static void Main(String[] args)
{
using (new MPI.Environment(ref args))
{
Intracommunicator comm = MPI.Communicator.world;
int p = comm.Size;
int rank = comm.Rank;
int range = 0, rem = 0, tag = 0;
int N = 0; // the number of subparts of an image
int noOfComponent = 0;
int noOfPersons = 0; // the no of persons in the training data (here it's 5)
string minLabel = "";
double minDistance = 0;
//this is used to get the distance of the training data and the predicted image
EuclideanDistance ed = new EuclideanDistance();
//this list will containt the parts of image after deviding it to N parts
List <double[][]> testSubImgs = new List <double[][]>();
//this will contain the weighs of each persono (N parts for each person)
List <double[][]> weights = new List <double[][]>();
//the label associated with each weight
List <string> labels = new List <string>();
if (rank == 0) // It's the root
{
//create an object of the algorithm
ModularFaceRecognitionAlgorithms mpca = new ModularFaceRecognitionAlgorithms();
//load the training data from file
mpca.loadTrainingData("C:/train.txt");
//prepare the image for testing
// u can change s1 to s2,s3,s4 ... s5 and watch out the result
String filePath = "C:/test/s4/10.bmp";
Matrix test = FileManager.GetBitMapColorMatrix(filePath);
//divide the image into N parts
testSubImgs = testSubImgs = mpca.devideImageToN(test, mpca.N);
//prepare local variables
noOfPersons = mpca.weights.Count / mpca.N;
N = mpca.N;
noOfComponent = mpca.numOfComponents;
weights = mpca.weights;
labels = mpca.labels;
if (p > 1) //this cond. to handle the exception of a single master process
{
//compute the no. of persons checked per process
//each process will be resposible for a nubmber of persons
//the process returns the dist. and label of the min distance of its persons
range = noOfPersons / (p - 1);
rem = noOfPersons % (p - 1);
if (range == 0) // in case for ex. we have 5 persons and 6 slaves
{
range = 1;
rem = 0;
}
}
else
{
Console.WriteLine("There's only a master process");
}
//broadcast the needed variables
comm.Broadcast(ref N, 0);
comm.Broadcast(ref noOfComponent, 0);
comm.Broadcast(ref range, 0);
comm.Broadcast(ref rem, 0);
comm.Broadcast(ref testSubImgs, 0);
comm.Broadcast(ref weights, 0);
comm.Broadcast(ref labels, 0);
comm.Broadcast(ref noOfPersons, 0);
string resLabel = ""; //the final resulted label
double resDistance = 0; // the final resulted distance
minLabel = ""; //used to receive the min label of each slave
minDistance = 0; //used to reciec the min distance of each slave
//in the following for loop we are receiving the min distance and label
//resulted from each slave and then get the min of them all
// the resulted resLabel and resDistance is the final result
//these line is used to handle if we have processes more than the noOfpersons
int endLoop = p - 1;
if (noOfPersons < (p - 1))
{
endLoop = noOfPersons;
}
for (int src = 1; src <= endLoop; src++)
{
comm.Receive(src, tag, out minDistance);
comm.Receive(src, tag, out minLabel);
if (src == 1 || minDistance < resDistance)
{
resLabel = minLabel;
resDistance = minDistance;
}
}
Console.WriteLine("resLabel = " + resLabel);
Console.WriteLine("resDistance = " + resDistance);
}
else
{
comm.Broadcast(ref N, 0);
comm.Broadcast(ref noOfComponent, 0);
comm.Broadcast(ref range, 0);
comm.Broadcast(ref rem, 0);
comm.Broadcast(ref testSubImgs, 0);
comm.Broadcast(ref weights, 0);
comm.Broadcast(ref labels, 0);
comm.Broadcast(ref noOfPersons, 0);
if (rank <= noOfPersons) //other wise do nothing
{
if (rank <= rem)
{
range++;
}
int start = 0;
if (rank <= rem)
{
start = (rank - 1) * N + (rank - 1) * N;
}
else
{
start = (rank - 1) * N + rem * N;
}
//As we mentioned before the range is the number of personse per process
// so in this for loop we are calculating the distance of each person
// and eventually send the min distance and lable to the master process
for (int i = 0; i < range; i++)
{
double dpj = 0;
int begin = i * N + start;
for (int j = begin, m = 0; m < N; j++, m++)
{
double dist = ed.getDistance(weights[j], testSubImgs[m]);
dpj += ((double)1 / noOfComponent) * dist;
}
double dp = ((double)1 / N) * dpj;
if (i == 0 || dp < minDistance)
{
minLabel = labels[begin];
minDistance = dp;
}
}
comm.Send(minDistance, 0, 0);
comm.Send(minLabel, 0, 0);
}
}
}
}
static void train(int metricType, int recognizer, int noOfComponents, int trainNums, int knn_k)
{
//set trainSet and testSet
Dictionary <String, List <int> > trainMap = new Dictionary <string, List <int> >();
Dictionary <String, List <int> > testMap = new Dictionary <string, List <int> >();
for (int i = 1; i <= 5; i++)
{
String label = "s" + i;
List <int> train = generateTrainNums(trainNums);
List <int> test = generateTestNums(train);
trainMap[label] = train;
testMap[label] = test;
}
//trainingSet & respective labels
List <Matrix> trainingSet = new List <Matrix>();
