/// <summary>
/// 图像匹配
/// </summary>
/// <param name="templateImage">模板图</param>
/// <param name="originalImage">原图</param>
/// <param name="nndrRatio">距离阈值，一般取0.5</param>
public static void matchImage(Mat templateImage, Mat originalImage, float nndrRatio)
{
DateTime start = DateTime.Now;
//指定特征点算法SURF
SURF surf = new SURF();
//获取模板图的特征点
KeyPoint[] templateKeyPoints = surf.Detect(templateImage);
//提取模板图的特征描述
//Mat templateDescriptors = new Mat(templateImage.Rows, templateImage.Cols, templateImage.Type());
Mat templateDescriptors = new Mat();
surf.Compute(templateImage, ref templateKeyPoints, templateDescriptors);
//获取原图的特征点
KeyPoint[] originalKeyPoints = surf.Detect(originalImage);
//提取原图的特征点描述;
Mat originalDescriptors = new Mat();
surf.Compute(originalImage, ref originalKeyPoints, originalDescriptors);
//开始匹配
DescriptorMatcher descriptorMatcher = DescriptorMatcher.Create("FlannBased");//或者使用
/**
* knnMatch方法的作用就是在给定特征描述集合中寻找最佳匹配
* 使用KNN-matching算法，令K=2，则每个match得到两个最接近的descriptor，然后计算最接近距离和次接近距离之间的比值，当比值大于既定值时，才作为最终match。
*/
DMatch[][] matches = descriptorMatcher.KnnMatch(templateDescriptors, originalDescriptors, 2);
List <DMatch> goodMatchesList = new List <DMatch>();
foreach (DMatch[] match in matches)
{
DMatch m1 = match[0];
DMatch m2 = match[1];
if (m1.Distance <= m2.Distance * nndrRatio)
{
goodMatchesList.Add(m1);
}
}
//当匹配后的特征点大于等于 4 个，则认为模板图在原图中，该值可以自行调整
if (goodMatchesList.Count >= 4)
{
//Console.WriteLine("模板图在原图匹配成功！");
List <KeyPoint> templateKeyPointList = templateKeyPoints.ToList();
List <KeyPoint> originalKeyPointList = originalKeyPoints.ToList();
List <Point2f> objectPoints = new List <Point2f>();
List <Point2f> scenePoints = new List <Point2f>();
foreach (DMatch goodMatch in goodMatchesList)
{
objectPoints.Add(templateKeyPointList[goodMatch.QueryIdx].Pt);
scenePoints.Add(originalKeyPointList[goodMatch.TrainIdx].Pt);
}
MatOfPoint2f objMatOfPoint2f = new MatOfPoint2f();
foreach (Point2f p in objectPoints)
{
objMatOfPoint2f.Add(p);
}
MatOfPoint2f scnMatOfPoint2f = new MatOfPoint2f();
foreach (Point2f p in scenePoints)
{
scnMatOfPoint2f.Add(p);
}
//使用 findHomography 寻找匹配上的关键点的变换
Mat homography = Cv2.FindHomography(objMatOfPoint2f, scnMatOfPoint2f, OpenCvSharp.HomographyMethod.Ransac, 3);
/**
* 透视变换(Perspective Transformation)是将图片投影到一个新的视平面(Viewing Plane)，也称作投影映射(Projective Mapping)。
*/
Mat templateCorners = new Mat(4, 1, MatType.CV_32FC2);
Mat templateTransformResult = new Mat(4, 1, MatType.CV_32FC2);
templateCorners.Set <Point2f>(0, 0, new Point2f(0, 0));
templateCorners.Set <Point2f>(1, 0, new Point2f(templateImage.Cols, 0));
templateCorners.Set <Point2f>(2, 0, new Point2f(templateImage.Cols, templateImage.Rows));
templateCorners.Set <Point2f>(3, 0, new Point2f(0, templateImage.Rows));
//使用 perspectiveTransform 将模板图进行透视变以矫正图象得到标准图片
Cv2.PerspectiveTransform(templateCorners, templateTransformResult, homography);
//矩形四个顶点
Point2f pointA = templateTransformResult.Get <Point2f>(0, 0);
Point2f pointB = templateTransformResult.Get <Point2f>(1, 0);
Point2f pointC = templateTransformResult.Get <Point2f>(2, 0);
Point2f pointD = templateTransformResult.Get <Point2f>(3, 0);
//将匹配的图像用用四条线框出来
Cv2.Line(originalImage, pointA, pointB, new Scalar(0, 255, 0), 1); //上 A->B
Cv2.Line(originalImage, pointB, pointC, new Scalar(0, 255, 0), 1); //右 B->C
Cv2.Line(originalImage, pointC, pointD, new Scalar(0, 255, 0), 1); //下 C->D
Cv2.Line(originalImage, pointD, pointA, new Scalar(0, 255, 0), 1); //左 D->A
