private static void FindMatch(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage, SurfSettings surfSettings, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, out Matrix<int> indices, out Matrix<byte> mask, out HomographyMatrix homography) { #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSettings != null) { hessianThresh = surfSettings.HessianThresh.Value; uniquenessThreshold = surfSettings.UniquenessThreshold.Value; } SURFDetector surfCPU = new SURFDetector(hessianThresh, false); #endregion int k = 2; Stopwatch watch; homography = null; //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; }
private void QuerySurfAlgo(string queryImagePath) { WriteQueryStatus("Start querying..."); SaveSurfSettingValues(); SurfSettings surfSetting = GetSurfSetting(); List <ImageRecord> searchImage = new List <ImageRecord>(); bool didRepoLoadingtookplace = false; long repoLoadingTimeInMs = 0; if (!SurfRepository.IsRepositoryInMemoryLoaded(surfSetting.Algorithm)) { didRepoLoadingtookplace = true; WriteQueryStatus("Loading Repository in memory..."); _stopWatch = Stopwatch.StartNew(); SurfRepository.LoadRespositoryFromFile(surfSetting.Algorithm); _stopWatch.Stop(); repoLoadingTimeInMs = _stopWatch.ElapsedMilliseconds; WriteQueryStatus(string.Format("Repository loading took {0} ms", repoLoadingTimeInMs)); } string msg; if (surfSetting.Algorithm == SurfAlgo.Linear) { SurfQuery2 surfQuery = new SurfQuery2(); _stopWatch = Stopwatch.StartNew(); searchImage = surfQuery.QueryImage(queryImagePath, surfSetting); _stopWatch.Stop(); if (didRepoLoadingtookplace) { msg = string.Format("Loading tooked {0} ms, quering tooked {1} ms", repoLoadingTimeInMs, _stopWatch.ElapsedMilliseconds); } else { msg = string.Format("Image Query Completed, it took {0} ms", _stopWatch.ElapsedMilliseconds); } WriteQueryStatus(msg); } else if (surfSetting.Algorithm == SurfAlgo.Flaan) { SurfQuery1 surfQuery = new SurfQuery1(); _stopWatch = Stopwatch.StartNew(); searchImage = surfQuery.QueryImage(queryImagePath, out msg, surfSetting); _stopWatch.Stop(); if (didRepoLoadingtookplace) { msg = string.Format("Loading Repo: {0} ms, Quering Total {1} ms, Details - ", repoLoadingTimeInMs, _stopWatch.ElapsedMilliseconds) + msg; } else { msg += string.Format(" Total {0} ms", _stopWatch.ElapsedMilliseconds); } WriteQueryStatus(msg); } this.Dispatcher.Invoke(() => { ImageList.ItemsSource = searchImage; lblTotalCount.Text = searchImage.Count.ToString(); }); }
private void QueryAccordSurfAlgo(string queryImagePath) { WriteQueryStatus("Start querying..."); SaveSurfSettingValues(); List <ImageRecord> searchImage = new List <ImageRecord>(); SurfSettings surfSetting = GetSurfSetting(); string msg; if (surfSetting.Algorithm == SurfAlgo.Linear) { SurfQuery4 surfQuery = new SurfQuery4(); _stopWatch = Stopwatch.StartNew(); searchImage = surfQuery.QueryImage(queryImagePath, out msg, surfSetting); _stopWatch.Stop(); } else { SurfQuery3 surfQuery = new SurfQuery3(); _stopWatch = Stopwatch.StartNew(); searchImage = surfQuery.QueryImage(queryImagePath, out msg, surfSetting); _stopWatch.Stop(); } msg += string.Format(" Total {0} ms", _stopWatch.ElapsedMilliseconds); WriteQueryStatus(msg); this.Dispatcher.Invoke(() => { ImageList.ItemsSource = searchImage; lblTotalCount.Text = searchImage.Count.ToString(); }); }
private void AccordSurfAlgoIndexing(SurfSettings surfSetting) { var imageFiles = getFiles(IndexDirectory, "*.gif|*.jpg|*.png|*.bmp|*.jpeg", SearchOption.TopDirectoryOnly); int totalFileCount = imageFiles.Length; Dispatcher.Invoke((Action) delegate { pbIndex.Minimum = 0; pbIndex.Maximum = totalFileCount; lblIndexStatus.Text = "Indexing.."; }); if (surfSetting.Algorithm == SurfAlgo.Linear) { SurfIndexer4 surfIndexer = new SurfIndexer4(); if (ExecInParallel) { surfIndexer.IndexFilesAsync(imageFiles, IndexBgWorker, WriteIndexStatus, surfSetting); } else { surfIndexer.IndexFiles(imageFiles, IndexBgWorker, WriteIndexStatus, surfSetting); } } else { SurfIndexer3 surfIndexer = new SurfIndexer3(); if (ExecInParallel) { surfIndexer.IndexFilesAsync(imageFiles, IndexBgWorker, WriteIndexStatus, surfSetting); } else { surfIndexer.IndexFiles(imageFiles, IndexBgWorker, WriteIndexStatus, surfSetting); } } }
private void mnuOpenSurfDetail_Click(object sender, RoutedEventArgs e) { string selectedObserverImage = string.Empty; string selectedModelImage = string.Empty; MenuItem mnu = sender as MenuItem; Border selectedBorder = null; if (mnu != null) { selectedBorder = ((ContextMenu)mnu.Parent).PlacementTarget as Border; selectedObserverImage = (string)selectedBorder.Tag; } selectedModelImage = QueryImageFullPath; bool isObserverImagePathFound = !string.IsNullOrWhiteSpace(selectedObserverImage); isObserverImagePathFound = isObserverImagePathFound && File.Exists(selectedObserverImage); bool isModelImagePathFound = !string.IsNullOrWhiteSpace(selectedModelImage); isModelImagePathFound = isObserverImagePathFound && File.Exists(selectedModelImage); if (isObserverImagePathFound & isModelImagePathFound) { SurfSettings surfSetting = GetSurfSetting(); if (SelectedAlgo == emAlgo.SURF) { DrawSurfMatches.MatchInWindow(selectedModelImage, selectedObserverImage, surfSetting); } else { AccordSurfWindow win = new AccordSurfWindow(selectedModelImage, selectedObserverImage, surfSetting); win.Show(); } } }
/// <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> private static Image<Bgr, Byte> Draw(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage, SurfSettings surfSettings, out long matchTime) { HomographyMatrix homography; VectorOfKeyPoint modelKeyPoints; VectorOfKeyPoint observedKeyPoints; Matrix<int> indices; Matrix<byte> mask; FindMatch(modelImage, observedImage, surfSettings, out matchTime, out modelKeyPoints, out observedKeyPoints, out indices, out mask, out homography); //Draw the matched keypoints Image<Bgr, Byte> result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints, indices, new Bgr(255, 0, 0), new Bgr(255, 255, 0), 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 return result; }
public AccordSurfWindow(string modelImagePath, string observerImagePath, SurfSettings setting) : this() { this.ModelImagePath = modelImagePath; this.ObserverImagePath = observerImagePath; this.SurfSetting = setting; }
public void IndexFiles(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action <string> logWriter, SurfSettings surfSetting = null) { #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } SURFDetector surfDectector = new SURFDetector(hessianThresh, false); #endregion List <SURFRecord2> surfRecord2List = new List <SURFRecord2>(); Stopwatch sw1, sw2; sw1 = Stopwatch.StartNew(); logWriter("Index started..."); int totalFileCount = imageFiles.Length; for (int i = 0; i < totalFileCount; i++) { var fi = imageFiles[i]; using (Image <Gray, byte> observerImage = new Image <Gray, byte>(fi.FullName)) { ImageFeature <float>[] observerFeatures = surfDectector.DetectFeatures(observerImage, null); if (observerFeatures.Length > 4) { SURFRecord2 record = new SURFRecord2 { Id = i, ImageName = fi.Name, ImagePath = fi.FullName, observerFeatures = observerFeatures }; surfRecord2List.Add(record); } else { Debug.WriteLine(fi.Name + " skip from index, because it didn't have significant feature"); } } IndexBgWorker.ReportProgress(i); } SurfRepository.AddSURFRecord2List(surfRecord2List); sw1.Stop(); logWriter(string.Format("Index Complete, it tooked {0} ms. Saving Repository...", sw1.ElapsedMilliseconds)); sw2 = Stopwatch.StartNew(); SurfRepository.SaveRepository(SurfAlgo.Linear); sw2.Stop(); logWriter(string.Format("Index tooked {0} ms. Saving Repository tooked {1} ms", sw1.ElapsedMilliseconds, sw2.ElapsedMilliseconds)); }
public static void MatchInWindow(string modelImagePath, string observedImagePath, SurfSettings surfSettings) { long matchTime; using (Image<Gray, Byte> modelImage = new Image<Gray, byte>(modelImagePath)) using (Image<Gray, Byte> observedImage = new Image<Gray, byte>(observedImagePath)) { Image<Bgr, byte> result = Draw(modelImage, observedImage, surfSettings, out matchTime); ImageViewer.Show(result, String.Format("Matched using {0} in {1} milliseconds", GpuInvoke.HasCuda ? "GPU" : "CPU", matchTime)); } }
