OpenCV图像拼接，图像分辨率大于1080 * 1080

我一直在使用OpenCV在树莓派和基于Windows操作系统的PC上将两张图像拼接在一起。

#include <stdio.h>
#include <iostream>
#include "opencv2/opencv.hpp"
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
int main (int argc, char** argv) {
   Mat image_1 = imread (argv[1]);
   Mat image_2 = imread (argv[2]);
   Mat gray_image_1;
   Mat gray_image_2;
   cvtColor (image_1, gray_image_1, CV_RGB2GRAY);
   cvtColor (image_2, gray_image_2, CV_RGB2GRAY);
   // Check if image files can be read
   if (!gray_image_1.data) {
       std::cout << "Error Reading Image 1" << std::endl;
       return 0;
   }
   if (!gray_image_2.data) {
       std::cout << "Error Reading Image 2" << std::endl;
       return 0;
   }
   // Detect the keypoints using SURF Detector
   // Based from Anna Huaman's 'Features2D + Homography to find a known    object' Tutorial
   int minHessian = 50;
   SurfFeatureDetector detector (minHessian);
   std::vector <KeyPoint> keypoints_object, keypoints_scene;
   detector.detect (gray_image_2, keypoints_object);
   detector.detect (gray_image_1, keypoints_scene);
   // Calculate Feature Vectors (descriptors)
   // Based from  Anna Huaman's 'Features2D + Homography to find a known object' Tutorial
   SurfDescriptorExtractor extractor;
   Mat descriptors_object, descriptors_scene;
   extractor.compute (gray_image_2, keypoints_object, descriptors_object);
   extractor.compute (gray_image_1, keypoints_scene, descriptors_scene);
   // Matching descriptor vectors using FLANN matcher
   // Based from  Anna Huaman's 'Features2D + Homography to find a known object' Tutorial
   FlannBasedMatcher matcher;
   std::vector <DMatch> matches;
   matcher.match (descriptors_object, descriptors_scene, matches);
   double max_dist = 0;
   double min_dist = 100;
   // Quick calculation of max and min distances between keypoints
   // Based from  Anna Huaman's 'Features2D + Homography to find a known object' Tutorial
   for (int i = 0; i < descriptors_object.rows; i++) {
      double dist = matches[i].distance;
      if (dist < min_dist) {
         min_dist = dist;
      }
   }
   // Use matches that have a distance that is less than 3 * min_dist
   std::vector <DMatch> good_matches;
   for (int i = 0; i < descriptors_object.rows; i++){
       if (matches[i].distance < 3 * min_dist) {
           good_matches.push_back (matches[i]);
       }
   }
   std::vector <Point2f> obj;
   std::vector <Point2f> scene;
   for (int i = 0; i < good_matches.size(); i++) {
       // Get the keypoints from the good matches
       obj.push_back (keypoints_object[good_matches[i].queryIdx].pt);
       scene.push_back (keypoints_scene[good_matches[i].trainIdx].pt);
   }
   // Find the Homography Matrix
   Mat H = findHomography (obj, scene, CV_RANSAC);
   // Use the Homography Matrix to warp the images
   cv::Mat result;
   warpPerspective (image_2, result, H, cv::Size (image_2.cols +   image_1.cols, image_2.rows));
   cv::Mat half (result, cv::Rect (0, 0, image_1.cols, image_1.rows));
   image_1.copyTo (half);
   // Write image
   imwrite("Update.jpg", result);
   waitKey (0);
   return 0;
}

我使用的两个图像作为输入结果是成功的。但是，只有当这两张图像的分辨率为<= 1080 * 1080像素时。

对于1440 * 1440和1944 * 1944分辨率，我发现findHomography不能工作，因为我不再得到超过3个好的匹配。findHomography需要至少4个好的匹配

I have try…

cv::resize(输入图像)-结果没有分辨率大小的图像产生足够好的匹配findHomography

min Hessian升高或降低-无变化

最小距离增加或减少-无变化

注意:两个图像重叠且尺寸相同。

---

谁有解决这个问题的办法?我花了几个小时研究这个问题，只得出结论，OpenCV图像拼接不能处理高分辨率图像。

下面我将包括两张高分辨率的图片，以供希望提供帮助的人使用。

colour_1_1440

colour_2_1440

我使用的是OpenCV 2.4.13，而不是新的OpenCV 3.1.0。