1347 lines
71 KiB
C#
1347 lines
71 KiB
C#
using System;
|
||
using System.Collections.Generic;
|
||
using System.Data;
|
||
using System.Diagnostics;
|
||
using System.Diagnostics.Contracts;
|
||
using System.Drawing;
|
||
using System.Drawing.Imaging;
|
||
using System.IO;
|
||
using System.Linq;
|
||
using System.Reflection;
|
||
using System.Runtime.InteropServices;
|
||
using System.Threading;
|
||
using HalconDotNet;
|
||
using System.Windows.Forms.VisualStyles;
|
||
using LeatherApp.Utils;
|
||
using Models;
|
||
using OpenCvSharp;
|
||
using OpenCvSharp.Extensions;
|
||
using PG_Detect;
|
||
using SqlSugar;
|
||
using HZH_Controls;
|
||
|
||
namespace LeatherApp.Device
|
||
{
|
||
public class DefectLib : IDisposable
|
||
{
|
||
//private const string dll_path = "yoloOrt.dll";
|
||
//[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
|
||
//extern static IntPtr CreateDetector(string model_path, string labels_path, bool is_gpu, int numberThreads);
|
||
|
||
private const string dll_path = "yolo_trt.dll";
|
||
[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
|
||
static extern IntPtr CreateDetector(string model_path);
|
||
|
||
[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
|
||
extern static bool DestroyDetector(IntPtr detector);
|
||
|
||
/// <summary>
|
||
/// 非0值表示成功，0表示失败 返回小图张数
|
||
/// </summary>
|
||
/// <param name="detector"></param>
|
||
/// <returns></returns>
|
||
[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
|
||
static extern int GetBatchSize(IntPtr detector);
|
||
|
||
//[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
|
||
//extern static bool Detect1(IntPtr detector, ref byte bgrs_data,
|
||
// int image_num, int width, int height, int channels,
|
||
// float conf_threshold, float iou_threshold,
|
||
// ref int output, int output_size, ref int object_num);
|
||
[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
|
||
static extern bool Detect(IntPtr detector, ref byte bgrs_data,
|
||
int image_num, int width, int height, int channels,
|
||
float conf_threshold, float iou_threshold,
|
||
ref float output, int output_size, ref int object_num,
|
||
int expand_pixel);
|
||
|
||
//-----
|
||
//private const string dll_path = "yolo_trt .dll";
|
||
//[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
|
||
//static extern IntPtr CreateDetector(string model_path);
|
||
|
||
//[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
|
||
//static extern bool DestroyDetector(IntPtr detector);
|
||
|
||
//[DllImport(dll_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.StdCall)]
|
||
//static extern bool Detect(IntPtr detector, ref byte bgrs_data,
|
||
// int image_num, int width, int height, int channels,
|
||
// float conf_threshold, float iou_threshold,
|
||
// ref int output, int output_size, ref int object_num);
|
||
|
||
//---
|
||
private IntPtr detector = IntPtr.Zero;
|
||
private readonly int image_width = 2048;
|
||
private readonly int image_hight = 2048;
|
||
private readonly int image_channels = 3;
|
||
private readonly int image_bytes = 2048 * 2048 * 3;
|
||
private readonly int detect_elem_size = 7; //维度数据结果，x y w h conf classid
|
||
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
|
||
//
|
||
public Action<int, int> QueueCountEvent;//0/1/2, 数量
|
||
public Action<DateTime,WarningEnum, string> WarningEvent;
|
||
/// <summary>
|
||
/// 检测结果JSON(原图,结果)
|
||
/// </summary>
|
||
public Action<DefectTask> finishEvent;
|
||
/// <summary>
|
||
/// 是否打开设备成功
|
||
/// </summary>
|
||
public bool IsInit { get; private set; } = false;
|
||
|
||
string rootPath = Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location);
|
||
private Thread t_task, t_task_operation ,t_task_maketag;
|
||
//=======task list
|
||
private List<DefectTask> taskList = new List<DefectTask>();
|
||
private List<DefectTask> taskOperationList = new List<DefectTask>();
|
||
private List<DefectTask> taskMakeTagList = new List<DefectTask>();
|
||
|
||
private string preModelName = "";
|
||
|
||
//传统算法核心
|
||
private PGDetect tPGDetect = new PGDetect();
|
||
public DefectLib()
|
||
{
|
||
}
|
||
public bool start()
|
||
{
|
||
try
|
||
{
|
||
//detector = CreateDetector(Config.model_path, Config.labels_path, true, 6);
|
||
//if (detector == IntPtr.Zero)
|
||
// throw new Exception("模型初始化失败！");
|
||
preModelName = "";
|
||
|
||
IsInit = true;
|
||
taskList.Clear();
|
||
taskOperationList.Clear();
|
||
taskMakeTagList.Clear();
|
||
|
||
t_task = new System.Threading.Thread(runStep);
|
||
t_task.IsBackground = true;
|
||
t_task.Start();
|
||
|
||
t_task_operation = new System.Threading.Thread(run2);
|
||
t_task_operation.IsBackground = true;
|
||
t_task_operation.Start();
|
||
|
||
t_task_maketag = new System.Threading.Thread(run3);
|
||
t_task_maketag.IsBackground = true;
|
||
t_task_maketag.Start();
|
||
|
||
return true;
|
||
}
|
||
catch (Exception ex)
|
||
{
|
||
WarningEvent?.Invoke(DateTime.Now,WarningEnum.High, ex.Message);
|
||
return false;
|
||
}
|
||
}
|
||
public void stop()
|
||
{
|
||
if (!IsInit) return;
|
||
|
||
try
|
||
{
|
||
IsInit = false;
|
||
//timer.Elapsed -= Timer_Elapsed;
