaiMuControl.Device;
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using Models;
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using OpenCvSharp;
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using S7.Net.Types;
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using System;
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using System.Collections.Generic;
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using System.Data;
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using System.Diagnostics;
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using System.Linq;
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using System.Reflection;
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using System.Runtime.InteropServices;
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using System.Text;
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using System.Threading;
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using System.Threading.Tasks;
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using static System.ComponentModel.Design.ObjectSelectorEditor;
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namespace GeBoShi.ImageDefect
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{
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public class DefectLib
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{
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#region C++dll
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private const string dll_path = "yolo_trt.dll";
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[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
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static extern IntPtr CreateDetector(string model_path);
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[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
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extern static bool DestroyDetector(IntPtr detector);
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/// <summary>
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/// 非0值表示成功，0表示失败 返回小图张数
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/// </summary>
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/// <param name="detector"></param>
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/// <returns></returns>
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[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
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static extern int GetBatchSize(IntPtr detector);
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[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
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static extern bool Detect(IntPtr detector, ref byte bgrs_data,
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int image_num, int width, int height, int channels,
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float conf_threshold, float iou_threshold,
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ref float output, int output_size, ref int object_num,
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int expand_pixel);
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#endregion
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public Action<WarningEnum, string> WarningEvent;
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/// <summary>
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/// 检测结果JSON(原图,结果)
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/// </summary>
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public Action<DefectTask> finishEvent;
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/// <summary>
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/// 是否打开设备成功
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/// </summary>
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public bool IsInit { get; private set; } = false;
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private IntPtr detector = IntPtr.Zero;
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private Thread t_task, t_task_operation, t_task_maketag;
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//=======task list
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private List<DefectTask> taskList = new List<DefectTask>();
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private List<DefectTask> taskOperationList = new List<DefectTask>();
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private List<DefectTask> taskMakeTagList = new List<DefectTask>();
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private string preModelName = "";
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#region 图片处理参数
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private readonly int image_width = 2048;
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private readonly int image_hight = 2048;
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private readonly int image_channels = 3;
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private readonly int image_bytes = 2048 * 2048 * 3;
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private readonly int detect_elem_size = 7; //维度数据结果，x y w h conf classid
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private readonly int detect_max_object_num = 20;//这个指的是一张子图可能最多给你返回的目标个球
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public Action<int, int> QueueCountEvent;//0/1/2, 数量
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#endregion
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public DefectLib()
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{
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}
