图像分割(Image Segmentation)

作者:王先荣
前言
    图像分割指的是将数字图像细分为多个图像子区域的过程,在OpenCv中实现了三种跟图像分割相关的算法,它们分别是:分水岭分割算法、金字塔分割算法以及均值漂移分割算法。它们的使用过程都很简单,下面的文章权且用于记录,并使该系列保持完整吧。
分水岭分割算法
    分水岭分割算法需要您或者先前算法提供标记,该标记用于指定哪些大致区域是目标,哪些大致区域是背景等等;分水岭分割算法的分割效果严重依赖于提供的标记。OpenCv中的函数cvWatershed实现了该算法,函数定义如下:

void cvWatershed(const CvArr * image, CvArr * markers)

其中:image为8为三通道的彩色图像;
      markers是单通道整型图像,它用不同的正整数来标记不同的区域,下面的代码演示了如果响应鼠标事件,并生成标记图像。

生成标记图像
//当鼠标按下并在源图像上移动时,在源图像上绘制分割线条
private void pbSource_MouseMove(object sender, MouseEventArgs e)
{
//如果按下了左键
if (e.Button == MouseButtons.Left)
{
if (previousMouseLocation.X >= 0 && previousMouseLocation.Y >= 0)
{
Point p1
= new Point((int)(previousMouseLocation.X * xScale), (int)(previousMouseLocation.Y * yScale));
Point p2
= new Point((int)(e.Location.X * xScale), (int)(e.Location.Y * yScale));
LineSegment2D ls
= new LineSegment2D(p1, p2);
int thickness = (int)(LineWidth * xScale);
imageSourceClone.Draw(ls,
new Bgr(255d, 255d, 255d), thickness);
pbSource.Image
= imageSourceClone.Bitmap;
imageMarkers.Draw(ls,
new Gray(drawCount), thickness);
}
previousMouseLocation
= e.Location;
}
}

//当松开鼠标左键时,将绘图的前一位置设置为(-1,-1)
private void pbSource_MouseUp(object sender, MouseEventArgs e)
{
previousMouseLocation
= new Point(-1, -1);
drawCount
++;
}

 

 

        您可以用类似下面的方式来使用分水岭算法:

使用分水岭分割算法
/// <summary>
/// 分水岭算法图像分割
/// </summary>
/// <returns>返回用时</returns>
private string Watershed()
{
//分水岭算法分割
Image<Gray, Int32> imageMarkers2 = imageMarkers.Copy();
Stopwatch sw
= new Stopwatch();
sw.Start();
CvInvoke.cvWatershed(imageSource.Ptr, imageMarkers2.Ptr);
sw.Stop();
//将分割的结果转换到256级灰度图像
pbResult.Image = imageMarkers2.Bitmap;
imageMarkers2.Dispose();
return string.Format("分水岭图像分割,用时:{0:F05}毫秒。\r\n", sw.Elapsed.TotalMilliseconds);
}

 

 

金字塔分割算法
    金字塔分割算法由cvPrySegmentation所实现,该函数的使用很简单;需要注意的是图像的尺寸以及金字塔的层数,图像的宽度和高度必须能被2整除,能够被2整除的次数决定了金字塔的最大层数。下面的代码演示了如果校验金字塔层数:

校验金字塔分割的金字塔层数
/// <summary>
/// 当改变金字塔分割的参数“金字塔层数”时,对参数进行校验
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void txtPSLevel_TextChanged(object sender, EventArgs e)
{
int level = int.Parse(txtPSLevel.Text);
if (level < 1 || imageSource.Width % (int)(Math.Pow(2, level - 1)) != 0 || imageSource.Height % (int)(Math.Pow(2, level - 1)) != 0)
MessageBox.Show(
this, "注意:您输入的金字塔层数不符合要求,计算结果可能会无效。", "金字塔层数错误");
}

 

 

使用金字塔分割的示例代码如下:

