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 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();
}
private void MatchBySurf(Mat src1, Mat src2)
{
Mat gray1 = new Mat();
Mat gray2 = new Mat();
Cv2.CvtColor(src1, gray1, ColorConversion.BgrToGray);
Cv2.CvtColor(src2, gray2, ColorConversion.BgrToGray);
SURF surf = new SURF(500, 4, 2, true);
// Detect the keypoints and generate their descriptors using SURF
KeyPoint[] keypoints1, keypoints2;
MatOfFloat descriptors1 = new MatOfFloat();
MatOfFloat descriptors2 = new MatOfFloat();
surf.Run(gray1, null, out keypoints1, descriptors1);
surf.Run(gray2, null, out keypoints2, descriptors2);
// Matching descriptor vectors with a brute force matcher
BFMatcher matcher = new BFMatcher(NormType.L2, false);
DMatch[] matches = matcher.Match(descriptors1, descriptors2);
// Draw matches
Mat view = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, matches, view);
using (new Window("SURF matching", WindowMode.AutoSize, view))
{
Cv2.WaitKey();
}
}
private VectorOfPoint _octagon; //Искомая область
/// <summary>
/// Конструктор.
/// </summary>
/// <param name="brickSingModel">Обрабатываемое изображение. Принимается Image<Bgr, Byte></param>
public SingDetectorMethodCanny(IInputArray brickSingModel)
{
_detector = new SURF(500);
using (Mat redMask = new Mat())
{
GetRedPixelMask(brickSingModel, redMask);
_modelKeypoints = new VectorOfKeyPoint();
_modelDescriptors = new Mat();
_detector.DetectAndCompute(redMask, null, _modelKeypoints, _modelDescriptors, false);
if (_modelKeypoints.Size == 0)
{
//throw new Exception("Изображение для обработки не загружено");
}
}
_modelDescriptorMatcher = new BFMatcher(DistanceType.L2);
_modelDescriptorMatcher.Add(_modelDescriptors);
_octagon = new VectorOfPoint(
new Point[] {
new Point(1, 0),
new Point(2, 0),
new Point(3, 1),
new Point(3, 2),
new Point(2, 3),
new Point(1, 3),
new Point(0, 2),
new Point(0, 1)
});
}
/// <summary>
/// Generate a SignMatcher with custom path
/// </summary>
/// <param name="knownSignsPath"> Folder with known signs </param>
public SignMatcher(string knownSignsPath)
{
KnownSignsPath = knownSignsPath;
// Upright-SURF to speedup the process with extended descriptors
detector = new SURF(500, 4, 3, true, true);
}
public static void FindMatch(Mat modelImage, Mat observedImage, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography)
{
int k = 2;
double uniquenessThreshold = 0.80;
double hessianThresh = 100;
Stopwatch watch;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
{
SURF surfCPU = new SURF(hessianThresh);
SIFT siftCPU = new SIFT();
//extract features from the object image
UMat modelDescriptors = new UMat();
//surfCPU.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
siftCPU.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
watch = Stopwatch.StartNew();
// extract features from the observed image
UMat observedDescriptors = new UMat();
//surfCPU.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
siftCPU.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
//Features2DToolbox.VoteForUniqueness(matches, 1, mask);
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints,
matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints,
observedKeyPoints, matches, mask, 2);
}
}
watch.Stop();
}
matchTime = watch.ElapsedMilliseconds;
}
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);
// 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();
}
}
private void MatchBySurf(Mat src1, Mat src2)
{
Mat gray1 = new Mat();
Mat gray2 = new Mat();
Cv2.CvtColor(src1, gray1, ColorConversion.BgrToGray);
Cv2.CvtColor(src2, gray2, ColorConversion.BgrToGray);
SURF surf = new SURF(500, 4, 2, true);
// Detect the keypoints and generate their descriptors using SURF
KeyPoint[] keypoints1, keypoints2;
MatOfFloat descriptors1 = new MatOfFloat();
MatOfFloat descriptors2 = new MatOfFloat();
surf.Run(gray1, null, out keypoints1, descriptors1);
surf.Run(gray2, null, out keypoints2, descriptors2);
// Matching descriptor vectors with a brute force matcher
BFMatcher matcher = new BFMatcher(NormType.L2, false);
DMatch[] matches = matcher.Match(descriptors1, descriptors2);
// Draw matches
Mat view = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, matches, view);
using (new Window("SURF matching", WindowMode.AutoSize, view))
{
Cv2.WaitKey();
}
}
public void TestSURF()
{
SURF detector = new SURF(500);
//ParamDef[] parameters = detector.GetParams();
EmguAssert.IsTrue(TestFeature2DTracker(detector, detector), "Unable to find homography matrix");
}
public void Run(Mat gray1, Mat gray2, Mat dst, int hessianThreshold, bool useBFMatcher)
{
var surf = SURF.Create(hessianThreshold, 4, 2, true);
KeyPoint[] keypoints1, keypoints2;
var descriptors1 = new Mat();
var descriptors2 = new Mat();
surf.DetectAndCompute(gray1, null, out keypoints1, descriptors1);
surf.DetectAndCompute(gray2, null, out keypoints2, descriptors2);
if (useBFMatcher)
{
if (descriptors1.Rows > 0 && descriptors2.Rows > 0) // occasionally there is nothing to match!
