public StopSignDetector(Image <Bgr, Byte> stopSignModel)
{
_detector = new SURFDetector(500);
using (Image <Gray, Byte> redMask = GetRedPixelMask(stopSignModel))
{
_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 TestSURF()
{
SURFDetector detector = new SURFDetector(500);
//ParamDef[] parameters = detector.GetParams();
EmguAssert.IsTrue(TestFeature2DTracker(detector, detector), "Unable to find homography matrix");
}
public List <VectorOfKeyPoint> SURF_BruteForceMatcher(Image <Gray, byte> model, Image <Gray, byte> observed, int hessianThreshould, out SURFDetector surfCPU)
{
surfCPU = new SURFDetector(hessianThreshould, false);
List <VectorOfKeyPoint> KeyPointsList = new List <VectorOfKeyPoint>();
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
try
{
modelKeyPoints = surfCPU.DetectKeyPointsRaw(model, null); // Extract features from the object image
observedKeyPoints = surfCPU.DetectKeyPointsRaw(observed, null); // Extract features from the observed image
if (modelKeyPoints.Size <= 0)
{
throw new System.ArgumentException("Can't find any keypoints in your model image!");
}
KeyPointsList.Add(modelKeyPoints);
KeyPointsList.Add(observedKeyPoints);
}
catch (Exception e)
{
Log.WriteLine("SURF_BruteForceMatcher: " + e.Message);
Console.WriteLine(e.Message);
throw e;
}
return(KeyPointsList);
}
public static extern void cvExtractSURF(
IntPtr image, IntPtr mask,
ref IntPtr keypoints,
ref IntPtr descriptors,
IntPtr storage,
SURFDetector parameters,
int useProvidedKeyPoints);
public StopSignDetector(Image <Bgr, Byte> stopSignModel)
{
_detector = new SURFDetector(500, false);
using (Image <Gray, Byte> redMask = GetRedPixelMask(stopSignModel))
{
ImageFeature <float>[] features = _detector.DetectFeatures(redMask, null);
if (features.Length == 0)
{
throw new Exception("No image feature has been found in the stop sign model");
}
_tracker = new Features2DTracker <float>(features);
}
_octagonStorage = new MemStorage();
_octagon = new Contour <Point>(_octagonStorage);
_octagon.PushMulti(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)
},
Emgu.CV.CvEnum.BACK_OR_FRONT.FRONT);
}
public void TestBOWKmeansTrainer()
{
Image <Gray, byte> box = EmguAssert.LoadImage <Gray, byte>("box.png");
SURFDetector detector = new SURFDetector(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 SurfDetector(String model)
{
surfCPU = new SURFDetector(500, false);
Image <Bgr, Byte> color = new Image <Bgr, byte>(model);
modelImage = color.Convert <Gray, Byte>();
//extract features from the object image
modelKeyPoints = surfCPU.DetectKeyPointsRaw(modelImage, null);
modelDescriptors = surfCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
matcher = new BruteForceMatcher <float>(DistanceType.L2);
matcher.Add(modelDescriptors);
//for computers with awesome GPUs...
