我一直在尝试在cv :: HoughLinesP（）的输出上运行kmeans。我的问题来自将HoughLinesP（），opencv_lines的输出转换为矩阵，Vector<Vec4i>是HoughLinesP(image_after_canny, opencv_lines, 2, 0.05*CV_PI/180, lower_hough_prob_min_no_of_intersections_trackbar + hough_prob_min_no_of_intersections_trackbar, lower_hough_prob_min_no_of_points_trackbar + hough_prob_min_no_of_points_trackbar, lower_hough_prob_max_gap_bw_points_trackbar + hough_prob_max_gap_bw_points_trackbar);
//opencv_lines is an Nx4 opencv[0] = [x1, y1, x2, y2]
//Spatial Clustering on line endpoints
//For each frame the endpoints should not change much relative to location
//sanity checking
// Mat samples = Mat(opencv_lines);
// samples.convertTo(samples, CV_32F);
// cout << samples.dims << endl;
// cout << samples.depth() << endl;
// cout << samples.type() << endl;
//Convert the opencv_lines into a matrix for kmeas
//There must be an better way
Mat samples = Mat(opencv_lines.size(), 4, CV_32FC1); //create a Nx4 matrix oftype 32F
for(int r = 0; r<opencv_lines.size(); r++){
Vec4i l_cur = opencv_lines[r];
for(int c = 0; c<4; c ++){
samples.at<float>(r,c) = opencv_lines[r][c];
}
}
cout << "Running Kmeans" << endl;
cv::kmeans(opencv_lines,K,labels, TermCriteria( CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, max_iteration, epsilone), attempts, cv::KMEANS_PP_CENTERS, centers);
的向量。这听起来非常简单，我敢打赌，但是，我是opencv和数据结构的新手，并尝试过很多东西。这是我正在尝试实现的代码。
Abort trap: 6
我收到的错误是：
...
检测线
OpenCV错误：kmeans中的断言失败（data0.dims＆lt; = 2＆amp;＆amp; type == CV_32F＆amp;＆amp; K＆gt; 0），文件/Development/OpenCV/modules/core/src/kmeans.cpp，line 230
libc ++ abi.dylib：以类型cv :: devception：/Development/OpenCV/modules/core/src/kmeans.cpp:230：error： ＆安培; type == CV_32F＆amp;＆amp; K>函数kmeans中的0
memecopy(samples.data, opencv_lines.data()*sizeof(float));我已尝试使用 Mat samples = Mat(opencv_lines.size(), 4, CV_32F); //create a Nx4 matrix oftype 32F
for(int r = 0; r<opencv_lines.size(); r++){
Vec4i l_cur = opencv_lines[r];
for(int c = 0; c<4; c ++){
samples.at<float>(r,c) = opencv_lines[r][c];
cout << opencv_lines[r][c] << " " ;
}
cout << ""<< endl;
}
samples=samples.reshape(1,samples.rows * 2);
samples.convertTo(samples, CV_32F);
cout << "M = "<< endl << " " << samples << endl << endl;
cout << "Running Kmeans" << endl;
cv::kmeans(opencv_lines,K,labels, TermCriteria( CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, max_iteration, epsilon), attempts, cv::KMEANS_PP_CENTERS, centers);
和其他各种循环结构执行此操作的多种方法。
我甚至切换到python实现并遇到了同样的问题！所以我正式卡住了。
PS我知道霍夫的输出工作正常且有线条。 更新： 我添加了一些print语句，并且一直在使用reshape（）函数，我认为我不满足data0.dims＆lt; = 2。
以下是新修改：
Fetching Video
Initializing Parameter
Begin Playback
Frame
Generating Skeleton Image
Detecting Lines
335 471 335 112
337 443 338 105
339 443 339 103
355 68 382 478
291 479 313 162
356 66 383 478
436 2 533 223
293 479 316 152
440 3 523 195
416 0 500 427
359 63 383 474
416 4 499 425
437 0 534 223
343 479 376 153
427 0 525 305
427 1 579 479
344 478 378 153
M =
[335, 471;
335, 112;
337, 443;
338, 105;
339, 443;
339, 103;
355, 68;
382, 478;
291, 479;
313, 162;
356, 66;
383, 478;
436, 2;
533, 223;
293, 479;
316, 152;
440, 3;
523, 195;
416, 0;
500, 427;
359, 63;
383, 474;
416, 4;
499, 425;
437, 0;
534, 223;
343, 479;
376, 153;
427, 0;
525, 305;
427, 1;
579, 479;
344, 478;
378, 153]
Running Kmeans
OpenCV Error: Assertion failed (data0.dims <= 2 && type == CV_32F && K > 0) in kmeans, file /Development/OpenCV/modules/core/src/kmeans.cpp, line 230
libc++abi.dylib: terminating with uncaught exception of type cv::Exception: Development/OpenCV/modules/core/src/kmeans.cpp:230: error: (-215) data0.dims <= 2 && type == CV_32F && K > 0 in function kmeans
Abort trap: 6
然而同样的错误：
Mat points(count, 2, CV_32F);
Mat points(count, 1, CV_32FC2);
Mat points(1, count, CV_32FC2);
std::vector<cv::Point2f> points(sampleCount);
有人可以帮助澄清文档中描述的输入结构。
群集数据。需要具有浮点坐标的N维点阵列。此数组的示例可以是：
Carbon::setToStringFormat('d-m-Y');
$dataslots = Dataslot::lists('maand', 'id');
答案 0 :(得分：0)
我最终改为使用python实现并使用sci-kit在hough输出上运行dbscan。通过这种方式，我能够通过过滤器enter image description here将概率性霍夫输出聚类到不同的区域中。
