基于SVM与人工神经网络的车牌识别OpenCV&C++实现

#include "OCR.h" const char OCR::strCharacters[] = { '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'B', 'C', 'D', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'R', 'S', 'T', 'V', 'W', 'X', 'Y', 'Z' }; const int OCR::numCharacters = 30; CharSegment::CharSegment(){} CharSegment::CharSegment(Mat i, Rect p) { img = i; pos = p; } OCR::OCR() { DEBUG = false; trained = false; saveSegments = false; charSize = 20; } OCR::OCR(string trainFile) { DEBUG = false; trained = false; saveSegments = false; charSize = 20; // 读取OCR.xml文件 FileStorage fs; fs.open("OCR.xml", FileStorage::READ); Mat TrainingData; Mat Classes; fs["TrainingDataF15"] >> TrainingData; fs["classes"] >> Classes; train(TrainingData, Classes, 10); } Mat OCR::preprocessChar(Mat in){ int h = in.rows; int w = in.cols; Mat transformMat = Mat::eye(2, 3, CV_32F); int m = max(w, h); transformMat.at<float>(0, 2) = m / 2 - w / 2; transformMat.at<float>(1, 2) = m / 2 - h / 2; Mat warpImage(m, m, in.type()); warpAffine(in, warpImage, transformMat, warpImage.size(), INTER_LINEAR, BORDER_CONSTANT, Scalar(0)); Mat out; resize(warpImage, out, Size(charSize, charSize)); return out; } bool OCR::verifySizes(Mat r){ // 正确的车牌字符宽高比为45/77 float aspect = 45.0f / 77.0f; float charAspect = (float)r.cols / (float)r.rows; float error = 0.35; // 允许误差达到35% float minHeight = 15; float maxHeight = 28; // 最小比例 float minAspect = 0.2; float maxAspect = aspect + aspect * error; // 区域像素 float area = countNonZero(r); // bb区域 float bbArea = r.cols * r.rows; // 像素占区域的百分比 float percPixels = area / bbArea; // 若一块区域的比率超过标准比率的80%，则认为该区域为黑色快，而不是一个字符 if (DEBUG) cout << "Aspect: " << aspect << " [" << minAspect << "," << maxAspect << "] " << "Area " << percPixels << " Char aspect " << charAspect << " Height char " << r.rows << "\n"; if (percPixels < 0.8 && charAspect > minAspect && charAspect < maxAspect && r.rows >= minHeight && r.rows < maxHeight) return true; else return false; } // 阈值分割 vector<CharSegment> OCR::segment(Plate plate){ Mat input = plate.plateImg; vector<CharSegment> output; Mat thresholdImage; threshold(input, thresholdImage, 60, 255, CV_THRESH_BINARY_INV); if (DEBUG) imshow("Threshold plate", thresholdImage); Mat img_contours; thresholdImage.copyTo(img_contours); // 找到可能的车牌的轮廓 vector< vector< Point> > contours; findContours(img_contours, contours, // 检测的轮廓数组，每一个轮廓用一个point类型的vector表示 CV_RETR_EXTERNAL, // 表示只检测外轮廓 CV_CHAIN_APPROX_NONE); // 轮廓的近似办法，这里存储所有的轮廓点 // 在白色的图上画出蓝色的轮廓 cv::Mat result; thresholdImage.copyTo(result); cvtColor(result, result, CV_GRAY2RGB); cv::drawContours(result, contours, -1, // 所有的轮廓都画出 cv::Scalar(255, 0, 0), // 颜色 1); // 线粗 // 对每个轮廓检测和提取最小区域的有界矩形区域 vector<vector<Point> >::iterator itc = contours.begin(); char res[20]; int i = 0; // 若没有达到设定的宽高比要求，移去该区域 while (itc != contours.end()) { Rect mr = boundingRect(Mat(*itc)); rectangle(result, mr, Scalar(0, 255, 0)); // 裁剪图像 Mat auxRoi(thresholdImage, mr); if (verifySizes(auxRoi)){ auxRoi = preprocessChar(auxRoi); output.push_back(CharSegment(auxRoi, mr)); //保存每个字符图片 sprintf(res, "PlateNumber%d.jpg", i); i++; imwrite(res, auxRoi); rectangle(result, mr, Scalar(0, 125, 255)); } ++itc; } if (DEBUG) cout << "Num chars: " << output.size() << "\n"; if (DEBUG) imshow("SEgmented Chars", result); return output; } Mat OCR::ProjectedHistogram(Mat img, int t) { int sz = (t) ? img.rows : img.cols; Mat mhist = Mat::zeros(1, sz, CV_32F); for (int j = 0; j<sz; j++){ Mat data = (t) ? img.row(j) : img.col(j); mhist.at<float>(j) = countNonZero(data); } // 