OpenCV C++案例实战《答题卡识别》

#include<iostream> #include"AnswerRecognition.h" using namespace std; using namespace cv; double EuDis(Point pt1, Point pt2) { return sqrt((pt2.x - pt1.x)*(pt2.x - pt1.x) + (pt2.y - pt1.y)*(pt2.y - pt1.y)); } void Answer::GetWarpImg(Mat src, Mat& WarpImg) { Mat gray; cvtColor(src, gray, COLOR_BGR2GRAY); Mat blur; GaussianBlur(gray, blur, Size(3, 3), 0); Mat canny; Canny(blur, canny, 100, 200); vector<vector<Point>>contours; findContours(canny, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); //找到最大矩形轮廓 int index = 0; double maxArea = 0; for (int i = 0; i < contours.size(); i++) { double area = contourArea(contours[i]); if (maxArea < area) { maxArea = area; index = i; } } //多边形近似 vector<vector<Point>>conPloy(contours.size()); double peri = arcLength(contours[index], true); approxPolyDP(contours[index], conPloy[index], 0.02*peri, true); //找到矩形四个顶点 vector<Point>srcPts; for (int i = 0; i < conPloy[index].size(); i++) { srcPts.push_back(Point(conPloy[index][i].x, conPloy[index][i].y)); } int width = src.cols / 2; int height = src.rows / 2; //将矩形四个顶点按T_L, B_L, B_R, T_R区分 int T_L, B_L, B_R, T_R; for (int i = 0; i < srcPts.size(); i++) { if (srcPts[i].x < width && srcPts[i].y < height) { T_L = i; } if (srcPts[i].x < width && srcPts[i].y > height) { B_L = i; } if (srcPts[i].x > width && srcPts[i].y > height) { B_R = i; } if (srcPts[i].x > width && srcPts[i].y < height) { T_R = i; } } /* 变换后，图像的长和宽应该变为： 长 = max（变换前左边长，变换前右边长） 宽 = max（变换前上边长，变换前下边长） 设变换后图像的左上角位置为原点位置。 */ double upWidth = EuDis(srcPts[T_R], srcPts[T_L]); double downWidth = EuDis(srcPts[B_R], srcPts[B_L]); double maxWidth = max(upWidth, downWidth); double leftHeight = EuDis(srcPts[B_L], srcPts[T_L]); double rightHeight = EuDis(srcPts[B_R], srcPts[T_R]); double maxHeight = max(leftHeight, rightHeight); Point2f AffineSrcPts[4] = { Point2f(srcPts[T_L]) ,Point2f(srcPts[T_R]) ,Point2f(srcPts[B_L]) ,Point2f(srcPts[B_R]) }; Point2f AffineDstPts[4] = { Point2f(0, 0),Point2f(maxWidth , 0),Point2f(0, maxHeight),Point2f(maxWidth , maxHeight) }; Mat M; //计算仿射变换矩阵 M = getPerspectiveTransform(AffineSrcPts, AffineDstPts); //对加载图形进行仿射变换操作 warpPerspective(src, WarpImg, M, Point(maxWidth, maxHeight)); } void Answer::GetAnswerArea(Mat&WarpImg, vector<Mat>&AnswerROI) { Mat gray; cvtColor(WarpImg, gray, COLOR_BGR2GRAY); Mat bin; threshold(gray, bin, 0, 255, THRESH_BINARY_INV | THRESH_OTSU); //使用 Size(15, 5)闭操作目的是为了将每一题选项连接起来。 Mat close; Mat kernel = getStructuringElement(MORPH_RECT, Size(15, 5)); morphologyEx(bin, close, MORPH_CLOSE, kernel); vector<vector<Point>>contours; findContours(close, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); RotatedRect rect; Rect box; for (int i = 0; i < contours.size(); i++) { Mat mask = Mat::zeros(WarpImg.size(), WarpImg.type()); mask = Scalar::all(255); double area = contourArea(contours[i]); if (area > 1000) { rect = minAreaRect(contours[i]); box = rect.boundingRect(); double ratio = rect.size.height / rect.size.width; //将每一选项都单独抠出来放进AnswerROI容器，以便后续识别。 if (ratio > 0.1) { //rectangle(WarpImg, Rect(box.tl(), box.br()), Scalar(0, 255, 0), 2); Mat ROI = WarpImg(Rect(box.tl(), box.br())); ROI.copyTo(mask(box)); AnswerROI.push_back(mask); } } } reverse(AnswerROI.begin(), AnswerROI.end()); } //计算ABCDE区域所有像素点个数 int get_pixsum(Mat image, int pixstart, int pixend) { int sum = 0; for (int i = pixstart; i < pixend; i++) { for (int j = 0; j < image.rows; j++) { if (image.at<uchar>(j, i) != 0) { sum++; } } } return sum; } //找到像素点最多区域的索引 int getMaxIndex(vector<int>Answer) { int max = 0; int index = -1; for (int i = 0; i < Answer.size(); i++) { if (Answer[i] > max) { max = Answer[i]; index = i; } } return index; } void Answer::GetAnswer(cv::Mat&WarpImg, vector<Mat>&AnswerROI, vector<int>Answers, int &Score) { const double Total = 5; //题目数量 double Count = 0; //答对数量 vector<int>Results; for (int i = 0; i < AnswerROI.size(); i++) { Mat gray; cvtColor(AnswerROI[i], gray, COLOR_BGR2GRAY); Mat bin; threshold(gray, bin, 0, 255, THRESH_BINARY_INV | THRESH_OTSU); vector<int>Answer; //计算ABCDE区域所有像素点个数 int A = get_pixsum(bin, 60, 110); int B = get_pixsum(bin, 110, 160); int C = get_pixsum(bin, 160, 210); int D = get_pixsum(bin, 210, 260); int E = get_pixsum(bin, 260, 310); Answer = { A,B,C,D,E }; //找到像素点最多区域的索引 int Index = getMaxIndex(Answer); Results.push_back(Index); //将选项在原图圈出来 vector<vector<Point>>contours; findContours(bin, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); reverse(contours.begin(), contours.end()); for (int j = 0; j < contours.size(); j++) { double area = contourArea(contours[j]); if (area > 200) { if (Answers[i] == Index) { drawContours(WarpImg, contours, Index, Scalar(0, 255, 0), 2); } else { drawContours(WarpImg, contours, Answers[i], Scalar(0, 0, 255), 2); } } } } //统计得分 for (int i = 0; i < Total; i++) { if (Results[i] == Answers[i]) { Count++; } } Score = (Count / Total) * 100; }