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程序问答发布时间：2022-06-02 发布网站：大佬教程 code.js-code.com

大佬教程收集整理的这篇文章主要介绍了OpenCV C ++ / Obj-C：检测一张纸/正方形检测，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。

如何解决OpenCV C ++ / Obj-C：检测一张纸/正方形检测？

开发过程中遇到OpenCV C ++ / Obj-C：检测一张纸/正方形检测的问题如何解决？下面主要结合日常开发的经验，给出你关于OpenCV C ++ / Obj-C：检测一张纸/正方形检测的解决方法建议，希望对你解决OpenCV C ++ / Obj-C：检测一张纸/正方形检测有所启发或帮助；

这是反复出现的主题，由于我找不到相关的实现，因此决定接受挑战。

我对OpenCV中存在的squares演示进行了一些修改，下面生成的C ++代码能够检测图像中的纸：

voID find_squares(Mat& image, vector<vector<Point> >& squares)
{
    // 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 every color plane of the image
    for (int c = 0; c < 3; 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);
                    }
            }
        }
    }
}

执行此过程后，纸页将成为以下位置中最大的正方形vector >

解决方法

我在测试应用程序中成功实现了OpenCV平方检测示例，但是现在需要过滤输出，因为它很乱-还是我的代码错误？

我对减少偏斜（如那样）和进一步处理的四个角落很感兴趣……

码：

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);
}

- (std::vector<std::vector<cv::Point> >)findSquaresInImage:(cv::Mat)_image
{
    std::vector<std::vector<cv::Point> > squares;
    cv::Mat pyr,timg,gray0(_image.size(),CV_8U),gray;
    int thresh = 50,N = 11;
    cv::pyrDown(_image,pyr,cv::Size(_image.cols/2,_image.rows/2));
    cv::pyrUp(pyr,_image.size());
    std::vector<std::vector<cv::Point> > contours;
    for( int c = 0; c < 3; c++ ) {
        int ch[] = {C,0};
        mixChAnnels(&timg,1,&gray0,ch,1);
        for( int l = 0; l < N; l++ ) {
            if( l == 0 ) {
                cv::CAnny(gray0,gray,thresh,5);
                cv::dilate(gray,cv::Mat(),cv::Point(-1,-1));
            }
            else {
                gray = gray0 >= (l+1)*255/N;
            }
            cv::findContours(gray,contours,CV_RETR_LIST,CV_CHAIN_APPROX_SIMPLE);
            std::vector<cv::Point> approx;
            for( size_t i = 0; i < contours.size(); i++ )
            {
                cv::approxPolyDP(cv::Mat(contours[i]),approx,arcLength(cv::Mat(contours[i]),truE)*0.02,truE);
                if( approx.size() == 4 && fabs(contourArea(cv::Mat(approX))) > 1000 && cv::isContourConvex(cv::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);
                    }
                }
            }
        }
    }
    return squares;
}

编辑17/08/2012：

要在图像上绘制检测到的正方形，请使用以下代码：

cv::Mat debugSquares( std::vector<std::vector<cv::Point> > squares,cv::Mat image )
{
    for ( int i = 0; i< squares.size(); i++ ) {
        // draw contour
        cv::drawContours(image,squares,i,cv::Scalar(255,0),8,std::vector<cv::Vec4i>(),cv::Point());

        // draw bounding rect
        cv::rect rect = boundingRect(cv::Mat(squares[i]));
        cv::rectangle(image,rect.tl(),rect.br(),cv::Scalar(0,255,2,0);

        // draw rotated rect
        cv::rotatedRect minRect = minAreaRect(cv::Mat(squares[i]));
        cv::Point2f rect_points[4];
        minRect.points( rect_points );
        for ( int j = 0; j < 4; j++ ) {
            cv::line( image,rect_points[j],rect_points[(j+1)%4],255),8 ); // blue
        }
    }

    return image;
}