542 lines
22 KiB
C#
542 lines
22 KiB
C#
using OpenCvSharp;
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using OpenCvSharp.Flann;
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using Sunny.UI.Win32;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Security.Cryptography;
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using System.Text;
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using System.Threading.Tasks;
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using static System.Net.Mime.MediaTypeNames;
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using Point = OpenCvSharp.Point;
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using Size = OpenCvSharp.Size;
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using OpenCvSharp;
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using OpenCvSharp.Features2D;
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using OpenCvSharp.Flann;
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using System.Drawing;
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namespace HisenceYoloDetection
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{
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public static class CheckDiffSciHelper
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{
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/// <summary>
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///
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/// </summary>
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/// <param name="path1">标准图像</param>
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/// <param name="path2">要对比的图像</param>
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/// <param name="IfWhiteWord"> 白板黑字为true </param>
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/// <param name="saveDir">存储路径</param>
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public static bool CheckDiffSci(string path1, Mat MatDet, Rect sqlrect, Rect detrect, bool IfWhiteWord, string saveDir)
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{
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// 读取和处理第一张图片。。
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Mat img1 = Cv2.ImRead(path1, ImreadModes.Color);
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if (img1.Empty())
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{
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Console.WriteLine($"Error loading image {path1}");
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return false;
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}
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// Cv2.Resize(img1, img1, new Size(550, 270));
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Mat gimg1 = new Mat();
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Cv2.CvtColor(img1, gimg1, ColorConversionCodes.BGR2GRAY);
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Mat thr1 = new Mat();
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if (IfWhiteWord)
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{
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Cv2.Threshold(gimg1, thr1, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
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}
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else
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{
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Cv2.Threshold(gimg1, thr1, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
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}
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// 读取和处理第二张图片
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Mat img2 = MatDet.Clone();
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if (img2.Empty())
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{
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// Console.WriteLine($"Error loading image {path2}");
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return false;
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}
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// Cv2.Resize(img2, img2, new Size(550, 270));
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Rect bottomleftRect= new Rect(0,img1.Height-30,30,30);
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Scalar avgColor1 = Cv2.Mean(new Mat(img1,bottomleftRect));
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Mat gimg2 = new Mat();
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Cv2.CvtColor(img2, gimg2, ColorConversionCodes.BGR2GRAY);
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Mat thr2 = new Mat();
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//Cv2.Threshold(gimg2, thr2, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
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if (IfWhiteWord)
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{
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Cv2.Threshold(gimg2, thr2, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
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}
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else
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{
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Cv2.Threshold(gimg2, thr2, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
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}
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//Rect area2 = new Rect(148,30,229,222);
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sqlrect.Width += 20;
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sqlrect.Height+= 100;
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detrect.Width += 20;
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detrect.Height+=100;
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Mat matCutblack1 = new Mat(thr1, sqlrect);
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if (IfWhiteWord)
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{
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matCutblack1.SetTo(Scalar.Black);
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}
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else
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{
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matCutblack1.SetTo(Scalar.Black);
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}
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Mat matCutblack2 = new Mat(thr2, detrect);
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if (IfWhiteWord)
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{
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matCutblack2.SetTo(Scalar.Black);
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}
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else
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{
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matCutblack2.SetTo(Scalar.Black);
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}
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Cv2.Resize(thr1, thr1, new Size(550, 270));
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Cv2.Resize(thr2, thr2, new Size(550, 270));
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DateTime dt = DateTime.Now;
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string filename = dt.Year.ToString() + dt.Month.ToString() + dt.Day.ToString() + dt.Hour.ToString() + dt.Minute.ToString() + dt.Millisecond.ToString();
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string savePath4 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "_thr1.png");
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// 保存结果
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Cv2.ImWrite(savePath4, thr1);
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string savePath3 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "_thr2.png");
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// 保存结果
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Cv2.ImWrite(savePath3, thr2);
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// 创建卷积核
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Mat filter1 = new Mat(15, 15, MatType.CV_32F, new Scalar(0));
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filter1.Row(7).SetTo(new Scalar(0.025));
