图像遍历反色处理，遍历多通道图片

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
using namespace cv;
// 下标M.at<float>(i,j)  方法1-1
cv::Mat inverseColor1(cv::Mat srcImage)
{
cv::Mat tempImage = srcImage.clone();
int row = tempImage.rows;
int col = tempImage.cols;
// 分别对各个通道进行反色处理
for (int i = 0; i < row; i++)
{
for (int j = 0; j < col; j++)
{
tempImage.at<cv::Vec3b>(i, j)[0] = 255 - tempImage.at<cv::Vec3b>(i, j)[0];
tempImage.at<cv::Vec3b>(i, j)[1] = 255 - tempImage.at<cv::Vec3b>(i, j)[1];
tempImage.at<cv::Vec3b>(i, j)[2] = 255 - tempImage.at<cv::Vec3b>(i, j)[2];
}
}
return tempImage;
}
// 方法1-2 下标M::at<float>(i,j)
cv::Mat inverseColor2(cv::Mat srcImage)
{
cv::Mat tempImage = srcImage.clone();

int row = tempImage.rows;
// 图像像素行实际的宽度
int step = tempImage.step;
// 直接对像素进行反色处理
for (int i = 0; i < row; i++)
{
for (int j = 0; j < step; j++)
{
tempImage.at<uchar>(i, j) = 255 - tempImage.at<uchar>(i, j);
}
}
return tempImage;
}
// 方法2  指针遍历Mat::ptr
cv::Mat inverseColor3(cv::Mat srcImage)
{
cv::Mat tempImage = srcImage.clone();
int row = tempImage.rows;
// 将3通道转换为单通道
int nStep = tempImage.cols * tempImage.channels();
for(int i = 0; i < row; i++)
{
// 取源图像的指针
const uchar* pSrcData = srcImage.ptr<uchar>(i);
// 将输出数据指针存放输出图像
uchar* pResultData = tempImage.ptr<uchar>(i);
for(int j=0; j < nStep; j++)
{
pResultData[j]= cv::saturate_cast<uchar>(255 - pSrcData[j]);
}
}
return tempImage;
}
// 方法3 使用迭代器MatConstIterator
cv::Mat inverseColor4(cv::Mat srcImage)
{
cv::Mat tempImage = srcImage.clone();
// 初始化源图像迭代器
cv::MatConstIterator_<cv::Vec3b> srcIterStart  = srcImage.begin<cv::Vec3b>();
cv::MatConstIterator_<cv::Vec3b> srcIterEnd = srcImage.end<cv::Vec3b>();
// 初始化输出图像迭代器
cv::MatIterator_<cv::Vec3b> resIterStart = tempImage.begin<cv::Vec3b>();
cv::MatIterator_<cv::Vec3b> resIterEnd = tempImage.end<cv::Vec3b>();
// 遍历图像反色处理
while( srcIterStart != srcIterEnd )
{
(*resIterStart)[0] = 255 - (*srcIterStart)[0];
(*resIterStart)[1] = 255 - (*srcIterStart)[1];
(*resIterStart)[2] = 255 - (*srcIterStart)[2];
// 迭代器递增
srcIterStart++;
resIterStart++;
}
return tempImage;
}

// 方法4 改进的指针方法isContinuous
cv::Mat inverseColor5(cv::Mat srcImage)
{
int row = srcImage.rows;
int col = srcImage.cols;
cv::Mat tempImage = srcImage.clone();
// 判断是否是连续图像，即是否有像素填充
if( srcImage.isContinuous() && tempImage.isContinuous() )
{
row = 1;
col = col * srcImage.rows * srcImage.channels();
}
// 遍历图像的每个像素
for(int i = 0; i < row; i++)
{
const uchar* pSrcData = srcImage.ptr<uchar>(i);
uchar* pResultData = tempImage.ptr<uchar>(i);
for(int j = 0; j < col; j++)
{
*pResultData++ = 255 - *pSrcData++;
}
}
return tempImage;
}
// 方法5 LUT查表法
cv::Mat inverseColor6(cv::Mat srcImage)
{
int row = srcImage.rows;
int col = srcImage.cols;
cv::Mat tempImage = srcImage.clone();
// 建立LUT 反色table
uchar LutTable[256];
for (int i = 0; i < 256; ++i)
LutTable[i] = 255 - i;
cv::Mat lookUpTable(1, 256, CV_8U);
uchar* pData = lookUpTable.data;
// 建立映射表
for( int i = 0; i < 256; ++i)
pData[i] = LutTable[i];
// 应用索引表进行查找
cv::LUT(srcImage, lookUpTable, tempImage);
return tempImage;
}

int main()
{
// 装载图像转为灰度图像
cv::Mat srcImage = cv::imread("..\\images\\flower3.jpg");
if (!srcImage.data)
return -1;
cv::imshow("srcImage", srcImage);

cv::Mat resultImg1,resultImg2,resultImg3;
cv::Mat resultImg4,resultImg5,resultImg6;

// 测试方法1-1
double tTime;
tTime = (double)getTickCount();
const int nTimes = 100;
for(int i  = 0;  i < nTimes; i++)
{
resultImg1 = inverseColor1(srcImage);
}
tTime= 1000*((double)getTickCount() - tTime)/getTickFrequency();
tTime /= nTimes;
std::cout << "test1-1: "  << tTime << std::endl;
// 测试方法1-2
tTime = (double)getTickCount();
for(int i  = 0;  i < nTimes; i++)
{
resultImg2 = inverseColor2(srcImage);
}
tTime= 1000*((double)getTickCount() - tTime)/getTickFrequency();
tTime /= nTimes;
std::cout << "test1-2: "  << tTime << std::endl;
// 测试方法2
tTime = (double)getTickCount();
for(int i  = 0;  i < nTimes; i++)
{
resultImg3 = inverseColor3(srcImage);
}
tTime= 1000*((double)getTickCount() - tTime)/getTickFrequency();
tTime /= nTimes;
std::cout << "test2: "  << tTime << std::endl;
// 测试方法3
tTime = (double)getTickCount();
for(int i  = 0;  i < nTimes; i++)
{
resultImg4 = inverseColor4(srcImage);
}
tTime= 1000*((double)getTickCount() - tTime)/getTickFrequency();
tTime /= nTimes;
std::cout << "test3: "  << tTime << std::endl;
// 测试方法4
tTime = (double)getTickCount();
for(int i  = 0;  i < nTimes; i++)
{
resultImg5 = inverseColor5(srcImage);
}
tTime= 1000*((double)getTickCount() - tTime)/getTickFrequency();
tTime /= nTimes;
std::cout << "test4: "  << tTime << std::endl;
// 测试方法5
tTime = (double)getTickCount();
for(int i  = 0;  i < nTimes; i++)
{
resultImg6 = inverseColor5(srcImage);
}
tTime= 1000*((double)getTickCount() - tTime)/getTickFrequency();
tTime /= nTimes;
std::cout << "test5: "  << tTime << std::endl;

cv::imshow("resultImg1", resultImg1);
cv::imshow("resultImg2", resultImg2);
cv::imshow("resultImg3", resultImg3);
cv::imshow("resultImg4", resultImg4);
cv::imshow("resultImg5", resultImg5);
cv::imshow("resultImg6", resultImg6);

cv::waitKey(0);
return 0;
}