OpenCV 360度行车辅助系统——红绿灯智能预判

1. AI学习红绿灯

此功能仅限前置摄像头。当视频刚开始播放时，通过自研AI算法学习红绿像素高频出现的位置，学习结束后根据学习结果判断是否使用AI红绿灯 区域，并在接下来的视频部分针对区域进行红绿灯识别。

检测到红灯则在视频底端显示“Red”字样，检测到绿灯则在视频底端显示“Green”字样。

1、收集前5%帧的红绿色素点

2、按照坐标大小排序，删掉前后10%，删掉异常大、小的值

3、筛选后判断结果数量是否充足，充足则开始使用，不足则用系统默认的区域

4、在应用的过程中持续监测红绿像素，如果出现大幅度像素密集点偏移，则重新学习

Mat player::RG_detector(Mat frame)
{Mat detected_frame = frame.clone();Mat roi(detected_frame, Rect(400,200,400,100));Rect detected_rect(400,200,400,100);//调整亮度frame.convertTo(img, img.type(), a, b);//转换为YCrCb颜色空间cvtColor(img, imgYCrCb, CV_BGR2YCrCb);imgRed.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);imgGreen.create(imgYCrCb.rows, imgYCrCb.cols, CV_8UC1);//分解YCrCb的三个成分vector<Mat> planes;split(imgYCrCb, planes);// 遍历以根据Cr分量拆分红色和绿色MatIterator_<uchar> it_Cr = planes[1].begin<uchar>(),it_Cr_end = planes[1].end<uchar>();MatIterator_<uchar> it_Red = imgRed.begin<uchar>();MatIterator_<uchar> it_Green = imgGreen.begin<uchar>();for (; it_Cr != it_Cr_end; ++it_Cr, ++it_Red, ++it_Green){// RED, 145<Cr<470if (*it_Cr > 145 && *it_Cr < 470)*it_Red = 255;else*it_Red = 0;// GREEN 95<Cr<110if (*it_Cr > 95 && *it_Cr < 110)*it_Green = 255;else*it_Green = 0;}redCount = processImgR(imgRed);greenCount = processImgG(imgGreen);cout << "red:" << redCount << ";  " << "green:" << greenCount << endl;if(currentFrameNumber < 50){putText(frame, "AI learning", hintTextPoint, font_face, font_scale, Scalar(135, 74, 32), thickness, 8, 0);}else{if(redCount > greenCount ){putText(frame, "Red", hintTextPoint, font_face, font_scale, Scalar(41, 41, 239), thickness, 8, 0);}else if(redCount < greenCount ){putText(frame, "Green", hintTextPoint, font_face, font_scale, Scalar(22, 210, 115), thickness, 8, 0);}}return frame;
}bool player::RGB_AI_learn_x_y(int x, int y)
{if(currentFrameNumber <= totalFrameNumber / 20){if(y <= frame.cols/2){RGB_learn_array_x.push_back(x);RGB_learn_array_y.push_back(y);}}else{sort(RGB_learn_array_x.begin(), RGB_learn_array_x.end());sort(RGB_learn_array_y.begin(), RGB_learn_array_y.end());if(int subArrayNum = RGB_learn_array_x.size() - RGB_learn_array_y.size() != 0){//删除多余元素if(subArrayNum > 0){for(int i=0; i<subArrayNum; i++){RGB_learn_array_x.pop_back();}}else if(subArrayNum < 0){for(int i=0; i>subArrayNum; i--){RGB_learn_array_y.pop_back();}}}int delete_count = 0, delete_total = RGB_learn_array_x.size()/10;for(delete_count = 0; delete_count < delete_total; delete_count++){//删除 10% 较大值RGB_learn_array_x.pop_back();RGB_learn_array_y.pop_back();}sort(RGB_learn_array_x.rbegin(), RGB_learn_array_x.rend());sort(RGB_learn_array_y.rbegin(), RGB_learn_array_y.rend());for(delete_count = 0; delete_count < delete_total; delete_count++){//删除 10% 较小值RGB_learn_array_x.pop_back();RGB_learn_array_y.pop_back();}sort(RGB_learn_array_x.begin(), RGB_learn_array_x.end());sort(RGB_learn_array_y.begin(), RGB_learn_array_y.end());long long int sum_x, sum_y;sum_x = accumulate(begin(RGB_learn_array_x),end(RGB_learn_array_x), 0);sum_y = accumulate(begin(RGB_learn_array_y),end(RGB_learn_array_y), 0);//计算均值RGB_learn_x = (int)sum_x / RGB_learn_array_x.size();RGB_learn_y = (int)sum_y / RGB_learn_array_y.size();//计算范围RGB_learn_w = RGB_learn_array_x.end() - RGB_learn_array_x.begin();RGB_learn_h = RGB_learn_array_y.end() - RGB_learn_array_y.begin();if(RGB_learn_array_x.size()>50)return true;//如果学习到大量红绿灯，认为学习有效else return false;}
}int player::processImgR(Mat src)
