图像缩放示例

二维图像的缩放属于仿射变换或者透视变换的范畴，一般可以通过OpenCV的warpAffine()或者warpPerspective()函数实现。

出于兴趣，根据仿射变换公式自己简单写了一个函数实现图像的缩放，缩放中心设置为图像中心。

代码如下：

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

using namespace std;
using namespace cv;

void zoomInAndOut(const float scale, const Mat srcImg, Mat &dstImg)
{
    Mat M=Mat::eye(3,3,CV_32FC1);
    int imgHeight=srcImg.rows;
    int imgWidth=srcImg.cols;

    uchar* pSrcData = (uchar*)srcImg.data;
    uchar* pDstData = (uchar*)dstImg.data;

    Point2f center(imgWidth / 2.0, imgHeight / 2.0);
    //计算仿射矩阵
    M.at<float>(0, 0) = scale;
    M.at<float>(0, 2) = (1 - scale)*center.x;
    M.at<float>(1, 1) = scale;
    M.at<float>(1, 2) = (1 - scale)*center.y;

    float a11 = M.at<float>(0, 0);
    float a12 = M.at<float>(0, 1);
    float a13 = M.at<float>(0, 2);
    float a21 = M.at<float>(1, 0);
    float a22 = M.at<float>(1, 1);
    float a23 = M.at<float>(1, 2);
    float a31 = M.at<float>(2, 0);
    float a32 = M.at<float>(2, 1);
    float a33 = M.at<float>(2, 2);

    float bx = a11*a22 - a21*a12;
    float by = a12*a21 - a11*a22;
    if ( abs(bx) > 1e-3 && abs(by) > 1e-3)
    {
        bx = 1.0 / bx;
        by = 1.0 / by;
        float cx = a13*a22 - a23*a12;
        float cy = a13*a21 - a23*a11;

        for (int j =0; j < imgHeight; j++)
        {
            for (int i = 0; i < imgWidth; i++)
            {
                float u = (a22*i - a12*j - cx) *bx;
                float v = (a21*i - a11*j - cy) *by;

                int u0 = floor(u);
                int v0 = floor(v);
                int u1 = floor(u0 + 1);
                int v1 = floor(v0 + 1);
                if (u0 >= 0 && v0 >= 0 && u1 < imgWidth && v1 < imgHeight)
                {
                    float dx = u - u0;
                    float dy = v - v0;
                    float weight1 = (1 - dx)*(1 - dy);
                    float weight2 = dx*(1 - dy);
                    float weight3 = (1 - dx)*dy;
                    float weight4 = dx*dy;

                    pDstData[j*imgWidth * 3 + i * 3 + 0] = weight1*pSrcData[v0*imgWidth * 3 + u0 * 3 + 0] +
                        weight2*pSrcData[v0*imgWidth * 3 + u1 * 3 + 0] +
                        weight3*pSrcData[v1*imgWidth * 3 + u0 * 3 + 0] +
                        weight4*pSrcData[v1*imgWidth * 3 + u1 * 3 + 0];
                    pDstData[j*imgWidth * 3 + i * 3 + 1] = weight1*pSrcData[v0*imgWidth * 3 + u0 * 3 + 1] +
                        weight2*pSrcData[v0*imgWidth * 3 + u1 * 3 + 1] +
                        weight3*pSrcData[v1*imgWidth * 3 + u0 * 3 + 1] +
                        weight4*pSrcData[v1*imgWidth * 3 + u1 * 3 + 1];
                    pDstData[j*imgWidth * 3 + i * 3 + 2] = weight1*pSrcData[v0*imgWidth * 3 + u0 * 3 + 2] +
                        weight2*pSrcData[v0*imgWidth * 3 + u1 * 3 + 2] +
                        weight3*pSrcData[v1*imgWidth * 3 + u0 * 3 + 2] +
                        weight4*pSrcData[v1*imgWidth * 3 + u1 * 3 + 2];
                }
                else
                {
                    pDstData[j*imgWidth * 3 + i * 3 + 0] =0;
                    pDstData[j*imgWidth * 3 + i * 3 + 1] =0;
                    pDstData[j*imgWidth * 3 + i * 3 + 2] =0;
                }
                    
            }
        }
    }
}

void main()
{
    string imgPath="data/source_images/";
    Mat srcImg = imread(imgPath+"moon.jpg");
    pyrDown(srcImg, srcImg);
    pyrDown(srcImg, srcImg);

    Mat dstImg = srcImg.clone();
    dstImg.setTo(0);

    namedWindow("showImg");
    imshow("showImg", srcImg);
    waitKey(10);

    float scale = 0;
    while (scale <= 2)
    {
        scale += 0.1;
        zoomInAndOut(scale, srcImg, dstImg);

        imshow("showImg", dstImg);
        waitKey(10);
    }

}

代码中采用反向映射方法，使用用双线性插值技术得到目标图像像素值