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 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;
Stopwatch watch;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
{
KAZE featureDetector = new KAZE();
//extract features from the object image
Mat modelDescriptors = new Mat();
featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
watch = Stopwatch.StartNew();
// extract features from the observed image
Mat observedDescriptors = new Mat();
featureDetector.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);
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;
}
public float classify(Image<Bgr, Byte> predImg)
{
using (SURF detector = new SURF(30))
using (BFMatcher matcher = new BFMatcher(DistanceType.L2))
using (Image<Gray, Byte> testImgGray = predImg.Convert<Gray, Byte>())
using (VectorOfKeyPoint testKeyPoints = new VectorOfKeyPoint())
using (Mat testBOWDescriptor = new Mat())
using( bowDE = new BOWImgDescriptorExtractor(detector, matcher))
{
float result = 0;
bowDE.SetVocabulary(vocabulary);
detector.DetectRaw(predImg, testKeyPoints, null);
bowDE.Compute(predImg, testKeyPoints, testBOWDescriptor);
if(!testBOWDescriptor.IsEmpty)
result = svmClassifier.Predict(testBOWDescriptor);
//result will indicate whether test image belongs to trainDescriptor label 1, 2
return result;
}
}
public void computeAndExtract()
{
using (detector = new SURF(30))
using (matcher = new BFMatcher(DistanceType.L2))
{
bowDE = new BOWImgDescriptorExtractor(detector, matcher);
BOWKMeansTrainer bowTrainer = new BOWKMeansTrainer(100, new MCvTermCriteria(100, 0.01), 3, Emgu.CV.CvEnum.KMeansInitType.PPCenters);
foreach(FileInfo[] folder in _folders)
foreach (FileInfo file in folder)
{
using (Image<Bgr, Byte> model = new Image<Bgr, byte>(file.FullName))
using (VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint())
//Detect SURF key points from images
{
detector.DetectRaw(model, modelKeyPoints);
//Compute detected SURF key points & extract modelDescriptors
Mat modelDescriptors = new Mat();
detector.Compute(model, modelKeyPoints, modelDescriptors);
//Add the extracted BoW modelDescriptors into BOW trainer
bowTrainer.Add(modelDescriptors);
}
input_num++;
}
//Cluster the feature vectors
bowTrainer.Cluster(vocabulary);
//Store the vocabulary
bowDE.SetVocabulary(vocabulary);
//training descriptors
tDescriptors = new Mat();
labels = new Matrix<int>(1, input_num);
int index = 0;
//compute and store BOWDescriptors and set labels
for (int i = 1; i <= _folders.Count; i++)
{
FileInfo[] files = _folders[i-1];
for (int j = 0; j < files.Length; j++)
{
FileInfo file = files[j];
using (Image<Bgr, Byte> model = new Image<Bgr, Byte>(file.FullName))
using (VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint())
using (Mat modelBOWDescriptor = new Mat())
{
detector.DetectRaw(model, modelKeyPoints);
bowDE.Compute(model, modelKeyPoints, modelBOWDescriptor);
tDescriptors.PushBack(modelBOWDescriptor);
labels[0, index++] = i;
}
}
}
}
}
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 = 0.8;
double hessianThresh = 300;
Stopwatch watch;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
#if !__IOS__
if ( CudaInvoke.HasCuda)
{
CudaSURF surfCuda = new CudaSURF((float) hessianThresh);
using (GpuMat gpuModelImage = new GpuMat(modelImage))
//extract features from the object image
using (GpuMat gpuModelKeyPoints = surfCuda.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat gpuModelDescriptors = surfCuda.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (CudaBFMatcher matcher = new CudaBFMatcher(DistanceType.L2))
{
surfCuda.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
using (GpuMat gpuObservedImage = new GpuMat(observedImage))
