图像畸变矫正

//
// Created by Qian.
//

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

using namespace std;

string image_file = "../test.png";   // 请确保路径正确

int main(int argc, char **argv) {

    // 本程序需要你自己实现去畸变部分的代码。尽管我们可以调用OpenCV的去畸变，但自己实现一遍有助于理解。
    // 畸变参数
    double k1 = -0.28340811, k2 = 0.07395907, p1 = 0.00019359, p2 = 1.76187114e-05;
    // 内参
    double fx = 458.654, fy = 457.296, cx = 367.215, cy = 248.375;

    cv::Mat image = cv::imread(image_file,CV_8UC1);   // 图像是灰度图，CV_8UC1
    int rows = image.rows, cols = image.cols;
    cv::Mat image_undistort = cv::Mat(rows, cols, CV_8UC1);   // 去畸变以后的图

    // 计算去畸变后图像的内容
    for (int v = 0; v < rows; v++)
        for (int u = 0; u < cols; u++) {

            double u_distorted = 0, v_distorted = 0;
            // TODO 按照公式，计算点(u,v)对应到畸变图像中的坐标(u_distorted, v_distorted) (~6 lines)
            // start your code here
            //首先转化成归一化坐标
            double x=(u-cx)/fx;
            double y=(v-cy)/fy;
            double r2=x*x+y*y;

            double x_distorted = x * (1 + k1 * r2 + k2 * r2 * r2)+2*p1*x*y+p2*(r2+2*x*x);
            double y_distorted= y * (1 + k1 * r2 + k2 * r2 * r2)+p1*(r2+2*y*y)+2*p2*x*y;
//            double x_distorted = x * (1 + k1 * r2 + k2 * r2 * r2);
//            double y_distorted= y * (1 + k1 * r2 + k2 * r2 * r2);

            //还原为像素坐标
            u_distorted = fx * x_distorted + cx;
            v_distorted = fy * y_distorted + cy;
            // end your code here

            // 赋值 (最近邻插值)
            if (u_distorted >= 0 && v_distorted >= 0 && u_distorted < cols && v_distorted < rows) {
                image_undistort.at<uchar>(v, u) = image.at<uchar>((int) v_distorted, (int) u_distorted);
            } else {
                image_undistort.at<uchar>(v, u) = 0;
            }
        }

    // 画图去畸变后图像
    cv::imshow("image undistorted", image_undistort);
    cv::waitKey();
}

    return 0;
}

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