车牌识别opencv+tesseract-ocr

#include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include "tesseract\baseapi.h" #include "tesseract\strngs.h" //#include<opencv2\opencv.hpp> #pragma comment(lib,"libtesseract302.lib") #pragma comment(lib, "liblept168.lib") #include <iostream> #include<opencv/highgui.h> #include<opencv/cxcore.h> #include"qmessagebox.h" #include <stdio.h > // using namespace cv; using namespace std; #include "chepaishibie.h" #include "cv.h" #include "highgui.h" #include "cxcore.h" #include<qfiledialog.h> #include<qtextcodec.h> #include<qmessagebox.h> #include <iostream> #include"qlineedit.h" void swap1(float *a, float *b); void swap2(int *a, int *b); bool isEligible(const RotatedRect &candidate); void OstuBeresenThreshold(const Mat &in, Mat &out); Mat getRplane(const Mat &in); chepaishibie::chepaishibie(QWidget *parent) : QMainWindow(parent) { ui.setupUi(this); } chepaishibie::~chepaishibie() { } Mat image;//定义读入图像 QImage img;// string name;//定义图片地址字符串 void chepaishibie::on_pushButton_clicked()//打开图片 { QString filename = QFileDialog::getOpenFileName(this, tr("Open Image"), "", tr("Image File(*.bmp *.jpg *.jpeg *.png)")); QTextCodec *code = QTextCodec::codecForName("gb18030");//转码 name = code->fromUnicode(filename).data();//获取图片地址 image = imread(name);//读取图片 //将图片长缩放为480 if (image.cols < 480) { float n = (float)480 / image.cols; cv::resize(image, image, Size((float)image.cols*n, (float)image.rows*n), 0, 0, CV_INTER_LINEAR); } else { float n = (float)image.cols / 480; cv::resize(image, image, Size((float)image.cols / n, (float)image.rows / n), 0, 0, CV_INTER_LINEAR); } //提示是否找到图片 if (!image.data) { QMessageBox msgBox; msgBox.setText(tr("image data is null")); msgBox.exec(); return; } else { cv::cvtColor(image, image, CV_BGR2RGB); img = QImage((const unsigned char*)(image.data), image.cols, image.rows, image.cols*image.channels(), QImage::Format_RGB888); ui.label->clear(); ui.label->setPixmap(QPixmap::fromImage(img)); ui.label->resize(ui.label->pixmap()->size()); } } int myOtsu(const IplImage *frame) //大津法求阈值 { #define GrayScale 256 //frame灰度级 int width = frame->width; int height = frame->height; int pixelCount[GrayScale] = { 0 }; float pixelPro[GrayScale] = { 0 }; int i, j, pixelSum = width * height, threshold = 0; uchar* data = (uchar*)frame->imageData; //统计每个灰度级中像素的个数 for (i = 0; i < height; i++) { for (j = 0; j < width; j++) { pixelCount[(int)data[i * width + j]]++; } } //计算每个灰度级的像素数目占整幅图像的比例 for (i = 0; i < GrayScale; i++) { pixelPro[i] = (float)pixelCount[i] / pixelSum; } //遍历灰度级[0,255],寻找合适的threshold float w0, w1, u0tmp, u1tmp, u0, u1, deltaTmp, deltaMax = 0; for (i = 0; i < GrayScale; i++) { w0 = w1 = u0tmp = u1tmp = u0 = u1 = deltaTmp = 0; for (j = 0; j < GrayScale; j++) { if (j <= i) //背景部分 { w0 += pixelPro[j]; u0tmp += j * pixelPro[j]; } else //前景部分 { w1 += pixelPro[j]; u1tmp += j * pixelPro[j]; } } u0 = u0tmp / w0; u1 = u1tmp / w1; deltaTmp = (float)(w0 *w1* pow((u0 - u1), 2)); if (deltaTmp > deltaMax) { deltaMax = deltaTmp; threshold = i; } } return threshold; } //识别按钮事件 void chepaishibie::on_pushButton_2_clicked() { const char* c_s = name.c_str(); IplImage *imgSrc = cvLoadImage(c_s); Mat mat123 = cvarrToMat(imgSrc); IplImage *imgH, *imgS, *imgV, *imgHSV, *imgGray; //通过分离颜色通道，选择蓝色区域为车牌 imgHSV = cvCreateImage(cvGetSize(imgSrc), imgSrc->depth, 3); imgH = cvCreateImage(cvGetSize(imgSrc), imgSrc->depth, 1); imgS = cvCreateImage(cvGetSize(imgSrc), imgSrc->depth, 1); imgV = cvCreateImage(cvGetSize(imgSrc), imgSrc->depth, 1); cvCvtColor(imgSrc, imgHSV, CV_BGR2HSV); cvSplit(imgHSV, imgH, imgS, imgV, 