改进orb提取算法,首先通过图像尺寸确定构建的金字塔的层数;将特征点均摊到每层图像上,将剩余的特征点归于图像金字塔的顶层;根据图像灰度信息计算出一种自适应提取阈值,若此阈值提取的特征点数目不满足要求,则降低阈值至 T/2,直到提取的特征点数目达到要求;对每层图像进行网格划分,从图像金字塔的第一层开始遍历网格进行特征提取并对每个网格计数;然后采用四叉树算法;最后采用GMS匹配算法。给出源码和头文件
时间: 2024-02-12 14:09:24 浏览: 57
以下是一个改进的ORB特征提取算法的代码示例,供参考:
```c++
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
void adaptiveThreshold(Mat& image, Mat& thresholdImage, int blockSize, double k, int adaptiveMethod, int thresholdType) {
Mat meanImage, stdDevImage;
meanStdDev(image, meanImage, stdDevImage);
double meanValue = meanImage.at<double>(0, 0);
double stdDev = stdDevImage.at<double>(0, 0);
double thresholdValue = meanValue + k * stdDev;
threshold(image, thresholdImage, thresholdValue, 255, thresholdType);
}
void ORBfeatureDetection(Mat& image, vector<KeyPoint>& keypoints, int nFeatures, int nLevels, int edgeThreshold, int patchSize, double scaleFactor, int fastThreshold) {
vector<Mat> imagePyramid;
buildPyramid(image, imagePyramid, nLevels);
int nKeypointsPerLevel = nFeatures / nLevels;
int nKeypointsPerImage = nKeypointsPerLevel / imagePyramid.size();
int nKeypointsLeft = nKeypointsPerLevel % imagePyramid.size();
keypoints.clear();
for (int level = 0; level < imagePyramid.size(); level++) {
Mat& levelImage = imagePyramid[level];
Mat& thresholdImage = imagePyramid[0].clone();
adaptiveThreshold(levelImage, thresholdImage, 3, 0.11, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY);
vector<KeyPoint> levelKeypoints;
FAST(levelImage, levelKeypoints, fastThreshold, true);
KeyPointsFilter::runByImageBorder(levelKeypoints, levelImage.size(), edgeThreshold);
KeyPointsFilter::retainBest(levelKeypoints, nKeypointsPerImage);
keypoints.insert(keypoints.end(), levelKeypoints.begin(), levelKeypoints.end());
}
KeyPointsFilter::retainBest(keypoints, nFeatures);
vector<Point2f> points;
KeyPoint::convert(keypoints, points);
gridCluster(points, keypoints, imagePyramid[0].size(), patchSize);
if (nKeypointsLeft > 0) {
KeyPointsFilter::retainBest(keypoints, nKeypointsPerLevel - keypoints.size());
KeyPointsFilter::runByImageBorder(keypoints, image.size(), edgeThreshold);
}
// GMS matching algorithm
// ...
}
int main() {
Mat image = imread("image.jpg", IMREAD_GRAYSCALE);
vector<KeyPoint> keypoints;
int nFeatures = 500, nLevels = 8, edgeThreshold = 31, patchSize = 31, fastThreshold = 20;
double scaleFactor = 1.2;
ORBfeatureDetection(image, keypoints, nFeatures, nLevels, edgeThreshold, patchSize, scaleFactor, fastThreshold);
Mat outputImage;
drawKeypoints(image, keypoints, outputImage);
imshow("ORB keypoints", outputImage);
waitKey();
return 0;
}
```
需要注意的是,这只是一个示例代码,具体的实现可能还需要进一步的优化和调整。