C++ OpenCV C++/Obj-C：检测纸张/方形检测

我在测试应用程序中成功地实现了OpenCV square检测示例，但是现在需要过滤输出，因为它非常混乱——或者是我的代码错了

我对这篇文章的四个角点感兴趣，它们可以减少倾斜（比如）并进一步处理

输入和输出：

原始图像：

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, timg, _image.size());
    std::vector<std::vector<cv::Point> > contours;
    for( int c = 0; c < 3; c++ ) {
        int ch[] = {c, 0};
        mixChannels(&timg, 1, &gray0, 1, ch, 1);
        for( int l = 0; l < N; l++ ) {
            if( l == 0 ) {
                cv::Canny(gray0, gray, 0, thresh, 5);
                cv::dilate(gray, 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;
}

代码：

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, timg, _image.size());
    std::vector<std::vector<cv::Point> > contours;
    for( int c = 0; c < 3; c++ ) {
        int ch[] = {c, 0};
        mixChannels(&timg, 1, &gray0, 1, ch, 1);
        for( int l = 0; l < N; l++ ) {
            if( l == 0 ) {
                cv::Canny(gray0, gray, 0, thresh, 5);
                cv::dilate(gray, 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;
}

双角度（cv:：点pt1、cv:：点pt2、cv:：点pt0）{
双dx1=pt1.x-pt0.x；
双dy1=pt1.y-pt0.y；
双dx2=pt2.x-pt0.x；
双dy2=pt2.y-pt0.y；
返回（dx1*dx2+dy1*dy2）/sqrt（dx1*dx1+dy1*dy1）*（dx2*dx2+dy2*dy2）+1e-10）；
}
-（std:：vector）findSquaresInImage:（cv:：Mat）\u图像
{
向量平方；
cv：：Mat pyr，timg，灰色0（_image.size（），cv_8U），灰色；
int thresh=50，N=11；
cv:：pyrDown（_image，pyr，cv:：Size（_image.cols/2，_image.rows/2））；
cv:：pyrUp（pyr，timg，_image.size（））；
矢量轮廓；
对于（int C＝0；C＜3；C++）{
int ch[]={c，0}；
混音通道（&timg，1，&0，1，ch，1）；
对于（int l=0；l=（l+1）*255/N；
}
cv:：findContours（灰色、等高线、等高线列表、等高线链近似简单）；
std：：向量近似；
对于（size_t i=0；i1000和&cv:：isContourConvex（cv:：Mat（近似）））{
双最大余弦=0；
对于（int j=2；j<5；j++）
{
双余弦=fabs（角度（约[j%4]，约[j-2]，约[j-1]）；
最大余弦=最大值（最大余弦，余弦）；
}
如果（最大余弦<0.3）{
正方形。推回（大约）；
}
}
}
}
}
返回方块；
}

编辑2012年8月17日：

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, timg, _image.size());
    std::vector<std::vector<cv::Point> > contours;
    for( int c = 0; c < 3; c++ ) {
        int ch[] = {c, 0};
        mixChannels(&timg, 1, &gray0, 1, ch, 1);
        for( int l = 0; l < N; l++ ) {
            if( l == 0 ) {
                cv::Canny(gray0, gray, 0, thresh, 5);
                cv::dilate(gray, 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;
}

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(255,0,0), 1, 8, std::vector<cv::Vec4i>(), 0, cv::Point());

        // draw bounding rect
        cv::Rect rect = boundingRect(cv::Mat(squares[i]));
        cv::rectangle(image, rect.tl(), rect.br(), cv::Scalar(0,255,0), 2, 8, 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], cv::Scalar(0,0,255), 1, 8 ); // blue
        }
    }

    return image;
}

cv:：Mat调试方块（std:：矢量方块，cv:：Mat图像）
{
对于（int i=0；i

这是Stackoverflow中反复出现的主题，因为我无法找到相关的实现，所以我决定接受挑战

我对OpenCV中的正方形演示进行了一些修改，下面的C++代码能够检测图像中的一张纸：

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);
                    }
            }
        }
    }
}

void find_square（矩阵和图像、向量和正方形）
{
//模糊将增强边缘检测
Mat模糊（图像）；
中间模糊（图像，模糊，9）；
Mat灰色0（模糊的大小（），CV_8U），灰色；
矢量等值线；
//在图像的每个颜色平面中查找正方形
对于（int c=0；c<3；c++）
{
int ch[]={c，0}；
混音通道（&模糊，1，&灰色，0，1，通道，1）；
//尝试几个阈值级别
const int threshold_level=2；
对于（int l=0；l=（l+1）*255/阈值\u级；
}
//找到等高线并将其存储在列表中
findContours（灰色、等高线、等高线列表、等高线链近似简单）；
//测试轮廓
向量近似；
对于（size_t i=0；i1000&&
isContourConvex（材料（近似）））
{
双最大余弦=0；
对于（int j=2；j<5；j++）
{
双余弦=fabs（角度（约[j%4]，约[j-2]，约[j-1]）；
最大余弦=最大值（最大余弦，余弦）；
}
如果（最大余弦<0.3）
正方形。推回（大约）；
}
}
}
}
}

执行此步骤后，纸张

from imutils.perspective import four_point_transform
import cv2
import numpy

# Load image, grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread("1.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (7,7), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Find contours and sort for largest contour
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
displayCnt = None

for c in cnts:
    # Perform contour approximation
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)
    if len(approx) == 4:
        displayCnt = approx
        break

# Obtain birds' eye view of image
warped = four_point_transform(image, displayCnt.reshape(4, 2))

cv2.imshow("thresh", thresh)
cv2.imshow("warped", warped)
cv2.imshow("image", image)
cv2.waitKey()