151 lines
4.6 KiB
C#
151 lines
4.6 KiB
C#
using OpenCvSharp;
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using System;
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using System.Drawing;
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using System.IO;
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using System.Linq;
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using System.Drawing.Imaging;
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using OpenCvSharp.Extensions;
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using System.Collections.Concurrent;
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using System.Threading.Tasks;
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using System.Diagnostics;
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using System.Threading;
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public class ProcessImg
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{
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// 对单个图像进行模板匹配
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public static (double score, Rect? coords) MatchTemplate(Mat img, string templatePath)
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{
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// 确保图像和模板文件存在
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// 读取图像和模板
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var template = Cv2.ImRead(templatePath, ImreadModes.Color);
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// 创建一个模板匹配的结果矩阵
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var result = new Mat();
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Cv2.MatchTemplate(img, template, result, TemplateMatchModes.CCoeffNormed);
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// 查找最大匹配值
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Cv2.MinMaxLoc(result, out _, out var maxVal, out _, out var maxLoc);
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// 如果找到的最大匹配值大于阈值
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double threshold = 0.3; // 可以根据需要调整阈值
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if (maxVal >= threshold)
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{
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// 计算匹配的坐标
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var topLeft = maxLoc;
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var rect = new Rect(topLeft.X, topLeft.Y, template.Width, template.Height);
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return (maxVal, rect);
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}
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return (0, null);
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}
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// 遍历文件夹中的所有图像文件进行模板匹配，并找到最佳得分图像
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public static Double ProcessImagesInFolder(string folderPath, Mat img)
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{
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// 获取所有图像文件
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var imageFiles = Directory.GetFiles(folderPath, "*.*", SearchOption.TopDirectoryOnly)
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.Where(file => file.EndsWith(".jpg", StringComparison.OrdinalIgnoreCase) ||
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file.EndsWith(".png", StringComparison.OrdinalIgnoreCase) ||
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file.EndsWith(".bmp", StringComparison.OrdinalIgnoreCase))
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.ToList();
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// 确保输出文件夹存在
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// 线程数量
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int numThreads = 5;
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// 用于存储每个图像的得分和坐标
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var bestMatch = new ConcurrentBag<(string imagePath, double score, Rect? coords)>();
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DateTime startTime = DateTime.Now;
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Stopwatch sw = new Stopwatch();
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sw.Start();
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Parallel.ForEach(imageFiles, new ParallelOptions { MaxDegreeOfParallelism = numThreads }, picPath =>
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{
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DateTime threadStartTime = DateTime.Now;
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var (score, coords) = MatchTemplate(img, picPath);
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bestMatch.Add((picPath, score, coords));
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DateTime threadEndTime = DateTime.Now;
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TimeSpan threadElapsed = threadEndTime - threadStartTime;
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Console.WriteLine($"线程处理 {picPath} 耗时: {threadElapsed.TotalMilliseconds}ms");
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});
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sw.Stop();
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TimeSpan totalElapsed = sw.Elapsed;
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Console.WriteLine($"处理完成，耗时: {totalElapsed.TotalSeconds}秒");
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// 查找最佳得分
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var best = bestMatch.OrderByDescending(m => m.score).FirstOrDefault();
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return best.score;
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//if (best.coords.HasValue)
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//{
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// Rect rect = best.coords.Value;
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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 Mat BitmapToMat(Bitmap bitmap)
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{
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if (bitmap == null)
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{
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throw new ArgumentException("Bitmap is null");
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}
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// 根据 Bitmap 的宽度、高度和像素格式创建一个与其相对应的 Mat
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Mat mat = new Mat(bitmap.Height, bitmap.Width, MatType.CV_8UC3); // 假设 Bitmap 是 24-bit RGB
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// 锁定 Bitmap 的内存区域以直接访问它的内存
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BitmapData bitmapData = bitmap.LockBits(
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new Rectangle(0, 0, bitmap.Width, bitmap.Height),
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ImageLockMode.ReadOnly,
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bitmap.PixelFormat
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);
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try
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{
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// 使用直接内存拷贝将 Bitmap 的数据拷贝到 Mat
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unsafe
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{
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byte* srcData = (byte*)bitmapData.Scan0;
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byte* dstData = (byte*)mat.DataPointer;
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int stride = bitmapData.Stride;
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int width = bitmap.Width * 3; // 24bpp 3个字节一个像素
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int height = bitmap.Height;
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for (int y = 0; y < height; y++)
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{
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Buffer.MemoryCopy(srcData + y * stride, dstData + y * mat.Step(), width, width);
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}
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}
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}
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finally
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{
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bitmap.UnlockBits(bitmapData);
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}
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return mat;
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}
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}
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