Mat findHomography( 
InputArray srcPoints, //原始平面中点的坐标
InputArray dstPoints, //目标平面中点的坐标
int method = 0, //用于计算单应性矩阵的方法
double ransacReprojThreshold = 3, 
OutputArray mask=noArray(), //通过鲁棒法（RANSAC或LMEDS）设置的可选输出掩码
const int maxIters = 2000, //RANSAC迭代的最大次数，2000是它可以达到的最大值
const double confidence = 0.995 //置信度
);

void perspectiveTransform(
InputArray src, //输入双通道或三通道浮点数组/图像
OutputArray dst, //输出与src相同大小和类型的数组/图像
InputArray m //3x3或4x4浮点转换矩阵
);

#include<opencv2/opencv.hpp>
#include<opencv2/xfeatures2d.hpp>
using namespace cv;
using namespace cv::xfeatures2d;

int main()
{
 Mat src1,src2;
 src1 = imread("E:/image/image/card.jpg");
 src2 = imread("E:/image/image/cards.jpg");
 if (src1.empty() || src2.empty())
 {
  printf("can ont load images....\n");
  return -1;
 }
 imshow("image1", src1);
 imshow("image2", src2);

 int minHessian = 400;
 //选择SURF特征
 Ptr<SURF>detector = SURF::create(minHessian);
 std::vector<KeyPoint>keypoints1;
 std::vector<KeyPoint>keypoints2;
 Mat descriptor1, descriptor2;
 //检测关键点并计算描述符
 detector->detectAndCompute(src1, Mat(), keypoints1, descriptor1);
 detector->detectAndCompute(src2, Mat(), keypoints2, descriptor2);

 //基于Flann的描述符匹配器
 FlannBasedMatcher matcher;
 std::vector<DMatch>matches;
 //从查询集中查找每个描述符的最佳匹配
 matcher.match(descriptor1, descriptor2, matches);
 double minDist = 1000;
 double maxDist = 0;
 for (int i = 0; i < descriptor1.rows; i++)
 {
  double dist = matches[i].distance;
  printf("%f \n", dist);
  if (dist > maxDist)
  {
   maxDist = dist;
  }
  if (dist < minDist)
  {
   minDist = dist;
  }

 }
 //DMatch类用于匹配关键点描述符的
 std::vector<DMatch>goodMatches;
 for (int i = 0; i < descriptor1.rows; i++)
 {
  double dist = matches[i].distance;
  if (dist < max(2*minDist, 0.02))
  {
   goodMatches.push_back(matches[i]);
  }
 }
 Mat matchesImg;
 drawMatches(src1, keypoints1, src2, keypoints2, goodMatches, matchesImg, Scalar::all(-1), 
  Scalar::all(-1), std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

 std::vector<Point2f>point1, point2;
 for (int i = 0; i < goodMatches.size(); i++)
 {
  point1.push_back(keypoints1[goodMatches[i].queryIdx].pt);
  point2.push_back(keypoints2[goodMatches[i].trainIdx].pt);
 }

 Mat H = findHomography(point1, point2, RANSAC);
 std::vector<Point2f>cornerPoints1(4);
 std::vector<Point2f>cornerPoints2(4);
 cornerPoints1[0] = Point(0, 0);
 cornerPoints1[1] = Point(src1.cols, 0);
 cornerPoints1[2] = Point(src1.cols, src1.rows);
 cornerPoints1[3] = Point(0,src1.rows);
 perspectiveTransform(cornerPoints1, cornerPoints2, H);

 //绘制出变换后的目标轮廓，由于左侧为图像src2故坐标点整体右移src1.cols
 line(matchesImg, cornerPoints2[0] + Point2f(src1.cols, 0), cornerPoints2[1] + Point2f(src1.cols, 0), Scalar(0,255,255), 4, 8, 0);
 line(matchesImg, cornerPoints2[1] + Point2f(src1.cols, 0), cornerPoints2[2] + Point2f(src1.cols, 0), Scalar(0,255,255), 4, 8, 0);
 line(matchesImg, cornerPoints2[2] + Point2f(src1.cols, 0), cornerPoints2[3] + Point2f(src1.cols, 0), Scalar(0,255,255), 4, 8, 0);
 line(matchesImg, cornerPoints2[3] + Point2f(src1.cols, 0), cornerPoints2[0] + Point2f(src1.cols, 0), Scalar(0,255,255), 4, 8, 0);

 //在原图上绘制出变换后的目标轮廓
 line(src2, cornerPoints2[0], cornerPoints2[1], Scalar(0,255,255), 4, 8, 0);
 line(src2, cornerPoints2[1], cornerPoints2[2], Scalar(0,255,255), 4, 8, 0);
 line(src2, cornerPoints2[2], cornerPoints2[3], Scalar(0,255,255), 4, 8, 0);
 line(src2, cornerPoints2[3], cornerPoints2[0], Scalar(0,255,255), 4, 8, 0);

 imshow("output", matchesImg);
 imshow("output2", src2);

 waitKey();
 return 0;
}