public string FindBestMatchFromScopeOfImages(ImageModel imageObject, IList <ImageModel> imageScenes)
{
IList <DMatch> bestMatches = new List <DMatch>();
string imagePath = string.Empty;
var descriptorObject = ConvertOpenCVTypes.ConvertFloatArrayToMat(imageObject.Descriptor);
Parallel.ForEach(imageScenes, (imageScene) =>
{
var descriptorScene = ConvertOpenCVTypes.ConvertFloatArrayToMat(imageScene.Descriptor);
var knnMatches = _matcher.KnnMatch(descriptorObject, descriptorScene, 2).ToList();
const float ratioThresh = 0.7f;
List <DMatch> goodMatches = new List <DMatch>();
for (int i = 0; i < knnMatches.Count; i++)
{
if (knnMatches[i][0].Distance < ratioThresh * knnMatches[i][1].Distance)
{
goodMatches.Add(knnMatches[i][0]);
}
}
lock (_lockObjectCalculating)
{
if (goodMatches.Count > 100 && goodMatches.Count > bestMatches.Count)
{
bestMatches = goodMatches;
imagePath = imageScene.ImagePath;
}
}
});
return(imagePath);
}
public DMatch[] Match(float[,] descriptorImage)
{
var descriptor = ConvertOpenCVTypes.ConvertFloatArrayToMat(descriptorImage);
if (!_matcher.Empty())
{
_matcher.Train();
}
return(_matcher.Match(descriptor));
}
public void AddImagesToTrain(IList <ImageModel> models)
{
var descriptionsScene =
models.Select(x =>
{
_indexIdMapImage.Add(_index, x.Id);
_index++;
return(ConvertOpenCVTypes.ConvertFloatArrayToMat(x.Descriptor));
});
_matcher.Add(descriptionsScene);
}
public ImageModel ConvertImagePathToImageModel(string imagePath)
{
Mat imgObject = Cv2.ImRead(imagePath, ImreadModes.Grayscale);
Mat descriptorsObject = new Mat();
double minHessian = 400;
SURF detector = SURF.Create(minHessian, extended: false);
var keypointsObject = detector.Detect(imgObject).Take(1000).ToArray();
detector.Compute(imgObject, ref keypointsObject, descriptorsObject);
var imageModel = new ImageModel()
{
ImagePath = imagePath,
ImageHash = ImageHelper.GetImageHash(imagePath),
Descriptor = ConvertOpenCVTypes.ConvertMatToFloatArray(descriptorsObject)
};
return(imageModel);
}
