static void Main(string[] args)
{
var img1 = new Mat(@"..\..\Images\left.png", LoadMode.GrayScale);
Cv2.ImShow("Left", img1);
Cv2.WaitKey(1); // do events
var img2 = new Mat(@"..\..\Images\right.png", LoadMode.GrayScale);
Cv2.ImShow("Right", img2);
Cv2.WaitKey(1); // do events
// detecting keypoints
// FastFeatureDetector, StarDetector, SIFT, SURF, ORB, BRISK, MSER, GFTTDetector, DenseFeatureDetector, SimpleBlobDetector
// SURF = Speeded Up Robust Features
var detector = new SURF(hessianThreshold: 400); //A good default value could be from 300 to 500, depending from the image contrast.
var keypoints1 = detector.Detect(img1);
var keypoints2 = detector.Detect(img2);
// computing descriptors, BRIEF, FREAK
// BRIEF = Binary Robust Independent Elementary Features
var extractor = new BriefDescriptorExtractor();
var descriptors1 = new Mat();
var descriptors2 = new Mat();
extractor.Compute(img1, ref keypoints1, descriptors1);
extractor.Compute(img2, ref keypoints2, descriptors2);
// matching descriptors
var matcher = new BFMatcher();
var matches = matcher.Match(descriptors1, descriptors2);
// drawing the results
var imgMatches = new Mat();
Cv2.DrawMatches(img1, keypoints1, img2, keypoints2, matches, imgMatches);
Cv2.ImShow("Matches", imgMatches);
Cv2.WaitKey(1); // do events
Cv2.WaitKey(0);
Cv2.DestroyAllWindows();
img1.Dispose();
img2.Dispose();
}
static void test_svm()
{
FileStorage fs = new FileStorage("test.yaml", FileStorage.Mode.Read);
FileNode n = fs["voca"];
Mat voca = new Mat();
n.ReadMat(voca);
SURF surf = new SURF(400);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
BOWImgDescriptorExtractor bowDex = new BOWImgDescriptorExtractor(surf, matcher);
bowDex.SetVocabulary(voca);
SVM svm = new SVM();
//FileStorage fs1 = new FileStorage("svm.yaml", FileStorage.Mode.Read);
svm.Read(fs.GetRoot());
foreach (string s in System.IO.Directory.GetFiles(@"C:\projects\local\testMQ\testMQ\bin\Debug\icons"))
{
Image <Bgr, Byte> test_img = new Image <Bgr, byte>(s);
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\projects\local\testMQ\testMQ\bin\Debug\mail_samples\email_icon_t.jpg");
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\projects\local\testMQ\testMQ\bin\Debug\phone_icons\icon_2.jpg");
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\test\35928233-email-icon-on-blue-background-clean-vector.jpg");
Mat ii = new Mat();
CvInvoke.CvtColor(test_img, ii, ColorConversion.Bgr2Gray);
MKeyPoint[] kp = surf.Detect(ii);
Mat desc = new Mat();
bowDex.Compute(ii, new VectorOfKeyPoint(kp), desc);
float r = svm.Predict(desc);
Program.logIt(string.Format("{0}={1}", s, r));
}
}
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();
}
static void Main(string[] args)
{
var img1 = new Mat(@"..\..\Images\left.png", LoadMode.GrayScale);
Cv2.ImShow("Left", img1);
Cv2.WaitKey(1); // do events
var img2 = new Mat(@"..\..\Images\right.png", LoadMode.GrayScale);
Cv2.ImShow("Right", img2);
Cv2.WaitKey(1); // do events
// detecting keypoints
// FastFeatureDetector, StarDetector, SIFT, SURF, ORB, BRISK, MSER, GFTTDetector, DenseFeatureDetector, SimpleBlobDetector
// SURF = Speeded Up Robust Features
var detector = new SURF(hessianThreshold: 400); //A good default value could be from 300 to 500, depending from the image contrast.