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public bool start()
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{
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try
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{
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//detector = CreateDetector(Config.model_path, Config.labels_path, true, 6);
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//if (detector == IntPtr.Zero)
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// throw new Exception("模型初始化失败！");
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preModelName = "";
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IsInit = true;
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taskList.Clear();
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taskOperationList.Clear();
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taskMakeTagList.Clear();
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t_task = new System.Threading.Thread(runStep);
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t_task.IsBackground = true;
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t_task.Start();
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t_task_operation = new System.Threading.Thread(run2);
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t_task_operation.IsBackground = true;
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t_task_operation.Start();
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t_task_maketag = new System.Threading.Thread(run3);
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t_task_maketag.IsBackground = true;
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t_task_maketag.Start();
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return true;
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}
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catch (Exception ex)
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{
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WarningEvent?.Invoke(WarningEnum.High, ex.Message);
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return false;
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}
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}
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public void stop()
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{
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if (!IsInit) return;
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try
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{
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IsInit = false;
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//timer.Elapsed -= Timer_Elapsed;
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preModelName = "";
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//释放模型
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if (detector != IntPtr.Zero)
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{
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DestroyDetector(detector);
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detector = IntPtr.Zero;
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}
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if (t_task != null)
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{
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bool b = t_task.Join(5000);
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if (!b) t_task.Abort();
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t_task = null;
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}
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if (t_task_operation != null)
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{
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bool b = t_task_operation.Join(5000);
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if (!b) t_task_operation.Abort();
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t_task_operation = null;
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}
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if (t_task_maketag != null)
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{
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bool b = t_task_maketag.Join(5000);
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if (!b) t_task_maketag.Abort();
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t_task_maketag = null;
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}
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taskList.Clear();
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taskOperationList.Clear();
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taskMakeTagList.Clear();
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}
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catch { }
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}
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/// <summary>
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/// 切割(先左右，后上下)
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/// </summary>
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/// <param name="mat"></param>
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/// <param name="width"></param>
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/// <param name="height"></param>
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/// <returns></returns>
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private Mat[] OpenCVToCutsMat(Mat mat, int width, int height)
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{
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Mat[] array = new Mat[mat.Width / width * mat.Height / height];
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int num = 0;
