图像元素遍历

// 功能：代码 图像遍历反色处理
// 作者：朱伟 zhu1988wei@163.com
// 来源：《OpenCV图像处理编程实例》
// 博客：http://blog.csdn.net/zhuwei1988
// 更新：2016-8-1
// 说明：版权所有，引用或摘录请联系作者，并按照上面格式注明出处，谢谢。// 
#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;
}

 

图像数据存储行与行之间可以不连续，isContinuous（）方法返回值指示是否连续，通过Mat::create()构造的矩阵总是连续的.