public static void SaveFlannIndex(Emgu.CV.Flann.Index index)
{
string repoFileStoragePath = Path.Combine(SaveDirectoryPath, "flannIndex.bin");
File.Delete(repoFileStoragePath);
FileStream stream = File.Create(repoFileStoragePath);
var formatter = new BinaryFormatter();
formatter.Serialize(stream, index);
stream.Close();
}
public static Emgu.CV.Flann.Index LoadFlannIndex()
{
Emgu.CV.Flann.Index index = null;
string repoFileStoragePath = Path.Combine(SaveDirectoryPath, "flannIndex.bin");
FileStream stream = File.OpenRead(repoFileStoragePath);
var formatter = new BinaryFormatter();
index = (Emgu.CV.Flann.Index)formatter.Deserialize(stream);
stream.Close();
return(index);
//Polenter.Serialization.SharpSerializerBinarySettings st = new Polenter.Serialization.SharpSerializerBinarySettings(Polenter.Serialization.BinarySerializationMode.SizeOptimized);
//Polenter.Serialization.SharpSerializer serializer = new Polenter.Serialization.SharpSerializer(st);
//serializer.Serialize(featureSets, repoFileStoragePath);
}
public static void AddFlannIndex(Emgu.CV.Flann.Index o, string key)
{
// NOTE: Apply expiration parameters as you see fit.
// I typically pull from configuration file.
// In this example, I want an absolute
// timeout so changes will always be reflected
// at that time. Hence, the NoSlidingExpiration.
if (TrackSize)
{
_cacheSizeinKb += MemorySize.GetBlobSizeinKb(o);
}
cache.Add(
key,
o,
DateTime.Now.AddMinutes(1440));
}
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);
}
private List <LoCaTeRanker> PerformExtendedSurfSearch(string queryImagePath, List <LoCaTeRanker> ImageList)
{
//If no images are found in Locate, return
if (ImageList.Count == 0)
{
return(ImageList);
}
SURFDetector surfDectector = new SURFDetector(1000, false);
Matrix <float> superMatrix = null;
List <SURFRecord1> observerSurfImageIndexList = new List <SURFRecord1>();
#region Computing Model Descriptors
Matrix <float> modelDescriptors;
VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint();
using (Image <Gray, byte> modelImage = new Image <Gray, byte>(queryImagePath))
{
modelDescriptors = surfDectector.DetectAndCompute(modelImage, null, modelKeyPoints);
}
#endregion
#region Computing Surf Descriptors
int rows = 0;
int numProcs = Environment.ProcessorCount;
int concurrencyLevel = numProcs * 2;
ConcurrentDictionary <long, Matrix <float> > obserableSurfPoints = new ConcurrentDictionary <long, Matrix <float> >(concurrencyLevel, ImageList.Count);
Parallel.ForEach(ImageList, img =>
{
string imagePath = img.ImagePath;
using (Image <Gray, byte> observerImage = new Image <Gray, byte>(imagePath))
{
VectorOfKeyPoint observerKeyPoints = new VectorOfKeyPoint();
Matrix <float> observerDescriptors = surfDectector.DetectAndCompute(observerImage, null, observerKeyPoints);
obserableSurfPoints.TryAdd(img.Id, observerDescriptors);
}
});
foreach (var rec in ImageList)
{
Matrix <float> observerDescriptors = obserableSurfPoints[rec.Id];
if (superMatrix != null)
{
superMatrix = superMatrix.ConcateVertical(observerDescriptors);
}
else
{
superMatrix = observerDescriptors;
}
int initRow = rows; int endRows = rows + observerDescriptors.Rows - 1;
observerSurfImageIndexList.Add(new SURFRecord1
{
Id = rec.Id,
ImageName = rec.ImageName,
ImagePath = rec.ImagePath,
IndexStart = rows,
IndexEnd = endRows,
Distance = 0
});
rows = endRows + 1;
}
//foreach (var rec in ImageList)
//{
// string imagePath = rec.ImagePath;
// using (Image<Gray, byte> observerImage = new Image<Gray, byte>(imagePath))
// {
// VectorOfKeyPoint observerKeyPoints = new VectorOfKeyPoint();
// Matrix<float> observerDescriptors = surfDectector.DetectAndCompute(observerImage, null, observerKeyPoints);
// if (superMatrix != null)
// superMatrix = superMatrix.ConcateVertical(observerDescriptors);
// else
// superMatrix = observerDescriptors;
// int initRow = rows; int endRows = rows + observerDescriptors.Rows - 1;
// observerSurfImageIndexList.Add(new SURFRecord1
// {
// Id = rec.Id,
// ImageName = rec.ImageName,
// ImagePath = rec.ImagePath,
// IndexStart = rows,
// IndexEnd = endRows,
// Distance = 0
// });
// rows = endRows + 1;
// }
//}
#endregion
Emgu.CV.Flann.Index flannIndex = new Emgu.CV.Flann.Index(superMatrix, 4);
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
flannIndex.KnnSearch(modelDescriptors, indices, dists, 2, 24);
IntervalTreeHelper.CreateTree(observerSurfImageIndexList);
for (int i = 0; i < indices.Rows; i++)
{
// filter out all inadequate pairs based on distance between pairs
