C++ OpenCV实现二维码检测功能
作者:Zero___Chen
这篇文章主要介绍了如何利用C++ OpenCV实现二维码检测功能,文中的示例代码讲解详细,感兴趣的小伙伴可以跟随小编一起学习一下
前言
本文将使用OpenCV C++ 进行二维码检测。
一、二维码检测
首先我们要先将图像进行预处理,通过灰度、滤波、二值化等操作提取出图像轮廓。在这里我还添加了形态学操作,消除噪点,有效将矩形区域连接起来。
Mat gray; cvtColor(src, gray, COLOR_BGR2GRAY); Mat blur; GaussianBlur(gray, blur, Size(3, 3), 0); Mat bin; threshold(blur, bin, 0, 255, THRESH_BINARY_INV | THRESH_OTSU); //通过Size(5,1)开运算消除边缘毛刺 Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 1)); Mat open; morphologyEx(bin, open, MORPH_OPEN, kernel); //通过Size(21,1)闭运算能够有效地将矩形区域连接 便于提取矩形区域 Mat kernel1 = getStructuringElement(MORPH_RECT, Size(21, 1)); Mat close; morphologyEx(open, close, MORPH_CLOSE, kernel1);
如图为经过一系列图像处理之后得到的效果。之后我们需要对该图进行轮廓提取,找到二维码所在的矩形区域。
//使用RETR_EXTERNAL找到最外轮廓 vector<vector<Point>>MaxContours; findContours(close, MaxContours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (int i = 0; i < MaxContours.size(); i++) { Mat mask = Mat::zeros(src.size(), CV_8UC3); mask = Scalar::all(255); double area = contourArea(MaxContours[i]); //通过面积阈值找到二维码所在矩形区域 if (area > 6000 && area < 100000) { //计算最小外接矩形 RotatedRect MaxRect = minAreaRect(MaxContours[i]); //计算最小外接矩形宽高比 double ratio = MaxRect.size.width / MaxRect.size.height; if (ratio > 0.8 && ratio < 1.2) { Rect MaxBox = MaxRect.boundingRect(); //rectangle(src, Rect(MaxBox.tl(), MaxBox.br()), Scalar(255, 0, 255), 2); //将矩形区域从原图抠出来 Mat ROI = src(Rect(MaxBox.tl(), MaxBox.br())); ROI.copyTo(mask(MaxBox)); ROI_Rect.push_back(mask); } } }
由以下代码段我们就可以很好的找出二维码所在的矩形区域,并将这些区域抠出来保存以便进行下面的识别工作。
//找到二维码所在的矩形区域 void Find_QR_Rect(Mat src, vector<Mat>&ROI_Rect) { Mat gray; cvtColor(src, gray, COLOR_BGR2GRAY); Mat blur; GaussianBlur(gray, blur, Size(3, 3), 0); Mat bin; threshold(blur, bin, 0, 255, THRESH_BINARY_INV | THRESH_OTSU); //通过Size(5,1)开运算消除边缘毛刺 Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 1)); Mat open; morphologyEx(bin, open, MORPH_OPEN, kernel); //通过Size(21,1)闭运算能够有效地将矩形区域连接 便于提取矩形区域 Mat kernel1 = getStructuringElement(MORPH_RECT, Size(21, 1)); Mat close; morphologyEx(open, close, MORPH_CLOSE, kernel1); //使用RETR_EXTERNAL找到最外轮廓 vector<vector<Point>>MaxContours; findContours(close, MaxContours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (int i = 0; i < MaxContours.size(); i++) { Mat mask = Mat::zeros(src.size(), CV_8UC3); mask = Scalar::all(255); double area = contourArea(MaxContours[i]); //通过面积阈值找到二维码所在矩形区域 if (area > 6000 && area < 100000) { //计算最小外接矩形 RotatedRect MaxRect = minAreaRect(MaxContours[i]); //计算最小外接矩形宽高比 double ratio = MaxRect.size.width / MaxRect.size.height; if (ratio > 0.8 && ratio < 1.2) { Rect MaxBox = MaxRect.boundingRect(); //rectangle(src, Rect(MaxBox.tl(), MaxBox.br()), Scalar(255, 0, 255), 2); //将矩形区域从原图抠出来 Mat ROI = src(Rect(MaxBox.tl(), MaxBox.br())); ROI.copyTo(mask(MaxBox)); ROI_Rect.push_back(mask); } } } }
如图所示,这是找到的二维码矩形。这里只展示其中之一。
二、二维码识别
通过findContours找到轮廓层级关系