if (GpuInvoke.HasCuda)
{
/*surfGPU = new GpuSURFDetector(500, 4, 4, false, 0.01f, true);
* gpuModelImage = new GpuImage<Gray, byte>(modelImage);
* gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw(gpuModelImage, null);
* gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints);
* matcher_gpu = new GpuBruteForceMatcher<float>(DistanceType.L2);
* modelKeyPoints = new VectorOfKeyPoint();
* surfGPU.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);*/
}
}
public void ProcessImage(Image <Gray, Byte> modelImage)
{
_modelKeyPoints = new VectorOfKeyPoint();
_modelImage = modelImage;
SURFDetector surfCPU = new SURFDetector(500, false);
_feature = surfCPU.DetectAndCompute(modelImage, null, _modelKeyPoints);
}
public void IndexFiles(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker,
Action <string> logWriter,
SurfSettings surfSetting = null)
{
#region Surf Dectator Region
double hessianThresh = 500;
double uniquenessThreshold = 0.8;
if (surfSetting != null)
{
hessianThresh = surfSetting.HessianThresh.Value;
uniquenessThreshold = surfSetting.UniquenessThreshold.Value;
}
SURFDetector surfDectector = new SURFDetector(hessianThresh, false);
#endregion
List <SURFRecord2> surfRecord2List = new List <SURFRecord2>();
Stopwatch sw1, sw2;
sw1 = Stopwatch.StartNew();
logWriter("Index started...");
int totalFileCount = imageFiles.Length;
for (int i = 0; i < totalFileCount; i++)
{
var fi = imageFiles[i];
using (Image <Gray, byte> observerImage = new Image <Gray, byte>(fi.FullName))
{
ImageFeature <float>[] observerFeatures = surfDectector.DetectFeatures(observerImage, null);
if (observerFeatures.Length > 4)
{
SURFRecord2 record = new SURFRecord2
{
Id = i,
ImageName = fi.Name,
ImagePath = fi.FullName,
observerFeatures = observerFeatures
};
surfRecord2List.Add(record);
}
else
{
Debug.WriteLine(fi.Name + " skip from index, because it didn't have significant feature");
}
}
IndexBgWorker.ReportProgress(i);
}
SurfRepository.AddSURFRecord2List(surfRecord2List);
sw1.Stop();
logWriter(string.Format("Index Complete, it tooked {0} ms. Saving Repository...", sw1.ElapsedMilliseconds));
sw2 = Stopwatch.StartNew();
SurfRepository.SaveRepository(SurfAlgo.Linear);
sw2.Stop();
logWriter(string.Format("Index tooked {0} ms. Saving Repository tooked {1} ms", sw1.ElapsedMilliseconds, sw2.ElapsedMilliseconds));
}
public void TestSURFBlankImage()
{
SURFDetector detector = new SURFDetector(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);
}
private static void FindMatch(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage, SurfSettings surfSettings, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, out Matrix <int> indices, out Matrix <byte> mask, out HomographyMatrix homography)
{
#region Surf Dectator Region
double hessianThresh = 500;
double uniquenessThreshold = 0.8;
if (surfSettings != null)
{
hessianThresh = surfSettings.HessianThresh.Value;
uniquenessThreshold = surfSettings.UniquenessThreshold.Value;
}
SURFDetector surfCPU = new SURFDetector(hessianThresh, false);
#endregion
int k = 2;
Stopwatch watch;
homography = null;
//extract features from the object image
modelKeyPoints = new VectorOfKeyPoint();
Matrix <float> modelDescriptors = surfCPU.DetectAndCompute(modelImage, null, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
observedKeyPoints = new VectorOfKeyPoint();
Matrix <float> observedDescriptors = surfCPU.DetectAndCompute(observedImage, null, observedKeyPoints);
BruteForceMatcher <float> matcher = new BruteForceMatcher <float>(DistanceType.L2);
matcher.Add(modelDescriptors);
