opencv检测虚线 [英] opencv detect dotted lines

问题描述

我有一张由虚线组成的图像:

I have an image which consists of somehow dotted lines:

注意:打开图像，将其包裹起来并查看所有小点

如何使用openCV检测和参数化这些行?

此图像是机器人上激光测距仪的值，我需要尽可能获得所有线条.

This image are the values of a laser range scanner on a robot and I need to get all the lines as good a possible.

HoughLinesP功能是否应该对此理想?

The HoughLinesP function should be ideal for this?

我尝试了以下代码:

    //converts laser range readings to a binary image.
    cv::Mat binaryImg = laserRangesToBinaryImage();

    cv::Mat cdst;
    cvtColor(binaryImg, cdst, CV_GRAY2BGR);

    std::vector<cv::Vec4i> lines;
    HoughLinesP(binaryImg, lines, 2, 5.0*CV_PI/180.0, 1, 2, 20 );
    for( size_t i = 0; i < lines.size(); i++ )
    {
        cv::Vec4i l = lines[i];
        line( cdst, cv::Point(l[0], l[1]), cv::Point(l[2], l[3]), cv::Scalar(0,0,255), 3, CV_AA);
    }

    cv::imshow("Hough output", cdst);

大约产生50至60行(在Ubuntu 14.04上使用openCV 2.8.3):

which results in about 50 to 60 lines (using openCV 2.8.3 on Ubuntu 14.04):

此处最大的问题是，存在多个分隔的线段，应在其中检测整条线.因此，这些网段未正确连接.有些行太短甚至没有被检测到.

The biggest problem here is that there are multiple separated line segments where a full line should be detected. So the segments aren't correctly connected. Some of the lines are too short or not even detected.

最佳结果应如下所示(手动创建)，其中包含约20个线段:

The optimal result should look like this (manually created) with about 20 line segments:

如何获得此结果?

推荐答案

如果您还没有，请查看此

If you didn't already, have a look at this tutorial.

基本上，您应该对这三个参数(

Basically, you should act on these three parameters (the last three parameters of the HoughLinesP function), until you reach the right length for the detected lines:

1. 阈值:的最小交集(在霍夫空间)检测"一行.
2. minLinLength :可以形成一条线的最小点数. 少于此点数的线将被忽略.
3. maxLineGap::要考虑的两点之间的最大差距 同一行.

1. threshold: The minimum number of intersections (in the Hough space) to "detect" a line.
2. minLinLength: The minimum number of points that can form a line. Lines with less than this number of points are disregarded.
3. maxLineGap: The maximum gap between two points to be considered in the same line.

数学形态学(结束)也可能会有所帮助，正如在评论.但是，帮助将是有限的:建议将小型3x3内核用于此类任务，因此，如果一行上的某些像素彼此之间相距太远，则无论如何都不会填满间隙.

Mathematical morphology (closing) could help too, as it was mentionned in the comments. However, the help will be limited: a small 3x3 kernel is recommended for such a task, so if some pixels on a line are too far from one another the gap won't be filled up anyway.

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