var keypoints1 = detector.Detect(img1);
var keypoints2 = detector.Detect(img2);
// computing descriptors, BRIEF, FREAK
// BRIEF = Binary Robust Independent Elementary Features
var extractor = new BriefDescriptorExtractor();
var descriptors1 = new Mat();
var descriptors2 = new Mat();
extractor.Compute(img1, ref keypoints1, descriptors1);
extractor.Compute(img2, ref keypoints2, descriptors2);
// matching descriptors
var matcher = new BFMatcher();
var matches = matcher.Match(descriptors1, descriptors2);
// drawing the results
var imgMatches = new Mat();
Cv2.DrawMatches(img1, keypoints1, img2, keypoints2, matches, imgMatches);
Cv2.ImShow("Matches", imgMatches);
Cv2.WaitKey(1); // do events
Cv2.WaitKey(0);
Cv2.DestroyAllWindows();
img1.Dispose();
img2.Dispose();
}
private void MatchBySurf(Mat src1, Mat src2)
{
var gray1 = new Mat();
var gray2 = new Mat();
Cv2.CvtColor(src1, gray1, ColorConversion.BgrToGray);
Cv2.CvtColor(src2, gray2, ColorConversion.BgrToGray);
var surf = new SURF(500, 4, 2, true);
KeyPoint[] kpt1 = surf.Detect(gray1, null);
KeyPoint[] kpt2 = surf.Detect(gray2, null);
// Detect the keypoints and generate their descriptors using SURF
KeyPoint[] keypoints1, keypoints2;
var descriptors1 = new MatOfFloat();
var descriptors2 = new MatOfFloat();
surf.Run(gray1, null, out keypoints1, descriptors1);
surf.Run(gray2, null, out keypoints2, descriptors2);
// Match descriptor vectors
var bfMatcher = new BFMatcher(NormType.L2, false);
var flannMatcher = new FlannBasedMatcher();
DMatch[] bfMatches = bfMatcher.Match(descriptors1, descriptors2);
DMatch[] flannMatches = flannMatcher.Match(descriptors1, descriptors2);
// Draw matches
var bfView = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, bfMatches, bfView);
var flannView = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, flannMatches, flannView);
using (new Window("SURF matching (by BFMather)", WindowMode.AutoSize, bfView))
using (new Window("SURF matching (by FlannBasedMatcher)", WindowMode.AutoSize, flannView))
{
Cv2.WaitKey();
}
}
static void Main(string[] args)
{
var image = new Image <Bgr, byte>("RGB.jpg").Resize(0.4, Inter.Area);
var image_gray = image.Convert <Gray, byte>();
var surfDetector = new SURF(1000);
var keyPoints = surfDetector.Detect(image_gray);
foreach (var point in keyPoints)
{
CvInvoke.Circle(image, new Point((int)point.Point.X, (int)point.Point.Y), 1, new MCvScalar(0, 0, 255, 255), 2);
}
CvInvoke.Imshow("result", image);
CvInvoke.WaitKey();
}
//Set target image to retrieve keypoints and descriptor(SURF)
//Target 등록하여 디스크립터 추출
public void setTarget(string filename)
{
target = Cv2.ImRead(filename, LoadMode.GrayScale);
t_keypoints = surfobj.Detect(target); //SURF keypoint
surfobj.Compute(target, ref t_keypoints, t_descriptor); //SURF descriptor
obj_corners = new Point2d[4];
//target의 크기에 맞는 사각형 (For RANSAC)
obj_corners[0] = new Point2d(0, 0);
obj_corners[1] = new Point2d(target.Cols, 0);
obj_corners[2] = new Point2d(target.Cols, target.Rows);
obj_corners[3] = new Point2d(0, target.Rows);
}
public UMat SURFDescriptor()
{
double hessianThresh = 800;
// public SURF(double hessianThresh, int nOctaves = 4, int nOctaveLayers = 2, bool extended = true, bool upright = false)
SURF surfAlgo = new SURF(hessianThresh, 4, 2, true, false);
VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint();