//用于存储检测到的二维码 vector<vector<Point>>QR_Rect; //遍历所有找到的矩形区域 for (int i = 0; i < ROI_Rect.size(); i++) { Mat gray; cvtColor(ROI_Rect[i], gray, COLOR_BGR2GRAY); Mat bin; threshold(gray, bin, 0, 255, THRESH_BINARY_INV|THRESH_OTSU); //通过hierarchy、RETR_TREE找到轮廓之间的层级关系 vector<vector<Point>>contours; vector<Vec4i>hierarchy; findContours(bin, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE); //父轮廓索引 int ParentIndex = -1; int cn = 0; //用于存储二维码矩形的三个“回” vector<Point>rect_points; for (int i = 0; i < contours.size(); i++) { //hierarchy[i][2] != -1 表示该轮廓有子轮廓 cn用于计数“回”中第几个轮廓 if (hierarchy[i][2] != -1 && cn == 0) { ParentIndex = i; cn++; } else if (hierarchy[i][2] != -1 && cn == 1) { cn++; } else if (hierarchy[i][2] == -1) { //初始化 ParentIndex = -1; cn = 0; } //如果该轮廓存在子轮廓,且有2级子轮廓则认定找到‘回' if (hierarchy[i][2] != -1 && cn == 2) { drawContours(canvas, contours, ParentIndex, Scalar::all(255), -1); RotatedRect rect; rect = minAreaRect(contours[ParentIndex]); rect_points.push_back(rect.center); } } }
以上代码段的整体思路为:首先经过图像预处理进行轮廓检测,
通过hierarchy、RETR_TREE找到轮廓之间的层级关系。根据hierarchy[i][2]是否为-1判断该轮廓是否有子轮廓。若该轮廓存在子轮廓,则统计有几个子轮廓。如果该轮廓存在子轮廓,且有2级子轮廓则认定找到‘回’ 。关于轮廓的层级关系,大家可以自行百度查找资料,理解一下其中原理。
//对找到的矩形区域进行识别是否为二维码 int Dectect_QR_Rect(Mat src,Mat &canvas,vector<Mat>&ROI_Rect) { //用于存储检测到的二维码 vector<vector<Point>>QR_Rect; //遍历所有找到的矩形区域 for (int i = 0; i < ROI_Rect.size(); i++) { Mat gray; cvtColor(ROI_Rect[i], gray, COLOR_BGR2GRAY); Mat bin; threshold(gray, bin, 0, 255, THRESH_BINARY_INV|THRESH_OTSU); //通过hierarchy、RETR_TREE找到轮廓之间的层级关系 vector<vector<Point>>contours; vector<Vec4i>hierarchy; findContours(bin, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE); //父轮廓索引 int ParentIndex = -1; int cn = 0; //用于存储二维码矩形的三个“回” vector<Point>rect_points; for (int i = 0; i < contours.size(); i++) { //hierarchy[i][2] != -1 表示该轮廓有子轮廓 cn用于计数“回”中第几个轮廓 if (hierarchy[i][2] != -1 && cn == 0) { ParentIndex = i; cn++; } else if (hierarchy[i][2] != -1 && cn == 1) { cn++; } else if (hierarchy[i][2] == -1) { //初始化 ParentIndex = -1; cn = 0; } //如果该轮廓存在子轮廓,且有2级子轮廓则认定找到‘回' if (hierarchy[i][2] != -1 && cn == 2) { drawContours(canvas, contours, ParentIndex, Scalar::all(255), -1); RotatedRect rect; rect = minAreaRect(contours[ParentIndex]); rect_points.push_back(rect.center); } } //将找到地‘回'连接起来 for (int i = 0; i < rect_points.size(); i++) { line(canvas, rect_points[i], rect_points[(i + 1) % rect_points.size()], Scalar::all(255), 5); } QR_Rect.push_back(rect_points); } return QR_Rect.size(); }
由以上代码段,我们就可以识别出二维码。效果如图所示。
三、二维码绘制
//框出二维码所在位置 Mat gray; cvtColor(canvas, gray, COLOR_BGR2GRAY); vector<vector<Point>>contours; findContours(gray, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); Point2f points[4]; for (int i = 0; i < contours.size(); i++) { RotatedRect rect = minAreaRect(contours[i]); rect.points(points); for (int j = 0; j < 4; j++) { line(src, points[j], points[(j + 1) % 4], Scalar(0, 255, 0), 2); } }
最终效果如图所示。
四、源码
#include<iostream> #include<opencv2/core.hpp> #include<opencv2/imgproc.hpp> #include<opencv2/highgui.hpp> using namespace std; using namespace cv; //找到二维码所在的矩形区域 void Find_QR_Rect(Mat src, vector<Mat>&ROI_Rect) { Mat gray; cvtColor(src, gray, COLOR_BGR2GRAY); Mat blur; GaussianBlur(gray, blur, Size(3, 3), 0); Mat bin; threshold(blur, bin, 0, 255, THRESH_BINARY_INV | THRESH_OTSU); //通过Size(5,1)开运算消除边缘毛刺 Mat kernel = getStructuringElement(MORPH_RECT, Size(5, 1)); Mat open; morphologyEx(bin, open, MORPH_OPEN, kernel); //通过Size(21,1)闭运算能够有效地将矩形区域连接 便于提取矩形区域 Mat kernel1 = getStructuringElement(MORPH_RECT, Size(21, 1)); Mat