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for (int i = 0; i < mat.Height / height; i++)
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{
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for (int j = 0; j < mat.Width / width; j++)
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{
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int x = j * width;
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int y = i * height;
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System.Drawing.Rectangle rectangle = new System.Drawing.Rectangle(x, y, width, height);
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Rect roi = new Rect(rectangle.X, rectangle.Y, rectangle.Width, rectangle.Height);
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array[num] = new Mat(mat, roi).Clone();
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num++;
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}
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}
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return array;
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}
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public static string ToGB2312(int codepage, string str)
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{
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try
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{
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Encoding cp850 = Encoding.GetEncoding(codepage);
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Encoding gb2312 = Encoding.GetEncoding("gb2312");//Encoding.Default ,936
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byte[] temp = cp850.GetBytes(str);
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return Encoding.GetEncoding("gb2312").GetString(temp, 0, temp.Length);
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}
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catch (Exception ex)//(UnsupportedEncodingException ex)
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{
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Console.Write(ex.Message);
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return null;
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}
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}
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private void runStep()
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{
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int step = 0;
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while (IsInit)
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{
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if (taskList.Count < 1)
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{
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Thread.Sleep(0);
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continue;
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}
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//
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step = 1;
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var task = pop();
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try
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{
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if (task != null)
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{
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Stopwatch stopwatch = Stopwatch.StartNew();
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step = 2;
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if (preModelName != task.modelName)
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{
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step = 3;
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//先释放模型
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if (detector != IntPtr.Zero)
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{
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step = 4;
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DestroyDetector(detector);
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detector = IntPtr.Zero;
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}
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step = 5;
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string modelPath = $"{ConfMgr.Instance.SysConfigParams.AIModelPath}\\{task.modelName}";
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detector = CreateDetector(modelPath);
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if (detector == IntPtr.Zero)
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throw new Exception($"模型({modelPath})初始化失败！");
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step = 6;
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preModelName = task.modelName;
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}
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//源图
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//Bitmap bmp = yolo1.Read2Bmp(file_path);
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//切割图像，输入图像格式14208*10640
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stopwatch.Start();
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//task.resizeBmp = OpenCVUtil.resize( task.bmp.Clone(), task.resize.Width, task.resize.Height);//在外面已做了resize
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//Cv2.CvtColor(task.bmp, task.bmpBgr2rgb, ColorConversionCodes.BGR2RGB);
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//task.bmps_cut = OpenCVToCutsMat(task.bmpBgr2rgb, image_width, image_hight); //这里cut时之前加的clone
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step = 7;
