作者:王先荣
前言
在很多情况下,我们需要从一段视频或者一系列图片中找到感兴趣的目标,比如说当人进入已经打烊的超市时发出警报。为了达到这个目的,我们首先需要“学习”背景模型,然后将背景模型和当前图像进行比较,从而得到前景目标。
背景建模
背景与前景都是相对的概念,以高速公路为例:有时我们对高速公路上来来往往的汽车感兴趣,这时汽车是前景,而路面以及周围的环境是背景;有时我们仅仅对闯入高速公路的行人感兴趣,这时闯入者是前景,而包括汽车之类的其他东西又成了背景。背景建模的方式很多,或高级或简单。不过各种背景模型都有自己适用的场合,即使是高级的背景模型也不能适用于任何场合。下面我将逐一介绍OpenCv中已经实现,或者在《学习OpenCv》这本书中介绍的背景建模方法。
1.帧差
帧差可说是最简单的一种背景模型,指定视频中的一幅图像为背景,用当前帧与背景进行比较,根据需要过滤较小的差异,得到的结果就是前景了。OpenCv中为我们提供了一种动态计算阀值,然后用帧差进行前景检测的函数——cvChangeDetection(注:EmguCv中没有封装cvChangeDetection,我将其声明到OpenCvInvoke类中,具体实现见文末代码)。而通过对两幅图像使用减法运算,然后再用指定阀值过滤的方法在《学习OpenCv》一书中有详细的介绍。它们的实现代码如下:
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
[DllImport("cvaux200.dll")]
public static extern void cvChangeDetection(IntPtr prev_frame, IntPtr curr_frame, IntPtr change_mask);
//backgroundMask为背景,imageBackgroundModel为背景模型,currentFrame为当前帧
if (backgroundMask == null)
backgroundMask = new Image<Gray, byte>(imageBackgroundModel.Size);
if (threshold == 0d)
//如果阀值为0,使用OpenCv中的自适应动态背景检测
OpenCvInvoke.cvChangeDetection(imageBackgroundModel.Ptr, currentFrame.Ptr, backgroundMask.Ptr);
else
{
//如果设置了阀值,使用帧差
Image<TColor, Byte> imageTemp = imageBackgroundModel.AbsDiff(currentFrame);
Image<Gray, Byte>[] images = imageTemp.Split();
backgroundMask.SetValue(0d);
foreach (Image<Gray, Byte> image in images)
backgroundMask._Or(image.ThresholdBinary(new Gray(threshold), new Gray(255d)));
}
backgroundMask._Not();
对于类似无人值守的仓库防盗之类的场合,使用帧差效果估计很好。
2.背景统计模型
背景统计模型是:对一段时间的背景进行统计,然后计算其统计数据(例如平均值、平均差分、标准差、均值漂移值等等),将统计数据作为背景的方法。OpenCv中并未实现简单的背景统计模型,不过在《学习OpenCv》中对其中的平均背景统计模型有很详细的介绍。在模仿该算法的基础上,我实现了一系列的背景统计模型,包括:平均背景、均值漂移、标准差和标准协方差。对这些统计概念我其实不明白,在维基百科上看了好半天 -_-
调用背景统计模型很简单,只需4步而已:
//(1)初始化对象
BackgroundStatModelBase<Bgr> bgModel = new BackgroundStatModelBase<Bgr>(BackgroundStatModelType.AccAvg);
//(2)更新一段时间的背景图像,视情况反复调用(2)
bgModel.Update(image);
//(3)设置当前帧
bgModel.CurrentFrame = currentFrame;
//(4)得到背景或者前景
Image<Gray,Byte> imageForeground = bgModel.ForegroundMask;
背景统计模型的实现代码如下:
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
/*
背景统计模型
作者:王先荣
时间:2010年2月19日
*/
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Drawing;
using System.Diagnostics;
using System.Runtime.InteropServices;
using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using Emgu.CV.UI;
using Emgu.CV.VideoSurveillance;
namespace ImageProcessLearn
{
//背景模型接口,在IBGFGDetector接口的基础上增加了一个CurrentFrame属性
public interface IBackgroundStatModel<TColor> : IDisposable
where TColor : struct, IColor
{
/// <summary>
/// 获取前景
/// </summary>
Image<Gray, byte> BackgroundMask { get; }
/// <summary>
/// 获取背景
/// </summary>
Image<Gray, byte> ForegroundMask { get; }
/// <summary>
/// 更新背景模型
/// </summary>
/// <param name="image"></param>
void Update(Image<TColor, byte> image);
/// <summary>
/// 计算统计数据
/// </summary>
void CalcStatData();
/// <summary>
/// 获取或者设置当前帧
/// </summary>
Image<TColor, Byte> CurrentFrame
{
get;
set;
}
}
/// <summary>
/// 使用帧差的方式来建立背景模型
/// </summary>
/// <typeparam name="TColor"></typeparam>
public class BackgroundStatModelFrameDiff<TColor> : IBackgroundStatModel<TColor>
where TColor : struct, IColor
{
//成员
private Image<TColor, Byte> imageBackgroundModel; //背景模型图像
private Image<TColor, Byte> currentFrame; //当前帧
private double threshold; //计算前景时所用的阀值,如果当前帧和背景的差别大于阀值,则被认为是前景
private Image<Gray, Byte> backgroundMask; //计算得到的背景图像
/// <summary>
/// 构造函数
/// </summary>
/// <param name="image">用于背景统计模型的背景</param>
public BackgroundStatModelFrameDiff(Image<TColor, Byte> image)
{
imageBackgroundModel = image;
currentFrame = null;
threshold = 15d;
backgroundMask = null;
}
public BackgroundStatModelFrameDiff()
: this(null)
{
}
/// <summary>
/// 设置或者获取计算前景时所用的阀值;如果阀值为0,则使用自适应的阀值
/// </summary>
public double Threshold
{
get
{
return threshold;
}
set
{
threshold = value >= 0 ? value : 15d;
}
}
/// <summary>
/// 更新背景模型
/// </summary>
/// <param name="image"></param>
public void Update(Image<TColor, Byte> image)
{
imageBackgroundModel = image;
}
/// <summary>
/// 获取或者设置当前帧
/// </summary>
public Image<TColor, Byte> CurrentFrame
{
get
{
return currentFrame;
}
set
{
currentFrame = value;
CalcBackgroundMask();
}
}
/// <summary>
/// 计算统计数据
/// </summary>
public void CalcStatData()
{
}
/// <summary>
/// 计算背景
/// </summary>
private void CalcBackgroundMask()
{
if (imageBackgroundModel == null || currentFrame == null || imageBackgroundModel.Size != currentFrame.Size)
throw new ArgumentException("在计算背景时发现参数错误。可能是:背景模型图像为空,当前帧为空,或者背景模型图像和当前帧的尺寸不一致。");
if (backgroundMask == null)
backgroundMask = new Image<Gray, byte>(imageBackgroundModel.Size);
if (threshold == 0d)
//如果阀值为0,使用OpenCv中的自适应动态背景检测
OpenCvInvoke.cvChangeDetection(imageBackgroundModel.Ptr, currentFrame.Ptr, backgroundMask.Ptr);
else
{
//如果设置了阀值,使用帧差
Image<TColor, Byte> imageTemp = imageBackgroundModel.AbsDiff(currentFrame);
Image<Gray, Byte>[] images = imageTemp.Split();
backgroundMask.SetValue(0d);
foreach (Image<Gray, Byte> image in images)
backgroundMask._Or(image.ThresholdBinary(new Gray(threshold), new Gray(255d)));
}
backgroundMask._Not();
}
/// <summary>
/// 获取背景
/// </summary>
public Image<Gray, Byte> BackgroundMask
