OpenCV探索之路（二十一）如何生成能在無opencv環境下運行的exe

本文轉載自查看原文 2017-07-31 16:21 10358 opencv探索

我們經常遇到這樣的需求：我們在VS寫好的程序，需要在一個沒有裝opencv甚至沒有裝vs的電腦下運行，跑出效果。比如，你在你的電腦用opencv+vs2015寫出一個程序，然后老師叫你把程序發給他，他要看看功能實現的怎么樣。老師的電腦肯定沒有整套的開發環境的，如果你想只把代碼發給他，讓他自己編譯，肯定會出現問題。所以，我們需要掌握如何生成一個不依賴開發環境的exe的方法。

下面將以一個實際例子說明如何生成一個不依賴開發環境的exe的方法。

比如我現在在VS2015下實現了一個圖像拼接功能的程序

#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];//變換后的坐標值
    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);    //右圖
    Mat image02 = imread(".\\src_pic\\left.jpg", 1);    //左圖
    imshow("p2", image01);
    imshow("p1", image02);

    //灰度圖轉換  
    Mat image1, image2;
    cvtColor(image01, image1, CV_RGB2GRAY);
    cvtColor(image02, image2, CV_RGB2GRAY);


    //提取特征點    
    SurfFeatureDetector surfDetector(800);  // 海塞矩陣閾值，在這里調整精度，值越大點越少，越精准 
    vector<KeyPoint> keyPoint1, keyPoint2;
    surfDetector.detect(image1, keyPoint1);
    surfDetector.detect(image2, keyPoint2);

    //特征點描述，為下邊的特征點匹配做准備    
    SurfDescriptorExtractor SurfDescriptor;
    Mat imageDesc1, imageDesc2;
    SurfDescriptor.compute(image1, keyPoint1, imageDesc1);
    SurfDescriptor.compute(image2, keyPoint2, imageDesc2);

    //獲得匹配特征點，並提取最優配對     
    FlannBasedMatcher matcher;
    vector<DMatch> matchePoints;
    matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());
    cout << "total match points: " << matchePoints.size() << endl;
   // sort(matchePoints.begin(), matchePoints.end()); //特征點排序    

    Mat img_match;
    drawMatches(image01, keyPoint1, image02, keyPoint2, matchePoints, img_match);
    imshow("match points",img_match);

    //獲取排在前N個的最優匹配特征點  
    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);
    }



    //獲取圖像1到圖像2的投影映射矩陣 尺寸為3*3  
    Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
    ////也可以使用getPerspectiveTransform方法獲得透視變換矩陣，不過要求只能有4個點，效果稍差  
    //Mat   homo=getPerspectiveTransform(imagePoints1,imagePoints2);  
    cout << "變換矩陣為：\n" << homo << endl << endl; //輸出映射矩陣      

    //計算配准圖的四個頂點坐標
    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;

    //圖像配准  
    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("直接經過透視矩陣變換", imageTransform1);
    imwrite(".\\dst_pic\\trans1.jpg", imageTransform1);


    //創建拼接后的圖,需提前計算圖的大小
    int dst_width = imageTransform1.cols;  //取最右點的長度為拼接圖的長度
    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;
}


//優化兩圖的連接處，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);//開始位置，即重疊區域的左邊界  
    
    double processWidth = img1.cols - start;//重疊區域的寬度  
    int rows = dst.rows;
    int cols = img1.cols; //注意，是列數*通道數
    double alpha = 1;//img1中像素的權重  
    for (int i = 0; i < rows; i++)
    {
        uchar* p = img1.ptr<uchar>(i);  //獲取第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);
            
        }
    }

}

那么怎樣才可以生成一個不依賴於環境的可執行程序exe呢？