|
||
preModelName = "";
|
||
//释放模型
|
||
if (detector != IntPtr.Zero)
|
||
{
|
||
DestroyDetector(detector);
|
||
detector = IntPtr.Zero;
|
||
}
|
||
if (t_task != null)
|
||
{
|
||
bool b = t_task.Join(5000);
|
||
if (!b) t_task.Abort();
|
||
t_task = null;
|
||
}
|
||
|
||
if (t_task_operation != null)
|
||
{
|
||
bool b = t_task_operation.Join(5000);
|
||
if (!b) t_task_operation.Abort();
|
||
t_task_operation = null;
|
||
}
|
||
|
||
if (t_task_maketag != null)
|
||
{
|
||
bool b = t_task_maketag.Join(5000);
|
||
if (!b) t_task_maketag.Abort();
|
||
t_task_maketag = null;
|
||
}
|
||
taskList.Clear();
|
||
taskOperationList.Clear();
|
||
taskMakeTagList.Clear();
|
||
}
|
||
catch { }
|
||
}
|
||
/// <summary>
|
||
///
|
||
/// </summary>
|
||
/// <param name="sumWidth">多个相机图像总宽(外部去除重合部分)</param>
|
||
/// <returns></returns>
|
||
public int GetWidthForResize(int sumWidth)
|
||
{
|
||
int count =(int) Math.Round(sumWidth * 1.0f / image_width, 0);
|
||
count = 8;
|
||
return count * image_width;
|
||
|
||
//int count = sumWidth / image_width;
|
||
////int remainder = sumWidth % image_width;
|
||
//if (count % 2 == 0)
|
||
// return count * image_width;
|
||
//else
|
||
// return count * image_width+ image_width;
|
||
}
|
||
/// <summary>
|
||
/// 保存图片
|
||
/// </summary>
|
||
/// <param name="Img"></param>图片
|
||
/// <param name="pictureUrl"></param>保存路径
|
||
/// <param name="pictureName"></param>保存名称
|
||
private void SaveImage(Bitmap Img, string pictureUrl, string pictureName)
|
||
{
|
||
if (!Directory.Exists(pictureUrl))
|
||
{
|
||
Directory.CreateDirectory(pictureUrl);
|
||
}
|
||
FileInfo FileUrl = new FileInfo(pictureUrl);//防止路径中有日期导致路径错误
|
||
|
||
try
|
||
{
|
||
using (Bitmap bitmap = new Bitmap(Img))
|
||
{
|
||
using (MemoryStream stream = new MemoryStream())
|
||
{
|
||
bitmap.Save(stream, ImageFormat.Bmp);
|
||
System.Drawing.Image img = System.Drawing.Image.FromStream(stream);
|
||
//img.Save(FileUrl +"\\"+ pictureName);
|
||
//img.Dispose();
|
||
string szURL = FileUrl + "\\" + pictureName;
|
||
img.Save(szURL, ImageFormat.Bmp);
|
||
img.Dispose();
|
||
}
|
||
}
|
||
}
|
||
catch (Exception)
|
||
{
|
||
if (Img != null)
|
||
{
|
||
Img.Dispose();
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
public bool LoadModel(string modelPath)
|
||
{
|
||
int step = 0;
|
||
try
|
||
{
|
||
if (!Config.OpenHalconDefect)
|
||
{
|
||
if (preModelName != modelPath)
|
||
{
|
||
//WarningEvent?.Invoke(DateTime.Now, WarningEnum.Normal, $"模型加载({modelPath})" );
|
||
step = 3;
|
||
//先释放模型
|
||
if (detector != IntPtr.Zero)
|
||
{
|
||
step = 4;
|
||
DestroyDetector(detector);
|
||
detector = IntPtr.Zero;
|
||
}
|
||
step = 5;
|
||
detector = CreateDetector($"./models/{modelPath}");
|
||
if (detector == IntPtr.Zero)
|
||
throw new Exception($"模型({modelPath})初始化失败！");
|
||
step = 6;
|
||
preModelName = modelPath;
|
||
}
|
||
}
|
||
else
|
||
{
|
||
HObject obj = new HObject();
|
||
Mat mat = new Mat($"./init.bmp");
|
||
OpenCVUtil.MatToHObject(mat, out obj);
|
||
//HOperatorSet.GenImageConst(out obj, "byte", 2048 * 8, 2048 * 2);//生成一幅图像，用byte格式，默认是黑色
|
||
//Mat initMat = new Mat(2048 * 2, 2048 * 8, MatType.CV_8UC3);
|
||
if (!tPGDetect.IniOptimization(obj, Config.HalconParamPath))
|
||
throw new Exception($"传统算法({Config.HalconParamPath})初始化失败！");
|
||
}
|
||
return true;
|
||
}
|
||
catch (Exception e)
|
||
{
|
||
WarningEvent?.Invoke(DateTime.Now, WarningEnum.Low, $"Load Model err({step}):" + e.Message);
|
||
return false;
|
||
}
|
||
}
|
||
private void runStep()
|
||
{
|
||
int step = 0;
|
||
while (IsInit)
|
||
{
|
||
if (taskList.Count < 1)
|
||
{
|
||
Thread.Sleep(20);
|
||
continue;
|
||
}
|
||
|
||
//
|
||
step = 1;
|
||
var task = pop();
|
||
try
|
||
{
|
||
if (task != null)
|
||
{
|
||
Stopwatch stopwatch = Stopwatch.StartNew();
|
||
step =2;
|
||
LoadModel(task.modelName);
|
||
//源图
|
||
//Bitmap bmp = yolo1.Read2Bmp(file_path);
|
||
//切割图像，输入图像格式14208*10640
|
||
stopwatch.Start();
|
||
if (!Config.OpenHalconDefect)
|
||
{
|
||
//task.resizeBmp = OpenCVUtil.resize( task.bmp.Clone(), task.resize.Width, task.resize.Height);//在外面已做了resize
|
||
|
||
//Cv2.CvtColor(task.bmp, task.bmpBgr2rgb, ColorConversionCodes.BGR2RGB);
|
||
//task.bmps_cut = OpenCVToCutsMat(task.bmpBgr2rgb, image_width, image_hight); //这里cut时之前加的clone
|
||
step = 7;
|
||
task.bmps_cut = OpenCVToCutsMat(task.bmp, image_width, image_hight); //这里cut时之前加的clone
|
||
stopwatch.Stop();
|
||
task.stopwatch[0] = stopwatch.ElapsedMilliseconds;
|
||
|
||
//Resize图像
|
||
//stopwatch.Restart();
|
||
//task.bmps_resize = yolo1.OpenCVToResizesMat(task.bmps_cut, task.resize.Width, task.resize.Height);
|
||
//stopwatch.Stop();
|
||
//task.stopwatch[1] = stopwatch.ElapsedMilliseconds;
|
||
|
||
//预处理模型
|
||
//stopwatch.Restart();
|
||
//task.tensors = yolo1.PreprocessImageMat(task.bmps_resize);
|
||
//stopwatch.Stop();
|
||
//task.stopwatch[2] = stopwatch.ElapsedMilliseconds;
|
||
step = 8;
|
||
lock (taskOperationList)
|
||
{
|
||
taskOperationList.Add(task);
|
||
QueueCountEvent?.BeginInvoke(1, taskOperationList.Count, null, null);
|
||
}
|
||
step = 9;
|
||
