private static void DarknessTest()
{
List <string> darkFingerprins = new List <string>(new[] { "57_6", "19_4", "45_6", "86_1", "38_4", "38_2", "4_5", "7_2", "14_2", "19_7" });
List <string> normalFingerprins = new List <string>(new[] { "1_7", "3_3", "5_3", "6_2", "8_8", "11_1", "21_5", "30_4", "30_8", "32_5" });
for (int threshold = 80; threshold < 120; threshold += 5)
{
double darkAverage = 0;
double normalAverage = 0;
for (int i = 0; i < darkFingerprins.Count; i++)
{
var image = ImageHelper.LoadImage(Constants.PathToDb + darkFingerprins[i] + ".tif");
darkAverage += QualityHelper.GetDarkness(image);
}
darkAverage /= darkFingerprins.Count;
darkAverage *= 100;
for (int j = 0; j < normalFingerprins.Count; j++)
{
var image = ImageHelper.LoadImage(Constants.PathToDb + normalFingerprins[j] + ".tif");
normalAverage += QualityHelper.GetDarkness(image);
}
normalAverage /= normalFingerprins.Count;
normalAverage *= 100;
string str = String.Format("{0} {1} {2} {3}\n", threshold, normalAverage, darkAverage, darkAverage - normalAverage);
File.AppendAllText(@"D:\darkness1.txt", str);
}
}
internal static Tuple<InputVector,InputVector,InputVector> GetMultyFuzzyInput(string fileName1, string fileName2, string fileName3, string fileNameEt, QualityHelper qhelper)
{
var res1 = GetMonoFuzzyInput(fileName1, fileNameEt, qhelper);
var res2 = GetMonoFuzzyInput(fileName2, fileNameEt, qhelper);
var res3 = GetMonoFuzzyInput(fileName3, fileNameEt,qhelper);
Tuple<InputVector, InputVector, InputVector> res =
new Tuple<InputVector, InputVector, InputVector>(res1, res2, res3);
return res;
}
public static long MultymodalAllDifTest(int step, double thresholdStep, double startThreshold, double endThreshold)
{
Stopwatch w = Stopwatch.StartNew();
string strFirst = String.Format("Threshold NumFull NumYes NumNo NumIdk\n");
File.AppendAllText(Constants.resultsPath + "MultymodalAllDifTest.txt", strFirst);
DecisionMaker decisionMaker = new DecisionMaker();
QualityHelper qhelper = new QualityHelper();
DbHelper dbHelper = new DbHelper();
Dictionary <int, List <Tuple <string, string, string> > > fpForMatchDictionary = new Dictionary <int, List <Tuple <string, string, string> > >();
Dictionary <int, int> BestFp = new Dictionary <int, int>();
for (int j = 1; j < 101; j += step)
{
var newFpForMatch = dbHelper.GetTestingFingerprintDiff(j);
fpForMatchDictionary.Add(j, newFpForMatch);
int bestFp = DbHelper.GetBestFingerprint(j);
BestFp.Add(j, bestFp);
}
for (double threshold = startThreshold; threshold < endThreshold + 0.001; threshold += thresholdStep)
{
int numYes = 0;
int numNo = 0;
int numIdk = 0;
int numAll = 0;
for (int j = 1; j < 101; j += step)
{
int bestFp = BestFp[j];
var fpForMatch = fpForMatchDictionary[j];
string fileNameEt = j.ToString() + "_" + bestFp.ToString() + ".tif";
foreach (var item in fpForMatch)
{
var input = InputHelper.GetMultymodalInput(item.Item1, item.Item2, item.Item3, fileNameEt);
if (input[0] > threshold && input[1] > threshold && input[2] > threshold)
{
numYes++;
}
else
{
numNo++;
}
numAll++;
}
}
string str = String.Format("{0} {1} {2} {3} {4}\n", threshold, numAll, numYes, numNo, 0);
File.AppendAllText(Constants.resultsPath + "MultymodalAllDifTest.txt", str);
}
w.Stop();
return(w.ElapsedMilliseconds);
}
public static void GetQualityDb()
{
for (int i = 41; i < 101; i++)
{
for (int j = 1; j < 9; j++)
{