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task.bmps_cut = OpenCVToCutsMat(task.bmp, image_width, image_hight); //这里cut时之前加的clone
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stopwatch.Stop();
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task.stopwatch[0] = stopwatch.ElapsedMilliseconds;
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//Resize图像
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//stopwatch.Restart();
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//task.bmps_resize = yolo1.OpenCVToResizesMat(task.bmps_cut, task.resize.Width, task.resize.Height);
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//stopwatch.Stop();
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//task.stopwatch[1] = stopwatch.ElapsedMilliseconds;
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//预处理模型
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//stopwatch.Restart();
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//task.tensors = yolo1.PreprocessImageMat(task.bmps_resize);
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//stopwatch.Stop();
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//task.stopwatch[2] = stopwatch.ElapsedMilliseconds;
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step = 8;
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lock (taskOperationList)
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{
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taskOperationList.Add(task);
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QueueCountEvent?.BeginInvoke(1, taskOperationList.Count, null, null);
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}
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step = 9;
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}
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}
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catch (Exception ex)
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{
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WarningEvent?.Invoke(WarningEnum.Low, $"DefectLib task1 err({step}):" + ex.Message);
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task.isSucceed = false;
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task.resultInfo = ex.Message;
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callback(task);
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}
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}
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}
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//推理
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private void run2()
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{
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QualifiedLimit qualifiedLimit;
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while (IsInit)
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{
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if (taskOperationList.Count < 1)
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{
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Thread.Sleep(0);
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continue;
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}
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//
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var task = pop2();
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int cut_count = 0, step = 0;
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try
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{
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if (task != null && task.bmps_cut.Count() > 0)
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{
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//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, "DefectLib tasks 运行推理...");
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cut_count = task.bmps_cut.Count();
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Stopwatch stopwatch = Stopwatch.StartNew();
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//====推理（必需单队列）
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stopwatch.Start();
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// 把数据转为byte数组，【h, w, c】的bgr格式，第一张在前，第二张在后
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byte[] imgData = new byte[image_bytes * cut_count];
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for (int i = 0; i < cut_count; i++)
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Marshal.Copy(task.bmps_cut[i].Data, imgData, image_bytes * i, image_bytes);
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step = 1;
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stopwatch.Stop();
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task.stopwatch[1] = stopwatch.ElapsedMilliseconds;
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stopwatch.Restart();
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task.output = new float[cut_count * detect_elem_size * detect_max_object_num];
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task.output_num = new int[cut_count];
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//执行推理
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step = 2;
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bool ok = Detect(detector, ref imgData[0], cut_count, image_width, image_hight, image_channels,
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0.25f, 0.45f, ref task.output[0], task.output.Length, ref task.output_num[0], task.expand_pixel);
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//bool ok = Detect(_detector, ref imgData[0], imgs.Count, ImageWidth, image_hight, image_channels,
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// 0.25f, 0.45f, ref output[0], output.Length, ref output_num[0]);