}
|
||
else
|
||
{
|
||
step = 20;
|
||
|
||
int object_num = 0;
|
||
//转换
|
||
OpenCVUtil.MatToHObject(task.bmp, out task.ho_Img);
|
||
|
||
stopwatch.Stop();
|
||
task.stopwatch[0] = stopwatch.ElapsedMilliseconds;
|
||
stopwatch.Restart();
|
||
step = 21;
|
||
//预处理
|
||
tPGDetect.Processing(task.ho_Img, out task.ho_imgG, out task.ho_ImageResult3, out task.ho_ImageSub,
|
||
Config.HalconParamPath,
|
||
out task.hv_RES, out task.hv_gauss_filter1, out task.hv_gauss_filter2,
|
||
out task.hv_Energy_value, out task.hv_Region_S_value, out task.hv_AreaThr, out task.hv_class);
|
||
|
||
stopwatch.Stop();
|
||
task.stopwatch[1] = stopwatch.ElapsedMilliseconds;
|
||
lock (taskOperationList)
|
||
{
|
||
taskOperationList.Add(task);
|
||
QueueCountEvent?.BeginInvoke(1, taskOperationList.Count, null, null);
|
||
}
|
||
}
|
||
}
|
||
}
|
||
catch (Exception ex)
|
||
{
|
||
WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"DefectLib task1 err({step}):" + ex.Message);
|
||
task.isSucceed = false;
|
||
task.resultInfo = ex.Message;
|
||
callback(task);
|
||
}
|
||
Thread.Sleep(5);
|
||
}
|
||
}
|
||
//推理
|
||
private void run2()
|
||
{
|
||
QualifiedLimit qualifiedLimit;
|
||
while (IsInit)
|
||
{
|
||
if (taskOperationList.Count < 1)
|
||
{
|
||
Thread.Sleep(20);
|
||
continue;
|
||
}
|
||
|
||
//
|
||
var task = pop2();
|
||
int cut_count=0,step = 0;
|
||
try
|
||
{
|
||
if (task != null && !Config.OpenHalconDefect && task.bmps_cut.Count()>0 )
|
||
{
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, "DefectLib tasks 运行推理...");
|
||
cut_count = task.bmps_cut.Count();
|
||
Stopwatch stopwatch = Stopwatch.StartNew();
|
||
//====推理（必需单队列）
|
||
stopwatch.Start();
|
||
// 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
|
||
byte[] imgData = new byte[image_bytes * cut_count];
|
||
for (int i = 0; i < cut_count; i++)
|
||
Marshal.Copy(task.bmps_cut[i].Data, imgData, image_bytes * i, image_bytes);
|
||
step = 1;
|
||
stopwatch.Stop();
|
||
task.stopwatch[1] = stopwatch.ElapsedMilliseconds;
|
||
stopwatch.Restart();
|
||
|
||
task.output = new float[cut_count * detect_elem_size * detect_max_object_num];
|
||
task.output_num = new int[cut_count];
|
||
//执行推理
|
||
step =2;
|
||
bool ok = Detect(detector, ref imgData[0], cut_count, image_width, image_hight, image_channels,
|
||
0.25f, 0.45f, ref task.output[0], task.output.Length, ref task.output_num[0],Config.expand_pixel);
|
||
//bool ok = Detect(_detector, ref imgData[0], imgs.Count, ImageWidth, image_hight, image_channels,
|
||
// 0.25f, 0.45f, ref output[0], output.Length, ref output_num[0]);
|
||
step = 3;
|
||
stopwatch.Stop();
|
||
task.stopwatch[2] = stopwatch.ElapsedMilliseconds;
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"DefectLib tasks 结果推理={ok}");
|
||
for (int i = 0; i < cut_count; i++)
|
||
task.bmps_cut[i].Dispose();
|
||
if (ok == false)
|
||
throw new Exception($"推理失败或者输入数组太小（{cut_count}）");
|
||
|
||
//
|
||
lock (taskMakeTagList)
|
||
{
|
||
taskMakeTagList.Add(task);
|
||
QueueCountEvent?.BeginInvoke(2, taskMakeTagList.Count, null, null);
|
||
}
|
||
}
|
||
else
|
||
{
|
||
Stopwatch stopwatch = Stopwatch.StartNew();
|
||
stopwatch.Start();
|
||
step = 22;
|
||
//算法处理
|
||
tPGDetect.Detect(task.ho_Img, task.ho_imgG, task.ho_ImageResult3, task.ho_ImageSub,
|
||
task.hv_Energy_value, task.hv_Region_S_value, task.hv_AreaThr, task.hv_class,
|
||
out task.hv_defectinfo, out task.object_num);
|
||
|
||
stopwatch.Stop();
|
||
task.stopwatch[2] = stopwatch.ElapsedMilliseconds;
|
||
lock (taskMakeTagList)
|
||
{
|
||
taskMakeTagList.Add(task);
|
||
QueueCountEvent?.BeginInvoke(2, taskMakeTagList.Count, null, null);
|
||
}
|
||
}
|
||
}
|
||
catch (Exception ex)
|
||
{
|
||
WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"DefectLib task2 err({step}):({cut_count}){ ex.Message}");
|
||
task.isSucceed = false;
|
||
task.resultInfo = ex.Message;
|
||
callback(task);
|
||
}
|
||
Thread.Sleep(5);
|
||
}
|
||
}
|
||
|
||
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
|
||
}
|
||
|
||
private double ContrastLow = 0.8;
|
||
private double ContrastTop = 1.2;
|
||
//private double ContrastToPercent(double val)
|
||
//{
|
||
// if (val < ContrastLow)
|
||
// return 0;
|
||
// else if (val > ContrastTop)
|
||
// return 100;
|
||
// double temp = 100 / (ContrastTop - ContrastLow);
|
||
// return temp * (val - ContrastLow);
|
||
//}
|
||
//private double PercentToContrast(double val)
|
||
//{
|
||
// double temp = val * (ContrastTop - ContrastLow);
|
||
// return temp + ContrastLow;
|
||
//}
|
||
private double ContrastToPercent(double val)
|
||
{
|
||
if (val < ContrastLow)
|
||
return 0;
|
||
else if (val > ContrastTop)
|
||
return 100;
|
||
double temp = 100 / (ContrastTop - ContrastLow);
|
||
return Math.Round(temp * (val - ContrastLow), 2);
|
||
}
|
||
private double PercentToContrast(double val)
|
||
{
|
||
double tt = 1;
|
||
if (val > 100)
|
||
tt = 100;
|
||
else if (val < 0)
|
||
tt = 0;
|
||
else
|
||
tt = val;
|
||
double temp = tt / 100 * (ContrastTop - ContrastLow);
|
||
return temp + ContrastLow;
|
||
}
|
||
