OpenCV探索之路（二十一）如何生成能在无opencv环境下运行的exe

2021-11-15 21:21:48

我们经常遇到这样的需求：我们在VS写好的程序，需要在一个没有装opencv甚至没有装vs的电脑下运行，跑出效果。比如，你在你的电脑用opencv+vs2015写出一个程序，然后老师叫你把程序发给他，他要看看功能实现的怎么样。老师的电脑肯定没有整套的开发环境的，如果你想只把代码发给他，让他自己编译，肯定会出现问题。所以，我们需要掌握如何生成一个不依赖开发环境的exe的方法。

下面将以一个实际例子说明如何生成一个不依赖开发环境的exe的方法。

比如我现在在VS2015下实现了一个图像拼接功能的程序

#include "highgui/highgui.hpp"

#include "opencv2/nonfree/nonfree.hpp"

#include "opencv2/legacy/legacy.hpp"

#include <iostream>  

using namespace cv;

using namespace std;

void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst);

typedef struct

{

    Point2f left_top;

    Point2f left_bottom;

    Point2f right_top;

    Point2f right_bottom;

}four_corners_t;

four_corners_t corners;

void CalcCorners(const Mat& H, const Mat& src)

{

    double v2[] = { 0, 0, 1 };//左上角

    double v1[3];//变换后的坐标值

    Mat V2 = Mat(3, 1, CV_64FC1, v2);  //列向量

    Mat V1 = Mat(3, 1, CV_64FC1, v1);  //列向量

    V1 = H * V2;

    //左上角(0,0,1)

    cout << "V2: " << V2 << endl;

    cout << "V1: " << V1 << endl;

    corners.left_top.x = v1[0] / v1[2];

    corners.left_top.y = v1[1] / v1[2];

    //左下角(0,src.rows,1)

    v2[0] = 0;

    v2[1] = src.rows;

    v2[2] = 1;

    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量

    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量

    V1 = H * V2;

    corners.left_bottom.x = v1[0] / v1[2];

    corners.left_bottom.y = v1[1] / v1[2];

    //右上角(src.cols,0,1)

    v2[0] = src.cols;

    v2[1] = 0;

    v2[2] = 1;

    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量

    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量

    V1 = H * V2;

    corners.right_top.x = v1[0] / v1[2];

    corners.right_top.y = v1[1] / v1[2];

    //右下角(src.cols,src.rows,1)

    v2[0] = src.cols;

    v2[1] = src.rows;

    v2[2] = 1;

    V2 = Mat(3, 1, CV_64FC1, v2);  //列向量

    V1 = Mat(3, 1, CV_64FC1, v1);  //列向量

    V1 = H * V2;

    corners.right_bottom.x = v1[0] / v1[2];

    corners.right_bottom.y = v1[1] / v1[2];

}

int main(int argc, char *argv[])

{

    Mat image01 = imread(".\\src_pic\\right.jpg", 1);    //右图

    Mat image02 = imread(".\\src_pic\\left.jpg", 1);    //左图

    imshow("p2", image01);

    imshow("p1", image02);

    //灰度图转换

    Mat image1, image2;

    cvtColor(image01, image1, CV_RGB2GRAY);

    cvtColor(image02, image2, CV_RGB2GRAY);

    //提取特征点

    SurfFeatureDetector surfDetector(800);  // 海塞矩阵阈值，在这里调整精度，值越大点越少，越精准

    vector<KeyPoint> keyPoint1, keyPoint2;

    surfDetector.detect(image1, keyPoint1);

    surfDetector.detect(image2, keyPoint2);

    //特征点描述，为下边的特征点匹配做准备

    SurfDescriptorExtractor SurfDescriptor;

    Mat imageDesc1, imageDesc2;

    SurfDescriptor.compute(image1, keyPoint1, imageDesc1);

    SurfDescriptor.compute(image2, keyPoint2, imageDesc2);

