public MatcherResult knnMatch(KeyPoints queryDescriptor, BFMatcher matcher, string leafCategory, int distanceCutoff = int.MaxValue, PreProcessedImage trainingData = null)
{
MatcherResult result = new MatcherResult();
result.Category = leafCategory;
using (VectorOfVectorOfDMatch vectorMatchesForSift = new VectorOfVectorOfDMatch())
{
matcher.KnnMatch(queryDescriptor.Descriptor, vectorMatchesForSift, 2, null);
int numberOfMatches = 0;
Dictionary <int, int> counts = new Dictionary <int, int>();
List <MDMatch> goodMatches = new List <MDMatch>(vectorMatchesForSift.Size);
for (int i = 0; i < vectorMatchesForSift.Size; i++)
{
// Do Ratio test: Reject matches where ratio of closest match with second closest if greater than 0.8
if (
(vectorMatchesForSift[i].Size == 1 ||
vectorMatchesForSift[i][0].Distance < 0.75 * vectorMatchesForSift[i][1].Distance) &&
vectorMatchesForSift[i][0].Distance < distanceCutoff)
{
goodMatches.Add(vectorMatchesForSift[i][0]);
numberOfMatches++;
}
}
//goodMatches = clusterBasedOnPoseEstimation(goodMatches, queryDescriptor, trainingData);
int maxResults = int.MaxValue;
result.MatchingPoints = goodMatches.Count;
if (!goodMatches.Any())
{
result.MatchDistance = float.MaxValue;
result.AverageDistance = 0;
result.MatchDistanceWeight = 0;
result.MatchingPointsWeight = 0;
}
else
{
result.MatchDistance = goodMatches.OrderBy(item => item.Distance).Take(maxResults).Sum(item => item.Distance);
result.AverageDistance = result.MatchDistance / result.MatchingPoints;
result.MatchDistanceWeight = 1 / Math.Pow(result.AverageDistance, 2);
result.MatchingPointsWeight = result.MatchingPoints * result.MatchingPoints;
}
}
return(result);
}
public void startComparingImages(Dictionary <String, BFMatcher> categoryBfmatcherMapping)
{
using (PreProcess preProcessAlgorithm = new PreProcess(queryImage))
{
using (Image <Gray, Byte> grayScaleImage = preProcessAlgorithm._ImageToGrayScaleUsingConvert())
{
using (FeatureExtractAlgorithm featureSet = new FeatureExtractAlgorithm(grayScaleImage))
{
using (KeyPoints leafDescriptor = featureSet.SIFTDescriptor())
{
foreach (var pair in categoryBfmatcherMapping)
{
if (pair.Key == "abies_concolor")
{
continue;
}
try
{
DescriptorMatcher learningAlgo = new DescriptorMatcher();
BFMatcher bfmatch = pair.Value;
string leafCategory = pair.Key;
MatcherResult result = learningAlgo.knnMatch(leafDescriptor, bfmatch, leafCategory);
finalResultSet.Add(result);
}
catch (Exception exception)
{
//TODO
//Console.WriteLine(exception.ToString());
}
}
finalResultSet = finalResultSet.OrderByDescending(item => item, new MatcherResultComparer()).Take(3).ToList();
}
}
}
}
}
private List <MatcherResult> QueryImage(string filePath, string expectedCategory)
{
using (PreProcess preProcessAlgorithm = new PreProcess(null))
{
PreProcessedImage preProcessedQueryImage = preProcessAlgorithm.Execute(filePath, expectedCategory);
List <MatcherResult> finalResultSet = new List <MatcherResult>(this.trainingDataset.Count);
DescriptorMatcher learningAlgo = new DescriptorMatcher();
foreach (PreProcessedImage trainingData in this.trainingDataset)
{
try
{
using (BFMatcher trainingMatcher = new BFMatcher(DistanceType.L1))
{
double areaRatio = trainingData.ContourArea / preProcessedQueryImage.ContourArea;
int contourDelta = trainingData.NumberOfContours - preProcessedQueryImage.NumberOfContours;
//if (areaRatio < 0.1 || areaRatio > 5)
//{
// continue;
//}
//if(contourDelta > 100 || contourDelta < -100)
//{
// continue;
//}
trainingMatcher.Add(trainingData.KeyPoints.Descriptor);
MatcherResult result = learningAlgo.knnMatch(preProcessedQueryImage.KeyPoints, trainingMatcher, trainingData.Category, 300, trainingData);
result.ContourRatio = CvInvoke.MatchShapes(trainingData.Contour, preProcessedQueryImage.Contour, Emgu.CV.CvEnum.ContoursMatchType.I3);
result.AreaRatio = areaRatio;
result.ContoursDelta = contourDelta;
//Console.WriteLine("QueryCategory: {0}, {1}", preProcessedQueryImage.Category, result.ToString());
finalResultSet.Add(result);
}
}
catch (Exception exception)
{
//Console.WriteLine(exception);
}
}
//IEnumerable<MatcherResult> highConfidenceResults = finalResultSet.Where(item => item.MatchingPoints >= 3);
//finalResultSet = highConfidenceResults.ToList();
//// If there are clusters with size great than or equal to 3 , then they have a very high probability of being correct.
//if (highConfidenceResults.Any())
//{
// //Console.WriteLine("High confidence:" + highConfidenceResults.Count() + " : " + highConfidenceResults.Max(item => item.MatchingPoints));
// finalResultSet = highConfidenceResults.ToList();
//}
//else
//{
// finalResultSet = finalResultSet
// .Where(item => item.MatchingPoints > 0)
// .GroupBy(item => item.Category)
// .Select(item =>
// new MatcherResult()
// {
// Category = item.Key,
// MatchingPoints = item.Sum(result => result.MatchingPoints),
// MatchDistance = item.Sum(result => result.MatchDistance)
// })
// .ToList();
//}
double totalWeightDistance = finalResultSet.Sum(item => item.MatchDistanceWeight);
double totalWeightPoints = finalResultSet.Sum(item => item.MatchingPointsWeight);
//finalResultSet = finalResultSet
// .Where(item => item.MatchingPoints > 0)
// .GroupBy(item => item.Category)
// .Select(item =>
// new MatcherResult()
// {
// Category = item.Key,
// MatchingPoints = item.Sum(result => result.MatchingPoints * result.MatchingPointsWeight) / totalWeightPoints,
// MatchDistance = item.Sum(result => result.MatchDistance * result.MatchDistanceWeight) / totalWeightDistance
// })
// .ToList();
finalResultSet = finalResultSet
.Where(item => item.MatchingPoints > 0)
.GroupBy(item => item.Category)
.Select(item =>
new MatcherResult()
{
Category = item.Key,
MatchingPoints = item.Sum(result => result.MatchingPointsWeight) / totalWeightPoints,
MatchDistance = item.Sum(result => result.MatchDistanceWeight) / totalWeightDistance
})
.ToList();
finalResultSet = finalResultSet.Where(item => item.MatchingPoints > 0).OrderByDescending(item => item, new MatcherResultWeightedComparer()).Take(3).ToList();
return(finalResultSet);
}
}