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();
}
public void New3()
{
var descriptorExtractor = KAZE.Create();
var descriptorMatcher = new BFMatcher();
new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
}
public void New2()
{
var descriptorExtractor = SURF.Create(100);
var descriptorMatcher = new BFMatcher();
new BOWImgDescriptorExtractor(descriptorExtractor, descriptorMatcher);
}
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 void MatchBySift(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 sift = SIFT.Create();
// Detect the keypoints and generate their descriptors using SIFT
using var descriptors1 = new Mat <float>();
using var descriptors2 = new Mat <float>();
sift.DetectAndCompute(gray1, null, out var keypoints1, descriptors1);
sift.DetectAndCompute(gray2, null, out var 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("SIFT matching (by BFMather)", bfView))
using (new Window("SIFT matching (by FlannBasedMatcher)", flannView))
{
Cv2.WaitKey();
}
}
public V2()
{
ProcessFunction = (object obj) =>
{
dynamic prt = obj as dynamic;
var descriptors1 = prt.descriptors1;
var descriptors2 = prt.descriptors2;
var keypoints1 = prt.keypoints1;
var keypoints2 = prt.keypoints2;
Mat gray1 = prt.gray1;
Mat gray2 = prt.gray2;
// Match descriptor vectors
var bfMatcher = new BFMatcher(NormTypes.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);
return(new { bfView, flannView });
};
PostProcessAction = () =>
{
Console.WriteLine("V2 terminou de processar!! Tempo gasto: {0}" + Environment.NewLine, ElapsedTime);
};
}
private static DMatch[][] GetMatches(Mat query, Mat train)
{
BFMatcher matcher = new BFMatcher();
var matches = matcher.KnnMatch(query, train, k: 2);
return(matches);
}
public void FindFeaturePointsBetweenTwoImages()
{
var filename = "";
var filename2 = "";
var orb = new ORBDetector(2000);
Image <Bgr, byte> left = new Image <Bgr, byte>(filename);
Image <Bgr, byte> right = new Image <Bgr, byte>(filename2);
var vectorLeft = new VectorOfKeyPoint();
var vectorRight = new VectorOfKeyPoint();
var matLeft = new Mat();
var matRight = new Mat();
orb.DetectAndCompute(left, null, vectorLeft, matLeft, false);
orb.DetectAndCompute(right, null, vectorRight, matRight, false);
var matcher = new BFMatcher(DistanceType.Hamming2, true);
var matches = new VectorOfVectorOfDMatch();
matcher.Add(matLeft);
matcher.KnnMatch(matRight, matches, 1, null);
CalculateEssentialMAtrix(vectorLeft, vectorRight, camera.CameraMatrix);
CalculateFundamentalMatrix(vectorLeft, vectorRight);
}
public Result AnalyzeImageEval(ref VectorOfKeyPoint keypointsEvalImage, ref Mat descriptorsEvalImage, float maxDistanceRatio,
float reprojectionThreshold, double confidence, int maxIters, int minimumNumbersInliers)
{
var matches = new VectorOfDMatch();
//Emgu.CV.Flann.KdTreeIndexParamses flannIndexParams = new Emgu.CV.Flann.KdTreeIndexParamses(4);
//var flannIndex = new Index(descriptorsQueryImage, flannIndexParams);
//DescriptorMatcher matcher = flannIndex;
BFMatcher bfmatcher = new BFMatcher(DistanceType.L2);
_util.MatchDescriptorsWithRatioTest(bfmatcher, ref descriptorsEvalImage, _descriptorsImageTrain[_LODIndex], ref matches, maxDistanceRatio);
if (matches.Size < minimumNumbersInliers)
{
return(new Result());
}
Mat homography = new Mat();
VectorOfDMatch inliers = new VectorOfDMatch();
VectorOfInt inliersMaskOut = new VectorOfInt();
_util.RefineMatchesWithHomography(keypointsEvalImage, _keypointsImageTrain[_LODIndex], matches, ref homography, inliers, inliersMaskOut, reprojectionThreshold, minimumNumbersInliers);
if (inliers.Size < minimumNumbersInliers)
{
return(new Result());
}
float bestROIMatch = 0;
bestROIMatch = (float)inliers.Size / (float)matches.Size;
return(new Result(ValueBanknote, new VectorOfPoint(), ColorContour, bestROIMatch, _trainsImage[_LODIndex], _keypointsImageTrain[_LODIndex], keypointsEvalImage, ref matches, ref inliers, ref inliersMaskOut, ref homography));
}
