06_Opencv图像混合

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/gzx110304/article/details/88676767

06_Opencv图像混合

一.图像的线性混合

• dst = alpha*src1 + beta*src2
• beta = 1-alpha
• alpha的取值为:0.0_{1.0之间,同理,beta取值也在0.0}1.0之间
• gama不取零的情况:dst = alpha*src1 + beta*src2 + gama

二.Opencv中图像线性混合的API

addWeighted(const CvArr *src1, double alpha, const CvArr *src2, double beta, double gamma, CvArr *dst)

• 第一个参数为输入的源图像src1, 第二个参数为输入图像src1的系数值, 第三个参数为输入的源图像src2, 第四个参数为输入图像src2的系数值, 第五个参数为gama值, 第六个参数为混合后的结果
• 只有图像src1与src2的大小和类型一样时,才能进行混合

三.使用读取像素的方式实现混合

    Mat src1 = imread("/Users/zhixingao/Downloads/android/OpencvForCPlus/素材/图像混合2.jpg");
    if(!src1.data) {
        printf("could not load image src1...\n");
        return -1;
    }
    
    Mat src2 = imread("/Users/zhixingao/Downloads/android/OpencvForCPlus/素材/图像混合1.jpg");
    if(!src2.data) {
        printf("could not load image src2...\n");
        return -1;
    }
    
    namedWindow("input image1", CV_WINDOW_AUTOSIZE);
    imshow("input image1", src1);
    
    namedWindow("input image2", CV_WINDOW_AUTOSIZE);
    imshow("input image2", src2);
    
    if(src1.cols == src2.cols && src1.rows == src2.rows && src1.type() == src2.type()) {
        //通过读写像素值的方式实现图像混合
        float alpha = 0.5;
        Mat dst(src1.size(), src1.type());
        
        int cols = src1.cols;
        int rows = src1.rows;
        int channels = src1.channels();
        
        for(int row=0; row<rows; row++) {
            for(int col=0; col<cols; col++) {
                if(channels == 3) {
                    Vec3b src1Intensity = src1.at<Vec3b>(row, col);
                    Vec3b src2Intensity = src2.at<Vec3b>(row, col);
                    
                    uchar src1B = src1Intensity[0];
                    uchar src1G = src1Intensity[1];
                    uchar src1R = src1Intensity[2];
                    
                    uchar src2B = src2Intensity[0];
                    uchar src2G = src2Intensity[1];
                    uchar src2R = src2Intensity[2];
                    
                    dst.at<Vec3b>(row, col)[0] = saturate_cast<uchar>(alpha * src1B + (1-alpha) * src2B);
                    dst.at<Vec3b>(row, col)[1] = saturate_cast<uchar>(alpha * src1G + (1-alpha) * src2G);
                    dst.at<Vec3b>(row, col)[2] = saturate_cast<uchar>(alpha * src1R + (1-alpha) * src2R);
                } else {
                    uchar src1Intensity = src1.at<uchar>(row, col);
                    uchar src2Intensity = src2.at<uchar>(row, col);
                    dst.at<uchar>(row, col) = saturate_cast<uchar>(alpha * src1Intensity + (1-alpha) * src2Intensity);
                }
            }
        }
        
        namedWindow("output image", CV_WINDOW_AUTOSIZE);
        imshow("output image", dst);
    } else {
        printf("can not blend src1 and src2 beacause of size and type is not same...\n");
        return -1;
    }

四.使用cv::addWeighted实现图像混合

    Mat src1 = imread("/Users/zhixingao/Downloads/android/OpencvForCPlus/素材/图像混合2.jpg");
    if(!src1.data) {
        printf("could not load image src1...\n");
        return -1;
    }
    
    Mat src2 = imread("/Users/zhixingao/Downloads/android/OpencvForCPlus/素材/图像混合1.jpg");
    if(!src2.data) {
        printf("could not load image src2...\n");
        return -1;
    }
    
    namedWindow("input image1", CV_WINDOW_AUTOSIZE);
    imshow("input image1", src1);
    
    namedWindow("input image2", CV_WINDOW_AUTOSIZE);
    imshow("input image2", src2);
    
    if(src1.cols == src2.cols && src1.rows == src2.rows && src1.type() == src2.type()) {
        float alpha = 0.5;
        Mat dst(src1.size(), src1.type());
        //通过addWeighted方式实现图像混合
        addWeighted(src1, alpha, src2, 1.0-alpha, 0.0, dst);
        
        namedWindow("output image", CV_WINDOW_AUTOSIZE);
        imshow("output image", dst);
    } else {
        printf("can not blend src1 and src2 beacause of size and type is not same...\n");
        return -1;
    }