/// <summary>
/// Extracts the features.
/// </summary>
/// <returns><c>true</c>, if features was extracted, <c>false</c> otherwise.</returns>
/// <param name="image">Image.</param>
/// <param name="keypoints">Keypoints.</param>
/// <param name="descriptors">Descriptors.</param>
bool extractFeatures(Mat image, ref KeyPoint[] keypoints, ref Mat descriptors)
{
if (image.Total() == 0)
{
return(false);
}
if (image.Channels() != 1)
{
return(false);
}
keypoints = m_detector.Detect(image, null);
if (keypoints.Length == 0)
{
return(false);
}
m_extractor.Compute(image, ref keypoints, descriptors);
//m_detector.DetectAndCompute(image, ref keypoints, descriptors);
// m_extractor.compute (image, keypoints, descriptors);
if (keypoints.Length == 0)
{
return(false);
}
return(true);
}
static private void CreateORB(
Mat imgGray,
KeyPoint[] keypoints,
out MatOfFloat descriptors)
{
descriptors = new MatOfFloat();
ORB orb1 = ORB.Create();
orb1.Compute(imgGray, ref keypoints, descriptors);
}
public static void matchImageORB(Mat templateImage, Mat originalImage, float nndrRatio)
{
DateTime start = DateTime.Now;
//指定特征点算法SURF
ORB surf = new ORB();
//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);
//FlannBasedMatcher descriptorMatcher = new FlannBasedMatcher();
//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_64FC2);
Mat templateTransformResult = new Mat(4, 1, MatType.CV_64FC2);
templateCorners.Set <Point2d>(0, 0, new Point2d(0, 0));
templateCorners.Set <Point2d>(1, 0, new Point2d(templateImage.Cols, 0));
templateCorners.Set <Point2d>(2, 0, new Point2d(templateImage.Cols, templateImage.Rows));
templateCorners.Set <Point2d>(3, 0, new Point2d(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
Point2d pointA = templateTransformResult.Get <Point2d>(0, 0);
Point2d pointB = templateTransformResult.Get <Point2d>(1, 0);
Point2d pointC = templateTransformResult.Get <Point2d>(2, 0);
Point2d pointD = templateTransformResult.Get <Point2d>(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);
}
}