EMGU CV SURF-Bild entsprechen

Habe ich die Arbeit mit der SURF feature detection Beispiel aus der EMGU CV Bibliothek.

So weit ist es erstaunlich; ich kann erkennen, passende Objekte zwischen 2 gegebenen Bilder, aber ich habe ein problem in Bezug auf, wenn die Bilder nicht übereinstimmen.

War ich auf der Suche nach Unterstützung aus den Foren, aber Sie sind nach unten aus, wo ich bin. Sollte jemand wissen, welche Parameter bestimmen, ob ein Bild, ein Spiel oder nicht. Wenn ich den test mit 2 Bildern, die nicht übereinstimmen, ist der code noch fortgesetzt, als wenn es war ein Spiel und zieht eine unscharf dicken roten Linie auf eine zufällige Position des Bildes, selbst wenn es nicht passt.

Wenn keine übereinstimmung vorhanden ist ich würde wünschen, Sie zu brechen aus dem code und nicht weiter fortfahren.

Anhang:

      static void Run()
      {
          Image<Gray, Byte> modelImage = new Image<Gray, byte>("HatersGonnaHate.png");
         Image<Gray, Byte> observedImage = new Image<Gray, byte>("box_in_scene.png");
         Stopwatch watch;
         HomographyMatrix homography = null;

         SURFDetector surfCPU = new SURFDetector(500, false);

         VectorOfKeyPoint modelKeyPoints;
         VectorOfKeyPoint observedKeyPoints;
         Matrix<int> indices;
         Matrix<float> dist;
         Matrix<byte> mask;

         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 matcher = new GpuBruteForceMatcher(GpuBruteForceMatcher.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, 2, 1))
               using (GpuMat<float> gpuMatchDist = new GpuMat<float>(gpuMatchIndices.Size, 1))
               {
                  observedKeyPoints = new VectorOfKeyPoint();
                  surfGPU.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);

                  matcher.KnnMatch(gpuObservedDescriptors, gpuModelDescriptors, gpuMatchIndices, gpuMatchDist, 2, null);

                  indices = new Matrix<int>(gpuMatchIndices.Size);
                  dist = new Matrix<float>(indices.Size);
                  gpuMatchIndices.Download(indices);
                  gpuMatchDist.Download(dist);

                  mask = new Matrix<byte>(dist.Rows, 1);

                  mask.SetValue(255);

                  Features2DTracker.VoteForUniqueness(dist, 0.8, mask);

                  int nonZeroCount = CvInvoke.cvCountNonZero(mask);
                  if (nonZeroCount >= 4)
                  {
                     nonZeroCount = Features2DTracker.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
                     if (nonZeroCount >= 4)
                        homography = Features2DTracker.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 3);
                  }

                  watch.Stop();
               }
            }
         }
         else
         {
            //extract features from the object image
            modelKeyPoints = surfCPU.DetectKeyPointsRaw(modelImage, null);
            //MKeyPoint[] kpts = modelKeyPoints.ToArray();
            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 matcher = new BruteForceMatcher(BruteForceMatcher.DistanceType.L2F32);
            matcher.Add(modelDescriptors);
            int k = 2;
            indices = new Matrix<int>(observedDescriptors.Rows, k);
            dist = new Matrix<float>(observedDescriptors.Rows, k);
            matcher.KnnMatch(observedDescriptors, indices, dist, k, null);

            mask = new Matrix<byte>(dist.Rows, 1);

            mask.SetValue(255);

            Features2DTracker.VoteForUniqueness(dist, 0.8, mask);

            int nonZeroCount = CvInvoke.cvCountNonZero(mask);
            if (nonZeroCount >= 4)
            {
               nonZeroCount = Features2DTracker.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
               if (nonZeroCount >= 4)
                  homography = Features2DTracker.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, indices, mask, 3);
            }

            watch.Stop();
         }

         //Draw the matched keypoints
        Image<Bgr, Byte> result = Features2DTracker.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
            indices, new Bgr(255, 255, 255), new Bgr(255, 255, 255), mask, Features2DTracker.KeypointDrawType.NOT_DRAW_SINGLE_POINTS);

         #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

         ImageViewer.Show(result, String.Format("Matched using {0} in {1} milliseconds", GpuInvoke.HasCuda ? "GPU" : "CPU", watch.ElapsedMilliseconds));
      }


   }

}

`