private List<DefectLabelInfo> HeBingDefect(string modelName, int Width, List<DefectLabelInfo> DefectLabelInfoList)
|
||
{
|
||
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<int> yPos = new List<int>();
|
||
List<double> ZXD = new List<double>();
|
||
|
||
List<DefectLabelInfo> HeBingList2 = new List<DefectLabelInfo>();
|
||
List<DefectLabelInfo> XcHeBingList2 = new List<DefectLabelInfo>();
|
||
List<int> xPos2 = new List<int>();
|
||
List<int> yPos2 = new List<int>();
|
||
List<double> ZXD2 = new List<double>();
|
||
|
||
List<DefectLabelInfo> HeBingList3 = new List<DefectLabelInfo>();
|
||
List<DefectLabelInfo> XcHeBingList3 = new List<DefectLabelInfo>();
|
||
List<int> xPos3 = new List<int>();
|
||
List<int> yPos3 = new List<int>();
|
||
List<double> ZXD3 = new List<double>();
|
||
|
||
List<DefectLabelInfo> HeBingList4 = new List<DefectLabelInfo>();
|
||
List<DefectLabelInfo> XcHeBingList4 = new List<DefectLabelInfo>();
|
||
List<int> xPos4 = new List<int>();
|
||
List<int> yPos4 = new List<int>();
|
||
List<double> ZXD4 = 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 (Config.getDefectCode(modelName, DefectLabelInfoList[q].classId) == "jietou")
|
||
{
|
||
int max = stpoint.y + 2000;
|
||
int min = stpoint.y - 2000 > 0? stpoint.y - 2000 : 0;
|
||
if (DefectLabelInfoList[q].y >= min && DefectLabelInfoList[q].y <= max)
|
||
{
|
||
HeBingList.Add(DefectLabelInfoList[q]);
|
||
xPos.Add((DefectLabelInfoList[q].i % colNum) * image_width + DefectLabelInfoList[q].x);
|
||
yPos.Add((DefectLabelInfoList[q].i / colNum) * image_hight + DefectLabelInfoList[q].y);
|
||
ZXD.Add(DefectLabelInfoList[q].confidence);
|
||
}
|
||
else
|
||
XcHeBingList.Add(DefectLabelInfoList[q]);
|
||
}
|
||
else if (Config.getDefectCode(modelName, DefectLabelInfoList[q].classId) == "hengdang")
|
||
{
|
||
int max = stpoint.y + 2000;
|
||
int min = stpoint.y - 2000 > 0 ? stpoint.y - 2000 : 0;
|
||
if (DefectLabelInfoList[q].y >= min && DefectLabelInfoList[q].y <= max)
|
||
{
|
||
HeBingList2.Add(DefectLabelInfoList[q]);
|
||
xPos2.Add((DefectLabelInfoList[q].i % colNum) * image_width + DefectLabelInfoList[q].x);
|
||
yPos2.Add((DefectLabelInfoList[q].i / colNum) * image_hight + DefectLabelInfoList[q].y);
|
||
ZXD2.Add(DefectLabelInfoList[q].confidence);
|
||
}
|
||
else
|
||
XcHeBingList2.Add(DefectLabelInfoList[q]);
|
||
}
|
||
else if (Config.getDefectCode(modelName, DefectLabelInfoList[q].classId) == "jt")
|
||
{
|
||
int max = stpoint.y + 2000;
|
||
int min = stpoint.y - 2000 > 0 ? stpoint.y - 2000 : 0;
|
||
if (DefectLabelInfoList[q].y >= min && DefectLabelInfoList[q].y <= max)
|
||
{
|
||
HeBingList3.Add(DefectLabelInfoList[q]);
|
||
xPos3.Add((DefectLabelInfoList[q].i % colNum) * image_width + DefectLabelInfoList[q].x);
|
||
yPos3.Add((DefectLabelInfoList[q].i / colNum) * image_hight + DefectLabelInfoList[q].y);
|
||
ZXD3.Add(DefectLabelInfoList[q].confidence);
|
||
}
|
||
else
|
||
XcHeBingList3.Add(DefectLabelInfoList[q]);
|
||
}
|
||
else if (Config.getDefectCode(modelName, DefectLabelInfoList[q].classId) == "tcy")
|
||
{
|
||
int max = stpoint.y + 2000;
|
||
int min = stpoint.y - 2000 > 0 ? stpoint.y - 2000 : 0;
|
||
if (DefectLabelInfoList[q].y >= min && DefectLabelInfoList[q].y <= max)
|
||
{
|
||
HeBingList4.Add(DefectLabelInfoList[q]);
|
||
xPos4.Add((DefectLabelInfoList[q].i % colNum) * image_width + DefectLabelInfoList[q].x);
|
||
yPos4.Add((DefectLabelInfoList[q].i / colNum) * image_hight + DefectLabelInfoList[q].y);
|
||
ZXD4.Add(DefectLabelInfoList[q].confidence);
|
||
}
|
||
else
|
||
XcHeBingList4.Add(DefectLabelInfoList[q]);
|
||
}
|
||
else
|
||
outList.Add(DefectLabelInfoList[q]);
|
||
}
|
||
//递归下次合并数据
|
||
List<DefectLabelInfo> dg1 = new List<DefectLabelInfo>();
|
||
List<DefectLabelInfo> dg2 = new List<DefectLabelInfo>();
|
||
List<DefectLabelInfo> dg3 = new List<DefectLabelInfo>();
|
||
List<DefectLabelInfo> dg4 = new List<DefectLabelInfo>();
|
||
if (XcHeBingList.Count >0)
|
||
dg1 = HeBingDefect(modelName, Width, XcHeBingList);
|
||
if (XcHeBingList2.Count > 0)
|
||
dg2 = HeBingDefect(modelName, Width, XcHeBingList2);
|
||
if (XcHeBingList3.Count > 0)
|
||
dg3 = HeBingDefect(modelName, Width, XcHeBingList3);
|
||
if (XcHeBingList4.Count > 0)
|
||
dg4 = HeBingDefect(modelName, Width, XcHeBingList4);
|
||
|
||
//多个jietou合并
|
||
if (HeBingList.Count>0)
|
||
{
|
||
var stIt = HeBingList.Find(x => (x.i % colNum) * image_width + x.x == xPos.Min());
|
||
var edIt = HeBingList.Find(x => (x.i % colNum) * image_width + x.x == xPos.Max());
|
||
var stIty = HeBingList.Find(x => ((x.i / colNum) * image_hight + x.y) == yPos.Min());
|
||
var edIty = HeBingList.Find(x => ((x.i / colNum) * image_hight + x.y) == yPos.Max());
|
||
var eZXD = HeBingList.Find(x => x.confidence == ZXD.Max());
|
||
int newW = Math.Abs(((edIt.i % colNum) * image_width + edIt.x) - ((stIt.i % colNum) * image_width + stIt.x)) + edIt.w;
|
||
int newh = Math.Abs(((edIt.i / colNum) * image_hight + edIt.y) - ((stIt.i / colNum) * image_hight + stIt.y)) + edIt.h;
|
||
outList.Add(new DefectLabelInfo() {
|
||
x=stIt.x,
|
||
y= edIty.y,
|
||
w = newW, //多图叠加
|
||
h = newh,
|
||
classId = eZXD.classId,
|