{
var bfMatcher = new BFMatcher(NormTypes.L2, false);
DMatch[] bfMatches = bfMatcher.Match(descriptors1, descriptors2);
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, bfMatches, dst);
}
}
else
{
var flannMatcher = new FlannBasedMatcher();
if (descriptors1.Width > 0 && descriptors2.Width > 0)
{
DMatch[] flannMatches = flannMatcher.Match(descriptors1, descriptors2);
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, flannMatches, dst);
}
}
}
public void TestLATCH()
{
SURF surf = new SURF(300);
LATCH latch = new LATCH();
EmguAssert.IsTrue(TestFeature2DTracker(surf, latch), "Unable to find homography matrix");
}
public StopSignDetector(IInputArray stopSignModel)
{
_detector = new SURF(500);
using (Mat redMask = new Mat())
{
GetRedPixelMask(stopSignModel, redMask);
_modelKeypoints = new VectorOfKeyPoint();
_modelDescriptors = new Mat();
_detector.DetectAndCompute(redMask, null, _modelKeypoints, _modelDescriptors, false);
if (_modelKeypoints.Size == 0)
{
throw new Exception("No image feature has been found in the stop sign model");
}
}
_modelDescriptorMatcher = new BFMatcher(DistanceType.L2);
_modelDescriptorMatcher.Add(_modelDescriptors);
_octagon = new VectorOfPoint(
new Point[]
{
new Point(1, 0),
new Point(2, 0),
new Point(3, 1),
new Point(3, 2),
new Point(2, 3),
new Point(1, 3),
new Point(0, 2),
new Point(0, 1)
});
}
public void TestDAISY()
{
SURF surf = new SURF(300);
DAISY daisy = new DAISY();
EmguAssert.IsTrue(TestFeature2DTracker(surf, daisy), "Unable to find homography matrix");
}
private void MatchBySurf(Mat src1, Mat src2)
{
using var gray1 = new Mat();
using var gray2 = new Mat();
Cv2.CvtColor(src1, gray1, ColorConversionCodes.BGR2GRAY);
Cv2.CvtColor(src2, gray2, ColorConversionCodes.BGR2GRAY);
using var surf = SURF.Create(200, 4, 2, true);
// Detect the keypoints and generate their descriptors using SURF
KeyPoint[] keypoints1, keypoints2;
using var descriptors1 = new Mat <float>();
using var descriptors2 = new Mat <float>();
surf.DetectAndCompute(gray1, null, out keypoints1, descriptors1);
surf.DetectAndCompute(gray2, null, out keypoints2, descriptors2);
// Match descriptor vectors
using var bfMatcher = new BFMatcher(NormTypes.L2, false);
using var flannMatcher = new FlannBasedMatcher();
DMatch[] bfMatches = bfMatcher.Match(descriptors1, descriptors2);
DMatch[] flannMatches = flannMatcher.Match(descriptors1, descriptors2);
// Draw matches
using var bfView = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, bfMatches, bfView);
using 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();
}
}
public void TestBOWKmeansTrainer()
{
Image <Gray, byte> box = EmguAssert.LoadImage <Gray, byte>("box.png");
SURF detector = new SURF(500);
VectorOfKeyPoint kpts = new VectorOfKeyPoint();
Mat descriptors = new Mat();
detector.DetectAndCompute(box, null, kpts, descriptors, false);
BOWKMeansTrainer trainer = new BOWKMeansTrainer(100, new MCvTermCriteria(), 3, CvEnum.KMeansInitType.PPCenters);
trainer.Add(descriptors);
Mat vocabulary = new Mat();
trainer.Cluster(vocabulary);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
BOWImgDescriptorExtractor extractor = new BOWImgDescriptorExtractor(detector, matcher);
extractor.SetVocabulary(vocabulary);
Mat descriptors2 = new Mat();
extractor.Compute(box, kpts, descriptors2);
}
private void FindAndDrawMatches()
{
using (var template = new Mat("Images\\Circle_Template.bmp", ImreadModes.GrayScale))
{
using (var surf = SURF.Create(1000))
{
using (var templateDescriptors = new Mat())
{
surf.DetectAndCompute(template, null, out KeyPoint[] templateKeyPoints, templateDescriptors);
