OpenCV线路检测 [英] OpenCV Line Detection

问题描述

我想在这张图片中找到中心框的边缘：

I am trying to find the edges of the centered box in this image:

我已经尝试过使用dough的dRho = img_width / 1000，dTheta = pi / 180和阈值= 250
它在这个图像上很好用，缩放到1/3的大小，但在全尺寸的图像，它只是得到线无处不在每个方向...

I have tried using a HoughLines using dRho=img_width/1000, dTheta=pi/180, and threshold=250 It works great on this image, scaled to 1/3 of the size, but on the full size image it just gets lines everywhere in every direction...

我可以做什么来调整这个更准确？

What can I do to tune this to be more accurate?

推荐答案

实现下面结果的代码是对此答案中提供的代码稍作修改：如何检测方形：

The code to achieve the result below is a slight modification of the one presented in this answer: how to detect a square:

原始程序可以在OpenCV中找到，它被称为 squares.cpp 。下面的代码被修改为仅在第一个颜色平面中搜索方块，但由于它仍然检测到许多方块，在程序结束时，我丢弃所有的方块，除了第一个，然后调用 draw_squares（） 以显示检测到的内容。

The original program can be found inside OpenCV, it's called squares.cpp. The code below was modified to search squares only in the first color plane, but as it still detects many squares, at the end of the program I discard all of them except the first, and then call draw_squares() to show what was detected. You can change this easilly to draw all of them and see everything that was detected.

您可以从现在开始做各种各样的事情，包括设置（ROI）区域感兴趣的是提取正方形内部的区域（忽略其周围的所有其他区域）。

You can do all sorts of thing from now own, including setting a (ROI) region of interest to extract the area that's inside the square (ignore everything else around it).

您可以看到检测到的矩形与图像中的线不完全对齐。您应该在图像中执行一些预处理（蚀刻？）操作，以减少线条的粗细并改善检测。但从这里开始，就在你身上：

You can see that the detected rectangle is not perfectly aligned with the lines in the image. You should perform some pre-processing (erode?) operations in the image to decrease the thickness of lines and improve the detection. But from here on it's all on you:

#include <cv.h>
#include <highgui.h>

using namespace cv;

double angle( cv::Point pt1, cv::Point pt2, cv::Point pt0 ) {
    double dx1 = pt1.x - pt0.x;
    double dy1 = pt1.y - pt0.y;
    double dx2 = pt2.x - pt0.x;
    double dy2 = pt2.y - pt0.y;
    return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}

void find_squares(Mat& image, vector<vector<Point> >& squares)
{
    // TODO: pre-processing

    // blur will enhance edge detection
    Mat blurred(image);
    medianBlur(image, blurred, 9);

    Mat gray0(blurred.size(), CV_8U), gray;
    vector<vector<Point> > contours;

    // find squares in the first color plane.
    for (int c = 0; c < 1; c++)
    {
        int ch[] = {c, 0};
        mixChannels(&blurred, 1, &gray0, 1, ch, 1);

        // try several threshold levels
        const int threshold_level = 2;
        for (int l = 0; l < threshold_level; l++)
        {
            // Use Canny instead of zero threshold level!
            // Canny helps to catch squares with gradient shading
            if (l == 0)
            {
                Canny(gray0, gray, 10, 20, 3); // 

                // Dilate helps to remove potential holes between edge segments
                dilate(gray, gray, Mat(), Point(-1,-1));
            }
            else
            {
                    gray = gray0 >= (l+1) * 255 / threshold_level;
            }

            // Find contours and store them in a list
            findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);

            // Test contours
            vector<Point> approx;
            for (size_t i = 0; i < contours.size(); i++)
            {
                    // approximate contour with accuracy proportional
                    // to the contour perimeter
                    approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);

                    // Note: absolute value of an area is used because
                    // area may be positive or negative - in accordance with the
                    // contour orientation
                    if (approx.size() == 4 &&
                            fabs(contourArea(Mat(approx))) > 1000 &&
                            isContourConvex(Mat(approx)))
                    {
                            double maxCosine = 0;

                            for (int j = 2; j < 5; j++)
                            {
                                    double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
                                    maxCosine = MAX(maxCosine, cosine);
                            }

                            if (maxCosine < 0.3)
                                    squares.push_back(approx);
                    }
            }
        }
    }
}

void draw_squares(Mat& img, vector<vector<Point> > squares)
{
    for (int i = 0; i < squares.size(); i++)
    {
        for (int j = 0; j < squares[i].size(); j++)
        {
            cv::line(img, squares[i][j], squares[i][(j+1) % 4], cv::Scalar(0, 255, 0), 1, CV_AA);
        }
    }
}


int main(int argc, char* argv[])
{
    Mat img = imread(argv[1]);

    vector<vector<Point> > squares;
    find_squares(img, squares);

    std::cout << "* " << squares.size() << " squares were found." << std::endl;

    // Ignore all the detected squares and draw just the first found
    vector<vector<Point> > tmp;
    if (squares.size() > 0)
    {
        tmp.push_back(squares[0]);
        draw_squares(img, tmp);
    }
    //imshow("squares", img);
    //cvWaitKey(0);

    imwrite("out.png", img);

    return 0;
}

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