MKeyPoint[] mKeyPoints = surfAlgo.Detect(preProcessedImageInGrayScale);
modelKeyPoints.Push(mKeyPoints);
VectorOfKeyPoint observedKeyPoints = new VectorOfKeyPoint();
UMat SurfDescriptors = new UMat();
surfAlgo.DetectAndCompute(preProcessedImageInGrayScale, null, modelKeyPoints, SurfDescriptors, true);
//image2.Source = BitmapSourceConvert.ToBitmapSource(modelDescriptors);
SurfDescriptors.Save("SURFDetection.jpg");
return(SurfDescriptors);
}
public ImageModel ConvertImagePathToImageModel(string imagePath)
{
Mat imgObject = Cv2.ImRead(imagePath, ImreadModes.Grayscale);
Mat descriptorsObject = new Mat();
double minHessian = 400;
SURF detector = SURF.Create(minHessian, extended: false);
var keypointsObject = detector.Detect(imgObject).Take(1000).ToArray();
detector.Compute(imgObject, ref keypointsObject, descriptorsObject);
var imageModel = new ImageModel()
{
ImagePath = imagePath,
ImageHash = ImageHelper.GetImageHash(imagePath),
Descriptor = ConvertOpenCVTypes.ConvertMatToFloatArray(descriptorsObject)
};
return(imageModel);
}
public AlgorithmResult DetectSurf(
string filename,
KeypointType kpsType,
double hessianThresh,
int octaves,
int octaveLayers)
{
AlgorithmResult result = new AlgorithmResult();
Image <Bgr, byte> image = ImageHelper.GetImage(filename);
Image <Bgr, byte> resultImage = new Image <Bgr, byte>(filename);
// Get features from image
var surf = new SURF(hessianThresh, octaves, octaveLayers);
var keyPoints = surf.Detect(image);
DrawKeypoints(
image,
new VectorOfKeyPoint(keyPoints),
resultImage,
new Bgr(Color.FromArgb(255, 77, 77)),
GetKeypointDraw(kpsType));
result.ImageArray = ImageHelper.SetImage(resultImage);
result.KeyDatas = new List <KeyPointModel>();
result.KeyDatas.AddRange(keyPoints.Select(k => new KeyPointModel()
{
X = k.Point.X,
Y = k.Point.Y,
Size = k.Size,
Angle = k.Angle,
Response = k.Response,
Octave = k.Octave,
ClassId = k.ClassId
}));
return(result);
}
static void train_svm()
{
int n_samples = 0;
SURF surf = new SURF(400);
List <Bitmap> samples = new List <Bitmap>();
List <Tuple <Bitmap, int> > data = new List <Tuple <Bitmap, int> >();
/*
* foreach (string s in System.IO.Directory.GetFiles("mail_samples"))
* {
* Bitmap f1 = new Bitmap(s);//ImageDecoder.DecodeFromFile(s);
* data.Add(new Tuple<Bitmap, int>(f1, +1));
* }
* foreach (string s in System.IO.Directory.GetFiles("phone_icons"))
* {
* Bitmap f1 = new Bitmap(s); //ImageDecoder.DecodeFromFile(s);
* data.Add(new Tuple<Bitmap, int>(f1, -1));
* }
*/
foreach (string s in System.IO.Directory.GetFiles(@"C:\test\iphone_icon"))
{
Bitmap f1 = new Bitmap(s);//ImageDecoder.DecodeFromFile(s);
if (string.Compare(System.IO.Path.GetFileNameWithoutExtension(s), "temp_1") == 0 ||
string.Compare(System.IO.Path.GetFileNameWithoutExtension(s), "scoll_selected_icon") == 0
)
{
data.Add(new Tuple <Bitmap, int>(f1, +1));
}
else
{
data.Add(new Tuple <Bitmap, int>(f1, 0));
}
}
n_samples = data.Count;
// computr bow
Mat m = new Mat();
foreach (Tuple <Bitmap, int> v in data)
{
Image <Bgr, Byte> i = new Image <Bgr, byte>(v.Item1);
Mat ii = new Mat();
CvInvoke.CvtColor(i, ii, ColorConversion.Bgr2Gray);
MKeyPoint[] kp = surf.Detect(ii);
Mat desc = new Mat();
surf.Compute(ii, new VectorOfKeyPoint(kp), desc);
m.PushBack(desc);
}
// Create the vocabulary with KMeans.
