void MOG_BGS_HSV::setThreshold(int value, void * userdata)
{
MOG_BGS_HSV *temp = (MOG_BGS_HSV *)userdata;
temp->pMOG.release();
temp->threshold = value;
temp->pMOG = createBackgroundSubtractorMOG2(temp->history, (double)(temp->threshold + 1) / 10.0, temp->shadows);
}
int BackSubs::show_forgnd_and_bgnd_init(int fpsIN, bool lostFlag)
{
StatusText = "NAN";
mog = createBackgroundSubtractorMOG2(BackSubs_History , BackSubs_Threshould , BackSubs_DetectShadows);
stable_bkgnd_phase = 0;
if (fpsIN>0)
fps = fpsIN;
if(fps<=0)
loopWait=33;
else
loopWait=1000/fps; // 1000/30=33; 1000/15=67
BgSubt_Status = BG_INITIALIZING ;
frame_counter = 0;
init_frames_number = 2;
cycles_number_after_lost = 0;
if (lostFlag) {
cycles_number_after_lost++;
BgSubt_Status = BG_RECOVER_FROM_LOST;
}
foreground = Mat();
return 0;
}
BallDetector::BallDetector(string fileName)
{
capture.open(fileName);
pMOG = createBackgroundSubtractorMOG2();
for (int i = 0; i < 100; i++)
capture.read(frame);
}
int main(int, char**) {
//create Background Subtractor objects
pMOG2 = createBackgroundSubtractorMOG2(); //MOG2 approach
// Load the cascades
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading face cascade\n"); return -1; };
int res = process_video();
return 0;
}
void BallDetector::learnBg()
{
pMOG = createBackgroundSubtractorMOG2();
for (int i = 0; i < COUNT; i++)
{
cap.read(frame);
medianBlur(frame, frame, 5);
pMOG->apply(frame, fg);
}
}
MOG_BGS_HSV::MOG_BGS_HSV(Data * data) : AbstractSegment(data)
{
shadows = false;
//Load settings if they exist
history = Settings::loadFromFile("MOG_BGS_History");
threshold = Settings::loadFromFile("MOG_BGS_Threshold");
pMOG = createBackgroundSubtractorMOG2(history, threshold, shadows);
//Create trackbar
createTrackbar("MOG_BGS_History", "Settings", &history, 2000, setHistory, this);
createTrackbar("MOG_BGS_Threshold", "Settings", &threshold, 500, setThreshold, this);
}
void VideoDemos( VideoCapture& surveillance_video, int starting_frame, bool clean_binary_images )
{
Mat previous_gray_frame, optical_flow, optical_flow_display;
Mat current_frame, thresholded_image, closed_image, first_frame;
Mat current_frame_gray, running_average_background;
Mat temp_running_average_background, running_average_difference;
Mat running_average_foreground_mask, running_average_foreground_image;
Mat selective_running_average_background;
Mat temp_selective_running_average_background, selective_running_average_difference;
Mat selective_running_average_foreground_mask, selective_running_average_background_mask, selective_running_average_foreground_image;
double running_average_learning_rate = 0.01;
surveillance_video.set(CV_CAP_PROP_POS_FRAMES,starting_frame);
surveillance_video >> current_frame;
first_frame = current_frame.clone();
cvtColor(current_frame, current_frame_gray, CV_BGR2GRAY);
current_frame.convertTo(running_average_background, CV_32F);
selective_running_average_background = running_average_background.clone();
int rad = running_average_background.depth();
MedianBackground median_background( current_frame, (float) 1.005, 1 );
Mat median_background_image, median_foreground_image;
int codec = static_cast<int>(surveillance_video.get(CV_CAP_PROP_FOURCC));
// V3.0.0 update on next line. OLD CODE was BackgroundSubtractorMOG2 gmm; //(50,16,true);
Ptr<BackgroundSubtractorMOG2> gmm = createBackgroundSubtractorMOG2();
Mat foreground_mask, foreground_image = Mat::zeros(current_frame.size(), CV_8UC3);
double frame_rate = surveillance_video.get(CV_CAP_PROP_FPS);
double time_between_frames = 1000.0/frame_rate;
Timestamper* timer = new Timestamper();
int frame_count = 0;
while ((!current_frame.empty()) && (frame_count++ < 1000))//1800))
{
double duration = static_cast<double>(getTickCount());
vector<Mat> input_planes(3);
split(current_frame,input_planes);
cvtColor(current_frame, current_frame_gray, CV_BGR2GRAY);
if (frame_count%2 == 0) // Skip every second frame so the flow is greater.
