本文主要是介绍EMGU.CV入门（十五、模板匹配），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、函数介绍
1.1 MatchTemplate
模板匹配函数
参数说明
参数1：输入图像
参数2：匹配模板
参数3：返回矩阵
参数4：算法类型
其中算法类型共计六种：
//// 摘要:// This function is similiar to cvCalcBackProjectPatch. It slids through image,// compares overlapped patches of size wxh with templ using the specified method// and stores the comparison results to result//// 参数:// image:// Image where the search is running. It should be 8-bit or 32-bit floating-point//// templ:// Searched template; must be not greater than the source image and the same data// type as the image//// result:// A map of comparison results; single-channel 32-bit floating-point. If image is// WxH and templ is wxh then result must be W-w+1xH-h+1.//// method:// Specifies the way the template must be compared with image regions//// mask:// Mask of searched template. It must have the same datatype and size with templ.// It is not set by default.public static void MatchTemplate(IInputArray image, IInputArray templ, IOutputArray result, TemplateMatchingType method, IInputArray mask = null)1.2 MinMaxLoc
参数1：输入MatchTemplate函数返回的矩阵
参数2、3、4、5:分别为最小值、最大值、最小值的位置、最大值的位置
//// 摘要:// Finds minimum and maximum element values and their positions. The extremums are// searched over the whole array, selected ROI (in case of IplImage) or, if mask// is not IntPtr.Zero, in the specified array region. If the array has more than// one channel, it must be IplImage with COI set. In case if multi-dimensional arrays// min_loc->x and max_loc->x will contain raw (linear) positions of the extremums//// 参数:// arr:// The source array, single-channel or multi-channel with COI set//// minVal:// Pointer to returned minimum value//// maxVal:// Pointer to returned maximum value//// minLoc:// Pointer to returned minimum location//// maxLoc:// Pointer to returned maximum location//// mask:// The optional mask that is used to select a subarray. Use IntPtr.Zero if not neededpublic static void MinMaxLoc(IInputArray arr, ref double minVal, ref double maxVal, ref Point minLoc, ref Point maxLoc, IInputArray mask = null)
1.3 Rectangle
绘制矩形
//// 摘要:// Draws a rectangle specified by a CvRect structure//// 参数:// img:// Image//// rect:// The rectangle to be drawn//// color:// Line color//// thickness:// Thickness of lines that make up the rectangle. Negative values make the function// to draw a filled rectangle.//// lineType:// Type of the line//// shift:// Number of fractional bits in the point coordinatespublic static void Rectangle(IInputOutputArray img, Rectangle rect, MCvScalar color, int thickness = 1, LineType lineType = LineType.EightConnected, int shift = 0
二、单匹配
2.1 效果
2.2 代码
// 1. 加载原图
var image1 = new Image<Bgr, byte>("bird1.png");
var image0 = image1.Mat.Clone();
var imgGray = new Mat();
CvInvoke.CvtColor(image0,imgGray,ColorConversion.Bgr2Gray);
PreviewImage1 = new WriteableBitmap(Bitmap2BitmapImage(Text(image1.Bitmap, "原图")));// 2. 原图转灰度
PreviewImage2 = new WriteableBitmap(Bitmap2BitmapImage(Text(imgGray.Bitmap, "灰度")));// 3. 加载模板
var img3 = new Mat("birdTemplate.png",0);
PreviewImage3 = new WriteableBitmap(Bitmap2BitmapImage(Text(img3.Bitmap, "模板")));// 需要用到的一些参数
var res = new Mat();
double minLoc = 0, maxLoc = 0;
Point minPoint = new Point();