{
get
{
return backgroundMask;
}
}
/// <summary>
/// 获取前景
/// </summary>
public Image<Gray, Byte> ForegroundMask
{
get
{
return backgroundMask.Not();
}
}
/// <summary>
/// 释放资源
/// </summary>
public void Dispose()
{
if (backgroundMask != null)
backgroundMask.Dispose();
}
}
/// <summary>
/// 使用平均背景来建立背景模型
/// </summary>
/// <typeparam name="TColor"></typeparam>
public class BackgroundStatModelAccAvg<TColor> : IBackgroundStatModel<TColor>
where TColor : struct, IColor
{
//成员
private Image<TColor, Single> imageAccSum; //累计图像
private Image<TColor, Single> imageAccDiff; //累计差值图像
private int frameCount; //已经累计的背景帧数
private Image<TColor, Single> previousFrame; //在背景建模时使用的前一帧图像
private Image<TColor, Byte> currentFrame; //当前帧图像
private double scale; //计算背景时所使用的缩放系数,大于平均值*scale倍数的像素认为是前景
private Image<Gray, Byte> backgroundMask; //计算得到的背景图像
private Image<TColor, Single> imageTemp; //临时图像
private bool isStatDataReady; //是否已经准备好统计数据
private Image<Gray, Single>[] imagesHi; //背景模型中各通道的最大值图像
private Image<Gray, Single>[] imagesLow; //背景模型中各通道的最小值图像
/// <summary>
/// 构造函数
/// </summary>
public BackgroundStatModelAccAvg()
{
imageAccSum = null;
imageAccDiff = null;
frameCount = 0;
previousFrame = null;
currentFrame = null;
scale = 6d;
backgroundMask = null;
isStatDataReady = false;
imagesHi = null;
imagesLow = null;
}
/// <summary>
/// 设置或者获取计算前景时所用的阀值
/// </summary>
public double Scale
{
get
{
return scale;
}
set
{
scale = value > 0 ? value : 6d;
}
}
/// <summary>
/// 更新背景模型
/// </summary>
/// <param name="image"></param>
public void Update(Image<TColor, Byte> image)
{
if (frameCount==0)
{
imageAccSum = new Image<TColor, Single>(image.Size);
imageAccSum.SetValue(0d);
imageAccDiff = new Image<TColor, float>(image.Size);
imageAccDiff.SetValue(0d);
}
imageTemp = image.ConvertScale<Single>(1d, 0d); //将图像转换成浮点型
imageAccSum.Acc(imageTemp);
if (previousFrame != null)
imageAccDiff.Acc(imageTemp.AbsDiff(previousFrame));
previousFrame = imageTemp.Copy();
frameCount++;
}
/// <summary>
/// 获取或者设置当前帧
/// </summary>
public Image<TColor, Byte> CurrentFrame
{
get
{
return currentFrame;
}
set
{
currentFrame = value;
CalcBackgroundMask();
}
}
/// <summary>
/// 计算统计数据
/// </summary>
public void CalcStatData()
{
//计算出最高及最低阀值图像
Image<TColor, Single> imageAvg = imageAccSum.ConvertScale<Single>(1d / frameCount, 0d);
Image<TColor, Single> imageAvgDiff = imageAccDiff.ConvertScale<Single>(1d / frameCount, 1d); //将平均值加1,为了确保总是存在差异
Image<TColor, Single> imageHi = imageAvg.Add(imageAvgDiff.ConvertScale<Single>(scale, 0d));
Image<TColor, Single> imageLow = imageAvg.Sub(imageAvgDiff.ConvertScale<Single>(scale, 0d));
imagesHi = imageHi.Split();
imagesLow = imageLow.Split();
isStatDataReady = true;
//释放资源
imageAvg.Dispose();
imageAvgDiff.Dispose();
imageHi.Dispose();
imageLow.Dispose();
}
/// <summary>
/// 计算背景
/// </summary>
private void CalcBackgroundMask()
{
if (imageAccSum == null || imageAccDiff == null || imageAccSum.Size != currentFrame.Size)
throw new ArgumentException("在计算背景时发生参数错误。可能是:还没有建立背景模型;或者当前帧的尺寸与背景尺寸不一致。");
if (!isStatDataReady)
CalcStatData();
imageTemp = currentFrame.ConvertScale<Single>(1d, 0d);
Image<Gray, Single>[] images = imageTemp.Split();
//计算背景图像
if (backgroundMask == null)
backgroundMask = new Image<Gray, byte>(currentFrame.Size);
backgroundMask.SetZero();
for (int i = 0; i < currentFrame.NumberOfChannels; i++)
backgroundMask._Or(images[i].InRange(imagesLow[i], imagesHi[i]));
//释放资源
for (int i = 0; i < images.Length; i++)
images[i].Dispose();
}
/// <summary>
/// 获取背景
/// </summary>
public Image<Gray, Byte> BackgroundMask
{
get
{
return backgroundMask;
}
}
/// <summary>
/// 获取前景
/// </summary>
public Image<Gray, Byte> ForegroundMask
{
get
{
return backgroundMask.Not();
}
}
/// <summary>
/// 释放资源
/// </summary>
public void Dispose()
{
if (imageAccSum != null)
imageAccSum.Dispose();
if (imageAccDiff != null)
imageAccDiff.Dispose();
if (previousFrame != null)
previousFrame.Dispose();
if (currentFrame != null)
currentFrame.Dispose();
if (backgroundMask != null)
backgroundMask.Dispose();
if (isStatDataReady)
{
for (int i = 0; i < imagesHi.Length; i++)
{
imagesHi[i].Dispose();
imagesLow[i].Dispose();
}
}
}
}
/// <summary>
/// 使用均值漂移来建立背景模型
/// </summary>
/// <typeparam name="TColor"></typeparam>
public class BackgroundStatModelRunningAvg<TColor> : IBackgroundStatModel<TColor>
where TColor : struct, IColor
{
//成员
private Image<TColor, Single> imageAcc; //累计图像
private Image<TColor, Single> imageAccDiff; //累计差值图像
private int frameCount; //已经累计的背景帧数
private Image<TColor, Single> previousFrame; //在背景建模时使用的前一帧图像
private Image<TColor, Byte> currentFrame; //当前帧图像
private double scale; //计算背景时所使用的缩放系数,大于平均值*scale倍数的像素认为是前景
private double alpha; //计算均值漂移时使用的权值
private Image<Gray, Byte> backgroundMask; //计算得到的背景图像
private Image<TColor, Single> imageTemp; //临时图像
private bool isStatDataReady; //是否已经准备好统计数据
private Image<Gray, Single>[] imagesHi; //背景模型中各通道的最大值图像
private Image<Gray, Single>[] imagesLow; //背景模型中各通道的最小值图像
/// <summary>
/// 构造函数
/// </summary>
public BackgroundStatModelRunningAvg()
