遍历图像

一、高效遍历图片

#include<opencv2opencv.hpp>
#include<iostream>
#include<string>

using namespace cv;
using namespace std;

void colorReduce(Mat &image, int div = 64);
int main() {
    Mat image = imread("C:\Users\Nelsoner\Desktop\Camera Roll\05.jpg");
    namedWindow("hah");
    for (int i = 1; i < 180; i += 10) {
        colorReduce(image, i);
        waitKey(550);
        imshow("hah", image);
        if (i == 178) {
            break;
        }

    }
}
void colorReduce(Mat &image, int div) {
    int nl = image.rows;   //行数
    int nc = image.cols * image.channels();   //每行的元素个数
    if (image.isContinuous())   //判断这幅图是否对行进行了填补，如果返回值是真，说明这图没有进行填补
    {
        nc = nc*nl;
        nl = 1;
    }
    //对于连续图形，本循环只执行1次
    for (int j = 0; j < nl; j++) {
        //得到第j行的首地址
        uchar *data = image.ptr<uchar>(j);
        for (int i = 0; i < nc; i++) {
            //处理每个像素
            //data[i] = data[i] / div*div + div / 2;   //颜色缩减公式
            data[i] = data[i] - data[i] % div + div / 2;
        }
    }
}

void colorReduce1(Mat &image, int div) {
    int nl = image.rows;
    int nc = image.cols;

    if (image.isContinuous()) {
        nc = nc * nl;
        nl = 1;
    }
    int n = static_cast<int>(log(static_cast<double>(div)) / log(2.0));
    uchar mask = 0xFF << n;   //位运算

    for (int j = 0; j < nl; j++) {
        uchar *data = image.ptr<uchar>(j);
        for (int i = 0; i < nc; i++) {
            *data ++= *data&mask + div / 2;
            *data++ = *data&mask + div / 2;
            *data++ = *data&mask + div / 2;
        }
    }
}

 二、迭代器遍历图像

void colorReduce(Mat &image, int div = 64);
int main() {
    Mat image = imread("C:\Users\Nelsoner\Desktop\Camera Roll\05.jpg");
    namedWindow("hah");
    for (int i = 1; i < 180; i += 10) {
        colorReduce(image, i);
        waitKey(550);
        imshow("hah", image);
        if (i == 178) {
            break;
        }

    }
}
void colorReduce(Mat &image, int div) {
    //得到初始位置的迭代器
    Mat_<Vec3b>::iterator it = image.begin<Vec3b>();
    //得到结束位置
    Mat_<Vec3b>::iterator itEnd = image.end<Vec3b>();

    for (; it != itEnd; it++) {
        (*it)[0] = (*it)[0]/div*div + div / 2;
        (*it)[1] = (*it)[1] / div*div + div / 2;
        (*it)[2] = (*it)[2] / div*div + div / 2;

    }
}
三、遍历图像和邻域操作（图像锐化）

#include<opencv2opencv.hpp>
#include<iostream>
#include<string>

using namespace cv;
using namespace std;

void sharpen(const Mat &image, Mat &result);
int main() {
    Mat image = imread("C:\Users\Nelsoner\Desktop\Camera Roll\05.jpg");
    Mat result;
    sharpen(image, result);
    namedWindow("hah");
    imshow("hah", result);
    //namedWindow("heh");
    //imshow("heh", image);
    waitKey(10000);
    
}

void sharpen(const Mat &image, Mat &result) {    //锐化
    result.create(image.size(), image.type());
    for (int j = 1; j < image.rows - 1; j++) {    //处理除了第一行和最后一行之外的所有行
        const uchar* previous = image.ptr<const uchar>(j - 1); //上一行
        const uchar* current = image.ptr<const uchar>(j);     //当前行
        const uchar* next = image.ptr<const uchar>(j + 1);   //下一行
        uchar* output = result.ptr<uchar>(j);  //输出行
        for (int i = 1; i < image.cols - 1; i++) {
            * output ++= saturate_cast<uchar>(10 * current[i] - current[i - 1] - current[i + 1] - previous[i] - next[i]);
        }
    }
    result.row(0).setTo(Scalar(0));
    result.row(result.rows - 1).setTo(Scalar(0));
    result.col(0).setTo(Scalar(0));
    result.col(result.cols - 1).setTo(Scalar(0));
}

void sharpen2D(const Mat &image, Mat &result) {
    Mat kernel(3,3,CV_32F,Scalar(0)); //构造核
    //对核进行赋值
    kernel.at<float>(1, 1) = 5.0;
    kernel.at<float>(0, 1) = -1.0;
    kernel.at<float>(2, 1) = -1.0;
    kernel.at<float>(1, 0) = -1.0;
    kernel.at<float>(1, 2) = -1.0;

    filter2D(image,result,image.depth(),kernel);
}