Point maxPoint = new Point();// 4. Sqdiff取最小值
CvInvoke.MatchTemplate(imgGray, img3, res, TemplateMatchingType.Sqdiff);
CvInvoke.MinMaxLoc(res,ref minLoc,ref maxLoc, ref minPoint,ref maxPoint);
var img4 = image0.Clone();
CvInvoke.Rectangle(img4, new Rectangle(minPoint, img3.Size), new MCvScalar(0, 0, 255), 2);
PreviewImage4 = new WriteableBitmap(Bitmap2BitmapImage(Text(img4.Bitmap, "Sqdiff")));// 5 .SqdiffNormed
CvInvoke.MatchTemplate(imgGray, img3, res, TemplateMatchingType.SqdiffNormed);
CvInvoke.MinMaxLoc(res, ref minLoc, ref maxLoc, ref minPoint, ref maxPoint);
var img5 = image0.Clone();
CvInvoke.Rectangle(img5, new Rectangle(minPoint, img3.Size), new MCvScalar(0, 0, 255), 2);
PreviewImage7 = new WriteableBitmap(Bitmap2BitmapImage(Text(img5.Bitmap, "SqdiffNormed")));// 6 .Ccoeff
CvInvoke.MatchTemplate(imgGray, img3, res, TemplateMatchingType.Ccoeff);
CvInvoke.MinMaxLoc(res, ref minLoc, ref maxLoc, ref minPoint, ref maxPoint);
var img6 = image0.Clone();
CvInvoke.Rectangle(img6, new Rectangle(maxPoint, img3.Size), new MCvScalar(0, 0, 255), 2);
PreviewImage5 = new WriteableBitmap(Bitmap2BitmapImage(Text(img6.Bitmap, "Ccoeff")));// 7 .CcoeffNormed
CvInvoke.MatchTemplate(imgGray, img3, res, TemplateMatchingType.CcoeffNormed);
CvInvoke.MinMaxLoc(res, ref minLoc, ref maxLoc, ref minPoint, ref maxPoint);
var img7 = image0.Clone();
CvInvoke.Rectangle(img7, new Rectangle(maxPoint, img3.Size), new MCvScalar(0, 0, 255), 2);
PreviewImage8 = new WriteableBitmap(Bitmap2BitmapImage(Text(img7.Bitmap, "CcoeffNormed")));// 8 .Ccorr
CvInvoke.MatchTemplate(imgGray, img3, res, TemplateMatchingType.Ccorr);
CvInvoke.MinMaxLoc(res, ref minLoc, ref maxLoc, ref minPoint, ref maxPoint);
var img8 = image0.Clone();
CvInvoke.Rectangle(img8, new Rectangle(maxPoint, img3.Size), new MCvScalar(0, 0, 255), 2);
PreviewImage6 = new WriteableBitmap(Bitmap2BitmapImage(Text(img8.Bitmap, "Ccorr")));// 9 .CcorrNormed
CvInvoke.MatchTemplate(imgGray, img3, res, TemplateMatchingType.CcorrNormed);
CvInvoke.MinMaxLoc(res, ref minLoc, ref maxLoc, ref minPoint, ref maxPoint);
var img9 = image0.Clone();
CvInvoke.Rectangle(img9, new Rectangle(maxPoint, img3.Size), new MCvScalar(0, 0, 255), 2);
PreviewImage9 = new WriteableBitmap(Bitmap2BitmapImage(Text(img9.Bitmap, "CcorrNormed")));
三、多匹配
3.1 效果
3.2 代码
// 1. 加载原图
var image1 = new Image<Bgr, byte>("Test.png");
var image0 = image1.Mat.Clone();
var imgGray = new Mat();
CvInvoke.CvtColor(image0,imgGray,ColorConversion.Bgr2Gray);
PreviewImage1 = new WriteableBitmap(Bitmap2BitmapImage(Text(image1.Bitmap, "原图")));// 2. 加载模板
var img3 = new Mat("testTemplate.png",0);
PreviewImage2 = new WriteableBitmap(Bitmap2BitmapImage(Text3(img3.Bitmap, "模板")));// 3. 匹配
var res = new Mat();
CvInvoke.MatchTemplate(imgGray, img3, res, TemplateMatchingType.CcoeffNormed);
var img4 = image0.Clone();
var m = new Matrix<float>(res.Rows, res.Cols);
res.CopyTo(m);
var image = res.ToImage<Gray, byte>();
for (int i = 0; i < res.Rows; i++)
{for (int j = 0; j < res.Cols; j++){if (m[i, j] > 0.8){CvInvoke.Rectangle(img4, new Rectangle(new Point(j, i), img3.Size), new MCvScalar(0, 0, 255), 2);}}
}PreviewImage3 = new WriteableBitmap(Bitmap2BitmapImage(Text(img4.Bitmap, "结果")));
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