private SurfSettings GetSurfSetting() { SurfSettings surfSetting = new SurfSettings(); this.Dispatcher.Invoke(() => { surfSetting.HessianThresh = Convert.ToDouble((cmbSurfThreshold.SelectedItem as ListBoxItem).Content); surfSetting.UniquenessThreshold = nudsurfUniqueThreshold.Value; surfSetting.GoodMatchThreshold = nudsurfGoodMatchThreshold.Value; surfSetting.Algorithm = (SurfAlgo)cmbSurfApproach.SelectedIndex; }); return(surfSetting); }
private void btnIndex_Click(object sender, RoutedEventArgs e) { btnIndex.IsEnabled = false; if (SelectedAlgo == emAlgo.SURF || SelectedAlgo == emAlgo.AccordSurf) { //Save Settings SaveSurfSettingValues(); //Pass Surf Settings SurfSettings surfSetting = GetSurfSetting(); IndexBgWorker.RunWorkerAsync(surfSetting); } else if (SelectedAlgo == emAlgo.Locate) { LocateSettings locateSetting = GetLocateSetting(); SaveLocateSetting(); IndexBgWorker.RunWorkerAsync(locateSetting); } else { IndexBgWorker.RunWorkerAsync(); } }
public void IndexFiles(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action<string> logWriter, SurfSettings surfSetting = null) { #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } SURFDetector surfDectector = new SURFDetector(hessianThresh, false); #endregion int rows = 0; Matrix<float> superMatrix = null; List<SURFRecord1> observerSurfImageIndexList = new List<SURFRecord1>(); Stopwatch sw1, sw2; sw1 = Stopwatch.StartNew(); logWriter("Index started..."); int totalFileCount = imageFiles.Length; for (int i = 0; i < totalFileCount; i++) { var fi = imageFiles[i]; using (Image<Gray, byte> observerImage = new Image<Gray, byte>(fi.FullName)) { VectorOfKeyPoint observerKeyPoints = new VectorOfKeyPoint(); Matrix<float> observerDescriptor = surfDectector.DetectAndCompute(observerImage, null, observerKeyPoints); if (observerDescriptor.Rows > 4) { int initRow = rows; int endRows = rows + observerDescriptor.Rows - 1; SURFRecord1 record = new SURFRecord1 { Id = i, ImageName = fi.Name, ImagePath = fi.FullName, IndexStart = rows, IndexEnd = endRows }; observerSurfImageIndexList.Add(record); if (superMatrix == null) superMatrix = observerDescriptor; else superMatrix = superMatrix.ConcateVertical(observerDescriptor); rows = endRows + 1; } else { Debug.WriteLine(fi.Name + " skip from index, because it didn't have significant feature"); } } IndexBgWorker.ReportProgress(i); } sw1.Stop(); logWriter(string.Format("Index Complete, it tooked {0} ms. Saving Repository...", sw1.ElapsedMilliseconds)); SurfDataSet surfDataset = new SurfDataSet { SurfImageIndexRecord = observerSurfImageIndexList, SuperMatrix = superMatrix }; sw2 = Stopwatch.StartNew(); SurfRepository.AddSuperMatrixList(surfDataset); SurfRepository.SaveRepository(SurfAlgo.Flaan); sw2.Stop(); logWriter(string.Format("Index tooked {0} ms. Saving Repository tooked {1} ms", sw1.ElapsedMilliseconds, sw2.ElapsedMilliseconds)); }
public void CalculateSurfDescriptor(Image img, SurfSettings surfSetting) { }
public void IndexFiles(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action<string> logWriter, SurfSettings surfSetting = null) { //For Time Profilling long readingTime, indexingTime, saveingTime; #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } float hessianThreshold2 = (float)hessianThresh / 1000000; SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); #endregion int rows = 0; List<SURFRecord1> observerSurfImageIndexList = new List<SURFRecord1>(); List<SpeededUpRobustFeaturePoint> listOfAllObserverImagesSurfPoints = new List<SpeededUpRobustFeaturePoint>(); Stopwatch sw1; sw1 = Stopwatch.StartNew(); logWriter("Index started..."); int totalFileCount = imageFiles.Length; for (int i = 0; i < totalFileCount; i++) { var fi = imageFiles[i]; using (Bitmap observerImage = (Bitmap)Image.FromFile(fi.FullName)) { List<SpeededUpRobustFeaturePoint> observerImageSurfPoints = surf.ProcessImage(observerImage); if (observerImageSurfPoints.Count > 4) { int initRow = rows; int endRows = rows + observerImageSurfPoints.Count - 1; SURFRecord1 record = new SURFRecord1 { Id = i, ImageName = fi.Name, ImagePath = fi.FullName, IndexStart = rows, IndexEnd = endRows }; observerSurfImageIndexList.Add(record); listOfAllObserverImagesSurfPoints.AddRange(observerImageSurfPoints); rows = endRows + 1; } else { Debug.WriteLine(fi.Name + " skip from index, because it didn't have significant feature"); } } IndexBgWorker.ReportProgress(i); } sw1.Stop(); readingTime = sw1.ElapsedMilliseconds; logWriter(string.Format("Reading Surb Complete, it tooked {0} ms. Saving Repository...", readingTime)); //------------Initialize Tree from Data sw1.Reset(); sw1.Start(); double[][] superMatrix = listOfAllObserverImagesSurfPoints.Select(c => c.Descriptor).ToArray(); int[] outputs = new int[superMatrix.Length]; for (int i = 0; i < outputs.Length; i++) outputs[i] = i; Accord.MachineLearning.Structures.KDTree<int> tree = Accord.MachineLearning.Structures.KDTree.FromData(superMatrix, outputs, Accord.Math.Distance.Euclidean, inPlace: true); sw1.Stop(); indexingTime = sw1.ElapsedMilliseconds; logWriter(string.Format("Intializing KD Tree: {0}", indexingTime)); //--------------Saving Indexed Records sw1.Reset(); sw1.Start(); SurfAccordDataSet surfAccordDataset = new SurfAccordDataSet { SurfImageIndexRecord = observerSurfImageIndexList, IndexedTree = tree }; string repoFileStoragePath = Path.Combine(DirectoryHelper.SaveDirectoryPath, "SurfAccordDataSet.bin"); if (File.Exists(repoFileStoragePath)) File.Delete(repoFileStoragePath); using (FileStream s = File.Create(repoFileStoragePath)) { //Polenter.Serialization.SharpSerializerBinarySettings bs = // new Polenter.Serialization.SharpSerializerBinarySettings(Polenter.Serialization.BinarySerializationMode.SizeOptimized); //Polenter.Serialization.SharpSerializer formatter = new Polenter.Serialization.SharpSerializer(bs); //formatter.Serialize(surfAccordDataset, s); System.Runtime.Serialization.Formatters.Binary.BinaryFormatter formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(); formatter.Serialize(s, surfAccordDataset); s.Close(); } sw1.Stop(); saveingTime = sw1.ElapsedMilliseconds; logWriter(string.Format("Saving Surf Accord Dataset: {0}", saveingTime)); //Invalidating Cache CacheHelper.Remove("SurfAccordDataSet"); CacheHelper.Remove("SurfAccordIntervalTree"); logWriter(string.Format("Reading: {0} ms, Indexing: {1} ms, Saving Indexed data {2}", readingTime, indexingTime, saveingTime)); }
private static void FindMatch(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage, SurfSettings surfSettings, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, out Matrix <int> indices, out Matrix <byte> mask, out HomographyMatrix homography) { #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSettings != null) { hessianThresh = surfSettings.HessianThresh.Value; uniquenessThreshold = surfSettings.UniquenessThreshold.Value; } SURFDetector surfCPU = new SURFDetector(hessianThresh, false); #endregion int k = 2; Stopwatch watch; homography = null; //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; }