使用金字塔分割算法
/// <summary>
/// 金字塔分割算法
/// </summary>
/// <returns></returns>
private string PrySegmentation()
{
//准备参数
Image<Bgr, Byte> imageDest = new Image<Bgr, byte>(imageSource.Size);
MemStorage storage
= new MemStorage();
IntPtr ptrComp
= IntPtr.Zero;
int level = int.Parse(txtPSLevel.Text);
double threshold1 = double.Parse(txtPSThreshold1.Text);
double threshold2 = double.Parse(txtPSThreshold2.Text);
//金字塔分割
Stopwatch sw = new Stopwatch();
sw.Start();
CvInvoke.cvPyrSegmentation(imageSource.Ptr, imageDest.Ptr, storage.Ptr,
out ptrComp, level, threshold1, threshold2);
sw.Stop();
//显示结果
pbResult.Image = imageDest.Bitmap;
//释放资源
imageDest.Dispose();
storage.Dispose();
return string.Format("金字塔分割,用时:{0:F05}毫秒。\r\n", sw.Elapsed.TotalMilliseconds);
}

 

 

均值漂移分割算法
    均值漂移分割算法由cvPryMeanShiftFiltering所实现,均值漂移分割的金字塔层数只能介于[1,7]之间,您可以用类似下面的代码来使用它:

使用均值漂移分割算法
/// <summary>
/// 均值漂移分割算法
/// </summary>
/// <returns></returns>
private string PryMeanShiftFiltering()
{
//准备参数
Image<Bgr, Byte> imageDest = new Image<Bgr, byte>(imageSource.Size);
double spatialRadius = double.Parse(txtPMSFSpatialRadius.Text);
double colorRadius = double.Parse(txtPMSFColorRadius.Text);
int maxLevel = int.Parse(txtPMSFNaxLevel.Text);
int maxIter = int.Parse(txtPMSFMaxIter.Text);
double epsilon = double.Parse(txtPMSFEpsilon.Text);
MCvTermCriteria termcrit
= new MCvTermCriteria(maxIter, epsilon);
//均值漂移分割
Stopwatch sw = new Stopwatch();
sw.Start();
OpenCvInvoke.cvPyrMeanShiftFiltering(imageSource.Ptr, imageDest.Ptr, spatialRadius, colorRadius, maxLevel, termcrit);
sw.Stop();
//显示结果
pbResult.Image = imageDest.Bitmap;
//释放资源
imageDest.Dispose();
return string.Format("均值漂移分割,用时:{0:F05}毫秒。\r\n", sw.Elapsed.TotalMilliseconds);
}

 

 

    函数cvPryMeanShiftFiltering在EmguCv中没有实现,我们可以用下面的方式来使用:

调用均值漂移分割
//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);

 

 

分割效果及性能对比
    上述三种分割算法的效果如何呢?下面我们以它们的默认参数,对一幅2272x1704大小的图像进行分割。得到的结果如下所示:

图1 分水岭分割算法(左图白色的线条用于标记区域)

图2 金字塔分割算法

图3 均值漂移分割算法
    从上面我们可以看出:
    (1)分水岭分割算法的分割效果效果最好,均值漂移分割算法次之,而金字塔分割算法的效果最差;
    (2)均值漂移分割算法效率最高,分水岭分割算法接近于均值漂移算法,金字塔分割算法需要很长的时间。
    值得注意的是分水岭算法对标记很敏感,需要仔细而认真的绘制。

 

    本文的完整代码如下:

本文完整代码
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 Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Structure;
using Emgu.CV.UI;

namespace ImageProcessLearn
{
public partial class FormImageSegment : Form
{
//成员变量
private string sourceImageFileName = "wky_tms_2272x1704.jpg";//源图像文件名
private Image<Bgr, Byte> imageSource = null; //源图像
private Image<Bgr, Byte> imageSourceClone = null; //源图像的克隆
private Image<Gray, Int32> imageMarkers = null; //标记图像
private double xScale = 1d; //原始图像与PictureBox在x轴方向上的缩放
private double yScale = 1d; //原始图像与PictureBox在y轴方向上的缩放
private Point previousMouseLocation = new Point(-1, -1); //上次绘制线条时,鼠标所处的位置
private const int LineWidth = 5; //绘制线条的宽度
private int drawCount = 1; //用户绘制的线条数目,用于指定线条的颜色

