OpenCV与AI深度学习 | OpenCV常用图像拼接方法(三)：基于特征匹配拼接

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原文链接：OpenCV常用图像拼接方法(三)：基于特征匹配拼接

    OpenCV常用图像拼接方法将分为四个部分与大家共享，这里是第三种方法，欢迎关注后续。

    OpenCV的常用图像拼接方法（三）：基于特征匹配的图像拼接，本次介绍SIFT特征匹配拼接方法，OpenCV版本为4.4.0。特点和适用范围：图像有足够重合相同特征区域，且待拼接图像之间无明显尺度变换和畸变。

优点：适应部分倾斜变化情况。缺点：需要有足够的相同特征区域进行匹配，速度较慢，拼接较大图片容易崩溃。

    如下是待拼接的两张图片：

    特征匹配图

    拼接结果图：

    拼接缝处理后(拼接处过渡更自然)：

    核心代码：

/********************直接图像拼接函数*************************/
bool ImageOverlap0(Mat &img1, Mat &img2) {
    Mat g1(img1, Rect(0, 0, img1.cols, img1.rows));  // init roi
    Mat g2(img2, Rect(0, 0, img2.cols, img2.rows));
    cvtColor(g1, g1, COLOR_BGR2GRAY);
    cvtColor(g2, g2, COLOR_BGR2GRAY);
    vector<cv::KeyPoint> keypoints_roi, keypoints_img;  /* keypoints found using SIFT */
    Mat descriptor_roi, descriptor_img;                           /* Descriptors for SIFT */
    FlannBasedMatcher matcher;                                   /* FLANN based matcher to match keypoints */
    vector<cv::DMatch> matches, good_matches;
    cv::Ptr<cv::SIFT> sift = cv::SIFT::create();
    int i, dist = 80;
    sift->detectAndCompute(g1, cv::Mat(), keypoints_roi, descriptor_roi);      /* get keypoints of ROI image */
    sift->detectAndCompute(g2, cv::Mat(), keypoints_img, descriptor_img);      /* get keypoints of the image */
    matcher.match(descriptor_roi, descriptor_img, matches);  //实现描述符之间的匹配
    double max_dist = 0;
    double min_dist = 5000;
    //-- Quick calculation of max and min distances between keypoints
    for (int i = 0; i < descriptor_roi.rows; i++) {
        double dist = matches[i].distance;
        if (dist < min_dist) min_dist = dist;
        if (dist > max_dist) max_dist = dist;
    }
    // 特征点筛选
    for (i = 0; i < descriptor_roi.rows; i++) {
        if (matches[i].distance < 3 * min_dist) {
            good_matches.push_back(matches[i]);
        }
    }
    printf("%ld no. of matched keypoints in right image\n", good_matches.size());
    /* Draw matched keypoints */
    Mat img_matches;
    //绘制匹配
    drawMatches(img1, keypoints_roi, img2, keypoints_img, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
    imshow("matches", img_matches);
    vector<Point2f> keypoints1, keypoints2;
    for (i = 0; i < good_matches.size(); i++) {
        keypoints1.push_back(keypoints_img[good_matches[i].trainIdx].pt);
        keypoints2.push_back(keypoints_roi[good_matches[i].queryIdx].pt);
    }
    //计算单应矩阵(仿射变换矩阵)
    Mat H = findHomography(keypoints1, keypoints2, RANSAC);
    Mat H2 = findHomography(keypoints2, keypoints1, RANSAC);

    Mat stitchedImage;
    //定义仿射变换后的图像(也是拼接结果图像)
    Mat stitchedImage2;
    //定义仿射变换后的图像(也是拼接结果图像)
    int mRows = img2.rows;
    if (img1.rows > img2.rows) {
        mRows = img1.rows;
    }
    int count = 0;
    for (int i = 0; i < keypoints2.size(); i++) {
        if (keypoints2[i].x >= img2.cols / 2)
            count++;
    }
    //判断匹配点位置来决定图片是左还是右
    if (count / float(keypoints2.size()) >= 0.5) {  //待拼接img2图像在右边
        cout << "img1 should be left" << endl;
        vector<Point2f>corners(4);
        vector<Point2f>corners2(4);
        corners[0] = Point(0, 0);
        corners[1] = Point(0, img2.rows);
        corners[2] = Point(img2.cols, img2.rows);
        corners[3] = Point(img2.cols, 0);
        stitchedImage = Mat::zeros(img2.cols + img1.cols, mRows, CV_8UC3);
        warpPerspective(img2, stitchedImage, H, Size(img2.cols + img1.cols, mRows));
        perspectiveTransform(corners, corners2, H);
        /* 
        circle(stitchedImage, corners2[0], 5, Scalar(0, 255, 0), 2, 8);
        circle(stitchedImage, corners2[1], 5, Scalar(0, 255, 255), 2, 8);
        circle(stitchedImage, corners2[2], 5, Scalar(0, 255, 0), 2, 8);
        circle(stitchedImage, corners2[3], 5, Scalar(0, 255, 0), 2, 8);
        */
        cout << corners2[0].x << ", " << corners2[0].y << endl;
        cout << corners2[1].x << ", " << corners2[1].y << endl;
        imshow("temp", stitchedImage);
        //imwrite("temp.jpg", stitchedImage);
        Mat half(stitchedImage, Rect(0, 0, img1.cols, img1.rows));
        img1.copyTo(half);
        imshow("result", stitchedImage);
    } else {  //待拼接图像img2在左边
        cout << "img2 should be left" << endl;
        stitchedImage = Mat::zeros(img2.cols + img1.cols, mRows, CV_8UC3);
        warpPerspective(img1, stitchedImage, H2, Size(img1.cols + img2.cols, mRows));
        imshow("temp", stitchedImage);
        //计算仿射变换后的四个端点
        vector<Point2f>corners(4);
        vector<Point2f>corners2(4);
        corners[0] = Point(0, 0);
        corners[1] = Point(0, img1.rows);
        corners[2] = Point(img1.cols, img1.rows);
        corners[3] = Point(img1.cols, 0);
        perspectiveTransform(corners, corners2, H2);
        //仿射变换对应端点
        /*
        circle(stitchedImage, corners2[0], 5, Scalar(0, 255, 0), 2, 8);
        circle(stitchedImage, corners2[1], 5, Scalar(0, 255, 255), 2, 8);
        circle(stitchedImage, corners2[2], 5, Scalar(0, 255, 0), 2, 8);
        circle(stitchedImage, corners2[3], 5, Scalar(0, 255, 0), 2, 8);
        */
        cout << corners2[0].x << ", " << corners2[0].y << endl;
        cout << corners2[1].x << ", " << corners2[1].y << endl;
        Mat half(stitchedImage, Rect(0, 0, img2.cols, img2.rows));
        img2.copyTo(half);
        imshow("result", stitchedImage);
    }
    imwrite("result.bmp", stitchedImage);
    return true;
}

THE END !

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