public List <ImageRecord> QueryImage(string queryImagePath, out string messageToLog, SurfSettings surfSetting = null) { List <ImageRecord> rtnImageList = new List <ImageRecord>(); #region Diagnostic Region Stopwatch sw = new Stopwatch(); long IndexingTime = 0; long QueryingTime = 0; long LoopTime = 0; #endregion Diagnostic Region SurfDataSet observerDataset = SurfRepository.GetSurfDataSet(); if (observerDataset == null) { throw new InvalidOperationException("Can't get the Surf Index, please index first"); } #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } SURFDetector surfDectector = new SURFDetector(hessianThresh, false); #endregion Surf Dectator Region Matrix <float> modelDescriptors; using (Image <Gray, byte> modelImage = new Image <Gray, byte>(queryImagePath)) { VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint(); modelDescriptors = surfDectector.DetectAndCompute(modelImage, null, modelKeyPoints); if (modelDescriptors.Rows < 4) { throw new InvalidOperationException("Model image didn't have any significant features to detect"); } Matrix <float> superMatrix = observerDataset.SuperMatrix; sw.Start(); Emgu.CV.Flann.Index flannIndex; if (!SurfRepository.Exists("flannIndex")) { flannIndex = new Emgu.CV.Flann.Index(superMatrix, 4); SurfRepository.AddFlannIndex(flannIndex, "flannIndex"); } else { flannIndex = SurfRepository.GetFlannIndex("flannIndex"); } sw.Stop(); IndexingTime = sw.ElapsedMilliseconds; sw.Reset(); var indices = new Matrix <int>(modelDescriptors.Rows, 2); // matrix that will contain indices of the 2-nearest neighbors found var dists = new Matrix <float>(modelDescriptors.Rows, 2); // matrix that will contain distances to the 2-nearest neighbors found sw.Start(); flannIndex.KnnSearch(modelDescriptors, indices, dists, 2, 24); sw.Stop(); QueryingTime = sw.ElapsedMilliseconds; sw.Reset(); List <SURFRecord1> imageList = observerDataset.SurfImageIndexRecord; imageList.ForEach(x => x.Distance = 0); //Create Interval Tree for Images IntervalTreeHelper.CreateTree(imageList); sw.Start(); for (int i = 0; i < indices.Rows; i++) { // filter out all inadequate pairs based on distance between pairs if (dists.Data[i, 0] < (uniquenessThreshold * dists.Data[i, 1])) { var img = IntervalTreeHelper.GetImageforRange(indices[i, 0]); if (img != null) { img.Distance++; } } } sw.Stop(); LoopTime = sw.ElapsedMilliseconds; string msg = String.Format("Indexing: {0}, Querying: {1}, Looping: {2}", IndexingTime, QueryingTime, LoopTime); messageToLog = msg; rtnImageList = imageList.Where(x => x.Distance > surfSetting.GoodMatchThreshold).OrderByDescending(x => x.Distance).Select(x => (ImageRecord)x).ToList(); } return(rtnImageList); }
public void CalculateSurfDescriptor(Image img, SurfSettings surfSetting) { }
public void IndexFiles(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action <string> logWriter, SurfSettings surfSetting = null) { //For Time Profilling long readingTime, indexingTime = 0, saveingTime = 0; #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } float hessianThreshold2 = (float)hessianThresh / 1000000; SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); #endregion int rows = 0; Stopwatch sw1; sw1 = Stopwatch.StartNew(); logWriter("Index started..."); string fullFileName = Path.Combine(DirectoryHelper.SaveDirectoryPath, "SurfAccordLinear.bin"); if (File.Exists(fullFileName)) { File.Delete(fullFileName); } using (FileStream fs = new FileStream(fullFileName, FileMode.Create, FileAccess.Write, FileShare.None)) { System.Runtime.Serialization.Formatters.Binary.BinaryFormatter bf = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(); int totalFileCount = imageFiles.Length; for (int i = 0; i < totalFileCount; i++) { var fi = imageFiles[i]; using (Bitmap observerImage = (Bitmap)Image.FromFile(fi.FullName)) { List <SpeededUpRobustFeaturePoint> observerImageSurfPoints = surf.ProcessImage(observerImage); if (observerImageSurfPoints.Count > 4) { SURFAccordRecord3 record = new SURFAccordRecord3 { Id = i, ImageName = fi.Name, ImagePath = fi.FullName, SurfDescriptors = observerImageSurfPoints }; bf.Serialize(fs, record); } else { Debug.WriteLine(fi.Name + " skip from index, because it didn't have significant feature"); } } IndexBgWorker.ReportProgress(i); } fs.Close(); } sw1.Stop(); readingTime = sw1.ElapsedMilliseconds; logWriter(string.Format("Reading Surb Complete, it tooked {0} ms. Saving Repository...", readingTime)); logWriter(string.Format("Reading: {0} ms, Indexing: {1} ms, Saving Indexed data {2}", readingTime, indexingTime, saveingTime)); }
private Bitmap CompareAndDrawImage(Bitmap modelImage, Bitmap observedImage, SurfSettings setting) { Stopwatch watch1 = new Stopwatch(); Stopwatch watch2 = new Stopwatch(); Bitmap returnBitmap; watch2.Start(); watch1.Reset(); watch1.Start(); double hessianThreshold = setting.HessianThresh.HasValue ? setting.HessianThresh.Value : 500; float hessianThreshold2 = (float)hessianThreshold / 1000000; Debug.WriteLine("hessianThreshold2: {0}", hessianThreshold2); SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); List<SpeededUpRobustFeaturePoint> surfPoints1 = surf.ProcessImage(modelImage); List<SpeededUpRobustFeaturePoint> surfPoints2 = surf.ProcessImage(observedImage); Debug.WriteLine("Surf points count: {0}", surfPoints1.Count); Debug.WriteLine("Surf points count: {0}", surfPoints2.Count); //long memoryFootprint = MemorySize.GetBlobSizeinKb(surfPoints2); //Debug.WriteLine("Surf extractor: {0} kb", memoryFootprint); watch1.Stop(); Debug.WriteLine("Surf Detection tooked {0} ms", watch1.ElapsedMilliseconds); watch1.Reset(); watch1.Start(); // Show the marked points in the original images Bitmap img1mark = new FeaturesMarker(surfPoints1, 2).Apply(modelImage); Bitmap img2mark = new FeaturesMarker(surfPoints2, 2).Apply(observedImage); // Concatenate the two images together in a single image (just to show on screen) Concatenate concatenate = new Concatenate(img1mark); returnBitmap = concatenate.Apply(img2mark); watch1.Stop(); Debug.WriteLine("Surf point plotting tooked {0} ms", watch1.ElapsedMilliseconds); //watch1.Reset(); watch1.Start(); //List<IntPoint>[] coretionalMatches = getMatches(surfPoints1, surfPoints2); //watch1.Stop(); //Debug.WriteLine("Correctional Match tooked {0} ms", watch1.ElapsedMilliseconds); //// Get the two sets of points //IntPoint[] correlationPoints11 = coretionalMatches[0].ToArray(); //IntPoint[] correlationPoints22 = coretionalMatches[1].ToArray(); //Debug.WriteLine("Correclation points count: {0}", correlationPoints11.Length); //Debug.WriteLine("Correclation points count: {0}", correlationPoints22.Length); Debug.WriteLine("Threshold: {0}", setting.UniquenessThreshold.Value); watch1.Reset(); watch1.Start(); // Step 2: Match feature points using a k-NN KNearestNeighborMatching matcher = new KNearestNeighborMatching(2); matcher.Threshold = setting.UniquenessThreshold.Value; IntPoint[][] matches = matcher.Match(surfPoints1, surfPoints2); watch1.Stop(); Debug.WriteLine("Knn Match tooked {0} ms", watch1.ElapsedMilliseconds); // Get the two sets of points IntPoint[] correlationPoints1 = matches[0]; IntPoint[] correlationPoints2 = matches[1]; Debug.WriteLine("Knn points count: {0}", correlationPoints1.Length); Debug.WriteLine("Knn points count: {0}", correlationPoints2.Length); //watch1.Reset(); watch1.Start(); //// Show the marked correlations in the concatenated image //PairsMarker pairs = new PairsMarker( // correlationPoints1, // Add image1's width to the X points to show the markings correctly // correlationPoints2.Apply(p => new IntPoint(p.X + modelImage.Width, p.Y)), Color.Blue); //returnBitmap = pairs.Apply(returnBitmap); //watch1.Stop(); //Debug.WriteLine("Match pair marking tooked {0} ms", watch1.ElapsedMilliseconds); if (correlationPoints1.Length < 4 || correlationPoints2.Length < 4) { MessageBox.Show("Insufficient points to attempt a fit."); return null; } watch1.Reset(); watch1.Start(); // Step 3: Create the homography matrix using a robust estimator //RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99); RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99); MatrixH homography = ransac.Estimate(correlationPoints1, correlationPoints2); watch1.Stop(); Debug.WriteLine("Ransac tooked {0} ms", watch1.ElapsedMilliseconds); watch1.Reset(); watch1.Start(); // Plot RANSAC results against correlation results IntPoint[] inliers1 = correlationPoints1.Submatrix(ransac.Inliers); IntPoint[] inliers2 = correlationPoints2.Submatrix(ransac.Inliers); watch1.Stop(); Debug.WriteLine("Ransac SubMatrix {0} ms", watch1.ElapsedMilliseconds); Debug.WriteLine("Ransac points count: {0}", inliers1.Length); Debug.WriteLine("Ransac points count: {0}", inliers2.Length); watch1.Reset(); watch1.Start(); PairsMarker inlierPairs = new PairsMarker( inliers1, // Add image1's width to the X points to show the markings correctly inliers2.Apply(p => new IntPoint(p.X + modelImage.Width, p.Y)), Color.Red); returnBitmap = inlierPairs.Apply(returnBitmap); watch1.Stop(); Debug.WriteLine("Ransac plotting tooked {0} ms", watch1.ElapsedMilliseconds); watch2.Stop(); return returnBitmap; }