List <String> labels = new List <String>();
HashSet <String> labelSet = new HashSet <string>(trainMap.Keys);
foreach (String label in labelSet)
{
List <int> cases = trainMap[label];
for (int i = 0; i < cases.Count; i++)
{
String filePath = "E:/faces/" + label + "/" + cases[i] + ".bmp";
Matrix temp;
try
{
temp = ImageGrapper.GetMatrixGivenPath(filePath);
if (recognizer != 0)
{
temp = convertTo1D(temp);
}
trainingSet.Add(temp);
labels.Add(label);
} catch (Exception e) {}
}
}
FaceRecognizer fr = null;
if (recognizer == 0)
{
fr = new ModularFaceRecognitionAlgorithms(trainingSet, labels, 200, 16, trainNums);
}
else if (recognizer == 1)
{
fr = new EigenFaceRecognizer(trainingSet, labels, 40);
}
else if (recognizer == 2)
{
fr = new LinearDescriminantAnalysis(trainingSet, labels);
}
else if (recognizer == 3)
{
fr = new LaplacianFaceRecognizer(trainingSet, labels, 20);
}
fr.saveTrainingData(@"E:\trainLPP.txt");
}
/*metricType:
* 0: CosineDissimilarity
* 1: L1Distance
* 2: EuclideanDistance
*
* energyPercentage:
* PCA: components = samples * energyPercentage
* LDA: components = (c-1) *energyPercentage
* LLP: components = (c-1) *energyPercentage
*
* featureExtractionMode
* 0: PCA
* 1: LDA
* 2: LLP
*
* trainNums: how many numbers in 1..10 are assigned to be training faces
* for each class, randomly generate the set
*
* knn_k: number of K for KNN algorithm
*
* */
static double test(int metricType, int noOfComponents, int faceRecognizerFlag, int trainNums, int knn_k)
{
//determine which metric is used
//metric
Metric metric = null;
if (metricType == 0)
{
metric = new CosineDissimilarity();
}
else if (metricType == 1)
{
metric = new L1Distance();
}
else if (metricType == 2)
{
metric = new EuclideanDistance();
}
//////////assert metric != null : "metricType is wrong!";////////
//set expectedComponents according to energyPercentage
//componentsRetained
// int trainingSize = trainNums * 10;
// int componentsRetained = 0;
// if(featureExtractionMode == 0)
// componentsRetained = (int) (trainingSize * energyPercentage);
// else if(featureExtractionMode == 1)
// componentsRetained = (int) ((10 -1) * energyPercentage);
// else if(featureExtractionMode == 2)
// componentsRetained = (int) ((10 -1) * energyPercentage);
//set trainSet and testSet
Dictionary <String, List <int> > trainMap = new Dictionary <string, List <int> >();
Dictionary <String, List <int> > testMap = new Dictionary <string, List <int> >();
for (int i = 1; i <= 40; i++)
{
String label = "s" + i;
List <int> train = generateTrainNums(trainNums);
List <int> test = generateTestNums(train);
trainMap[label] = train;
testMap[label] = test;
}
//trainingSet & respective labels
List <Matrix> trainingSet = new List <Matrix>();
List <String> labels = new List <String>();
HashSet <String> labelSet = new HashSet <string>(trainMap.Keys);
foreach (String label in labelSet)
{
List <int> cases = trainMap[label];
for (int i = 0; i < cases.Count; i++)
{
String filePath = "E:/faces/" + label + "/" + cases[i] + ".bmp";
Matrix temp;
try {
temp = FileManager.GetBitMapColorMatrix(filePath);
if (faceRecognizerFlag == 3)
{
trainingSet.Add(temp);
}
else
{
trainingSet.Add(convertTo1D(temp));
}
labels.Add(label);
} catch (Exception e) {
}
}
}
//testingSet & respective true labels
List <Matrix> testingSet = new List <Matrix>();
List <String> trueLabels = new List <String>();
labelSet = new HashSet <string>(trainMap.Keys);
foreach (string label in labelSet)
{
List <int> cases = testMap[label];
for (int i = 0; i < cases.Count(); i++)
{
String filePath = "E:/faces/" + label + "/" + cases[i] + ".bmp";
Matrix temp;
try
{
temp = FileManager.GetBitMapColorMatrix(filePath);
testingSet.Add(convertTo1D(temp));
trueLabels.Add(label);
}
catch (Exception e) { }
}
}
//set featureExtraction
FaceRecognizer fe = null;
if (faceRecognizerFlag == 0)
{
fe = new EigenFaceRecognizer(trainingSet, labels, noOfComponents);
}
else if (faceRecognizerFlag == 1)
{
fe = new LDA(trainingSet, labels, noOfComponents);
}
else if (faceRecognizerFlag == 2)
{
fe = new FisherFaceRecognizer(trainingSet, labels, noOfComponents);
}
else if (faceRecognizerFlag == 3)
{
fe = new ModularFaceRecognitionAlgorithms(trainingSet, labels, 200, 16, trainNums);
}
FileManager.convertMatricetoImage(fe.getWeightMatrix(), faceRecognizerFlag);
//use test cases to validate
//testingSet trueLables
List <ProjectMatrix> projectSet = fe.getProjectSet();
int accurateNum = 0;
for (int i = 0; i < testingSet.Count; i++)
{
Matrix testCase = fe.getWeightMatrix().Transpose().Multiply(testingSet[i].Subtract(fe.getMeanMatrix()));
String result = KNN.assignLabel(projectSet.ToArray(), testCase, knn_k, metric);
if (result == trueLabels[i])
{
accurateNum++;
}
}
double accuracy = accurateNum / (double)testingSet.Count;
Console.WriteLine("The accuracy is " + accuracy);
return(accuracy);
}