Ejemplo n.º 1
0
        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);
                    }
                }
            }
        }
Ejemplo n.º 2
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");
        }
Ejemplo n.º 3
0
        /*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);
        }