public List <ImageRecord> QueryImage(string queryImagePath, out string messageToLog, SurfSettings surfSetting = null) { List <ImageRecord> rtnImageList = new List <ImageRecord>(); #region Diagnostic Region Stopwatch sw = new Stopwatch(); Stopwatch sw1 = new Stopwatch(); long _loadingTime = 0, _modelImageDectionlong = 0, _queryingTime = 0, _matchingTime = 0; #endregion Diagnostic Region #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; int minGoodMatchPercent = 0; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; minGoodMatchPercent = surfSetting.GoodMatchThreshold.Value; } float hessianThreshold2 = (float)hessianThresh / 1000000; SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); #endregion Surf Dectator Region #region Get Model Dectection and Validation sw.Reset(); sw.Start(); List <SpeededUpRobustFeaturePoint> modelImageSurfPoints; using (Bitmap modelImage = (Bitmap)Image.FromFile(queryImagePath)) { modelImageSurfPoints = surf.ProcessImage(modelImage); } if (modelImageSurfPoints == null || modelImageSurfPoints.Count < 4) { throw new InvalidOperationException("Insuffucient interesting point in query image, try another query image"); } sw.Stop(); _modelImageDectionlong = sw.ElapsedMilliseconds; #endregion #region Search Images sw.Reset(); sw.Start(); string fullFileName = Path.Combine(DirectoryHelper.SaveDirectoryPath, "SurfAccordLinear.bin"); if (!File.Exists(fullFileName)) { string exMsg = string.Format("Can't get the Surf Index at {0}, please index first", fullFileName); throw new FileNotFoundException(fullFileName); } using (FileStream fs = new FileStream(fullFileName, FileMode.Open, FileAccess.Read, FileShare.None)) { System.Runtime.Serialization.Formatters.Binary.BinaryFormatter bf = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(); long fileLength = fs.Length; while (fs.Position < fileLength) { SURFAccordRecord3 record = (SURFAccordRecord3)bf.Deserialize(fs); KNearestNeighborMatching matcher = new KNearestNeighborMatching(2); matcher.Threshold = uniquenessThreshold; sw1.Start(); AForge.IntPoint[][] matches = matcher.Match(modelImageSurfPoints, record.SurfDescriptors); sw1.Stop(); var countOfMatchPoint = matches[0].Length; if (countOfMatchPoint > 0) { double totalnumberOfModelFeature = modelImageSurfPoints.Count; double matchPercentage = ((totalnumberOfModelFeature - (double)countOfMatchPoint) / totalnumberOfModelFeature); matchPercentage = (1 - matchPercentage) * 100; matchPercentage = Math.Round(matchPercentage); if (matchPercentage >= minGoodMatchPercent) { record.Distance = matchPercentage; rtnImageList.Add(record.Clone()); } } record = null; } fs.Close(); } sw.Stop(); _matchingTime = sw1.ElapsedMilliseconds; _queryingTime = sw.ElapsedMilliseconds; #endregion string msg = String.Format("Loading: {0}, Model detection: {1}, Querying: {2}, Matching: {3}", _loadingTime, _modelImageDectionlong, _queryingTime, _matchingTime); messageToLog = msg; if (rtnImageList.Count > 0) { rtnImageList = rtnImageList.OrderByDescending(rec => rec.Distance) .ToList <ImageRecord>(); } return(rtnImageList); }
public List<ImageRecord> QueryImage(string queryImagePath, out string messageToLog, SurfSettings surfSetting = null) { List<ImageRecord> rtnImageList = new List<ImageRecord>(); #region Diagnostic Region Stopwatch sw = new Stopwatch(); long _loadingTime, _modelImageDectionlong, _queryingTime, _treeQuery, _loopTime = 0; #endregion Diagnostic Region #region Get KD-Tree Index sw.Reset(); sw.Start(); //--------------Getting Indexed Records SurfAccordDataSet surfAccordDataset; bool isExist= CacheHelper.Get<SurfAccordDataSet>("SurfAccordDataSet", out surfAccordDataset); if (!isExist) { string repoFileStoragePath = Path.Combine(DirectoryHelper.SaveDirectoryPath, "SurfAccordDataSet.bin"); if (!File.Exists(repoFileStoragePath)) { string exMsg = string.Format("Can't get the Surf Index at {0}, please index first", repoFileStoragePath); throw new FileNotFoundException(exMsg); } using (FileStream s = File.OpenRead(repoFileStoragePath)) { //Polenter.Serialization.SharpSerializerBinarySettings bs = // new Polenter.Serialization.SharpSerializerBinarySettings(Polenter.Serialization.BinarySerializationMode.SizeOptimized); //Polenter.Serialization.SharpSerializer formatter = new Polenter.Serialization.SharpSerializer(bs); //formatter.Serialize(surfAccordDataset, s); System.Runtime.Serialization.Formatters.Binary.BinaryFormatter formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(); surfAccordDataset = (SurfAccordDataSet)formatter.Deserialize(s); s.Close(); } CacheHelper.Add<SurfAccordDataSet>(surfAccordDataset, "SurfAccordDataSet"); } if (surfAccordDataset == null) throw new InvalidOperationException("Can't get the Surf Index, please index first"); sw.Stop(); _loadingTime = sw.ElapsedMilliseconds; #endregion #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; int goodMatchDistThreshold = 0; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; goodMatchDistThreshold = surfSetting.GoodMatchThreshold.Value; } float hessianThreshold2 = (float)hessianThresh / 1000000; SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); #endregion Surf Dectator Region #region Get Model Dectection and Validation sw.Reset(); sw.Start(); List<SpeededUpRobustFeaturePoint> modelImageSurfPoints; using (Bitmap modelImage = (Bitmap)Image.FromFile(queryImagePath)) { modelImageSurfPoints = surf.ProcessImage(modelImage); } if (modelImageSurfPoints == null || modelImageSurfPoints.Count < 4) { throw new InvalidOperationException("Insuffucient interesting point in query image, try another query image"); } sw.Stop(); _modelImageDectionlong = sw.ElapsedMilliseconds; #endregion #region Search Images sw.Reset(); sw.Start(); //------------Search Images Accord.MachineLearning.Structures.KDTree<int> tree = surfAccordDataset.IndexedTree; double[][] listofQueryDescriptors = modelImageSurfPoints.Select(ft => ft.Descriptor).ToArray(); double[] myscores = new double[listofQueryDescriptors.Length]; int[] labels = Enumerable.Repeat(-1, listofQueryDescriptors.Length).ToArray(); for (int i = 0; i < listofQueryDescriptors.Length; i++) { KDTreeNodeCollection<int> neighbors = tree.ApproximateNearest(listofQueryDescriptors[i],2, 90d); //KDTreeNodeCollection<int> neighbors = tree.Nearest(listofQueryDescriptors[i], uniquenessThreshold, 2); Dictionary<int, double> keyValueStore = new Dictionary<int, double>(); double similarityDist = 0; foreach (KDTreeNodeDistance<int> point in neighbors) { int label = point.Node.Value; double d = point.Distance; // Convert to similarity measure if (keyValueStore.ContainsKey(label)) { similarityDist = keyValueStore[label]; similarityDist += 1.0 / (1.0 + d); keyValueStore[label] = similarityDist; } else { similarityDist = 1.0 / (1.0 + d); keyValueStore.Add(label, similarityDist); } } if (keyValueStore.Count > 0) { int maxIndex = keyValueStore.Aggregate((l, r) => l.Value > r.Value ? l : r).Key; labels[i] = maxIndex; double sumOfAllValues = keyValueStore.Values.Sum(); myscores[i] = keyValueStore[maxIndex] / sumOfAllValues; } } sw.Stop(); _queryingTime = sw.ElapsedMilliseconds; sw.Reset(); sw.Start(); List<SURFRecord1> listOfSurfImages = surfAccordDataset.SurfImageIndexRecord; //----------Create Interval Tree from ImageMetaData IntervalTreeLib.IntervalTree<SURFRecord1, int> intervalTree; bool isTreeExist = CacheHelper.Get<IntervalTreeLib.IntervalTree<SURFRecord1, int>>("SurfAccordIntervalTree", out intervalTree); if (!isTreeExist) { intervalTree = new IntervalTreeLib.IntervalTree<SURFRecord1, int>(); foreach (var record in listOfSurfImages) { intervalTree.AddInterval(record.IndexStart, record.IndexEnd, record); } CacheHelper.Add<IntervalTreeLib.IntervalTree<SURFRecord1, int>>(intervalTree, "SurfAccordIntervalTree"); } //--------------Matching Target image similarity for (int i = 0; i < listofQueryDescriptors.Length; i++) { int rowNum = labels[i]; if (rowNum == -1) continue; double dist = myscores[i]; SURFRecord1 rec = intervalTree.Get(rowNum, IntervalTreeLib.StubMode.ContainsStartThenEnd).FirstOrDefault(); rec.Distance++; } sw.Stop(); _loopTime = sw.ElapsedMilliseconds; #endregion string msg = String.Format("Loading: {0}, Model detection: {1}, Querying: {2}, Looping: {3}", _loadingTime, _modelImageDectionlong, _queryingTime, _loopTime); messageToLog = msg; rtnImageList = listOfSurfImages.Where(rec => rec.Distance > goodMatchDistThreshold) .OrderByDescending(rec => rec.Distance) .ToList<ImageRecord>(); return rtnImageList; }