int[,] nfiqMask = QualityHelper.GetQualityMap(Constants.PathToDb + i + "_" + j + ".tif");
double nfiqQuality = QualityHelper.GetAverageQualityNfiq(nfiqMask);
string str = String.Format("{0} {1} {2}\n", i, j, nfiqQuality);
File.AppendAllText(Constants.qualityDb + "qualityAll.txt", str);
}
}
}
private static void OneFingerQualityTest()
{
int i = 88;
int j = 3;
var image = ImageHelper.LoadImage(Constants.PathToDb + i + "_" + j + ".tif");
int[,] nfiqMask =
QualityHelper.GetQualityMap(Constants.PathToDb + i + "_" + j + ".tif");
double nfiqBadReg = QualityHelper.GetLowQualityBlocksNfiq(nfiqMask);
double nfiqQuality = QualityHelper.GetAverageQualityNfiq(nfiqMask);
double darkness = QualityHelper.GetDarkness(image);
double background = QualityHelper.GetBackgroundPercentage(image);
i++;
}
private static void FullQualityTest()
{
Stopwatch w = Stopwatch.StartNew();
for (int i = 1; i < 101; i++)
{
for (int j = 1; j < 9; j++)
{
var image = ImageHelper.LoadImage(Constants.PathToDb + i + "_" + j + ".tif");
int[,] nfiqMask =
QualityHelper.GetQualityMap(Constants.PathToDb + i + "_" + j + ".tif");
double nfiqBadReg = QualityHelper.GetLowQualityBlocksNfiq(nfiqMask);
double nfiqQuality = QualityHelper.GetAverageQualityNfiq(nfiqMask);
double darkness = QualityHelper.GetDarkness(image);
double background = QualityHelper.GetBackgroundPercentage(image);
string str = String.Format("{0} {1} {2} {3} {4} {5}\n", i, j, nfiqBadReg, nfiqQuality, darkness, background);
File.AppendAllText(Constants.qualityDb + "qualityTest2.txt", str);
}
}
w.Stop();
//File.AppendAllText(@"D:\qualityTest1.txt", w.ElapsedMilliseconds.ToString());
}
internal static InputVector GetMonoFuzzyInput(string fileName1, string fileNameEt, QualityHelper qhelper)
{
//var img1 = ImageHelper.LoadImage(Constants.PathToDb + fileName1);
//var imgEt = ImageHelper.LoadImage(Constants.PathToDb + fileNameEt);
double identity1 = Matcher.GetIdentity(fileNameEt, fileName1);
//var map = QualityHelper.GetQualityMap(Constants.PathToDb + fileName1);
//double awerageQuality = QualityHelper.GetAverageQualityNfiq(map);
//double badBlocks = QualityHelper.GetLowQualityBlocksNfiq(map);
//double darkness = QualityHelper.GetDarkness(img1);
//double background = QualityHelper.GetBackgroundPercentage(img1);
InputVector input = new InputVector();
input.AverageQuality = qhelper.GetAverageQualityNfiqS(fileName1);
input.Background = qhelper.GetBackgroundS(fileName1);
input.BadBlocks = qhelper.GetLowQualityBlocksNfiqS(fileName1);
input.Darkness = qhelper.GetDarknessS(fileName1);
input.Identity = identity1;
return input;
}
private static void OneFingerTest(string fileName1, string fileName2)
{
var img1 = ImageHelper.LoadImage(Constants.PathToDb + fileName1);
var img2 = ImageHelper.LoadImage(Constants.PathToDb + fileName2);
double identity1 = Matcher.GetIdentity(fileName2, fileName1);
var map = QualityHelper.GetQualityMap(Constants.PathToDb + fileName1);
double awerageQuality = QualityHelper.GetAverageQualityNfiq(map);
double badBlocks = QualityHelper.GetLowQualityBlocksNfiq(map);
double darkness = QualityHelper.GetDarkness(img1);
double background = QualityHelper.GetBackgroundPercentage(img1);
InputVector input = new InputVector();
input.AverageQuality = awerageQuality;
input.Background = background;
input.BadBlocks = badBlocks;
input.Darkness = darkness;
input.Identity = identity1;
DecisionMaker m = new DecisionMaker();