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filter1.Col(7).SetTo(new Scalar(0.025));
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// 应用卷积
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Mat final_result1 = new Mat();
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Cv2.Filter2D(thr1, final_result1, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Cv2.Filter2D(final_result1, final_result1, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Cv2.Filter2D(final_result1, final_result1, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Mat final_result2 = new Mat();
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Cv2.Filter2D(thr2, final_result2, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Cv2.Filter2D(final_result2, final_result2, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Cv2.Filter2D(final_result2, final_result2, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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//裁剪才行
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//string savePath2 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + "_final_result1.png");
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////保存结果
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//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
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//Cv2.ImWrite(savePath2, final_result1);
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//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + "_final_result2.png");
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////保存结果
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//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
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//Cv2.ImWrite(savePath, final_result2);
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// 计算图像差异
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Mat devIMG = new Mat();
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Mat devIMG_ = new Mat();
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Cv2.Subtract(final_result1, final_result2, devIMG);
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Cv2.Subtract(final_result2, final_result1, devIMG_);
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//string savePathd = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "devIMG.png");
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//// 保存结果
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//Cv2.ImWrite(savePathd, devIMG);
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//string savePathd1 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "devIMG_.png");
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//// 保存结果
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//Cv2.ImWrite(savePathd1, devIMG_);
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// 对差异图像应用阈值
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Cv2.Threshold(devIMG, devIMG, 20, 255, ThresholdTypes.Binary);
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Cv2.Threshold(devIMG_, devIMG_, 20, 255, ThresholdTypes.Binary);
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// 结合差异
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Mat sumIMG = new Mat();
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Cv2.Add(devIMG, devIMG_, sumIMG);
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// 应用形态学操作
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Mat kernelCL = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3));
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Mat blackhatImg = new Mat();
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Cv2.Dilate(sumIMG, blackhatImg, kernelCL);
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// 处理轮廓和保存结果
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Point[][] contours = new Point[10000][];
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Cv2.FindContours(blackhatImg, out contours, out _, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);
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bool isMatch = true;
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foreach (var contour in contours)
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{
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if (Cv2.ContourArea(contour) <= 500)
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{
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Cv2.DrawContours(blackhatImg, new Point[][] { contour }, -1, Scalar.Black, thickness: Cv2.FILLED);
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// 框选轮廓
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string savePath2 = Path.Combine("D:\\Hisence\\Test\\2\\ng", Path.GetFileNameWithoutExtension(path1) + filename + "_Rect.png");
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// 保存结果
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//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
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Cv2.ImWrite(savePath2, img2);
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string savePath = Path.Combine("D:\\Hisence\\Test\\2\\ng", Path.GetFileNameWithoutExtension(path1) + filename + "_diff.png");
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// 保存结果
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//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
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Cv2.ImWrite(savePath, blackhatImg);
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}
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else
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{
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Rect boundingRect = Cv2.BoundingRect(contour);
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Cv2.Rectangle(img2, boundingRect, Scalar.Red, thickness: 2);
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isMatch = false;
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string savePath2 = Path.Combine("D:\\Hisence\\Test\\2\\ok", Path.GetFileNameWithoutExtension(path1) + filename + "_Rect.png");
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// 保存结果
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//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
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Cv2.ImWrite(savePath2, img2);
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string savePath = Path.Combine("D:\\Hisence\\Test\\2\\ok", Path.GetFileNameWithoutExtension(path1) + filename + "_diff.png");
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// 保存结果
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//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
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Cv2.ImWrite(savePath, blackhatImg);
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}
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}
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return isMatch;
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}
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public static Rect strChangeRect(string strrect)
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{
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if (!string.IsNullOrEmpty(strrect))
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{
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string[] rectstr = strrect.Split(",");
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int areaX = int.Parse(rectstr[0]);
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int areaY = int.Parse(rectstr[1]);
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int areaWidth = int.Parse(rectstr[2]);
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int areaHeight = int.Parse(rectstr[3]);
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Rect rect = new Rect(areaX, areaY, areaWidth, areaHeight);
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return rect;
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}
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else
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{
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return new Rect(0, 0, 0, 0);
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}
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}
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public static string rectChangeStr(Rect area)
