EmguCV SURF - 确定匹配的点对

我目前正在修改 EmguCV（版本 3.0.0.2157）SurfFeature 示例（在这里看到的 https://github.com/neutmute/emgucv/blob/master/Emgu.CV.Example/SURFFeature/DrawMatches.cs#L33).

我试图确定匹配的点对的数量，以便计算输入图像之间的相似度百分比。

据我了解，这些信息存储在mask变量，但我不知道如何访问它？

（这个问题之前已经被问过here https://stackoverflow.com/questions/14374423/use-surf-to-match-images-in-opencv-emgucv，但引用的示例源代码使用的是旧版本的 EmguCV）

提前致谢！

p 确定匹配项

    public static Image<Bgr, Byte> Draw(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage, out long matchTime, out int nonofZeroCount)
    {
        int returnValue = 0;

        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);

                        returnValue = nonZeroCount;
                    }

                    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);
            }

            returnValue = nonZeroCount;
            watch.Stop();
        }

        int p = mask.ManagedArray.OfType<byte>().ToList().Where(q => q == 1).Count();

        //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 && p > 20)
        {  //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;

        nonofZeroCount = returnValue;

        return result;
    }