m.GetAnswerForFinger(input, 0.5);
}
public static long FuzzyDifTest(int step, double thresholdStep, double startThreshold, double endThreshold)
{
Stopwatch w = Stopwatch.StartNew();
string strFirst = String.Format("Threshold NumFull NumYes NumNo NumIdk\n");
File.AppendAllText(Constants.resultsPath + "FuzzyDifTest.txt", strFirst);
DecisionMaker decisionMaker = new DecisionMaker();
QualityHelper qhelper = new QualityHelper();
DbHelper dbHelper = new DbHelper();
Dictionary<int, List<Tuple<string, string, string>>> fpForMatchDictionary = new Dictionary<int, List<Tuple<string, string, string>>>();
Dictionary<int, int> BestFp = new Dictionary<int, int>();
for (int j = 1; j < 101; j += step)
{
var newFpForMatch = dbHelper.GetTestingFingerprintDiff(j);
fpForMatchDictionary.Add(j, newFpForMatch);
int bestFp = DbHelper.GetBestFingerprint(j);
BestFp.Add(j, bestFp);
}
for (double threshold = startThreshold; threshold < endThreshold + 0.001; threshold += thresholdStep)
{
int numYes = 0;
int numNo = 0;
int numIdk = 0;
int numAll = 0;
for (int j = 1; j < 101; j += step)
{
int bestFp = BestFp[j];
var fpForMatch = fpForMatchDictionary[j];
string fileNameEt = j.ToString() + "_" + bestFp.ToString() + ".tif";
foreach (var item in fpForMatch)
{
var input = InputHelper.GetMultyFuzzyInput(item.Item1, item.Item2, item.Item3, fileNameEt, qhelper);
var answer = decisionMaker.GetAnswer(input, threshold);
switch (answer.ValueName)
{
case "yes":
numYes++;
break;
case "no":
numNo++;
break;
case "idk":
numIdk++;
break;
default:
break;
}
numAll++;
}
}
string str = String.Format("{0} {1} {2} {3} {4}\n", threshold, numAll, numYes, numNo, numIdk);
File.AppendAllText(Constants.resultsPath + "FuzzyDifTest.txt", str);
}
w.Stop();
return w.ElapsedMilliseconds;
}
public static long MonomodalDifTest(int step, double thresholdStep, double startThreshold, double endThreshold)
{
Stopwatch w = Stopwatch.StartNew();
string strFirst = String.Format("Threshold NumFull NumYes NumNo NumIdk\n");
File.AppendAllText(Constants.resultsPath + "MonomodalSameTest.txt", strFirst);
DecisionMaker decisionMaker = new DecisionMaker();
QualityHelper qhelper = new QualityHelper();
DbHelper dbHelper = new DbHelper();
Dictionary<int, List<Tuple<string, string, string>>> fpForMatchDictionary = new Dictionary<int, List<Tuple<string, string, string>>>();
Dictionary<int, int> BestFp = new Dictionary<int, int>();
for (int j = 1; j < 101; j += step)
{
var newFpForMatch = dbHelper.GetTestingFingerprintDiff(j);
fpForMatchDictionary.Add(j, newFpForMatch);
int bestFp = DbHelper.GetBestFingerprint(j);
BestFp.Add(j, bestFp);
}
for (double threshold = startThreshold; threshold < endThreshold + 0.001; threshold += thresholdStep)
{
int numYes = 0;
int numNo = 0;
int numIdk = 0;
int numAll = 0;
for (int j = 1; j < 101; j += step)
{
int bestFp = BestFp[j];
var fpForMatch = fpForMatchDictionary[j];
string fileNameEt = j.ToString() + "_" + bestFp.ToString() + ".tif";
foreach (var item in fpForMatch)
{
var input1 = InputHelper.GetMonomodalInput(item.Item1, fileNameEt);
if (input1 < threshold)
{
numNo++;
}
else
{
numYes++;
}
numAll++;
var input2 = InputHelper.GetMonomodalInput(item.Item2, fileNameEt);
if (input2 < threshold)
{
numNo++;
}
else
{
numYes++;
}
numAll++;
var input3 = InputHelper.GetMonomodalInput(item.Item3, fileNameEt);
if (input3 < threshold)
{
numNo++;
}
else
{
numYes++;
}
numAll++;