using (var image = new Mat("Images\\Circle.bmp", ImreadModes.GrayScale))
{
using (var imageDescriptors = new Mat())
{
surf.DetectAndCompute(image, null, out KeyPoint[] imageKeyPoints, imageDescriptors);
using (var matcher = new BFMatcher())
{
var matches = matcher.Match(imageDescriptors, templateDescriptors);
using (var overLay = image.Overlay())
{
Cv2.DrawMatches(image, imageKeyPoints, template, templateKeyPoints, matches, overLay);
this.Result.Source = overLay.ToBitmapSource();
}
}
}
}
}
}
}
}
private void FindAndDrawHomo()
{
using (var template = new Mat("Images\\Circle_Template.bmp", ImreadModes.Color))
{
using (var surf = SURF.Create(1000))
{
using (var templateDescriptors = new Mat())
{
surf.DetectAndCompute(template, null, out KeyPoint[] templateKeyPoints, templateDescriptors);
using (var image = new Mat("Images\\Circle.bmp", ImreadModes.Color))
{
using (var imageDescriptors = new Mat())
{
surf.DetectAndCompute(image, null, out KeyPoint[] imageKeyPoints, imageDescriptors);
using (var matcher = new BFMatcher())
{
var matches = matcher.Match(imageDescriptors, templateDescriptors);
var goodMatches = matches;//.Where(m => m.Distance < 0.2).ToArray();
using (var srcPoints = InputArray.Create(goodMatches.Select(m => templateKeyPoints[m.TrainIdx].Pt)))
{
using (var dstPoints = InputArray.Create(goodMatches.Select(m => imageKeyPoints[m.QueryIdx].Pt)))
{
using (var h**o = Cv2.FindHomography(srcPoints, dstPoints, HomographyMethods.Rho))
{
////using (var overlay = image.Overlay())
////{
//// DrawBox(template, h**o, overlay);
//// this.Result.Source = overlay.ToBitmapSource();
////}
using (var tmp = image.Overlay())
{
Cv2.BitwiseNot(template, template);
Cv2.WarpPerspective(template, tmp, h**o, tmp.Size());
using (var overlay = tmp.Overlay())
{
for (var r = 0; r < tmp.Rows; r++)
{
for (var c = 0; c < tmp.Cols; c++)
{
overlay.Set(r, c,
tmp.At <int>(r, c) == 0
? new Vec4b(0, 0, 0, 0)
: new Vec4b(0, 0, 255, 150));
}
}
this.Result.Source = overlay.ToBitmapSource();
}
}
}
}
}
}
}
}
}
}
}
}
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));
}
}
public void New2()
{
var descriptorExtractor = SURF.Create(100);
var descriptorMatcher = new BFMatcher();
new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
}
/**
* Extracts all the important points from a Mat image
**/
public static VectorOfKeyPoint extractKeyPoints(Mat image, int minHessian = 400)
{
VectorOfKeyPoint res = new VectorOfKeyPoint();
SURF detector = new SURF(minHessian);
detector.DetectRaw(image, res);
return(res);
}
public void New4()
{
using (var ip = new LinearIndexParams())
using (var sp = new SearchParams())
using (var descriptorExtractor = SURF.Create(100))
using (var descriptorMatcher = new FlannBasedMatcher(ip, sp))
using (new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher)) { }
}
public void SURFDraw(Mat image, Mat testImage)
{
VectorOfKeyPoint keyPoint = new VectorOfKeyPoint();
SURF surfCPU = new SURF(500, 4, 2, true, false);
surfCPU.DetectRaw(image, keyPoint);
Features2DToolbox.DrawKeypoints(image, keyPoint, testImage, new Bgr(Color.Red), Features2DToolbox.KeypointDrawType.Default);
}
public static Tuple <UMat, VectorOfKeyPoint> DetectAndCompute
(SURF surf, Image <Bgr, byte> image, bool b, IInputArray inputArray = null)
{
var keypoints = new VectorOfKeyPoint();
var desc = new UMat();
surf.DetectAndCompute(image, inputArray, keypoints, desc, b);
return(new Tuple <UMat, VectorOfKeyPoint>(desc, keypoints));
}
public void New2Flann()
{
using (var descriptorExtractor = SURF.Create(100))
using (var descriptorMatcher = new FlannBasedMatcher())
{