using (GpuMat gpuObservedKeyPoints = surfCuda.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat gpuObservedDescriptors = surfCuda.ComputeDescriptorsRaw(gpuObservedImage, null, gpuObservedKeyPoints))
//using (GpuMat tmp = new GpuMat())
//using (Stream stream = new Stream())
{
matcher.KnnMatch(gpuObservedDescriptors, gpuModelDescriptors, matches, k);
surfCuda.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, 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();
}
}
else
#endif
{
using (UMat uModelImage = modelImage.ToUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.ToUMat(AccessType.Read))
{
SURF surfCPU = new SURF(hessianThresh);
//extract features from the object image
UMat modelDescriptors = new UMat();
surfCPU.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);
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);
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;
}
public Mat Calculate(Bitmap referenceBitmap, Bitmap currentBitmap)
{
Mat homography;
using (var detector = new SURF(threshold))
using (var model = new Image<Gray, byte>(referenceBitmap))
using (var modelMat = model.Mat.ToUMat(AccessType.Read))
using (var modelKeyPoints = new VectorOfKeyPoint())
using (var modelDescriptors = new UMat())
using (var observed = new Image<Gray, byte>(currentBitmap))
using (var observedMat = observed.Mat.ToUMat(AccessType.Read))
using (var observedKeyPoints = new VectorOfKeyPoint())
using (var observedDescriptors = new UMat())
using (var matcher = new BFMatcher(DistanceType.L2))
using (var matches = new VectorOfVectorOfDMatch())
{
detector.DetectAndCompute(modelMat, null, modelKeyPoints, modelDescriptors, false);
detector.DetectAndCompute(observedMat, null, observedKeyPoints, observedDescriptors, false);
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
homography = TryFindHomography(modelKeyPoints, observedKeyPoints, matches);
}
return homography;
}
private void CalculateMatch(string username, string keyDescClass)
{
try
{
string decKeyClass = DecryptText(keyDescClass);
KeypointsDescriptors kpDescriptors = DeSerializeObject <KeypointsDescriptors>(decKeyClass);
Matrix <byte> useDescriptors = kpDescriptors.descriptors;
VectorOfKeyPoint useKeyPoints = kpDescriptors.keyPoints;
using (var db = new BioMexDatabaseEntities1())
{
BFMatcher matcher = new Emgu.CV.Features2D.BFMatcher(DistanceType.L2);
Matrix <byte> mask;
User use = (from person in db.Users where ((person.US_USERNAME == username)) select person).FirstOrDefault();
var regUser = new UserClass(use.US_USERNAME, use.US_PASSWORD, use.IdealFeatures.FirstOrDefault().IF_PASSWORD_COUNT, use.IdealFeatures.FirstOrDefault().IF_SHIFT_CLASS,
use.IdealFeatures.FirstOrDefault().IF_TYPING_SPEED, 0, DeserializeIntegers(use.IdealFeatures.FirstOrDefault().IF_KEY_ORDER.ToString()).ToArray(), DeserializeIntegers(use.IdealFeatures.FirstOrDefault().IF_KEY_LATENCIES.ToString()).ToArray(),
DeserializeIntegers(use.IdealFeatures.FirstOrDefault().IF_KEY_PRESS_DURATION.ToString()).ToArray(), DeserializeIntegers(use.IdealFeatures.FirstOrDefault().IF_PAIRED_KEYS.ToString()).ToArray());
if (use != null && use.US_USERNAME.Equals(username))
{
matcher.Add(useDescriptors);
Matrix <byte> observedDesc = DeSerializeObject <Matrix <byte> >(use.Features.FirstOrDefault().FaceFeature.FF_DESCRIPTORS);
VectorOfKeyPoint observedKeypoints = DeSerializeObject <VectorOfKeyPoint>(use.Features.FirstOrDefault().FaceFeature.FF_KEY_POINTS);
Matrix <int> indices = new Matrix <int>(observedDesc.Rows, 2);
VectorOfVectorOfDMatch vectMatch = new VectorOfVectorOfDMatch();
using (Matrix <float> Dist = new Matrix <float>(observedDesc.Rows, 2))
{
matcher.KnnMatch(observedDesc, vectMatch, 2, null);
mask = new Matrix <byte>(Dist.Rows, 1);
mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(vectMatch, 0.8, mask.ToUMat().ToMat(AccessType.Read));