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{
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string[] rectsql = new string[4];
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rectsql[0] = Convert.ToString(area.X);
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rectsql[1] = Convert.ToString(area.Y);
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rectsql[2] = Convert.ToString(area.Width);
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rectsql[3] = Convert.ToString(area.Height);
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string strrect = rectsql.Join(",");
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return strrect;
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}
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public static class CheckDiffSciHelper1
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{
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/// <summary>
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///
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/// </summary>
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/// <param name="path1">标准图像</param>
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/// <param name="path2">要对比的图像</param>
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/// <param name="IfWhiteWord"> 白板黑字为true </param>
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/// <param name="saveDir">存储路径</param>
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public static bool CheckDiffSci(string path1, Mat MatDet, Rect sqlrect, Rect detrect, bool IfWhiteWord, string saveDir)
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{
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// 读取和处理第一张图片
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Mat img1 = Cv2.ImRead(path1, ImreadModes.Color);
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if (img1.Empty())
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{
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Console.WriteLine($"Error loading image {path1}");
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return false;
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}
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// Cv2.Resize(img1, img1, new Size(550, 270));
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img1 = RemoveBorders(img1);
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Mat gimg1 = new Mat();
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Cv2.CvtColor(img1, gimg1, ColorConversionCodes.BGR2GRAY);
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Mat thr1 = new Mat();
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if (IfWhiteWord)
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{
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Cv2.Threshold(gimg1, thr1, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
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}
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else
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{
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Cv2.Threshold(gimg1, thr1, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
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}
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// 读取和处理第二张图片
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Mat img2 = MatDet.Clone();
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if (img2.Empty())
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{
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// Console.WriteLine($"Error loading image {path2}");
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return false;
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}
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// Cv2.Resize(img2, img2, new Size(550, 270));
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img2 = RemoveBorders(img2);
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Mat gimg2 = new Mat();
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Cv2.CvtColor(img2, gimg2, ColorConversionCodes.BGR2GRAY);
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Mat thr2 = new Mat();
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//Cv2.Threshold(gimg2, thr2, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
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if (IfWhiteWord)
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{
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Cv2.Threshold(gimg2, thr2, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
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}
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else
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{
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Cv2.Threshold(gimg2, thr2, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
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}
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//Rect area2 = new Rect(148,30,229,222);
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sqlrect.Width += 20;
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sqlrect.Height += 20;
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detrect.Width += 20;
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detrect.Height += 20;
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Mat matCutblack1 = new Mat(thr1, sqlrect);
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if (IfWhiteWord)
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{
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matCutblack1.SetTo(Scalar.Black);
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}
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else
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{
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matCutblack1.SetTo(Scalar.Black);
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}
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Mat matCutblack2 = new Mat(thr2, detrect);
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if (IfWhiteWord)
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{
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matCutblack2.SetTo(Scalar.Black);
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}
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else
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{
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matCutblack2.SetTo(Scalar.Black);
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}
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Cv2.Resize(thr1, thr1, new Size(845, 498));
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Cv2.Resize(thr2, thr2, new Size(845, 498));
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DateTime dt = DateTime.Now;
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string filename = dt.Year.ToString() + dt.Month.ToString() + dt.Day.ToString() + dt.Hour.ToString() + dt.Minute.ToString() + dt.Millisecond.ToString();
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string savePath4 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "_thr1.png");
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// 保存结果
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Cv2.ImWrite(savePath4, thr1);
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string savePath3 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "_thr2.png");
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// 保存结果
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Cv2.ImWrite(savePath3, thr2);
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// 创建卷积核
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Mat filter1 = new Mat(15, 15, MatType.CV_32F, new Scalar(0));
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filter1.Row(7).SetTo(new Scalar(0.025));
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filter1.Col(7).SetTo(new Scalar(0.025));
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// 应用卷积
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Mat final_result1 = new Mat();
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Cv2.Filter2D(thr1, final_result1, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Cv2.Filter2D(final_result1, final_result1, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Cv2.Filter2D(final_result1, final_result1, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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//Cv2.Filter2D(final_result1, final_result1, -1, filter2, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Mat final_result2 = new Mat();