indices = new Matrix <int>(observedDescriptors.Rows, k);
using (Matrix <float> dist = new Matrix <float>(observedDescriptors.Rows, k))
{
matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
mask = new Matrix <byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
}
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
}
watch.Stop();
matchTime = watch.ElapsedMilliseconds;
}
public static Image <Bgr, Byte> DrawFeatures(Image <Gray, Byte> modelImage)
{
SURFDetector surfCPU = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint();
Matrix <float> modelDescriptors = surfCPU.DetectAndCompute(modelImage, null, modelKeyPoints);
Image <Bgr, Byte> result = Features2DToolbox.DrawKeypoints(modelImage, modelKeyPoints, new Bgr(0, 0, 255), Features2DToolbox.KeypointDrawType.DEFAULT);
return(result);
}
private void CreateSurfaceTracker()
{
surfaceParameters = new SURFDetector(500, false);
using (Image <Bgr, Byte> stopSignModel = new Image <Bgr, Byte>(Properties.Resources.SignModel))
using (Image <Gray, Byte> redMask = GetRedPixelMask(stopSignModel))
{
featureTracker = new Features2DTracker <float>(surfaceParameters.DetectFeatures(redMask, null));
}
}
private ImageFeature <float>[] SURF(Bitmap M)
{
Image <Gray, byte> image = new Image <Gray, byte>(M);
SURFDetector sift = new SURFDetector(1.0f, true);
VectorOfKeyPoint keys = new VectorOfKeyPoint();
MKeyPoint[] key = sift.DetectKeyPoints(image, null);
ImageFeature <float>[] res = sift.ComputeDescriptors(image, null, key);
return(res);
}
public Homography()
{
InitializeComponent();
camera = new Capture(@"F:\Working\Final phase\DataSet\20150409_13-34-33.asf");
surfParam = new SURFDetector(500, false);
modelImage = new Image <Rgb, byte>(@"C:\Users\ahmed nady\Pictures\modelImage.bmp");
modelFeatures = surfParam.DetectFeatures(modelImage.Convert <Gray, byte>(), null);
//Create a Feature Tracker
tracker = new Features2DTracker <float>(modelFeatures);
}
public Surf()
{
InitializeComponent();
camera = new Capture(0);
surfParam = new SURFDetector(500, false);
modelImage = new Image <Rgb, byte>(@"C:\Users\ahmed nady\Desktop\hand.jpg");
//extract features from the object image
modelFeatures = surfParam.DetectFeatures(modelImage.Convert <Gray, byte>(), null);
//Create a Feature Tracker
tracker = new Features2DTracker <float>(modelFeatures);
}
public Tuple <Image <Bgr, byte>, HomographyMatrix> DrawHomography(Image <Gray, byte> model, Image <Gray, byte> observed, double uniquenessThreshold)
{
HomographyMatrix homography = null;
Image <Bgr, Byte> result = observed.Convert <Bgr, byte>();
SURFDetector surfCPU = new SURFDetector(500, false);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix <int> indices;
Matrix <byte> mask;
int k = 2;
modelKeyPoints = surfCPU.DetectKeyPointsRaw(model, null); // Extract features from the object image
Matrix <float> modelDescriptors = surfCPU.ComputeDescriptorsRaw(model, null, modelKeyPoints);
observedKeyPoints = surfCPU.DetectKeyPointsRaw(observed, null); // Extract features from the observed image
if (modelKeyPoints.Size <= 0)
{
throw new System.ArgumentException("Can't find any keypoints in your model image!");
}
if (observedKeyPoints.Size > 0)
{
Matrix <float> observedDescriptors = surfCPU.ComputeDescriptorsRaw(observed, null, observedKeyPoints);
BruteForceMatcher <float> matcher = new BruteForceMatcher <float> (DistanceType.L2);
matcher.Add(modelDescriptors);
indices = new Matrix <int> (observedDescriptors.Rows, k);
using (Matrix <float> dist = new Matrix <float>(observedDescriptors.Rows, k)) {
matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