}
}
}
}
catch (Exception ex)
{
System.Diagnostics.Debug.WriteLine("Error while matching");
System.Diagnostics.Debug.WriteLine(ex.Message);
}
}
private void timer1_Tick(object sender, EventArgs e)
{
if (time == 10)
{
Mat frame = new Mat();
capture.Retrieve(frame, 0);
Mat grayVideo = new Mat();
CvInvoke.CvtColor(frame, grayVideo, ColorConversion.Bgr2Gray);
UMat videoDescriptors = new UMat();
VectorOfKeyPoint videoKeyPoints = new VectorOfKeyPoint();
calculatedescriptors(grayVideo, videoDescriptors, videoKeyPoints);
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
BFMatcher matcher = new BFMatcher(DistanceType.L2);
matcher.Add(originalImageDescriptors);
matcher.KnnMatch(videoDescriptors, matches, 2, null);
Mat mask = mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
Mat homography = new Mat();
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(originalImageKeyPoints, videoKeyPoints,
matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(originalImageKeyPoints, videoKeyPoints, matches, mask, 2);
}
Mat result = new Mat();
Features2DToolbox.DrawMatches(grayImage, originalImageKeyPoints, grayVideo, videoKeyPoints,
matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask);
if (homography != null)
{
//draw a rectangle along the projected model
Rectangle rect = new Rectangle(Point.Empty, grayImage.Size);
PointF[] pts = new PointF[]
{
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
pts = CvInvoke.PerspectiveTransform(pts, homography);
Point[] points = Array.ConvertAll<PointF, Point>(pts, Point.Round);
using (VectorOfPoint vp = new VectorOfPoint(points))
{
CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255), 5);
}
viewer.Image = result;
}
time = 0;
}
else
{
time++;
}
}
protected override void DisposeObject()
{
if (_modelKeypoints != null)
{
_modelKeypoints.Dispose();
_modelKeypoints = null;
}
if (_modelDescriptors != null)
{
_modelDescriptors.Dispose();
_modelDescriptors = null;
}
if (_modelDescriptorMatcher != null)
{
_modelDescriptorMatcher.Dispose();
_modelDescriptorMatcher = null;
}
if (_octagon != null)
{
_octagon.Dispose();
_octagon = null;
}
}
public void TestBOWKmeansTrainer2()
{
Image<Gray, byte> box = EmguAssert.LoadImage<Gray, byte>("box.png");
Brisk detector = new Brisk(30, 3, 1.0f);
VectorOfKeyPoint kpts = new VectorOfKeyPoint();
Mat descriptors = new Mat();
detector.DetectAndCompute(box, null, kpts, descriptors, false);
Mat descriptorsF = new Mat();
descriptors.ConvertTo(descriptorsF, CvEnum.DepthType.Cv32F);
//Matrix<float> descriptorsF = descriptors.Convert<float>();
BOWKMeansTrainer trainer = new BOWKMeansTrainer(100, new MCvTermCriteria(), 3, CvEnum.KMeansInitType.PPCenters);
trainer.Add(descriptorsF);
Mat vocabulary = new Mat();
trainer.Cluster(vocabulary);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
BOWImgDescriptorExtractor extractor = new BOWImgDescriptorExtractor(detector, matcher);
Mat vocabularyByte = new Mat();
vocabulary.ConvertTo(vocabularyByte, CvEnum.DepthType.Cv8U);
extractor.SetVocabulary(vocabularyByte);
Mat descriptors2 = new Mat();
extractor.Compute(box, kpts, descriptors2);
}
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);
}
public static bool TestFeature2DTracker(Feature2D keyPointDetector, Feature2D descriptorGenerator)
{
//for (int k = 0; k < 1; k++)
{
Feature2D feature2D = null;
if (keyPointDetector == descriptorGenerator)
{
feature2D = keyPointDetector as Feature2D;
}
Mat modelImage = EmguAssert.LoadMat("box.png");
//Image<Gray, Byte> modelImage = new Image<Gray, byte>("stop.jpg");
//modelImage = modelImage.Resize(400, 400, true);
//modelImage._EqualizeHist();
#region extract features from the object image
Stopwatch stopwatch = Stopwatch.StartNew();