归一化直方图 double min, max; minMaxLoc(mhist, &min, &max); if (max>0) mhist.convertTo(mhist, -1, 1.0f / max, 0); return mhist; } Mat OCR::getVisualHistogram(Mat *hist, int type) { int size = 100; Mat imHist; if (type == HORIZONTAL){ imHist.create(Size(size, hist->cols), CV_8UC3); } else{ imHist.create(Size(hist->cols, size), CV_8UC3); } imHist = Scalar(55, 55, 55); for (int i = 0; i<hist->cols; i++){ float value = hist->at<float>(i); int maxval = (int)(value*size); Point pt1; Point pt2, pt3, pt4; if (type == HORIZONTAL) { pt1.x = pt3.x = 0; pt2.x = pt4.x = maxval; pt1.y = pt2.y = i; pt3.y = pt4.y = i + 1; line(imHist, pt1, pt2, CV_RGB(220, 220, 220), 1, 8, 0); line(imHist, pt3, pt4, CV_RGB(34, 34, 34), 1, 8, 0); pt3.y = pt4.y = i + 2; line(imHist, pt3, pt4, CV_RGB(44, 44, 44), 1, 8, 0); pt3.y = pt4.y = i + 3; line(imHist, pt3, pt4, CV_RGB(50, 50, 50), 1, 8, 0); } else { pt1.x = pt2.x = i; pt3.x = pt4.x = i + 1; pt1.y = pt3.y = 100; pt2.y = pt4.y = 100 - maxval; line(imHist, pt1, pt2, CV_RGB(220, 220, 220), 1, 8, 0); line(imHist, pt3, pt4, CV_RGB(34, 34, 34), 1, 8, 0); pt3.x = pt4.x = i + 2; line(imHist, pt3, pt4, CV_RGB(44, 44, 44), 1, 8, 0); pt3.x = pt4.x = i + 3; line(imHist, pt3, pt4, CV_RGB(50, 50, 50), 1, 8, 0); } } return imHist; } void OCR::drawVisualFeatures(Mat character, Mat hhist, Mat vhist, Mat lowData){ Mat img(121, 121, CV_8UC3, Scalar(0, 0, 0)); Mat ch; Mat ld; cvtColor(character, ch, CV_GRAY2RGB); resize(lowData, ld, Size(100, 100), 0, 0, INTER_NEAREST); cvtColor(ld, ld, CV_GRAY2RGB); Mat hh = getVisualHistogram(&hhist, HORIZONTAL); Mat hv = getVisualHistogram(&vhist, VERTICAL); Mat subImg = img(Rect(0, 101, 20, 20)); ch.copyTo(subImg); subImg = img(Rect(21, 101, 100, 20)); hh.copyTo(subImg); subImg = img(Rect(0, 0, 20, 100)); hv.copyTo(subImg); subImg = img(Rect(21, 0, 100, 100)); ld.copyTo(subImg); line(img, Point(0, 100), Point(121, 100), Scalar(0, 0, 255)); line(img, Point(20, 0), Point(20, 121), Scalar(0, 0, 255)); imshow("Visual Features", img); cvWaitKey(0); } // 特征提取 Mat OCR::features(Mat in, int sizeData){ //Histogram features Mat vhist = ProjectedHistogram(in, VERTICAL); Mat hhist = ProjectedHistogram(in, HORIZONTAL); Mat lowData; resize(in, lowData, Size(sizeData, sizeData)); if (DEBUG) drawVisualFeatures(in, hhist, vhist, lowData); int numCols = vhist.cols + hhist.cols + lowData.cols*lowData.cols; Mat out = Mat::zeros(1, numCols, CV_32F); int j = 0; for (int i = 0; i<vhist.cols; i++) { out.at<float>(j) = vhist.at<float>(i); j++; } for (int i = 0; i<hhist.cols; i++) { out.at<float>(j) = hhist.at<float>(i); j++; } for (int x = 0; x<lowData.cols; x++) { for (int y = 0; y<lowData.rows; y++){ out.at<float>(j) = (float)lowData.at<unsigned char>(x, y); j++; } } if (DEBUG) cout << out << "\n===========================================\n"; return out; } // 用于创建所有需要的矩阵并用训练数据、类标签矩 // 阵、在隐藏层的神经元数量来训练一个识别系统 void OCR::train(Mat TrainData, Mat classes, int nlayers){ Mat layers(1, 3, CV_32SC1); layers.at<int>(0) = TrainData.cols; layers.at<int>(1) = nlayers; layers.at<int>(2) = numCharacters; ann.create(layers, CvANN_MLP::SIGMOID_SYM, 1, 1); // 创建一个矩阵，其中存放n个训练数据，并将其分为m类 Mat trainClasses; trainClasses.create(TrainData.rows, numCharacters, CV_32FC1); for (int i = 0; i < trainClasses.rows; i++) { for (int k = 0; k < trainClasses.cols; k++) { if (k == classes.at<int>(i)) trainClasses.at<float>(i, k) = 1; else trainClasses.at<float>(i, k) = 0; } } Mat weights(1, TrainData.rows, CV_32FC1, Scalar::all(1)); // 分类器学习 ann.train(TrainData, train