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step = 3;
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stopwatch.Stop();
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task.stopwatch[2] = stopwatch.ElapsedMilliseconds;
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//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"DefectLib tasks 结果推理={ok}");
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for (int i = 0; i < cut_count; i++)
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task.bmps_cut[i].Dispose();
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if (ok == false)
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throw new Exception($"推理失败或者输入数组太小（{cut_count}）");
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//
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lock (taskMakeTagList)
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{
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taskMakeTagList.Add(task);
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QueueCountEvent?.BeginInvoke(2, taskMakeTagList.Count, null, null);
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}
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}
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}
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catch (Exception ex)
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{
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WarningEvent?.Invoke(WarningEnum.Low, $"DefectLib task2 err({step}):({cut_count}){ex.Message}");
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task.isSucceed = false;
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task.resultInfo = ex.Message;
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callback(task);
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}
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}
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}
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//打标
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private void run3()
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{
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QualifiedLimit qualifiedLimit;
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while (IsInit)
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{
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if (taskMakeTagList.Count < 1)
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{
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Thread.Sleep(0);
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continue;
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}
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//
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var task = pop3();
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int liStep = 0;
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try
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{
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Stopwatch stopwatch = Stopwatch.StartNew();
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stopwatch.Restart();
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int cut_count = task.bmps_cut.Count();//上面bmps_cut已销毁
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int colNum = task.bmp.Width / image_width;
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int rowNum = task.bmp.Height / image_hight;
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int count = 0;
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liStep = 3;
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//车用革去除接头处横档误判
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bool haveJieTou = false;
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List<DefectLabelInfo> DefectLabelInfoList = new List<DefectLabelInfo>();
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for (int i = 0; i < cut_count; i++)
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{
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liStep = i * 100;
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task.resultInfo += $"第 {i}/{cut_count} 张小图(大图索引{task.photoIndex}): 缺陷数 = {task.output_num[i]}\n";
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//task.resultInfo +=$"大图({task.tag})[{task.bmp.Width}*{task.bmp.Height}],第 {i + 1}/{cut_count} 张小图[{task.bmps_cut[i].Width}*{task.bmps_cut[i].Height}]: 瑕疵output_num = {output_num[i]}\n";
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#region 检测信息汇总
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for (int j = 0; j < task.output_num[i]; j++)//缺陷数
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{
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liStep += j;//0
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//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"-------");
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int index = count * detect_elem_size;
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// 打印输出信息（示例代码，根据实际情况进行修改和格式化）
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// 获取输出信息
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int x = (int)task.output[index];
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int y = (int)task.output[index + 1];
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int w = (int)task.output[index + 2];
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int h = (int)task.output[index + 3];
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int classId = (int)task.output[index + 4];
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double confidence = Math.Round(task.output[index + 5], 2); //置信度