||
confidence = eZXD.confidence,
|
||
contrast = eZXD.contrast,
|
||
cmH = Math.Round(edIt.h * 1.0 / Config.cm2px_y, 2),
|
||
cmW = Math.Round(newW * 1.0 / Config.cm2px_x, 2),
|
||
i = stIt.i,
|
||
j = stIt.j,
|
||
});
|
||
}
|
||
//多个hengdang合并
|
||
if (HeBingList2.Count > 0)
|
||
{
|
||
var stIt = HeBingList2.Find(x => (x.i % colNum) * image_width + x.x == xPos2.Min());
|
||
var edIt = HeBingList2.Find(x => (x.i % colNum) * image_width + x.x == xPos2.Max());
|
||
var stIty = HeBingList2.Find(x => ((x.i / colNum) * image_hight + x.y) == yPos2.Min());
|
||
var edIty = HeBingList2.Find(x => ((x.i / colNum) * image_hight + x.y) == yPos2.Max());
|
||
var eZXD = HeBingList2.Find(x => x.confidence == ZXD2.Max());
|
||
int newW = Math.Abs(((edIt.i % colNum) * image_width + edIt.x) - ((stIt.i % colNum) * image_width + stIt.x)) + edIt.w;
|
||
int newh = Math.Abs(((edIt.i / colNum) * image_hight + edIt.y) - ((stIt.i / colNum) * image_hight + stIt.y)) + edIt.h;
|
||
outList.Add(new DefectLabelInfo()
|
||
{
|
||
x = stIt.x,
|
||
y = edIty.y,
|
||
w = newW, //多图叠加
|
||
h = newh,
|
||
classId = eZXD.classId,
|
||
confidence = eZXD.confidence,
|
||
contrast = eZXD.contrast,
|
||
cmH = Math.Round(edIt.h * 1.0 / Config.cm2px_y, 2),
|
||
cmW = Math.Round(newW * 1.0 / Config.cm2px_x, 2),
|
||
i = stIt.i,
|
||
j = stIt.j,
|
||
});
|
||
}
|
||
//多个jt合并
|
||
if (HeBingList3.Count > 0)
|
||
{
|
||
var stIt = HeBingList3.Find(x => (x.i % colNum) * image_width + x.x == xPos3.Min());
|
||
var edIt = HeBingList3.Find(x => (x.i % colNum) * image_width + x.x == xPos3.Max());
|
||
var stIty = HeBingList3.Find(x => ((x.i / colNum) * image_hight + x.y) == yPos3.Min());
|
||
var edIty = HeBingList3.Find(x => ((x.i / colNum) * image_hight + x.y) == yPos3.Max());
|
||
var eZXD = HeBingList3.Find(x => x.confidence == ZXD3.Max());
|
||
int newW = Math.Abs(((edIt.i % colNum) * image_width + edIt.x) - ((stIt.i % colNum) * image_width + stIt.x)) + edIt.w;
|
||
int newh = Math.Abs(((edIt.i / colNum) * image_hight + edIt.y) - ((stIt.i / colNum) * image_hight + stIt.y)) + edIt.h;
|
||
outList.Add(new DefectLabelInfo()
|
||
{
|
||
x = stIt.x,
|
||
y = edIty.y,
|
||
w = newW, //多图叠加
|
||
h = newh,
|
||
classId = eZXD.classId,
|
||
confidence = eZXD.confidence,
|
||
contrast = eZXD.contrast,
|
||
cmH = Math.Round(edIt.h * 1.0 / Config.cm2px_y, 2),
|
||
cmW = Math.Round(newW * 1.0 / Config.cm2px_x, 2),
|
||
i = stIt.i,
|
||
j = stIt.j,
|
||
});
|
||
}
|
||
//多个tcy合并
|
||
if (HeBingList4.Count > 0)
|
||
{
|
||
var stIt = HeBingList4.Find(x => (x.i % colNum) * image_width + x.x == xPos4.Min());
|
||
var edIt = HeBingList4.Find(x => (x.i % colNum) * image_width + x.x == xPos4.Max());
|
||
var stIty = HeBingList4.Find(x => ((x.i / colNum) * image_hight + x.y) == yPos4.Min());
|
||
var edIty = HeBingList4.Find(x => ((x.i / colNum) * image_hight + x.y) == yPos4.Max());
|
||
var eZXD = HeBingList4.Find(x => x.confidence == ZXD4.Max());
|
||
int newW = Math.Abs(((edIt.i % colNum) * image_width + edIt.x) - ((stIt.i % colNum) * image_width + stIt.x)) + edIt.w;
|
||
int newh = Math.Abs(((edIt.i / colNum) * image_hight + edIt.y) - ((stIt.i / colNum) * image_hight + stIt.y)) + edIt.h;
|
||
outList.Add(new DefectLabelInfo()
|
||
{
|
||
x = stIt.x,
|
||
y = edIty.y,
|
||
w = newW, //多图叠加
|
||
h = newh,
|
||
classId = eZXD.classId,
|
||
confidence = eZXD.confidence,
|
||
contrast = eZXD.contrast,
|
||
cmH = Math.Round(edIt.h * 1.0 / Config.cm2px_y, 2),
|
||
cmW = Math.Round(newW * 1.0 / Config.cm2px_x, 2),
|
||
i = stIt.i,
|
||
j = stIt.j,
|
||
});
|
||
}
|
||
outList = outList.Concat(dg1).ToList<DefectLabelInfo>();//保留重复项
|
||
outList = outList.Concat(dg2).ToList<DefectLabelInfo>();//保留重复项
|
||
outList = outList.Concat(dg3).ToList<DefectLabelInfo>();//保留重复项
|
||
outList = outList.Concat(dg4).ToList<DefectLabelInfo>();//保留重复项
|
||
return outList;
|
||
}
|
||
//打标
|
||
private void run3()
|
||
{
|
||
QualifiedLimit qualifiedLimit;
|
||
while (IsInit)
|
||
{
|
||
if (taskMakeTagList.Count < 1)
|
||
{
|
||
Thread.Sleep(20);
|
||
continue;
|
||
}
|
||
|
||
//
|
||
var task = pop3();
|
||
int liStep = 0;
|
||
try
|
||
{
|
||
Stopwatch stopwatch = Stopwatch.StartNew();
|
||
stopwatch.Restart();
|
||
|
||
int colNum = task.bmp.Width / image_width;
|
||
int rowNum = task.bmp.Height / image_hight;
|
||
int count = 0;
|
||
liStep = 3;
|
||
|
||
if (!Config.OpenHalconDefect)
|
||
{
|
||
int cut_count = task.bmps_cut.Count();//上面bmps_cut已销毁
|
||
//车用革去除接头处横档误判
|
||
bool haveJieTou = false;
|
||
List<DefectLabelInfo> DefectLabelInfoList = new List<DefectLabelInfo>();
|
||
|
||
for (int i = 0; i < cut_count; i++)
|
||
{
|
||
liStep = i * 100;
|
||
//task.resultInfo += $"第 {i}/{cut_count} 张小图(大图索引{task.photoIndex}): 缺陷数 = {task.output_num[i]}\n";
|
||
//task.resultInfo +=$"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";
|
||
#region 检测信息汇总
|
||
for (int j = 0; j < task.output_num[i]; j++)//缺陷数
|
||
{
|
||
liStep += j;//0
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"-------");
|
||
int index = count * detect_elem_size;
|
||
|
||
// 打印输出信息（示例代码，根据实际情况进行修改和格式化）
|
||
// 获取输出信息
|
||