0); cvInRangeS(imgH, cvScalar(94, 0, 0, 0), cvScalar(115, 0, 0, 0), imgH); cvInRangeS(imgS, cvScalar(90, 0, 0, 0), cvScalar(255, 0, 0, 0), imgS); cvInRangeS(imgV, cvScalar(36, 0, 0, 0), cvScalar(255, 0, 0, 0), imgV); IplImage *imgTemp, *imgHsvBinnary; imgTemp = cvCreateImage(cvGetSize(imgSrc), imgSrc->depth, 1); imgHsvBinnary = cvCreateImage(cvGetSize(imgSrc), imgSrc->depth, 1); cvAnd(imgH, imgS, imgTemp); cvAnd(imgTemp, imgV, imgHsvBinnary); //形态学去噪 //定义结构元素 IplConvKernel *element = 0; int values[2] = { 255, 255 }; int rows = 2, cols = 1, anchor_x = 0, anchor_y = 1; element = cvCreateStructuringElementEx(cols, rows, anchor_x, anchor_y, CV_SHAPE_CUSTOM, values); //膨胀腐蚀 cvDilate(imgHsvBinnary, imgHsvBinnary, element, 1); cvErode(imgHsvBinnary, imgHsvBinnary, element, 1); /*cvNamedWindow("imgh1"); cvShowImage("imgh1", imgHsvBinnary);*/ Mat mat12 = cvarrToMat(imgHsvBinnary); float n = (float)mat12.cols / mat12.rows; //图片缩放长为1000 cout << "长宽比1" << n << endl; if (mat12.cols < 1000) { float n = (float)1000 / mat12.cols; cv::resize(mat12, mat12, Size((float)mat12.cols*n, (float)mat12.rows*n), 0, 0, CV_INTER_LINEAR); } else { float n = (float)mat12.cols / 1000; cv::resize(mat12, mat12, Size((float)mat12.cols / n, (float)mat12.rows / n), 0, 0, CV_INTER_LINEAR); } cout << "长" << mat12.cols << "宽" << mat12.rows << endl; float n1 = (float)mat12.cols / mat12.rows; cout << "长宽比1" << n1 << endl; //resize(mat12, mat12, Size(850, 540), 0, 0, CV_INTER_LINEAR); //Mat element1 = getStructuringElement(MORPH_RECT, Size(13, 13)); //闭形态学的结构元素 //morphologyEx(mat12, mat12, MORPH_CLOSE, element1); //中值滤波 medianBlur(mat12, mat12, 3); //Mat element1 = getStructuringElement(MORPH_RECT, Size(13, 13)); //闭形态学的结构元素 //morphologyEx(mat12, mat12, MORPH_CROSS, element1); medianBlur(mat12, mat12, 5); int blockSize = 11; int constValue = 10; Mat img_gray; //adaptiveThreshold(mat12, mat12, 255, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY_INV, blockSize, constValue);//自适应二值化 imwrite("1221.jpg", mat12); Mat r = imread("1221.jpg", CV_LOAD_IMAGE_GRAYSCALE); Mat r1 = imread("1221.jpg", 1); adaptiveThreshold(r, r, 255, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY_INV, blockSize, constValue);//自适应二值化 vector <vector< Point> > contours; //cout <<contours.size() << endl; vector<Vec4i> hierarchy; findContours(r, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); //只检测外轮廓对候选的轮廓进行进一步筛选 int index = 0; //cout << "thy" << contours.size() << endl; for (; index = 0; index = hierarchy[index][0]) { Scalar color(rand() & 255, rand() & 200, rand() & 255); drawContours(r, contours, index, color, FILLED, 8, hierarchy); } vector <vector< Point >>::iterator itc = contours.begin(); RotatedRect mr = minAreaRect(Mat(*itc)); vector <RotatedRect> rects; while (itc != contours.end()) { RotatedRect mr = minAreaRect(Mat(*itc)); //返回每个轮廓的最小有界矩形区域 if (!isEligible(mr)) //判断矩形轮廓是否符合要求 { itc = contours.erase(itc); } else { rects.push_back(mr); ++itc; } } cv::resize(mat123, mat123, Size(mat12.cols, mat12.rows), 0, 0, CV_INTER_LINEAR); //测试是否找到了号码区域 cout << "qwe" << rects.size() << endl; Point2f vertices[4]; if (rects.size() == 0){ QMessageBox msgBox2; msgBox2.setText(tr("don't find the license plate")); msgBox2.exec(); return; } rects[0].points(vertices); for (int i = 0; i < 4; i++) line(mat123, vertices[i], vertices[(i + 1) % 4], CV_RGB(0, 0, 255), 3); imwrite("mat123.jpg", mat123); Mat matimg = imread("mat123.jpg", 1); float mat123x = (float)matimg.cols / 2; float mat123y = (float)matimg.rows / 2; cv::resize(matimg, matimg, Size(mat123x, mat123y), 0, 0, CV_INTER_LINEAR); QImage imgcp