基于qt+opencv的车牌识别项目服务器端

#include "recognisebusiness.h" #include "opencv2/ml.hpp" using namespace ml; RecogniseBusiness::RecogniseBusiness() { } RecogniseBusiness::~RecogniseBusiness() { } QImage RecogniseBusiness::disnoising(QImage& image) { Mat srcMat=QImage2cvMat(image); cv::GaussianBlur(srcMat, srcMat, Size(5, 5), 0, 0, BORDER_DEFAULT); return cvMat2QImage(srcMat); } QImage RecogniseBusiness::grey(QImage& image) { Mat srcMat=QImage2cvMat(image); cv::cvtColor(srcMat, srcMat, CV_BGR2GRAY); return cvMat2QImage(srcMat); } QImage RecogniseBusiness::sobel(QImage& image) { Mat srcMat=QImage2cvMat(image); int scale = 1; int delta = 0; int ddepth = CV_16S; //Generate grad_x and grad_y Mat grad_x, grad_y; Mat abs_grad_x, abs_grad_y; //Gradient X Sobel( srcMat, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT ); convertScaleAbs( grad_x, abs_grad_x ); //Gradient Y Sobel( srcMat, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT ); convertScaleAbs( grad_y, abs_grad_y ); addWeighted( abs_grad_x, 1, abs_grad_y, 0, 0, srcMat ); return cvMat2QImage(srcMat); } QImage RecogniseBusiness::binary(QImage& image) { Mat srcMat=QImage2cvMat(image); threshold(srcMat, srcMat, 0, 255, CV_THRESH_OTSU+CV_THRESH_BINARY); return cvMat2QImage(srcMat); } QImage RecogniseBusiness::dilateCondition(QImage& image,QImage con) { Mat srcMat=QImage2cvMat(image); Mat conMat=QImage2cvMat(con); //颜色信息膨胀 Mat element = getStructuringElement(MORPH_RECT, Size(21, 3) ); srcMat=dilateCondition(srcMat,srcMat,conMat,element); //小范围膨胀 Mat element1 = getStructuringElement(MORPH_RECT, Size(3, 3) ); dilate(srcMat,srcMat,element1); return cvMat2QImage(srcMat); } QImage RecogniseBusiness::erode(QImage& image) { Mat srcMat=QImage2cvMat(image); Mat element2 = getStructuringElement(MORPH_RECT, Size(7, 3) ); cv::erode(srcMat, srcMat, element2); return cvMat2QImage(srcMat); } int RecogniseBusiness::possible(QImage& image,QImage con,vector<QImage>& rv) { Mat srcMat=QImage2cvMat(image); Mat conMat=QImage2cvMat(con); //寻找可能的车牌轮廓 vector< vector< Point> > contours; findContours(srcMat, contours, // a vector of contours CV_RETR_EXTERNAL, // 提取外部轮廓 CV_CHAIN_APPROX_NONE); // all pixels of each contours //Start to iterate to each contour founded vector<vector<Point> >::iterator itc = contours.begin(); vector<RotatedRect> rects; //Remove patch that are no inside limits of aspect ratio and area. //筛出倾斜角过大的矩形 float minH=conMat.rows/100; float minW=conMat.cols/15; while (itc != contours.end()) { //Create bounding rect of object RotatedRect minRect = minAreaRect(Mat(*itc)); //筛选 float r = (float)minRect.size.width / (float)minRect.size.height; float angle = minRect.angle; Size rect_size = minRect.size; if (r < 1) { angle = 90 + angle; swap(rect_size.width, rect_size.height); } //如果抓取的方块旋转超过m_angle角度，则不是车牌，放弃处理 if (angle - 30 < 0 && angle + 30 > 0&&rect_size.height>minH&&rect_size.width>minW) { //Create and rotate image Mat rotmat = getRotationMatrix2D(minRect.center, angle, 1); Mat img_rotated; warpAffine(conMat, img_rotated, rotmat, conMat.size(), CV_INTER_CUBIC); Mat resultMat; resultMat = showResultMat(img_rotated, rect_size, minRect.center); rects.push_back(minRect); rv.push_back(this->cvMat2QImage(resultMat)); ++itc; } else { itc = contours.erase(itc); } }//end of while return rv.size(); } int RecogniseBusiness::plateLocate(QImage& s, vector<QImage>& rv) { //QImage转cvMat Mat srcImage=QImage2cvMat(s); //高斯模糊 cv::Mat dstImage(srcImage.clone()); cv::GaussianBlur(srcImage, dstImage, Size(5, 5), 0, 0, BORDER_DEFAULT); // cv::imshow("beforegaus",srcImage); // cv::imshow("aftergaus",dstImage); //灰度化 cv::cvtColor(dstImage, dstImage, CV_BGR2GRAY); // cv::imshow("aftercvtColor",dstImage); //sobel算子 int scale = 1; int delta = 0; int ddepth = CV_16S; //Generate grad_x and grad_y Mat grad_x, grad_y; Mat abs_grad_x, abs_grad_y; //Gradient X //Scharr( dstImage, grad_x, ddepth, 1, 0, scale, delta, BORDER_DEFAULT ); Sobel( dstImage, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT ); convertScaleAbs( grad_x, abs_grad_x ); //Gradient Y //Scharr( dstImage, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT ); Sobel( dstImage, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT ); convertScaleAbs( grad_y, abs_grad_y ); //Total Gradient (approximate) // addWeighted( abs_grad_x, SOBEL_X_WEIGHT, abs_grad_y, SOBEL_Y_WEIGHT, 0, grad ); // 在调用参数中有两个常量SOBEL_X_WEIGHT与SOBEL_Y_WEIGHT代表水平方向和垂直方向的权值，默认前者是1，后者是0， // 代表仅仅做水平方向求导，而不做垂直方向求导。这样做的意义是，如果我们做了垂直方向求导，会检测出很多水平边缘。 addWeighted( abs_grad_x, 1, abs_grad_y, 0, 0, dstImage ); // cv::imshow("afterSobel",dstImage); //二值化 threshold(dstImage, dstImage, 0, 255, CV_THRESH_OTSU+CV_THRESH_BINARY); //threshold(dstImage, dstImage, 75, 255, CV_THRESH_BINARY); // cv::imshow("afterthreshold",dstImage); //颜色信息膨胀 Mat element = getStructuringElement(MORPH_RECT, Size(21, 3) ); dstImage=dilateCondition(dstImage,dstImage,srcImage,element); // dilate(dstImage,dstImage,element); //小范围膨胀 Mat element1 = getStructuringElement(MORPH_RECT, Size(3, 3) ); dilate(dstImage,dstImage,element1); // cv::imshow("small",dstImage); //腐蚀 Mat element2 = getStructuringElement(MORPH_RECT, Size(7, 3) ); cv::erode(dstImage, dstImage, element2); // cv::imshow("erode",dstImage); //寻找可能的车牌轮廓 vector< vector< Point> > contours; findContours(dstImage, contours, // a vector of contours CV_RETR_EXTERNAL, // 提取外部轮廓 CV_CHAIN_APPROX_NONE); // all pixels of each contours //Start to iterate to each contour founded vector<vector<Point> >::iterator itc = contours.begin(); vector<RotatedRect> rects; //Remove patch that are no inside limits of aspect ratio and area. //筛出倾斜角过大的矩形 float minH=srcImage.rows/100; float minW=srcImage.cols/15; while (itc != contours.end()) { //Create bounding rect of object RotatedRect minRect = minAreaRect(Mat(*itc)); //筛选 float r = (float)minRect.size.width / (float)minRect.size.height; float angle = minRect.angle; Size rect_size = minRect.size; if (r < 1) { angle = 90 + angle; swap(rect_size.width, rect_size.height); } //如果抓取的方块旋转超过m_angle角度，则不是车牌，放弃处理 if (angle - 30 < 0 && angle + 30 > 0&&rect_size.height>minH&&rect_size.width>minW) { //Create and rotate image Mat rotmat = getRotationMatrix2D(minRect.center, angle, 1); Mat img_rotated; warpAffine(srcImage, img_rotated, rotmat, srcImage.size(), CV_INTER_CUBIC); Mat resultMat; resultMat