int x = (int)task.output[index];
|
||
int y = (int)task.output[index + 1];
|
||
int w = (int)task.output[index + 2];
|
||
int h = (int)task.output[index + 3];
|
||
int classId = (int)task.output[index + 4];
|
||
double confidence = Math.Round(task.output[index + 5], 2); //置信度
|
||
double contrast = Math.Round(task.output[index + 6], 3);//对比度
|
||
//y = image_hight-y-h;//转到右下角为原点
|
||
count++;
|
||
|
||
//var cmW = Math.Round(w * task.widthRatio / Config.cm2px_x, 2);
|
||
//var cmH = Math.Round(h * task.widthRatio / Config.cm2px_y, 2);
|
||
var cmW = Math.Round(w * 1.0 / Config.cm2px_x, 2);
|
||
var cmH = Math.Round(h * 1.0 / Config.cm2px_y, 2);
|
||
// 打印输出信息
|
||
//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";
|
||
|
||
DefectLabelInfoList.Add(new DefectLabelInfo()
|
||
{
|
||
x = (int)task.output[index],
|
||
y = (int)task.output[index + 1],
|
||
w = (int)task.output[index + 2],
|
||
h = (int)task.output[index + 3],
|
||
classId = (int)task.output[index + 4],
|
||
confidence = Math.Round(task.output[index + 5], 2), //置信度
|
||
contrast = ContrastToPercent(Math.Round(task.output[index + 6], 3)),//对比度
|
||
//cmW = Math.Round(w * task.widthRatio / Config.cm2px_x, 2),
|
||
//cmH = Math.Round(h * task.widthRatio / Config.cm2px_y, 2),
|
||
cmW = Math.Round(w * 1.0 / Config.cm2px_x, 2),
|
||
cmH = Math.Round(h * 1.0 / Config.cm2px_y, 2),
|
||
i = i,
|
||
j = j,
|
||
});
|
||
}
|
||
|
||
#endregion
|
||
#if false
|
||
for (int j = 0; j < task.output_num[i]; j++)//缺陷数
|
||
{
|
||
liStep += j;//0
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"-------");
|
||
int index = count * detect_elem_size;
|
||
|
||
// 打印输出信息（示例代码，根据实际情况进行修改和格式化）
|
||
// 获取输出信息
|
||
int x = (int)task.output[index];
|
||
int y = (int)task.output[index + 1];
|
||
int w = (int)task.output[index + 2];
|
||
int h = (int)task.output[index + 3];
|
||
int classId = (int)task.output[index + 4];
|
||
double confidence = Math.Round(task.output[index + 5],2); //置信度
|
||
double contrast = Math.Round(task.output[index + 6], 3);//对比度
|
||
//y = image_hight-y-h;//转到右下角为原点
|
||
count++;
|
||
|
||
var cmW = Math.Round(w * task.widthRatio / Config.cm2px_x, 2);
|
||
var cmH = Math.Round(h * task.widthRatio / Config.cm2px_y, 2);
|
||
// 打印输出信息
|
||
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";
|
||
//是否满足此产品标准
|
||
if (task.qualifiedLimitList != null && task.qualifiedLimitList.Count > 0)
|
||
{
|
||
qualifiedLimit = task.qualifiedLimitList.FirstOrDefault(m => m.Code == Config.getDefectCode(classId));
|
||
if (qualifiedLimit != null)
|
||
{
|
||
//if ((qualifiedLimit.ZXD > 0 && qualifiedLimit.ZXD > confidence)
|
||
// || (qualifiedLimit.ContrastTop + qualifiedLimit.ContrastLower > 0 && x> qualifiedLimit.ContrastLower && x < qualifiedLimit.ContrastTop)
|
||
// || (qualifiedLimit.Area > 0 && qualifiedLimit.Area > cmW * cmH))
|
||
if (confidence <= qualifiedLimit.ZXD)//confidence > qualifiedLimit.ZXD 是瑕疵 才继续判断下面的两个条件
|
||
{
|
||
task.resultInfo += $" 置信度不满足此产品瑕疵标准，跳过！ \n";
|
||
continue;
|
||
}
|
||
|
||
//下限<对比度<上限：不是瑕疵
|
||
var ContrastTotal = qualifiedLimit.ContrastTop + qualifiedLimit.ContrastLower;
|
||
if (qualifiedLimit.IsOR)
|
||
{
|
||
if (!(
|
||
(qualifiedLimit.Area <= 0 || cmW * cmH >= qualifiedLimit.Area) ||
|
||
(ContrastTotal <= 0 || (contrast < qualifiedLimit.ContrastLower || 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 || cmW * cmH >= qualifiedLimit.Area) &&
|
||
(ContrastTotal <=0 || (contrast < qualifiedLimit.ContrastLower || 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;
|
||
}
|
||
}
|
||
|
||
|
||
//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((i % colNum) * image_width + x, (i / colNum) * image_hight + y);
|
||
var point2 = new OpenCvSharp.Point(point1.X + w, point1.Y + 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；");
|
||
var cmX = Math.Round(point1.X * task.widthRatio / Config.cm2px_x, 2);
|
||
var cmY = Math.Round((task.bmp.Height-point1.Y-h) * task.widthRatio / Config.cm2px_y, 2);//外面计Y从右下角为原点
|
||
liStep++;//3
|
||
task.resultInfo += $" 转换到大图坐标(cm)[widthRatio={task.widthRatio}]：x={cmX},y={cmY}; w={cmW},h={cmH}\n";
|
||
task.excelTable.Rows.Add($"{task.photoIndex}", cmX, cmY, cmW, cmH, j + 1, classId, confidence, 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 = (w > 236 ? 20 : 256 - w) / 2;
|
||
decY = (h > 236 ? 20 : 256 - h) / 2;
|
||
left = point1.X - decX;
|
||
top = point1.Y - decY;
|
||
if (left < 0) left = 0;
|
||
if (top < 0) top = 0;
|
||
|
||
int width = w + decX * 2;
|
||
int height = 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={h}; top={top},mat.Height={ task.bmp.Height}\n================\n";
|
||
WarningEvent?.Invoke(DateTime.Now,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
|
||
//WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"缺陷小图数量:{task.lstDefectBmp.Count}");
|
||
}
|
||
#endif
|
||
}
|
||
|
||
#region 合并接头横档
|
||
liStep = 3000;
|
||
if (DefectLabelInfoList.Count > 0)
|
||
DefectLabelInfoList = HeBingDefect(task.modelName, task.bmp.Width, DefectLabelInfoList);
|
||
liStep++;
|
||
#endregion
|
||
|
||
//结果过滤
|
||
#region 结果过滤
|
||
//降序排序，先得到是否有接头检出
|
||
