private void FrmMain_Shown(object sender, EventArgs e)
{
this.list = Ptma.LoadFromPath("config.xml");
for (int i = 0; i < list.Count; i++)
{
TrainedTemplate trainedTemplate = new TrainedTemplate();
trainedTemplate.templateImage = Cv2.ImRead(list[i].imageSrc, OpenCvSharp.LoadMode.Color);
SURF featureDetector = new SURF();
//获取模板图的特征点
KeyPoint[] templateKeyPoints = featureDetector.Detect(trainedTemplate.templateImage);
//提取模板图的特征点
Mat templateDescriptors = new Mat(trainedTemplate.templateImage.Rows, trainedTemplate.templateImage.Cols, trainedTemplate.templateImage.Type());
SURF descriptorExtractor = new SURF();
descriptorExtractor.Compute(trainedTemplate.templateImage, ref templateKeyPoints, templateDescriptors);
trainedTemplate.templateDescriptors = templateDescriptors;
trainedTemplate.templateKeyPoints = templateKeyPoints;
this.toolList.Add(trainedTemplate);
}
this.dgvMain.DataSource = this.list;
Thread bgThread = new Thread(CaptureAndAnalyse);
bgThread.IsBackground = true;
bgThread.Start();
}
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);
}
}