{
imageAcc = null;
imageAccDiff = null;
frameCount = 0;
previousFrame = null;
currentFrame = null;
scale = 6d;
alpha = 0.5d;
backgroundMask = null;
isStatDataReady = false;
imagesHi = null;
imagesLow = null;
}
/// <summary>
/// 设置或者获取计算前景时所用的阀值
/// </summary>
public double Scale
{
get
{
return scale;
}
set
{
scale = value > 0 ? value : 6d;
}
}
/// <summary>
/// 设置或者获取计算均值漂移是使用的权值
/// </summary>
public double Alpha
{
get
{
return alpha;
}
set
{
alpha = value > 0 && value < 1 ? value : 0.5d;
}
}
/// <summary>
/// 更新背景模型
/// </summary>
/// <param name="image"></param>
public void Update(Image<TColor, Byte> image)
{
imageTemp = image.ConvertScale<Single>(1d, 0d); //将图像转换成浮点型
if (imageAcc == null)
{
imageAcc = imageTemp.Copy();
}
else
imageAcc.RunningAvg(imageTemp, alpha);
if (previousFrame != null)
{
if (imageAccDiff == null)
imageAccDiff = imageTemp.AbsDiff(previousFrame);
else
imageAccDiff.RunningAvg(imageTemp.AbsDiff(previousFrame), alpha);
}
previousFrame = imageTemp.Copy();
frameCount++;
}
/// <summary>
/// 获取或者设置当前帧
/// </summary>
public Image<TColor, Byte> CurrentFrame
{
get
{
return currentFrame;
}
set
{
currentFrame = value;
CalcBackgroundMask();
}
}
/// <summary>
/// 计算统计数据
/// </summary>
public void CalcStatData()
{
//计算出最高及最低阀值图像
Image<TColor, Single> imageHi = imageAcc.Add(imageAccDiff.ConvertScale<Single>(scale, 0d));
Image<TColor, Single> imageLow = imageAcc.Sub(imageAccDiff.ConvertScale<Single>(scale, 0d));
imagesHi = imageHi.Split();
imagesLow = imageLow.Split();
isStatDataReady = true;
//释放资源
imageHi.Dispose();
imageLow.Dispose();
}
/// <summary>
/// 计算背景
/// </summary>
private void CalcBackgroundMask()
{
if (imageAcc == null || imageAccDiff == null || imageAcc.Size != currentFrame.Size)
throw new ArgumentException("在计算背景时发生参数错误。可能是:还没有建立背景模型;或者当前帧的尺寸与背景尺寸不一致。");
if (!isStatDataReady)
CalcStatData();
imageTemp = currentFrame.ConvertScale<Single>(1d, 0d);
Image<Gray, Single>[] images = imageTemp.Split();
//计算背景图像
if (backgroundMask == null)
backgroundMask = new Image<Gray, byte>(currentFrame.Size);
backgroundMask.SetZero();
for (int i = 0; i < currentFrame.NumberOfChannels; i++)
backgroundMask._Or(images[i].InRange(imagesLow[i], imagesHi[i]));
//释放资源
for (int i = 0; i < images.Length; i++)
images[i].Dispose();
}
/// <summary>
/// 获取背景
/// </summary>
public Image<Gray, Byte> BackgroundMask
{
get
{
return backgroundMask;
}
}
/// <summary>
/// 获取前景
/// </summary>
public Image<Gray, Byte> ForegroundMask
{
get
{
return backgroundMask.Not();
}
}
/// <summary>
/// 释放资源
/// </summary>
public void Dispose()
{
if (imageAcc != null)
imageAcc.Dispose();
if (imageAccDiff != null)
imageAccDiff.Dispose();
if (previousFrame != null)
previousFrame.Dispose();
if (currentFrame != null)
currentFrame.Dispose();
if (backgroundMask != null)
backgroundMask.Dispose();
if (isStatDataReady)
{
for (int i = 0; i < imagesHi.Length; i++)
{
imagesHi[i].Dispose();
imagesLow[i].Dispose();
}
}
}
}
/// <summary>
/// 使用标准方差来建立背景模型
/// </summary>
/// <typeparam name="TColor"></typeparam>
public class BackgroundStatModelSquareAcc<TColor> : IBackgroundStatModel<TColor>
where TColor : struct, IColor
{
//成员
private Image<TColor, Single> imageAccSum; //累计图像
private Image<TColor, Single> imageAccSquare; //累计平方图像
private int frameCount; //已经累计的背景帧数
private Image<TColor, Single> previousFrame; //在背景建模时使用的前一帧图像
private Image<TColor, Byte> currentFrame; //当前帧图像
private double scale; //计算背景时所使用的缩放系数,大于平均值*scale倍数的像素认为是前景
private Image<Gray, Byte> backgroundMask; //计算得到的背景图像
private Image<TColor, Single> imageTemp; //临时图像
private bool isStatDataReady; //是否已经准备好统计数据
private Image<Gray, Single>[] imagesHi; //背景模型中各通道的最大值图像
private Image<Gray, Single>[] imagesLow; //背景模型中各通道的最小值图像
/// <summary>
/// 构造函数
/// </summary>
public BackgroundStatModelSquareAcc()
{
imageAccSum = null;
imageAccSquare = null;
frameCount = 0;
previousFrame = null;
currentFrame = null;
scale = 6d;
backgroundMask = null;
isStatDataReady = false;
imagesHi = null;
imagesLow = null;
}
/// <summary>
/// 设置或者获取计算前景时所用的阀值
/// </summary>
public double Scale
{
get
{
return scale;
}
set
{
scale = value > 0 ? value : 6d;
}
}
/// <summary>
/// 更新背景模型
/// </summary>
/// <param name="image"></param>
public void Update(Image<TColor, Byte> image)
{
if (frameCount == 0)
{
imageAccSum = new Image<TColor, Single>(image.Size);
imageAccSum.SetZero();
imageAccSquare = new Image<TColor, float>(image.Size);
imageAccSquare.SetZero();
}
imageTemp = image.ConvertScale<Single>(1d, 0d); //将图像转换成浮点型
imageAccSum.Acc(imageTemp);
CvInvoke.cvSquareAcc(imageTemp.Ptr, imageAccSquare.Ptr, IntPtr.Zero);
previousFrame = imageTemp.Copy();
frameCount++;
}
/// <summary>
/// 获取或者设置当前帧
/// </summary>
public Image<TColor, Byte> CurrentFrame
{
get
{
return currentFrame;
}
set
{
currentFrame = value;
CalcBackgroundMask();
}
}
/// <summary>
/// 计算统计数据
/// </summary>
public void CalcStatData()
{
//计算出标准差、最高及最低阀值图像
Image<TColor, Single> imageAvg = imageAccSum.ConvertScale<Single>(1d / frameCount, 0d);
Image<TColor, Single> imageSd = imageAccSquare.ConvertScale<Single>(1d / frameCount, 0d);
