public void Process(Settings settings)
{
var imagesPath = settings.ImagesPath;
var images = _fileInputOutputHelper.ReadImages(imagesPath, FileType.jpg);
var options = new ParallelOptions
{
MaxDegreeOfParallelism = 16
};
Parallel.ForEach(images, options, (imageContext) =>
{
ProcessSingleImage(imageContext, settings);
_fileInputOutputHelper.SaveImage(imageContext);
imageContext.Dispose();
});
}
public void Process(Settings settings)
{
var imagesPath = settings.ImagesPath;
var context = new ImageProcessingContext();
var images = _fileInputOutputHelper.ReadImages(imagesPath, FileType.jpg);
foreach (var image in images)
{
_bitmapConverter.ApplyGrayScale(image);
//So far gaussian is too slow
//_bitmapConverter.ApplyGaussianBlur(image, settings);
_fileInputOutputHelper.SaveImage(image, true);
}
}
public void GetLicensesHistogramAverages(string path)
{
var images = _fileInputOutputHelper.ReadImages(path, FileType.png);
var avgByImage = _licensePlateAreaValidator.GetHistogramAverages(images).ToList();
var avgs = avgByImage.Select(x => x.Averages).Where(x => x[0] > 0 && x[1] > 0).ToList();
var lowestSatAvg = avgs.Min(x => x[0]);
var meanSatAvg = avgs.Average(x => x[0]);
var highestSatAvg = avgs.Max(x => x[0]);
var lowestValueAvg = avgs.Min(x => x[1]);
var meanValueAvg = avgs.Average(x => x[1]);
var highestValueAvg = avgs.Max(x => x[1]);
foreach (var image in avgByImage)
{
_fileInputOutputHelper.SaveImage(image.Image);
}
}
