OpenCV2特征点的图像拼接

前面介绍了OpenCV3 角点检测，OpenCV2 特征检测与匹配，这些特征点可以用于识别，分类等任务，也可以用于图像的拼接，3D重建，地图重建（vslam）。
OpenCV2 中有自带的图像拼接函数Stitch，其主要步骤是：
1.检测需要匹配的两幅图的特征点（OpenCV 用的SURF ,ORB（快）），
2.对这两幅图的特征进行匹配找到共同的特征点，
3.根据共同的特征点进行配准，找到第二幅图到第一幅图的单应矩阵， findHomography()。
4.将第二幅图映射到一个共同的坐标的图上，第一幅图也复制上去形成初步的拼接，
5.对拼接处做EMA平滑处理，做到无缝连接。

代码如下，参考代码

#include "highgui/highgui.hpp"    
#include "opencv2/nonfree/nonfree.hpp"    
#include "opencv2/legacy/legacy.hpp"   
#include   
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("g5.jpg", 1);    //右图
    Mat image02 = imread("g4.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 Detector(2000);  
    vector<KeyPoint> keyPoint1, keyPoint2;
    Detector.detect(image1, keyPoint1);
    Detector.detect(image2, keyPoint2);
    //特征点描述，为下边的特征点匹配做准备    
    SurfDescriptorExtractor Descriptor;
    Mat imageDesc1, imageDesc2;
    Descriptor.compute(image1, keyPoint1, imageDesc1);
    Descriptor.compute(image2, keyPoint2, imageDesc2);
    FlannBasedMatcher matcher;
    vector<vector<DMatch> > matchePoints;
    vector<DMatch> GoodMatchePoints;
    vector<Mat> train_desc(1, imageDesc1);
    matcher.add(train_desc);
    matcher.train();
    matcher.knnMatch(imageDesc2, matchePoints, 2);
    cout << "total match points: " << matchePoints.size() << endl;

    // Lowe's algorithm,获取优秀匹配点
    for (int i = 0; i < matchePoints.size(); i++)
    {
        if (matchePoints[i][0].distance < 0.4 * matchePoints[i][1].distance)
        {
            GoodMatchePoints.push_back(matchePoints[i][0]);
        }
    }

    Mat first_match;
    drawMatches(image02, keyPoint2, image01, keyPoint1, GoodMatchePoints, first_match);
    imshow("first_match ", first_match);

    vector<Point2f> imagePoints1, imagePoints2;

    for (int i = 0; i<GoodMatchePoints.size(); i++)
    {
        imagePoints2.push_back(keyPoint2[GoodMatchePoints[i].queryIdx].pt);
      imagePoints1.push_back(keyPoint1[GoodMatchePoints[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("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)));
    imshow("b_dst", dst);
    OptimizeSeam(image02, imageTransform1, dst);
    imshow("dst", dst);
    imwrite("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++)
        {
            //如果遇到图像trans中无像素的黑点，则完全拷贝img1中的数据
            if (t[j * 3] == 0 && t[j * 3 + 1] == 0 && t[j * 3 + 2] == 0)
            {
                alpha = 1;
            }
            else
            {
                //img1中像素的权重，与当前处理点距重叠区域左边界的距离成正比，实验证明，这种方法确实好  
                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);
        }
    }
}

OpenCV中自带的stitcher算法代码如下

#include 
#include 
#include 
#include 
#include 
using namespace std;
using namespace cv;
bool try_use_gpu = false;
vector<Mat> imgs;
string result_name = "dst1.jpg";
int main(int argc, char * argv[])
{
    Mat img1 = imread("1.jpg");
    Mat img2 = imread("2.jpg");

    imshow("p1", img1);
    imshow("p2", img2);

    if (img1.empty() || img2.empty())
    {
        cout << "Can't read image" << endl;
        return -1;
    }
    imgs.push_back(img1);
    imgs.push_back(img2);
    Stitcher stitcher = Stitcher::createDefault(try_use_gpu);
    // 使用stitch函数进行拼接
    Mat pano;
    Stitcher::Status status = stitcher.stitch(imgs, pano);
    if (status != Stitcher::OK)
    {
        cout << "Can't stitch images, error code = " << int(status) << endl;
        return -1;
    }
    imwrite(result_name, pano);
    Mat pano2 = pano.clone();
    // 显示源图像，和结果图像
    imshow("全景图像", pano);
    if (waitKey() == 27)
        return 0;
}

效果如下：（拍摄远景的拼接效果会好些）