mask = new Matrix <byte> (dist.Rows, 1);
mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
}
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 10)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 10)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
}
result = Features2DToolbox.DrawMatches(model, modelKeyPoints, observed, observedKeyPoints,
indices, new Bgr(255, 255, 255), new Bgr(255, 255, 255), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
}
return(new Tuple <Image <Bgr, byte>, HomographyMatrix>(result, homography));
}
//主要是在這端對比的
public void FindMatch(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage, out long matchTime, out VectorOfKeyPoint modelKeyPoints,
out VectorOfKeyPoint observedKeyPoints, out Matrix <int> indices, out Matrix <byte> mask, out HomographyMatrix homography)
{
int k = 2;
double uniquenessThreshold = 0.8;
SURFDetector surfCPU = new SURFDetector(500, false); //設定處理特徵值的方式
Stopwatch watch; //監看處理時間
homography = null; //如果相同，取得四邊形
//extract features from the object image
modelKeyPoints = new VectorOfKeyPoint();
Matrix <float> modelDescriptors = surfCPU.DetectAndCompute(modelImage, null, modelKeyPoints); //modelKeyPoints : 算出 特徵點? //modelDescriptors :
// extract features from the observed image
observedKeyPoints = new VectorOfKeyPoint();
Matrix <float> observedDescriptors = surfCPU.DetectAndCompute(observedImage, null, observedKeyPoints); //observedKeyPoints : 取得特徵點 //
//ImagePrecess processor = new ImagePrecess(observedImage.ToBitmap(),320,240);
//observedDescriptors = processor.GetImageFeature();
//observedKeyPoints=processor.GetImageVectorOfKeyPoint();
watch = Stopwatch.StartNew();
//
BruteForceMatcher <float> matcher = new BruteForceMatcher <float>(DistanceType.L2);
matcher.Add(modelDescriptors);
indices = new Matrix <int>(observedDescriptors.Rows, k);
using (Matrix <float> dist = new Matrix <float>(observedDescriptors.Rows, k))
{
matcher.KnnMatch(observedDescriptors, indices, dist, k, null); //取得對比
mask = new Matrix <byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);//會把剛剛match完的結果抓來看是不是不明確或是不確定的，而跑完的結果放在mask中。
}
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
}
watch.Stop();
matchTime = watch.ElapsedMilliseconds;
}
/// <summary>
/// 計算特徵點
/// </summary>
/// <param name="srcImage">來源影像</param>
/// <returns>回傳特徵類別</returns>
public static SURFFeatureData CalSURFFeature(Image <Bgr, Byte> srcImage)
{
SURFDetector surfCPU = new SURFDetector(new MCvSURFParams(1200, false)); //預設500
VectorOfKeyPoint keyPoints;
Matrix <float> descriptors = null;
using (Image <Gray, Byte> grayImg = srcImage.Convert <Gray, Byte>())
{
keyPoints = surfCPU.DetectKeyPointsRaw(grayImg, null);
descriptors = surfCPU.ComputeDescriptorsRaw(grayImg, null, keyPoints);
}
return(new SURFFeatureData(srcImage.Copy(), keyPoints, descriptors));
}
Matrix <float> ComputeSingleDescriptors(string fileName)
{
Matrix <float> descs;
detector = new SURFDetector(surfHessianThresh, surfExtendedFlag);
using (Image <Gray, byte> img = new Image <Gray, byte>(fileName))
{
VectorOfKeyPoint keyPoints = detector.DetectKeyPointsRaw(img, null);
descs = detector.ComputeDescriptorsRaw(img, null, keyPoints);
}
return(descs);
}
Matrix <float> CursorComputeSingleDescriptor(ref Image <Gray, byte> source)
{
Matrix <float> descs;
cursorDetector = new SURFDetector(surfHessianThresh, surfExtendedFlag);
VectorOfKeyPoint keyPoints = detector.DetectKeyPointsRaw(source, null);
descs = detector.ComputeDescriptorsRaw(source, null, keyPoints);
//descs = detector.DetectAndCompute(source, null, keyPoints);