public List <ImageRecord> QueryImage(string queryImagePath, SurfSettings surfSetting = null) { List <ImageRecord> rtnImageList = new List <ImageRecord>(); var observerFeatureSets = SurfRepository.GetSurfRecordList(); #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; int minGoodMatchPercent = 50; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; minGoodMatchPercent = surfSetting.GoodMatchThreshold.Value; } SURFDetector surfDectector = new SURFDetector(hessianThresh, false); #endregion using (Image <Gray, byte> modelImage = new Image <Gray, byte>(queryImagePath)) { ImageFeature <float>[] modelFeatures = surfDectector.DetectFeatures(modelImage, null); if (modelFeatures.Length < 4) { throw new InvalidOperationException("Model image didn't have any significant features to detect"); } Features2DTracker <float> tracker = new Features2DTracker <float>(modelFeatures); foreach (var surfRecord in observerFeatureSets) { string queryImageName = System.IO.Path.GetFileName(queryImagePath); string modelImageName = surfRecord.ImageName; Features2DTracker <float> .MatchedImageFeature[] matchedFeatures = tracker.MatchFeature(surfRecord.observerFeatures, 2); Features2DTracker <float> .MatchedImageFeature[] uniqueFeatures = Features2DTracker <float> .VoteForUniqueness(matchedFeatures, uniquenessThreshold); Features2DTracker <float> .MatchedImageFeature[] uniqueRotOriFeatures = Features2DTracker <float> .VoteForSizeAndOrientation(uniqueFeatures, 1.5, 20); int goodMatchCount = 0; goodMatchCount = uniqueRotOriFeatures.Length; bool isMatch = false; double totalnumberOfModelFeature = modelFeatures.Length; double matchPercentage = ((totalnumberOfModelFeature - (double)goodMatchCount) / totalnumberOfModelFeature); matchPercentage = (1 - matchPercentage) * 100; matchPercentage = Math.Round(matchPercentage); if (matchPercentage >= minGoodMatchPercent) { HomographyMatrix homography = Features2DTracker <float> .GetHomographyMatrixFromMatchedFeatures(uniqueRotOriFeatures); if (homography != null) { isMatch = homography.IsValid(5); if (isMatch) { surfRecord.Distance = matchPercentage; rtnImageList.Add((ImageRecord)surfRecord); } } } //bool isMatch = false; //if (uniqueFeatures.Length > 4) //{ // HomographyMatrix homography = // Features2DTracker<float>.GetHomographyMatrixFromMatchedFeatures(uniqueRotOriFeatures); // if (homography != null) // { // isMatch = homography.IsValid(5); // } //} //if (isMatch) //{ // surfRecord.Distance = goodMatchCount; // rtnImageList.Add((ImageRecord)surfRecord); //} //int goodMatchCount = 0; //foreach (Features2DTracker<float>.MatchedImageFeature ms in matchedFeatures) //{ // if (ms.SimilarFeatures[0].Distance < uniquenessThreshold) // goodMatchCount++; //} //double totalnumberOfModelFeature = modelFeatures.Length; //double matchPercentage = ((totalnumberOfModelFeature - (double)goodMatchCount) / totalnumberOfModelFeature); //matchPercentage = (1 - matchPercentage) * 100; //matchPercentage = Math.Round(matchPercentage); //if (matchPercentage >= minGoodMatchPercent) //{ // surfRecord.Distance = matchPercentage; // rtnImageList.Add((ImageRecord)surfRecord); //} } } rtnImageList = rtnImageList.OrderByDescending(x => x.Distance).ToList(); return(rtnImageList); }
public List<ImageRecord> QueryImage(string queryImagePath, SurfSettings surfSetting = null) { List<ImageRecord> rtnImageList = new List<ImageRecord>(); var observerFeatureSets = SurfRepository.GetSurfRecordList(); #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; int minGoodMatchPercent = 50; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; minGoodMatchPercent = surfSetting.GoodMatchThreshold.Value; } SURFDetector surfDectector = new SURFDetector(hessianThresh, false); #endregion using (Image<Gray, byte> modelImage = new Image<Gray, byte>(queryImagePath)) { ImageFeature<float>[] modelFeatures = surfDectector.DetectFeatures(modelImage, null); if (modelFeatures.Length < 4) throw new InvalidOperationException("Model image didn't have any significant features to detect"); Features2DTracker<float> tracker = new Features2DTracker<float>(modelFeatures); foreach (var surfRecord in observerFeatureSets) { string queryImageName = System.IO.Path.GetFileName(queryImagePath); string modelImageName = surfRecord.ImageName; Features2DTracker<float>.MatchedImageFeature[] matchedFeatures = tracker.MatchFeature(surfRecord.observerFeatures, 2); Features2DTracker<float>.MatchedImageFeature[] uniqueFeatures = Features2DTracker<float>.VoteForUniqueness(matchedFeatures, uniquenessThreshold); Features2DTracker<float>.MatchedImageFeature[] uniqueRotOriFeatures = Features2DTracker<float>.VoteForSizeAndOrientation(uniqueFeatures, 1.5, 20); int goodMatchCount = 0; goodMatchCount = uniqueRotOriFeatures.Length; bool isMatch = false; double totalnumberOfModelFeature = modelFeatures.Length; double matchPercentage = ((totalnumberOfModelFeature - (double)goodMatchCount) / totalnumberOfModelFeature); matchPercentage = (1 - matchPercentage) * 100; matchPercentage = Math.Round(matchPercentage); if (matchPercentage >= minGoodMatchPercent) { HomographyMatrix homography = Features2DTracker<float>.GetHomographyMatrixFromMatchedFeatures(uniqueRotOriFeatures); if (homography != null) { isMatch = homography.IsValid(5); if (isMatch) { surfRecord.Distance = matchPercentage; rtnImageList.Add((ImageRecord)surfRecord); } } } //bool isMatch = false; //if (uniqueFeatures.Length > 4) //{ // HomographyMatrix homography = // Features2DTracker<float>.GetHomographyMatrixFromMatchedFeatures(uniqueRotOriFeatures); // if (homography != null) // { // isMatch = homography.IsValid(5); // } //} //if (isMatch) //{ // surfRecord.Distance = goodMatchCount; // rtnImageList.Add((ImageRecord)surfRecord); //} //int goodMatchCount = 0; //foreach (Features2DTracker<float>.MatchedImageFeature ms in matchedFeatures) //{ // if (ms.SimilarFeatures[0].Distance < uniquenessThreshold) // goodMatchCount++; //} //double totalnumberOfModelFeature = modelFeatures.Length; //double matchPercentage = ((totalnumberOfModelFeature - (double)goodMatchCount) / totalnumberOfModelFeature); //matchPercentage = (1 - matchPercentage) * 100; //matchPercentage = Math.Round(matchPercentage); //if (matchPercentage >= minGoodMatchPercent) //{ // surfRecord.Distance = matchPercentage; // rtnImageList.Add((ImageRecord)surfRecord); //} } } rtnImageList = rtnImageList.OrderByDescending(x => x.Distance).ToList(); return rtnImageList; }
private void AccordSurfAlgoIndexing(SurfSettings surfSetting) { var imageFiles = getFiles(IndexDirectory, "*.gif|*.jpg|*.png|*.bmp|*.jpeg", SearchOption.TopDirectoryOnly); int totalFileCount = imageFiles.Length; Dispatcher.Invoke((Action)delegate { pbIndex.Minimum = 0; pbIndex.Maximum = totalFileCount; lblIndexStatus.Text = "Indexing.."; }); if (surfSetting.Algorithm == SurfAlgo.Linear) { SurfIndexer4 surfIndexer = new SurfIndexer4(); if (ExecInParallel) surfIndexer.IndexFilesAsync(imageFiles, IndexBgWorker, WriteIndexStatus, surfSetting); else surfIndexer.IndexFiles(imageFiles, IndexBgWorker, WriteIndexStatus, surfSetting); } else { SurfIndexer3 surfIndexer = new SurfIndexer3(); if (ExecInParallel) surfIndexer.IndexFilesAsync(imageFiles, IndexBgWorker, WriteIndexStatus, surfSetting); else surfIndexer.IndexFiles(imageFiles, IndexBgWorker, WriteIndexStatus, surfSetting); } }
