//GMM.h
#pragma once
#include <iostream>
#include "opencv2/opencv.hpp"
using namespace cv;
using namespace std;
//定义gmm模型用到的变量
#define GMM_MAX_COMPONT 6 //每个GMM最多的高斯模型个数
#define GMM_LEARN_ALPHA 0.005
#define GMM_THRESHOD_SUMW 0.7
#define TRAIN_FRAMES 60 // 对前 TRAIN_FRAMES 帧建模
class MOG_BGS
{
public:
MOG_BGS(void);
~MOG_BGS(void);
void init(const Mat _image);
void processFirstFrame(const Mat _image);
void trainGMM(const Mat _image);
void getFitNum(const Mat _image);
void testGMM(const Mat _image);
Mat getMask(void){ return m_mask; };
private:
Mat m_weight[GMM_MAX_COMPONT]; //权值
Mat m_mean[GMM_MAX_COMPONT]; //均值
Mat m_sigma[GMM_MAX_COMPONT]; //方差
Mat m_mask;
Mat m_fit_num;
};
//GMM.cpp
#include"stdafx.h"
#include "GMM.h"
MOG_BGS::MOG_BGS(void)
{
}
MOG_BGS::~MOG_BGS(void)
{
}
// 全部初始化为0
void MOG_BGS::init(const Mat _image)
{
/****initialization the three parameters ****/
for (int i = 0; i < GMM_MAX_COMPONT; i++)
{
m_weight[i] = Mat::zeros(_image.size(), CV_32FC1);
m_mean[i] = Mat::zeros(_image.size(), CV_8UC1);
m_sigma[i] = Mat::zeros(_image.size(), CV_32FC1);
}
m_mask = Mat::zeros(_image.size(), CV_8UC1);
m_fit_num = Mat::ones(_image.size(), CV_8UC1);
}
//gmm第一帧初始化函数实现
//捕获到第一帧时对高斯分布进行初始化．主要包括对每个高斯分布的权值、期望和方差赋初值．
//其中第一个高斯分布的权值为1，期望为第一个像素数据．其余高斯分布权值为0，期望为0．
//每个高斯分布都被赋予适当的相等的初始方差 15
void MOG_BGS::processFirstFrame(const Mat _image)
{
for (int i = 0; i < GMM_MAX_COMPONT; i++)
{
if (i == 0)
{
m_weight[i].setTo(1.0);
_image.copyTo(m_mean[i]);
m_sigma[i].setTo(15.0);
}
else
{
m_weight[i].setTo(0.0);
m_mean[i].setTo(0);
m_sigma[i].setTo(15.0);
}
}
}
// 通过新的帧来训练GMM
void MOG_BGS::trainGMM(const Mat _image)
{
for (int i = 0; i < _image.rows; i++)
{
for (int j = 0; j < _image.cols; j++)
{
int num_fit = 0;
/**************************** Update parameters Start ******************************************/
for (int k = 0; k < GMM_MAX_COMPONT; k++)
{
int delm = abs(_image.at<uchar>(i, j) - m_mean[k].at<uchar>(i, j));
long dist = delm * delm;
// 判断是否匹配：采样值与高斯分布的均值的距离小于3倍方差（表示匹配）
if (dist < 3.0 * m_sigma[k].at<float>(i, j))
{
// 如果匹配
/****update the weight****/
m_weight[k].at<float>(i, j) += GMM_LEARN_ALPHA * (1 - m_weight[k].at<float>(i, j));
/****update the average****/
m_mean[k].at<uchar>(i, j) += (GMM_LEARN_ALPHA / m_weight[k].at<float>(i, j)) * delm;
/****update the variance****/
m_sigma[k].at<float>(i, j) += (GMM_LEARN_ALPHA / m_weight[k].at<float>(i, j)) * (dist - m_sigma[k].at<float>(i, j));
}
else
{
// 如果不匹配。则该该高斯模型的权值变小
m_weight[k].at<float>(i, j) += GMM_LEARN_ALPHA * (0 - m_weight[k].at<float>(i, j));
num_fit++; // 不匹配的模型个数
}
}
/**************************** Update parameters End ******************************************/
/*********************** Sort Gaussian component by 'weight / sigma' Start ****************************/
//对gmm各个高斯进行排序,从大到小排序,排序依据为 weight / sigma
for (int kk = 0; kk < GMM_MAX_COMPONT; kk++)
{
for (int rr = kk; rr< GMM_MAX_COMPONT; rr++)
{
if (m_weight[rr].at<float>(i, j) / m_sigma[rr].at<float>(i, j) > m_weight[kk].at<float>(i, j) / m_sigma[kk].at<float>(i, j))
{
//权值交换
float temp_weight = m_weight[rr].at<float>(i, j);
m_weight[rr].at<float>(i, j) = m_weight[kk].at<float>(i, j);
m_weight[kk].at<float>(i, j) = temp_weight;
//均值交换
uchar temp_mean = m_mean[rr].at<uchar>(i, j);
m_mean[rr].at<uchar>(i, j) = m_mean[kk].at<uchar>(i, j);
m_mean[kk].at<uchar>(i, j) = temp_mean;
//方差交换
float temp_sigma = m_sigma[rr].at<float>(i, j);
m_sigma[rr].at<float>(i, j) = m_sigma[kk].at<float>(i, j);
m_sigma[kk].at<float>(i, j) = temp_sigma;
}
}
}
/*********************** Sort Gaussian model by 'weight / sigma' End ****************************/
/*********************** Create new Gaussian component Start ****************************/
if (num_fit == GMM_MAX_COMPONT && 0 == m_weight[GMM_MAX_COMPONT - 1].at<float>(i, j))
