Difference between revisions of "SURF feature detector in CSharp"

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(Undo revision 991 by Inuxejiq (talk))
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!Component || Requirement || Detail  
 
!Component || Requirement || Detail  
 
|-
 
|-
−
|Emgu CV || [[Version_History#Emgu.CV-2.0.0.0_Alpha|Version 2.0.0.0 Alpha]] ||   
+
|Emgu CV || [[Version_History#Emgu.CV-2.4.0|Version 2.4.0]] ||   
 
|-
 
|-
 
|Operation System || Cross Platform ||  
 
|Operation System || Cross Platform ||  
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using System;
 
using System;
 
using System.Collections.Generic;
 
using System.Collections.Generic;
−
using System.Windows.Forms;
+
using System.Diagnostics;
 
using System.Drawing;
 
using System.Drawing;
 +
using System.Runtime.InteropServices;
 
using Emgu.CV;
 
using Emgu.CV;
−
using Emgu.CV.UI;
 
 
using Emgu.CV.CvEnum;
 
using Emgu.CV.CvEnum;
 +
using Emgu.CV.Features2D;
 
using Emgu.CV.Structure;
 
using Emgu.CV.Structure;
 +
using Emgu.CV.Util;
 +
using Emgu.CV.GPU;
  
 
namespace SURFFeatureExample
 
namespace SURFFeatureExample
 
{
 
{
−
   static class Program
+
   public static class DrawMatches
 
   {
 
   {
 
       /// <summary>
 
       /// <summary>
−
       /// The main entry point for the application.
+
       /// Draw the model image and observed image, the matched features and homography projection.
 
       /// </summary>
 
       /// </summary>
−
       [STAThread]
+
       /// <param name="modelImageFileName">The model image</param>
−
       static void Main()
+
       /// <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)
 
       {
 
       {
−
         Application.EnableVisualStyles();
+
         Image<Gray, Byte> modelImage = new Image<Gray, byte>(modelImageFileName);
−
         Application.SetCompatibleTextRenderingDefault(false);
+
        Image<Gray, Byte> observedImage = new Image<Gray, byte>(observedImageFileName);
−
         Run();
+
         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();
  
−
      static void Run()
+
              // extract features from the observed image
−
      {
+
              using (GpuImage<Gray, Byte> gpuObservedImage = new GpuImage<Gray, byte>(observedImage))
−
        MCvSURFParams surfParam = new MCvSURFParams(500, false);
+
              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);
  
−
        Image<Gray, Byte> modelImage = new Image<Gray, byte>("box.png");
+
                  //gpu implementation of voteForUniquess
−
        //extract features from the object image
+
                  using (GpuMat<float> col0 = gpuMatchDist.Col(0))
−
        SURFFeature[] modelFeatures = modelImage.ExtractSURF(ref surfParam);
+
                  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);
 +
                  }
  
−
        Image<Gray, Byte> observedImage = new Image<Gray, byte>("box_in_scene.png");
+
                  observedKeyPoints = new VectorOfKeyPoint();
−
        // extract features from the observed image
+
                  surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
−
        SURFFeature[] imageFeatures = observedImage.ExtractSURF(ref surfParam);
 
  
−
        //Create a SURF Tracker using k-d Tree
+
                  //wait for the stream to complete its tasks
−
        SURFTracker tracker = new SURFTracker(modelFeatures);
+
                  //We can perform some other CPU intesive stuffs here while we are waiting for the stream to complete.
−
        //Comment out above and uncomment below if you wish to use spill-tree instead
+
                  stream.WaitForCompletion();
−
        //SURFTracker tracker = new SURFTracker(modelFeatures, 50, .7, .1);
 
  
−
        SURFTracker.MatchedSURFFeature[] matchedFeatures = tracker.MatchFeature(imageFeatures, 2, 20);
+
                  gpuMask.Download(mask);
−
        matchedFeatures = SURFTracker.VoteForUniqueness(matchedFeatures, 0.8);
+
                  gpuMatchIndices.Download(indices);
−
        matchedFeatures = SURFTracker.VoteForSizeAndOrientation(matchedFeatures, 1.5, 20);
 
−
        HomographyMatrix homography = SURFTracker.GetHomographyMatrixFromMatchedFeatures(matchedFeatures);
 
  
−
        //Merge the object image and the observed image into one image for display
+
                  if (GpuInvoke.CountNonZero(gpuMask) >= 4)
−
        Image<Gray, Byte> res = modelImage.ConcateVertical(observedImage);
+
                  {
 +
                    int nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
 +
                    if (nonZeroCount >= 4)
 +
                        homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 2);
 +
                  }
  
−
        #region draw lines between the matched features
+
                  watch.Stop();
−
         foreach (SURFTracker.MatchedSURFFeature matchedFeature in matchedFeatures)
+
              }
 +
            }
 +
         } else
 
         {
 
         {
−
             PointF p = matchedFeature.ObservedFeature.Point.pt;
+
             //extract features from the object image
−
             p.Y += modelImage.Height;
+
            modelKeyPoints = surfCPU.DetectKeyPointsRaw(modelImage, null);
−
             res.Draw(new LineSegment2DF(matchedFeature.ModelFeatures[0].Point.pt, p), new Gray(0), 1);
+
            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();
 
         }
 
         }
−
        #endregion
 
  
−
         #region draw the project region on the image
+
        //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)
 
         if (homography != null)
 
         {  //draw a rectangle along the projected model
 
         {  //draw a rectangle along the projected model
Line 81: Line 153:
 
             homography.ProjectPoints(pts);
 
             homography.ProjectPoints(pts);
  
−
             for (int i = 0; i < pts.Length; i++)
+
             result.DrawPolyline(Array.ConvertAll<PointF, Point>(pts, Point.Round), true, new Bgr(Color.Red), 5);
−
              pts[i].Y += modelImage.Height;
 
−
 
 
−
            res.DrawPolyline(Array.ConvertAll<PointF, Point>(pts, Point.Round), true, new Gray(255.0), 5);
 
 
         }
 
         }
 
         #endregion
 
         #endregion
  
−
         ImageViewer.Show(res);
+
         matchTime = watch.ElapsedMilliseconds;
 +
 
 +
        return result;
 
       }
 
       }
 
   }
 
   }
 
}
 
}
−
</source>
 
−
 
 
== Result ==
 
== Result ==
 
[[image:SURFExample.png]]
 
[[image:SURFExample.png]]

Revision as of 03:03, 27 May 2012

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

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