{Mat tmp;vector<vector<Point>> contours;vector<Vec4i> hierarchy;vector<Point> hull;CvPoint2D32f tempNode;CvMemStorage* storage = cvCreateMemStorage();CvSeq* pointSeq = cvCreateSeq(CV_32FC2, sizeof(CvSeq), sizeof(CvPoint2D32f), storage);Rect* trackBox;Rect* result;int resultNum = 0;int area = 0;src.copyTo(tmp);//提取轮廓findContours(tmp, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);if (contours.size() > 0){trackBox = new Rect[contours.size()];result = new Rect[contours.size()];//确定要跟踪的区域for (int i = 0; i < contours.size(); i++){cvClearSeq(pointSeq);// 获取凸包的点集convexHull(Mat(contours[i]), hull, true);int hullcount = (int)hull.size();// 凸包的保存点for (int j = 0; j < hullcount - 1; j++){//if(HoughCircle_detector(src) > 1){bool final_learn_result;if(currentFrameNumber <= totalFrameNumber / 20){//前面 5% 帧用来学习红绿灯位置final_learn_result = RGB_AI_learn_x_y(hull[j].x, hull[j].y);}else{if(final_learn_result == true){//学习有效，使用AI识别红绿灯if(hull[j].x >= RGB_learn_x-RGB_learn_w/2 && hull[j].x <= RGB_learn_x+RGB_learn_w/2 &&hull[j].y >= RGB_learn_y-RGB_learn_h/2 && hull[j].y <= RGB_learn_y+RGB_learn_h/2){//cout<<"AI success!"<<endl;tempNode.x = hull[j].x;tempNode.y = hull[j].y;cvSeqPush(pointSeq, &tempNode);}}else{if(hull[j].x >= 400 && hull[j].x <= 800 &&hull[j].y >= 400 && hull[j].y <= 550){tempNode.x = hull[j].x;tempNode.y = hull[j].y;cvSeqPush(pointSeq, &tempNode);}}}}}trackBox[i] = cvBoundingRect(pointSeq);}if (isFirstDetectedR){lastTrackBoxR = new Rect[contours.size()];for (int i = 0; i < contours.size(); i++)lastTrackBoxR[i] = trackBox[i];lastTrackNumR = contours.size();isFirstDetectedR = false;}else{for (int i = 0; i < contours.size(); i++){for (int j = 0; j < lastTrackNumR; j++){if (isIntersected(trackBox[i], lastTrackBoxR[j])){result[resultNum] = trackBox[i];break;}}resultNum++;}delete[] lastTrackBoxR;lastTrackBoxR = new Rect[contours.size()];for (int i = 0; i < contours.size(); i++){lastTrackBoxR[i] = trackBox[i];}lastTrackNumR = contours.size();}delete[] trackBox;}else{isFirstDetectedR = true;result = NULL;}cvReleaseMemStorage(&storage);if (result != NULL){for (int i = 0; i < resultNum; i++){area += result[i].area();}}delete[] result;return area;
}int player::processImgG(Mat src)
{Mat tmp;vector<vector<Point> > contours;vector<Vec4i> hierarchy;vector< Point > hull;CvPoint2D32f tempNode;CvMemStorage* storage = cvCreateMemStorage();CvSeq* pointSeq = cvCreateSeq(CV_32FC2, sizeof(CvSeq), sizeof(CvPoint2D32f), storage);Rect* trackBox;Rect* result;int resultNum = 0;int area = 0;src.copyTo(tmp);// Extract the contourfindContours(tmp, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);if (contours.size() > 0){trackBox = new Rect[contours.size()];result = new Rect[contours.size()];// Determine the area to trackfor (int i = 0; i < contours.size(); i++){cvClearSeq(pointSeq);// Get the point set of the convex hullconvexHull(Mat(contours[i]), hull, true);int hullcount = (int)hull.size();// Save points of the convex hullfor (int j = 0; j < hullcount - 1; j++){//if(HoughCircle_detector(src) > 1){bool final_learn_result;if(currentFrameNumber <= totalFrameNumber / 20){//前面 5% 帧用来学习红绿灯位置final_learn_result = RGB_AI_learn_x_y(hull[j].x, hull[j].y);}else{if(final_learn_result == true){//学习有效，使用AI识别红绿灯if(hull[j].x >= RGB_learn_x-RGB_learn_w/2 && hull[j].x <= RGB_learn_x+RGB_learn_w/2 &&hull[j].y >= RGB_learn_y-RGB_learn_h/2 && hull[j].y <= RGB_learn_y+RGB_learn_h/2){//cout<<"AI success!"<<endl;tempNode.x = hull[j].x;tempNode.y = hull[j].y;cvSeqPush(pointSeq, &tempNode);}}else{if(hull[j].x >= 400 && hull[j].x <= 800 &&hull[j].y >= 400 && hull[j].y <= 550){tempNode.x = hull[j].x;tempNode.y = hull[j].y;cvSeqPush(pointSeq, &tempNode);}}}}}trackBox[i] = cvBoundingRect(pointSeq);}if (isFirstDetectedG){lastTrackBoxG = new Rect[contours.size()];for (int i = 0; i < contours.size(); i++)lastTrackBoxG[i] = trackBox[i];lastTrackNumG = contours.size();isFirstDetectedG = false;}else{for (int i = 0; i < contours.size(); i++){for (int j = 0; j < lastTrackNumG; j++){if (isIntersected(trackBox[i], lastTrackBoxG[j])){result[resultNum] = trackBox[i];break;}}resultNum++;}delete[] lastTrackBoxG;lastTrackBoxG = new Rect[contours.size()];for (int i = 0; i < contours.size(); i++){lastTrackBoxG[i] = trackBox[i];}lastTrackNumG = contours.size();}delete[] trackBox;}else{isFirstDetectedG = true;result = NULL;}cvReleaseMemStorage(&storage);if (result != NULL){for (int i = 0; i < resultNum; i++){area += result[i].area();}}delete[] result;return area;
}