public void IndexFiles(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action <string> logWriter, SurfSettings surfSetting = null) { #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } SURFDetector surfDectector = new SURFDetector(hessianThresh, false); #endregion int rows = 0; Matrix <float> superMatrix = null; List <SURFRecord1> observerSurfImageIndexList = new List <SURFRecord1>(); Stopwatch sw1, sw2; sw1 = Stopwatch.StartNew(); logWriter("Index started..."); int totalFileCount = imageFiles.Length; for (int i = 0; i < totalFileCount; i++) { var fi = imageFiles[i]; using (Image <Gray, byte> observerImage = new Image <Gray, byte>(fi.FullName)) { VectorOfKeyPoint observerKeyPoints = new VectorOfKeyPoint(); Matrix <float> observerDescriptor = surfDectector.DetectAndCompute(observerImage, null, observerKeyPoints); if (observerDescriptor.Rows > 4) { int initRow = rows; int endRows = rows + observerDescriptor.Rows - 1; SURFRecord1 record = new SURFRecord1 { Id = i, ImageName = fi.Name, ImagePath = fi.FullName, IndexStart = rows, IndexEnd = endRows }; observerSurfImageIndexList.Add(record); if (superMatrix == null) { superMatrix = observerDescriptor; } else { superMatrix = superMatrix.ConcateVertical(observerDescriptor); } rows = endRows + 1; } else { Debug.WriteLine(fi.Name + " skip from index, because it didn't have significant feature"); } } IndexBgWorker.ReportProgress(i); } sw1.Stop(); logWriter(string.Format("Index Complete, it tooked {0} ms. Saving Repository...", sw1.ElapsedMilliseconds)); SurfDataSet surfDataset = new SurfDataSet { SurfImageIndexRecord = observerSurfImageIndexList, SuperMatrix = superMatrix }; sw2 = Stopwatch.StartNew(); SurfRepository.AddSuperMatrixList(surfDataset); SurfRepository.SaveRepository(SurfAlgo.Flaan); sw2.Stop(); logWriter(string.Format("Index tooked {0} ms. Saving Repository tooked {1} ms", sw1.ElapsedMilliseconds, sw2.ElapsedMilliseconds)); }
private SurfSettings GetSurfSetting() { SurfSettings surfSetting = new SurfSettings(); this.Dispatcher.Invoke(() => { surfSetting.HessianThresh = Convert.ToDouble((cmbSurfThreshold.SelectedItem as ListBoxItem).Content); surfSetting.UniquenessThreshold = nudsurfUniqueThreshold.Value; surfSetting.GoodMatchThreshold = nudsurfGoodMatchThreshold.Value; surfSetting.Algorithm = (SurfAlgo)cmbSurfApproach.SelectedIndex; }); return surfSetting; }
/// <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> private static Image <Bgr, Byte> Draw(Image <Gray, Byte> modelImage, Image <Gray, byte> observedImage, SurfSettings surfSettings, out long matchTime) { HomographyMatrix homography; VectorOfKeyPoint modelKeyPoints; VectorOfKeyPoint observedKeyPoints; Matrix <int> indices; Matrix <byte> mask; FindMatch(modelImage, observedImage, surfSettings, out matchTime, out modelKeyPoints, out observedKeyPoints, out indices, out mask, out homography); //Draw the matched keypoints Image <Bgr, Byte> result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints, indices, new Bgr(255, 0, 0), new Bgr(255, 255, 0), 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 return(result); }
public List <ImageRecord> QueryImage(string queryImagePath, out string messageToLog, SurfSettings surfSetting = null) { List <ImageRecord> rtnImageList = new List <ImageRecord>(); #region Diagnostic Region Stopwatch sw = new Stopwatch(); long _loadingTime, _modelImageDectionlong, _queryingTime, _treeQuery, _loopTime = 0; #endregion Diagnostic Region #region Get KD-Tree Index sw.Reset(); sw.Start(); //--------------Getting Indexed Records SurfAccordDataSet surfAccordDataset; bool isExist = CacheHelper.Get <SurfAccordDataSet>("SurfAccordDataSet", out surfAccordDataset); if (!isExist) { string repoFileStoragePath = Path.Combine(DirectoryHelper.SaveDirectoryPath, "SurfAccordDataSet.bin"); if (!File.Exists(repoFileStoragePath)) { string exMsg = string.Format("Can't get the Surf Index at {0}, please index first", repoFileStoragePath); throw new FileNotFoundException(exMsg); } using (FileStream s = File.OpenRead(repoFileStoragePath)) { //Polenter.Serialization.SharpSerializerBinarySettings bs = // new Polenter.Serialization.SharpSerializerBinarySettings(Polenter.Serialization.BinarySerializationMode.SizeOptimized); //Polenter.Serialization.SharpSerializer formatter = new Polenter.Serialization.SharpSerializer(bs); //formatter.Serialize(surfAccordDataset, s); System.Runtime.Serialization.Formatters.Binary.BinaryFormatter formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(); surfAccordDataset = (SurfAccordDataSet)formatter.Deserialize(s); s.Close(); } CacheHelper.Add <SurfAccordDataSet>(surfAccordDataset, "SurfAccordDataSet"); } if (surfAccordDataset == null) { throw new InvalidOperationException("Can't get the Surf Index, please index first"); } sw.Stop(); _loadingTime = sw.ElapsedMilliseconds; #endregion #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; int goodMatchDistThreshold = 0; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; goodMatchDistThreshold = surfSetting.GoodMatchThreshold.Value; } float hessianThreshold2 = (float)hessianThresh / 1000000; SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); #endregion Surf Dectator Region #region Get Model Dectection and Validation sw.Reset(); sw.Start(); List <SpeededUpRobustFeaturePoint> modelImageSurfPoints; using (Bitmap modelImage = (Bitmap)Image.FromFile(queryImagePath)) { modelImageSurfPoints = surf.ProcessImage(modelImage); } if (modelImageSurfPoints == null || modelImageSurfPoints.Count < 4) { throw new InvalidOperationException("Insuffucient interesting point in query image, try another query image"); } sw.Stop(); _modelImageDectionlong = sw.ElapsedMilliseconds; #endregion #region Search Images sw.Reset(); sw.Start(); //------------Search Images Accord.MachineLearning.Structures.KDTree <int> tree = surfAccordDataset.IndexedTree; double[][] listofQueryDescriptors = modelImageSurfPoints.Select(ft => ft.Descriptor).ToArray(); double[] myscores = new double[listofQueryDescriptors.Length]; int[] labels = Enumerable.Repeat(-1, listofQueryDescriptors.Length).ToArray(); for (int i = 0; i < listofQueryDescriptors.Length; i++) { KDTreeNodeCollection <int> neighbors = tree.ApproximateNearest(listofQueryDescriptors[i], 2, 90d); //KDTreeNodeCollection<int> neighbors = tree.Nearest(listofQueryDescriptors[i], uniquenessThreshold, 2); Dictionary <int, double> keyValueStore = new Dictionary <int, double>(); double similarityDist = 0; foreach (KDTreeNodeDistance <int> point in neighbors) { int label = point.Node.Value; double d = point.Distance; // Convert to similarity measure if (keyValueStore.ContainsKey(label)) { similarityDist = keyValueStore[label]; similarityDist += 1.0 / (1.0 + d); keyValueStore[label] = similarityDist; } else { similarityDist = 1.0 / (1.0 + d); keyValueStore.Add(label, similarityDist); } } if (keyValueStore.Count > 0) { int maxIndex = keyValueStore.Aggregate((l, r) => l.Value > r.Value ? l : r).Key; labels[i] = maxIndex; double sumOfAllValues = keyValueStore.Values.Sum(); myscores[i] = keyValueStore[maxIndex] / sumOfAllValues; } } sw.Stop(); _queryingTime = sw.ElapsedMilliseconds; sw.Reset(); sw.Start(); List <SURFRecord1> listOfSurfImages = surfAccordDataset.SurfImageIndexRecord; //----------Create Interval Tree from ImageMetaData IntervalTreeLib.IntervalTree <SURFRecord1, int> intervalTree; bool isTreeExist = CacheHelper.Get <IntervalTreeLib.IntervalTree <SURFRecord1, int> >("SurfAccordIntervalTree", out intervalTree); if (!isTreeExist) { intervalTree = new IntervalTreeLib.IntervalTree <SURFRecord1, int>(); foreach (var record in listOfSurfImages) { intervalTree.AddInterval(record.IndexStart, record.IndexEnd, record); } CacheHelper.Add <IntervalTreeLib.IntervalTree <SURFRecord1, int> >(intervalTree, "SurfAccordIntervalTree"); } //--------------Matching Target image similarity for (int i = 0; i < listofQueryDescriptors.Length; i++) { int rowNum = labels[i]; if (rowNum == -1) { continue; } double dist = myscores[i]; SURFRecord1 rec = intervalTree.Get(rowNum, IntervalTreeLib.StubMode.ContainsStartThenEnd).FirstOrDefault(); rec.Distance++; } sw.Stop(); _loopTime = sw.ElapsedMilliseconds; #endregion string msg = String.Format("Loading: {0}, Model detection: {1}, Querying: {2}, Looping: {3}", _loadingTime, _modelImageDectionlong, _queryingTime, _loopTime); messageToLog = msg; rtnImageList = listOfSurfImages.Where(rec => rec.Distance > goodMatchDistThreshold) .OrderByDescending(rec => rec.Distance) .ToList <ImageRecord>(); return(rtnImageList); }