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)
});
}
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 static void FindMatch(string modelFileName, string observedFileName, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask)
{
int k = 2;
double uniquenessThreshold = 0.8;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
{
using (UMat uModelImage = CvInvoke.Imread(modelFileName, ImreadModes.Color).GetUMat(AccessType.Read))
using (UMat uObservedImage = CvInvoke.Imread(observedFileName, ImreadModes.Color).GetUMat(AccessType.Read))
{
SIFT sift = new SIFT();
UMat modelDescriptors = new UMat();
sift.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
UMat observedDescriptors = new UMat();
sift.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);
}
}
}
public void Run(Mat gray1, Mat gray2, Mat dst2, bool useBFMatcher, int pointsToMatch)
{
var sift = SIFT.Create(pointsToMatch);
KeyPoint[] keypoints1, keypoints2;
var descriptors1 = new Mat();
var descriptors2 = new Mat();
sift.DetectAndCompute(gray1, null, out keypoints1, descriptors1);
sift.DetectAndCompute(gray2, null, out keypoints2, descriptors2);
if (useBFMatcher)
{
var bfMatcher = new BFMatcher(NormTypes.L2, false);
DMatch[] bfMatches = bfMatcher.Match(descriptors1, descriptors2);
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, bfMatches, dst2);
}
else
{
var flannMatcher = new FlannBasedMatcher();
DMatch[] flannMatches = flannMatcher.Match(descriptors1, descriptors2);
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, flannMatches, dst2);
}
kp1 = keypoints1;
kp2 = keypoints2;
}
public static void GetMatches(VectorOfKeyPoint imageKeypoints, IInputArray imageDescriptors, VectorOfKeyPoint patternKeypoints, IInputArray patternDescriptors, out VectorOfVectorOfDMatch matches, out Mat homography)
{
int k = 2;
double uniquenessThreshold = 0.8;
homography = null;
matches = new VectorOfVectorOfDMatch();
var matcher = new BFMatcher(DistanceType.L2);
matcher.Add(patternDescriptors);
matcher.KnnMatch(imageDescriptors, matches, k, null);
var 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(patternKeypoints, imageKeypoints, matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(patternKeypoints, imageKeypoints, matches, mask, 2);
}
}
}
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();
}
}
}
}
}
}
}
}
}
}
}
}
private Mat 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);
using var surf = AKAZE.Create();
// Detect the keypoints and generate their descriptors using SURF
using var descriptors1 = new Mat <float>();
using var descriptors2 = new Mat <float>();
surf.DetectAndCompute(gray1, null, out var keypoints1, descriptors1);
surf.DetectAndCompute(gray2, null, out var keypoints2, descriptors2);
// Match descriptor vectors
using var bfMatcher = new BFMatcher(NormTypes.L2, false);
DMatch[] bfMatches = bfMatcher.Match(descriptors1, descriptors2);
// Draw matches
var bfView = new Mat();
Cv2.DrawMatches(gray1, keypoints1, gray2, keypoints2, bfMatches, bfView, flags: DrawMatchesFlags.NotDrawSinglePoints);
return(bfView);
}
private void trainModel(String plantCategoryName, List <String> eachLeafSpecies)
{
BFMatcher match;
if (!categoryBfmatcherMapping.ContainsKey(plantCategoryName))
{
match = new BFMatcher(DistanceType.L2);
categoryBfmatcherMapping.Add(plantCategoryName, match);
}
else
{
//Find values associated with key
match = categoryBfmatcherMapping[plantCategoryName];
}
foreach (String leaf in eachLeafSpecies)
{
using (Image <Bgr, Byte> image = loadImage(leaf))
{
using (PreProcess preProcessAlgorithm = new PreProcess(image))
{
using (Image <Hsv, Byte> HSVImage = preProcessAlgorithm._ImageToHSV())
{
using (Image <Gray, Byte> grayScaleImage = preProcessAlgorithm._ImageToGrayScaleUsingConvert())
{
using (FeatureExtractAlgorithm featureSet = new FeatureExtractAlgorithm(grayScaleImage))
{
KeyPoints leafDescriptor = featureSet.SIFTDescriptor();
match.Add(leafDescriptor.Descriptor);
}
}
}
}
}
