IList <PreProcessedImage> ITrainModel.startTrainingModel()
{
IList <PreProcessedImage> result = new List <PreProcessedImage>(this.trainingModel.Sum(item => item.listOfImages.Count));
using (PreProcess preProcesAlgorithm = new PreProcess(null))
{
int count = 0;
foreach (ImageCategory imageCategory in this.trainingModel)
{
Console.WriteLine(count++ + " Training category: " + imageCategory.leafCategory + "total files: " + imageCategory.listOfImages.Count);
foreach (string imagePath in imageCategory.listOfImages)
{
PreProcessedImage preProcessedImage = preProcesAlgorithm.Execute(imagePath, imageCategory.leafCategory);
result.Add(preProcessedImage);
}
}
}
return(result);
}
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);
}
}