if (dists.Data[i, 0] < (0.3 * dists.Data[i, 1]))
{
var img = IntervalTreeHelper.GetImageforRange(indices[i, 0]);
if (img != null)
{
img.Distance++;
}
}
}
int maxMatch = Convert.ToInt32(observerSurfImageIndexList.Select(rec => rec.Distance).Max());
observerSurfImageIndexList = observerSurfImageIndexList.OrderByDescending(rec => rec.Distance).ToList();
int totalImageCount = observerSurfImageIndexList.Count;
for (int i = 0; i < totalImageCount; i++)
{
long id = observerSurfImageIndexList[i].Id;
var img2 = ImageList.Where(img => img.Id == id).SingleOrDefault();
if (img2 != null)
{
double countofMatch = observerSurfImageIndexList[i].Distance;
if (countofMatch > 0)
{
img2.SurfRank = (totalImageCount - i);
}
}
}
return(ImageList);
}
private List<LoCaTeRanker> PerformExtendedSurfSearch(string queryImagePath, List<LoCaTeRanker> ImageList)
{
//If no images are found in Locate, return
if (ImageList.Count == 0)
return ImageList;
SURFDetector surfDectector = new SURFDetector(1000, false);
Matrix<float> superMatrix = null;
List<SURFRecord1> observerSurfImageIndexList = new List<SURFRecord1>();
#region Computing Model Descriptors
Matrix<float> modelDescriptors;
VectorOfKeyPoint modelKeyPoints = new VectorOfKeyPoint();
using (Image<Gray, byte> modelImage = new Image<Gray, byte>(queryImagePath))
{
modelDescriptors = surfDectector.DetectAndCompute(modelImage, null, modelKeyPoints);
}
#endregion
#region Computing Surf Descriptors
int rows = 0;
int numProcs = Environment.ProcessorCount;
int concurrencyLevel = numProcs * 2;
ConcurrentDictionary<long, Matrix<float>> obserableSurfPoints = new ConcurrentDictionary<long, Matrix<float>>(concurrencyLevel, ImageList.Count);
Parallel.ForEach(ImageList, img =>
{
string imagePath = img.ImagePath;
using (Image<Gray, byte> observerImage = new Image<Gray, byte>(imagePath))
{
VectorOfKeyPoint observerKeyPoints = new VectorOfKeyPoint();
Matrix<float> observerDescriptors = surfDectector.DetectAndCompute(observerImage, null, observerKeyPoints);
obserableSurfPoints.TryAdd(img.Id, observerDescriptors);
}
});
foreach (var rec in ImageList)
{
Matrix<float> observerDescriptors = obserableSurfPoints[rec.Id];
if (superMatrix != null)
superMatrix = superMatrix.ConcateVertical(observerDescriptors);
else
superMatrix = observerDescriptors;
int initRow = rows; int endRows = rows + observerDescriptors.Rows - 1;
observerSurfImageIndexList.Add(new SURFRecord1
{
Id = rec.Id,
ImageName = rec.ImageName,
ImagePath = rec.ImagePath,
IndexStart = rows,
IndexEnd = endRows,
Distance = 0
});
rows = endRows + 1;
}
//foreach (var rec in ImageList)
//{
// string imagePath = rec.ImagePath;
// using (Image<Gray, byte> observerImage = new Image<Gray, byte>(imagePath))
// {
// VectorOfKeyPoint observerKeyPoints = new VectorOfKeyPoint();
// Matrix<float> observerDescriptors = surfDectector.DetectAndCompute(observerImage, null, observerKeyPoints);
// if (superMatrix != null)
// superMatrix = superMatrix.ConcateVertical(observerDescriptors);
// else
// superMatrix = observerDescriptors;
// int initRow = rows; int endRows = rows + observerDescriptors.Rows - 1;
// observerSurfImageIndexList.Add(new SURFRecord1
// {
// Id = rec.Id,
// ImageName = rec.ImageName,
// ImagePath = rec.ImagePath,
// IndexStart = rows,
// IndexEnd = endRows,
// Distance = 0
// });
// rows = endRows + 1;
// }
//}
#endregion
Emgu.CV.Flann.Index flannIndex = new Emgu.CV.Flann.Index(superMatrix, 4);
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
flannIndex.KnnSearch(modelDescriptors, indices, dists, 2, 24);
IntervalTreeHelper.CreateTree(observerSurfImageIndexList);
for (int i = 0; i < indices.Rows; i++)
{
// filter out all inadequate pairs based on distance between pairs
if (dists.Data[i, 0] < (0.3 * dists.Data[i, 1]))
{
var img = IntervalTreeHelper.GetImageforRange(indices[i, 0]);
if (img != null) img.Distance++;
}
}
int maxMatch = Convert.ToInt32(observerSurfImageIndexList.Select(rec => rec.Distance).Max());
observerSurfImageIndexList = observerSurfImageIndexList.OrderByDescending(rec => rec.Distance).ToList();
int totalImageCount = observerSurfImageIndexList.Count;
for (int i = 0; i < totalImageCount; i++)
{
long id = observerSurfImageIndexList[i].Id;
var img2 = ImageList.Where(img => img.Id == id).SingleOrDefault();
if (img2 != null)
{
double countofMatch = observerSurfImageIndexList[i].Distance;
if (countofMatch > 0)
{
img2.SurfRank = (totalImageCount - i);
}
}
}
return ImageList;
}
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;
}