using (new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher)) { }
using (new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher)) { }
}
}
public void Detect()
{
// This parameter should introduce same result of http://opencv.jp/wordpress/wp-content/uploads/lenna_SURF-150x150.png
using var gray = Image("lenna.png", 0);
using var surf = SURF.Create(500, 4, 2, true);
var keyPoints = surf.Detect(gray);
Console.WriteLine($"KeyPoint has {keyPoints.Length} items.");
}
public void New4()
{
var descriptorExtractor = SURF.Create(100);
LinearIndexParams ip = new LinearIndexParams();
SearchParams sp = new SearchParams();
var descriptorMatcher = new FlannBasedMatcher(ip, sp);
new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
}
public void TestSURFBlankImage()
{
SURF detector = new SURF(500);
Image <Gray, Byte> img = new Image <Gray, byte>(1024, 900);
VectorOfKeyPoint vp = new VectorOfKeyPoint();
Mat descriptors = new Mat();
detector.DetectAndCompute(img, null, vp, descriptors, false);
}
/// <summary>
/// Generates a SignMatcher with default path
/// </summary>
public SignMatcher()
{
if (Directory.Exists(@"KnownSigns/"))
{
KnownSignsPath = @"KnownSigns/";
}
// Upright-SURF to speedup the process with extended descriptors
detector = new SURF(500, 4, 3, true, true);
}
public void DetectAndCompute()
{
using (var gray = Image("lenna.png", ImreadModes.Grayscale))
using (var surf = SURF.Create(500))
using (Mat descriptor = new Mat())
{
surf.DetectAndCompute(gray, null, out var keyPoints, descriptor);
Console.WriteLine($"keyPoints has {keyPoints.Length} items.");
Console.WriteLine($"descriptor has {descriptor.Rows} items.");
}
}
public void Compute()
{
using (var color = Image("lenna.png", ImreadModes.Color))
using (var gray = Image("lenna.png", ImreadModes.GrayScale))
using (var descriptors = new Mat())
using (var latch = LATCH.Create())
using (var surf = SURF.Create(500))
{
var keypoints = surf.Detect(gray);
latch.Compute(color, ref keypoints, descriptors);
}
}
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();
}
public void Clear()
{
k = 2;
uniquenessThreshold = 0.8;
hessianThresh = 300;
CPU = new SURF(hessianThresh);
matcher = new BFMatcher(DistanceType.L2);
descriptor = new BriefDescriptorExtractor();
homography = null;
matches = new VectorOfVectorOfDMatch();
modelDescriptors = new Mat();
observedDescriptors = new Mat();
result = new Mat();
}
private void FillImageSet(List <ImageData> set, string prefix)
{
UtilityHelper.refreshDirectory(prefix);
if (dialog.ShowDialog() == DialogResult.OK)
{
var files = Directory.GetFiles(dialog.SelectedPath, "*.dcm");
foreach (var file in files)
{
var ds = new DicomImage(file);
var dsBones = new DicomImage(file)
{
WindowWidth = 100,
WindowCenter = 500
};
var image = ds.RenderImage().AsBitmap();
var imageBones = dsBones.RenderImage().AsBitmap();
string newName = prefix + "/" + Path.GetFileName(file).Replace(".dcm", ".jpg");
string newBonesName = prefix + "/" + Path.GetFileName(file).Replace(".dcm", "_bones.jpg");
image.Save(newName);
imageBones.Save(newBonesName);
Feature2D s;
switch (algorithm)
{
case Algo.ORB:
s = new ORBDetector();
break;
case Algo.SURF:
s = new SURF(0.8);
break;
default:
s = new SIFT();
break;
}
Mat mat = CvInvoke.Imread(newBonesName, ImreadModes.Grayscale);
Mat matOrig = CvInvoke.Imread(newName, ImreadModes.Unchanged);
var vec = new VectorOfKeyPoint();
Mat modelDescriptors = new Mat();
s.DetectAndCompute(mat, null, vec, modelDescriptors, false);
ImageData id = new ImageData(matOrig, mat)
{
KeyPoints = vec,
Descriptors = modelDescriptors
};
set.Add(id);
}
}
}