VectorOfKeyPoint modelKeypoints = new VectorOfKeyPoint();
Mat modelDescriptors = new Mat();
if (feature2D != null)
{
feature2D.DetectAndCompute(modelImage, null, modelKeypoints, modelDescriptors, false);
}
else
{
keyPointDetector.DetectRaw(modelImage, modelKeypoints);
descriptorGenerator.Compute(modelImage, modelKeypoints, modelDescriptors);
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time to extract feature from model: {0} milli-sec", stopwatch.ElapsedMilliseconds));
#endregion
//Image<Gray, Byte> observedImage = new Image<Gray, byte>("traffic.jpg");
Image<Gray, Byte> observedImage = EmguAssert.LoadImage<Gray, byte>("box_in_scene.png");
//Image<Gray, Byte> observedImage = modelImage.Rotate(45, new Gray(0.0));
//image = image.Resize(400, 400, true);
//observedImage._EqualizeHist();
#region extract features from the observed image
stopwatch.Reset();
stopwatch.Start();
VectorOfKeyPoint observedKeypoints = new VectorOfKeyPoint();
using (Mat observedDescriptors = new Mat())
{
if (feature2D != null)
{
feature2D.DetectAndCompute(observedImage, null, observedKeypoints, observedDescriptors, false);
}
else
{
keyPointDetector.DetectRaw(observedImage, observedKeypoints);
descriptorGenerator.Compute(observedImage, observedKeypoints, observedDescriptors);
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time to extract feature from image: {0} milli-sec", stopwatch.ElapsedMilliseconds));
#endregion
//Merge the object image and the observed image into one big image for display
Image<Gray, Byte> res = modelImage.ToImage<Gray, Byte>().ConcateVertical(observedImage);
Rectangle rect = new Rectangle(Point.Empty, modelImage.Size);
PointF[] pts = new PointF[] {
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)};
Mat homography = null;
stopwatch.Reset();
stopwatch.Start();
int k = 2;
DistanceType dt = modelDescriptors.Depth == CvEnum.DepthType.Cv8U ? DistanceType.Hamming : DistanceType.L2;
//using (Matrix<int> indices = new Matrix<int>(observedDescriptors.Rows, k))
//using (Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k))
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
using (BFMatcher matcher = new BFMatcher(dt))
{
//ParamDef[] parameterDefs = matcher.GetParams();
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
//mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(matches, 0.8, 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);
}
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time for feature matching: {0} milli-sec", stopwatch.ElapsedMilliseconds));
bool success = false;
if (homography != null)
{
PointF[] points = pts.Clone() as PointF[];
points = CvInvoke.PerspectiveTransform(points, homography);
//homography.ProjectPoints(points);
for (int i = 0; i < points.Length; i++)
points[i].Y += modelImage.Height;
res.DrawPolyline(
#if NETFX_CORE
Extensions.
#else
Array.
#endif
ConvertAll<PointF, Point>(points, Point.Round), true, new Gray(255.0), 5);
success = true;
}
//Emgu.CV.UI.ImageViewer.Show(res);
return success;
}
/*
stopwatch.Reset(); stopwatch.Start();
//set the initial region to be the whole image
using (Image<Gray, Single> priorMask = new Image<Gray, float>(observedImage.Size))
{
priorMask.SetValue(1.0);
homography = tracker.CamShiftTrack(
observedFeatures,
(RectangleF)observedImage.ROI,
priorMask);
}
Trace.WriteLine(String.Format("Time for feature tracking: {0} milli-sec", stopwatch.ElapsedMilliseconds));
if (homography != null) //set the initial tracking window to be the whole image
{
PointF[] points = pts.Clone() as PointF[];
homography.ProjectPoints(points);
for (int i = 0; i < points.Length; i++)
points[i].Y += modelImage.Height;
res.DrawPolyline(Array.ConvertAll<PointF, Point>(points, Point.Round), true, new Gray(255.0), 5);
return true;
}
else
{
return false;
}*/