MCvTermCriteria tc = new MCvTermCriteria(100, 0.00001);
BOWKMeansTrainer bowTrainer = new BOWKMeansTrainer(16, tc, 3, KMeansInitType.PPCenters);
bowTrainer.Add(m);
Mat voca = new Mat();
bowTrainer.Cluster(voca);
//
BFMatcher matcher = new BFMatcher(DistanceType.L2);
BOWImgDescriptorExtractor bowDex = new BOWImgDescriptorExtractor(surf, matcher);
bowDex.SetVocabulary(voca);
//
Mat tDesc = new Mat();
//Matrix<int> tLabel = new Matrix<int>(1, n_samples);
Matrix <int> tLabel = new Matrix <int>(n_samples, 1);
//foreach (Tuple<Bitmap, int> v in data)
for (int j = 0; j < data.Count; j++)
{
Image <Bgr, Byte> i = new Image <Bgr, byte>(data[j].Item1);
Mat ii = new Mat();
CvInvoke.CvtColor(i, ii, ColorConversion.Bgr2Gray);
MKeyPoint[] kp = surf.Detect(ii);
Mat desc = new Mat();
bowDex.Compute(ii, new VectorOfKeyPoint(kp), desc);
tDesc.PushBack(desc);
//tLabel[0, j] = data[j].Item2;
tLabel[j, 0] = data[j].Item2;
}
//
//SVM model = new SVM();
//model.SetKernel(Emgu.CV.ML.SVM.SvmKernelType.Linear);
//model.Type = SVM.SvmType.CSvc;
//model.C = 1;
//model.TermCriteria = new MCvTermCriteria(100, 0.00001);
SVM svm = new SVM();
svm.C = 312.5;
svm.Gamma = 0.50625000000000009;
svm.SetKernel(SVM.SvmKernelType.Rbf);
svm.Type = SVM.SvmType.CSvc;
svm.Nu = 0.5;
TrainData td = new TrainData(tDesc, Emgu.CV.ML.MlEnum.DataLayoutType.RowSample, tLabel);
bool tained = svm.TrainAuto(td);
using (FileStorage fs = new FileStorage("voca.yaml", FileStorage.Mode.Write))
{
svm.Write(fs);
fs.Write(voca, "voca");
}
//using (FileStorage fs = new FileStorage("svm.yaml", FileStorage.Mode.Write))
//{
// svm.Write(fs);
//}
//svm.Save("svm.yaml");
// test
{
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\test\scroll_left.jpg");
Image <Bgr, Byte> test_img = new Image <Bgr, byte>(@"C:\test\iphone_icon\temp_1.jpg");
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\projects\local\testMQ\testMQ\bin\Debug\phone_icons\icon_2.jpg");
//Image<Bgr, Byte> test_img = new Image<Bgr, byte>(@"C:\test\35928233-email-icon-on-blue-background-clean-vector.jpg");
Mat ii = new Mat();
CvInvoke.CvtColor(test_img, ii, ColorConversion.Bgr2Gray);
MKeyPoint[] kp = surf.Detect(ii);
Mat desc = new Mat();
bowDex.Compute(ii, new VectorOfKeyPoint(kp), desc);
float r = svm.Predict(desc);
}
}
public void TestSURFDetector2()
{
//Trace.WriteLine("Size of MCvSURFParams: " + Marshal.SizeOf(typeof(MCvSURFParams)));
Image <Gray, byte> box = EmguAssert.LoadImage <Gray, byte>("box.png");
SURF detector = new SURF(400);
Stopwatch watch = Stopwatch.StartNew();
VectorOfKeyPoint vp1 = new VectorOfKeyPoint();
Mat descriptors1 = new Mat();
detector.DetectAndCompute(box, null, vp1, descriptors1, false);
watch.Stop();
EmguAssert.WriteLine(String.Format("Time used: {0} milliseconds.", watch.ElapsedMilliseconds));
watch.Reset();
watch.Start();
MKeyPoint[] keypoints = detector.Detect(box, null);
//ImageFeature<float>[] features2 = detector.Compute(box, keypoints);
watch.Stop();
EmguAssert.WriteLine(String.Format("Time used: {0} milliseconds.", watch.ElapsedMilliseconds));
watch.Reset();
watch.Start();
//MCvSURFParams p = detector.SURFParams;
//SURFFeature[] features3 = box.ExtractSURF(ref p);
//watch.Stop();
//EmguAssert.WriteLine(String.Format("Time used: {0} milliseconds.", watch.ElapsedMilliseconds));
// EmguAssert.IsTrue(features1.Length == features2.Length);
//EmguAssert.IsTrue(features2.Length == features3.Length);
PointF[] pts =
#if NETFX_CORE
Extensions.
#else
Array.
