public static void Main(string[] args)
{
var statistics = new Dictionary<string, int>();
IImageBinarizer binarizer = new ImageBinarizer();
var directoryInfo = new DirectoryInfo(Environment.CurrentDirectory).Parent.Parent.Parent;
var path = Path.Combine(directoryInfo.FullName, Program.DataDirectoryName);
directoryInfo = new DirectoryInfo(path);
var imageClassifier = new ImageClassifier(binarizer, Program.Size * Program.Size);
//Train
foreach (DirectoryInfo directory in directoryInfo.EnumerateDirectories())
{
foreach (var file in directory.EnumerateFiles("*.bmp"))
{
string fileName = Path.GetFileNameWithoutExtension(file.FullName);
imageClassifier.Train(new ImageUnit { Id = fileName, Image = new Bitmap(file.FullName) });
}
}
foreach (DirectoryInfo directory in directoryInfo.EnumerateDirectories())
{
foreach (var file in directory.EnumerateFiles("*.bmp"))
{
string fileName = Path.GetFileNameWithoutExtension(file.FullName);
var patternDirectory = directory.EnumerateDirectories().FirstOrDefault(p => p.Name == ("Randomized_" + fileName));
foreach (var patternFile in patternDirectory.EnumerateFiles("*.bmp"))
{
string patternFileName = Path.GetFileNameWithoutExtension(patternFile.FullName);
string[] nameParts = patternFileName.Split(new[] { "_" }, StringSplitOptions.RemoveEmptyEntries);
string percentValue = nameParts[1];
if (statistics.ContainsKey(percentValue) == false)
{
statistics.Add(percentValue, 0);
}
string classificationResult = imageClassifier.Classify(new Bitmap(patternFile.FullName));
if (classificationResult.Equals(fileName, StringComparison.CurrentCulture))
{
statistics[percentValue] += 1;
}
}
}
}
Console.WriteLine("Classification result: ");
foreach (var statistic in statistics)
{
Console.WriteLine("Percent - {0} Match - {1}", statistic.Key, statistic.Value);
}
Console.ReadLine();
}
public static string Run(string DataDirectoryName)
{
var statistics = new Dictionary<string, int>();
IImageBinarizer binarizer = new ImageBinarizer();
var directoryInfo = new DirectoryInfo(Environment.CurrentDirectory).Parent.Parent.Parent;
var path = Path.Combine(directoryInfo.FullName, DataDirectoryName);
directoryInfo = new DirectoryInfo(path);
imageClassifier = new ImageClassifier(binarizer, Size * Size);
//Train
foreach (DirectoryInfo directory in directoryInfo.EnumerateDirectories())
{
foreach (var file in directory.EnumerateFiles("*.bmp"))
{
string fileName = Path.GetFileNameWithoutExtension(file.FullName);
imageClassifier.Train(new ImageUnit { Id = fileName, Image = new Bitmap(file.FullName) });
}
}
foreach (DirectoryInfo directory in directoryInfo.EnumerateDirectories())
{
foreach (var file in directory.EnumerateFiles("*.bmp"))
{
string fileName = Path.GetFileNameWithoutExtension(file.FullName);
var patternDirectory = directory.EnumerateDirectories().FirstOrDefault(p => p.Name == ("Randomized_" + fileName));
foreach (var patternFile in patternDirectory.EnumerateFiles("*.bmp"))
{
string patternFileName = Path.GetFileNameWithoutExtension(patternFile.FullName);
string[] nameParts = patternFileName.Split(new[] { "_" }, StringSplitOptions.RemoveEmptyEntries);
string percentValue = nameParts[1];
if (statistics.ContainsKey(percentValue) == false)
{
statistics.Add(percentValue, 0);
}
string classificationResult = imageClassifier.Classify(new Bitmap(patternFile.FullName));
if (classificationResult.Equals(fileName, StringComparison.CurrentCulture))
{
statistics[percentValue] += 1;
}
}
}
}
return statistics.Aggregate("Classification result: \n\n", (current, statistic) => current + String.Format("Noise percent: {0}\n Matched: {1}\n\n", statistic.Key, statistic.Value));
}