}
}
string str = String.Format("{0} {1} {2} {3} {4}\n", threshold, numAll, numYes, numNo, 0);
File.AppendAllText(Constants.resultsPath + "MonomodalDifTest.txt", str);
}
w.Stop();
return w.ElapsedMilliseconds;
}
public static long FuzzySameTest(int step, double thresholdStep, double startThreshold, double endThreshold)
{
Stopwatch w = Stopwatch.StartNew();
string strFirst = String.Format("Threshold NumFull NumYes NumNo NumIdk\n");
File.AppendAllText(Constants.resultsPath + "FuzzySameTest.txt", strFirst);
DecisionMaker decisionMaker = new DecisionMaker();
QualityHelper qhelper = new QualityHelper();
Dictionary <int, List <Tuple <string, string, string> > > fpForMatchDictionary = new Dictionary <int, List <Tuple <string, string, string> > >();
Dictionary <int, int> BestFp = new Dictionary <int, int>();
for (int j = 1; j < 101; j += step)
{
int bestFp = DbHelper.GetBestFingerprint(j);
BestFp.Add(j, bestFp);
var newFpForMatch = DbHelper.GetTestingFingerprintSame(j, bestFp);
fpForMatchDictionary.Add(j, newFpForMatch);
}
for (double threshold = startThreshold; threshold < endThreshold + 0.001; threshold += thresholdStep)
{
int numYes = 0;
int numNo = 0;
int numIdk = 0;
int numAll = 0;
for (int j = 1; j < 101; j += step)
{
int bestFp = BestFp[j];
var fpForMatch = fpForMatchDictionary[j];
string fileNameEt = j.ToString() + "_" + bestFp.ToString() + ".tif";
foreach (var item in fpForMatch)
{
var input = InputHelper.GetMultyFuzzyInput(item.Item1, item.Item2, item.Item3, fileNameEt, qhelper);
var answer = decisionMaker.GetAnswer(input, threshold);
switch (answer.ValueName)
{
case "yes":
numYes++;
break;
case "no":
numNo++;
break;
case "idk":
numIdk++;
break;
default:
break;
}
numAll++;
}
}
string str = String.Format("{0} {1} {2} {3} {4}\n", threshold, numAll, numYes, numNo, numIdk);
File.AppendAllText(Constants.resultsPath + "FuzzySameTest.txt", str);
}
w.Stop();
return(w.ElapsedMilliseconds);
}
internal static InputVector GetMonoFuzzyInput(string fileName1, string fileNameEt, QualityHelper qhelper)
{
//var img1 = ImageHelper.LoadImage(Constants.PathToDb + fileName1);
//var imgEt = ImageHelper.LoadImage(Constants.PathToDb + fileNameEt);
double identity1 = Matcher.GetIdentity(fileNameEt, fileName1);
//var map = QualityHelper.GetQualityMap(Constants.PathToDb + fileName1);
//double awerageQuality = QualityHelper.GetAverageQualityNfiq(map);
//double badBlocks = QualityHelper.GetLowQualityBlocksNfiq(map);
//double darkness = QualityHelper.GetDarkness(img1);
//double background = QualityHelper.GetBackgroundPercentage(img1);
InputVector input = new InputVector();
input.AverageQuality = qhelper.GetAverageQualityNfiqS(fileName1);
input.Background = qhelper.GetBackgroundS(fileName1);
input.BadBlocks = qhelper.GetLowQualityBlocksNfiqS(fileName1);
input.Darkness = qhelper.GetDarknessS(fileName1);
input.Identity = identity1;
return(input);
}
internal static Tuple <InputVector, InputVector, InputVector> GetMultyFuzzyInput(string fileName1, string fileName2, string fileName3, string fileNameEt, QualityHelper qhelper)
{
var res1 = GetMonoFuzzyInput(fileName1, fileNameEt, qhelper);
var res2 = GetMonoFuzzyInput(fileName2, fileNameEt, qhelper);
var res3 = GetMonoFuzzyInput(fileName3, fileNameEt, qhelper);
Tuple <InputVector, InputVector, InputVector> res =
new Tuple <InputVector, InputVector, InputVector>(res1, res2, res3);
return(res);
}