imageSd.Sub(imageAvg.Pow(2d));
imageSd = imageSd.Pow(0.5d);
Image<TColor, Single> imageHi = imageAvg.Add(imageSd.ConvertScale<Single>(scale, 0d));
Image<TColor, Single> imageLow = imageAvg.Sub(imageSd.ConvertScale<Single>(scale, 0d));
imagesHi = imageHi.Split();
imagesLow = imageLow.Split();
isStatDataReady = true;
//释放资源
imageAvg.Dispose();
imageSd.Dispose();
imageHi.Dispose();
imageLow.Dispose();
}
/// <summary>
/// 计算背景
/// </summary>
private void CalcBackgroundMask()
{
if (imageAccSum == null || imageAccSquare == null || imageAccSum.Size != currentFrame.Size)
throw new ArgumentException("在计算背景时发生参数错误。可能是:还没有建立背景模型;或者当前帧的尺寸与背景尺寸不一致。");
if (!isStatDataReady)
CalcStatData();
imageTemp = currentFrame.ConvertScale<Single>(1d, 0d);
Image<Gray, Single>[] images = imageTemp.Split();
//计算背景图像
if (backgroundMask == null)
backgroundMask = new Image<Gray, byte>(currentFrame.Size);
backgroundMask.SetZero();
for (int i = 0; i < currentFrame.NumberOfChannels; i++)
backgroundMask._Or(images[i].InRange(imagesLow[i], imagesHi[i]));
//释放资源
for (int i = 0; i < images.Length; i++)
images[i].Dispose();
}
/// <summary>
/// 获取背景
/// </summary>
public Image<Gray, Byte> BackgroundMask
{
get
{
return backgroundMask;
}
}
/// <summary>
/// 获取前景
/// </summary>
public Image<Gray, Byte> ForegroundMask
{
get
{
return backgroundMask.Not();
}
}
/// <summary>
/// 释放资源
/// </summary>
public void Dispose()
{
if (imageAccSum != null)
imageAccSum.Dispose();
if (imageAccSquare != null)
imageAccSquare.Dispose();
if (previousFrame != null)
previousFrame.Dispose();
if (currentFrame != null)
currentFrame.Dispose();
if (backgroundMask != null)
backgroundMask.Dispose();
if (isStatDataReady)
{
for (int i = 0; i < imagesHi.Length; i++)
{
imagesHi[i].Dispose();
imagesLow[i].Dispose();
}
}
}
}
/// <summary>
/// 使用标准协方差来建立背景模型
/// </summary>
/// <typeparam name="TColor"></typeparam>
public class BackgroundStatModelMultiplyAcc<TColor> : IBackgroundStatModel<TColor>
where TColor : struct, IColor
{
//成员
private Image<TColor, Single> imageAccSum; //累计图像
private Image<TColor, Single> imageAccMultiply; //累计平方图像
private int frameCount; //已经累计的背景帧数
private Image<TColor, Single> previousFrame; //在背景建模时使用的前一帧图像
private Image<TColor, Byte> currentFrame; //当前帧图像
private double scale; //计算背景时所使用的缩放系数,大于平均值*scale倍数的像素认为是前景
private Image<Gray, Byte> backgroundMask; //计算得到的背景图像
private Image<TColor, Single> imageTemp; //临时图像
private bool isStatDataReady; //是否已经准备好统计数据
private Image<Gray, Single>[] imagesHi; //背景模型中各通道的最大值图像
private Image<Gray, Single>[] imagesLow; //背景模型中各通道的最小值图像
/// <summary>
/// 构造函数
/// </summary>
public BackgroundStatModelMultiplyAcc()
{
imageAccSum = null;
imageAccMultiply = null;
frameCount = 0;
previousFrame = null;
currentFrame = null;
scale = 6d;
backgroundMask = null;
isStatDataReady = false;
imagesHi = null;
imagesLow = null;
}
/// <summary>
/// 设置或者获取计算前景时所用的阀值
/// </summary>
public double Scale
{
get
{
return scale;
}
set
{
scale = value > 0 ? value : 6d;
}
}
/// <summary>
/// 更新背景模型
/// </summary>
/// <param name="image"></param>
public void Update(Image<TColor, Byte> image)
{
if (frameCount == 0)
{
imageAccSum = new Image<TColor, Single>(image.Size);
imageAccSum.SetZero();
imageAccMultiply = new Image<TColor, float>(image.Size);
imageAccMultiply.SetZero();
}
imageTemp = image.ConvertScale<Single>(1d, 0d); //将图像转换成浮点型
imageAccSum.Acc(imageTemp);
if (previousFrame != null)
CvInvoke.cvMultiplyAcc(previousFrame.Ptr, imageTemp.Ptr, imageAccMultiply.Ptr, IntPtr.Zero);
previousFrame = imageTemp.Copy();
frameCount++;
}
/// <summary>
/// 获取或者设置当前帧
/// </summary>
public Image<TColor, Byte> CurrentFrame
{
get
{
return currentFrame;
}
set
{
currentFrame = value;
CalcBackgroundMask();
}
}
/// <summary>
/// 计算统计数据
/// </summary>
public void CalcStatData()
{
//计算出标准协方差、最高及最低阀值图像
Image<TColor, Single> imageAvg = imageAccSum.ConvertScale<Single>(1d / frameCount, 0d);
Image<TColor, Single> imageScov = imageAccMultiply.ConvertScale<Single>(1d / frameCount, 0d);
imageScov.Sub(imageAvg.Pow(2d));
imageScov = imageScov.Pow(0.5d);
Image<TColor, Single> imageHi = imageAvg.Add(imageScov.ConvertScale<Single>(scale, 0d));
Image<TColor, Single> imageLow = imageAvg.Sub(imageScov.ConvertScale<Single>(scale, 0d));
imagesHi = imageHi.Split();
imagesLow = imageLow.Split();
isStatDataReady = true;
//释放资源
imageAvg.Dispose();
imageScov.Dispose();
imageHi.Dispose();
imageLow.Dispose();
}
/// <summary>
/// 计算背景
/// </summary>
private void CalcBackgroundMask()
{
if (imageAccSum == null || imageAccMultiply == null || imageAccSum.Size != currentFrame.Size)
throw new ArgumentException("在计算背景时发生参数错误。可能是:还没有建立背景模型;或者当前帧的尺寸与背景尺寸不一致。");
if (!isStatDataReady)
CalcStatData();
imageTemp = currentFrame.ConvertScale<Single>(1d, 0d);
Image<Gray, Single>[] images = imageTemp.Split();
//计算背景图像
if (backgroundMask == null)
backgroundMask = new Image<Gray, byte>(currentFrame.Size);
backgroundMask.SetZero();
for (int i = 0; i < currentFrame.NumberOfChannels; i++)
backgroundMask._Or(images[i].InRange(imagesLow[i], imagesHi[i]));
//释放资源
