opencv-图像扫描，查表和处理时间选择(修改)

#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];   //写入流执行数据结构变换
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;//各个运算循环次数常量100

//ScanImageAndReduceC运算时间---
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;

//ScanImageAndReduceIterator运算时间---
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;

//ScanImageAndReduceRandomAccess运算时间---
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;

//The Core FunctionLTU
Mat lookUpTable(1, 256, CV_8U);
uchar* p = lookUpTable.data;
for( int i = 0; i < 256; ++i)
p[i] = table[i];
//LUT运算时间
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;
int nCols = I.cols * channels;

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;
}