    //获得匹配特征点，并提取最优配对

    FlannBasedMatcher matcher;

    vector<DMatch> matchePoints;

    matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());

    cout << "total match points: " << matchePoints.size() << endl;

   // sort(matchePoints.begin(), matchePoints.end()); //特征点排序    

    Mat img_match;

    drawMatches(image01, keyPoint1, image02, keyPoint2, matchePoints, img_match);

    imshow("match points",img_match);

    //获取排在前N个的最优匹配特征点

    vector<Point2f> imagePoints1, imagePoints2;

    for (int i = 0; i<matchePoints.size(); i++)

    {

        imagePoints1.push_back(keyPoint1[matchePoints[i].queryIdx].pt);

        imagePoints2.push_back(keyPoint2[matchePoints[i].trainIdx].pt);

    }

    //获取图像1到图像2的投影映射矩阵 尺寸为3*3

    Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);

    ////也可以使用getPerspectiveTransform方法获得透视变换矩阵，不过要求只能有4个点，效果稍差

    //Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);

    cout << "变换矩阵为：\n" << homo << endl << endl; //输出映射矩阵      

    //计算配准图的四个顶点坐标

    CalcCorners(homo, image01);

    cout << "left_top:" << corners.left_top << endl;

    cout << "left_bottom:" << corners.left_bottom << endl;

    cout << "right_top:" << corners.right_top << endl;

    cout << "right_bottom:" << corners.right_bottom << endl;

    //图像配准

    Mat imageTransform1, imageTransform2;

    warpPerspective(image01, imageTransform1, homo, Size(MAX(corners.right_top.x, corners.right_bottom.x),image02.rows));

    //warpPerspective(image01, imageTransform2, adjustMat*homo, Size(image02.cols*1.3, image02.rows*1.8));

    imshow("直接经过透视矩阵变换", imageTransform1);

    imwrite(".\\dst_pic\\trans1.jpg", imageTransform1);

    //创建拼接后的图,需提前计算图的大小

    int dst_width = imageTransform1.cols;  //取最右点的长度为拼接图的长度

    int dst_height = image02.rows;

    Mat dst(dst_height, dst_width,CV_8UC3);

    dst.setTo(0);

    imageTransform1.copyTo(dst(Rect(0, 0, imageTransform1.cols, imageTransform1.rows)));

    image02.copyTo(dst(Rect(0, 0, image02.cols, image02.rows)));

    OptimizeSeam(image02, imageTransform1, dst);

    imshow("dst", dst);

    imwrite(".\\dst_pic\\dst.jpg", dst);

    waitKey();

    return 0;

}

//优化两图的连接处，使得拼接自然

void OptimizeSeam(Mat& img1, Mat& trans, Mat& dst)

{

    int start = MIN(corners.left_top.x, corners.left_bottom.x);//开始位置，即重叠区域的左边界  

    double processWidth = img1.cols - start;//重叠区域的宽度

    int rows = dst.rows;

    int cols = img1.cols; //注意，是列数*通道数

    double alpha = 1;//img1中像素的权重

    for (int i = 0; i < rows; i++)

    {

        uchar* p = img1.ptr<uchar>(i);  //获取第i行的首地址

        uchar* t = trans.ptr<uchar>(i);

        uchar* d = dst.ptr<uchar>(i);

        for (int j = start; j < cols; j++)

        {

            if (t[j*3] == 0 && t[j*3+1] == 0 && t[j*3+2] == 0)

            {

                alpha = 1;

            }

            else

            {

                alpha = (processWidth - (j - start)) / processWidth;

            }

            d[j*3] = p[j*3] * alpha + t[j*3] * (1 - alpha);

            d[j*3+1] = p[j*3+1] * alpha + t[j*3+1] * (1 - alpha);

            d[j*3+2] = p[j*3+2] * alpha + t[j*3+2] * (1 - alpha);

        }

    }

}