for (int i = 0; i < images.Length; i++)
images[i].Dispose();
}
/// <summary>
/// 获取背景
/// </summary>
public Image<Gray, Byte> BackgroundMask
{
get
{
return backgroundMask;
}
}
/// <summary>
/// 获取前景
/// </summary>
public Image<Gray, Byte> ForegroundMask
{
get
{
return backgroundMask.Not();
}
}
/// <summary>
/// 释放资源
/// </summary>
public void Dispose()
{
if (imageAccSum != null)
imageAccSum.Dispose();
if (imageAccMultiply != null)
imageAccMultiply.Dispose();
if (previousFrame != null)
previousFrame.Dispose();
if (currentFrame != null)
currentFrame.Dispose();
if (backgroundMask != null)
backgroundMask.Dispose();
if (isStatDataReady)
{
for (int i = 0; i < imagesHi.Length; i++)
{
imagesHi[i].Dispose();
imagesLow[i].Dispose();
}
}
}
}
/// <summary>
/// 背景统计模型
/// </summary>
public class BackgroundStatModelBase<TColor> : IBackgroundStatModel<TColor>
where TColor : struct, IColor
{
//成员变量
IBackgroundStatModel<TColor> bgModel;
BackgroundStatModelType type;
/// <summary>
/// 构造函数
/// </summary>
/// <param name="type">背景模型类型</param>
public BackgroundStatModelBase(BackgroundStatModelType type)
{
this.type = type;
switch (type)
{
case BackgroundStatModelType.FrameDiff:
bgModel = new BackgroundStatModelFrameDiff<TColor>();
break;
case BackgroundStatModelType.AccAvg:
bgModel = new BackgroundStatModelAccAvg<TColor>();
break;
case BackgroundStatModelType.RunningAvg:
bgModel = new BackgroundStatModelRunningAvg<TColor>();
break;
case BackgroundStatModelType.SquareAcc:
bgModel = new BackgroundStatModelSquareAcc<TColor>();
break;
case BackgroundStatModelType.MultiplyAcc:
bgModel = new BackgroundStatModelMultiplyAcc<TColor>();
break;
default:
throw new ArgumentException("不存在的背景模型", "type");
}
}
/// <summary>
/// 获取背景模型类型
/// </summary>
public BackgroundStatModelType BackgroundStatModelType
{
get
{
return type;
}
}
/// <summary>
/// 更新背景模型
/// </summary>
/// <param name="image"></param>
public void Update(Image<TColor, Byte> image)
{
bgModel.Update(image);
}
/// <summary>
/// 计算统计数据
/// </summary>
public void CalcStatData()
{
bgModel.CalcStatData();
}
/// <summary>
/// 设置或者获取当前帧
/// </summary>
public Image<TColor, Byte> CurrentFrame
{
get
{
return bgModel.CurrentFrame;
}
set
{
bgModel.CurrentFrame = value;
}
}
/// <summary>
/// 获取背景
/// </summary>
public Image<Gray, Byte> BackgroundMask
{
get
{
return bgModel.BackgroundMask;
}
}
/// <summary>
/// 获取前景
/// </summary>
public Image<Gray, Byte> ForegroundMask
{
get
{
return bgModel.ForegroundMask;
}
}
/// <summary>
/// 释放资源
/// </summary>
public void Dispose()
{
if (bgModel != null)
bgModel.Dispose();
}
}
/// <summary>
/// 背景模型类型
/// </summary>
public enum BackgroundStatModelType
{
FrameDiff, //帧差
AccAvg, //平均背景
RunningAvg, //均值漂移
MultiplyAcc, //计算协方差
SquareAcc //计算方差
}
}
3.编码本背景模型
编码本的基本思路是这样的:针对每个像素在时间轴上的变动,建立多个(或者一个)包容近期所有变化的Box(变动范围);在检测时,用当前像素与Box去比较,如果当前像素落在任何Box的范围内,则为背景。
在OpenCv中已经实现了编码本背景模型,不过实现方式与《学习OpenCv》中提到的方式略有不同,主要有:(1)使用单向链表来容纳Code Element;(2)清除消极的Code Element时,并未重置t。OpenCv中的以下函数与编码本背景模型相关:
cvCreateBGCodeBookModel 建立背景模型
cvBGCodeBookUpdate 更新背景模型
cvBGCodeBookClearStale 清除消极的Code Element
cvBGCodeBookDiff 计算得到背景与前景(注意:该函数仅仅设置背景像素为0,而对前景像素未处理,因此在调用前需要将所有的像素先置为前景)
cvReleaseBGCodeBookModel 释放资源
在EmguCv中只实现了一部分编码本背景模型,在类BGCodeBookModel<TColor>中,可惜它把cvBGCodeBookDiff给搞忘记了 -_-
下面的代码演示了如果使用编码本背景模型:
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
//(1)初始化对象
if (rbCodeBook.Checked)
{
if (bgCodeBookModel != null)
{
bgCodeBookModel.Dispose();
bgCodeBookModel = null;
}
bgCodeBookModel = new BGCodeBookModel<Bgr>();
}
//(2)背景建模或者前景检测
bool stop = false;
while (!stop)
{
Image<Bgr, Byte> image = capture.QueryFrame().Clone(); //当前帧
bool isBgModeling, isFgDetecting; //是否正在建模,是否正在前景检测
lock (lockObject)
{
stop = !isVideoCapturing;
isBgModeling = isBackgroundModeling;
isFgDetecting = isForegroundDetecting;
}
//得到设置的参数
SettingParam param = (SettingParam)this.Invoke(new GetSettingParamDelegate(GetSettingParam));
//code book
if (param.ForegroundDetectType == ForegroundDetectType.CodeBook)
{
if (bgCodeBookModel != null)
{
//背景建模
if (isBgModeling)
{
bgCodeBookModel.Update(image);
//背景建模一段时间之后,清理陈旧的条目 (因为清理操作不会重置t,所以这里用求余数的办法来决定清理的时机)
if (backgroundModelFrameCount % CodeBookClearPeriod == CodeBookClearPeriod - 1)
bgCodeBookModel.ClearStale(CodeBookStaleThresh, Rectangle.Empty, null);
backgroundModelFrameCount++;
pbBackgroundModel.Image = bgCodeBookModel.BackgroundMask.Bitmap;
//如果达到最大背景建模次数,停止背景建模
if (param.MaxBackgroundModelFrameCount != 0 && backgroundModelFrameCount > param.MaxBackgroundModelFrameCount)
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel));
}
//前景检测
if (isFgDetecting)
{
Image<Gray, Byte> imageFg = new Image<Gray, byte>(image.Size);
imageFg.SetValue(255d); //CodeBook在得出前景时,仅仅将背景像素置零,所以这里需要先将所有的像素都假设为前景
CvInvoke.cvBGCodeBookDiff(bgCodeBookModel.Ptr, image.Ptr, imageFg.Ptr, Rectangle.Empty);
pbBackgroundModel.Image = imageFg.Bitmap;
}
}
}
//更新视频图像
pbVideo.Image = image.Bitmap;
}
//(3)释放对象
if (bgCodeBookModel != null)
{
try
{
bgCodeBookModel.Dispose();
}
catch { }
}
4.高级背景统计模型
