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@ -57,7 +57,6 @@ int CPlateDetect::plateDetect(Mat src, vector<Mat>& resultVec, int index)
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int CPlateDetect::plateDetectDeep(Mat src, vector<CPlate>& resultVec, bool showDetectArea, int index)
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{
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vector<Mat> resultPlates;
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vector<CPlate> color_Plates;
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@ -72,30 +71,47 @@ int CPlateDetect::plateDetectDeep(Mat src, vector<CPlate>& resultVec, bool showD
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m_plateLocate->plateColorLocate(src, color_Plates, index);
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m_plateJudge->plateJudge(color_Plates, color_result_Plates);
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//m_plateJudge->plateJudge(color_Plates, color_result_Plates);
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//下面代码是sunjunlishi添加
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for (int i=0;i<color_Plates.size();++i)
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{
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//暂时屏蔽车牌的判断，车牌判断采用 字符跳变的方式判断
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//字符跳变的形式 | | | | | | |,根据跳变的规律直接判断车牌，更准，效率更高
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//当然判断跳变用的还是原始代码，在后面我加上了主食
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color_result_Plates.push_back(color_Plates[i]);
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}
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for (int i = 0; i< color_result_Plates.size(); i++)
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{
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CPlate plate = color_result_Plates[i];
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RotatedRect minRect = plate.getPlatePos();
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Point2f rect_points[4];
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minRect.points(rect_points);
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all_result_Plates.push_back(plate);
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}
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//颜色和边界闭操作同时采用
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{
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m_plateLocate->plateSobelLocate(src, sobel_Plates, index);
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m_plateJudge->plateJudge(sobel_Plates, sobel_result_Plates);
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//m_plateJudge->plateJudge(sobel_Plates, sobel_result_Plates);
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//下面代码是sunjunlishi添加
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for (int i=0;i<sobel_Plates.size();++i)
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{
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//暂时屏蔽车牌的判断，车牌判断采用 字符跳变的方式判断
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//字符跳变的形式 | | | | | | |,根据跳变的规律直接判断车牌，更准，效率更高
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//当然判断跳变用的还是原始代码，在后面我加上了主食
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sobel_result_Plates.push_back(sobel_Plates[i]);
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}
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for (int i = 0; i< sobel_result_Plates.size(); i++)
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{
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CPlate plate = sobel_result_Plates[i];
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if (0)
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{
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imshow("plate_mat", plate.getPlateMat());
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waitKey(0);
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destroyWindow("plate_mat");
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}
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plate.bColored = false;
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all_result_Plates.push_back(plate);
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@ -108,72 +124,59 @@ int CPlateDetect::plateDetectDeep(Mat src, vector<CPlate>& resultVec, bool showD
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CPlate plate = all_result_Plates[i];
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resultVec.push_back(plate);
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}
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return 0;
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}
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int CPlateDetect::showResult(const Mat& result)
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{
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const int RESULTWIDTH = 1000; //640 930
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const int RESULTHEIGHT = 810; //540 710
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namedWindow("EasyPR", CV_WINDOW_AUTOSIZE);
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/*if(cbgImage_ && cbgImage_->width == result.cols/4*4 && cbgImage_->height == result.rows)
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{
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for (int i=0;i<cbgImage_->height;++i)
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{
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for (int j=0;j<cbgImage_->width;++j)
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{
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cbgImage_->imageData[i*cbgImage_->widthStep+j*3] = result.data[i*result.step[0]+j*3+2];
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cbgImage_->imageData[i*cbgImage_->widthStep+j*3+1] = result.data[i*result.step[0]+j*3+2+1];
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cbgImage_->imageData[i*cbgImage_->widthStep+j*3+2] = result.data[i*result.step[0]+j*3];
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}
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const int RESULTWIDTH = 930; //640 930
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const int RESULTHEIGHT = 710; //540 710
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}
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}*/
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Mat img_window;
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img_window.create(RESULTHEIGHT, RESULTWIDTH, CV_8UC3);
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//Mat img_window;
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//img_window.create(RESULTHEIGHT, RESULTWIDTH, CV_8UC3);
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int nRows = result.rows;
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int nCols = result.cols;
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//int nRows = result.rows;
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//int nCols = result.cols;
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Mat result_resize;
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if (nCols <= img_window.cols && nRows <= img_window.rows) {
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result_resize = result;
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//Mat result_resize;
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//if (nCols <= img_window.cols && nRows <= img_window.rows) {
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// result_resize = result;
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} else if (nCols > img_window.cols && nRows <= img_window.rows) {
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float scale = float(img_window.cols) / float(nCols);
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resize(result, result_resize, Size(), scale, scale, CV_INTER_AREA);
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//} else if (nCols > img_window.cols && nRows <= img_window.rows) {
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// float scale = float(img_window.cols) / float(nCols);
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// resize(result, result_resize, Size(), scale, scale, CV_INTER_AREA);
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} else if (nCols <= img_window.cols && nRows > img_window.rows) {
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float scale = float(img_window.rows) / float(nRows);
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resize(result, result_resize, Size(), scale, scale, CV_INTER_AREA);
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//} else if (nCols <= img_window.cols && nRows > img_window.rows) {
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// float scale = float(img_window.rows) / float(nRows);
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// resize(result, result_resize, Size(), scale, scale, CV_INTER_AREA);
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} else if (nCols > img_window.cols && nRows > img_window.rows) {
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Mat result_middle;
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float scale = float(img_window.cols) / float(nCols);
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resize(result, result_middle, Size(), scale, scale, CV_INTER_AREA);
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//} else if (nCols > img_window.cols && nRows > img_window.rows) {
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// Mat result_middle;
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// float scale = float(img_window.cols) / float(nCols);
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// resize(result, result_middle, Size(), scale, scale, CV_INTER_AREA);
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if (result_middle.rows > img_window.rows) {
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float scale = float(img_window.rows) / float(result_middle.rows);
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resize(result_middle, result_resize, Size(), scale, scale, CV_INTER_AREA);
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// if (result_middle.rows > img_window.rows) {
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// float scale = float(img_window.rows) / float(result_middle.rows);
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// resize(result_middle, result_resize, Size(), scale, scale, CV_INTER_AREA);
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}
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else {
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result_resize = result_middle;
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}
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} else {
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result_resize = result;
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}
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// }
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// else {
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// result_resize = result_middle;
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// }
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//} else {
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// result_resize = result;
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//}
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Mat imageRoi = img_window(Rect((RESULTWIDTH - result_resize.cols) / 2, (RESULTHEIGHT - result_resize.rows) / 2,
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result_resize.cols, result_resize.rows));
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addWeighted(imageRoi, 0, result_resize, 1, 0, imageRoi);
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//Mat imageRoi = img_window(Rect((RESULTWIDTH - result_resize.cols) / 2, (RESULTHEIGHT - result_resize.rows) / 2,
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// result_resize.cols, result_resize.rows));
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//addWeighted(imageRoi, 0, result_resize, 1, 0, imageRoi);
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imshow("EasyPR", img_window);
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waitKey();
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//imshow("EasyPR", img_window);
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destroyWindow("EasyPR");
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return 0;
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}
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liuruoze
10 years ago
