opencv实现分水岭,金字塔,均值漂移算法进行分割

1、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.struct

2、ure; using emgu.cv.ui;namespaceimageprocesslearnpublic partialclass formimagesegment : form/ 成员变量privateimage<bgr, byte> imagesourceclone=null ;/ 源图像的克隆privateimage<gray, int32> imagemarkers =null ;/ 标记图像privatedouble xscale = 1d;/ 原始图像与 picturebox 在 x 轴方向上的缩放privatedouble yscale = 1d;/

3、原始图像与 picturebox 在 y 轴方向上的缩放privatepoint previousmouselocation =newpoint(-1, - 1 );/ 上次绘制线条时，鼠标所处的位置privateconst int linewidth =5;/绘制线条的宽度privatestring sourceimagefilename =“wky_tms_2272x1704.jpg“;/ 源图像文件名private image<bgr, byte> imagesource =null ;/ 源图像privateint drawcount =1 ;/ 用户绘制的线条数目，用于指

4、定线条的颜色public formimagesegment()initializecomponent();/ 窗体加载时privatevoid formimagesegment_load(object sender, eventargs e)法“);/ 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(txtpsthreshol

5、d1,“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,“颜色窗的半径“); 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privatevoid formimagesegment_formclosing

6、(object sender, formclosingeventargs e)if (imagesource !=null ) imagesource.dispose();if (imagesourceclone !=null )imagesourceclone.dispose(); if (imagemarkers !=null )imagemarkers.dispose();/ 加载源图像privatevoid btnloadimage_click(object sender, eventargs e)openfiledialog ofd =new openfiledialog(); of

7、d.checkfileexists =true ; ofd.defaultext =“jpg“ ;ofd.filter =“图片文件|*.jpg;*.png;*.bmp|全部文件|*.*“ ; if (ofd.showdialog(this ) = dialogresult.ok)if (ofd.filename !=“ )sourceimagefilename = ofd.filename; loadimage();ofd.dispose();/ 清除分割线条privatevoid btnclearmarkers_click(object sender, eventargs e)if (im

8、agesourceclone !=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privatevoid pbsource_mousemove(object sender, mouseeventargs e)/ 假如按下了左键if (e.button = mousebuttons.lef

9、t)if (previousmouselocation.x >=0 && previousmouselocation.y >=0)pointp1=newpoint( int )(previousmouselocation.x (int )(previousmouselocation.y * yscale);*xscale),point p2 = new point( int )(e.location.x* xscale), ( int )(e.location.y* yscale); linesegment2d ls =new linesegment2d(p1, p

10、2);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 ）privatevoid pbsource_mouseup(

11、object sender, mouseeventargs e)previousmouselocation =new point(- 1, - 1 ); drawcount+;/ 加载源图像privatevoid loadimage()if (imagesource !=null ) imagesource.dispose();imagesource =new image<bgr,byte >(sourceimagefilename); if (imagesourceclone !=null )imagesourceclone.dispose(); imagesourceclone

12、 = 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;d

13、rawcount1=;/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();/ <summa

14、ry>/ 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pb

15、result.image = imagemarkers2.bitmap; imagemarkers2.dispose();return string.format(“ 分 水 岭 图 像 分 割 ， 用 时 ：sw.elapsed.totalmilliseconds);/ <summary>/ 金字塔分割算法/ </summary>/ <returns></returns>private string prysegmentation()0:f05毫 秒 。 rn“,/预备参数image<bgr, byte> imagedest n=e

16、w image<bgr, byte>(imagesource.size);memstorage storage n=ew 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.s

17、tart();cvinvoke.cvpyrsegmentation(imagesource.ptr, imagedest.ptrs,torage.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);/ <summa

18、ry>/ 均值漂移分割算法/ </summary>/ <returns></returns>private string prymeanshiftfiltering()/预备参数image<bgr, byte> imagedestn=ew image<bgr, byte>(imagesource.size);double spatialradius = double.parse(txtpmsfspatialradius.text); double colorradius = double.parse(txtpmsfcolorra

19、dius.text);int maxlevel = int.parse(txtpmsfnaxlevel.text); int maxiter = int.parse(txtpmsfmaxiter.text);double epsilon = double.parse(txtpmsfepsilon.text);mcvtermcriteria termcrit/均值漂移分割n=ew mcvtermcriteria(maxiter, epsilon);stopwatch sw =new stopwatch(); sw.start();opencvinvoke.cvpyrmeanshiftfilter

20、ing(imagesource.ptr, imagedest.ptr, spatialradiu colorradius, maxlevel, termcrit);sw.stop();/显示结果pbresult.image = imagedest.bitmap;/释放资源imagedest.dispose();returnstring.format(“ 均sw.elapsed.totalmilliseconds);值 漂 移 分 割 ， 用 时 ： 0:f05 毫 秒 。 rn“,/ <summary>/ 当转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“>&l