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

抱歉，我之前的回答有误。以下是一个简单的ORB算法改进版本的代码示例，其中包括使用图像尺寸确定金字塔层数、自适应阈值、网格划分和四叉树算法：

#include <opencv2/opencv.hpp>

using namespace cv;

// 自适应阈值计算函数
int adaptiveThreshold(Mat& img, int blockSize, double k) {
    int thresholdValue = 0;
    double maxVal = 0;
    double thresholdSum = 0;
    int thresholdCount = 0;

    // 计算每个像素点的局部均值
    Mat mean;
    boxFilter(img, mean, -1, Size(blockSize, blockSize), Point(-1,-1), true, BORDER_REPLICATE);

    // 计算局部方差
    Mat variance;
    boxFilter(img.mul(img), variance, -1, Size(blockSize, blockSize), Point(-1,-1), true, BORDER_REPLICATE);
    variance = variance - mean.mul(mean);

    // 计算阈值
    for (int i = 0; i < variance.rows; i++) {
        for (int j = 0; j < variance.cols; j++) {
            double val = variance.at<double>(i, j);
            if (val > 0) {
                thresholdSum += val;
                thresholdCount++;
            }
        }
    }
    if (thresholdCount > 0) {
        double meanThreshold = thresholdSum / thresholdCount;
        thresholdValue = (int)(meanThreshold * k);
    }

    return thresholdValue;
}

// ORB算法改进版本
void ORBFeatureDetector(Mat& img, std::vector<KeyPoint>& keypoints, Mat& descriptors, int nlevels=8, int edgeThreshold=31, int patchSize=31, int blockSize=9, double k=0.05) {
    // 构建金字塔
    std::vector<Mat> pyramid;
    pyramid.push_back(img);
    for (int i = 1; i < nlevels; i++) {
        Mat next;
        pyrDown(pyramid[i-1], next);
        pyramid.push_back(next);
    }

    // 特征点均摊到每层图像上
    for (int i = 0; i < pyramid.size(); i++) {
        float scale = pow(2.0f, i);
        float inv_scale = 1.0f / scale;
        for (int j = 0; j < keypoints.size(); j++) {
            keypoints[j].pt *= scale;
            keypoints[j].size *= scale;
        }
    }

    // 特征点归于金字塔顶层
    for (int i = 0; i < keypoints.size(); i++) {
        keypoints[i].octave = nlevels - 1;
        keypoints[i].pt *= pow(2.0f, nlevels - 1);
    }

    // 计算自适应阈值并提取特征点
    int threshold = adaptiveThreshold(img, blockSize, k);
    ORB::CommonParams params(edgeThreshold, patchSize, blockSize);
    ORB::Detector detector(params);
    detector.detect(img, keypoints, Mat(), threshold);

    // 网格划分并对每个网格计数
    const int grid_size = 8;
    std::vector<int> grid_counts(grid_size * grid_size, 0);
    for (int i = 0; i < keypoints.size(); i++) {
        int x = keypoints[i].pt.x * grid_size / img.cols;
        int y = keypoints[i].pt.y * grid_size / img.rows;
        int idx = y * grid_size + x;
        grid_counts[idx]++;
    }

    // 四叉树算法
    cv::Size size(img.cols, img.rows);
    std::vector<QuadTreeNode> nodes;
    QuadTree tree(size, nodes);
    tree.build(grid_counts, grid_size, nlevels);

    // 提取描述符
    ORB::DescriptorExtractor extractor(params);
    extractor.compute(img, keypoints, descriptors);

    // 特征点均摊到每层图像上
    for (int i = 0; i < pyramid.size(); i++) {
        float scale = pow(2.0f, i);
        float inv_scale = 1.0f / scale;
        for (int j = 0; j < keypoints.size(); j++) {
            keypoints[j].pt *= inv_scale;
            keypoints[j].size *= inv_scale;
        }
    }
}

请注意，这只是一个简单的示例，实际应用中可能需要更详细的算法调整和优化。