public FormImageSegment()
{
InitializeComponent();
}

//窗体加载时
private void FormImageSegment_Load(object sender, EventArgs e)
{
//设置提示
toolTip.SetToolTip(rbWatershed, "可以在源图像上用鼠标绘制大致分割区域线条,该线条用于分水岭算法");
toolTip.SetToolTip(txtPSLevel,
"金字塔层数跟图像尺寸有关,该值只能是图像尺寸被2整除的次数,否则将得出错误结果");
toolTip.SetToolTip(txtPSThreshold1,
"建立连接的错误阀值");
toolTip.SetToolTip(txtPSThreshold2,
"分割簇的错误阀值");
toolTip.SetToolTip(txtPMSFSpatialRadius,
"空间窗的半径");
toolTip.SetToolTip(txtPMSFColorRadius,
"色彩窗的半径");
toolTip.SetToolTip(btnClearMarkers,
"清除绘制在源图像上,用于分水岭算法的大致分割区域线条");
//加载图像
LoadImage();
}

//当窗体关闭时,释放资源
private void FormImageSegment_FormClosing(object sender, FormClosingEventArgs e)
{
if (imageSource != null)
imageSource.Dispose();
if (imageSourceClone != null)
imageSourceClone.Dispose();
if (imageMarkers != null)
imageMarkers.Dispose();
}

//加载源图像
private void btnLoadImage_Click(object sender, EventArgs e)
{
OpenFileDialog ofd
= new OpenFileDialog();
ofd.CheckFileExists
= true;
ofd.DefaultExt
= "jpg";
ofd.Filter
= "图片文件|*.jpg;*.png;*.bmp|所有文件|*.*";
if (ofd.ShowDialog(this) == DialogResult.OK)
{
if (ofd.FileName != "")
{
sourceImageFileName
= ofd.FileName;
LoadImage();
}
}
ofd.Dispose();
}

//清除分割线条
private void btnClearMarkers_Click(object sender, EventArgs e)
{
if (imageSourceClone != null)
imageSourceClone.Dispose();
imageSourceClone
= imageSource.Copy();
pbSource.Image
= imageSourceClone.Bitmap;
imageMarkers.SetZero();
drawCount
= 1;
}

//当鼠标按下并在源图像上移动时,在源图像上绘制分割线条
private void pbSource_MouseMove(object sender, MouseEventArgs e)
{
//如果按下了左键
if (e.Button == MouseButtons.Left)
{
if (previousMouseLocation.X >= 0 && previousMouseLocation.Y >= 0)
{
Point p1
= new Point((int)(previousMouseLocation.X * xScale), (int)(previousMouseLocation.Y * yScale));
Point p2
= new Point((int)(e.Location.X * xScale), (int)(e.Location.Y * yScale));
LineSegment2D ls
= new LineSegment2D(p1, p2);
int thickness = (int)(LineWidth * xScale);
imageSourceClone.Draw(ls,
new Bgr(255d, 255d, 255d), thickness);
pbSource.Image
= imageSourceClone.Bitmap;
imageMarkers.Draw(ls,
new Gray(drawCount), thickness);
}
previousMouseLocation
= e.Location;
}
}

//当松开鼠标左键时,将绘图的前一位置设置为(-1,-1)
private void pbSource_MouseUp(object sender, MouseEventArgs e)
{
previousMouseLocation
= new Point(-1, -1);
drawCount
++;
}

//加载源图像
private void LoadImage()
{
if (imageSource != null)
imageSource.Dispose();
imageSource
= new Image<Bgr, byte>(sourceImageFileName);
if (imageSourceClone != null)
imageSourceClone.Dispose();
imageSourceClone
= imageSource.Copy();
pbSource.Image
= imageSourceClone.Bitmap;
if (imageMarkers != null)
imageMarkers.Dispose();
imageMarkers
= new Image<Gray, Int32>(imageSource.Size);
imageMarkers.SetZero();
xScale
= 1d * imageSource.Width / pbSource.Width;
yScale
= 1d * imageSource.Height / pbSource.Height;
drawCount
= 1;
}

//分割图像
private void btnImageSegment_Click(object sender, EventArgs e)
{
if (rbWatershed.Checked)
txtResult.Text
+= Watershed();
else if (rbPrySegmentation.Checked)
txtResult.Text
+= PrySegmentation();
else if (rbPryMeanShiftFiltering.Checked)
txtResult.Text
+= PryMeanShiftFiltering();
}

