sumit patel's profile - activity

I tried with different directives of openmp, but i can't get difference. Or i get errors. (access violation). I am using ms visual studio 2012 (v110) on windows 8.1 . I tried for basic openmp program after changing in configuration of vs 2012. And i got 8 line output. Without enable openmp support, i got one line output. I placed my code here, Please suggest me , where i can use openmp and how.

#include <opencv/highgui.h>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <opencv\cv.h>
#include <opencv2\objdetect\objdetect.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\imgcodecs.hpp>
#include <opencv2\core\core.hpp>
#include <opencv\cvaux.hpp>
#include <fstream>
#include <ctime>
#include <stdio.h>
#include <math.h>
#include <vector>
#include <windows.h>
#include <atlstr.h>
#include <iostream>
#include <sstream>
#include <iomanip>
#include <omp.h>

using namespace cv;
using namespace cv::ml;
using namespace std;

void printDim(Mat mat,const char *name)
{
    std::cout<<name<<" :"<<mat.rows<<"x"<<mat.cols<<"  depth :"<<mat.depth()<<"  channel :"<<mat.channels()<<std::endl;
}

void readData(cv::Mat& data, int cl)
{
    vector<const char *> filenames;
    filenames.push_back("Data/ik147_1.txt");
    filenames.push_back("Data/ik147_2.txt");
    filenames.push_back("Data/ik147_3.txt");
    vector<Mat> raw_data(cl);//= new std::vector<cv::Mat>(4);
    int row;
    int i;
    double  min,max;

    #pragma omp parallel for 

    for( i =0;i<cl;i++)
    {
        ifstream file( filenames[i] );
        while( file>>row )
        {
            raw_data[i].push_back(row);
        }
        minMaxLoc(raw_data[i],&min,&max);
      cout<<filenames[i]<<" min :"<<min<<", max :"<<max<<std::endl;

    }
    int N=raw_data[0].rows;
   // cv::Mat data(N,3,CV_32FC1);
    cout<<"Number of data (row) : "<< N << endl;
    int columns_to_read=cl;
    data.create(N,columns_to_read,CV_32FC1);

    //#pragma omp parallel for private(i) 
    for( i=0;i<columns_to_read;i++)
    {
        raw_data[i](Rect(0,0,1,N)).copyTo(data(Rect(i,0,1,N)));
    }

}

void resize1(Mat src, string name, string name1)
{
    Size size(512,512);//the dst image size,e.g.100x100
    Mat dst;
    resize(src,dst,size);//resize image
    //cout<<"" <<endl;
    imshow(name,dst);
    imwrite(name1,dst);
}

int main()
{

    int fn;
    int width, height;
    cout << "Enter number of read files :" << endl;
    cin>> fn;
    cout<< "   "<< endl;

    cout << "Enter width and height :" << endl;
    cin>> width >> height;
    cout<< "   "<< endl;
    cout << "Image has width : " <<width <<" and height : " << height << endl;
    cout<< "   "<< endl;


    LARGE_INTEGER frequency;
    LARGE_INTEGER start;
    LARGE_INTEGER end;
    double interval;

    QueryPerformanceFrequency(&frequency);
    QueryPerformanceCounter(&start);

    Mat image = Mat::zeros(height, width, CV_8UC3);

    Mat data1; 
    readData(data1, fn);
    printDim(data1,"data1");
    cout << "----------------------- " << endl;

    Mat data=data1.clone();
    printDim(data,"data");
    data1.release();
    printDim(data1,"release data1 ");

    Mat train_data1 = data.reshape(fn, width );      // Mat train_data1 = data.reshape(3, 512 );
    printDim(train_data1, "Dimension of train_data1");

    double Min, Max;
    minMaxLoc(train_data1, &Min, &Max);

    Mat image1;
    train_data1.convertTo(image1,CV_8UC3);

    imwrite("3channel.jpg",image1);
    resize1(image1,"source","source.jpg");

    int nthreads;
    int i;
    int N=data.rows; //number of data points
    cout << "N of vv rows:"<< N << endl;
    cout <<"  "<<endl;
    int K=10;
    int clusterCount=K;
    int sampleCount = N;
    Mat labelsMatKMeans;
    Mat centers;
    /*#pragma omp parallel 
    {
    #pragma omp ...

