基于opencv的简单答题卡识别

#include <opencv2/opencv.hpp> #include <iostream> #include "math.h" using namespace cv; using namespace std; #if 0 const bool X_Axis = true; const bool Y_Axis = false; void FindAnchorPoint(const Mat& src, const Mat& matchMask, vector<Point2f>& anchorPoint); void projectionAlgorithm(Mat src, vector<int>& UpJumpWave, vector<int>& DownJumpWave, bool Axis, int maxInterval); void checkKeypoint( Mat& _src, vector<Point2f>& allPoint, vector<Point2f>& testkeyPoint); int main1(int argc,char** argv) { //变量 //读取图片 Mat standImage = imread("D:\\workspace\\OpenCVApplication1\\OpenCVApplication1\\Debug\\1.jpg"); Mat perImage = imread("D:\\workspace\\OpenCVApplication1\\OpenCVApplication1\\Debug\\2.jpg"); //Mat perImage = imread("perspective.jpg"); //Mat matchMask = imread("Circle.jpg"); //-----------生成模板图片R = 11 Mat matchMask; matchMask.create(Size(24, 24), CV_8UC3); matchMask.setTo(255); circle(matchMask, Point(11, 11), 11, Scalar(0), -1); resize(perImage, perImage, Size(600, 600)); vector<Point2f> stdAncherPoint(4); vector<Point2f> perAncherPoint(4); FindAnchorPoint(standImage, matchMask, stdAncherPoint); FindAnchorPoint(perImage, matchMask, perAncherPoint); Mat change = getPerspectiveTransform(perAncherPoint, stdAncherPoint); Mat resultPerImage; warpPerspective(perImage, resultPerImage, change, resultPerImage.size()); FindAnchorPoint(resultPerImage, matchMask, perAncherPoint); Mat grayImage = resultPerImage;//.clone(); Mat show = resultPerImage.clone(); cvtColor(grayImage, grayImage, CV_BGR2GRAY); threshold(grayImage, grayImage,90,255, THRESH_BINARY_INV); vector<Mat> vectorGrayImage(4); vectorGrayImage[0] = grayImage(Rect(perAncherPoint[0].x+4, 0, 15, standImage.rows));//LEFT vectorGrayImage[1] = grayImage(Rect(perAncherPoint[1].x+4 , 0, 15, standImage.rows));//RIGHT vectorGrayImage[2] = grayImage(Rect(0,perAncherPoint[0].y+4, standImage.cols, 15));//TOP vectorGrayImage[3] = grayImage(Rect(0,perAncherPoint[2].y+4, standImage.cols, 15));//BOTTOM vector<vector<int>> upJumpWave(4); vector<vector<int>> downJumpWave(4); for (size_t i = 0; i < 4; i++) { if (i<2) projectionAlgorithm(vectorGrayImage[i], upJumpWave[i], downJumpWave[i], Y_Axis, 2); else projectionAlgorithm(vectorGrayImage[i], upJumpWave[i], downJumpWave[i], X_Axis, 2); } //-----------------------绘制检测的跳变线-------------------------// for (size_t i = 0; i < upJumpWave[0].size(); i++) { line(grayImage, Point(perAncherPoint[0].x + 11, upJumpWave[0][i]), Point(perAncherPoint[0].x + 22, upJumpWave[0][i]), Scalar(255, 255, 255)); } for (size_t i = 0; i < upJumpWave[3].size(); i++) { line(grayImage, Point(upJumpWave[3][i], perAncherPoint[3].y), Point(upJumpWave[3][i], perAncherPoint[3].y + 11), Scalar(255, 255, 255)); } for (size_t i = 0; i < upJumpWave[1].size(); i++) { line(grayImage, Point(perAncherPoint[1].x, upJumpWave[1][i]), Point(perAncherPoint[1].x + 11, upJumpWave[1][i]), Scalar(255, 255, 255)); } for (size_t i = 0; i < upJumpWave[2].size(); i++) { line(grayImage, Point(upJumpWave[2][i], perAncherPoint[0].y + 11), Point(upJumpWave[2][i], perAncherPoint[0].y + 22), Scalar(255, 255, 255)); } //-------------把所有的点存储在容器里，以供下面的函数调用--------------// vector<Point2f> allPoint; for (size_t i = 1; i < upJumpWave[2].size(); i++)//存储上半部分图卡点（准考证号区+旁边那个看不清的区域） { for (size_t j = 0; j < 10; j++) { allPoint.push_back(Point(upJumpWave[2][i], upJumpWave[1][j])); } } for (size_t i = 0; i < upJumpWave[3].size(); i++)//存储下半部分图卡点（答题区） { for (size_t j = 10; j < upJumpWave[1].size(); j++) { allPoint.push_back(Point(upJumpWave[3][i], upJumpWave[1][j])); } } //--------------检测涂上铅笔的区域--------------// vector<Point2f> testKeyPoint; checkKeypoint(grayImage, allPoint, testKeyPoint); for (size_t i = 0; i < testKeyPoint.size(); i++) { rectangle(show, Rect(testKeyPoint.at(i), Point(testKeyPoint.at(i).x + 12, testKeyPoint.at(i).y + 5)), Scalar(0, 0, 255)); } waitKey(); return 0; } //--------------------------------注释代码部分为未用掩码操作--------------------------// void FindAnchorPoint(const Mat& src,const Mat& matchMask,vector<Point2f>& anchorPoint) { Mat matchResult; matchResult.create(Size(src.cols - matchMask.cols, src.rows - matchMask.rows), CV_16SC1); //----模板匹配找四个定位点，同时得归一化（初始数据范围太大，自己通过image watch 查看） matchTemplate(src, matchMask, matchResult, TM_CCOEFF_NORMED, Mat()); normalize(matchResult, matchResult, 0, 1, NORM_MINMAX); //----查找匹配的四个点,分成四个区域查找，因为一个区域没办法查找四个值 /*Mat topleft = matchResult(Rect(Point(0, 0), Point(matchResult.cols / 2, matchResult.rows / 2))); Mat topright = matchResult(Rect(Point(matchResult.cols / 2, 0), Point(matchResult.cols, matchResult.rows / 2))); Mat botleft = matchResult(Rect(Point(0, matchResult.rows / 2), Point(matchResult.cols / 2, matchResult.rows))); Mat botright = matchResult(Rect(Point(matchResult.cols / 2, matchResult.rows / 2), Point(matchResult.cols , matchResult.rows)));*/ double maxValue[4] = { 0 }, minValue[4] = {0}; vector<Point2i> maxPoint(4), minPoint(4); Mat topleftMask = Mat::zeros(matchResult.size(), CV_8UC1); Mat toprightMask = Mat::zeros(matchResult.size(), CV_8UC1); Mat botleftMask = Mat::zeros(matchResult.size(), CV_8UC1); Mat botrightMask = Mat::zeros(matchResult.size(), CV_8UC1); topleftMask(Rect(Point(0, 0), Point(matchResult.cols / 2, matchResult.rows / 2))).setTo(255); toprightMask(Rect(Point(matchResult.cols / 2, 0), Point(matchResult.cols, matchResult.rows / 2))).setTo(255); botleftMask(Rect(Point(0, matchResult.rows / 2), Point(matchResult.cols / 2, matchResult.rows))).setTo(255); botrightMask(Rect(Point(matchResult.cols / 2, matchResult.rows / 2), Point(matchResult.cols, matchResult.rows))).setTo(255); vector<Mat> vectorMask;//注意此处如果用vector<Mat> vectorMask(4);对应的下面写法是vectorMask[0]=topleftMask; vectorMask.push_back(topleftMask); vectorMask.push_back(toprightMask); vectorMask.push_back(botleftMask); vectorMask.push_back(botrightMask); for (size_t i = 0; i < vectorMask.size(); i++) { minMaxLoc(matchResult, &minValue[i], &maxValue[i], &minPoint[i], &maxPoint[i], vectorMask[i]); } anchorPoint.assign(maxPoint.begin(), maxPoint.end()); //minMaxLoc(topleft, &minValue[0], &maxValue[0], &minPoint[0], &maxPoint[0]); //minMaxLoc(topright, &minValue[1], &maxValue[1], &minPoint[1], &maxPoint[1]); //maxPoint[1].x = maxPoint[1].x + matchResult.cols / 2; //minMaxLoc(botleft, &minValue[2], &maxValue[2], &minPoint[2], &maxPoint[2]); //maxPoint[2].y = maxPoint[2].y + matchResult.rows / 2; //minMaxLoc(botright, &minValue[3], &maxValue[3], &minPoint[3], &maxPoint[3]); //maxPoint[3].x = maxPoint[3].x + matchResult.cols / 2; //maxPoint[3].x = maxPoint[3].y + matchResult.rows / 2; } //------------------------------------图像投影算法-----------------------------------------------// //*************@src------------------输入矩阵为单通道********************************************// //*************@leftUpJumpWave-------上升跳变沿存储**********************************************// //*************@rightDownJumpWave----下降跳变沿存储**********************************************// //*************@maxInterval----------允许高电平（像素）最大间隔，也可以说是允许的最大误差********// //-----------------------------------------------------------------------------------------------// void projectionAlgorithm(Mat src,vector<int>& UpJumpWave,vector<int>& DownJumpWave,bool Axis,int maxInterval) { vector<int> pixNum(src.rows > src.cols ? src.rows : src.cols); //------对X、Y做直方图类似的投影，统计一行或者一列的非零个数--------// if (Axis) { for (size_t i = 0; i < src.cols; i++) { Mat col = src.col(i);//一列数据 pixNum[i] = countNonZe