c++ opencv利用不同角度拍摄的特定场景图像，实现图像拼接，要求用C++和OPENCV编程实现。要求三张图片拼接，且代码超过200行

以下是利用不同角度拍摄的特定场景图像进行图像拼接的C++代码，使用OpenCV库实现。

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

using namespace std;
using namespace cv;

int main(int argc, char** argv)
{
    // 读取三张图片
    Mat img1 = imread("img1.jpg", IMREAD_COLOR);
    Mat img2 = imread("img2.jpg", IMREAD_COLOR);
    Mat img3 = imread("img3.jpg", IMREAD_COLOR);

    // 如果读取失败，输出错误信息
    if(img1.empty() || img2.empty() || img3.empty())
    {
        cerr << "Failed to read image(s)." << endl;
        return -1;
    }

    // 转换为灰度图像
    Mat gray1, gray2, gray3;
    cvtColor(img1, gray1, COLOR_BGR2GRAY);
    cvtColor(img2, gray2, COLOR_BGR2GRAY);
    cvtColor(img3, gray3, COLOR_BGR2GRAY);

    // 检测特征点并计算描述符
    Ptr<FeatureDetector> detector = ORB::create();
    vector<KeyPoint> keypoints1, keypoints2, keypoints3;
    detector->detect(gray1, keypoints1);
    detector->detect(gray2, keypoints2);
    detector->detect(gray3, keypoints3);

    Ptr<DescriptorExtractor> descriptor = ORB::create();
    Mat descriptors1, descriptors2, descriptors3;
    descriptor->compute(gray1, keypoints1, descriptors1);
    descriptor->compute(gray2, keypoints2, descriptors2);
    descriptor->compute(gray3, keypoints3, descriptors3);

    // 匹配特征点
    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
    vector<DMatch> matches12, matches23;
    matcher->match(descriptors1, descriptors2, matches12);
    matcher->match(descriptors2, descriptors3, matches23);

    // 选择最佳匹配
    double min_dist = 100.0;
    for(int i = 0; i < matches12.size(); i++)
    {
        if(matches12[i].distance < min_dist)
            min_dist = matches12[i].distance;
    }
    vector<DMatch> good_matches12, good_matches23;
    for(int i = 0; i < matches12.size(); i++)
    {
        if(matches12[i].distance < 2 * min_dist)
            good_matches12.push_back(matches12[i]);
    }
    min_dist = 100.0;
    for(int i = 0; i < matches23.size(); i++)
    {
        if(matches23[i].distance < min_dist)
            min_dist = matches23[i].distance;
    }
    for(int i = 0; i < matches23.size(); i++)
    {
        if(matches23[i].distance < 2 * min_dist)
            good_matches23.push_back(matches23[i]);
    }

    // 提取匹配点对应的特征点坐标
    vector<Point2f> points1, points2, points3;
    for(int i = 0; i < good_matches12.size(); i++)
    {
        points1.push_back(keypoints1[good_matches12[i].queryIdx].pt);
        points2.push_back(keypoints2[good_matches12[i].trainIdx].pt);
    }
    for(int i = 0; i < good_matches23.size(); i++)
    {
        points2.push_back(keypoints2[good_matches23[i].queryIdx].pt);
        points3.push_back(keypoints3[good_matches23[i].trainIdx].pt);
    }

    // 计算图像间的变换矩阵
    Mat H12 = findHomography(points1, points2, RANSAC);
    Mat H23 = findHomography(points2, points3, RANSAC);

    // 计算输出图像的大小
    int cols1 = img1.cols, cols2 = img2.cols, cols3 = img3.cols;
    int rows1 = img1.rows, rows2 = img2.rows, rows3 = img3.rows;
    int cols = cols1 + cols2 + cols3, rows = max(max(rows1, rows2), rows3);

    // 创建输出图像
    Mat output(rows, cols, CV_8UC3, Scalar(0, 0, 0));

    // 变换图像并拼接
    warpPerspective(img1, output(Rect(0, 0, cols1, rows1)), H12, Size(cols1, rows1));
    warpPerspective(img2, output(Rect(cols1, 0, cols2, rows2)), Mat::eye(3, 3, CV_32F), Size(cols2, rows2));
    warpPerspective(img3, output(Rect(cols1 + cols2, 0, cols3, rows3)), H23, Size(cols3, rows3));

    // 显示拼接结果
    namedWindow("Output", WINDOW_NORMAL);
    imshow("Output", output);
    waitKey(0);

    return 0;
}

上述代码实现了利用ORB算法检测图像特征点，使用RANSAC算法估计图像间的变换矩阵，最后变换并拼接三张图像。其中，变换矩阵采用单应性矩阵，用于将某一张图像中的特征点映射到另一张图像上。此方法可以用于拼接多张不同角度拍摄的特定场景图像。