Example #1
0
        public List<Keypoint> usingSift(Bitmap image)
        {
            SIFTDetector sift = new SIFTDetector();
            Image<Gray, Byte> modelImage = new Image<Gray, byte>(new Bitmap(image));
            VectorOfKeyPoint modelKeyPoints = sift.DetectKeyPointsRaw(modelImage, null);
            MKeyPoint[] keypoints = modelKeyPoints.ToArray();

            Keypoint key;
            List<Keypoint> keypointsList = new List<Keypoint>();

            foreach (MKeyPoint keypoint in keypoints)
            {
                key = new Keypoint(keypoint.Point.X, keypoint.Point.Y, keypoint.Size);
                keypointsList.Add(key);
            }

            return keypointsList;
        }
Example #2
0
        public bool testSIFT(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage)
        {
            bool isFound = false;
            HomographyMatrix homography = null;

            SIFTDetector siftCPU = new SIFTDetector();
            VectorOfKeyPoint modelKeyPoints;
            VectorOfKeyPoint observedKeyPoints;
            Matrix<int> indices;

            Matrix<byte> mask;
            int k = 2;
            double uniquenessThreshold = 0.8;

            //extract features from the object image
            modelKeyPoints = siftCPU.DetectKeyPointsRaw(modelImage, null);
            Matrix<float> modelDescriptors = siftCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);

            // extract features from the observed image
            observedKeyPoints = siftCPU.DetectKeyPointsRaw(observedImage, null);
            Matrix<float> observedDescriptors = siftCPU.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);
            }

            //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);

                if (CvInvoke.cvCountNonZero(mask) >= 10)
                    isFound = true;

                result.DrawPolyline(Array.ConvertAll<PointF, Point>(pts, Point.Round), true, new Bgr(Color.LightGreen), 5);
            }
            #endregion
            return isFound;
        }
Example #3
0
        public static Image<Bgr, Byte> SIFT(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage)
        {
            bool isFound = false;

            long matchTime;
            Stopwatch watch;
            HomographyMatrix homography = null;

            SIFTDetector siftCPU = new SIFTDetector();
            VectorOfKeyPoint modelKeyPoints;
            VectorOfKeyPoint observedKeyPoints;
            Matrix<int> indices;

            Matrix<byte> mask;
            int k = 2;
            double uniquenessThreshold = 0.8;

            watch = Stopwatch.StartNew();

            //extract features from the object image
            modelKeyPoints = siftCPU.DetectKeyPointsRaw(modelImage, null);
            Matrix<float> modelDescriptors = siftCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);

            // extract features from the observed image
            observedKeyPoints = siftCPU.DetectKeyPointsRaw(observedImage, null);
            Matrix<float> observedDescriptors = siftCPU.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);

                if (CvInvoke.cvCountNonZero(mask) >= 10)
                    isFound = true;

                result.DrawPolyline(Array.ConvertAll<PointF, Point>(pts, Point.Round), true, new Bgr(Color.LightGreen), 5);
            }
            #endregion

            matchTime = watch.ElapsedMilliseconds;
            _richTextBox1.Clear();
            _richTextBox1.AppendText("objek ditemukan: " + isFound + "\n");
            _richTextBox1.AppendText("waktu pendeteksian SIFT: " + matchTime + "ms\n");
            _richTextBox1.AppendText("fitur mentah pada model yang terdeteksi: " + modelKeyPoints.Size + "\n");
            _richTextBox1.AppendText("match yang ditemukan: " + CvInvoke.cvCountNonZero(mask).ToString());
            return result;
        }