Cv2.PutText(originalImage, "time:" + DateTime.Now.Subtract(start).TotalMilliseconds + "ms", new Point(10, originalImage.Height - 10), FontFace.HersheySimplex, 0.5, new Scalar(255, 255, 255));
Cv2.ImWrite(@"C:\Users\Administrator\Desktop\result.jpg", originalImage);
}
}
public static MatchResult Match(Bitmap src, TrainedTemplate trainedTemplate)
{
try
{
Mat originalImage = Bitmap2Mat(src);
DateTime begin = DateTime.Now;
SURF surf = new SURF();
//获取原图的特征点
KeyPoint[] originalKeyPoints = surf.Detect(originalImage);
//提取原图的特征点描述;
Mat originalDescriptors = new Mat();
surf.Compute(originalImage, ref originalKeyPoints, originalDescriptors);
//开始匹配
DescriptorMatcher descriptorMatcher = DescriptorMatcher.Create("FlannBased");
/**
* knnMatch方法的作用就是在给定特征描述集合中寻找最佳匹配
* 使用KNN-matching算法，令K=2，则每个match得到两个最接近的descriptor，然后计算最接近距离和次接近距离之间的比值，当比值大于既定值时，才作为最终match。
*/
DMatch[][] matches = descriptorMatcher.KnnMatch(trainedTemplate.templateDescriptors, originalDescriptors, 2);
List <DMatch> goodMatchesList = new List <DMatch>();
foreach (DMatch[] match in matches)
{
DMatch m1 = match[0];
DMatch m2 = match[1];
if (m1.Distance <= m2.Distance * nndrRatio)
{
goodMatchesList.Add(m1);
}
}
//当匹配后的特征点大于等于 4 个，则认为模板图在原图中，该值可以自行调整
if (goodMatchesList.Count >= 4)
{
//Console.WriteLine("模板图在原图匹配成功！");
List <KeyPoint> templateKeyPointList = trainedTemplate.templateKeyPoints.ToList();
List <KeyPoint> originalKeyPointList = originalKeyPoints.ToList();
List <Point2f> objectPoints = new List <Point2f>();
List <Point2f> scenePoints = new List <Point2f>();
foreach (DMatch goodMatch in goodMatchesList)
{
objectPoints.Add(templateKeyPointList[goodMatch.QueryIdx].Pt);
scenePoints.Add(originalKeyPointList[goodMatch.TrainIdx].Pt);
}
MatOfPoint2f objMatOfPoint2f = new MatOfPoint2f();
foreach (Point2f p in objectPoints)
{
objMatOfPoint2f.Add(p);
}
MatOfPoint2f scnMatOfPoint2f = new MatOfPoint2f();
foreach (Point2f p in scenePoints)
{
scnMatOfPoint2f.Add(p);
}
//使用 findHomography 寻找匹配上的关键点的变换
Mat homography = Cv2.FindHomography(objMatOfPoint2f, scnMatOfPoint2f, OpenCvSharp.HomographyMethod.Ransac, 3);
/**
* 透视变换(Perspective Transformation)是将图片投影到一个新的视平面(Viewing Plane)，也称作投影映射(Projective Mapping)。
*/
Mat templateCorners = new Mat(4, 1, MatType.CV_32FC2);
Mat templateTransformResult = new Mat(4, 1, MatType.CV_32FC2);
templateCorners.Set <Point2f>(0, 0, new Point2f(0, 0));
templateCorners.Set <Point2f>(1, 0, new Point2f(trainedTemplate.templateImage.Cols, 0));
templateCorners.Set <Point2f>(2, 0, new Point2f(trainedTemplate.templateImage.Cols, trainedTemplate.templateImage.Rows));
templateCorners.Set <Point2f>(3, 0, new Point2f(0, trainedTemplate.templateImage.Rows));
//使用 perspectiveTransform 将模板图进行透视变以矫正图象得到标准图片
Cv2.PerspectiveTransform(templateCorners, templateTransformResult, homography);
//矩形四个顶点
Point2f pointA = templateTransformResult.Get <Point2f>(0, 0);
Point2f pointB = templateTransformResult.Get <Point2f>(1, 0);
Point2f pointC = templateTransformResult.Get <Point2f>(2, 0);
Point2f pointD = templateTransformResult.Get <Point2f>(3, 0);
MatchResult matchResult = new MatchResult();
matchResult.top = (int)pointA.Y;
matchResult.left = (int)pointA.X;
matchResult.right = (int)pointB.X;
matchResult.bottom = (int)pointD.Y;
matchResult.time = DateTime.Now.Subtract(begin).TotalMilliseconds;
return(matchResult);
}
else
{
return(null);
}
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
return(null);
}
}