{
if ( previous_gray_frame.data )
{
Mat lucas_kanade_flow;
timer->ignoreTimeSinceLastRecorded();
LucasKanadeOpticalFlow(previous_gray_frame, current_frame_gray, lucas_kanade_flow);
timer->recordTime("Lucas Kanade Optical Flow");
calcOpticalFlowFarneback(previous_gray_frame, current_frame_gray, optical_flow, 0.5, 3, 15, 3, 5, 1.2, 0);
cvtColor(previous_gray_frame, optical_flow_display, CV_GRAY2BGR);
drawOpticalFlow(optical_flow, optical_flow_display, 8, Scalar(0, 255, 0), Scalar(0, 0, 255));
timer->recordTime("Farneback Optical Flow");
char frame_str[100];
sprintf( frame_str, "Frame = %d", frame_count);
Mat temp_output = JoinImagesHorizontally( current_frame, frame_str, optical_flow_display, "Farneback Optical Flow", 4 );
Mat optical_flow_output = JoinImagesHorizontally( temp_output, "", lucas_kanade_flow, "Lucas Kanade Optical Flow", 4 );
imshow("Optical Flow", optical_flow_output );
}
std::swap(previous_gray_frame, current_frame_gray);
}
// Static background image
Mat difference_frame, binary_difference;
Mat structuring_element(3,3,CV_8U,Scalar(1));
timer->ignoreTimeSinceLastRecorded();
absdiff(current_frame,first_frame,difference_frame);
cvtColor(difference_frame, thresholded_image, CV_BGR2GRAY);
threshold(thresholded_image,thresholded_image,30,255,THRESH_BINARY);
if (clean_binary_images)
{
morphologyEx(thresholded_image,closed_image,MORPH_CLOSE,structuring_element);
morphologyEx(closed_image,binary_difference,MORPH_OPEN,structuring_element);
current_frame.copyTo(binary_difference, thresholded_image);
}
else
{
binary_difference.setTo(Scalar(0,0,0));
current_frame.copyTo(binary_difference, thresholded_image);
}
timer->recordTime("Static difference");
// Running Average (three channel version)
vector<Mat> running_average_planes(3);
split(running_average_background,running_average_planes);
accumulateWeighted(input_planes[0], running_average_planes[0], running_average_learning_rate);
accumulateWeighted(input_planes[1], running_average_planes[1], running_average_learning_rate);
accumulateWeighted(input_planes[2], running_average_planes[2], running_average_learning_rate);
merge(running_average_planes,running_average_background);
running_average_background.convertTo(temp_running_average_background,CV_8U);
absdiff(temp_running_average_background,current_frame,running_average_difference);
split(running_average_difference,running_average_planes);
// Determine foreground points as any point with a difference of more than 30 on any one channel:
threshold(running_average_difference,running_average_foreground_mask,30,255,THRESH_BINARY);
split(running_average_foreground_mask,running_average_planes);
bitwise_or( running_average_planes[0], running_average_planes[1], running_average_foreground_mask );
bitwise_or( running_average_planes[2], running_average_foreground_mask, running_average_foreground_mask );
if (clean_binary_images)
{
morphologyEx(running_average_foreground_mask,closed_image,MORPH_CLOSE,structuring_element);
morphologyEx(closed_image,running_average_foreground_mask,MORPH_OPEN,structuring_element);
}
running_average_foreground_image.setTo(Scalar(0,0,0));
current_frame.copyTo(running_average_foreground_image, running_average_foreground_mask);
timer->recordTime("Running Average");
// Running Average with selective update
vector<Mat> selective_running_average_planes(3);
// Find Foreground mask
selective_running_average_background.convertTo(temp_selective_running_average_background,CV_8U);
absdiff(temp_selective_running_average_background,current_frame,selective_running_average_difference);
split(selective_running_average_difference,selective_running_average_planes);
// Determine foreground points as any point with an average difference of more than 30 over all channels:
Mat temp_sum = (selective_running_average_planes[0]/3 + selective_running_average_planes[1]/3 + selective_running_average_planes[2]/3);
threshold(temp_sum,selective_running_average_foreground_mask,30,255,THRESH_BINARY_INV);
// Update background
split(selective_running_average_background,selective_running_average_planes);
accumulateWeighted(input_planes[0], selective_running_average_planes[0], running_average_learning_rate,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[1], selective_running_average_planes[1], running_average_learning_rate,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[2], selective_running_average_planes[2], running_average_learning_rate,selective_running_average_foreground_mask);