}
}
public bool MatchDescriptorsWithRatioTest(BFMatcher descriptorMatcher, ref Mat descriptorsEvalImage, Mat trainDescriptors, ref VectorOfDMatch matchesFilteredOut, float maxDistanceRatio)
{
if (trainDescriptors.Rows < 4)
{
return(false);
}
matchesFilteredOut.Clear();
descriptorMatcher.Add(trainDescriptors);
VectorOfVectorOfDMatch matchesKNN = new VectorOfVectorOfDMatch();
descriptorMatcher.KnnMatch(descriptorsEvalImage, matchesKNN, 2, null);
for (int matchPos = 0; matchPos < matchesKNN.Size; ++matchPos)
{
if (matchesKNN[matchPos].Size >= 2)
{
if (matchesKNN[matchPos][0].Distance <= maxDistanceRatio * matchesKNN[matchPos][1].Distance)
{
matchesFilteredOut.Push(new MDMatch[] { matchesKNN[matchPos][0] });
}
}
}
return(!(matchesFilteredOut.Size == 0));
}
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 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 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 FindMatch(Mat observedImage)
{
int k = 2;
mask = new Mat();
homography = null;
matches = new VectorOfVectorOfDMatch();
uObservedImage = observedImage.GetUMat(AccessType.ReadWrite);
// extract features from the observed image
ORBCPU.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
matcher = new BFMatcher(DistanceType.L2);
matcher.Add(objDescriptors);
if (objDescriptors.Size.Height > 3 && observedDescriptors.Size.Height > 3)
{
matcher.KnnMatch(observedDescriptors, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 4)
{
//nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(objKeyPoints, observedKeyPoints,
//matches, mask, 1, 2);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(objKeyPoints,
observedKeyPoints, matches, mask, 3);
}
}
}
}
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 static VectorOfVectorOfDMatch KnnMatch(this BFMatcher matcher, UMat pdesc, int i, IInputArray o = null)
{
var puzzelmatches = new VectorOfVectorOfDMatch();
matcher.KnnMatch(pdesc, puzzelmatches, i, o);
return(puzzelmatches);
}
public StopSignDetector(IInputArray stopSignModel)
{
_detector = new KAZE();
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)
});
}
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 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);
}
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);
}
/// <summary>
/// To avoid NaN's when best match has zero distance we will use inversed ratio.
/// KNN match will return 2 nearest matches for each query descriptor
/// </summary>
List <DMatch> GetMatches(BFMatcher matcher, Mat queryDescriptors, Mat trainDescriptors)
{
List <DMatch> matchesList = new List <DMatch>();
if (enableRatioTest)
{
float minRatio = 1.0f / 1.5f;
DMatch[][] dm = matcher.KnnMatch(queryDescriptors, trainDescriptors, 2);
for (int i = 0; i < dm.Length; i++)
{
DMatch bestMatch = dm[i][0];
DMatch betterMatch = dm[i][1];
float distanceRatio = bestMatch.Distance / betterMatch.Distance;
if (distanceRatio < minRatio)
{
matchesList.Add(bestMatch);
}
}
}
else
{
matchesList.AddRange(matcher.Match(queryDescriptors, trainDescriptors));
}
return(matchesList);
}
private static void BForceMatcherSample()
{
var src1 = new Mat("data/match1.png");
var src2 = new Mat("data/match2.png");
var gray1 = new Mat();
var gray2 = new Mat();
Cv2.CvtColor(src1, gray1, ColorConversion.BgrToGray);
Cv2.CvtColor(src2, gray2, ColorConversion.BgrToGray);
var fast = new FastFeatureDetector(10);
var descriptorExtractor = new BriefDescriptorExtractor(32);
var descriptors1 = new Mat();
var descriptors2 = new Mat();
KeyPoint[] keypoints1 = fast.Run(gray1, null);
descriptorExtractor.Compute(gray1, ref keypoints1, descriptors1);
KeyPoint[] keypoints2 = fast.Run(gray2, null);
descriptorExtractor.Compute(gray2, ref keypoints2, descriptors2);
// Match descriptor vectors
var bfMatcher = new BFMatcher(NormType.L2, false);
DMatch[][] bfMatches = bfMatcher.KnnMatch(descriptors1, descriptors2, 3, null, false);
bfMatches.ToString();
var view = new Mat();