List<DefectLabelInfo> DefectLabelInfoListByDefect = new List<DefectLabelInfo>();
|
||
List<DefectLabelInfo> DefectLabelInfoListByClassID = DefectLabelInfoList.OrderByDescending(t => t.classId).ToList();//降序
|
||
for (int q = 0; q < DefectLabelInfoListByClassID.Count; q++)
|
||
{
|
||
//是否满足此产品标准
|
||
if (task.qualifiedLimitList != null && task.qualifiedLimitList.Count > 0)
|
||
{
|
||
qualifiedLimit = task.qualifiedLimitList.FirstOrDefault(m => m.Code == Config.getDefectCode(task.modelName, DefectLabelInfoListByClassID[q].classId));
|
||
if (qualifiedLimit != null)
|
||
{
|
||
// 打印输出信息
|
||
task.resultInfo += $"----{DefectLabelInfoListByClassID[q].i}----col:{DefectLabelInfoListByClassID[q].i % colNum}/{colNum} row:{DefectLabelInfoListByClassID[q].i / colNum}/{colNum}-----------\n目标:{DefectLabelInfoListByClassID[q].j + 1} 类别ID:{DefectLabelInfoListByClassID[q].classId} 置信度:{DefectLabelInfoListByClassID[q].confidence} 对比度:{DefectLabelInfoListByClassID[q].contrast}\n";
|
||
|
||
//if ((qualifiedLimit.ZXD > 0 && qualifiedLimit.ZXD > confidence)
|
||
// || (qualifiedLimit.ContrastTop + qualifiedLimit.ContrastLower > 0 && x> qualifiedLimit.ContrastLower && x < qualifiedLimit.ContrastTop)
|
||
// || (qualifiedLimit.Area > 0 && qualifiedLimit.Area > cmW * cmH))
|
||
if (DefectLabelInfoListByClassID[q].confidence <= qualifiedLimit.ZXD)//confidence > qualifiedLimit.ZXD 是瑕疵 才继续判断下面的两个条件
|
||
{
|
||
task.resultInfo += $" 置信度不满足此产品瑕疵标准，跳过！ \n";
|
||
continue;
|
||
}
|
||
|
||
//下限<对比度<上限：不是瑕疵
|
||
var ContrastTotal = qualifiedLimit.ContrastTop + qualifiedLimit.ContrastLower;
|
||
if (qualifiedLimit.IsOR)
|
||
{
|
||
//if (!(
|
||
// (ContrastTotal <= 0 || (PercentToContrast(DefectLabelInfoListByClassID[q].contrast) < qualifiedLimit.ContrastLower || PercentToContrast(DefectLabelInfoListByClassID[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 (!(ContrastTotal <= 0 || (PercentToContrast(DefectLabelInfoListByClassID[q].contrast) < qualifiedLimit.ContrastLower || PercentToContrast(DefectLabelInfoListByClassID[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 (qualifiedLimit.Area > 0 && DefectLabelInfoListByClassID[q].cmW * DefectLabelInfoListByClassID[q].cmH < qualifiedLimit.Area)
|
||
{
|
||
task.resultInfo += $" 不满足此产品瑕疵标准面积，跳过！ \n";
|
||
continue;
|
||
}
|
||
if (Config.getDefectCode(task.modelName, DefectLabelInfoListByClassID[q].classId) == "jietou")
|
||
haveJieTou = true;
|
||
if (haveJieTou && (Config.getDefectCode(task.modelName, DefectLabelInfoListByClassID[q].classId) == "hengdang"))
|
||
{
|
||
task.resultInfo += $" 判断为接头处横档，跳过！ \n";
|
||
continue;
|
||
}
|
||
if (Config.getDefectCode(task.modelName, DefectLabelInfoListByClassID[q].classId) == "na")
|
||
{
|
||
task.resultInfo += $" 判断为正向标签na，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}])");
|
||
}
|
||
}
|
||
//检测缺陷
|
||
DefectLabelInfoListByDefect.Add(DefectLabelInfoListByClassID[q]);
|
||
liStep++;//1
|
||
}
|
||
|
||
//y方向位置排序
|
||
List<DefectLabelInfo> DefectLabelInfoListByYdis = DefectLabelInfoListByDefect.OrderByDescending(t => (t.i / colNum) * image_hight + t.y).ToList();//降序
|
||
for (int q = 0; q < DefectLabelInfoListByYdis.Count; q++)
|
||
{
|
||
//打标
|
||
int penLine = 50;
|
||
int xx = (DefectLabelInfoListByYdis[q].i % colNum) * image_width + DefectLabelInfoListByYdis[q].x - 2 * penLine;
|
||
int yy = (DefectLabelInfoListByYdis[q].i / colNum) * image_hight + DefectLabelInfoListByYdis[q].y - 2 * penLine;
|
||
var point1 = new OpenCvSharp.Point(xx >0? xx:0, yy >0? yy:0);
|
||
var point2 = new OpenCvSharp.Point(point1.X + DefectLabelInfoListByYdis[q].w + 4 * penLine, point1.Y + DefectLabelInfoListByYdis[q].h + 4 * penLine);
|
||
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), penLine);//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 / Config.cm2px_x, 2);
|
||
var cmX = Math.Round((double)px / Config.cm2px_x, 2);
|
||
//var cmY = Math.Round((task.bmp.Height - point1.Y - DefectLabelInfoListByClassID[q].h) * task.widthRatio / Config.cm2px_y, 2);//外面计Y从右下角为原点
|
||
var cmY = Math.Round((task.bmp.Height - point1.Y - DefectLabelInfoListByYdis[q].h) * 1.0 / Config.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, DefectLabelInfoListByYdis[q].cmW, DefectLabelInfoListByYdis[q].cmH, DefectLabelInfoListByYdis[q].j + 1, DefectLabelInfoListByYdis[q].classId, DefectLabelInfoListByYdis[q].confidence, DefectLabelInfoListByYdis[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 = ((DefectLabelInfoListByYdis[q].w + 4 * penLine) > 236 ? 20 : (256 - DefectLabelInfoListByYdis[q].w - 4 * penLine)) / 2;
|
||
decY = ((DefectLabelInfoListByYdis[q].h + 4 * penLine) > 236 ? 20 : (256 - DefectLabelInfoListByYdis[q].h - 4 * penLine)) / 2;
|
||
//left = point1.X - decX;
|
||
//top = point1.Y - decY;
|
||
left = point1.X - 20;
|
||
top = point1.Y - 20;
|
||
if (left < 0) left = 0;
|
||
if (top < 0) top = 0;
|
||
|
||
//int width = DefectLabelInfoListByClassID[q].w + decX * 2;
|
||
//int height = DefectLabelInfoListByClassID[q].h + decY * 2;
|
||
int width = point2.X - point1.X + 20 * 2;
|
||
