探索OpenCvSharp：用C#和Winform构建图像处理世界

OpencvSharp资料采用C#加Winform编写包含接近50个Demo,直接运行即可。 例程包含模板匹配、边缘识别、人脸识别灰度变化、标定等。最近在图像处理领域折腾发现了一个超棒的资源——基于OpenCvSharp用C# 加Winform编写且包含近50个可直接运行Demo的项目简直是图像处理爱好者和开发者的福音。今天就来跟大家唠唠这里面的精彩内容。丰富多样的例程模板匹配模板匹配在图像识别里是个常用技术比如在一张大图里找某个小图标在哪。在这个项目里实现模板匹配的代码大概长这样using OpenCvSharp; class TemplateMatching { public static void MatchTemplateExample() { Mat sourceImage Cv2.ImRead(source.jpg); Mat templateImage Cv2.ImRead(template.jpg); Mat result new Mat(); Cv2.MatchTemplate(sourceImage, templateImage, result, TemplateMatchModes.CCoeffNormed); Core.MinMaxLoc(result, out _, out double maxVal, out _, out Point maxLoc); double threshold 0.8; if (maxVal threshold) { int width templateImage.Width; int height templateImage.Height; Rect rect new Rect(maxLoc, new Size(width, height)); Cv2.Rectangle(sourceImage, rect, Scalar.Red, 2); } Cv2.ImShow(Matched Image, sourceImage); Cv2.WaitKey(0); Cv2.DestroyAllWindows(); } }代码分析首先通过Cv2.ImRead读取源图像和模板图像。然后用Cv2.MatchTemplate函数进行模板匹配这里用的匹配模式是TemplateMatchModes.CCoeffNormed它会返回一个匹配结果的矩阵。接着用Core.MinMaxLoc找到矩阵里的最大值也就是最佳匹配位置。设置一个阈值如果最大值大于阈值就认为匹配成功在源图像上画出匹配区域的矩形框最后显示图像。边缘识别边缘识别能提取图像中物体的轮廓对于图像分析很关键。下面是简单的边缘识别代码using OpenCvSharp; class EdgeDetection { public static void CannyEdgeDetection() { Mat image Cv2.ImRead(input.jpg, ImreadModes.Grayscale); Mat edges new Mat(); Cv2.Canny(image, edges, 50, 150); Cv2.ImShow(Edges, edges); Cv2.WaitKey(0); Cv2.DestroyAllWindows(); } }代码分析先把图像以灰度模式读进来因为Canny边缘检测算法在灰度图上效果更好。Cv2.Canny函数接收图像、输出边缘图像、以及两个阈值较低阈值用于边缘连接较高阈值用于检测明显的边缘。最后显示检测到的边缘图像。人脸识别人脸识别一直是个热门话题。在这个项目里利用OpenCvSharp也能轻松实现简单的人脸识别using OpenCvSharp; using OpenCvSharp.Dnn; class FaceRecognition { public static void FaceRecognitionExample() { string modelFile res10_300x300_ssd_iter_140000_fp16.caffemodel; string configFile deploy.prototxt; Net net DnnDnn.Net.ReadNetFromCaffe(configFile, modelFile); Mat image Cv2.ImRead(group_photo.jpg); Mat blob DnnBlob.FromImage(image, 1.0, new Size(300, 300), new Scalar(104.0, 177.0, 123.0), false, false); net.SetInput(blob); Mat detections net.Forward(); for (int i 0; i detections.Rows; i) { float confidence detections.Atfloat(i, 2); if (confidence 0.5) { int x1 (int)(detections.Atfloat(i, 3) * image.Width); int y1 (int)(detections.Atfloat(i, 4) * image.Height); int x2 (int)(detections.Atfloat(i, 5) * image.Width); int y2 (int)(detections.Atfloat(i, 6) * image.Height); Rect rect new Rect(x1, y1, x2 - x1, y2 - y1); Cv2.Rectangle(image, rect, Scalar.Green, 2); } } Cv2.ImShow(Face Detection, image); Cv2.WaitKey(0); Cv2.DestroyAllWindows(); } }代码分析这里用到了深度学习模型先读取模型文件和配置文件创建网络。把图像转成Blob数据格式输入网络网络会输出检测结果。遍历检测结果根据置信度判断是否为有效人脸如果是就画出人脸的矩形框。灰度变化灰度变化能简化图像信息便于后续处理。using OpenCvSharp; class GrayscaleConversion { public static void ConvertToGrayscale() { Mat colorImage Cv2.ImRead(color_image.jpg); Mat grayImage new Mat(); Cv2.CvtColor(colorImage, grayImage, ColorConversionCodes.BGR2GRAY); Cv2.ImShow(Color Image, colorImage); Cv2.ImShow(Grayscale Image, grayImage); Cv2.WaitKey(0); Cv2.DestroyAllWindows(); } }代码分析读取彩色图像后用Cv2.CvtColor函数把彩色图像转成灰度图像这里指定了从BGR颜色空间转成灰度的代码ColorConversionCodes.BGR2GRAY最后分别显示彩色图像和灰度图像。标定标定在机器视觉里用于确定相机的参数以便更准确地测量和识别。虽然代码相对复杂些但这个项目里也有详细实现。// 这里只给出简单框架实际标定代码更复杂 using OpenCvSharp; using OpenCvSharp.Calib3d; class CameraCalibration { public static void CalibrateCamera() { // 准备棋盘格角点数据 Size patternSize new Size(9, 6); Point3f[] objectPoints new Point3f[patternSize.Width * patternSize.Height]; for (int i 0; i patternSize.Height; i) { for (int j 0; j patternSize.Width; j) { objectPoints[i * patternSize.Width j] new Point3f(j, i, 0); } } // 存储所有图像的角点 ListPoint3f[] objectPointsList new ListPoint3f[](); ListPoint2f[] imagePointsList new ListPoint2f[](); // 读取图像并寻找角点 for (int i 0; i numImages; i) { Mat image Cv2.ImRead($image_{i}.jpg); Mat gray new Mat(); Cv2.CvtColor(image, gray, ColorConversionCodes.BGR2GRAY); bool found Cv2.FindChessboardCorners(gray, patternSize, out Point2f[] corners, ChessboardFlags.AdaptiveThresh | ChessboardFlags.NormalizeImage); if (found) { Cv2.CornerSubPix(gray, corners, new Size(11, 11), new Size(-1, -1), new TermCriteria(TermCriteriaTypes.Eps TermCriteriaTypes.MaxIter, 30, 0.1)); objectPointsList.Add(objectPoints); imagePointsList.Add(corners); } } // 标定相机 Mat cameraMatrix Mat.Zeros(3, 3, MatType.CV_64F); Mat distCoeffs Mat.Zeros(5, 1, MatType.CV_64F); Calib3d.CalibrateCamera(objectPointsList, imagePointsList, gray.Size(), cameraMatrix, distCoeffs, out _, out _, CalibrationFlags.None); } }代码分析首先定义棋盘格的尺寸生成棋盘格角点的三维坐标。循环读取图像把彩色图像转成灰度图后找棋盘格角点如果找到就优化角点位置并存储。最后用这些角点数据进行相机标定得到相机矩阵和畸变系数。OpencvSharp资料采用C#加Winform编写包含接近50个Demo,直接运行即可。 例程包含模板匹配、边缘识别、人脸识别灰度变化、标定等。这个基于OpenCvSharpC# 加Winform的项目通过这一个个精彩的Demo为我们打开了图像处理的大门无论是新手学习还是老手拓展思路都非常有价值强烈推荐大家下载来亲自运行体验一番