使用较多色调可能会严重影响我们的算法性能。但是，有时只用少得多的结果就可以得到相同的最终结果。
在这种情况下，我们通常会减少颜色空间。这意味着我们将颜色空间当前值与新的输入值分开，以较少的颜色结束。
例如，零到九之间的每个值都取新值零，每个值在十到十九之间取值十等等。
当你用一个int值除一个uchar（unsigned char - 又名0到255之间的值）值时，结果也是char。这些值可能只是char值。因此，任何小数将被舍入。利用这个事实，uchar域中的上层操作可以表示为：
然后放一个程序喽：
时间的衡量：
OpenCV提供了两个简单的函数来实现这个cv::getTickCount()和cv::getTickFrequency()。
第一个返回系统CPU的运行的次数。第二个相当于频率，一秒内运行了多少次。
放程序：
图像在内存中的存储：
和
我们可以使用cv::Mat::isContinuouw()函数来询问矩阵是否属于这种情况。继续进入下一部分以查找示例。
访问图像中像素的三种方法：
方法1：
方法2：
方法3：
此外：批量的修改图像，这个无需一个一个的检索图像的像素，可以直接修改！在官网中的介绍并不是很清楚，在一个整体的程序中会更清楚一点。
LUT(I, lookUpTable, J);
整体的一个程序如下：
#include <opencv2/core.hpp>
#include <opencv2/core/utility.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/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
<< "./how_to_scan_images <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], IMREAD_GRAYSCALE);
else
I = imread(argv[1], IMREAD_COLOR);
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;
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));
//! [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);
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;
}
//! [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;
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;
}
//! [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]
CMakeLists.txt文件如下：
cmake_minimum_required(VERSION 2.8)
set(CMAKE_CXX_FLAGS "-std=c++11")
project( DisplayImage )
find_package( OpenCV REQUIRED )
include_directories( ${OpenCV_INCLUDE_DIRS} )
add_executable( DisplayImage main.cpp )
target_link_libraries( DisplayImage ${OpenCV_LIBS} )
install(TARGETS DisplayImage RUNTIME DESTINATION bin)
运行和测试结果如下：
