OpenCV学习 day3 像素操作 图像融合 调整亮度和对比度

1.像素的操作

 

 通过读取像素 反转像素值：

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main()
{
    Mat src = imread("D:/learning/image/3.jpg", IMREAD_UNCHANGED); 
    if (src.empty())
    {
        cout << "Could not find the image!" << endl;
        return -1;
    }
    namedWindow("InputImage"); 
    imshow("InputImage", src); 
    
    int height = src.rows;
    int width = src.cols;
                            
    Mat dst;
    dst.create(src.size(), src.type());
    for (int row = 0; row < height; row++) {
        for (int col = 0; col < width; col++) {
            if (dst.channels() == 1) {
                int gray_pixel = src.at<uchar>(row, col);  //获取单通道图像像素值
                dst.at<uchar>(row, col) = 255 - gray_pixel;
            }
            else if(dst.channels() == 3){
                int b = src.at<Vec3b>(row, col)[0];  //获取三通道图像像素值
                int g = src.at<Vec3b>(row, col)[1];
                int r = src.at<Vec3b>(row, col)[2];
                dst.at<Vec3b>(row, col)[0] = 255 - b;
                dst.at<Vec3b>(row, col)[1] = 255 - g;
                dst.at<Vec3b>(row, col)[2] = 255 - r;
            }
        }
    }
    imshow("image invert", dst);
    waitKey(0);
    return 0;
}

或者直接bitwise_not()函数

bitwise_not(src, dst);

   

2. 调整亮度和对比度

 

 亮度和对比度的调节属于点操作

f(i, j)是原图的像素值，g(i, j)是调整后的像素值，α调整对比度，β调整亮度

int main(int argc, char** argv) {
    Mat src, dst;
    src = imread("D:/learning/image/4.jpg");
    if (!src.data) {
        cout << "could not src1..." << endl;
        return -1;
    }
    int height = src.rows;
    int width = src.cols;
    char input_win[] = "input image";
    namedWindow(input_win, WINDOW_AUTOSIZE);
    imshow(input_win, src);
    dst = Mat::zeros(src.size(), src.type());
    float alpha = 1.2;
    float beta = 30;
    Mat m1;
    src.convertTo(m1, CV_32F);  //转换像素数据类型  原像素是整数类型
    for (int row = 0; row < height; row++) {
        for (int col = 0; col < width; col++) {
            if (src.channels() == 3) {
                float b = m1.at<Vec3f>(row, col)[0];
                float g = m1.at<Vec3f>(row, col)[1];
                float r = m1.at<Vec3f>(row, col)[2];
                dst.at<Vec3b>(row, col)[0] = saturate_cast<uchar>(b * alpha + beta);  //将像素限制在0-255之间
                dst.at<Vec3b>(row, col)[1] = saturate_cast<uchar>(g * alpha + beta);
                dst.at<Vec3b>(row, col)[2] = saturate_cast<uchar>(r * alpha + beta);
            }
            else if (src.channels() == 1) {
                float v = src.at<uchar>(row, col);
                dst.at<uchar>(row, col) = saturate_cast<uchar>(v * alpha + beta);
            }
        }
    }
    imshow("output image", dst);
    waitKey(0);
    return 0;

 

   

 

 

 3. 图像融合

通过addWeighted函数

int main(int argc, char** argv) {
    Mat src1, src2, dst;
    src1 = imread("D:/learning/image/4.jpg");
    src2 = imread("D:/learning/image/5.jpg");
    if (!src1.data) {
        cout << "could not src1..." << endl;
        return -1;
    }
    if (!src2.data) {
        cout << "could not src2..." << endl;
        return -1;
    }
    double alpha = 0.5;
    if (src1.rows == src2.rows && src1.cols == src2.cols && src1.type() == src2.type()) {
        addWeighted(src1, alpha, src2, 1.0 - alpha, 0.0, dst);
        //multiply(src1, src2, dst, 1.0);
        imshow("blend image", dst);
    }
    else {
        cout << "can not blend" << endl;
    }
    waitKey(0);
    return 0;
}