public static 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); Stopwatch watch; homography = null; #if !IOS if (GpuInvoke.HasCuda) { GpuSURFDetector surfGPU = new GpuSURFDetector(surfCPU.SURFParams, 0.01f); using (GpuImage<Gray, Byte> gpuModelImage = new GpuImage<Gray, byte>(modelImage)) //extract features from the object image using (GpuMat<float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw(gpuModelImage, null)) using (GpuMat<float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints)) using (GpuBruteForceMatcher<float> matcher = new GpuBruteForceMatcher<float>(DistanceType.L2)) { modelKeyPoints = new VectorOfKeyPoint(); surfGPU.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints); watch = Stopwatch.StartNew(); // extract features from the observed image using (GpuImage<Gray, Byte> gpuObservedImage = new GpuImage<Gray, byte>(observedImage)) using (GpuMat<float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw(gpuObservedImage, null)) using (GpuMat<float> gpuObservedDescriptors = surfGPU.ComputeDescriptorsRaw(gpuObservedImage, null, gpuObservedKeyPoints)) using (GpuMat<int> gpuMatchIndices = new GpuMat<int>(gpuObservedDescriptors.Size.Height, k, 1, true)) using (GpuMat<float> gpuMatchDist = new GpuMat<float>(gpuObservedDescriptors.Size.Height, k, 1, true)) using (GpuMat<Byte> gpuMask = new GpuMat<byte>(gpuMatchIndices.Size.Height, 1, 1)) using (Stream stream = new Stream()) { matcher.KnnMatchSingle(gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream); indices = new Matrix<int>(gpuMatchIndices.Size); mask = new Matrix<byte>(gpuMask.Size); //gpu implementation of voteForUniquess using (GpuMat<float> col0 = gpuMatchDist.Col(0)) using (GpuMat<float> col1 = gpuMatchDist.Col(1)) { GpuInvoke.Multiply(col1, new MCvScalar(uniquenessThreshold), col1, stream); GpuInvoke.Compare(col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream); } observedKeyPoints = new VectorOfKeyPoint(); surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints); //wait for the stream to complete its tasks //We can perform some other CPU intesive stuffs here while we are waiting for the stream to complete. stream.WaitForCompletion(); gpuMask.Download(mask); gpuMatchIndices.Download(indices); if (GpuInvoke.CountNonZero(gpuMask) >= 4) { int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20); if (nonZeroCount >= 4) homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2); } watch.Stop(); } } } else #endif { //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; }
/// <summary> /// Draw the model image and observed image, the matched features and homography projection. /// </summary> /// <param name="modelImage">The model image</param> /// <param name="observedImage">The observed image</param> /// <param name="matchTime">The output total time for computing the homography matrix.</param> /// <returns>The model image and observed image, the matched features and homography projection.</returns> public static Image<Bgr, Byte> Draw(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage, out long matchTime) { Stopwatch watch; HomographyMatrix homography = null; SURFDetector surfCPU = new SURFDetector (500, false); VectorOfKeyPoint modelKeyPoints; VectorOfKeyPoint observedKeyPoints; Matrix<int> indices; Matrix<byte> mask; int k = 2; double uniquenessThreshold = 0.8; if (GpuInvoke.HasCuda) { GpuSURFDetector surfGPU = new GpuSURFDetector (surfCPU.SURFParams, 0.01f); using (GpuImage<Gray, Byte> gpuModelImage = new GpuImage<Gray, byte> (modelImage)) //extract features from the object image using (GpuMat<float> gpuModelKeyPoints = surfGPU.DetectKeyPointsRaw (gpuModelImage, null)) using (GpuMat<float> gpuModelDescriptors = surfGPU.ComputeDescriptorsRaw (gpuModelImage, null, gpuModelKeyPoints)) using (GpuBruteForceMatcher<float> matcher = new GpuBruteForceMatcher<float> (DistanceType.L2)) { modelKeyPoints = new VectorOfKeyPoint (); surfGPU.DownloadKeypoints (gpuModelKeyPoints, modelKeyPoints); watch = Stopwatch.StartNew (); // extract features from the observed image using (GpuImage<Gray, Byte> gpuObservedImage = new GpuImage<Gray, byte> (observedImage)) using (GpuMat<float> gpuObservedKeyPoints = surfGPU.DetectKeyPointsRaw (gpuObservedImage, null)) using (GpuMat<float> gpuObservedDescriptors = surfGPU.ComputeDescriptorsRaw (gpuObservedImage, null, gpuObservedKeyPoints)) using (GpuMat<int> gpuMatchIndices = new GpuMat<int> (gpuObservedDescriptors.Size.Height, k, 1, true)) using (GpuMat<float> gpuMatchDist = new GpuMat<float> (gpuObservedDescriptors.Size.Height, k, 1, true)) using (GpuMat<Byte> gpuMask = new GpuMat<byte> (gpuMatchIndices.Size.Height, 1, 1)) using (Stream stream = new Stream ()) { matcher.KnnMatchSingle (gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream); indices = new Matrix<int> (gpuMatchIndices.Size); mask = new Matrix<byte> (gpuMask.Size); //gpu implementation of voteForUniquess using (GpuMat<float> col0 = gpuMatchDist.Col (0)) using (GpuMat<float> col1 = gpuMatchDist.Col (1)) { GpuInvoke.Multiply (col1, new MCvScalar (uniquenessThreshold), col1, stream); GpuInvoke.Compare (col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream); } observedKeyPoints = new VectorOfKeyPoint (); surfGPU.DownloadKeypoints (gpuObservedKeyPoints, observedKeyPoints); //wait for the stream to complete its tasks //We can perform some other CPU intesive stuffs here while we are waiting for the stream to complete. stream.WaitForCompletion (); gpuMask.Download (mask); gpuMatchIndices.Download (indices); if (GpuInvoke.CountNonZero (gpuMask) >= 4) { int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation (modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20); if (nonZeroCount >= 4) homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures (modelKeyPoints, observedKeyPoints, indices, mask, 2); } watch.Stop (); } } } else { //extract features from the object image modelKeyPoints = surfCPU.DetectKeyPointsRaw (modelImage, null); Matrix<float> modelDescriptors = surfCPU.ComputeDescriptorsRaw (modelImage, null, modelKeyPoints); watch = Stopwatch.StartNew (); // extract features from the observed image observedKeyPoints = surfCPU.DetectKeyPointsRaw (observedImage, null); Matrix<float> observedDescriptors = surfCPU.ComputeDescriptorsRaw (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 (); } //Draw the matched keypoints Image<Bgr, Byte> result = Features2DToolbox.DrawMatches (modelImage, modelKeyPoints, observedImage, observedKeyPoints, indices, new Bgr (255, 255, 255), new Bgr (255, 255, 255), mask, Features2DToolbox.KeypointDrawType.DEFAULT); #region draw the projected region on the image if (homography != null) { //draw a rectangle along the projected model Rectangle rect = modelImage.ROI; 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) }; homography.ProjectPoints (pts); result.DrawPolyline (Array.ConvertAll<PointF, Point> (pts, Point.Round), true, new Bgr (Color.Red), 5); } #endregion matchTime = watch.ElapsedMilliseconds; return result; }
