【计算机视觉】常用背景模型介绍
转自:http://www.cnblogs.com/xrwang/archive/2010/02/21/ForegroundDetection.html
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:
帧差:
[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:
/* 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何使用编码本背景模型:
//(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: [cpp] view plain copy print? //(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 [cpp] view plain copy print? 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。
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。