Пример #1
0
        /// <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;
        }
Пример #2
0
        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;
        }