如上图所示，这是一段交通路口处的视频监控录像。录像中的每一个像素在大部分时间内属于静态背景，小部分时间内属于动态前景。所以将视频中的所有帧的图像叠加，然后求平均，平均后的图像会比较接近实际的背景。
代码：
#include<opencv2/opencv.hpp> #include<iostream> #include<Eigen/Dense> #include<string> cv::Mat getAverageBG(cv::VideoCapture& capture,int start,int end){long frameCount = end - start;int index = 0;int width = capture.get(CV_CAP_PROP_FRAME_WIDTH);int height = capture.get(CV_CAP_PROP_FRAME_HEIGHT);Eigen::MatrixXd averageBgR(height, width);Eigen::MatrixXd averageBgG(height, width);Eigen::MatrixXd averageBgB(height, width);for (int i = 0; i < height; i++){for (int j = 0; j < width; j++){averageBgR(i, j) = 0;averageBgG(i, j) = 0;averageBgB(i, j) = 0;}}for (int i = start; i < end; i++){index++;//移动内部指针到视频图像的第i帧capture.set(CV_CAP_PROP_POS_FRAMES, i);cv::Mat curMat;capture.read(curMat);for (int i = 0; i < height; i++){cv::Vec3b* p = curMat.ptr<cv::Vec3b>(i);for (int j = 0; j < width; j++){averageBgR(i, j) += p[j][0] ;averageBgG(i, j) += p[j][1] ;averageBgB(i, j) += p[j][2] ;}}std::cout << index<<"/"<<frameCount << std::endl;}//for every frame cv::Mat result(height,width,CV_8UC3);for (int i = 0; i < height; i++){for (int j = 0; j < width; j++){cv::Vec3b p;//std::cout << averageBgR(i, j) << std::endl;p[0] = static_cast<uchar>(averageBgR(i, j) / (float)frameCount);p[1] = static_cast<uchar>(averageBgG(i, j) / (float)frameCount);p[2] = static_cast<uchar>(averageBgB(i, j) / (float)frameCount);result.at<cv::Vec3b>(i, j) = p;}}return result; } int main(int argc,char* argv[]){cv::VideoCapture capture;if (argc < 3){return 1;}std::string fileName(argv[1]);std::string outName(argv[2]);if (!capture.open(fileName)){std::cout << "can not open video" << std::endl;return 1;}long frameCount = static_cast<long>(capture.get(CV_CAP_PROP_FRAME_COUNT));cv::Mat bg = getAverageBG(capture, 0, frameCount);cv::imwrite(outName, bg);cv::imshow("", bg);cv::waitKey();return 0; }
结果：