close; morphologyEx(open, close, MORPH_CLOSE, kernel1); //使用RETR_EXTERNAL找到最外轮廓 vector<vector<Point>>MaxContours; findContours(close, MaxContours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); for (int i = 0; i < MaxContours.size(); i++) { Mat mask = Mat::zeros(src.size(), CV_8UC3); mask = Scalar::all(255); double area = contourArea(MaxContours[i]); //通过面积阈值找到二维码所在矩形区域 if (area > 6000 && area < 100000) { //计算最小外接矩形 RotatedRect MaxRect = minAreaRect(MaxContours[i]); //计算最小外接矩形宽高比 double ratio = MaxRect.size.width / MaxRect.size.height; if (ratio > 0.8 && ratio < 1.2) { Rect MaxBox = MaxRect.boundingRect(); //rectangle(src, Rect(MaxBox.tl(), MaxBox.br()), Scalar(255, 0, 255), 2); //将矩形区域从原图抠出来 Mat ROI = src(Rect(MaxBox.tl(), MaxBox.br())); ROI.copyTo(mask(MaxBox)); ROI_Rect.push_back(mask); } } } } //对找到的矩形区域进行识别是否为二维码 int Dectect_QR_Rect(Mat src,Mat &canvas,vector<Mat>&ROI_Rect) { //用于存储检测到的二维码 vector<vector<Point>>QR_Rect; //遍历所有找到的矩形区域 for (int i = 0; i < ROI_Rect.size(); i++) { Mat gray; cvtColor(ROI_Rect[i], gray, COLOR_BGR2GRAY); Mat bin; threshold(gray, bin, 0, 255, THRESH_BINARY_INV|THRESH_OTSU); //通过hierarchy、RETR_TREE找到轮廓之间的层级关系 vector<vector<Point>>contours; vector<Vec4i>hierarchy; findContours(bin, contours, hierarchy, RETR_TREE, CHAIN_APPROX_NONE); //父轮廓索引 int ParentIndex = -1; int cn = 0; //用于存储二维码矩形的三个“回” vector<Point>rect_points; for (int i = 0; i < contours.size(); i++) { //hierarchy[i][2] != -1 表示该轮廓有子轮廓 cn用于计数“回”中第几个轮廓 if (hierarchy[i][2] != -1 && cn == 0) { ParentIndex = i; cn++; } else if (hierarchy[i][2] != -1 && cn == 1) { cn++; } else if (hierarchy[i][2] == -1) { //初始化 ParentIndex = -1; cn = 0; } //如果该轮廓存在子轮廓,且有2级子轮廓则认定找到‘回' if (hierarchy[i][2] != -1 && cn == 2) { drawContours(canvas, contours, ParentIndex, Scalar::all(255), -1); RotatedRect rect; rect = minAreaRect(contours[ParentIndex]); rect_points.push_back(rect.center); } } //将找到地‘回'连接起来 for (int i = 0; i < rect_points.size(); i++) { line(canvas, rect_points[i], rect_points[(i + 1) % rect_points.size()], Scalar::all(255), 5); } QR_Rect.push_back(rect_points); } return QR_Rect.size(); } int main() { Mat src = imread("6.png"); if (src.empty()) { cout << "No image data!" << endl; system("pause"); return 0; } vector<Mat>ROI_Rect; Find_QR_Rect(src, ROI_Rect); Mat canvas = Mat::zeros(src.size(), src.type()); int flag = Dectect_QR_Rect(src, canvas, ROI_Rect); //imshow("canvas", canvas); if (flag <= 0) { cout << "Can not detect QR code!" << endl; system("pause"); return 0; } cout << "检测到" << flag << "个二维码。" << endl; //框出二维码所在位置 Mat gray; cvtColor(canvas, gray, COLOR_BGR2GRAY); vector<vector<Point>>contours; findContours(gray, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); Point2f points[4]; for (int i = 0; i < contours.size(); i++) { RotatedRect rect = minAreaRect(contours[i]); rect.points(points); for (int j = 0; j < 4; j++) { line(src, points[j], points[(j + 1) % 4], Scalar(0, 255, 0), 2); } } imshow("source", src); waitKey(0); destroyAllWindows(); system("pause"); return 0; }
总结
本文使用OpenCV C++进行二维码检测,关键步骤有以下几点。
1、图像预处理,筛选出二维码所在的矩形区域,并将该区域抠出来进行后续的识别工作。
2、对筛选出的矩形区域进行轮廓检测,判断它们之前的层级关系,以此来识别二维码。
3、最后根据检测到的二维码“回”字,将其绘制出来就可以了。
以上就是C++ OpenCV实现二维码检测功能的详细内容,更多关于C++ OpenCV二维码检测的资料请关注脚本之家其它相关文章!