OpenCV开发笔记（八十）：基于特征点匹配实现全景图片拼接
前言
一个摄像头视野不大的时候，我们希望进行两个视野合并，这样让正视的视野增大，从而可以看到更广阔的标准视野。拼接的方法分为两条路，第一条路是Sticher类，第二条思路是特征点匹配。
本篇使用特征点匹配，进行两张图来视野合并拼接。
100%的点匹配
换了一幅图：
所以，opencv传统的方式，对这些特征点是有一些要求的。（注意：这两张图使用sStitcher类实现全景图片拼接，是无法拼接成功的！！！）
cv::Mat leftImageMat = cv::imread("D:/qtProject/openCVDemo/openCVDemo/modules/openCVManager/images/30.jpg");
cv::Mat rightImageMat = cv::imread("D:/qtProject/openCVDemo/openCVDemo/modules/openCVManager/images/31.jpg");
// 提取特征点
cv::Ptr<cv::xfeatures2d::SurfFeatureDetector> pSurfFeatureDetector = cv::xfeatures2d::SurfFeatureDetector::create();
std::vector<cv::KeyPoint> leftKeyPoints;
std::vector<cv::KeyPoint> rightKeyPoints;
cv::Mat leftMatch;
cv::Mat rightMatch;
std::vector<cv::DMatch> matches;
pSurfFeatureDetector->detectAndCompute(leftImageGrayMat, cv::Mat(), leftKeyPoints, leftMatch);
pSurfFeatureDetector->detectAndCompute(rightImageGrayMat, cv::Mat(), rightKeyPoints, rightMatch);
// 暴力匹配
cv::Ptr<cv::FlannBasedMatcher> pFlannBasedMatcher = cv::FlannBasedMatcher::create();
pFlannBasedMatcher->match(leftMatch, rightMatch, matches);
// 筛选匹配点, 根据match的距离从小到大排序
std::sort(matches.begin(), matches.end());
// 筛选匹配点，根据排序留下最好的匹配点
std::vector<cv::DMatch> goodMatchs;
// 阈值最小个数点
int count = 40;
// 阈值点个数 小于 总量的10% 则使用 总量的10%
int validPoints = (int)(matches.size() * 1.0f);
if(validPoints > count)
{
count = validPoints;
}
// 所有匹配点小于阈值，那么就取所有点
if(matches.size() < count)
{
count = matches.size();
}
// 将筛选出的点当作较好的点
for(int index = 0; index < count; index++)
{
goodMatchs.push_back(matches.at(index));
}
// 匹配结果
cv::Mat matchedMat;
// 绘制结果, 注意顺序
cv::drawMatches(leftImageMat, leftKeyPoints, rightImageMat, rightKeyPoints, goodMatchs, matchedMat);
#if 1
cv::namedWindow("matchedMat", cv::WINDOW_NORMAL);
cv::resizeWindow("matchedMat", cv::Size(800, 300));
cv::imshow("matchedMat", matchedMat);
#endif
// 准备匹配的点
std::vector<cv::Point2f> leftImagePoints;
std::vector<cv::Point2f> rightImagePoints;
for(int index = 0; index < goodMatchs.size(); index++)
{
leftImagePoints.push_back(rightKeyPoints.at(goodMatchs.at(index).trainIdx).pt);
rightImagePoints.push_back(leftKeyPoints.at(goodMatchs.at(index).queryIdx).pt);
}
// 使用暴力匹配的点计算透视变换矩阵
cv::Mat m = cv::findHomography(leftImagePoints, rightImagePoints, CV_RANSAC);
// 计算第二张图的变换大小
cv::Point2f leftTopPoint2f;
cv::Point2f leftBottomPoint2f;
cv::Point2f rightTopPoint2f;
cv::Point2f rightBottomPoint2f;
cv::Mat H = m.clone();
cv::Mat src = leftImageMat.clone();
{
cv::Mat V1;
cv::Mat V2;
// 左上角(0, 0, 1)
double v2[3] = {0, 0, 1};
// 变换后的坐标值
double v1[3];
//列向量
V2 = cv::Mat(3, 1, CV_64FC1, v2);
V1 = cv::Mat(3, 1, CV_64FC1, v1);
V1 = H * V2;
leftTopPoint2f.x = v1[0] / v1[2];
leftTopPoint2f.y = v1[1] / v1[2];
// 左下角(0, src.rows, 1)
v2[0] = 0;
v2[1] = src.rows;
v2[2] = 1;
V2 = cv::Mat(3, 1, CV_64FC1, v2);
V1 = cv::Mat(3, 1, CV_64FC1, v1);
V1 = H * V2;
leftBottomPoint2f.x = v1[0] / v1[2];
leftBottomPoint2f.y = v1[1] / v1[2];
// 右上角(src.cols, 0, 1)
v2[0] = src.cols;
v2[1] = 0;
v2[2] = 1;
V2 = cv::Mat(3, 1, CV_64FC1, v2);
V1 = cv::Mat(3, 1, CV_64FC1, v1);
V1 = H * V2;
rightTopPoint2f.x = v1[0] / v1[2];
rightTopPoint2f.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 = cv