如何计算Lucas Kanade流量

Question

我目前正在开展一个对象跟踪项目,并使用过c ++,opencv.我成功地使用了Farneback密集光流来实现分割方法,例如k均值(使用每帧中的位移).现在我想用Lucas Kanade稀疏方法做同样的事情.但是这个函数的输出是:

nextPts - 2D点的输出向量(具有单精度浮点坐标),包含第二个图像中输入要素的计算新位置; 当传递OPTFLOW_USE_INITIAL_FLOW标志时,向量必须与输入中的大小相同.

(如官方网站所述)

我的问题是我将如何将结果以Mat流程为例.我到目前为止尝试过:

//实现Lucas Kanade算法

cvCalcOpticalFlowPyrLK(frame1_1C, frame2_1C, pyramid1, pyramid2,
frame1_features, frame2_features, number_of_features,
optical_flow_window, 5, optical_flow_found_feature,
optical_flow_feature_error, optical_flow_termination_criteria,
0);
 // Calculate each feature point's coordinates in every frame 
 CvPoint p,q;
 p.x = (int) frame1_features[i].x;
 p.y = (int) frame1_features[i].y;

 q.x = (int) frame2_features[i].x;
 q.y = (int) frame2_features[i].y;
// Creating the arrows for imshow

angle = atan2((double) p.y - q.y, (double) p.x - q.x);
hypotenuse = sqrt(square(p.y - q.y) + square(p.x - q.x));

/* Here we lengthen the arrow by a factor of three. */

q.x = (int) (p.x - 3 * hypotenuse * cos(angle));
q.y = (int) (p.y - 3 * hypotenuse * sin(angle));

cvLine(frame1, p, q, line_color, line_thickness, CV_AA, 0);

p.x = (int) (q.x + 9 * cos(angle + pi / 4));
p.y = (int) (q.y + 9 * sin(angle + pi / 4));
cvLine(frame1, p, q, line_color, line_thickness, CV_AA, 0);
p.x = (int) (q.x + 9 * cos(angle - pi / 4));
p.y = (int) (q.y + 9 * sin(angle - pi / 4));
cvLine(frame1, p, q, line_color, line_thickness, CV_AA, 0);

allocateOnDemand(&framenew, frame_size, IPL_DEPTH_8U, 3);
cvConvertImage(frame1, framenew, CV_CVTIMG_FLIP);

cvShowImage("Optical Flow", framenew);

这是光流演示.有什么想法我应该得到类似于Farneback光流结果的Mat流？

(http://docs.opencv.org/2.4/modules/video/doc/motion_analysis_and_object_tracking.html#calcopticalflowfarneback)

更新:非常好的答案.但现在我有显示kmeans图像的问题.使用farneback我使用:

cv::kmeans(m, K, bestLabels,
                TermCriteria( CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 10, 1.0),
                3, KMEANS_PP_CENTERS, centers);
        int colors[K];
        for (int i = 0; i < K; i++) {
            colors[i] = 255 / (i + 1);
        }

        namedWindow("Kmeans", WINDOW_NORMAL);

        Mat clustered = Mat(flow.rows, flow.cols, CV_32F);

        for (int i = 0; i < flow.cols * flow.rows; i++) {
            clustered.at<float>(i / flow.cols, i % flow.cols) =
                    (float) (colors[bestLabels.at<int>(0, i)]);
        }
        clustered.convertTo(clustered, CV_8U);
        imshow("Kmeans", clustered);

有任何想法吗 ？？

Answer 1

要获得类似Farneback算法的图像,您必须首先了解输出是什么.

在OpenCV文档中,您有:

prev(y,x) ~ next(y + flow(y,x)[1], x +flow(y,x)[0])

因此,它是一个图像1和2之间位移的矩阵.假设你没有计算的点将没有移动0,0; 你可以模拟这个,你只需要为每个(x,y)具有新位置的点放置(x', y'):

cv::Mat LKFlowMatrix(img.rows, img.cols, CV_32FC2, cv::Scalar(0,0));
LKFlowMatrix.at<cv::Vec2f>(y,x) = cv::Vec2f(x-x', y-y') ;

另外,不要忘记使用status = 0过滤"未找到的点"

通过The Way,你的函数不是它的opencv c ++版本:

cvCalcOpticalFlowPyrLK应该cv::calcOpticalFlowFarneback在c ++中 cvShowImage应该是cv::imshowc ++等等

**更新**

既然你需要的是kmeans的输入(我想这是OpenCV版本),并且你只想使用稀疏点,那么你可以这样做:

cv::Mat prevImg, nextImg;
// load your images

std::vector<cv:Point2f> initial_points, new_points;
// fill the initial points vector 

std::vector<uchar> status;
std::vector<float> error;

cv::calcOpticalFlowPyrLK(prevImage, nextImage, initial_points, new_points, status, errors);

std::vector<cv::Vec2f> vectorForKMeans;
for(size_t t = 0; t < status.size(); t++){
  if(status[t] != 0)
    vectorForKmeans.push_back(cv::Vec2f(initial_points[i] - new_points[i]));
}

// Do kmeans to vectorForKMeans