OpenCV 之Mat::push_back()

  OpenCV的数据结构Mat是相当强大的，Mat的push_back()

template<typename _Tp> inline void Mat::push_back(const _Tp& elem)
{
    if( !data )
    {
        *this = Mat(1, 1, DataType<_Tp>::type, (void*)&elem).clone();
        return;
    }
    CV_Assert(DataType<_Tp>::type == type() && cols == 1
              /* && dims == 2 (cols == 1 implies dims == 2) */);
    uchar* tmp = dataend + step[0];
    if( !isSubmatrix() && isContinuous() && tmp <= datalimit )
    {
        *(_Tp*)(data + (size.p[0]++)*step.p[0]) = elem;
        dataend = tmp;
    }
    else
        push_back_(&elem);
}

template<typename _Tp> inline void Mat::push_back(const Mat_<_Tp>& m)
{
    push_back((const Mat&)m);
}

从上面可以看出，当dig.push_back()的参数是Mat small的时候，会把这个small的数据排成一行放到dig中，在这之前先reshape().这样就可以作为一个Mat的虚拟的vector来使用。注意，这个功能必须要求isContinues(),即small的数据必须是在内存中连续存放的。

一下是一个使用的例子

// Main entry code OpenCV

#include <cv.h>
#include <highgui.h>
#include <cvaux.h>

#include <iostream>
#include <vector>

using namespace std;
using namespace cv;

int main ( int argc, char** argv )
{
    cout << "OpenCV Training SVM Automatic Number Plate Recognition\n";
    cout << "\n";

    char* path_Plates;
    char* path_NoPlates;
    int numPlates;
    int numNoPlates;
    int imageWidth=144;
    int imageHeight=33;

    //Check if user specify image to process
    if(argc >= 5 )
    {
        numPlates= atoi(argv[1]);
        numNoPlates= atoi(argv[2]);
        path_Plates= argv[3];
        path_NoPlates= argv[4];

    }else{
        cout << "Usage:\n" << argv[0] << " <num Plate Files> <num Non Plate Files> <path to plate folder files> <path to non plate files> \n";
        return 0;
    }        

    Mat classes;//(numPlates+numNoPlates, 1, CV_32FC1);
    Mat trainingData;//(numPlates+numNoPlates, imageWidth*imageHeight, CV_32FC1 );

    Mat trainingImages;
    vector<int> trainingLabels;

    for(int i=0; i< numPlates; i++)
    {

        stringstream ss(stringstream::in | stringstream::out);
        ss << path_Plates << i << ".jpg";
        Mat img=imread(ss.str(), 0);
        img= img.reshape(1, 1);
        trainingImages.push_back(img);
        trainingLabels.push_back(1);
    }

    for(int i=0; i< numNoPlates; i++)
    {
        stringstream ss(stringstream::in | stringstream::out);
        ss << path_NoPlates << i << ".jpg";
        Mat img=imread(ss.str(), 0);
        img= img.reshape(1, 1);
        trainingImages.push_back(img);
        trainingLabels.push_back(0);

    }

    Mat(trainingImages).copyTo(trainingData);
    //trainingData = trainingData.reshape(1,trainingData.rows);
    trainingData.convertTo(trainingData, CV_32FC1);
    Mat(trainingLabels).copyTo(classes);

    FileStorage fs("SVM.xml", FileStorage::WRITE);
    fs << "TrainingData" << trainingData;
    fs << "classes" << classes;
    fs.release();

    return 0;
}