OpenCV访问像素点的灰度值

1.Mat矩阵数值的存储方式

这里以指针的方式访问图像素为例

(1)单通道

定义一个单通道图像：

   cv::Mat img_1 = (320, 640, CV_8UC1, Scalar(0));

对于单通道M(i,j)即为第i行j列的其灰度值；程序中表示为：

 img_1.ptr<uchar>(i)[j];

(2)多通道

这里以RGB图像为例，每一个子列依次为B、G、R，，第一个分量是蓝色，第二个是绿色，第三个是红色。

定义一个3通道BGR图像：

  cv::Mat img_1 = (320, 640, CV_8UC3, Scalar(0， 0 ，0));

对于多通道M(i,j*3)即为第i行j列的B通道其灰度值，M(i,j*3+1) 即为第i行j列的G通道其灰度值，M(i,j*3+1) 即为第i行j列的B通道其灰度值；程序中表示为：

第i行j列的B通道其灰度值：

    img_1.ptr<uchar>(i)[j*3];

第i行j列的G通道其灰度值：

 img_1.ptr<uchar>(i)[j*3+1];

第i行j列的R通道其灰度值：

 img_1.ptr<uchar>(i)[j*3+2];

2.示例程序，以三种方法（指针，at，迭代器）

////获得图像像素值

#include <iostream>

#include <vector>

#include <algorithm>

#include <opencv2/core/core.hpp>

#include <opencv2/imgproc/imgproc.hpp>

#include <opencv2/opencv.hpp>


using namespace std;

using namespace cv;


void get_setImagePixel3(char *imagePath, int x, int y)

{

    Mat image = imread(imagePath, 1);

    //得宽高

    int w = image.cols;

    int h = image.rows;

    int channels = image.channels();



    if (channels == 1)

    {

        //得到初始位置的迭代器 

        Mat_<uchar>::iterator it = image.begin<uchar>();

        //得到终止位置的迭代器 

        Mat_<uchar>::iterator itend = image.end<uchar>();

        int pixel = *(it + y * w + x);

        cout << "灰度图像,处的灰度值为" << pixel << endl;

    }

    else

    {

        //得到初始位置的迭代器 

        Mat_<Vec3b>::iterator it = image.begin<Vec3b>();

        //得到终止位置的迭代器 

        Mat_<Vec3b>::iterator itend = image.end<Vec3b>();

        //读取

        it = it + y * w + x;

        int b = (*it)[0];

        cout << b << endl;

        int g = (*it)[1];

        cout << g << endl;

        int r = (*it)[2];

        cout << r << endl;



        //设置像素值

        (*it)[0] = 255;

        (*it)[1] = 255;

        (*it)[2] = 255;


    }

    imshow("cc", image);


}





int main()

{

    vector<int> v = {1,2,3,4,6};

   

    cout << "*********通过指针访问像素的灰度值********************"  << endl;

    //通过指针访问像素的灰度值

    //单通道

    Mat img1(20, 30, CV_32FC1, Scalar(0));

    img1.ptr<float>(19)[25] = 23456.1789;

    cout << "img(19,25):" << img1.ptr<float>(19)[25] <<endl;

    //多通道,创建一个B、G、R通道灰度值为0的图像，图像大小20*30，每个灰度值为16位无符号2进制表示

    Mat img2(20, 30, CV_16UC3, Scalar(0, 0, 0));

    cout << "img(1,2):" << int(img1.ptr<uchar>(19)[25]) << endl;


    Mat img = imread("test1.jpg");

    int numRow = img.rows;

    int numCol = img.cols;

    int numCol_channel = img.cols*img.channels();

    cout << "numRow:" << numRow << endl;

    cout << "numCol:" << numCol << endl;

    cout << "numCol_channel:" << numCol_channel << endl;

    cout << "45行，483列B通道灰度值" << int(img.ptr<uchar>(45)[483*3]) << endl;

    cout << "45行，483列G通道灰度值" << int(img.ptr<uchar>(45)[483*3+1]) << endl;

    cout << "45行，483列R通道灰度值" << int(img.ptr<uchar>(45)[483*3+2]) << endl;



Mat img_B(numRow, numCol, CV_8UC3, Scalar(0, 0, 0));

    Mat img_G(numRow, numCol, CV_8UC3, Scalar(0, 0, 0));

    Mat img_R(numRow, numCol, CV_8UC3, Scalar(0, 0, 0));

    for (int i = 0; i < numRow; i++)

    {

        for (int j = 0; j < numCol; j++)

        {

            img_B.ptr<uchar>(i)[j*3] = img.ptr<uchar>(i)[j*3];

            img_G.ptr<uchar>(i)[j*3+1] = img.ptr<uchar>(i)[j*3+1];

            img_R.ptr<uchar>(i)[j*3+2] = img.ptr<uchar>(i)[j*3+2];

        }

    }

    imshow("img", img);

    imshow("img_B", img_B);

    imshow("img_G", img_G);

    imshow("img_R", img_R);


    cout << endl;

    cout << endl;

    cout << endl;


    cout << "*********at只适合灰度值为8位的图像********************" << endl;

    //注意：at只适合灰度值为8位的图像

    //单通道

    Mat img3(20, 30, CV_8UC1, Scalar(0));

    cout << "img（7,8）" << int(img3.at<uchar>(7, 8)) << endl;

    //多通道

    Mat img4(20, 30, CV_8UC3, Scalar(0));

    //BGR通道

    cout << "B通道灰度值" << int(img4.at<Vec3b>(3, 4)[0]) << endl;

    cout << "G通道灰度值" << int(img4.at<Vec3b>(3, 4)[1]) << endl;

    cout << "R通道灰度值" << int(img4.at<Vec3b>(3, 4)[2]) << endl;


    waitKey(0);

    system("pause");

    return 0;

}

OpenCV访问像素点的灰度值

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