631 lines
26 KiB
C#
631 lines
26 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 System;
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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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public static Mat ProcessImage(Mat image, Rect fillRect)
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{
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// 获取图像尺寸
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int width = image.Width;
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int height = image.Height;
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// 定义左下角 30x30 矩形框
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int rectSize = 30;
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int rectX = 0;
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int rectY = height - rectSize; // 确保是左下角
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// 检查左下角矩形框是否在图像范围内
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if (rectY < 0 || rectX < 0 || rectSize > width || rectSize > height)
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{
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Console.WriteLine("图像尺寸不足以获取指定区域");
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return image;
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}
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// 定义感兴趣区域 (ROI) 并计算其平均颜色
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Rect roi = new Rect(rectX, rectY, rectSize, rectSize);
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Mat roiMat = new Mat(image, roi);
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Scalar meanColor = Cv2.Mean(roiMat);
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// 创建 Scalar 类型的颜色填充
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Scalar fillColor = new Scalar(meanColor.Val0, meanColor.Val1, meanColor.Val2);
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// 修改 fillRect 的 Y 和 Height 属性以覆盖整个图像的高度
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fillRect.Y = 0; // 起始位置为顶部
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fillRect.Height = height; // 高度覆盖整个图像
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// 检查填充矩形是否在图像范围内
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if (fillRect.X < 0 || fillRect.X + fillRect.Width > width)
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{
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Console.WriteLine("填充区域超出图像范围");
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return image;
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}
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// 使用 OpenCV 的 rectangle 函数进行填充
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Cv2.Rectangle(image, fillRect.TopLeft, fillRect.BottomRight, fillColor, Cv2.FILLED);
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return image;
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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, ref Mat ResultMat,Rect sqlrect, Rect detrect, bool IfWhiteWord, string saveDir,string SN)
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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($"Error loading image {path1}");
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return false;
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}
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// Cv2.Resize(img1, img1, new Size(550, 270));
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img1 = ProcessImage(img1, sqlrect);
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Mat gimg1 = new Mat();
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Cv2.CvtColor(img1, gimg1, ColorConversionCodes.BGR2GRAY);
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Mat thr1 = new Mat();
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if (IfWhiteWord)
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{
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Cv2.Threshold(gimg1, thr1, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
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}
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else
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{
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Cv2.Threshold(gimg1, thr1, 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
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}
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// 读取和处理第二张图片
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Mat img2 = MatDet.Clone();
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ResultMat= MatDet.Clone();
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if (img2.Empty())
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{
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// Console.WriteLine($"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 = ProcessImage(img2, detrect);
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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 = SN;
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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\\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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else
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{
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Rect boundingRect = Cv2.BoundingRect(contour);
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Cv2.Resize(img2, img2, new Size(550, 270));
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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\\ng",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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CheckDiffSciHelper1.ResizeImage(savePath2, savePath2, 640, 480, 75);
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ResultMat = img2.Clone();
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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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}
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// 新增的白色面积占比判断
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double whiteArea1 = Cv2.CountNonZero(thr1);
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double whiteArea2 = Cv2.CountNonZero(thr2);
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double ratio1 = whiteArea1 / (thr1.Rows * thr1.Cols);
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double ratio2 = whiteArea2 / (thr2.Rows * thr2.Cols);
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if (Math.Abs(ratio1 - ratio2) >= 0.90)
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{
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isMatch = true;
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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,string SN)
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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 = SN;
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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
|
||
////string savePath = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path2) + "_diff.png");
|
||
//Cv2.ImWrite(savePath, final_result2);
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
// 计算图像差异
|
||
Mat devIMG = new Mat();
|
||
Mat devIMG_ = new Mat();
|
||
Cv2.Subtract(final_result1, final_result2, devIMG);
|
||
Cv2.Subtract(final_result2, final_result1, devIMG_);
|
||
//string savePathd = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "devIMG.png");
|
||
// 保存结果
|
||
|
||
// Cv2.ImWrite(savePathd, devIMG);
|
||
//string savePathd1 = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "devIMG_.png");
|
||
// 保存结果
|
||
|
||
//Cv2.ImWrite(savePathd1, devIMG_);
|
||
// 对差异图像应用阈值
|
||
Cv2.Threshold(devIMG, devIMG, 8, 255, ThresholdTypes.Binary);
|
||
Cv2.Threshold(devIMG_, devIMG_, 8, 255, ThresholdTypes.Binary);
|
||
|
||
// 结合差异
|
||
Mat sumIMG = new Mat();
|
||
Cv2.Add(devIMG, devIMG_, sumIMG);
|
||
//string savePaths = Path.Combine(saveDir, Path.GetFileNameWithoutExtension(path1) + filename + "sumIMG.png");
|
||
// 保存结果
|
||
|
||
//Cv2.ImWrite(savePaths, sumIMG);
|
||
// 应用形态学操作
|
||
Mat kernelCL = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3));
|
||
Mat blackhatImg = new Mat();
|
||
Cv2.Dilate(sumIMG, blackhatImg, kernelCL);
|
||
|
||
// 处理轮廓和保存结果
|
||
Point[][] contours = new Point[10000][];
|
||
Cv2.FindContours(blackhatImg, out contours, out _, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);
|
||
bool isMatch = true;
|
||
foreach (var contour in contours)
|
||
{
|
||
if (Cv2.ContourArea(contour) <= 500)
|
||
{
|
||
Cv2.DrawContours(blackhatImg, new Point[][] { contour }, -1, Scalar.Black, thickness: Cv2.FILLED);
|
||
// 框选轮廓
|
||
|
||
}
|
||
else
|
||
{
|
||
Rect boundingRect = Cv2.BoundingRect(contour);
|
||
Cv2.Rectangle(img2, boundingRect, Scalar.Red, thickness: 2);
|
||
isMatch = false;
|
||
}
|
||
}
|
||
|
||
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);
|
||
//ResizeImage(savePath2, savePath2, 640, 480, 75);
|
||
//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;
|
||
}
|
||
|
||
public static void ResizeImage(string inputPath, string outputPath, int newWidth, int newHeight, int quality)
|
||
{
|
||
// 加载原始图像
|
||
using (Mat originalImage = Cv2.ImRead(inputPath))
|
||
{
|
||
// 创建一个Mat对象用于存储缩放后的图像
|
||
using (Mat resizedImage = new Mat())
|
||
{
|
||
// 缩放图像
|
||
Cv2.Resize(originalImage, resizedImage, new OpenCvSharp.Size(newWidth, newHeight));
|
||
|
||
// 保存图像为JPEG格式，并设置压缩质量
|
||
SaveJpeg(outputPath, resizedImage, quality);
|
||
Console.WriteLine($"Image saved to {outputPath}");
|
||
}
|
||
}
|
||
}
|
||
|
||
static void SaveJpeg(string path, Mat image, int quality)
|
||
{
|
||
// 设置JPEG编码参数
|
||
var encodeParams = new[] { new ImageEncodingParam(ImwriteFlags.JpegQuality, quality) };
|
||
|
||
// 保存图像
|
||
Cv2.ImWrite(path, image, encodeParams);
|
||
}
|
||
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;
|
||
}
|
||
}
|
||
}
|
||
}
|
||
|
||
|