}
}
public Image Compare2Features(
string destFeatureFile,
string origFeatureFile,
string vpFileDest,
string vpFileOrig,
string destImageFile = "",
string origImageFile = "",
bool needMatchedImage = false)
{
EmguType destFeatures = Utils.ReadJsonFile<EmguType>(destFeatureFile);
EmguType origFeatures = Utils.ReadJsonFile<EmguType>(origFeatureFile);
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
BFMatcher matcher = new BFMatcher(DistanceType.L2);
matcher.Add(origFeatures.Descriptors);
matcher.KnnMatch(destFeatures.Descriptors, matches, 2, null);
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
Mat homography = null;
int Count = CvInvoke.CountNonZero(mask); //用于寻找模板在图中的位置
if (Count >= 4)
{
Count = Features2DToolbox.VoteForSizeAndOrientation(origFeatures.KeyPoints, destFeatures.KeyPoints, matches, mask, 1.5, 20);
if (Count >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(origFeatures.KeyPoints, destFeatures.KeyPoints, matches, mask, 2);
}
Mat showImage = null;
Mat pointImage = null;
if (needMatchedImage
&& !string.IsNullOrWhiteSpace(destImageFile)
&& !string.IsNullOrWhiteSpace(origImageFile))
{
Image<Gray, Byte> destImage = new Image<Gray, Byte>(destImageFile);
Image<Gray, Byte> origImage = new Image<Gray, Byte>(origImageFile);
showImage = new Mat(origImage.Size, DepthType.Cv8U, 3);
pointImage = new Mat(origImage.Size, DepthType.Cv8U, 3);
//add optical vp line
string vpPath = Path.GetDirectoryName(vpFileDest);
List<FileInfo> files = new List<FileInfo>();
DirectoryInfo dirInfo = new DirectoryInfo(vpPath);
FileInfo[] fsinfo = dirInfo.GetFiles();
FileInfo[] vppFiles = fsinfo.Where(p => p.Name.Contains(".jpgpp.dat")).OrderBy(p => p.Name).ToArray();
//FileInfo[] vpdFiles = fsinfo.Where(p => p.Name.Contains(".jpgpd.dat")).OrderBy(p => p.Name).ToArray();
for (int k = 0; k < vppFiles.Length - 1; k++)
{
VectorOfPointF vpDest = Utils.ReadJsonFile<VectorOfPointF>(vppFiles[k+1].FullName);
//VectorOfPointF vpOrig = Utils.ReadJsonFile<VectorOfPointF>(vpdFiles[k].FullName);
VectorOfPointF vpOrig = Utils.ReadJsonFile<VectorOfPointF>(vppFiles[k].FullName);
//Restart the start point of motion tracking.
if ((k+1) % Constants.DETECTIVE_GROUP_COUNT == 0)
continue;
Point[] pointsDest = Array.ConvertAll<PointF, Point>(vpDest.ToArray(), Point.Round);
Point[] pointsOirg = Array.ConvertAll<PointF, Point>(vpOrig.ToArray(), Point.Round);
for (int i = 0; i < pointsDest.Length; i++)
{
Point[] ps = { pointsDest[i], pointsOirg[i] };
CvInvoke.Polylines(pointImage, ps, true, new MCvScalar(0, 0, 255, 255));
//CvInvoke.Circle(pointImage, pointsOirg[i], 1, new MCvScalar(0, 255, 0, 255));
}
}
Image<Bgr, Byte> firstImg = new Image<Bgr, Byte>(origImageFile);
Image<Bgr, Byte> lastImg = new Image<Bgr, Byte>("D:\\MyPrj\\mygitcode\\MyCode\\ExamVideoProcess\\ExamVideoProcess\\bin\\x64\\Debug\\initVideo\\30Grayimg.jpg");
CvInvoke.AddWeighted(firstImg, 0.5, lastImg, 0.5, 0.0, showImage, DepthType.Cv8U);
CvInvoke.AddWeighted(showImage, 0.5, pointImage, 0.5, 0.0, showImage, DepthType.Cv8U);
/*
Features2DToolbox.DrawMatches(origImage.Convert<Gray, Byte>().Mat, origFeatures.KeyPoints, destImage.Convert<Gray, Byte>().Mat, destFeatures.KeyPoints, matches, showImage, new MCvScalar(255, 0, 255), new MCvScalar(0, 255, 255), mask);
if (homography != null) //如果在图中找到了模板，就把它画出来
{
Rectangle rect = new Rectangle(Point.Empty, origImage.Size);
PointF[] points = new PointF[]
{
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
points = CvInvoke.PerspectiveTransform(points, homography);
Point[] points2 = Array.ConvertAll<PointF, Point>(points, Point.Round);
VectorOfPoint vp = new VectorOfPoint(points2);
CvInvoke.Polylines(showImage, vp, true, new MCvScalar(255, 0, 0, 255), 15);
}
*/
return showImage.Bitmap;
}
return null;
}