int height = point2.Y - point1.Y + 20 * 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(DateTime.Now, WarningEnum.Normal, task.resultInfo);
|
||
}
|
||
|
||
//保存
|
||
//string filename = $"{Config.appBasePath}\\temp\\{task.tag}\\{task.tag}_X{mmX.ToString()}_Y{mmY.ToString()}_W{mmW.ToString()}_H{mmH.ToString()}_目标{j + 1}_类别{classId}_置信度{confidence}.bmp";
|
||
//OpenCvSharp.Extensions.BitmapConverter.ToBitmap(new Mat(task.bmp, roi)).Save(filename, ImageFormat.Jpeg);
|
||
|
||
task.lstDefectBmp.Add(new Mat(task.bmpTag, roi).Clone());
|
||
liStep++;//7
|
||
}
|
||
#endregion
|
||
}
|
||
else
|
||
{
|
||
stopwatch.Restart();
|
||
//打标
|
||
var defectinfo = task.hv_defectinfo.ToOArr();
|
||
List<DefectLabelInfo> DefectLabelInfoList = new List<DefectLabelInfo>();
|
||
for (int i = 0; i < task.object_num; i++)
|
||
{
|
||
liStep = 23 + i;
|
||
#region 检测信息汇总
|
||
int index = i * 6;
|
||
|
||
// 打印输出信息（示例代码，根据实际情况进行修改和格式化）
|
||
// 获取输出信息
|
||
int x = (int)defectinfo[index].ToInt();
|
||
int y = (int)defectinfo[index + 1].ToInt();
|
||
int w = (int)defectinfo[index + 2].ToInt();
|
||
int h = (int)defectinfo[index + 3].ToInt();
|
||
int classId = -99;//(int)defectinfo[index + 4].ToInt();
|
||
double confidence = Math.Round(defectinfo[index + 5].ToDouble(), 2); //置信度
|
||
|
||
int px = (x - task.xw) > 0 ? (x - task.xw) : 0; ;
|
||
var cmX = Math.Round(px * 1.0 / Config.cm2px_x, 2);
|
||
var cmY = Math.Round((task.bmp.Height - y - h) * 1.0 / Config.cm2px_y, 2);//外面计Y从右下角为原点
|
||
|
||
DefectLabelInfoList.Add(new DefectLabelInfo()
|
||
{
|
||
x = x,
|
||
y = y,
|
||
w = w,
|
||
h = h,
|
||
classId = classId,
|
||
confidence = confidence, //置信度
|
||
cmW = Math.Round(w * 1.0 / Config.cm2px_x, 2),
|
||
cmH = Math.Round(h * 1.0 / Config.cm2px_y, 2),
|
||
i = i,
|
||
j = i,
|
||
});
|
||
//y = image_hight-y-h;//转到右下角为原点
|
||
task.excelTable.Rows.Add($"{task.photoIndex}", cmX, cmY,
|
||
Math.Round(w * 1.0 / Config.cm2px_x, 2), Math.Round(h * 1.0 / Config.cm2px_y, 2),
|
||
i, classId, confidence, 0);
|
||
|
||
int penLine = 20;
|
||
//打标
|
||
int xx = x - 2 * penLine;
|
||
int yy = y - 2 * penLine;
|
||
var point1 = new OpenCvSharp.Point(xx >0? xx:0, yy >0?yy:0);
|
||
var point2 = new OpenCvSharp.Point(point1.X + w + 4* penLine , point1.Y + h + 4 * penLine);
|
||
Cv2.Rectangle(task.bmpTag, point1, point2, new Scalar(0.0, 0.0, 255.0), penLine);//画打标点
|
||
|
||
//切小图
|
||
int left, top, decX, decY;
|
||
|
||
//int tdecX = (w / 2) / 2;
|
||
//int tdecY = w / h > 4 ? w / 8 : (h / 1) / 2;
|
||
|
||
//decX = (w > 236 ? tdecX : 256 - w) / 2;
|
||
//decY = (h > 236 ? tdecY : 256 - h) / 2;
|
||
|
||
//left = point1.X - decX;
|
||
//top = point1.Y - decY;
|
||
left = point1.X - 20;
|
||
top = point1.Y - 20;
|
||
if (left < 0) left = 0;
|
||
if (top < 0) top = 0;
|
||
|
||
//int width = w + decX * 2;
|
||
//int height = h + decY * 2;
|
||
int width = point2.X - point1.X + 20 * 2;
|
||
int height = point2.Y - point1.Y + 20 * 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;
|
||
|
||
Rect roi = new Rect(left, top, width, height);
|
||
task.lstDefectBmp.Add(new Mat(task.bmpTag, roi).Clone());
|
||
#endregion
|
||
}
|
||
}
|
||
liStep = 99;
|
||
stopwatch.Stop();
|
||
task.stopwatch[3] = stopwatch.ElapsedMilliseconds;
|
||
|
||
task.isSucceed = true;
|
||
callback(task);
|
||
}
|
||
catch (Exception ex)
|
||
{
|
||
WarningEvent?.Invoke(DateTime.Now,WarningEnum.Low, $"DefectLib task3 err({liStep}):" + ex.Message);
|
||
task.isSucceed = false;
|
||
task.resultInfo = ex.Message;
|
||
callback(task);
|
||
}
|
||
Thread.Sleep(5);
|
||
}
|
||
}
|
||
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);
|
||
});
|
||
/// <summary>
|
||
/// 切割(先左右，后上下)
|
||
/// </summary>
|
||
/// <param name="mat"></param>
|
||
/// <param name="width"></param>
|
||
/// <param name="height"></param>
|
||
/// <returns></returns>
|
||
private Mat[] OpenCVToCutsMat(Mat mat, int width, int height)
|
||
{
|
||
Mat[] array = new Mat[mat.Width / width * mat.Height / height];
|
||
int num = 0;
|
||
for (int i = 0; i < mat.Height / height; i++)
|
||
{
|
||
for (int j = 0; j < mat.Width / width; j++)
|
||
{
|
||
int x = j * width;
|
||
int y = i * height;
|
||
System.Drawing.Rectangle rectangle = new System.Drawing.Rectangle(x, y, width, height);
|
||
Rect roi = new Rect(rectangle.X, rectangle.Y, rectangle.Width, rectangle.Height);
|
||
array[num] = new Mat(mat, roi).Clone();
|
||
num++;
|
||
}
|
||
}
|
||
|
||
return array;
|
||
}
|
||
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>
|
||
/// 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];
|
||
|
||
public int xw;
|
||
|
||
//传统算法使用
|
||
public HObject ho_Img = new HObject();
|
||
public HObject ho_imgG = new HObject();
|
||
public HObject ho_ImageResult3 = new HObject();
|
||
public HObject ho_ImageSub = new HObject();
|
||
public HTuple hv_RES, hv_gauss_filter1, hv_gauss_filter2, hv_Energy_value, hv_Region_S_value, hv_AreaThr, hv_class, hv_defectinfo;
|
||
public int object_num = 0;
|
||
|
||
//图片位置
|
||
public double CurrDis;
|
||
}
|
||
|
||
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;
|
||
}
|
||
}
|
||
|
||
public void Dispose()
|
||
{
|
||
stop();
|
||
}
|
||
}
|
||
}
|