Subversion Repositories OpenCV2-Cookbook

/*------------------------------------------------------------------------------------------*\
   This file contains material supporting chapter 10 of the cookbook:  
   Computer Vision Programming using the OpenCV Library.
   by Robert Laganiere, Packt Publishing, 2011.

   This program is free software; permission is hereby granted to use, copy, modify,
   and distribute this source code, or portions thereof, for any purpose, without fee,
   subject to the restriction that the copyright notice may not be removed
   or altered from any source or altered source distribution.
   The software is released on an as-is basis and without any warranties of any kind.
   In particular, the software is not guaranteed to be fault-tolerant or free from failure.
   The author disclaims all warranties with regard to this software, any use,
   and any consequent failure, is purely the responsibility of the user.
 
   Copyright (C) 2010-2011 Robert Laganiere, www.laganiere.name
\*------------------------------------------------------------------------------------------*/

#if !defined BGFGSeg
#define BGFGSeg

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include "videoprocessor.h"

class BGFGSegmentor : public FrameProcessor {
       
        cv::Mat gray;                   // current gray-level image
        cv::Mat background;             // accumulated background
        cv::Mat backImage;              // background image
        cv::Mat foreground;             // foreground image
        double learningRate;    // learning rate in background accumulation
        int threshold;                  // threshold for foreground extraction

  public:

        BGFGSegmentor() : threshold(10), learningRate(0.01) {}

        // Set the threshold used to declare a foreground
        void setThreshold(int t) {

                threshold= t;
        }

        // Set the learning rate
        void setLearningRate(double r) {

                learningRate= r;
        }

        // processing method
        void process(cv:: Mat &frame, cv:: Mat &output) {

                // convert to gray-level image
                cv::cvtColor(frame, gray, CV_BGR2GRAY);

                // initialize background to 1st frame
                if (background.empty())
                        gray.convertTo(background, CV_32F);
               
                // convert background to 8U
                background.convertTo(backImage,CV_8U);

                // compute difference between current image and background
                cv::absdiff(backImage,gray,foreground);

                // apply threshold to foreground image
                cv::threshold(foreground,output,threshold,255,cv::THRESH_BINARY_INV);

                // accumulate background
                cv::accumulateWeighted(gray, background, learningRate, output);
        }
};

#endif