opencv的图像遍历

注意Mat的数据结构，opencv中的数据结构

这是opencv自带的tutorial：

#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <iostream>#include <sstream>using namespace std;using namespace cv;static void help(){    cout        << "\n--------------------------------------------------------------------------" << endl        << "This program shows how to scan image objects in OpenCV (cv::Mat). As use case"        << " we take an input image and divide the native color palette (255) with the "  << endl        << "input. Shows C operator[] method, iterators and at function for on-the-fly item address calculation."<< endl        << "Usage:"                                                                       << endl        << "./howToScanImages imageNameToUse divideWith [G]"                              << endl        << "if you add a G parameter the image is processed in gray scale"                << endl        << "--------------------------------------------------------------------------"   << endl        << endl;}Mat& ScanImageAndReduceC(Mat& I, const uchar* table);Mat& ScanImageAndReduceIterator(Mat& I, const uchar* table);Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar * table);int main( int argc, char* argv[]){    help();    if (argc < 3)    {        cout << "Not enough parameters" << endl;        return -1;    }    Mat I, J;//读取的图像是灰度还是彩色    if( argc == 4 && !strcmp(argv[3],"G") )        I = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);    else        I = imread(argv[1], CV_LOAD_IMAGE_COLOR);    if (!I.data)    {        cout << "The image" << argv[1] << " could not be loaded." << endl;        return -1;    }    int divideWith = 0; // convert our input string to number - C++ style//将输入的字符串变为int，更加安全c++    stringstream s;    s << argv[2];    s >> divideWith;    if (!s || !divideWith)    {        cout << "Invalid number entered for dividing. " << endl;        return -1;    }    uchar table[256];    for (int i = 0; i < 256; ++i)       table[i] = (uchar)(divideWith * (i/divideWith));    const int times = 100;    double t;    t = (double)getTickCount();    for (int i = 0; i < times; ++i)    {        cv::Mat clone_i = I.clone();        J = ScanImageAndReduceC(clone_i, table);    }    t = 1000*((double)getTickCount() - t)/getTickFrequency();    t /= times;    cout << "Time of reducing with the C operator [] (averaged for "         << times << " runs): " << t << " milliseconds."<< endl;    t = (double)getTickCount();    for (int i = 0; i < times; ++i)    {        cv::Mat clone_i = I.clone();        J = ScanImageAndReduceIterator(clone_i, table);    }    t = 1000*((double)getTickCount() - t)/getTickFrequency();    t /= times;    cout << "Time of reducing with the iterator (averaged for "        << times << " runs): " << t << " milliseconds."<< endl;    t = (double)getTickCount();    for (int i = 0; i < times; ++i)    {        cv::Mat clone_i = I.clone();        ScanImageAndReduceRandomAccess(clone_i, table);    }    t = 1000*((double)getTickCount() - t)/getTickFrequency();    t /= times;    cout << "Time of reducing with the on-the-fly address generation - at function (averaged for "        << times << " runs): " << t << " milliseconds."<< endl;    Mat lookUpTable(1, 256, CV_8U);    uchar* p = lookUpTable.data;    for( int i = 0; i < 256; ++i)        p[i] = table[i];    t = (double)getTickCount();//直接查表    for (int i = 0; i < times; ++i)        LUT(I, lookUpTable, J);    t = 1000*((double)getTickCount() - t)/getTickFrequency();    t /= times;    cout << "Time of reducing with the LUT function (averaged for "        << times << " runs): " << t << " milliseconds."<< endl;    return 0;}Mat& ScanImageAndReduceC(Mat& I, const uchar* const table){    // accept only char type matrices    CV_Assert(I.depth() != sizeof(uchar));    int channels = I.channels();    int nRows = I.rows;    int nCols = I.cols * channels;//判断是否连续    if (I.isContinuous())    {        nCols *= nRows;        nRows = 1;    }    int i,j;    uchar* p;//智能指针    for( i = 0; i < nRows; ++i)    {        p = I.ptr<uchar>(i);        for ( j = 0; j < nCols; ++j)        {            p[j] = table[p[j]];        }    }    return I;}Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table){    // accept only char type matrices    CV_Assert(I.depth() != sizeof(uchar));    const int channels = I.channels();    switch(channels)    {    case 1:        {            MatIterator_<uchar> it, end;            for( it = I.begin<uchar>(), end = I.end<uchar>(); it != end; ++it)                *it = table[*it];            break;        }    case 3:        {            MatIterator_<Vec3b> it, end;            for( it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end; ++it)            {                (*it)[0] = table[(*it)[0]];                (*it)[1] = table[(*it)[1]];                (*it)[2] = table[(*it)[2]];            }        }    }    return I;}Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table){    // accept only char type matrices    CV_Assert(I.depth() != sizeof(uchar));    const int channels = I.channels();    switch(channels)    {    case 1:        {            for( int i = 0; i < I.rows; ++i)                for( int j = 0; j < I.cols; ++j )                    I.at<uchar>(i,j) = table[I.at<uchar>(i,j)];            break;        }    case 3:        {//采用的是Mat_型的，有operator重载，不用at，更加方便         Mat_<Vec3b> _I = I;         for( int i = 0; i < I.rows; ++i)            for( int j = 0; j < I.cols; ++j )               {                   _I(i,j)[0] = table[_I(i,j)[0]];                   _I(i,j)[1] = table[_I(i,j)[1]];                   _I(i,j)[2] = table[_I(i,j)[2]];            }         I = _I;         break;        }    }    return I;}