private Bitmap CompareAndDrawImage(Bitmap modelImage, Bitmap observedImage, SurfSettings setting) { Stopwatch watch1 = new Stopwatch(); Stopwatch watch2 = new Stopwatch(); Bitmap returnBitmap; watch2.Start(); watch1.Reset(); watch1.Start(); double hessianThreshold = setting.HessianThresh.HasValue ? setting.HessianThresh.Value : 500; float hessianThreshold2 = (float)hessianThreshold / 1000000; Debug.WriteLine("hessianThreshold2: {0}", hessianThreshold2); SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); List <SpeededUpRobustFeaturePoint> surfPoints1 = surf.ProcessImage(modelImage); List <SpeededUpRobustFeaturePoint> surfPoints2 = surf.ProcessImage(observedImage); Debug.WriteLine("Surf points count: {0}", surfPoints1.Count); Debug.WriteLine("Surf points count: {0}", surfPoints2.Count); //long memoryFootprint = MemorySize.GetBlobSizeinKb(surfPoints2); //Debug.WriteLine("Surf extractor: {0} kb", memoryFootprint); watch1.Stop(); Debug.WriteLine("Surf Detection tooked {0} ms", watch1.ElapsedMilliseconds); watch1.Reset(); watch1.Start(); // Show the marked points in the original images Bitmap img1mark = new FeaturesMarker(surfPoints1, 2).Apply(modelImage); Bitmap img2mark = new FeaturesMarker(surfPoints2, 2).Apply(observedImage); // Concatenate the two images together in a single image (just to show on screen) Concatenate concatenate = new Concatenate(img1mark); returnBitmap = concatenate.Apply(img2mark); watch1.Stop(); Debug.WriteLine("Surf point plotting tooked {0} ms", watch1.ElapsedMilliseconds); //watch1.Reset(); watch1.Start(); //List<IntPoint>[] coretionalMatches = getMatches(surfPoints1, surfPoints2); //watch1.Stop(); //Debug.WriteLine("Correctional Match tooked {0} ms", watch1.ElapsedMilliseconds); //// Get the two sets of points //IntPoint[] correlationPoints11 = coretionalMatches[0].ToArray(); //IntPoint[] correlationPoints22 = coretionalMatches[1].ToArray(); //Debug.WriteLine("Correclation points count: {0}", correlationPoints11.Length); //Debug.WriteLine("Correclation points count: {0}", correlationPoints22.Length); Debug.WriteLine("Threshold: {0}", setting.UniquenessThreshold.Value); watch1.Reset(); watch1.Start(); // Step 2: Match feature points using a k-NN KNearestNeighborMatching matcher = new KNearestNeighborMatching(2); matcher.Threshold = setting.UniquenessThreshold.Value; IntPoint[][] matches = matcher.Match(surfPoints1, surfPoints2); watch1.Stop(); Debug.WriteLine("Knn Match tooked {0} ms", watch1.ElapsedMilliseconds); // Get the two sets of points IntPoint[] correlationPoints1 = matches[0]; IntPoint[] correlationPoints2 = matches[1]; Debug.WriteLine("Knn points count: {0}", correlationPoints1.Length); Debug.WriteLine("Knn points count: {0}", correlationPoints2.Length); //watch1.Reset(); watch1.Start(); //// Show the marked correlations in the concatenated image //PairsMarker pairs = new PairsMarker( // correlationPoints1, // Add image1's width to the X points to show the markings correctly // correlationPoints2.Apply(p => new IntPoint(p.X + modelImage.Width, p.Y)), Color.Blue); //returnBitmap = pairs.Apply(returnBitmap); //watch1.Stop(); //Debug.WriteLine("Match pair marking tooked {0} ms", watch1.ElapsedMilliseconds); if (correlationPoints1.Length < 4 || correlationPoints2.Length < 4) { MessageBox.Show("Insufficient points to attempt a fit."); return(null); } watch1.Reset(); watch1.Start(); // Step 3: Create the homography matrix using a robust estimator //RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99); RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99); MatrixH homography = ransac.Estimate(correlationPoints1, correlationPoints2); watch1.Stop(); Debug.WriteLine("Ransac tooked {0} ms", watch1.ElapsedMilliseconds); watch1.Reset(); watch1.Start(); // Plot RANSAC results against correlation results IntPoint[] inliers1 = correlationPoints1.Submatrix(ransac.Inliers); IntPoint[] inliers2 = correlationPoints2.Submatrix(ransac.Inliers); watch1.Stop(); Debug.WriteLine("Ransac SubMatrix {0} ms", watch1.ElapsedMilliseconds); Debug.WriteLine("Ransac points count: {0}", inliers1.Length); Debug.WriteLine("Ransac points count: {0}", inliers2.Length); watch1.Reset(); watch1.Start(); PairsMarker inlierPairs = new PairsMarker( inliers1, // Add image1's width to the X points to show the markings correctly inliers2.Apply(p => new IntPoint(p.X + modelImage.Width, p.Y)), Color.Red); returnBitmap = inlierPairs.Apply(returnBitmap); watch1.Stop(); Debug.WriteLine("Ransac plotting tooked {0} ms", watch1.ElapsedMilliseconds); watch2.Stop(); return(returnBitmap); }
public void IndexFilesAsync(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action <string> logWriter, SurfSettings surfSetting = null) { throw new NotImplementedException(); }
public void IndexFiles(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action<string> logWriter, SurfSettings surfSetting = null) { //For Time Profilling long readingTime, indexingTime = 0 , saveingTime = 0; #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } float hessianThreshold2 = (float)hessianThresh / 1000000; SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); #endregion int rows = 0; Stopwatch sw1; sw1 = Stopwatch.StartNew(); logWriter("Index started..."); string fullFileName = Path.Combine(DirectoryHelper.SaveDirectoryPath, "SurfAccordLinear.bin"); if (File.Exists(fullFileName)) File.Delete(fullFileName); using (FileStream fs = new FileStream(fullFileName, FileMode.Create, FileAccess.Write, FileShare.None)) { System.Runtime.Serialization.Formatters.Binary.BinaryFormatter bf = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(); int totalFileCount = imageFiles.Length; for (int i = 0; i < totalFileCount; i++) { var fi = imageFiles[i]; using (Bitmap observerImage = (Bitmap)Image.FromFile(fi.FullName)) { List<SpeededUpRobustFeaturePoint> observerImageSurfPoints = surf.ProcessImage(observerImage); if (observerImageSurfPoints.Count > 4) { SURFAccordRecord3 record = new SURFAccordRecord3 { Id = i, ImageName = fi.Name, ImagePath = fi.FullName, SurfDescriptors = observerImageSurfPoints }; bf.Serialize(fs, record); } else { Debug.WriteLine(fi.Name + " skip from index, because it didn't have significant feature"); } } IndexBgWorker.ReportProgress(i); } fs.Close(); } sw1.Stop(); readingTime = sw1.ElapsedMilliseconds; logWriter(string.Format("Reading Surb Complete, it tooked {0} ms. Saving Repository...", readingTime)); logWriter(string.Format("Reading: {0} ms, Indexing: {1} ms, Saving Indexed data {2}", readingTime, indexingTime, saveingTime)); }
public void IndexFiles(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action <string> logWriter, SurfSettings surfSetting = null) { //For Time Profilling long readingTime, indexingTime, saveingTime; #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } float hessianThreshold2 = (float)hessianThresh / 1000000; SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); #endregion int rows = 0; List <SURFRecord1> observerSurfImageIndexList = new List <SURFRecord1>(); List <SpeededUpRobustFeaturePoint> listOfAllObserverImagesSurfPoints = new List <SpeededUpRobustFeaturePoint>(); Stopwatch sw1; sw1 = Stopwatch.StartNew(); logWriter("Index started..."); int totalFileCount = imageFiles.Length; for (int i = 0; i < totalFileCount; i++) { var fi = imageFiles[i]; using (Bitmap observerImage = (Bitmap)Image.FromFile(fi.FullName)) { List <SpeededUpRobustFeaturePoint> observerImageSurfPoints = surf.ProcessImage(observerImage); if (observerImageSurfPoints.Count > 4) { int initRow = rows; int endRows = rows + observerImageSurfPoints.Count - 1; SURFRecord1 record = new SURFRecord1 { Id = i, ImageName = fi.Name, ImagePath = fi.FullName, IndexStart = rows, IndexEnd = endRows }; observerSurfImageIndexList.Add(record); listOfAllObserverImagesSurfPoints.AddRange(observerImageSurfPoints); rows = endRows + 1; } else { Debug.WriteLine(fi.Name + " skip from index, because it didn't have significant feature"); } } IndexBgWorker.ReportProgress(i); } sw1.Stop(); readingTime = sw1.ElapsedMilliseconds; logWriter(string.Format("Reading Surb Complete, it tooked {0} ms. Saving Repository...", readingTime)); //------------Initialize Tree from Data sw1.Reset(); sw1.Start(); double[][] superMatrix = listOfAllObserverImagesSurfPoints.Select(c => c.Descriptor).ToArray(); int[] outputs = new int[superMatrix.Length]; for (int i = 0; i < outputs.Length; i++) { outputs[i] = i; } Accord.MachineLearning.Structures.KDTree <int> tree = Accord.MachineLearning.Structures.KDTree.FromData(superMatrix, outputs, Accord.Math.Distance.Euclidean, inPlace: true); sw1.Stop(); indexingTime = sw1.ElapsedMilliseconds; logWriter(string.Format("Intializing KD