halcon学习之边缘提取5

5， derivate_gauss.hdev  

a）高斯导数滤波用作 平滑滤波器（使用分水岭watershed得到contours）（用于很多小块的图像）

read_image...

derivate_gauss(Meningg5,Smoothed, 2, 'none')

derivate_gauss(Image : DerivGauss（这里指输出图像） : Sigma, Component : )

用一个图片Image和一个高斯函数的导数求卷积，从而计算出不同的特征值。sigma控制高斯函数，当sigma为一个值时候，行和列的方向上sigma相同，当sigma为两个值时候，第一个控制列的程度，第二个控制行的程度。‘none’这里指Smoothing only，其余参数请自行查看帮助文档。

convert_image_type(Smoothed, SmoothedByte,'byte')

convert_image_type(Image : ImageConverted : NewType : )转换图像类型

watersheds(SmoothedByte, Basins, Watersheds)

watersheds(Image : Basins, Watersheds : : )

找出图片的分水岭和凹陷块区域,用于图片分割

b） 高斯导数滤波用作边缘检测

read_image....

derivate_gauss(Image,GradientAmp1, 1.5,'gradient')

threshold....

c) 高斯导数滤波用作角检测?

derivate(Image, Det, 1.5, 'det')

threshold(Det, Corners, 20, 1000000)

d ) 高斯导数滤波用作边缘检测( 二阶导数)

derivate_gauss(Image, EdgesAreZero, 3, '2nd_ddg')

zero_crossing(EdgesAreZero, Edges)

（zero_crossing returns the zero crossings of the input image as a region. A pixel is accepted as a zero crossing if its gray value (in Image) is zero, or if at least one of its neighbors of the 4-neighborhood has a different sign.

This operator is intended to be used after edge operators returning the second derivative of the image (e.g., laplace_of_gauss), which were possibly followed by a smoothing operator. In this case, the zero crossings are (candidates for) edges.）

6， 各种滤波

diff_of_gauss        +              zero_crossing

laplace_of_gauss   +             zero_crossing

derivate_gauss      +             zero_crossing   

7，显示一个保存边界的XLD对象

read_image

edges_sub_pix(Image,Edges, 'mderiche2', 0.7, 10,20)

edges_sub_pix(Image : Edges : Filter, Alpha, Low, High : )

使用Deriche, Lanser, Shen, or Canny滤波器来精确检测边缘

edges_image(Image, ImaAmp, ImaDir,'mderiche2', 0.7,'nms',10, 20)

使用Deriche, Lanser, Shen, or Canny滤波器来检测边缘,并得到边缘的幅值和方向

count_seconds(a1)  记录当前时间

disp_xld(Edges, WindowID)

count_seconds(a2)

Time：=a2-a1

记录执行下面一步所用的时间

8, edge_segments.hdev 边缘分割

read_image...

get_image_size(Image, Width, Height)

dev_open_window_fit_image

显示

edges_image(Image, ImaAmp, ImaDir,'lanser2', 0.5,'nms',20, 40)  

lanser精度很高, 用来计算边界

threshold

connection

用来提取出边界

边界的显示：

count_obj(ConnectedRegions, Number)

gen_empty_obj(XLDContours)   用来存储边界

for i:=1 to Number by 1

         select_obj(ConnectedRegions, SingleEdgeObject, i)

                      select_obj(Objects : ObjectSelected : Index : )

         split_skeleton_lines(SingleEdgeObject, 2, BeginRow, BeginCol, EndRow, EndCol)

split_skeleton_lines(SkeletonRegion : : MaxDistance : BeginRow, BeginCol, EndRow, EndCol)

  寻找骨骼线，删除小分支，得到骨骼线的头和尾节点,主意这里可能产生的头和尾是一个数组，其中可能有多个头节点和尾节点

         for k:= 0 to |BeginRow|-1 by 1

         gen_contour_polygon_xld( Contour,[BeginRow[k], endRow[k]], [BeginCol[k], EndCol[k]])

         concat_obj(XLDContours, Contour, XLDContours)

         concat_obj(Objects1, Objects2 : ObjectsConcat : : )

          把两个对象连起来

         endfor

endfor