{
//if there is no exit component fit,then start a new component
//当有新值出现的时候，若目前分布个数小于M，新添一个分布，以新采样值作为均值，并赋予较大方差和较小权值
for (int k = 0; k < GMM_MAX_COMPONT; k++)
{
if (0 == m_weight[k].at<float>(i, j))
{
m_weight[k].at<float>(i, j) = GMM_LEARN_ALPHA;
m_mean[k].at<uchar>(i, j) = _image.at<uchar>(i, j);
m_sigma[k].at<float>(i, j) = 15.0;
//normalization the weight,let they sum to 1
for (int q = 0; q < GMM_MAX_COMPONT && q != k; q++)
{
//对其他的高斯模型的权值进行更新，保持权值和为1
/****update the other unfit's weight,u and sigma remain unchanged****/
m_weight[q].at<float>(i, j) *= (1 - GMM_LEARN_ALPHA);
}
break; //找到第一个权值不为0的即可
}
}
}
else if (num_fit == GMM_MAX_COMPONT && m_weight[GMM_MAX_COMPONT - 1].at<float>(i, j) != 0)
{
//如果GMM_MAX_COMPONT都曾经赋值过，则用新来的高斯代替权值最弱的高斯，权值不变，只更新均值和方差
m_mean[GMM_MAX_COMPONT - 1].at<uchar>(i, j) = _image.at<uchar>(i, j);
m_sigma[GMM_MAX_COMPONT - 1].at<float>(i, j) = 15.0;
}
/*********************** Create new Gaussian component End ****************************/
}
}
}
//对输入图像每个像素gmm选择合适的高斯分量个数
//排序后最有可能是背景分布的排在最前面，较小可能的短暂的分布趋向于末端．我们将排序后的前fit_num个分布选为背景模型;
//在排过序的分布中，累积概率超过GMM_THRESHOD_SUMW的前fit_num个分布被当作背景模型，剩余的其它分布被当作前景模型．
void MOG_BGS::getFitNum(const Mat _image)
{
for (int i = 0; i < _image.rows; i++)
{
for (int j = 0; j < _image.cols; j++)
{
float sum_w = 0.0; //重新赋值为0，给下一个像素做累积
for (uchar k = 0; k < GMM_MAX_COMPONT; k++)
{
sum_w += m_weight[k].at<float>(i, j);
if (sum_w >= GMM_THRESHOD_SUMW) //如果这里THRESHOD_SUMW=0.6的话，那么得到的高斯数目都为1，因为每个像素都有一个权值接近1
{
m_fit_num.at<uchar>(i, j) = k + 1;
break;
}
}
}
}
}
//gmm测试函数的实现
void MOG_BGS::testGMM(const Mat _image)
{
for (int i = 0; i < _image.rows; i++)
{
for (int j = 0; j < _image.cols; j++)
{
int k = 0;
for (; k < m_fit_num.at<uchar>(i, j); k++)
{
if (abs(_image.at<uchar>(i, j) - m_mean[k].at<uchar>(i, j)) < (uchar)(2.5 * m_sigma[k].at<float>(i, j)))
{
m_mask.at<uchar>(i, j) = 0;
break;
}
}
if (k == m_fit_num.at<uchar>(i, j))
{
m_mask.at<uchar>(i, j) = 255;
}
}
}
}
//main.cpp
#include"stdafx.h"
#include "opencv2/opencv.hpp"
#include "GMM.h"
#include <iostream>
#include <cstdio>
using namespace cv;
using namespace std;
int main(int argc, char* argv[])
{
Mat frame, gray, mask;
VideoCapture capture;
capture.open("camera1.avi");
string outfile = "out.avi";
if (!capture.isOpened())
{
cout << "No camera or video input!\n" << endl;
return -1;
}
//获取视频帧率、大小和总帧数
double fps = capture.get(CV_CAP_PROP_FPS);//获取视频文件的帧率
int frame_width = (int)capture.get(CV_CAP_PROP_FRAME_WIDTH);
int frame_height = (int)capture.get(CV_CAP_PROP_FRAME_HEIGHT);
cout << "frame_width:" << frame_width << endl;
cout << "frame_height" << frame_height << endl;
int frame_number = capture.get(CV_CAP_PROP_FRAME_COUNT);//总帧数
cout << "frame_number:" << frame_number << endl;
//创建输出视频文件
VideoWriter Save_result(outfile, CV_FOURCC('M', 'J', 'P', 'G'), fps, Size(frame_width, frame_height), false);
//Mat dstImg(frame_height, frame_width, IPL_DEPTH_8U, 3);//创建要保存的图像
MOG_BGS Mog_Bgs;
int count = 0;
while (1)
{
count++;
cout << count << endl;
capture >> frame;
if (frame.empty())
break;
cvtColor(frame, gray, CV_RGB2GRAY);
if (count == 1)
{
Mog_Bgs.init(gray);
Mog_Bgs.processFirstFrame(gray);
cout << " Using " << TRAIN_FRAMES << " frames to training GMM..." << endl;
}
else if (count < TRAIN_FRAMES)
{
Mog_Bgs.trainGMM(gray);
}
else if (count == TRAIN_FRAMES)
{
Mog_Bgs.getFitNum(gray);
cout << " Training GMM complete!" << endl;
}
else
{
Mog_Bgs.testGMM(gray);
mask = Mog_Bgs.getMask();
morphologyEx(mask, mask, MORPH_OPEN, Mat());
erode(mask, mask, Mat(7, 7, CV_8UC1), Point(-1, -1)); // You can use Mat(5, 5, CV_8UC1) here for less distortion
dilate(mask, mask, Mat(7, 7, CV_8UC1), Point(-1, -1));
imshow("mask", mask);
Save_result << mask;
}
imshow("input", frame);
if (cvWaitKey(10) == 'q')
break;
}
return 0;
}