Tree: {0}", indexingTime)); //--------------Saving Indexed Records sw1.Reset(); sw1.Start(); SurfAccordDataSet surfAccordDataset = new SurfAccordDataSet { SurfImageIndexRecord = observerSurfImageIndexList, IndexedTree = tree }; string repoFileStoragePath = Path.Combine(DirectoryHelper.SaveDirectoryPath, "SurfAccordDataSet.bin"); if (File.Exists(repoFileStoragePath)) { File.Delete(repoFileStoragePath); } using (FileStream s = File.Create(repoFileStoragePath)) { //Polenter.Serialization.SharpSerializerBinarySettings bs = // new Polenter.Serialization.SharpSerializerBinarySettings(Polenter.Serialization.BinarySerializationMode.SizeOptimized); //Polenter.Serialization.SharpSerializer formatter = new Polenter.Serialization.SharpSerializer(bs); //formatter.Serialize(surfAccordDataset, s); System.Runtime.Serialization.Formatters.Binary.BinaryFormatter formatter = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(); formatter.Serialize(s, surfAccordDataset); s.Close(); } sw1.Stop(); saveingTime = sw1.ElapsedMilliseconds; logWriter(string.Format("Saving Surf Accord Dataset: {0}", saveingTime)); //Invalidating Cache CacheHelper.Remove("SurfAccordDataSet"); CacheHelper.Remove("SurfAccordIntervalTree"); logWriter(string.Format("Reading: {0} ms, Indexing: {1} ms, Saving Indexed data {2}", readingTime, indexingTime, saveingTime)); }
public void IndexFilesAsync(FileInfo[] imageFiles, System.ComponentModel.BackgroundWorker IndexBgWorker, Action<string> logWriter, SurfSettings surfSetting = null) { throw new NotImplementedException(); }
public AccordSurfWindow(string modelImagePath, string observerImagePath, SurfSettings setting) : this() { this.ModelImagePath = modelImagePath; this.ObserverImagePath = observerImagePath; this.SurfSetting = setting; }
public static void MatchInWindow(string modelImagePath, string observedImagePath, SurfSettings surfSettings) { long matchTime; using (Image <Gray, Byte> modelImage = new Image <Gray, byte>(modelImagePath)) using (Image <Gray, Byte> observedImage = new Image <Gray, byte>(observedImagePath)) { Image <Bgr, byte> result = Draw(modelImage, observedImage, surfSettings, out matchTime); ImageViewer.Show(result, String.Format("Matched using {0} in {1} milliseconds", GpuInvoke.HasCuda ? "GPU" : "CPU", matchTime)); } }
public List<ImageRecord> QueryImage(string queryImagePath, out string messageToLog, SurfSettings surfSetting = null) { List<ImageRecord> rtnImageList = new List<ImageRecord>(); #region Diagnostic Region Stopwatch sw = new Stopwatch(); Stopwatch sw1 = new Stopwatch(); long _loadingTime = 0, _modelImageDectionlong = 0, _queryingTime = 0, _matchingTime = 0; #endregion Diagnostic Region #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; int minGoodMatchPercent = 0; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; minGoodMatchPercent = surfSetting.GoodMatchThreshold.Value; } float hessianThreshold2 = (float)hessianThresh / 1000000; SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2); #endregion Surf Dectator Region #region Get Model Dectection and Validation sw.Reset(); sw.Start(); List<SpeededUpRobustFeaturePoint> modelImageSurfPoints; using (Bitmap modelImage = (Bitmap)Image.FromFile(queryImagePath)) { modelImageSurfPoints = surf.ProcessImage(modelImage); } if (modelImageSurfPoints == null || modelImageSurfPoints.Count < 4) { throw new InvalidOperationException("Insuffucient interesting point in query image, try another query image"); } sw.Stop(); _modelImageDectionlong = sw.ElapsedMilliseconds; #endregion #region Search Images sw.Reset(); sw.Start(); string fullFileName = Path.Combine(DirectoryHelper.SaveDirectoryPath, "SurfAccordLinear.bin"); if (!File.Exists(fullFileName)) { string exMsg = string.Format("Can't get the Surf Index at {0}, please index first", fullFileName); throw new FileNotFoundException(fullFileName); } using (FileStream fs = new FileStream(fullFileName, FileMode.Open, FileAccess.Read, FileShare.None)) { System.Runtime.Serialization.Formatters.Binary.BinaryFormatter bf = new System.Runtime.Serialization.Formatters.Binary.BinaryFormatter(); long fileLength = fs.Length; while (fs.Position < fileLength) { SURFAccordRecord3 record = (SURFAccordRecord3)bf.Deserialize(fs); KNearestNeighborMatching matcher = new KNearestNeighborMatching(2); matcher.Threshold = uniquenessThreshold; sw1.Start(); AForge.IntPoint[][] matches = matcher.Match(modelImageSurfPoints, record.SurfDescriptors); sw1.Stop(); var countOfMatchPoint = matches[0].Length; if (countOfMatchPoint > 0) { double totalnumberOfModelFeature = modelImageSurfPoints.Count; double matchPercentage = ((totalnumberOfModelFeature - (double)countOfMatchPoint) / totalnumberOfModelFeature); matchPercentage = (1 - matchPercentage) * 100; matchPercentage = Math.Round(matchPercentage); if (matchPercentage >= minGoodMatchPercent) { record.Distance = matchPercentage; rtnImageList.Add(record.Clone()); } } record = null; } fs.Close(); } sw.Stop(); _matchingTime = sw1.ElapsedMilliseconds; _queryingTime = sw.ElapsedMilliseconds; #endregion string msg = String.Format("Loading: {0}, Model detection: {1}, Querying: {2}, Matching: {3}", _loadingTime, _modelImageDectionlong, _queryingTime, _matchingTime); messageToLog = msg; if (rtnImageList.Count > 0) { rtnImageList = rtnImageList.OrderByDescending(rec => rec.Distance) .ToList<ImageRecord>(); } return rtnImageList; }
public List<ImageRecord> QueryImage(string queryImagePath, out string messageToLog, SurfSettings surfSetting = null) { List<ImageRecord> rtnImageList = new List<ImageRecord>(); #region Diagnostic Region Stopwatch sw = new Stopwatch(); long IndexingTime = 0; long QueryingTime = 0; long LoopTime = 0; #endregion Diagnostic Region SurfDataSet observerDataset = SurfRepository.GetSurfDataSet(); if (observerDataset == null) throw new InvalidOperationException("Can't get the Surf Index, please index first"); #region Surf Dectator Region double hessianThresh = 500; double uniquenessThreshold = 0.8; if (surfSetting != null) { hessianThresh = surfSetting.HessianThresh.Value; uniquenessThreshold = surfSetting.UniquenessThreshold.Value; } SURFDetector surfDectector = new SURFDetector(hessianThresh, false); #endregion Surf Dectator Region Matrix<float> modelDescriptors; using (Image<Gray, byte> modelImage = new Image<Gray, byte>(queryImagePath)) { VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint(); modelDescriptors = surfDectector.DetectAndCompute(modelImage, null, modelKeyPoints); if (modelDescriptors.Rows < 4) throw new InvalidOperationException("Model image didn't have any significant features to detect"); Matrix<float> superMatrix = observerDataset.SuperMatrix; sw.Start(); Emgu.CV.Flann.Index flannIndex; if (!SurfRepository.Exists("flannIndex")) { flannIndex = new Emgu.CV.Flann.Index(superMatrix, 4); SurfRepository.AddFlannIndex(flannIndex, "flannIndex"); } else flannIndex = SurfRepository.GetFlannIndex("flannIndex"); sw.Stop(); IndexingTime = sw.ElapsedMilliseconds; sw.Reset(); var indices = new Matrix<int>(modelDescriptors.Rows, 2); // matrix that will contain indices of the 2-nearest neighbors found var dists = new Matrix<float>(modelDescriptors.Rows, 2); // matrix that will contain distances to the 2-nearest neighbors found sw.Start(); flannIndex.KnnSearch(modelDescriptors, indices, dists, 2, 24); sw.Stop(); QueryingTime = sw.ElapsedMilliseconds; sw.Reset(); List<SURFRecord1> imageList = observerDataset.SurfImageIndexRecord; imageList.ForEach(x => x.Distance = 0); //Create Interval Tree for Images IntervalTreeHelper.CreateTree(imageList); sw.Start(); for (int i = 0; i < indices.Rows; i++) { // filter out all inadequate pairs based on distance between pairs if (dists.Data[i, 0] < (uniquenessThreshold * dists.Data[i, 1])) { var img = IntervalTreeHelper.GetImageforRange(indices[i, 0]); if (img != null) img.Distance++; } } sw.Stop(); LoopTime = sw.ElapsedMilliseconds; string msg = String.Format("Indexing: {0}, Querying: {1}, Looping: {2}", IndexingTime, QueryingTime, LoopTime); messageToLog = msg; rtnImageList = imageList.Where(x => x.Distance > surfSetting.GoodMatchThreshold).OrderByDescending(x => x.Distance).Select(x => (ImageRecord)x).ToList(); } return rtnImageList; }