C#Halcon从零开发_Day15_AOI缺陷检测策略1_Bolb分析+特征分析_检测瓶口缺陷

*AOI缺陷检测策略 Blob分析+特征分析
*极坐标系转换:将圆环图片转为矩形图片，有利于均值滤波等适合矩形图像的操作

*定义变量 瓶口外径圆和内径圆的距离
dev_get_window (WindowHandle)
dis1:=60
for Index := 1 to 16 by 1
    read_image (Image,'bottles/bottle_mouth_'+Index$'.02')
    
    ***Blob分析
    *目的1：运用差集 找出中间圆环区域
    threshold (Image, Region, 0, 50)
    *去除杂质 开运算
    opening_circle (Region, RegionOpening, 3.5)
    *填充内部空洞
    fill_up (RegionOpening, RegionFillUp)
    *获取最小外接圆
    smallest_circle (RegionFillUp, Row, Column, Radius)
    *生成圆形区域
    gen_circle (Circle, Row, Column, Radius)
    *靠腐蚀40，来获取内侧圆
    erosion_circle (Circle, RegionErosion, 40)
    *差集找圆环
    difference (Circle, RegionErosion, RegionDifference)
    *抠图
    reduce_domain (Image, RegionDifference, ImageReduced)
    *目的2：转成矩形图，特征检测
    *ImageReduced 原来的环形图  
    *PolarTransImage 输出的矩形图
    * Row :Column 圆心
    * 0 : 开始角度
    * rad(360) 从0到360
    * Radius-40 半径的开始
    *Radius 半径的结束
    * 700 ：输出矩形的款
    * 100 输出矩形的高
    *'nearest_neighbor' 插值算法
    polar_trans_image_ext (ImageReduced, PolarTransImage, Row, Column, 0, rad(360), Radius-40, Radius, 700, 100, 'nearest_neighbor')
    *灰度值均衡化  扩大范围缩放为 0-255
    scale_image_max (PolarTransImage, ImageScaleMax)
    *Blob分析
    mean_image (ImageScaleMax, ImageMean, 100, 3)
    dyn_threshold (PolarTransImage, ImageMean, RegionDynThresh, 80, 'light')
    *开运算 去杂质
    opening_circle (RegionDynThresh, RegionOpening1, 4)
    connection (RegionOpening1, ConnectedRegions)
    count_obj (ConnectedRegions, Number)
    
  if(Number<=0)
       set_display_font (WindowHandle, 16, 'mono', 'false', 'false')
        dev_disp_text ('OK', 'window', 0, 0, 'green', [], [])
  else
      set_display_font (WindowHandle, 16, 'mono', 'false', 'false')
        dev_disp_text ('NG', 'window', 0, 0, 'green', [], [])
  endif
       
    
endfor

实现效果：