opencv实现分水岭,金字塔,均值漂移算法进行分割

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 imageSource = null; /源图像 private Image imageSourceClone = null; /源图像的克隆 private Image 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 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(sourceImageFileName); if (imageSourceClone != null) imageSourceClone.Dispose(); imageSourceClone = imageSource.Copy(); pbSource.Image = imageSourceClone.Bitmap; if (imageMarkers != null) imageMarkers.Dispose(); imageMarkers = new Image(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(); / / 分水岭算法图像分割 / / 返回用时 private string Watershed() /分水岭算法分割 Image 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。rn“, sw.Elapsed.TotalMilliseconds); / / 金字塔分割算法 / / private string PrySegmentation() /准备参数 Image imageDest = new Image(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。rn“, sw.Elapsed.TotalMilliseconds); / / 均值漂移分割算法 / / private string PryMeanShiftFiltering() /准备参数 Image imageDest = new Image(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。rn“, sw.Elapsed.TotalMilliseconds); / / 当改变金字塔分割的参数“金字塔层数” 时，对参数进行校验 / / / private void txtPSLevel_TextChanged(object sender, Eve