SURF feature detector in CSharp

From Emgu CV: OpenCV in .NET (C#, VB, C++ and more)
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This project is part of the Emgu.CV.Example solution

System Requirement

Component Requirement Detail
Emgu CV Version 2.4.0
Operation System Cross Platform

Source Code

<source lang="csharp"> using System; using System.Collections.Generic; using System.Diagnostics; using System.Drawing; using System.Runtime.InteropServices; using Emgu.CV; using Emgu.CV.CvEnum; using Emgu.CV.Features2D; using Emgu.CV.Structure; using Emgu.CV.Util; using Emgu.CV.GPU;

namespace SURFFeatureExample {

  public static class DrawMatches
  {
     /// <summary>
     /// Draw the model image and observed image, the matched features and homography projection.
     /// </summary>
     /// <param name="modelImageFileName">The model image</param>
     /// <param name="observedImageFileName">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(String modelImageFileName, String observedImageFileName, out long matchTime)
     {
        Image<Gray, Byte> modelImage = new Image<Gray, byte>(modelImageFileName);
        Image<Gray, Byte> observedImage = new Image<Gray, byte>(observedImageFileName);
        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;
     }
  }

}

Result

SURFExample.png