EmguCV基本操作（一）

1 EmguCV

1.1 访问图片

 //默认彩色模式打开 Mat srcImg = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\22.jpg", LoadImageType.Color); if (srcImg.IsEmpty) { 
    Console.WriteLine("文件图片找不到"); return; } Console.WriteLine($"图像宽度为：{srcImg.Cols}");//获取图片的列 Console.WriteLine($"图像高度为：{srcImg.Rows}");//获取图片的行 Console.WriteLine($"图像通道数为：{srcImg.NumberOfChannels}");//获取图片通道数 CvInvoke.NamedWindow("srcImg window", NamedWindowType.AutoSize); CvInvoke.Imshow("srcImg window", srcImg); CvInvoke.CvtColor(srcImg, srcImg, ColorConversion.Bgr2Gray);//转化为灰度图像 CvInvoke.Imshow("outImg window", srcImg); CvInvoke.WaitKey(0); CvInvoke.DestroyWindow("srcImg window");//销毁指定窗口 CvInvoke.DestroyAllWindows();//销毁所有窗口 

2 点、矩形框

Point pt = new Point(100, 100); pt.X = 10; pt.Y = 20; Rectangle rect = new Rectangle(); rect.X = 10; rect.Y = 20; rect.Width = 100; rect.Height = 200; bool bFlg = rect.Contains(new Point(0,0));//判断点是否在矩形框内 Size size = rect.Size; //MCvScalar mCvScalar = new MCvScalar(); Mat img = new Mat(500,500,DepthType.Cv8U,3); img.SetTo(new MCvScalar(0, 0, 0)); CvInvoke.Line(img, new Point(10, 10), new Point(250, 250), new MCvScalar(0, 255, 0),2,LineType.EightConnected); CvInvoke.Circle(img, new Point(250, 250), 100, new MCvScalar(0, 0, 255), 2); CvInvoke.Circle(img, new Point(350, 350), 100, new MCvScalar(0, 0, 255), -1); CvInvoke.Rectangle(img, new Rectangle(100, 100, 300, 200), new MCvScalar(255, 0, 0), 2); CvInvoke.Rectangle(img, new Rectangle(50, 50, 300, 200), new MCvScalar(255, 0, 0), -1); //椭圆 CvInvoke.Ellipse(img, new RotatedRect(new PointF(100, 110), new SizeF(300, 200),0),new MCvScalar(0,255,255),2,LineType.FourConnected); CvInvoke.Ellipse(img, new Point(250, 250), new Size(150, 100), 0, 0, 360, new MCvScalar(0, 0, 255), 2); CvInvoke.PutText(img, "hello world", new Point(100, 100), FontFace.HersheyComplex, 2, new MCvScalar(255, 255, 0), 5); CvInvoke.Imshow("--->", img); CvInvoke.WaitKey(0); 

3 访问图像像素

1. 索引访问

{ 
    Console.WriteLine("通过索引访问像素值"); Image<Bgr, Byte> img = new Image<Bgr, byte>(@"C:\Users\Administrator\Desktop\22.jpg"); for (int i = 0; i < img.Rows; i++) { 
    for (int j = 0; j < img.Cols; j++) { 
    if (i==j) { 
    img[i,j] = new Bgr(0, 0, 255);//把第i行，第j列设置成红色 } } } CvInvoke.Imshow("Image",img); CvInvoke.WaitKey(0); } 

2. 通过Data访问

{ 
    Console.WriteLine("通过Data索引访问，速度比较快 返回TDepth类型"); //单通道灰度图像像素访问 Image<Bgr, Byte> img = new Image<Bgr, byte>(@"C:\Users\Administrator\Desktop\22.jpg"); for (int i = 0; i < img.Rows; i++) { 
    for (int j = 0; j < img.Cols; j++) { 
    if (i == j) { 
    img.Data[i, j, 0] = 0;//像素值改变赋值（第100行100列 0通道）蓝 img.Data[i, j, 1] = 255;//像素值改变赋值（第100行100列 0通道）//绿 img.Data[i, j, 2] = 0;//像素值改变赋值（第100行100列 0通道）//红 //img[i, j] = new Bgr(0, 0, 255);//把第i行，第j列设置成红色 } } } CvInvoke.Imshow("Image", img); CvInvoke.WaitKey(0); } 

3. Image类与Mat类的转化

Console.WriteLine("Image类与Mat类的转换"); //Image类转Mat Image<Bgr, Byte> img = new Image<Bgr, byte>(@"C:\Users\Administrator\Desktop\22.jpg"); Mat matResult = img.Mat; //Mat转Image类 Mat img2 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\22.jpg", LoadImageType.Color); Image<Bgr, Byte> result = img2.ToImage<Bgr, Byte>(); 