#endif
ConvertAll <MKeyPoint, PointF>(keypoints, delegate(MKeyPoint mkp)
{
return(mkp.Point);
});
//SURFFeature[] features = box.ExtractSURF(pts, null, ref detector);
//int count = features.Length;
/*
* for (int i = 0; i < features1.Length; i++)
* {
* Assert.AreEqual(features1[i].KeyPoint.Point, features2[i].KeyPoint.Point);
* float[] d1 = features1[i].Descriptor;
* float[] d2 = features2[i].Descriptor;
*
* for (int j = 0; j < d1.Length; j++)
* Assert.AreEqual(d1[j], d2[j]);
* }*/
foreach (MKeyPoint kp in keypoints)
{
box.Draw(new CircleF(kp.Point, kp.Size), new Gray(255), 1);
}
}
private void стартToolStripMenuItem_Click(object sender, EventArgs e)
{
try
{
labelstrip.Text = "Получение изображения";
// берем изображение из PictureBox и переводим bitmap в Mat
Bitmap image1b = (Bitmap)objectPictureBox.Image;
Bitmap image2b = (Bitmap)scenePictureBox.Image;
Mat image1 = BitmapConverter.ToMat(image1b);
Mat image2 = BitmapConverter.ToMat(image2b);
//создаем SURF переменную
SURF surf = new SURF(form.getThreshold(), form.getOctaves(), form.getOctavesLayer(), form.getDecriptors(), false);
labelstrip.Text = "Нахождение особых точек и их дескриптеров";
//создаем по 2 переменных для записи ключевых точек и дескриптеров
Mat descriptors1 = new Mat();
Mat descriptors2 = new Mat();
KeyPoint[] points1, points2;
//находим особые точки
points1 = surf.Detect(image1);
points2 = surf.Detect(image2);
//нахождение дескрипторов точек
surf.Compute(image1, ref points1, descriptors1);
surf.Compute(image2, ref points2, descriptors2);
//матчим массивы дескрипторов
FlannBasedMatcher matcher = new FlannBasedMatcher();
DMatch[] matches;
matches = matcher.Match(descriptors1, descriptors2);
//Вычисление максимального и минимального расстояния среди всех дескрипторов
double max_dist = 0; double min_dist = 100;
for (int i = 0; i < descriptors1.Rows; i++)
{
double dist = matches[i].Distance;
if (dist < min_dist)
{
min_dist = dist;
}
if (dist > max_dist)
{
max_dist = dist;
}
}
labelstrip.Text = "Отбор точек";
// Отобрать только хорошие матчи, расстояние меньше чем 3 * min_dist
List <DMatch> good_matches = new List <DMatch>();
for (int i = 0; i < matches.Length; i++)
{
if (matches[i].Distance < form.getMinDinst() * min_dist)
{
good_matches.Add(matches[i]);
}
}
Mat image3 = new Mat();
Cv2.DrawMatches(image1, points1, image2, points2, good_matches, image3, Scalar.RandomColor(), Scalar.RandomColor(), null, DrawMatchesFlags.NotDrawSinglePoints);
labelstrip.Text = "Локализация объекта";
//Использование гомографии
// Локализация объектов
Point2f[] vector = new Point2f[good_matches.Count];
Point2d[] vector1 = new Point2d[good_matches.Count];
Point2d[] vector2 = new Point2d[good_matches.Count];
for (int i = 0; i < good_matches.Count; i++)
{
vector[i] = points1[good_matches[i].QueryIdx].Pt;
vector1[i].X = vector[i].X;
vector1[i].Y = vector[i].Y;
vector[i] = points2[good_matches[i].TrainIdx].Pt;
vector2[i].X = vector[i].X;
vector2[i].Y = vector[i].Y;
}
Mat H = Cv2.FindHomography(vector1, vector2, HomographyMethod.Ransac);
//Получить "углы" изображения с целевым объектом
Point2d[] vector3 = new Point2d[4];
vector3[0].X = 0; vector3[0].Y = 0;
vector3[1].X = image1.Cols; vector3[1].Y = 0;
vector3[2].X = image1.Cols; vector3[2].Y = image1.Rows;
vector3[3].X = 0; vector3[3].Y = image1.Rows;
Point2d pointtest; pointtest.X = 0; pointtest.Y = 0;
List <Point2d> vector4 = new List <Point2d>()
{
pointtest, pointtest, pointtest, pointtest
};
//Отобразить углы целевого объекта, используя найденное преобразование, на сцену
Cv2.PerspectiveTransform(InputArray.Create(vector3), OutputArray.Create(vector4), H); //?
Point2d point1;
Point2d point2;
int k;
for (int i = 0; i < 4; i++)
{
if (i == 3)
{
k = 0;
}
else
{
k = i + 1;
}
point1.X = vector4[i].X + image1.Cols;
point1.Y = vector4[i].Y + 0;
point2.X = vector4[k].X + image1.Cols;
point2.Y = vector4[k].Y + 0;
Cv2.Line(image3, point1, point2, Scalar.RandomColor(), 4);
}
labelstrip.Text = "Объект локализован";
Bitmap image3b = BitmapConverter.ToBitmap(image3);
imageForm.getresultimage(image3b);
imageForm.ShowDialog();
}
catch (Exception ex)
{
MessageBox.Show(Convert.ToString(ex));
labelstrip.Text = "Произошла ошибка";
}
}
/// <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);
}
}
