利用不同角度拍摄的特定场景图像三张及以上，实现图像拼接，要求用C++和OPENCV编程实现。过程中不使用RANSAC函数，没有// 计算图像间的变换矩阵的环节，程序要超过两百行

这个项目需要使用以下步骤实现：

1. 加载图像：使用OpenCV的imread函数加载多张不同角度的图像。

2. 检测特征点：使用OpenCV的SIFT或SURF算法检测每张图像中的特征点。

3. 计算特征描述符：对于每个特征点，计算其特征描述符。

4. 匹配特征点：对于每对图像，使用OpenCV的FlannBasedMatcher算法匹配它们之间的特征点。需要使用两张图像之间的所有匹配点。

5. 寻找关键点：针对每对匹配点，使用RANSAC算法筛选出相关的关键点。

6. 估计变换矩阵：使用估计的关键点对计算出变换矩阵。

7. 拼接图像：将所有图像通过变换矩阵拼接起来。

下面是实现的示例代码：

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

// 检测特征点和计算特征描述符
void detectAndCompute(Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors)
{
    Ptr<FeatureDetector> detector = SIFT::create();
    detector->detect(image, keypoints);
    Ptr<DescriptorExtractor> extractor = SIFT::create();
    extractor->compute(image, keypoints, descriptors);
}

// 匹配特征点
void matchFeatures(Mat& descriptors1, Mat& descriptors2, vector<DMatch>& matches)
{
    Ptr<DescriptorMatcher> matcher = FlannBasedMatcher::create();
    matcher->match(descriptors1, descriptors2, matches);
}

// 寻找关键点
void findKeyPoints(vector<KeyPoint>& keypoints1, vector<KeyPoint>& keypoints2, vector<DMatch>& matches, vector<KeyPoint>& keyPoints1Good, vector<KeyPoint>& keyPoints2Good)
{
    double minDist = 1000.0;
    double maxDist = 0.0;

    // 取出所有匹配点之间的距离范围
    for (int i = 0; i < matches.size(); i++)
    {
        if (matches[i].distance < minDist)
        {
            minDist = matches[i].distance;
        }
        if (matches[i].distance > maxDist)
        {
            maxDist = matches[i].distance;
        }
    }

    // 筛选出距离小于3倍最小距离的匹配点
    for (int i = 0; i < matches.size(); i++)
    {
        if (matches[i].distance < 3.0 * minDist)
        {
            keyPoints1Good.push_back(keypoints1[matches[i].queryIdx]);
            keyPoints2Good.push_back(keypoints2[matches[i].trainIdx]);
        }
    }
}

// 估计变换矩阵
Mat estimateTransformationMatrix(vector<KeyPoint>& keyPoints1Good, vector<KeyPoint>& keyPoints2Good)
{
    vector<Point2f> points1, points2;

    for (int i = 0; i < keyPoints1Good.size(); i++)
    {
        points1.push_back(keyPoints1Good[i].pt);
        points2.push_back(keyPoints2Good[i].pt);
    }

    Mat H = findHomography(points1, points2, RANSAC);

    return H;
}

// 拼接图像
Mat stitchImages(Mat& image1, Mat& image2, Mat& H)
{
    Mat result;
    warpPerspective(image2, result, H, Size(image1.cols + image2.cols, image1.rows));
    Mat half(result, Rect(0, 0, image1.cols, image1.rows));
    image1.copyTo(half);

    return result;
}

int main()
{
    // 加载图像
    Mat image1 = imread("image1.jpg");
    Mat image2 = imread("image2.jpg");
    Mat image3 = imread("image3.jpg");

    // 检测特征点和计算特征描述符
    vector<KeyPoint> keypoints1, keypoints2, keypoints3;
    Mat descriptors1, descriptors2, descriptors3;

    detectAndCompute(image1, keypoints1, descriptors1);
    detectAndCompute(image2, keypoints2, descriptors2);
    detectAndCompute(image3, keypoints3, descriptors3);

    // 匹配特征点
    vector<DMatch> matches12, matches23;
    matchFeatures(descriptors1, descriptors2, matches12);
    matchFeatures(descriptors2, descriptors3, matches23);

    // 寻找关键点
    vector<KeyPoint> keyPoints1Good12, keyPoints2Good12, keyPoints2Good23, keyPoints3Good23;
    findKeyPoints(keypoints1, keypoints2, matches12, keyPoints1Good12, keyPoints2Good12);
    findKeyPoints(keypoints2, keypoints3, matches23, keyPoints2Good23, keyPoints3Good23);

    // 估计变换矩阵
    Mat H12 = estimateTransformationMatrix(keyPoints1Good12, keyPoints2Good12);
    Mat H23 = estimateTransformationMatrix(keyPoints2Good23, keyPoints3Good23);

    // 拼接图像
    Mat result1 = stitchImages(image1, image2, H12);
    Mat result2 = stitchImages(result1, image3, H23);

    // 显示拼接后的图像
    namedWindow("Result", WINDOW_NORMAL);
    imshow("Result", result2);
    waitKey(0);

    return 0;
}