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Cv2.Filter2D(thr2, final_result2, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Cv2.Filter2D(final_result2, final_result2, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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Cv2.Filter2D(final_result2, final_result2, -1, filter1, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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//Cv2.Filter2D(final_result2, final_result2, -1, filter2, anchor: new Point(-1, -1), 0, BorderTypes.Reflect);
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//裁剪才行
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//string savePath2 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + "_final_result1.png");
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//// 保存结果
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////string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
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//Cv2.ImWrite(savePath2, final_result1);
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//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + "_final_result2.png");
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//// 保存结果
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////string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
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//Cv2.ImWrite(savePath, final_result2);
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// 计算图像差异
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Mat devIMG = new Mat();
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Mat devIMG_ = new Mat();
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Cv2.Subtract(final_result1, final_result2, devIMG);
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Cv2.Subtract(final_result2, final_result1, devIMG_);
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string savePathd = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "devIMG.png");
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// 保存结果
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Cv2.ImWrite(savePathd, devIMG);
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string savePathd1 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "devIMG_.png");
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// 保存结果
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Cv2.ImWrite(savePathd1, devIMG_);
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// 对差异图像应用阈值
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Cv2.Threshold(devIMG, devIMG, 8, 255, ThresholdTypes.Binary);
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Cv2.Threshold(devIMG_, devIMG_, 8, 255, ThresholdTypes.Binary);
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// 结合差异
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Mat sumIMG = new Mat();
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Cv2.Add(devIMG, devIMG_, sumIMG);
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string savePaths = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "sumIMG.png");
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// 保存结果
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Cv2.ImWrite(savePaths, sumIMG);
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// 应用形态学操作
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Mat kernelCL = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3));
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Mat blackhatImg = new Mat();
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Cv2.Dilate(sumIMG, blackhatImg, kernelCL);
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// 处理轮廓和保存结果
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Point[][] contours = new Point[10000][];
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Cv2.FindContours(blackhatImg, out contours, out _, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);
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bool isMatch = true;
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foreach (var contour in contours)
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{
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if (Cv2.ContourArea(contour) <= 500)
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{
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Cv2.DrawContours(blackhatImg, new Point[][] { contour }, -1, Scalar.Black, thickness: Cv2.FILLED);
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// 框选轮廓
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}
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else
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{
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Rect boundingRect = Cv2.BoundingRect(contour);
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Cv2.Rectangle(img2, boundingRect, Scalar.Red, thickness: 2);
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isMatch = false;
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}
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}
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string savePath2 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "_Rect.png");
|
|
// 保存结果
|
|
//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
|
|
Cv2.ImWrite(savePath2, img2);
|
|
string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "_diff.png");
|
|
// 保存结果
|
|
//string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
|
|
Cv2.ImWrite(savePath, blackhatImg);
|
|
return isMatch;
|
|
}
|
|
static Mat RemoveBorders(Mat image)
|
|
{
|
|
// 将图像转换为灰度图
|
|
Mat grayImage = new Mat();
|
|
Cv2.CvtColor(image, grayImage, ColorConversionCodes.BGR2GRAY);
|
|
|
|
// 使用自适应二值化将图像变为黑白图
|
|
Mat binaryImage = new Mat();
|
|
Cv2.AdaptiveThreshold(grayImage, binaryImage, 255, AdaptiveThresholdTypes.MeanC, ThresholdTypes.Binary, 11, 2);
|
|
|
|
// 反转颜色
|
|
Mat invertedBinaryImage = new Mat();
|
|
Cv2.BitwiseNot(binaryImage, invertedBinaryImage);
|
|
|
|
// 查找轮廓
|
|
Point[][] contours;
|
|
HierarchyIndex[] hierarchy;
|
|
Cv2.FindContours(invertedBinaryImage, out contours, out hierarchy, RetrievalModes.External, ContourApproximationModes.ApproxSimple);
|
|
|
|
// 找到包含最大面积的轮廓
|
|
double maxArea = 0;
|
|
Point[] maxContour = null;
|
|
foreach (var contour in contours)
|
|
{
|
|
double area = Cv2.ContourArea(contour);
|
|
if (area > maxArea)
|
|
{
|
|
maxArea = area;
|
|
maxContour = contour;
|
|
}
|
|
}
|
|
|
|
if (maxContour == null)
|
|
{
|
|
Console.WriteLine("未找到有效轮廓！");
|
|
return image;
|
|
}
|
|
|
|
// 找到平行四边形的四个顶点
|
|
Point[] approx = Cv2.ApproxPolyDP(maxContour, Cv2.ArcLength(maxContour, true) * 0.02, true);
|
|
Point2f[] srcPoints = approx.Select(p => new Point2f(p.X, p.Y)).ToArray();
|
|
|
|
if (srcPoints.Length != 4)
|
|
{
|
|
Console.WriteLine("未找到平行四边形的四个顶点！");
|
|
return image;
|
|
}
|
|
|
|
// 按顺时针顺序对顶点进行排序
|
|
srcPoints = OrderPoints(srcPoints);
|
|
|
|
// 确定目标图像的四个顶点
|
|
Point2f[] dstPoints = new Point2f[]
|
|
{
|
|
new Point2f(0, 0),
|
|
new Point2f(image.Width - 1, 0),
|
|
new Point2f(image.Width - 1, image.Height - 1),
|
|
new Point2f(0, image.Height - 1)
|
|
};
|
|
|
|
// 计算透视变换矩阵
|
|
Mat transformMatrix = Cv2.GetPerspectiveTransform(srcPoints, dstPoints);
|
|
|
|
// 应用透视变换
|
|
Mat warpedImage = new Mat();
|
|
Cv2.WarpPerspective(image, warpedImage, transformMatrix, new Size(image.Width, image.Height));
|
|
|
|
return warpedImage;
|
|
}
|
|
|
|
private static Point2f[] OrderPoints(Point2f[] points)
|
|
{
|
|
// 对顶点进行排序，顺时针顺序
|
|
Point2f[] orderedPoints = new Point2f[4];
|
|
|
|
// 计算质心
|
|
Point2f center = new Point2f(points.Average(p => p.X), points.Average(p => p.Y));
|
|
|
|
foreach (var point in points)
|
|
{
|
|
if (point.X < center.X && point.Y < center.Y)
|
|
orderedPoints[0] = point; // 左上
|
|
else if (point.X > center.X && point.Y < center.Y)
|
|
orderedPoints[1] = point; // 右上
|
|
else if (point.X > center.X && point.Y > center.Y)
|
|
orderedPoints[2] = point; // 右下
|
|
else if (point.X < center.X && point.Y > center.Y)
|
|
orderedPoints[3] = point; // 左下
|
|
}
|
|
|
|
return orderedPoints;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|