private Statistic CalculateStatistic(Classifier classifier, Action<int> report)
{
var statistic = new Statistic(classifier.ScaleFactor, classifier.MinNeighbours);
var stopWatch = new Stopwatch();
stopWatch.Start();
for (var i = 0; i < images_.Length; ++i)
{
var testimage = images_[i];
testimage.DetectedAreas.Clear();
testimage.DetectedAreas.AddRange(classifier.Detect(testimage.GrayImage));
statistic.ProcessSigns(testimage.SignsAreas.ToArray(), testimage.DetectedAreas.ToArray());
report(i);
if (slowcheckbox_.Checked)
Thread.Sleep(1000);
}
stopWatch.Stop();
statistic.Time = stopWatch.ElapsedMilliseconds / 1000;
return statistic;
}
private Statistic CalculateStatistic(Classifier classifier, Action <int> report)
{
var statistic = new Statistic(classifier.ScaleFactor, classifier.MinNeighbours);
var stopWatch = new Stopwatch();
stopWatch.Start();
for (var i = 0; i < images_.Length; ++i)
{
var testimage = images_[i];
testimage.DetectedAreas.Clear();
testimage.DetectedAreas.AddRange(classifier.Detect(testimage.GrayImage));
statistic.ProcessSigns(testimage.SignsAreas.ToArray(), testimage.DetectedAreas.ToArray());
report(i);
if (slowcheckbox_.Checked)
{
Thread.Sleep(1000);
}
}
stopWatch.Stop();
statistic.Time = stopWatch.ElapsedMilliseconds / 1000;
return(statistic);
}
private void RunDetectionAsync(object sender, DoWorkEventArgs e)
{
var worker = (BackgroundWorker)sender;
var configuration = (RunConfiguration)e.Argument;
using (var classifier = new Classifier())
{
classifier.ScaleFactor = configuration.Scale;
classifier.MinNeighbours = configuration.MinNeighbours;
CalculateStatistic(classifier, worker.ReportProgress);
}
}