OpenCV从入门到放弃系列之——如何扫描图像、利用查找表和计时

目的

• 如何遍历图像中的每一个像素？
• OpenCV的矩阵值是如何存储的？
• 如何测试我们所实现算法的性能？
• 查找表是什么？为什么要用它？

测试用例

颜色空间缩减。具体做法就是：将现有颜色空间值除以某个输入值，以获得较少的颜色数。例如，颜色0到9可取为新值0，10到19可取为10。

计算公式：

Lnew = (Lold / 10) * 10

如果对图像矩阵的每一个像素进行这个操作的话，是比较费时的，因为有大量的乘除操作。 这个时候我们的查找表就派上用场了，提前把值计算好，然后要用的时候，直接赋值即可。

• 创建查找表

int divideWith; // convert our input string to number - C++ style
stringstream s;
s << argv[2];
s >> divideWith;
if (!s) {
    cout << "Invalid number entered for dividing. " << endl;
    return -1;
}

uchar table[256];
for (int i = 0; i < 256; ++i)
	table[i] = divideWith* (i/divideWith);

• 计时

具体用的是getTickCount()和getTickFrequency()两个函数。第一个函数返回的是CPU自某个事件以来走过的时钟周期数，第二个函数返回你的CPU一秒钟所走的时钟周期数。

double t = (double)getTickCount();
// 做点什么 ...
t = ((double)getTickCount() - t)/getTickFrequency();
cout << "Times passed in seconds: " << t << endl;

1. 高效的方法 Efficient Way

因为图像中的每个像素是可以顺序存储的，所以可以使用下标进行访问，访问前使用isContinuous()来判断矩阵是否连续存储的。

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 * channels;
    int nCols = I.cols;

    if (I.isContinuous())
    {
        nCols *= nRows;
        nRows = 1;
    }

    int i,j;
    uchar* p;
    for( i = 0; i < nRows; ++i)
    {
    	// 获取每一行开始的指针
        p = I.ptr<uchar>(i);
        for ( j = 0; j < nCols; ++j)
        {
            p[j] = table[p[j]];
        }
    }
    return I;
}

另外一种方法来实现遍历功能，就是使用data，data会从Mat中返回指向矩阵第一行第一列的指针。注意如果该指针为NULL则表明对象里面无输入，所以这是一种简单的检查图像是否被成功读入的方法。当矩阵是连续存储时，我们就可以通过遍历data来扫描整个图像。

uchar* p = I.data;

for( unsigned int i =0; i < ncol*nrows; ++i)
    *p++ = table[*p];

2. 迭代法

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;
}

3. 通过相关返回值的On-the-fly地址计算

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:
        {
         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;
}

4. 核心函数LUT (The Core Function)

operationsOnArrays:LUT() 包含于core module的函数，首先我们建立一个mat型用于查表：

 Mat lookUpTable(1, 256, CV_8U);
    uchar* p = lookUpTable.data;
    for( int i = 0; i < 256; ++i)
        p[i] = table[i];

然后我们调用函数(I是输入J是输出)

LUT(I, lookUpTable, J);

性能表现

Efficient Way 79.4717 milliseconds

Iterator 83.7201 milliseconds

On-The-Fly RA 93.7878 milliseconds

LUT function 32.5759 milliseconds

结论：尽量使用OpenCV内置函数。调用LUT函数可以获得最快的速度。这是因为OpenCV库可以通过英特尔线程架构启用多线程。如果你喜欢使用指针的方法来扫描图像，迭代法是一个不错的选择，不过速度上较慢。

四种方法完整的代码：

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <sstream>

using namespace std;
using namespace cv;

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;
    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; // convert our input string to number - C++ style
    stringstream s;
    s << argv[2];
    s >> divideWith;
    if (!s)
    {
        cout << "Invalid number entered for dividing. " << endl;
        return -1;
    }

    uchar table[256];
    for (int i = 0; i < 256; ++i)
       table[i] = divideWith* (i/divideWith);

    const int times = 100;
    double t;

    t = (double)getTickCount();    

    for (int i = 0; i < times; ++i)
        J = ScanImageAndReduceC(I.clone(), 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)
        J = ScanImageAndReduceIterator(I.clone(), 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)
        ScanImageAndReduceRandomAccess(I.clone(), 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();    

    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 * channels;
    int nCols = I.cols;

    if (I.isContinuous())
    {
        nCols *= nRows;
        nRows = 1;
    }

    int i,j;
    uchar* p;
    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:
        {
         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;
}