public static bool FindModelImageInObservedImage( Image<Gray, byte> modelImage, Image<Gray, byte> observedImage ) { var surfCpu = new SURFDetector(500, false); VectorOfKeyPoint modelKeyPoints; VectorOfKeyPoint observedKeyPoints; Matrix<int> indices; Matrix<byte> mask; int k = 2; double uniquenessThreshold = 0.8; if ( GpuInvoke.HasCuda ) { GpuSURFDetector surfGpu = new GpuSURFDetector(surfCpu.SURFParams, 0.01f); using ( GpuImage<Gray, byte> gpuModelImage = new GpuImage<Gray, byte>( modelImage ) ) //extract features from the object image using ( GpuMat<float> gpuModelKeyPoints = surfGpu.DetectKeyPointsRaw( gpuModelImage, null ) ) using ( GpuMat<float> gpuModelDescriptors = surfGpu.ComputeDescriptorsRaw( gpuModelImage, null, gpuModelKeyPoints ) ) using ( GpuBruteForceMatcher<float> matcher = new GpuBruteForceMatcher<float>( DistanceType.L2 ) ) { modelKeyPoints = new VectorOfKeyPoint(); surfGpu.DownloadKeypoints( gpuModelKeyPoints, modelKeyPoints ); // extract features from the observed image using ( GpuImage<Gray, byte> gpuObservedImage = new GpuImage<Gray, byte>( observedImage ) ) using ( GpuMat<float> gpuObservedKeyPoints = surfGpu.DetectKeyPointsRaw( gpuObservedImage, null ) ) using ( GpuMat<float> gpuObservedDescriptors = surfGpu.ComputeDescriptorsRaw( gpuObservedImage, null, gpuObservedKeyPoints ) ) using ( GpuMat<int> gpuMatchIndices = new GpuMat<int>( gpuObservedDescriptors.Size.Height, k, 1, true ) ) using ( GpuMat<float> gpuMatchDist = new GpuMat<float>( gpuObservedDescriptors.Size.Height, k, 1, true ) ) using ( GpuMat<Byte> gpuMask = new GpuMat<byte>( gpuMatchIndices.Size.Height, 1, 1 ) ) using ( var stream = new Emgu.CV.GPU.Stream() ) { matcher.KnnMatchSingle( gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, k, null, stream ); indices = new Matrix<int>( gpuMatchIndices.Size ); mask = new Matrix<byte>( gpuMask.Size ); //gpu implementation of voteForUniquess using ( GpuMat<float> col0 = gpuMatchDist.Col( 0 ) ) using ( GpuMat<float> col1 = gpuMatchDist.Col( 1 ) ) { GpuInvoke.Multiply( col1, new MCvScalar( uniquenessThreshold ), col1, stream ); GpuInvoke.Compare( col0, col1, gpuMask, CMP_TYPE.CV_CMP_LE, stream ); } observedKeyPoints = new VectorOfKeyPoint(); surfGpu.DownloadKeypoints( gpuObservedKeyPoints, observedKeyPoints ); //wait for the stream to complete its tasks //We can perform some other CPU intesive stuffs here while we are waiting for the stream to complete. stream.WaitForCompletion(); gpuMask.Download( mask ); gpuMatchIndices.Download( indices ); if ( GpuInvoke.CountNonZero( gpuMask ) >= 4 ) { int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20); if ( nonZeroCount >= 4 ) { Features2DToolbox.GetHomographyMatrixFromMatchedFeatures( modelKeyPoints, observedKeyPoints, indices, mask, 2 ); } if ( (double)nonZeroCount / mask.Height > 0.02 ) { return true; } } } } } else { //extract features from the object image modelKeyPoints = surfCpu.DetectKeyPointsRaw( modelImage, null ); Matrix<float> modelDescriptors = surfCpu.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints); // extract features from the observed image observedKeyPoints = surfCpu.DetectKeyPointsRaw( observedImage, null ); Matrix<float> observedDescriptors = surfCpu.ComputeDescriptorsRaw(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 ) { Features2DToolbox.GetHomographyMatrixFromMatchedFeatures( modelKeyPoints, observedKeyPoints, indices, mask, 2 ); } } if ( (double)nonZeroCount/mask.Height > 0.02 ) { return true; } } //Draw the matched keypoints //var result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints, indices, new Bgr(0, 0, 255), new Bgr(255, 0, 0), mask, Features2DToolbox.KeypointDrawType.DEFAULT); //result.Save( @"C:\Users\D.Markachev\Desktop\bleh-keypoints.jpg" ); return false; }