正方形检测找不到正方形

Posted 2023-02-16

【中文标题】正方形检测找不到正方形

【英文标题】：Square detection doesn't find squares

【发布时间】：2011-12-05 14:44:52

【问题描述】：

我正在使用 OpenCV 库示例中提供的程序 squares.c。它适用于每张图片，但我真的无法弄清楚为什么它无法识别该图片中绘制的正方形

http://desmond.imageshack.us/Himg12/scaled.php?server=12&filename=26725680.jpg&res=medium

康力之后：

扩张之后：

结果图片（红色） http://img267.imageshack.us/img267/8016/resultuq.jpg

如您所见，未检测到正方形。

检测后我需要提取正方形中包含的区域...没有ROI怎么可能？

【问题讨论】：

您的图片似乎丢失了。您是否有机会重新上传或重新创建它们？

【参考方案1】：

我建议您在这张图片中的正方形太薄了。 squares.c 中的第一步是在传递给 Canny 边缘检测器之前缩小和备份图像以减少噪声。

缩放与 5x5 内核卷积，因此在您的情况下，这可能会导致在如此薄的边缘丢失任何梯度。

如果要将正方形的边缘覆盖在连续背景上，请尝试使它们的边缘至少为 5 像素。

【讨论】：

可惜正方形已经画好了，我只需要提取出来

【参考方案2】：

下面的源代码展示了 Square Detector 程序的一个小变种。它并不完美，但它说明了解决问题的一种方法。

您可以区分此代码与原始代码并检查所做的所有更改，但主要是：

将阈值级别的数量减少到 2。

在findSquares()的开头，扩张图像以检测细白方块，然后模糊整个图像，这样算法就不会将海洋和天空检测为单独的正方形。

编译后，使用以下语法运行应用程序：./app <image>

// The "Square Detector" program.
// It loads several images sequentially and tries to find squares in
// each image

#include "highgui.h"
#include "cv.h"

#include <iostream>
#include <math.h>
#include <string.h>

using namespace cv;
using namespace std;

void help()

        cout <<
        "\nA program using pyramid scaling, Canny, contours, contour simpification and\n"
        "memory storage (it's got it all folks) to find\n"
        "squares in a list of images pic1-6.png\n"
        "Returns sequence of squares detected on the image.\n"
        "the sequence is stored in the specified memory storage\n"
        "Call:\n"
        "./squares\n"
    "Using OpenCV version %s\n" << CV_VERSION << "\n" << endl;



int thresh = 50, N = 2; // karlphillip: decreased N to 2, was 11.
const char* wndname = "Square Detection Demo";

// helper function:
// finds a cosine of angle between vectors
// from pt0->pt1 and from pt0->pt2
double angle( Point pt1, Point pt2, 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);


// returns sequence of squares detected on the image.
// the sequence is stored in the specified memory storage
void findSquares( const Mat& image, vector<vector<Point> >& squares )

    squares.clear();

    Mat pyr, timg, gray0(image.size(), CV_8U), gray;

    // karlphillip: dilate the image so this technique can detect the white square,
    Mat out(image);
    dilate(out, out, Mat(), Point(-1,-1));
    // then blur it so that the ocean/sea become one big segment to avoid detecting them as 2 big squares.
    medianBlur(out, out, 7);

    // down-scale and upscale the image to filter out the noise
    pyrDown(out, pyr, Size(out.cols/2, out.rows/2));
    pyrUp(pyr, timg, out.size());
    vector<vector<Point> > contours;

    // find squares in every color plane of the image
    for( int c = 0; c < 3; c++ )
    
        int ch[] = c, 0;
        mixChannels(&timg, 1, &gray0, 1, ch, 1);

        // try several threshold levels
        for( int l = 0; l < N; l++ )
        
            // hack: use Canny instead of zero threshold level.
            // Canny helps to catch squares with gradient shading
            if( l == 0 )
            
                // apply Canny. Take the upper threshold from slider
                // and set the lower to 0 (which forces edges merging)
                Canny(gray0, gray, 0, thresh, 5);
                // dilate canny output to remove potential
                // holes between edge segments
                dilate(gray, gray, Mat(), Point(-1,-1));
            
            else
            
                // apply threshold if l!=0:
                //     tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
                gray = gray0 >= (l+1)*255/N;
            

            // find contours and store them all as a list
            findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);

            vector<Point> approx;

            // test each contour
            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);

                // square contours should have 4 vertices after approximation
                // relatively large area (to filter out noisy contours)
                // and be convex.
                // 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++ )
                    
                        // find the maximum cosine of the angle between joint edges
                        double cosine = fabs(angle(approx[j%4], approx[j-2], approx[j-1]));
                        maxCosine = MAX(maxCosine, cosine);
                    

                    // if cosines of all angles are small
                    // (all angles are ~90 degree) then write quandrange
                    // vertices to resultant sequence
                    if( maxCosine < 0.3 )
                        squares.push_back(approx);
                
            
        
    



// the function draws all the squares in the image
void drawSquares( Mat& image, const vector<vector<Point> >& squares )

    for( size_t i = 0; i < squares.size(); i++ )
    
        const Point* p = &squares[i][0];
        int n = (int)squares[i].size();
        polylines(image, &p, &n, 1, true, Scalar(0,255,0), 3, CV_AA);
    

    imshow(wndname, image);



int main(int argc, char** argv)

    if (argc < 2)
    
        cout << "Usage: ./program <file>" << endl;
        return -1;
    

//    static const char* names[] =  "pic1.png", "pic2.png", "pic3.png",
//        "pic4.png", "pic5.png", "pic6.png", 0 ;
    static const char* names[] =  argv[1], 0 ;

    help();
    namedWindow( wndname, 1 );
    vector<vector<Point> > squares;

    for( int i = 0; names[i] != 0; i++ )
    
        Mat image = imread(names[i], 1);
        if( image.empty() )
        
            cout << "Couldn't load " << names[i] << endl;
            continue;
        

        findSquares(image, squares);
        drawSquares(image, squares);
        imwrite("out.jpg", image);

        int c = waitKey();
        if( (char)c == 27 )
            break;
    

    return 0;

输出：

【讨论】：

谢谢您 karlphillip...您的更正现在脚本工作正常。但是如果我想提取一个只包含正方形区域的子图像呢？可能吗？ （在这种情况下，我们没有 ROI，只有一个正方形序列）

是的，您需要从一组 4 个 cv::Point 中创建一个 cv::Mat。因为 *** 不是聊天，所以让我们为每个线程保留一个问题。如果您有更多问题，请随时在新主题中提问。

但是为了说明这个例子的过程，由于应用程序有一个正方形向量，你应该这样做：for (size_t x = 0; x < squares.size(); x++) Rect roi(squares[x][0].x, squares[x][0].y, squares[x][1].x - squares[x][0].x, squares[x][3].y - squares[x][0].y); Mat subimage(image, roi); ，它会生成一个新的 Mat为原始图像中检测到的所有正方形调用subimage。

记住：图像中检测到的点可能不代表一个完美的正方形（如上图所示），但我刚刚向您建议的代码 假设他们这样做。

卡尔，谢谢你的好意。正如你所建议的，我创建了一个新线程：***.com/questions/7755647/…