/// <summary>
/// 分水岭算法图像分割
/// </summary>
/// <returns>返回用时</returns>
private string Watershed()
{
//分水岭算法分割
Image<Gray, Int32> imageMarkers2 = imageMarkers.Copy();
Stopwatch sw
= new Stopwatch();
sw.Start();
CvInvoke.cvWatershed(imageSource.Ptr, imageMarkers2.Ptr);
sw.Stop();
//将分割的结果转换到256级灰度图像
pbResult.Image = imageMarkers2.Bitmap;
imageMarkers2.Dispose();
return string.Format("分水岭图像分割,用时:{0:F05}毫秒。\r\n", sw.Elapsed.TotalMilliseconds);
}

/// <summary>
/// 金字塔分割算法
/// </summary>
/// <returns></returns>
private string PrySegmentation()
{
//准备参数
Image<Bgr, Byte> imageDest = new Image<Bgr, byte>(imageSource.Size);
MemStorage storage
= new MemStorage();
IntPtr ptrComp
= IntPtr.Zero;
int level = int.Parse(txtPSLevel.Text);
double threshold1 = double.Parse(txtPSThreshold1.Text);
double threshold2 = double.Parse(txtPSThreshold2.Text);
//金字塔分割
Stopwatch sw = new Stopwatch();
sw.Start();
CvInvoke.cvPyrSegmentation(imageSource.Ptr, imageDest.Ptr, storage.Ptr,
out ptrComp, level, threshold1, threshold2);
sw.Stop();
//显示结果
pbResult.Image = imageDest.Bitmap;
//释放资源
imageDest.Dispose();
storage.Dispose();
return string.Format("金字塔分割,用时:{0:F05}毫秒。\r\n", sw.Elapsed.TotalMilliseconds);
}

/// <summary>
/// 均值漂移分割算法
/// </summary>
/// <returns></returns>
private string PryMeanShiftFiltering()
{
//准备参数
Image<Bgr, Byte> imageDest = new Image<Bgr, byte>(imageSource.Size);
double spatialRadius = double.Parse(txtPMSFSpatialRadius.Text);
double colorRadius = double.Parse(txtPMSFColorRadius.Text);
int maxLevel = int.Parse(txtPMSFNaxLevel.Text);
int maxIter = int.Parse(txtPMSFMaxIter.Text);
double epsilon = double.Parse(txtPMSFEpsilon.Text);
MCvTermCriteria termcrit
= new MCvTermCriteria(maxIter, epsilon);
//均值漂移分割
Stopwatch sw = new Stopwatch();
sw.Start();
OpenCvInvoke.cvPyrMeanShiftFiltering(imageSource.Ptr, imageDest.Ptr, spatialRadius, colorRadius, maxLevel, termcrit);
sw.Stop();
//显示结果
pbResult.Image = imageDest.Bitmap;
//释放资源
imageDest.Dispose();
return string.Format("均值漂移分割,用时:{0:F05}毫秒。\r\n", sw.Elapsed.TotalMilliseconds);
}

/// <summary>
/// 当改变金字塔分割的参数“金字塔层数”时,对参数进行校验
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void txtPSLevel_TextChanged(object sender, EventArgs e)
{
int level = int.Parse(txtPSLevel.Text);
if (level < 1 || imageSource.Width % (int)(Math.Pow(2, level - 1)) != 0 || imageSource.Height % (int)(Math.Pow(2, level - 1)) != 0)
MessageBox.Show(
this, "注意:您输入的金字塔层数不符合要求,计算结果可能会无效。", "金字塔层数错误");
}

/// <summary>
/// 当改变均值漂移分割的参数“金字塔层数”时,对参数进行校验
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void txtPMSFNaxLevel_TextChanged(object sender, EventArgs e)
{
int maxLevel = int.Parse(txtPMSFNaxLevel.Text);
if (maxLevel < 0 || maxLevel > 8)
MessageBox.Show(
this, "注意:均值漂移分割的金字塔层数只能在0至8之间。", "金字塔层数错误");
}
}
}

 

 

感谢您耐心看完本文,希望对您有所帮助。

posted @ 2010-02-28 09:33  Wuya  阅读(59136)  评论(28编辑  收藏