图像卷积

卷积流程

1.卷积核旋转180°

2.卷积核和图像对应元素点乘并求和

3.结果输出到输出图像的对应位置

4.移动卷积核重复1-3

图像卷积处理后，由模糊图像的作用。

#include <opencv2\opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main()
{
    //待卷积矩阵
    uchar points[25] = { 1,2,3,4,5,
        6,7,8,9,10,
        11,12,13,14,15,
        16,17,18,19,20,
        21,22,23,24,25 };
    Mat img(5, 5, CV_8UC1, points);
    //卷积模板
    Mat kernel = (Mat_<float>(3, 3) << 1, 2, 1,
        2, 0, 2,
        1, 2, 1);
    Mat kernel_norm = kernel / 12;  //卷积模板归一化
                                    //未归一化卷积结果和归一化卷积结果
    Mat result, result_norm;
    filter2D(img, result, CV_32F, kernel, Point(-1, -1), 2, BORDER_CONSTANT);
    filter2D(img, result_norm, CV_32F, kernel_norm, Point(-1, -1), 2, BORDER_CONSTANT);
    cout << "result:" << endl << result << endl;
    cout << "result_norm:" << endl << result_norm << endl;
    //图像卷积
    Mat lena = imread("lena.png");
    if (lena.empty())
    {
        cout << "请确认图像文件名称是否正确" << endl;
        return -1;
    }
    Mat lena_fillter;
    filter2D(lena, lena_fillter, -1, kernel_norm, Point(-1, -1), 2, BORDER_CONSTANT);
    imshow("lena_fillter", lena_fillter);
    imshow("lena", lena);
    waitKey(0);
    return 0;
}