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double contrast = Math.Round(task.output[index + 6], 3);//对比度
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//y = image_hight-y-h;//转到右下角为原点
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count++;
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//var cmW = Math.Round(w * task.widthRatio / task.cm2px_x, 2);
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//var cmH = Math.Round(h * task.widthRatio / task.cm2px_y, 2);
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// 打印输出信息
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task.resultInfo += $"----{i}----col:{i % colNum}/{colNum} row:{i / colNum}/{colNum}-----------\n目标:{j + 1} 类别ID:{classId} 置信度:{confidence} 对比度:{contrast} 坐标:({x},{y})-({x + w},{y + h}) 宽高:w={w},h={h}; \n";
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DefectLabelInfoList.Add(new DefectLabelInfo()
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{
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x = (int)task.output[index],
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y = (int)task.output[index + 1],
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w = (int)task.output[index + 2],
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h = (int)task.output[index + 3],
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classId = (int)task.output[index + 4],
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confidence = Math.Round(task.output[index + 5], 2), //置信度
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contrast = Math.Round(task.output[index + 6], 3),//对比度
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cmW = Math.Round(w * task.widthRatio / task.cm2px_x, 2),
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cmH = Math.Round(h * task.widthRatio / task.cm2px_y, 2),
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i = i,
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j = j,
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});
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}
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#endregion
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}
|
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#region 合并接头横档
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if(DefectLabelInfoList.Count >0)
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DefectLabelInfoList = HeBingDefect(task.bmp.Width, "jietou", DefectLabelInfoList);
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if (DefectLabelInfoList.Count > 0)
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DefectLabelInfoList = HeBingDefect(task.bmp.Width, "hengdang", DefectLabelInfoList);
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#endregion
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//结果过滤
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#region 结果过滤
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//降序排序，先得到是否有接头检出
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List<DefectLabelInfo> DefectLabelInfoListByClassID = DefectLabelInfoList.OrderByDescending(t => t.classId).ToList();//降序
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for (int q = 0; q < DefectLabelInfoList.Count; q++)
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{
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//是否满足此产品标准
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if (task.qualifiedLimitList != null && task.qualifiedLimitList.Count > 0)
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{
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qualifiedLimit = task.qualifiedLimitList.FirstOrDefault(m => m.Code == task.labelDic[DefectLabelInfoList[q].classId]);
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if (qualifiedLimit != null)
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{
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//if ((qualifiedLimit.ZXD > 0 && qualifiedLimit.ZXD > confidence)
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// || (qualifiedLimit.ContrastTop + qualifiedLimit.ContrastLower > 0 && x> qualifiedLimit.ContrastLower && x < qualifiedLimit.ContrastTop)
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// || (qualifiedLimit.Area > 0 && qualifiedLimit.Area > cmW * cmH))
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if (DefectLabelInfoList[q].confidence <= qualifiedLimit.ZXD)//confidence > qualifiedLimit.ZXD 是瑕疵 才继续判断下面的两个条件
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{
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task.resultInfo += $" 置信度不满足此产品瑕疵标准，跳过！ \n";
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continue;
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}
|
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//下限<对比度<上限：不是瑕疵
|
||
var ContrastTotal = qualifiedLimit.ContrastTop + qualifiedLimit.ContrastLower;
|
||
if (qualifiedLimit.IsOR)
|
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{
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if (!(
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(qualifiedLimit.Area <= 0 || DefectLabelInfoList[q].cmW * DefectLabelInfoList[q].cmH >= qualifiedLimit.Area) ||
|
||
(ContrastTotal <= 0 || (DefectLabelInfoList[q].contrast < qualifiedLimit.ContrastLower || DefectLabelInfoList[q].contrast > qualifiedLimit.ContrastTop))))
|
||
{
|
||
task.resultInfo += $" 不满足此产品瑕疵标准，跳过！ \n";
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"判断不是瑕疵：类别ID:{classId}； 置信度({confidence},[{qualifiedLimit.ZXD}]); isOr({qualifiedLimit.IsOR}); 面积({cmW * cmH},[{ qualifiedLimit.Area}]); 对比度({contrast},[{qualifiedLimit.ContrastLower}-{qualifiedLimit.ContrastTop}])");
|
||
continue;
|
||
}
|
||
}
|
||
else
|
||
{
|
||
if (!(
|
||
