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我要建立一个由朝下的相机覆盖的地面全景图像(在固定高度,离地面约 1 米)。这可能会运行到数千帧,因此 Stitcher 类的内置panorama方法并不真正适合 - 它太慢而且内存占用很大。

相反,我假设地板和运动是平面的(这里不是不合理的),并试图在我看到每一帧时建立一个累积的单应性。也就是说,对于每一帧,我计算从前一帧到新一帧的单应性。然后,我通过将其与所有先前单应性的乘积相乘来获得累积单应性。

假设我H01在第 0 帧和第 1 帧之间,然后H12在第 1 帧和第 2 帧之间。为了将第 2 帧放置到马赛克上,我需要得到H01*H12. 随着帧数的增加,这种情况继续下去,这样我就得到了H01*H12*H23*H34*H45*....

在代码中,这类似于:

cv::Mat previous, current;

// Init cumulative homography
cv::Mat cumulative_homography = cv::Mat::eye(3);

video_stream >> previous;
for(;;) {

        video_stream >> current;
        // Here I do some checking of the frame, etc

        // Get the homography using my DenseMosaic class (using Farneback to get OF)
        cv::Mat tmp_H = DenseMosaic::get_homography(previous,current);

        // Now normalise the homography by its bottom right corner
        tmp_H /= tmp_H.at<double>(2, 2);

        cumulative_homography *= tmp_H;

        previous = current.clone( );
}

它工作得很好,除了随着相机在视点中“向上”移动,单应性比例减小。随着它向下移动,比例再次增加。这给了我的全景图一种我真的不想要的透视效果。

例如,这是在向前移动几秒钟的视频上拍摄的,然后向后移动。第一帧看起来不错: 框架 2 植入到框架 1

当我们向前推进几帧时,问题就来了: 单应性按比例缩小,导致帧在全景图上变小

然后当我们再次返回时,您可以看到框架再次变大: 在此处输入图像描述

我不知道这是从哪里来的。

我正在使用 Farneback 密集光流来计算像素-像素对应关系,如下所示(稀疏特征匹配在此数据上效果不佳)并且我检查了我的流向量 - 它们通常非常好,所以它不是跟踪问题。我还尝试切换输入的顺序以找到单应性(以防我混淆了帧号),但仍然没有更好。

cv::calcOpticalFlowFarneback(grey_1, grey_2, flow_mat, 0.5, 6,50, 5, 7, 1.5, flags);

// Using the flow_mat optical flow map, populate grid point correspondences between images
std::vector<cv::Point2f> points_1, points_2;
median_motion = DenseMosaic::dense_flow_to_corresp(flow_mat, points_1, points_2);
cv::Mat H = cv::findHomography(cv::Mat(points_2), cv::Mat(points_1), CV_RANSAC, 1);

我认为可能的另一件事是我在转换中包含的翻译,以确保我的全景在场景中居中:

cv::warpPerspective(init.clone(), warped, translation*homography, init.size());

但是在应用翻译之前检查了单应性中的值,我提到的缩放问题仍然存在。

任何提示都将不胜感激。我可以输入很多代码,但似乎无关紧要,如果缺少某些内容,请告诉我

更新 我尝试将*=运算符切换为完全乘法并尝试反转单应性相乘的顺序,但没有运气。下面是我计算单应性的代码:

/**
\brief Calculates the homography between the current and previous frames


*/
cv::Mat DenseMosaic::get_homography()
{

    cv::Mat grey_1, grey_2; // Grayscale versions of frames


    cv::cvtColor(prev, grey_1, CV_BGR2GRAY);
    cv::cvtColor(cur, grey_2, CV_BGR2GRAY);

    // Calculate the dense flow
    int flags = cv::OPTFLOW_FARNEBACK_GAUSSIAN;
    if (frame_number > 2) {
        flags = flags | cv::OPTFLOW_USE_INITIAL_FLOW;
    }
    cv::calcOpticalFlowFarneback(grey_1, grey_2, flow_mat, 0.5, 6,50, 5, 7, 1.5, flags);

    // Convert the flow map to point correspondences
    std::vector<cv::Point2f> points_1, points_2;
    median_motion = DenseMosaic::dense_flow_to_corresp(flow_mat, points_1, points_2);

    // Use the correspondences to get the homography
    cv::Mat H = cv::findHomography(cv::Mat(points_2), cv::Mat(points_1), CV_RANSAC, 1);

    return H;
}

这是我用来从流程图中查找对应关系的函数:

/**
\brief Calculate pixel->pixel correspondences given a map of the optical flow across the image
\param[in]  flow_mat Map of the optical flow across the image
\param[out] points_1 The set of points from #cur
\param[out] points_2 The set of points from #prev
\param[in]  step_size The size of spaces between the grid lines
\return The median motion as a point

Uses a dense flow map (such as that created by cv::calcOpticalFlowFarneback) to obtain a set of point correspondences across a grid.
*/
cv::Point2f DenseMosaic::dense_flow_to_corresp(const cv::Mat &flow_mat, std::vector<cv::Point2f> &points_1, std::vector<cv::Point2f> &points_2, int step_size)
{

    std::vector<double> tx, ty;
    for (int y = 0; y < flow_mat.rows; y += step_size) {
        for (int x = 0; x < flow_mat.cols; x += step_size) {
            /* Flow is basically the delta between left and right points */
            cv::Point2f flow = flow_mat.at<cv::Point2f>(y, x);
            tx.push_back(flow.x);
            ty.push_back(flow.y);


            /*  There's no need to calculate for every single point,
            if there's not much change, just ignore it
            */
            if (fabs(flow.x) < 0.1 && fabs(flow.y) < 0.1)
                continue;

            points_1.push_back(cv::Point2f(x, y));
            points_2.push_back(cv::Point2f(x + flow.x, y + flow.y));
        }
    }

    // I know this should be median, not mean, but it's only used for plotting the 
    // general motion direction so it's unimportant.
    cv::Point2f t_median;
    cv::Scalar mtx = cv::mean(tx);
    t_median.x = mtx[0];
    cv::Scalar mty = cv::mean(ty);
    t_median.y = mty[0];

    return t_median;
}
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1 回答 1

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事实证明,这是因为我的观点接近特征,这意味着跟踪特征的非平面性导致了单应性的偏斜。我设法通过使用estimateRigidTransform代替来防止这种情况(它更像是一种黑客而不是一种方法......) findHomography,因为这不会估计透视变化。

在这种特殊情况下,这样做是有意义的,因为视图只会经历严格的转换。

于 2014-07-30T15:37:15.437 回答