我在我的测试应用程序中成功实现了 OpenCV 平方检测示例,但现在需要过滤输出,因为它非常混乱 - 还是我的代码错误?
我对论文的四个角点感兴趣,用于减少偏斜(像那样)和进一步处理。
输入输出:
原图:
点击
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; }
编辑 2012 年 8 月 17 日:
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; }
这是 Stackoverflow 中反复出现的主题,由于我无法找到相关的实现,我决定接受挑战。
我对 OpenCV 中的正方形演示进行了一些修改,下面生成的 C++ 代码能够检测到图像中的一张纸:
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); } } } } }
执行此过程后,这张纸将是 中最大的正方形vector<vector<Point> >:
vector<vector<Point> >
我让你编写函数来找到最大的正方形。;)