(qualifiedLimit.Area <= 0 || DefectLabelInfoList[q].cmW * DefectLabelInfoList[q].cmH >= qualifiedLimit.Area) &&
|
||
(ContrastTotal <= 0 || (DefectLabelInfoList[q].contrast < qualifiedLimit.ContrastLower || DefectLabelInfoList[q].contrast > qualifiedLimit.ContrastTop))))
|
||
{
|
||
task.resultInfo += $" 不满足此产品瑕疵标准，跳过！ \n";
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"判断不是瑕疵：类别ID:{classId}； 置信度({confidence},[{qualifiedLimit.ZXD}]); isOr({qualifiedLimit.IsOR}); 面积({cmW * cmH},[{ qualifiedLimit.Area}]); 对比度({contrast},[{qualifiedLimit.ContrastLower}-{qualifiedLimit.ContrastTop}])");
|
||
continue;
|
||
}
|
||
}
|
||
if (task.labelDic[DefectLabelInfoList[q].classId] == "jietou")
|
||
haveJieTou = true;
|
||
if (haveJieTou && (task.labelDic[DefectLabelInfoList[q].classId] == "hengdang"))
|
||
{
|
||
task.resultInfo += $" 判断为接头处横档，跳过！ \n";
|
||
continue;
|
||
}
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"判断是瑕疵：类别ID:{classId}； 置信度({confidence},[{qualifiedLimit.ZXD}]); isOr({qualifiedLimit.IsOR}); 面积({cmW * cmH},[{ qualifiedLimit.Area}]); 对比度({contrast},[{qualifiedLimit.ContrastLower}-{qualifiedLimit.ContrastTop}])");
|
||
}
|
||
}
|
||
liStep++;//1
|
||
//打标
|
||
var point1 = new OpenCvSharp.Point((DefectLabelInfoList[q].i % colNum) * image_width + DefectLabelInfoList[q].x, (DefectLabelInfoList[q].i / colNum) * image_hight + DefectLabelInfoList[q].y);
|
||
var point2 = new OpenCvSharp.Point(point1.X + DefectLabelInfoList[q].w, point1.Y + DefectLabelInfoList[q].h);
|
||
liStep++;//2
|
||
task.resultInfo += $" 转换到大图坐标(px)：p1={point1.X},{point1.Y}; p2={point2.X},{point2.Y}\n";
|
||
Cv2.Rectangle(task.bmpTag, point1, point2, new Scalar(0.0, 0.0, 255.0), 1);//画打标点
|
||
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"保存第 {count} 行缺陷信息；");
|
||
int px = (point1.X - task.xw) > 0 ? (point1.X - task.xw) : 0;
|
||
var cmX = Math.Round(px * task.widthRatio / task.cm2px_x, 2);
|
||
var cmY = Math.Round((task.bmp.Height - point1.Y - DefectLabelInfoList[q].h) * task.widthRatio / task.cm2px_y, 2);//外面计Y从右下角为原点
|
||
liStep++;//3
|
||
task.resultInfo += $" 转换到大图坐标(cm)[widthRatio={task.widthRatio}]：x={cmX},y={cmY}; w={DefectLabelInfoList[q].cmW},h={DefectLabelInfoList[q].cmH}\n";
|
||
task.excelTable.Rows.Add($"{task.photoIndex}", cmX, cmY, DefectLabelInfoList[q].cmW, DefectLabelInfoList[q].cmH, DefectLabelInfoList[q].j + 1, DefectLabelInfoList[q].classId, DefectLabelInfoList[q].confidence, Utils.ContrastToPercent(DefectLabelInfoList[q].contrast));
|
||
|
||
liStep++;//4
|
||
//切缺陷小图
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"保存第 {count} 个缺陷小图；");
|
||
int left, top, decX, decY;
|
||
decX = (DefectLabelInfoList[q].w > 236 ? 20 : 256 - DefectLabelInfoList[q].w) / 2;
|
||
decY = (DefectLabelInfoList[q].h > 236 ? 20 : 256 - DefectLabelInfoList[q].h) / 2;
|
||
left = point1.X - decX;
|
||
top = point1.Y - decY;
|
||
if (left < 0) left = 0;
|
||
if (top < 0) top = 0;
|
||
|
||
int width = DefectLabelInfoList[q].w + decX * 2;
|
||
int height = DefectLabelInfoList[q].h + decY * 2;
|
||
if (left + width > task.bmp.Width - 1) width = task.bmp.Width - left - 1;
|
||
if (top + height > task.bmp.Height - 1) height = task.bmp.Height - top - 1;
|
||
liStep++;//5
|
||
Rect roi = new Rect(left, top, width, height);
|
||
liStep++;//6
|
||
if (height < 1 || width < 1)
|
||
{
|
||
task.resultInfo += $" 打标到大图坐标Rect(px)：left={left},top={top}; width={width},height={height}\n";
|
||
task.resultInfo += $" test point1.Y={point1.Y},h={DefectLabelInfoList[q].h}; top={top},mat.Height={task.bmp.Height}\n================\n";
|
||
WarningEvent?.Invoke(WarningEnum.Normal, task.resultInfo);
|
||
}
|
||
|
||
//保存
|
||
//string filename = $"{Config.appBasePath}\\temp\\{task.tag}\\{task.tag}_X{mmX.ToString()}_Y{mmY.ToString()}_W{mmW.ToString()}_H{mmH.ToString()}_目标{j + 1}_类别{classId}_置信度{confidence}.bmp";
|
||
//OpenCvSharp.Extensions.BitmapConverter.ToBitmap(new Mat(task.bmp, roi)).Save(filename, ImageFormat.Jpeg);
|
||
|
||
task.lstDefectBmp.Add(new Mat(task.bmpTag, roi).Clone());
|
||
liStep++;//7
|
||
}
|
||
#endregion
|
||
liStep = 99;
|
||
stopwatch.Stop();
|
||
task.stopwatch[3] = stopwatch.ElapsedMilliseconds;
|
||
|
||
task.isSucceed = true;
|
||
callback(task);
|
||
}
|
||
catch (Exception ex)
|
||
{
|
||
WarningEvent?.Invoke(WarningEnum.Low, $"DefectLib task3 err({liStep}):" + ex.Message);
|
||
task.isSucceed = false;
|
||
task.resultInfo = ex.Message;
|
||
callback(task);
|
||
}
|
||
}
|
||
}
|
||
|
||
#region 词条合并
|
||
private List<DefectLabelInfo> HeBingDefect(int Width, string label, List<DefectLabelInfo> DefectLabelInfoList)
|
||
{
|
||
int fw = 500;
|
||
List<DefectLabelInfo> outList = new List<DefectLabelInfo>();
|
||
|
||
List<DefectLabelInfo> HeBingList = new List<DefectLabelInfo>();
|
||
List<DefectLabelInfo> XcHeBingList = new List<DefectLabelInfo>();
|
||
List<int> xPos = new List<int>();
|
||
List<double> xZXD = new List<double>();
|
||
|
||
DefectLabelInfo stpoint = DefectLabelInfoList[0];
|
||
|
||
int colNum = Width / image_width;
|
||
//寻找在一条线上
|
||
for (int q = 0; q < DefectLabelInfoList.Count; q++)
|
||
{
|
||
if (SysMgr.Instance.GetDefectCode()[DefectLabelInfoList[q].classId] == label)
|
||
{
|
||
int max = stpoint.y + fw;
|
||
int min = stpoint.y - fw > 0 ? stpoint.y - fw : 0;
|
||
if (DefectLabelInfoList[q].y >= min && DefectLabelInfoList[q].y <= max)
|
||
{
|
||
HeBingList.Add(DefectLabelInfoList[q]);
|
||
xPos.Add(DefectLabelInfoList[q].x);
|
||
xZXD.Add(DefectLabelInfoList[q].confidence);
|
||
}
|
||
else
|
||
XcHeBingList.Add(DefectLabelInfoList[q]);
|
||
}
|
||
else
|
||
outList.Add(DefectLabelInfoList[q]);
|
||
}
|
||
//递归下次合并数据
|
||
List<DefectLabelInfo> dg1 = new List<DefectLabelInfo>();
|
||
if (XcHeBingList.Count > 0)
|
||