在OpenCv还实现了两种高级的背景统计模型,它们为别是:(1)FGD——复杂背景下的前景物体检测(Foreground object detection from videos containing complex background);(2)MOG——高斯混合模型(Mixture Of Gauss)。包括以下函数:
CvCreateFGDetectorBase 建立前景检测对象
CvFGDetectorProcess 更新前景检测对象
CvFGDetectorGetMask 获取前景
CvFGDetectorRelease 释放资源
EmguCv将其封装到类FGDetector<TColor>中。我个人觉得OpenCv在实现这个模型的时候做得不太好,因为它将背景建模和前景检测糅合到一起了,无论你是否愿意,在建模的过程中也会检测前景,而只希望前景检测的时候,同时也会建模。我比较喜欢将背景建模和前景检测进行分离的设计。
调用的过程很简单,代码如下:
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
//(1)创建对象
if (rbMog.Checked)
{
if (fgDetector != null)
{
fgDetector.Dispose();
fgDetector = null;
}
fgDetector = new FGDetector<Bgr>(FORGROUND_DETECTOR_TYPE.FGD);
}
else if (rbFgd.Checked)
{
if (fgDetector != null)
{
fgDetector.Dispose();
fgDetector = null;
}
fgDetector = new FGDetector<Bgr>(FORGROUND_DETECTOR_TYPE.MOG);
}
//背景建模及前景检测
bool stop = false;
while (!stop)
{
Image<Bgr, Byte> image = capture.QueryFrame().Clone(); //当前帧
bool isBgModeling, isFgDetecting; //是否正在建模,是否正在前景检测
lock (lockObject)
{
stop = !isVideoCapturing;
isBgModeling = isBackgroundModeling;
isFgDetecting = isForegroundDetecting;
}
//得到设置的参数
SettingParam param = (SettingParam)this.Invoke(new GetSettingParamDelegate(GetSettingParam));
if (param.ForegroundDetectType == ForegroundDetectType.Fgd || param.ForegroundDetectType == ForegroundDetectType.Mog)
{
if (fgDetector != null && (isBgModeling || isFgDetecting))
{
//背景建模
fgDetector.Update(image);
backgroundModelFrameCount++;
pbBackgroundModel.Image = fgDetector.BackgroundMask.Bitmap;
//如果达到最大背景建模次数,停止背景建模
if (param.MaxBackgroundModelFrameCount != 0 && backgroundModelFrameCount > param.MaxBackgroundModelFrameCount)
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel));
//前景检测
if (isFgDetecting)
{
pbBackgroundModel.Image = fgDetector.ForgroundMask.Bitmap;
}
}
}
//更新视频图像
pbVideo.Image = image.Bitmap;
}
//(3)释放资源
if (fgDetector != null)
{
try
{
fgDetector.Dispose();
}
catch { }
}
前景检测
在建立好背景模型之后,通过对当前图像及背景的某种比较,我们可以得出前景。在上面的介绍中,已经包含了对前景的代码,在此不再重复。一般情况下,得到的前景包含了很多噪声,为了消除噪声,我们可以对前景图像进行开运算及闭运算,然后再丢弃比较小的轮廓。
本文的代码
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
using System.Diagnostics;
using System.Runtime.InteropServices;
using System.Threading;
using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using Emgu.CV.UI;
using Emgu.CV.VideoSurveillance;
namespace ImageProcessLearn
{
public partial class FormForegroundDetect : Form
{
//成员变量
Capture capture = null; //视频捕获对象
Thread captureThread = null; //视频捕获线程
private bool isVideoCapturing = true; //是否正在捕获视频
private bool isBackgroundModeling = false; //是否正在背景建模
private int backgroundModelFrameCount = 0; //已经建模的视频帧数
private bool isForegroundDetecting = false; //是否正在进行前景检测
private object lockObject = new object(); //用于锁定的对象
//各种前景检测方法对应的对象
BGCodeBookModel<Bgr> bgCodeBookModel = null; //编码本前景检测
private const int CodeBookClearPeriod = 40; //编码本的清理周期,更新这么多次背景之后,清理掉很少使用的陈旧条目
private const int CodeBookStaleThresh = 20; //在清理编码本时,使用的阀值(stale大于该阀值的条目将被删除)
FGDetector<Bgr> fgDetector = null; //Mog或者Fgd检测
BackgroundStatModelFrameDiff<Bgr> bgModelFrameDiff = null; //帧差
BackgroundStatModelAccAvg<Bgr> bgModelAccAvg = null; //平均背景
BackgroundStatModelRunningAvg<Bgr> bgModelRunningAvg = null; //均值漂移
BackgroundStatModelSquareAcc<Bgr> bgModelSquareAcc = null; //标准方差
BackgroundStatModelMultiplyAcc<Bgr> bgModelMultiplyAcc = null; //标准协方差
public FormForegroundDetect()
{
InitializeComponent();
}
//窗体加载时
private void FormForegroundDetect_Load(object sender, EventArgs e)
{
//设置Tooltip
toolTip.Active = true;
toolTip.SetToolTip(rbMog, "高斯混合模型(Mixture Of Gauss)");
toolTip.SetToolTip(rbFgd, "复杂背景下的前景物体检测(Foreground object detection from videos containing complex background)");
toolTip.SetToolTip(txtMaxBackgroundModelFrameCount, "在背景建模时,使用的最大帧数,超出该值之后,将自动停止背景建模。\r\n对于帧差,总是只捕捉当前帧作为背景。\r\n如果设为零,背景检测将不会自动停止。");
//打开摄像头视频捕获线程
capture = new Capture(0);
captureThread = new Thread(new ParameterizedThreadStart(CaptureWithEmguCv));
captureThread.Start(null);
}
//窗体关闭前
private void FormForegroundDetect_FormClosing(object sender, FormClosingEventArgs e)
{
//终止视频捕获
isVideoCapturing = false;
if (captureThread != null)
captureThread.Abort();
if (capture != null)
capture.Dispose();
//释放对象
if (bgCodeBookModel != null)
{
try
{
bgCodeBookModel.Dispose();
}
catch { }
}
if (fgDetector != null)
{
try
{
fgDetector.Dispose();
}
catch { }
}
if (bgModelFrameDiff != null)
bgModelFrameDiff.Dispose();
if (bgModelAccAvg != null)
bgModelAccAvg.Dispose();
if (bgModelRunningAvg != null)
bgModelRunningAvg.Dispose();
if (bgModelSquareAcc != null)
bgModelSquareAcc.Dispose();
if (bgModelMultiplyAcc != null)
bgModelMultiplyAcc.Dispose();
}
//EmguCv视频捕获
private void CaptureWithEmguCv(object objParam)
{
if (capture == null)
return;
bool stop = false;
while (!stop)
{
Image<Bgr, Byte> image = capture.QueryFrame().Clone(); //当前帧
bool isBgModeling, isFgDetecting; //是否正在建模,是否正在前景检测
lock (lockObject)
{
stop = !isVideoCapturing;
isBgModeling = isBackgroundModeling;
isFgDetecting = isForegroundDetecting;
}
//得到设置的参数
SettingParam param = (SettingParam)this.Invoke(new GetSettingParamDelegate(GetSettingParam));
//code book
