OpenCV如何扫描图像、利用查找表和计时

1. 代码及注释

#include 
#include 
#include "opencv2/imgcodecs.hpp"
#include 
#include 
#include 

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
        << "./how_to_scan_images   [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)   // main函数需要至少3个参数，argv[0]为exe文件的完整路径，此外必须提供“图像文件的路径”和“颜色缩减的参数”
    {
        cout << "Not enough parameters" << endl;
        return -1;
    }

    Mat I, J;
    if( argc == 4 && !strcmp(argv[3],"G") ) // strcmp() 函数的声明：int strcmp(const char *str1, const char *str2)，如果返回值等于 0，则表示 str1 等于 str2。
        I = imread(argv[1], IMREAD_GRAYSCALE);  // ???IMREAD_GRAYSCALE和CV_LOAD_IMAGE_GRAYSCALE有什么区别
    else
        I = imread(argv[1], IMREAD_COLOR);  // ???IMREAD_COLOR和CV_LOAD_IMAGE_COLOR有什么区别

    if (I.empty())
    {
        cout << "The image" << argv[1] << " could not be loaded." << endl;
        return -1;
    }

    //! [dividewith]
    int divideWith = 0; // convert our input string to number - C++ style
    stringstream s; // 数据类型转换，将string类型转换为int类型
    s << argv[2];
    s >> divideWith;
    if (!s || !divideWith)
    {
        cout << "Invalid number entered for dividing. " << endl;
        return -1;
    }

    uchar table[256];   // uchar类型（无符号字符，即0到255之间取值的数）
    for (int i = 0; i < 256; ++i)
       table[i] = (uchar)(divideWith * (i/divideWith));
    //! [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;

    //! [table-init]
    Mat lookUpTable(1, 256, CV_8U);
    uchar* p = lookUpTable.ptr();
    for( int i = 0; i < 256; ++i)
        p[i] = table[i];
    //! [table-init]

    t = (double)getTickCount();

    for (int i = 0; i < times; ++i)
        //! [table-use]
        LUT(I, lookUpTable, J);
        //! [table-use]

    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;
}

//! [scan-c]
Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)
{
    // accept only char type matrices
    CV_Assert(I.depth() == CV_8U);  // 若I.depth() == CV_8U为true，则继续执行后面的语句

    int channels = I.channels();

    int nRows = I.rows;
    int nCols = I.cols * channels;  // 对多通道图像来说，矩阵中的列会包含多个子列，其子列个数与通道数相等

    // 很多情况下，因为内存足够大，可实现连续存储，因此，图像中的各行就能一行一行地连接起来，形成一个长行。连续存储有助于提升图像扫描速度，我们可以使用isContinuous()来去判断矩阵是否是连续存储的
    if (I.isContinuous())
    {
        nCols *= nRows;
        nRows = 1;
    }

    int i,j;
    uchar* p;
    for( i = 0; i < nRows; ++i)
    {
        p = I.ptr<uchar>(i);    // p指向第i行的首元素
        for ( j = 0; j < nCols; ++j)
        {
            p[j] = table[p[j]]; // p[j]为第i行第j列元素的引用
        }
    }
    return I;
}
//! [scan-c]

//! [scan-iterator]
Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table)
{
    // accept only char type matrices
    CV_Assert(I.depth() == CV_8U);

    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;    // typedef Vec Vec3b;图像矩阵的每个像素可以看作一个vector，该vector包含3个uchar元素。it为迭代器，指向单个像素，即指向Vec3b类型
            for( it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end; ++it)
            {
                (*it)[0] = table[(*it)[0]]; // 修改B
                (*it)[1] = table[(*it)[1]]; // 修改G
                (*it)[2] = table[(*it)[2]]; // 修改R
            }
        }
    }

    return I;
}
//! [scan-iterator]

//! [scan-random]
Mat& ScanImageAndReduceRandomAccess(Mat& I, const uchar* const table)
{
    // accept only char type matrices
    CV_Assert(I.depth() == CV_8U);

    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:
        {
         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;
}
//! [scan-random]

2. 参考内容

OpenCV如何扫描图像、利用查找表和计时

OpenCV 中 imread cvtColor cv::IMREAD_GRAYSCALE与CV_BGR2GRAY得到灰度图不一致问题

C++编程语言中stringstream类介绍