OpenCV学习：如何扫描图像、利用查找表和计时

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这篇博客介绍了OpenCV中如何进行图像扫描、颜色空间缩减以及利用查找表提高效率。颜色缩减通过除法运算减少颜色数量，但为优化性能，可以使用查找表预先存储计算结果。OpenCV的getTickCount()和getTickFrequency()函数用于计时。此外，文章提到了LUT()函数，它是进行数组操作并快速替换图像值的有效工具。

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OpenCV_tutorials资料学习笔记

OpenCV如何扫描图像、利用查找表和计时

颜色缩减方法：将现有颜色空间值除以某个输入值，获得较少的颜色数。例如，颜色值0到9可取为新值0,10到19可取为10等等。

颜色空间缩减算法为：

1）遍历图像矩阵的每一个像素

2）对像素应用上述公式

ps：乘法和除法运算特别费时，尽可能用低代价的加、减、赋值等运算替换。对于较大的图像，有效的方法是预先计算所有可能的值，然后需要这些值的时候，利用查找表直接赋值即可。查找表是一维或多维数组，存储了不同输入值所对应的输出值，其优势在于只需读取、无需计算。

颜色空间缩减中的查找表计算：

uchar table[256];	//存储查找表
for (int i = 0; i < 256; ++i)
       table[i] = (uchar)(divideWith * (i/divideWith));	//设置查找表，颜色空间缩减公式计算得到

计时：

OpenCV提供了两个简便的可用于计时的函数 getTickCount() 和 getTickFrequency()。第一个函数返回你的CPU自某个事件（如启动电脑）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。

以秒为单位对某运算计时：

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

Opencv四种扫描图像的方法

1）经典的C风格运算符[]（指针）访问，是推荐的效率最高的查找表赋值方法

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; 
}

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。一个灰度图像操作如下：

uchar* p = I.data;

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

2）迭代法 The iterator method

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.at读取

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）利用opencv自带的核心函数LUT计算

在图像处理中，对于一个给定的值，将其替换成其他的值是一个很常见的操作，OpenCV 提供里一个函数直接实现该操作，并不需要你自己扫描图像，就是:operationsOnArrays:LUT() <lut> ，一个包含于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];

然后调用函数

LUT(I, lookUpTable, J);

LUT函数为：

void cv::LUT( InputArray _src, InputArray _lut, OutputArray _dst, int interpolation )
{
    Mat src = _src.getMat(), lut = _lut.getMat();
    CV_Assert( interpolation == 0 );
    int cn = src.channels();
    int lutcn = lut.channels();

    CV_Assert( (lutcn == cn || lutcn == 1) &&
        lut.total() == 256 && lut.isContinuous() &&
        (src.depth() == CV_8U || src.depth() == CV_8S) );
    _dst.create( src.dims, src.size, CV_MAKETYPE(lut.depth(), cn));
    Mat dst = _dst.getMat();

    LUTFunc func = lutTab[lut.depth()];
    CV_Assert( func != 0 );

    const Mat* arrays[] = {&src, &dst, 0};
    uchar* ptrs[2];
    NAryMatIterator it(arrays, ptrs);
    int len = (int)it.size;

    for( size_t i = 0; i < it.nplanes; i++, ++it )
        func(ptrs[0], lut.data, ptrs[1], len, cn, lutcn);
}

附录：opencv提供的函数实现（自己写的注释，请谨慎参考）

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>	//opencv头文件
#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[])	//argc是参数个数，第一参数argv[0]是运行程序名，系统自己会给定！
{
    help();
    if (argc < 3)	//判断输入的参数数目是否符合需要
    {
        cout << "Not enough parameters" << endl;
        return -1;
    }

    Mat I, J;
    if( argc == 4 && !strcmp(argv[3],"G") )	//第四个参数argv[3]是用来区分读取图像的类型，==G时，读取灰度图像
        I = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);//第二个参数argv[1]是读取图像路径，包括图像后缀名
    else
        I = imread(argv[1], CV_LOAD_IMAGE_COLOR);

    if (!I.data)	//判断图像读取是否成功
    {
        cout << "The image" << argv[1] << " could not be loaded." << endl;
        return -1;
    }

    int divideWith = 0; // convert our input string to number - C++ style		//divideWith是颜色空间缩减的参数，公式中的除数和乘数
    stringstream s;		//字符流数据存储
    s << argv[2];	//用<<读取符号，将第三个参数argv[2]即缩减参数输出到s变量中
    s >> divideWith;	//将字符型数据转为int型，存到divideWith中：stringstream 类，把第三个命令行参数由字符串转换为整数
    if (!s || !divideWith)	//参数为0时退出函数
    {
        cout << "Invalid number entered for dividing. " << endl;
        return -1;
    }

    uchar table[256];	//存储查找表
    for (int i = 0; i < 256; ++i)
       table[i] = (uchar)(divideWith * (i/divideWith));	//设置查找表，颜色空间缩减公式计算得到

    const int times = 100;
    double t;

    t = (double)getTickCount();	//getTickcount返回你的CPU自某个事件（如启动电脑）以来走过的时钟周期数

    for (int i = 0; i < times; ++i)
    {
        cv::Mat clone_i = I.clone();	//将图像I复制拷贝到clone_i,具有自己的矩阵头，矩阵指针，还有自己的数据矩阵
        J = ScanImageAndReduceC(clone_i, table);
    }

    t = 1000*((double)getTickCount() - t)/getTickFrequency();	//getTickFrequency返回你的CPU一秒钟所走的时钟周期数
    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);	//迭代器方法遍历图像各个像素
    }

    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);	//Mat.at访问像素
    }

    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);	//opencv自带的查找表函数

    t = 1000*((double)getTickCount() - t)/getTickFrequency();
    t /= times;

    cout << "Time of reducing with the LUT function (averaged for "
        << times << " runs): " << t << " milliseconds."<< endl;
	system("pause");
    return 0;
}

Mat& ScanImageAndReduceC(Mat& I, const uchar* const table)	//const uchar* const table是指向const uchar(无符号字符常量)的const常量指针table
{
    // accept only char type matrices
    CV_Assert(I.depth() != sizeof(uchar));	//若括号中的表达式值为false，则返回一个错误信息。

    int channels = I.channels();	//读取图像I的通道数

    int nRows = I.rows;
    int nCols = I.cols * channels;

    if (I.isContinuous())	//isContinuous判断矩阵元素存储是否连续
    {
        nCols *= nRows;	//矩阵存储连续，修改成一维矩阵存储格式，row为1
        nRows = 1;
    }

    int i,j;
    uchar* p;
    for( i = 0; i < nRows; ++i)
    {
        p = I.ptr<uchar>(i);	//p指针指向图像I的第i行首地址
        for ( j = 0; j < nCols; ++j)
        {
            p[j] = table[p[j]];	//将(i,j)位置的图像像素值修改成原有像素在查找表中的对应值 
        }
    }
    return I;
}

Mat& ScanImageAndReduceIterator(Mat& I, const uchar* const table)	//用C++特有的迭代法遍历图像
{
    // accept only char type matrices
    CV_Assert(I.depth() != sizeof(uchar));

    const int channels = I.channels();	//读取图像通道数
    switch(channels)	//注意不同通道数的不同处理
    {
    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;
}

运行结果