if (param.ForegroundDetectType == ForegroundDetectType.CodeBook)
{
if (bgCodeBookModel != null && (isBgModeling || isFgDetecting))
{
//背景建模
if (isBgModeling)
{
bgCodeBookModel.Update(image);
//背景建模一段时间之后,清理陈旧的条目
if (backgroundModelFrameCount % CodeBookClearPeriod == CodeBookClearPeriod - 1)
bgCodeBookModel.ClearStale(CodeBookStaleThresh, Rectangle.Empty, null);
backgroundModelFrameCount++;
pbBackgroundModel.Image = bgCodeBookModel.BackgroundMask.Bitmap;
//如果达到最大背景建模次数,停止背景建模
if (param.MaxBackgroundModelFrameCount != 0 && backgroundModelFrameCount > param.MaxBackgroundModelFrameCount)
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel));
}
//前景检测
if (isFgDetecting)
{
Image<Gray, Byte> imageFg = new Image<Gray, byte>(image.Size);
imageFg.SetValue(255d); //CodeBook在得出前景时,仅仅将背景像素置零,所以这里需要先将所有的像素都假设为前景
CvInvoke.cvBGCodeBookDiff(bgCodeBookModel.Ptr, image.Ptr, imageFg.Ptr, Rectangle.Empty);
pbBackgroundModel.Image = imageFg.Bitmap;
}
}
}
//fgd or mog
else if (param.ForegroundDetectType == ForegroundDetectType.Fgd || param.ForegroundDetectType == ForegroundDetectType.Mog)
{
if (fgDetector != null && (isBgModeling || isFgDetecting))
{
//背景建模
fgDetector.Update(image);
backgroundModelFrameCount++;
pbBackgroundModel.Image = fgDetector.BackgroundMask.Bitmap;
//如果达到最大背景建模次数,停止背景建模
if (param.MaxBackgroundModelFrameCount != 0 && backgroundModelFrameCount > param.MaxBackgroundModelFrameCount)
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel));
//前景检测
if (isFgDetecting)
{
pbBackgroundModel.Image = fgDetector.ForgroundMask.Bitmap;
}
}
}
//帧差
else if (param.ForegroundDetectType == ForegroundDetectType.FrameDiff)
{
if (bgModelFrameDiff != null)
{
//背景建模
if (isBgModeling)
{
bgModelFrameDiff.Update(image);
backgroundModelFrameCount++;
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel)); //对于帧差,只需要捕获当前帧作为背景即可
}
//前景检测
if (isFgDetecting)
{
bgModelFrameDiff.Threshold = param.Threshold;
bgModelFrameDiff.CurrentFrame = image;
pbBackgroundModel.Image = bgModelFrameDiff.ForegroundMask.Bitmap;
}
}
}
//平均背景
else if (param.ForegroundDetectType == ForegroundDetectType.AccAvg)
{
if (bgModelAccAvg!=null)
{
//背景建模
if (isBgModeling)
{
bgModelAccAvg.Update(image);
backgroundModelFrameCount++;
//如果达到最大背景建模次数,停止背景建模
if (param.MaxBackgroundModelFrameCount != 0 && backgroundModelFrameCount > param.MaxBackgroundModelFrameCount)
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel));
}
//前景检测
if (isFgDetecting)
{
bgModelAccAvg.CurrentFrame = image;
pbBackgroundModel.Image = bgModelAccAvg.ForegroundMask.Bitmap;
}
}
}
//均值漂移
else if (param.ForegroundDetectType == ForegroundDetectType.RunningAvg)
{
if (bgModelRunningAvg != null)
{
//背景建模
if (isBgModeling)
{
bgModelRunningAvg.Update(image);
backgroundModelFrameCount++;
//如果达到最大背景建模次数,停止背景建模
if (param.MaxBackgroundModelFrameCount != 0 && backgroundModelFrameCount > param.MaxBackgroundModelFrameCount)
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel));
}
//前景检测
if (isFgDetecting)
{
bgModelRunningAvg.CurrentFrame = image;
pbBackgroundModel.Image = bgModelRunningAvg.ForegroundMask.Bitmap;
}
}
}
//计算方差
else if (param.ForegroundDetectType == ForegroundDetectType.SquareAcc)
{
if (bgModelSquareAcc != null)
{
//背景建模
if (isBgModeling)
{
bgModelSquareAcc.Update(image);
backgroundModelFrameCount++;
//如果达到最大背景建模次数,停止背景建模
if (param.MaxBackgroundModelFrameCount != 0 && backgroundModelFrameCount > param.MaxBackgroundModelFrameCount)
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel));
}
//前景检测
if (isFgDetecting)
{
bgModelSquareAcc.CurrentFrame = image;
pbBackgroundModel.Image = bgModelSquareAcc.ForegroundMask.Bitmap;
}
}
}
//协方差
else if (param.ForegroundDetectType == ForegroundDetectType.MultiplyAcc)
{
if (bgModelMultiplyAcc != null)
{
//背景建模
if (isBgModeling)
{
bgModelMultiplyAcc.Update(image);
backgroundModelFrameCount++;
//如果达到最大背景建模次数,停止背景建模
if (param.MaxBackgroundModelFrameCount != 0 && backgroundModelFrameCount > param.MaxBackgroundModelFrameCount)
this.Invoke(new NoParamAndReturnDelegate(StopBackgroundModel));
}
//前景检测
if (isFgDetecting)
{
bgModelMultiplyAcc.CurrentFrame = image;
pbBackgroundModel.Image = bgModelMultiplyAcc.ForegroundMask.Bitmap;
}
}
}
//更新视频图像
pbVideo.Image = image.Bitmap;
}
}
//用于在工作线程中更新结果的委托及方法
private delegate void AddResultDelegate(string result);
private void AddResultMethod(string result)
{
//txtResult.Text += result;
}
//用于在工作线程中获取设置参数的委托及方法
private delegate SettingParam GetSettingParamDelegate();
private SettingParam GetSettingParam()
{
ForegroundDetectType type = ForegroundDetectType.FrameDiff;
if (rbFrameDiff.Checked)
type = ForegroundDetectType.FrameDiff;
else if (rbAccAvg.Checked)
type = ForegroundDetectType.AccAvg;
else if (rbRunningAvg.Checked)
type = ForegroundDetectType.RunningAvg;
else if (rbMultiplyAcc.Checked)
type = ForegroundDetectType.MultiplyAcc;
else if (rbSquareAcc.Checked)
type = ForegroundDetectType.SquareAcc;
else if (rbCodeBook.Checked)
type = ForegroundDetectType.CodeBook;
else if (rbMog.Checked)
type = ForegroundDetectType.Mog;
else
type = ForegroundDetectType.Fgd;
int maxFrameCount = 0;