4. 案例

{ 
    //图像减色算法 Console.WriteLine("图像减色算法"); Image<Bgr, Byte> img = new Image<Bgr, Byte>(@"C:\Users\Administrator\Desktop\22.jpg"); byte reduceValue = 64;//减色系数 for (int i = 0; i < img.Rows; i++) { 
    for (int j = 0; j < img.Cols; j++) { 
    img.Data[i, j, 0] = (byte)((byte)(byte)((byte)(img.Data[i, j, 0] / reduceValue) * reduceValue) + (byte)(reduceValue / 2)); img.Data[i, j, 1] = (byte)((byte)(byte)((byte)(img.Data[i, j, 1] / reduceValue) * reduceValue) + (byte)(reduceValue / 2)); img.Data[i, j, 2] = (byte)((byte)(byte)((byte)(img.Data[i, j, 2] / reduceValue) * reduceValue) + (byte)(reduceValue / 2)); } } CvInvoke.Imshow("img", img); CvInvoke.WaitKey(0); } { 
    //模拟雪花或者椒盐噪声 Console.WriteLine("模拟雪花或者椒盐噪声"); Image<Bgr, Byte> img = new Image<Bgr, Byte>(@"C:\Users\Administrator\Desktop\22.jpg"); int snowNum = 5000;//椒盐噪声的数量 Random random = new Random(); for (int k = 0; k < snowNum; k++) { 
    int i = random.Next() % img.Rows; int j = random.Next() % img.Cols; img.Data[i, j, 0] = 255; img.Data[i, j, 1] = 255; img.Data[i, j, 2] = 255; } CvInvoke.Imshow("img", img); CvInvoke.WaitKey(0); } 

4 对比度亮度调整

4.1 原理

 Image<Bgr, Byte> img = new Image<Bgr, byte>(@"C:\Users\Administrator\Desktop\22.jpg"); Image<Bgr, Byte> img2 = img.Clone();//创建img副本 int contrast = 100;//对比度 Byte brightness = 16;//亮度 for (int i = 0; i < img.Rows; i++) { 
    for (int j = 0; j < img.Cols; j++) { 
    int color_B = (int)((0.01 * contrast) * img.Data[i, j, 0] + brightness); int color_G = (int)((0.01 * contrast) * img.Data[i, j, 1] + brightness); int color_R = (int)((0.01 * contrast) * img.Data[i, j, 2] + brightness); if (color_B>255) { 
    color_B = 255; } if (color_G > 255) { 
    color_G = 255; } if (color_R > 255) { 
    color_R = 255; } if (color_B < 0) { 
    color_B = 0; } if (color_G < 0) { 
    color_G = 0; } if (color_R < 0) { 
    color_R = 0; } img.Data[i, j, 0] = (Byte)color_B; img.Data[i, j, 1] = (Byte)color_G; img.Data[i, j, 2] = (Byte)color_R; } } CvInvoke.Imshow("img show", img); CvInvoke.WaitKey(0); 

5 图像通道分离与合并

Mat img3 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\22.jpg", LoadImageType.Color); Mat[] channelsArray = img3.Split(); CvInvoke.Imshow("B", channelsArray[0]); CvInvoke.Imshow("G", channelsArray[1]); CvInvoke.Imshow("R", channelsArray[2]); //通道合并 Mat dst = new Mat(); VectorOfMat vectorOfMat = new VectorOfMat(); for (int i = 0; i < 3; i++) { 
    CvInvoke.Threshold(channelsArray[i], channelsArray[i], 100, 255, ThresholdType.Binary); vectorOfMat.Push(channelsArray[i]); } CvInvoke.Merge(vectorOfMat, dst); CvInvoke.Imshow("img vectorOfMat", dst); CvInvoke.Imshow("img show", img3); CvInvoke.WaitKey(0); 

6 图像基本运算

1. 图像加法

// 两图片必须有相同的大小和位深度 Mat img1 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\22.jpg", LoadImageType.Color); Mat img2 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\11.png", LoadImageType.Color); Mat dstImg = new Mat(); CvInvoke.Add(img1, img2, dstImg); //按照权重叠加 CvInvoke.AddWeighted(img1, 0.5, img2, 0.5, 0, dstImg); 

2. 图像加减

 Mat img1 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\22.jpg", LoadImageType.Color); Mat img2 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\11.PNG", LoadImageType.Color); Mat dstImg = new Mat(); //CvInvoke.Subtract(img1, img2, dstImg);//差值 小于0 取0 CvInvoke.AbsDiff(img1, img2, dstImg);//差值的绝对值 CvInvoke.Imshow("result Img", dstImg); CvInvoke.WaitKey(0); 

3. 图像的乘除法

 Mat img1 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\22.jpg", LoadImageType.Color); Mat img2 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\11.PNG", LoadImageType.Color); Mat temp = new Mat(new Size(img1.Cols,img1.Rows),DepthType.Cv8U,3); temp.SetTo(new MCvScalar(1,5,1)); Mat dstImg = new Mat(); CvInvoke.Multiply(img1, temp, dstImg, 1);//矩阵相乘 //CvInvoke.Divide(img1, temp, dstImg, 1);//矩阵相除 CvInvoke.Imshow("result Img", dstImg); CvInvoke.WaitKey(0); 

4. 图像逻辑运算 按位操作

Mat img1 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\111.png", LoadImageType.Color); Mat img2 = CvInvoke.Imread(@"C:\Users\Administrator\Desktop\222.png", LoadImageType.Color); Mat dstImg = new Mat(); //CvInvoke.BitwiseAnd(img1, img2, dstImg);//图像相与 //CvInvoke.BitwiseOr(img1, img2, dstImg);//图像相或 //CvInvoke.BitwiseNot(img1,dstImg);//逻辑非 CvInvoke.BitwiseXor(img1, img2, dstImg);//图像异或 CvInvoke.Imshow("result Img", dstImg); CvInvoke.WaitKey(0);