Cv2.DrawMatches(src1, keypoints1, src2, keypoints2, bfMatches, view);
Window.ShowImages(view);
}
public Image <Bgr, byte> pointComp(Image <Bgr, byte> baseImg, Image <Bgr, byte> twistedImg)
{
Image <Gray, byte> baseImgGray = baseImg.Convert <Gray, byte>();
Image <Gray, byte> twistedImgGray = twistedImg.Convert <Gray, byte>();
Brisk descriptor = new Brisk();
GFTTDetector detector = new GFTTDetector(40, 0.01, 5, 3, true);
VectorOfKeyPoint GFP1 = new VectorOfKeyPoint();
UMat baseDesc = new UMat();
UMat bimg = twistedImgGray.Mat.GetUMat(AccessType.Read);
VectorOfKeyPoint GFP2 = new VectorOfKeyPoint();
UMat twistedDesc = new UMat();
UMat timg = baseImgGray.Mat.GetUMat(AccessType.Read);
detector.DetectRaw(bimg, GFP1);
descriptor.Compute(bimg, GFP1, baseDesc);
detector.DetectRaw(timg, GFP2);
descriptor.Compute(timg, GFP2, twistedDesc);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
matcher.Add(baseDesc);
matcher.KnnMatch(twistedDesc, matches, 2, null);
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
//int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(GFP1, GFP1, matches, mask, 1.5, 20);
Image <Bgr, byte> res = baseImg.CopyBlank();
Features2DToolbox.DrawMatches(twistedImg, GFP1, baseImg, GFP2, matches, res, new MCvScalar(255, 0, 0), new MCvScalar(255, 0, 0), mask);
return(res);
}
private void button3_Click(object sender, EventArgs e)
{
GFTTDetector detector = new GFTTDetector(40, 0.01, 5, 3, true);
var baseImgGray = baseImg.Convert <Gray, byte>();
var twistedImgGray = twistedImg.Convert <Gray, byte>();
//генератор описания ключевых точек
Brisk descriptor = new Brisk();
//поскольку в данном случае необходимо посчитать обратное преобразование
//базой будет являться изменённое изображение
VectorOfKeyPoint GFP1 = new VectorOfKeyPoint();
UMat baseDesc = new UMat();
UMat bimg = twistedImgGray.Mat.GetUMat(AccessType.Read);
VectorOfKeyPoint GFP2 = new VectorOfKeyPoint();
UMat twistedDesc = new UMat();
UMat timg = baseImgGray.Mat.GetUMat(AccessType.Read);
//получение необработанной информации о характерных точках изображений
detector.DetectRaw(bimg, GFP1);
//генерация описания характерных точек изображений
descriptor.Compute(bimg, GFP1, baseDesc);
detector.DetectRaw(timg, GFP2);
descriptor.Compute(timg, GFP2, twistedDesc);
//класс позволяющий сравнивать описания наборов ключевых точек
BFMatcher matcher = new BFMatcher(DistanceType.L2);
//массив для хранения совпадений характерных точек
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
//добавление описания базовых точек
matcher.Add(baseDesc);
//сравнение с описанием изменённых
matcher.KnnMatch(twistedDesc, matches, 2, null);
//3й параметр - количество ближайших соседей среди которых осуществляется поиск совпадений
//4й параметр - маска, в данном случае не нужна
//маска для определения отбрасываемых значений (аномальных и не уникальных)
Mat mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
//определение уникальных совпадений
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
Mat homography;
//получение матрицы гомографии
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(GFP1, GFP2, matches, mask, 2);
var destImage = new Image <Bgr, byte>(baseImg.Size);
CvInvoke.WarpPerspective(twistedImg, destImage, homography, destImage.Size);
twistedImg = destImage;
imageBox2.Image = destImage.Resize(640, 480, Inter.Linear);
}
private static void BowTest()
{
DescriptorMatcher matcher = new BFMatcher();
Feature2D extractor = AKAZE.Create();
Feature2D detector = AKAZE.Create();
TermCriteria criteria = new TermCriteria(CriteriaType.Count | CriteriaType.Eps, 10, 0.001);
BOWKMeansTrainer bowTrainer = new BOWKMeansTrainer(200, criteria, 1);
BOWImgDescriptorExtractor bowDescriptorExtractor = new BOWImgDescriptorExtractor(extractor, matcher);
Mat img = null;
KeyPoint[] keypoint = detector.Detect(img);
Mat features = new Mat();
extractor.Compute(img, ref keypoint, features);
bowTrainer.Add(features);
throw new NotImplementedException();
}