I follow opencv documentation. I have four txt file (512 by 512 type:double) They are channels value.I cannot upload them here.. They are generated with envy software. I combine three files and made data of 262144 by 3 dimension. I generate image from it.

function for read data :

  void readData(cv::Mat& data)
    {
        std::vector<const char *> filenames;
        filenames.push_back("Data/ik147_1.txt");
        filenames.push_back("Data/ik147_2.txt");
        filenames.push_back("Data/ik147_3.txt");
      //  filenames.push_back("Data/ik147_4.txt");
        string line;
        vector<Mat> raw_data(cl);//= new std::vector<cv::Mat>(4);
        int row;
        double  min,max;
        for(int i =0;i<cl;i++)
        {
            ifstream file( filenames[i] );
            while( file>>row )
            {
                raw_data[i].push_back(row);
            }
            minMaxLoc(raw_data[i],&min,&max);
          cout<<filenames[i]<<" min :"<<min<<", max :"<<max<<std::endl;

        }
        int N=raw_data[0].rows;
       // cv::Mat data(N,3,CV_32FC1);
        cout<<"Number of data (row) : "<< N << endl;
        int columns_to_read=cl;
        data.create(N,columns_to_read,CV_32FC1);

        for(int i=0;i<columns_to_read;i++)
        {
            raw_data[i](Rect(0,0,1,N)).copyTo(data(Rect(i,0,1,N)));
        }

    }
int main()
{
int width = 512, height = 512;
    Mat image = Mat::zeros(height, width, CV_8UC3);
    Mat img = Mat::zeros(height, width, CV_8UC3);
    Mat img_kmeans = Mat::zeros(height, width, CV_8UC3);
//  Mat image3 = Mat::zeros(height, width, CV_8UC3);

    Mat data, train_data11, test_data11;
    readData(data);
    printDim(data,"data");
    //print(mat1,3);                                                 // direct reshape train to 512 by 512//
//  printDim(mat1, "Dimension of Mat1");
    Mat train_data1 = data.reshape(3, 512 );      //    Mat train_data1 = data.reshape(3, 512 );
    printDim(train_data1, "Dimension of train_data1");
    Mat image1;

    double Min, Max;
    minMaxLoc(train_data1, &Min, &Max);
    //  train_data.convertTo(train_data,CV_8UC3);
    train_data1.convertTo(image1,CV_8U,255.0/(Max-Min),-255.0/Min);

    printDim(image1,"Dimension of Image");
    //Mat img1 = image1.reshape(3, 512 );
    //printDim(img1,"Dimension of Image");
    imshow( "window_name", image1 );
    imwrite("3channel.jpg",image1);


    /*For take 30% of total data, i randomly generate numbers and with help of it, i store pixel values of image in other matrix(78642 by 3) . Than perform kmeans for generating labels.  I use 10 clusters. Than I perform training phase.* /

            int N1=78642;
        Mat location(N1, 1, CV_32FC1);

           RNG rng((unsigned)time(NULL)); 
        RNG rng_center(20); 


            //RNG rng(0xFFFF);

            cout<<"New 1 :"<< endl;
            //rng.fill(location, RNG::UNIFORM, Scalar(0,5), Scalar(4,8));
            rng.fill(location, RNG::UNIFORM, 0, 1);
            //print(location,3);

            cout<<"New 2 :"<< endl;
            location=78642 * location;
            //print(location,3);


            for(int i=0;i<N1;++i)
            {
            location.at<float>(i,0)= cvCeil(location.at<float>(i,0));

           // labelsMat.at<int>(i)= cvCeil(labelsMat.at<int>(i));
        }
            location=abs(location); 

            cout<<"New 2 :"<< endl;
            //print(location,3);


            //cout << "test of .data : "<< (int)location.data << endl;

            Mat image3 = imread("3channel.jpg", 1);
            imshow("value test",image3);
            vector<int> c1;
            vector<int> r1;
        //  int i,j;
            for(int i=0;i<location.rows;i++)
                //for(int j=0; j<image.cols; j++ )
                {
                //  cout<< "value of t : "<< location.at<float>(i,0) << endl;
                    int t=location.at<float>(i,0);
            //      cout<< "value of t.1 : "<< t << endl;
                    int s ...

I generated clusters with k-means clustering algorithm. I used 5 centres ( defined in text file like (0,0) (256,200) (500,500) ). I got output windows with 5 groups of points at different place.