invertImage(selective_running_average_foreground_mask,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[0], selective_running_average_planes[0], running_average_learning_rate/3.0,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[1], selective_running_average_planes[1], running_average_learning_rate/3.0,selective_running_average_foreground_mask);
accumulateWeighted(input_planes[2], selective_running_average_planes[2], running_average_learning_rate/3.0,selective_running_average_foreground_mask);
merge(selective_running_average_planes,selective_running_average_background);
if (clean_binary_images)
{
morphologyEx(selective_running_average_foreground_mask,closed_image,MORPH_CLOSE,structuring_element);
morphologyEx(closed_image,selective_running_average_foreground_mask,MORPH_OPEN,structuring_element);
}
selective_running_average_foreground_image.setTo(Scalar(0,0,0));
current_frame.copyTo(selective_running_average_foreground_image, selective_running_average_foreground_mask);
timer->recordTime("Selective Running Average");
// Median background
timer->ignoreTimeSinceLastRecorded();
median_background.UpdateBackground( current_frame );
timer->recordTime("Median");
median_background_image = median_background.GetBackgroundImage();
Mat median_difference;
absdiff(median_background_image,current_frame,median_difference);
cvtColor(median_difference, median_difference, CV_BGR2GRAY);
threshold(median_difference,median_difference,30,255,THRESH_BINARY);
median_foreground_image.setTo(Scalar(0,0,0));
current_frame.copyTo(median_foreground_image, median_difference);
// Update the Gaussian Mixture Model
// V3.0.0 update on next line. OLD CODE was gmm(current_frame, foreground_mask);
gmm->apply(current_frame, foreground_mask);
// Clean the resultant binary (moving pixel) mask using an opening.
threshold(foreground_mask,thresholded_image,150,255,THRESH_BINARY);
Mat moving_incl_shadows, shadow_points;
threshold(foreground_mask,moving_incl_shadows,50,255,THRESH_BINARY);
absdiff( thresholded_image, moving_incl_shadows, shadow_points );
Mat cleaned_foreground_mask;
if (clean_binary_images)
{
morphologyEx(thresholded_image,closed_image,MORPH_CLOSE,structuring_element);
morphologyEx(closed_image,cleaned_foreground_mask,MORPH_OPEN,structuring_element);
}
else cleaned_foreground_mask = thresholded_image.clone();
foreground_image.setTo(Scalar(0,0,0));
current_frame.copyTo(foreground_image, cleaned_foreground_mask);
timer->recordTime("Gaussian Mixture Model");
// Create an average background image (just for information)
Mat mean_background_image;
timer->ignoreTimeSinceLastRecorded();
// V3.0.0 update on next line. OLD CODE was gmm.getBackgroundImage(mean_background_image);
gmm->getBackgroundImage(mean_background_image);
duration = static_cast<double>(getTickCount())-duration;
duration /= getTickFrequency()/1000.0;
int delay = (time_between_frames>duration) ? ((int) (time_between_frames-duration)) : 1;
char c = cvWaitKey(delay);
char frame_str[100];
sprintf( frame_str, "Frame = %d", frame_count);
Mat temp_static_output = JoinImagesHorizontally( current_frame, frame_str, first_frame, "Static Background", 4 );
Mat static_output = JoinImagesHorizontally( temp_static_output, "", binary_difference, "Foreground", 4 );
imshow("Static Background Model", static_output );
Mat temp_running_output = JoinImagesHorizontally( current_frame, frame_str, temp_running_average_background, "Running Average Background", 4 );
Mat running_output = JoinImagesHorizontally( temp_running_output, "", running_average_foreground_image, "Foreground", 4 );
imshow("Running Average Background Model", running_output );
Mat temp_selective_output = JoinImagesHorizontally( current_frame, frame_str, temp_selective_running_average_background, "Selective Running Average Background", 4 );
Mat selective_output = JoinImagesHorizontally( temp_selective_output, "", selective_running_average_foreground_image, "Foreground", 4 );
imshow("Selective Running Average Background Model", selective_output );
Mat temp_median_output = JoinImagesHorizontally( current_frame, frame_str, median_background_image, "Median Background", 4 );
Mat median_output = JoinImagesHorizontally( temp_median_output, "", median_foreground_image, "Foreground", 4 );
imshow("Median Background Model", median_output );
Mat temp_gaussian_output = JoinImagesHorizontally( current_frame, frame_str, mean_background_image, "GMM Background", 4 );
Mat gaussian_output = JoinImagesHorizontally( temp_gaussian_output, "", foreground_image, "Foreground", 4 );
imshow("Gaussian Mixture Model", gaussian_output );
timer->putTimes( current_frame );
imshow( "Computation Times", current_frame );
surveillance_video >> current_frame;
}
cvDestroyAllWindows();
}