int.TryParse(txtMaxBackgroundModelFrameCount.Text, out maxFrameCount);
double threshold = 15d;
double.TryParse(txtThreshold.Text, out threshold);
if (threshold <= 0)
threshold = 15d;
return new SettingParam(type, maxFrameCount, threshold);
}
//没有参数及返回值的委托
private delegate void NoParamAndReturnDelegate();
//开始背景建模
private void btnStartBackgroundModel_Click(object sender, EventArgs e)
{
if (rbCodeBook.Checked)
{
if (bgCodeBookModel != null)
{
bgCodeBookModel.Dispose();
bgCodeBookModel = null;
}
bgCodeBookModel = new BGCodeBookModel<Bgr>();
}
else if (rbMog.Checked)
{
if (fgDetector != null)
{
fgDetector.Dispose();
fgDetector = null;
}
fgDetector = new FGDetector<Bgr>(FORGROUND_DETECTOR_TYPE.FGD);
}
else if (rbFgd.Checked)
{
if (fgDetector != null)
{
fgDetector.Dispose();
fgDetector = null;
}
fgDetector = new FGDetector<Bgr>(FORGROUND_DETECTOR_TYPE.MOG);
}
else if (rbFrameDiff.Checked)
{
if (bgModelFrameDiff != null)
{
bgModelFrameDiff.Dispose();
bgModelFrameDiff = null;
}
bgModelFrameDiff = new BackgroundStatModelFrameDiff<Bgr>();
}
else if (rbAccAvg.Checked)
{
if (bgModelAccAvg != null)
{
bgModelAccAvg.Dispose();
bgModelAccAvg = null;
}
bgModelAccAvg = new BackgroundStatModelAccAvg<Bgr>();
}
else if (rbRunningAvg.Checked)
{
if (bgModelRunningAvg != null)
{
bgModelRunningAvg.Dispose();
bgModelRunningAvg = null;
}
bgModelRunningAvg = new BackgroundStatModelRunningAvg<Bgr>();
}
else if (rbSquareAcc.Checked)
{
if (bgModelSquareAcc != null)
{
bgModelSquareAcc.Dispose();
bgModelSquareAcc = null;
}
bgModelSquareAcc = new BackgroundStatModelSquareAcc<Bgr>();
}
else if (rbMultiplyAcc.Checked)
{
if (bgModelMultiplyAcc != null)
{
bgModelMultiplyAcc.Dispose();
bgModelMultiplyAcc = null;
}
bgModelMultiplyAcc = new BackgroundStatModelMultiplyAcc<Bgr>();
}
backgroundModelFrameCount = 0;
isBackgroundModeling = true;
btnStartBackgroundModel.Enabled = false;
btnStopBackgroundModel.Enabled = true;
btnStartForegroundDetect.Enabled = false;
btnStopForegroundDetect.Enabled = false;
}
//停止背景建模
private void btnStopBackgroundModel_Click(object sender, EventArgs e)
{
StopBackgroundModel();
}
//停止背景建模
private void StopBackgroundModel()
{
lock (lockObject)
{
isBackgroundModeling = false;
}
btnStartBackgroundModel.Enabled = true;
btnStopBackgroundModel.Enabled = false;
btnStartForegroundDetect.Enabled = true;
btnStopForegroundDetect.Enabled = false;
}
//开始前景检测
private void btnStartForegroundDetect_Click(object sender, EventArgs e)
{
isForegroundDetecting = true;
btnStartBackgroundModel.Enabled = false;
btnStopBackgroundModel.Enabled = false;
btnStartForegroundDetect.Enabled = false;
btnStopForegroundDetect.Enabled = true;
}
//停止前景检测
private void btnStopForegroundDetect_Click(object sender, EventArgs e)
{
lock (lockObject)
{
isForegroundDetecting = false;
}
btnStartBackgroundModel.Enabled = true;
btnStopBackgroundModel.Enabled = false;
btnStartForegroundDetect.Enabled = true;
btnStopForegroundDetect.Enabled = false;
}
}
//前景检测方法枚举
public enum ForegroundDetectType
{
FrameDiff,
AccAvg,
RunningAvg,
MultiplyAcc,
SquareAcc,
CodeBook,
Mog,
Fgd
}
//设置参数
public struct SettingParam
{
public ForegroundDetectType ForegroundDetectType;
public int MaxBackgroundModelFrameCount;
public double Threshold;
public SettingParam(ForegroundDetectType foregroundDetectType, int maxBackgroundModelFrameCount, double threshold)
{
ForegroundDetectType = foregroundDetectType;
MaxBackgroundModelFrameCount = maxBackgroundModelFrameCount;
Threshold = threshold;
}
}
}
另外,细心的读者发现我忘记贴OpenCvInvoke类的实现代码了,这里补上。多谢指正。
![](https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif)
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Drawing;
using System.Runtime.InteropServices;
using Emgu.CV.Structure;
using Emgu.CV.CvEnum;
namespace ImageProcessLearn
{
/// <summary>
/// 声明一些没有包含在EmguCv中的OpenCv函数
/// </summary>
public static class OpenCvInvoke
{
//自适应动态背景检测
[DllImport("cvaux200.dll")]
public static extern void cvChangeDetection(IntPtr prev_frame, IntPtr curr_frame, IntPtr change_mask);
//均值漂移分割
[DllImport("cv200.dll")]
public static extern void cvPyrMeanShiftFiltering(IntPtr src, IntPtr dst, double spatialRadius, double colorRadius, int max_level, MCvTermCriteria termcrit);
//开始查找轮廓
[DllImport("cv200.dll")]
public static extern IntPtr cvStartFindContours(IntPtr image, IntPtr storage, int header_size, RETR_TYPE mode, CHAIN_APPROX_METHOD method, Point offset);
//查找下一个轮廓
[DllImport("cv200.dll")]
public static extern IntPtr cvFindNextContour(IntPtr scanner);
//用新轮廓替换scanner指向的当前轮廓
[DllImport("cv200.dll")]
public static extern void cvSubstituteContour(IntPtr scanner, IntPtr new_contour);
//结束轮廓查找
[DllImport("cv200.dll")]
public static extern IntPtr cvEndFindContour(ref IntPtr scanner);
}
}
后记
值得注意的是,本文提到的OpenCv函数目前属于CvAux系列,以后也许会加入到正式的图像处理Cv系列,也许以后会消失。最重要的是它们还没有正式的文档。
其实关于背景模型的方法还有很多,比如《Video-object segmentation using multi-sprite background subtraction》可以在摄像机运动的情况下建立背景,《Nonparametric background generation》利用mean-shift算法处理动态的背景模型,如果我的时间和能力允许,也许会去尝试实现它们。另外,《Wallflower: Principles and practice of background maintenance》比较了各种背景建模方式的差异,我希望能够尝试翻译出来。
感谢您耐心看完本文,希望对您有所帮助。