I want to find points with cluster numbers.

ex. (1,1) is in cluster no. 1 ; (2,10) is in cluster no. 3

Give me any idea or suggestion about this. 

code:

(Include header files)
using namespace cv;
using namespace cv::ml;
using namespace std;
void readCenters(cv::Mat&, const char *);

int CLUSTER_COUNT=5;//number of clusters
Mat train_data;
Mat test_data;

void printDim(cv::Mat mat,const char *name)

void splitData(cv::Mat &data,cv::Mat &train_data,cv::Mat &test_data)


void readData(cv::Mat& data)
{
    std::vector<const char *> filenames;
    filenames.push_back("Data/ik147_1.txt");
    filenames.push_back("Data/ik147_2.txt");
string line;
    vector<Mat> raw_data(2);//= new std::vector<cv::Mat>(4);
    int row;
    double  min,max;
    for(int i =0;i<2;i++)
    {
        ifstream file( filenames[i] );
        while( file>>row )
        {
            raw_data[i].push_back(row);
        }
        minMaxLoc(raw_data[i],&min,&max);
      cout<<filenames[i]<<" min :"<<min<<", max :"<<max<<std::endl;

    }
    int N=raw_data[0].rows;
   // cv::Mat data(N,3,CV_32FC1);
    cout<<"N value : "<< N << endl;
    int columns_to_read=2;
    data.create(N,columns_to_read,CV_32FC1);

    for(int i=0;i<columns_to_read;i++)
    {
        raw_data[i](Rect(0,0,1,N)).copyTo(data(Rect(i,0,1,N)));
    }

}

void computeLabelledData(Mat& data,Mat &data_with_labels)
{
    Mat labels,centers;    
    kmeans(data, CLUSTER_COUNT, labels,
               TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0),
               3, KMEANS_PP_CENTERS, centers);


    data_with_labels.create(data.rows,data.cols+1,CV_32FC1);
    data.copyTo(data_with_labels(Rect(0,0,data.cols,data.rows)));
    labels.copyTo(data_with_labels(Rect(data.cols,0,labels.cols,labels.rows)));
}

void print(Mat mat, int prec)
{      
    for(int i=0; i<mat.size().height; i++)
    {
        cout << "[";
        for(int j=0; j<mat.size().width; j++)
        {
            cout << setprecision(prec) << mat.at<float>(i,j);
            if(j != mat.size().width-1)
                cout << ", ";
            else
                cout << "]" << endl; 
        }
    }
}
int main()
{
    // Data for visual representation
    int width = 512, height = 512;
    Mat image = Mat::zeros(height, width, CV_8UC3);

        Mat data;
    readData(data);
    printDim(data,"data");
    Mat data_with_labels,train_data_labels,test_data_labels;
     computeLabelledData(data,data_with_labels);
      splitData(data_with_labels,train_data_labels,test_data_labels);
        //splitData(data,train_data,test_data);

    Mat train_data,train_labels;
    train_data_labels(cv::Rect(0,0,train_data_labels.cols-1,train_data_labels.rows)).copyTo(train_data);
    train_data_labels(cv::Rect(train_data_labels.cols-1,0,1,train_data_labels.rows)).copyTo(train_labels);

    Mat test_data,test_labels,predicted_labels,confusion_matrix;
    test_data_labels(cv::Rect(0,0,test_data_labels.cols-1,test_data_labels.rows)).copyTo(test_data);
    test_data_labels(cv::Rect(test_data_labels.cols-1,0,1,test_data_labels.rows)).copyTo(test_labels);

    printDim(train_data,"train_data"); 
    printDim(test_data,"test_data"); 

    cout <<"train data : "  << endl;
    //print(train_data, 3);
    cout <<"test data : "  << endl;
    //print(test_data, 3);


    int N=test_data.rows; //number of data points
    int K=5;  //number of labels

    RNG rng((unsigned)time(NULL)); 
    RNG rng_center(20);
    int clusterCount=K;
    int sampleCount = N;
    Mat centers(5,2,CV_32FC1);
    readCenters(centers,"centers.txt");
    Mat labelsMat(N,1,CV_32SC1);
    Mat labelsMatKMeans;
    Mat img_kmeans(512, 512, CV_8UC3);

    Point center;
    center.x=0;//centers.at<float>(k,0);
    center.y=0;//:centers.at<float>(k,1 ...