return(descs);
}
public void TestGridAdaptedFeatureDetectorRepeatedRun()
{
Image <Gray, byte> box = EmguAssert.LoadImage <Gray, byte>("box.png");
SURFDetector surfdetector = new SURFDetector(400);
GridAdaptedFeatureDetector detector = new GridAdaptedFeatureDetector(surfdetector, 1000, 2, 2);
VectorOfKeyPoint kpts1 = new VectorOfKeyPoint();
detector.DetectRaw(box, kpts1);
VectorOfKeyPoint kpts2 = new VectorOfKeyPoint();
detector.DetectRaw(box, kpts2);
EmguAssert.IsTrue(kpts1.Size == kpts2.Size);
}
Matrix <float> CursorComputeSingleDescriptor()
{
tempCursorSourceImage = new Image <Gray, byte>(cursorSourceSkin.Bitmap);
Matrix <float> descs;
cursorDetector = new SURFDetector(surfHessianThresh, surfExtendedFlag);
VectorOfKeyPoint keyPoints = detector.DetectKeyPointsRaw(tempCursorSourceImage, null);
descs = detector.ComputeDescriptorsRaw(tempCursorSourceImage, null, keyPoints);
//descs = detector.DetectAndCompute(source, null, keyPoints);
return(descs);
}
public SurfStopSignDetector()
{
detector = new SURFDetector(500, false);
Image <Bgr, Byte> stopSignModel = new Image <Bgr, Byte>("models\\stop-sign-model.png");
Image <Gray, Byte> redMask = GetRedPixelMask(stopSignModel);
tracker = new Features2DTracker <float>(detector.DetectFeatures(redMask, null));
octagonStorage = new MemStorage();
octagon = new Contour <Point>(octagonStorage);
octagon.PushMulti(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) }, Emgu.CV.CvEnum.BACK_OR_FRONT.FRONT);
}
public List <Keypoint> usingSurf(Bitmap image)
{
SURFDetector surf = new SURFDetector(750, false);
Image <Gray, Byte> modelImage = new Image <Gray, byte>(new Bitmap(image));
VectorOfKeyPoint modelKeyPoints = surf.DetectKeyPointsRaw(modelImage, null);
MKeyPoint[] keypoints = modelKeyPoints.ToArray();
Keypoint key;
List <Keypoint> keypointsList = new List <Keypoint>();
foreach (MKeyPoint keypoint in keypoints)
{
key = new Keypoint(keypoint.Point.X, keypoint.Point.Y, keypoint.Size);
keypointsList.Add(key);
}
return(keypointsList);
}
/// <summary>
/// 計算特徵點
/// </summary>
/// <param name="srcImage">來源影像</param>
/// <returns>回傳特徵類別</returns>
public static SURFFeatureData CalSURFFeature(Image <Bgr, Byte> srcImage)
{
SURFDetector surfCPU = new SURFDetector(new MCvSURFParams(1200, false)); //預設500
VectorOfKeyPoint keyPoints;
Matrix <float> descriptors = null;
Stopwatch watch;
watch = Stopwatch.StartNew();
using (Image <Gray, Byte> grayImg = srcImage.Convert <Gray, Byte>())
{
keyPoints = surfCPU.DetectKeyPointsRaw(grayImg, null);
descriptors = surfCPU.ComputeDescriptorsRaw(grayImg, null, keyPoints);
}
watch.Stop();
Console.WriteLine("\nExtract SURF time=> " + watch.ElapsedMilliseconds.ToString() + "ms");
//抽取出的特徵點數量
Console.WriteLine("keypoint size:" + keyPoints.Size);
return(new SURFFeatureData(srcImage.Copy(), keyPoints, descriptors));
}
private static bool IsModelInObserved( Image<Gray, byte> modelImage, Image<Gray, byte> observedImage, double similarityThreshold = 0.075 )
{
var surfCpu = new SURFDetector(500, false);
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
//extract features from the object image
var modelKeyPoints = surfCpu.DetectKeyPointsRaw( modelImage, null );
Matrix<float> modelDescriptors = surfCpu.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
var observedKeyPoints = surfCpu.DetectKeyPointsRaw( observedImage, null );
Matrix<float> observedDescriptors = surfCpu.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(DistanceType.L2);
matcher.Add( modelDescriptors );
var indices = new Matrix<int>( observedDescriptors.Rows, k );
