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112 lines
3.0 KiB
C++
112 lines
3.0 KiB
C++
#include "opencv2/core.hpp"
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#include <opencv2/core/utility.hpp>
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#include "opencv2/imgproc.hpp"
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#include "opencv2/video/background_segm.hpp"
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#include "opencv2/videoio.hpp"
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#include "opencv2/highgui.hpp"
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#include <stdio.h>
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using namespace std;
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using namespace cv;
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static void help()
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{
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printf("\nDo background segmentation, especially demonstrating the use of cvUpdateBGStatModel().\n"
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"Learns the background at the start and then segments.\n"
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"Learning is togged by the space key. Will read from file or camera\n"
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"Usage: \n"
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" ./bgfg_segm [--camera]=<use camera, if this key is present>, [--file_name]=<path to movie file> \n\n");
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}
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const char* keys =
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{
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"{c camera | | use camera or not}"
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"{m method |mog2 | method (knn or mog2) }"
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"{s smooth | | smooth the mask }"
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"{fn file_name|tree.avi | movie file }"
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};
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//this is a sample for foreground detection functions
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int main(int argc, const char** argv)
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{
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help();
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CommandLineParser parser(argc, argv, keys);
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bool useCamera = parser.has("camera");
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bool smoothMask = parser.has("smooth");
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string file = parser.get<string>("file_name");
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string method = parser.get<string>("method");
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VideoCapture cap;
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bool update_bg_model = true;
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if( useCamera )
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cap.open(0);
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else
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cap.open(file.c_str());
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parser.printMessage();
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if( !cap.isOpened() )
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{
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printf("can not open camera or video file\n");
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return -1;
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}
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namedWindow("image", WINDOW_NORMAL);
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namedWindow("foreground mask", WINDOW_NORMAL);
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namedWindow("foreground image", WINDOW_NORMAL);
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namedWindow("mean background image", WINDOW_NORMAL);
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Ptr<BackgroundSubtractor> bg_model = method == "knn" ?
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createBackgroundSubtractorKNN().dynamicCast<BackgroundSubtractor>() :
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createBackgroundSubtractorMOG2().dynamicCast<BackgroundSubtractor>();
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Mat img0, img, fgmask, fgimg;
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for(;;)
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{
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cap >> img0;
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if( img0.empty() )
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break;
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resize(img0, img, Size(640, 640*img0.rows/img0.cols), INTER_LINEAR);
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if( fgimg.empty() )
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fgimg.create(img.size(), img.type());
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//update the model
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bg_model->apply(img, fgmask, update_bg_model ? -1 : 0);
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if( smoothMask )
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{
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GaussianBlur(fgmask, fgmask, Size(11, 11), 3.5, 3.5);
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threshold(fgmask, fgmask, 10, 255, THRESH_BINARY);
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}
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fgimg = Scalar::all(0);
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img.copyTo(fgimg, fgmask);
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Mat bgimg;
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bg_model->getBackgroundImage(bgimg);
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imshow("image", img);
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imshow("foreground mask", fgmask);
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imshow("foreground image", fgimg);
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if(!bgimg.empty())
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imshow("mean background image", bgimg );
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char k = (char)waitKey(30);
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if( k == 27 ) break;
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if( k == ' ' )
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{
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update_bg_model = !update_bg_model;
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if(update_bg_model)
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printf("Background update is on\n");
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else
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printf("Background update is off\n");
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}
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}
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return 0;
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}
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