我想使用在OpenCV 3.0中集成的AKAZE。 为此,我测试了以下代码:
#include <opencv2/features2d.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/opencv.hpp>
#include <vector>
#include <iostream>
#include <qcoreapplication.h>
#include <QDebug>
using namespace std;
using namespace cv;
const float inlier_threshold = 2.5f; // Distance threshold to identify inliers
const float nn_match_ratio = 0.8f; // Nearest neighbor matching ratio
int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
Mat img1 = cv::imread("img1.jpg",IMREAD_GRAYSCALE);
Mat img2 = imread("img2.jpg", IMREAD_GRAYSCALE);
Mat homography;
FileStorage fs("H1to3p.xml", FileStorage::READ);
fs.getFirstTopLevelNode() >> homography;
vector<KeyPoint> kpts1, kpts2;
Mat desc1, desc2;
Ptr<AKAZE> akaze = AKAZE::create();
//ERROR after detectAndCompute(...)
akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
akaze->detectAndCompute(img2, noArray(), kpts2, desc2);
BFMatcher matcher(NORM_HAMMING);
vector< vector<DMatch> > nn_matches;
matcher.knnMatch(desc1, desc2, nn_matches, 2);
vector<KeyPoint> matched1, matched2, inliers1, inliers2;
vector<DMatch> good_matches;
for(size_t i = 0; i < nn_matches.size(); i++) {
DMatch first = nn_matches[i][0];
float dist1 = nn_matches[i][0].distance;
float dist2 = nn_matches[i][1].distance;
if(dist1 < nn_match_ratio * dist2) {
matched1.push_back(kpts1[first.queryIdx]);
matched2.push_back(kpts2[first.trainIdx]);
}
}
for(unsigned i = 0; i < matched1.size(); i++) {
Mat col = Mat::ones(3, 1, CV_64F);
col.at<double>(0) = matched1[i].pt.x;
col.at<double>(1) = matched1[i].pt.y;
col = homography * col;
col /= col.at<double>(2);
double dist = sqrt( pow(col.at<double>(0) - matched2[i].pt.x, 2) +
pow(col.at<double>(1) - matched2[i].pt.y, 2));
if(dist < inlier_threshold) {
int new_i = static_cast<int>(inliers1.size());
inliers1.push_back(matched1[i]);
inliers2.push_back(matched2[i]);
good_matches.push_back(DMatch(new_i, new_i, 0));
}
}
Mat res;
drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
imwrite("res.png", res);
double inlier_ratio = inliers1.size() * 1.0 / matched1.size();
cout << "A-KAZE Matching Results" << endl;
cout << "*******************************" << endl;
cout << "# Keypoints 1: \t" << kpts1.size() << endl;
cout << "# Keypoints 2: \t" << kpts2.size() << endl;
cout << "# Matches: \t" << matched1.size() << endl;
cout << "# Inliers: \t" << inliers1.size() << endl;
cout << "# Inliers Ratio: \t" << inlier_ratio << endl;
cout << endl;
return a.exec();
}
在行akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
之后引发了以下异常:
OpenCV Error: Insufficient memory (Failed to allocate 72485160 bytes) in OutOfMemoryError, file C:\opencv\sources\modules\core\src\alloc.cpp, line 52.
OpenCV Error: Assertion failed (u != 0) in create, file C:\opencv\sources\modules\core\src\matrix.cpp, line 411 terminate called after throwing an instance of 'cv::Exception'
what(): C:\opencv\sources\modules\core\src\matrix.cpp:411: error: (-215) u != 0
我在Windows 7下编译了OpenCV mit mingw 4.92。
有人回答了吗?
谢谢