using ( var dist = new Matrix<float>( observedDescriptors.Rows, k ) )
{
matcher.KnnMatch( observedDescriptors, indices, dist, k, null );
mask = new Matrix<byte>( dist.Rows, 1 );
mask.SetValue( 255 );
Features2DToolbox.VoteForUniqueness( dist, uniquenessThreshold, mask );
}
int keypointMatchCount = CvInvoke.cvCountNonZero( mask );
if ( keypointMatchCount >= 4 )
{
keypointMatchCount = Features2DToolbox.VoteForSizeAndOrientation( modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20 );
if ( keypointMatchCount >= 4 )
{
Features2DToolbox.GetHomographyMatrixFromMatchedFeatures( modelKeyPoints, observedKeyPoints, indices, mask, 2 );
}
}
var similarity = (double)keypointMatchCount / observedKeyPoints.Size;
return similarity > similarityThreshold;
}
private void SURFfeature(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, out Matrix <int> indices, out Matrix <byte> mask, out HomographyMatrix homography)
{
int k = 2;
double uniquenessThreshold = 0.8;
SURFDetector surfCPU = new SURFDetector(300, false);
homography = null;
//extract features from the object image
modelKeyPoints = new VectorOfKeyPoint();
Matrix <float> modelDescriptors = surfCPU.DetectAndCompute(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = new VectorOfKeyPoint();
Matrix <float> observedDescriptors = surfCPU.DetectAndCompute(observedImage, null, observedKeyPoints);
BruteForceMatcher <float> matcher = new BruteForceMatcher <float>(DistanceType.L2);
matcher.Add(modelDescriptors);
indices = new Matrix <int>(observedDescriptors.Rows, k);
using (Matrix <float> dist = new Matrix <float>(observedDescriptors.Rows, k))
{
matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
mask = new Matrix <byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
}
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
}
}
public void TestDetectorColor()
{
Image <Bgr, byte> box = EmguAssert.LoadImage <Bgr, byte>("box.png");
Image <Gray, byte> gray = box.Convert <Gray, Byte>();
SURFDetector surf = new SURFDetector(400);
OpponentColorDescriptorExtractor opponentSurf = new OpponentColorDescriptorExtractor(surf);
SIFTDetector sift = new SIFTDetector();
OpponentColorDescriptorExtractor opponentSift = new OpponentColorDescriptorExtractor(sift);
//using (Util.VectorOfKeyPoint kpts = surf.DetectKeyPointsRaw(gray, null))
using (Util.VectorOfKeyPoint kpts = new VectorOfKeyPoint())
{
sift.DetectRaw(gray, kpts);
for (int i = 1; i < 2; i++)
{
using (Mat surfDescriptors = new Mat())
{
opponentSurf.Compute(box, kpts, surfDescriptors);
//EmguAssert.IsTrue(surfDescriptors.Width == (surf.SURFParams.Extended == 0 ? 64 : 128) * 3);
}
//TODO: Find out why the following test fails
using (Mat siftDescriptors = new Mat())
{
sift.Compute(gray, kpts, siftDescriptors);
EmguAssert.IsTrue(siftDescriptors.Cols == sift.GetDescriptorSize());
}
int siftDescriptorSize = sift.GetDescriptorSize();
using (Mat siftDescriptors = new Mat())
{
opponentSift.Compute(box, kpts, siftDescriptors);
EmguAssert.IsTrue(siftDescriptors.Cols == siftDescriptorSize * 3);
}
}
}
}
public StopSignDetector(Image <Bgr, Byte> stopSignModel)
{
_detector = new SURFDetector(500, false);
using (Image <Gray, Byte> redMask = GetRedPixelMask(stopSignModel))
{
ImageFeature <float>[] temp = _detector.DetectFeatures(redMask, null);
_tracker = new Features2DTracker <float>(temp);
}
_octagonStorage = new MemStorage();
_octagon = new Contour <Point>(_octagonStorage);
_octagon.PushMulti(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)
},
Emgu.CV.CvEnum.BACK_OR_FRONT.FRONT
);
}