dg1 = HeBingDefect(Width, label, XcHeBingList);
|
||
|
||
|
||
//多个jietou合并
|
||
if (HeBingList.Count > 0)
|
||
{
|
||
var stIt = HeBingList.Find(x => x.x == xPos.Min());
|
||
var edIt = HeBingList.Find(x => x.x == xPos.Max());
|
||
var eZXD = HeBingList.Find(x => x.confidence == xZXD.Max());
|
||
int newW = Math.Abs((edIt.i % colNum) * image_width + edIt.x) - ((stIt.i % colNum) * image_width + stIt.x) + edIt.w;
|
||
outList.Add(new DefectLabelInfo()
|
||
{
|
||
x = stIt.x,
|
||
y = edIt.y,
|
||
w = newW, //多图叠加
|
||
h = edIt.h,
|
||
classId = eZXD.classId,
|
||
confidence = eZXD.confidence,
|
||
contrast = eZXD.contrast,
|
||
cmH = Math.Round(edIt.h * 1.0 / ConfMgr.Instance.SysConfigParams.Cm2px_y, 2),
|
||
cmW = Math.Round(newW * 1.0 / ConfMgr.Instance.SysConfigParams.Cm2px_x, 2),
|
||
i = stIt.i,
|
||
j = stIt.j,
|
||
});
|
||
}
|
||
|
||
outList = outList.Concat(dg1).ToList<DefectLabelInfo>();//保留重复项
|
||
return outList;
|
||
}
|
||
#endregion
|
||
|
||
#region 结果回调
|
||
private void callback(DefectTask task)
|
||
{
|
||
//返回成功/失败,异步调用
|
||
if (task.finishEvent != null || (task.finishEvent = finishEvent) != null)
|
||
//task.finishEvent.BeginInvoke(result, errInfo, res => task.finishEvent.EndInvoke(res), null);
|
||
System.Threading.ThreadPool.QueueUserWorkItem(waitCallback, task);
|
||
}
|
||
//异步回调
|
||
WaitCallback waitCallback = new WaitCallback(o =>
|
||
{
|
||
var task = (DefectTask)o;
|
||
task.finishEvent(task);
|
||
});
|
||
#endregion
|
||
|
||
#region 处理队列
|
||
public void add(DefectTask task)
|
||
{
|
||
lock (taskList)
|
||
{
|
||
taskList.Add(task);
|
||
QueueCountEvent?.BeginInvoke(0, taskList.Count, null, null);
|
||
}
|
||
}
|
||
|
||
|
||
private DefectTask pop()
|
||
{
|
||
lock (taskList)
|
||
{
|
||
if (taskList.Count < 1)
|
||
return null;
|
||
|
||
//int index = 0;// taskList.FindIndex(p => { return p.isSync; });
|
||
//if (index < 0) index = 0;
|
||
var task = taskList[0];
|
||
taskList.RemoveAt(0);
|
||
QueueCountEvent?.BeginInvoke(0, taskList.Count, null, null);
|
||
return task;
|
||
}
|
||
}
|
||
private DefectTask pop2()
|
||
{
|
||
lock (taskOperationList)
|
||
{
|
||
if (taskOperationList.Count < 1)
|
||
return null;
|
||
|
||
//int index = 0;// taskList.FindIndex(p => { return p.isSync; });
|
||
//if (index < 0) index = 0;
|
||
var task = taskOperationList[0];
|
||
taskOperationList.RemoveAt(0);
|
||
QueueCountEvent?.BeginInvoke(1, taskOperationList.Count, null, null);
|
||
return task;
|
||
}
|
||
}
|
||
private DefectTask pop3()
|
||
{
|
||
lock (taskMakeTagList)
|
||
{
|
||
if (taskMakeTagList.Count < 1)
|
||
return null;
|
||
|
||
//int index = 0;// taskList.FindIndex(p => { return p.isSync; });
|
||
//if (index < 0) index = 0;
|
||
var task = taskMakeTagList[0];
|
||
taskMakeTagList.RemoveAt(0);
|
||
QueueCountEvent?.BeginInvoke(2, taskMakeTagList.Count, null, null);
|
||
return task;
|
||
}
|
||
}
|
||
#endregion
|
||
|
||
public void Dispose()
|
||
{
|
||
stop();
|
||
}
|
||
|
||
private class DefectLabelInfo
|
||
{
|
||
public int x { get; set; }
|
||
public int y { get; set; }
|
||
public int w { get; set; }
|
||
public int h { get; set; }
|
||
public int classId { get; set; }
|
||
public double confidence { get; set; } //置信度
|
||
public double contrast { get; set; }//对比度
|
||
|
||
public double cmW { get; set; }
|
||
public double cmH { get; set; }
|
||
|
||
public int i { get; set; }//小图index
|
||
public int j { get; set; }//缺陷index
|
||
}
|
||
}
|
||
|
||
public class DefectTask
|
||
{
|
||
public DefectTask()
|
||
{
|
||
// 创建一个 DataTable 对象来存储数据
|
||
excelTable = new DataTable("MyData");
|
||
// 添加列到 DataTable
|
||
excelTable.Columns.Add("FileName", typeof(string));
|
||
excelTable.Columns.Add("X", typeof(decimal));
|
||
excelTable.Columns.Add("Y", typeof(decimal));
|
||
excelTable.Columns.Add("W", typeof(decimal));
|
||
excelTable.Columns.Add("H", typeof(decimal));
|
||
excelTable.Columns.Add("目标", typeof(int));
|
||
excelTable.Columns.Add("类别", typeof(int));
|
||
excelTable.Columns.Add("置信度", typeof(decimal));
|
||
excelTable.Columns.Add("对比度", typeof(decimal));
|
||
// 向 DataTable 中添加数据行
|
||
//excelTable.Rows.Add("John Doe", 30);
|
||
//excelTable.Rows.Add("Jane Smith", 25);
|
||
}
|
||
/// <summary>
|
||
/// 处理参数
|
||
/// </summary>
|
||
public int expand_pixel;
|
||
public int cm2px_x;
|
||
public int cm2px_y;
|
||
|
||
public Dictionary<int, string> labelDic;
|
||
/// <summary>
|
||
/// 模型名字
|
||
/// </summary>
|
||
public string modelName;
|
||
|
||
public Models.Records record;
|
||
//public string bmpPath;//源图路径（仅目录）
|
||
/// <summary>
|
||
/// 源图(resize后的)
|
||
/// </summary>
|
||
public Mat bmp;
|
||
//public Mat bmpBgr2rgb=new Mat();
|
||
|
||
public System.Drawing.Size resize = new System.Drawing.Size(224, 224);
|
||
//public Mat resizeBmp;//resize后 BGR2RGB图，只用于识别
|
||
public Mat bmpTag;
|
||
/// <summary>
|
||
/// 图片索引0-n
|
||
/// </summary>
|
||
public int photoIndex;//excel中对应的图像路径标识
|
||
public float widthRatio;//宽度比例，resize前/resize后
|
||
//切割后
|
||
public Mat[] bmps_cut;
|
||
|
||
//用于比对参数 }//置信度 面积 对比度
|
||
public List<QualifiedLimit> qualifiedLimitList;
|
||
|
||
//推理后结果用于打标
|
||
public float[] output;
|
||
public int[] output_num;
|
||
|
||
//
|
||
/// <summary>
|
||
/// 完成后回调
|
||
/// </summary>
|
||
public Action<DefectTask> finishEvent;
|
||
//==结果返回
|
||
public bool isSucceed;//转换是否成功
|
||
public string resultInfo = "";//成功或失败信息
|
||
|
||
public List<Mat> lstDefectBmp = new List<Mat>();
|
||
/// <summary>
|
||
/// fileIndex,x_mm,y_mm,w_mm,h_mm,目标,类别ID,置信度
|
||
/// </summary>
|
||
public DataTable excelTable = new DataTable();
|
||
public long[] stopwatch = new long[4];
|
||
|
||
/// <summary>
|
||
/// 等比例填充的宽度
|
||
/// </summary>
|
||
public int xw;
|
||
}
|
||
}
|