HALCON示例程序check_blister_mixed.hedv药品胶囊缺陷检测

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#胶囊 #缺陷 #检测 #check_blister_mixed.hedv #halcon

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本文详细介绍HALCON示例程序check_blister_mixed.hedv，该程序利用高斯混合模型(GMM)对药品胶囊进行缺陷检测。通过提取胶囊、分割连通域、筛选区域并进行颜色空间分析，实现对不同颜色胶囊的识别与分类。最终，通过对比训练数据与检测结果，判断胶囊的缺失、错误类型及数量。

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HALCON示例程序check_blister_mixed.hedv药品胶囊缺陷检测

示例程序源码（加注释）

读入图片与显示相关设置
dev_close_window ()
read_image (Image, ‘blister/blister_mixed_reference’)
dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle)
set_display_font (WindowHandle, 14, ‘mono’, ‘true’, ‘false’)
dev_update_off ()
dev_display (Image)
dev_set_draw (‘margin’)
首先提取每个胶囊
disp_message (WindowHandle, ‘Train gmm classifier on pill types’, ‘window’, -1, -1, ‘black’, ‘true’)
注意注意敲黑板，划重点。下边的这个函数是halcon函数的封装，里边代码有好多。内容是上一篇文章介绍过，大同小异，所以不理解的话看一下上一篇例子。下边的代码块是extract_pill_types里边的内容。
extract_pill_types (Image, Chambers, ChambersUnion, Classes, PhiRef, RowRef, ColumnRef, PillTypeCount)

threshold (Image, Region, 90, 255)
connection (Region, ConnectedRegions)
select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 5000, 999999)
shape_trans (SelectedRegions, Blister, 'convex')
gen_empty_region (Chambers)
for I := 0 to 4 by 1
    Row := 107 + I * 70
    for J := 0 to 2 by 1
        Column := 177 + J * 150
        gen_rectangle2 (Rectangle, Row, Column, 0, 64, 30)
        concat_obj (Chambers, Rectangle, Chambers)
    endfor
endfor
difference (Blister, Chambers, Pattern)
union1 (Chambers, ChambersUnion)
orientation_region (Blister, PhiRef)
PhiRef := rad(180) + PhiRef
area_center (Blister, Area, RowRef, ColumnRef)

*  +++ Extract pattern for classification +++
select_shape (Chambers, PillType1, 'row', 'and', 1, 145)
union1 (PillType1, PillType1)
select_shape (Chambers, PillType2, 'row', 'and', 145, 270)
union1 (PillType2, PillType2)
select_shape (Chambers, PillType3, 'row', 'and', 270, 390)
union1 (PillType3, PillType3)

* Extract color space of yellow pills
reduce_domain (Image, PillType1, ImageReduced)
decompose3 (ImageReduced, ImageR, ImageG, ImageB)
threshold (ImageB, Region, 60, 95)

* Extract color space of red pills
reduce_domain (Image, PillType2, ImageReduced)
decompose3 (ImageReduced, ImageR, ImageG, ImageB)
* invert_image反转图像的灰度值
invert_image (ImageB, ImageInvert)
* hysteresis_threshold使用迟滞阈值操作
* 函数原型：hysteresis_threshold(Image : RegionHysteresis : Low, High, MaxLength : )
* Image ：输入图像；RegionHysteresis ：输出区域；Low：低于这个灰度不会被选中；High：高于这个灰度立刻被选中；
* MaxLength ：在Low与High之间的根据MaxLength 对比度进行选择，和carry边缘类似。
hysteresis_threshold (ImageInvert, RegionHysteresis2, 190, 200, 5)

* Extract color space of green pills
reduce_domain (Image, PillType3, ImageReduced)
decompose3 (ImageReduced, ImageR, ImageG, ImageB)
invert_image (ImageB, ImageInvert)
hysteresis_threshold (ImageInvert, RegionHysteresis3, 180, 200, 10)
 
 * 求两个区域的交集
intersection (Region, PillType1, PillType1)
intersection (RegionHysteresis2, PillType2, PillType2)
PillTypeCount := [3,6,6]
intersection (RegionHysteresis3, PillType3, PillType3)
concat_obj (PillType1, PillType2, Classes)
concat_obj (Classes, PillType3, Classes)
return ()

NumClasses := |PillTypeCount|

创建创建高斯混合模型
输入参数： NumDim：几个特征；NumClasses:样本分类个数；NumCenters:类中心的个数；CovarType:
协方差矩阵（‘Spherical’,‘diag’,‘full’）;Preprocessing:预处理是否使用，及特征向量转换类型；
NumComponents:预处理参数，转换特征数；RandSend:迭代次数，初始化GMM；
输出参数：GMMHandle:高斯混合模型
create_class_gmm (3, 3, [1,5], ‘spherical’, ‘normalization’, 10, 42, GMMHandle)
含义：增加训练样本（图片区），放入GMM 进行训练数据
输入参数：Image:训练的图像（多通道）;ClassRegions:图像中用于训练的类区域;
GMMHandle:高斯混合模型;Randomize:高斯噪声标准差
add_samples_image_class_gmm (Image, Classes, GMMHandle, 0)
含义：训练高斯混合模型
输入参数：GMMHandle：高斯混合模型;MixIter: 期望最大值算法中迭代最大数;
Threshold:期望误差阈值，超过此值，计算取消;
ClassPriors:类的先验概率类型;（‘training’,‘uniform’）'uniform’表示使用相同的权重，1/NumClasses;'training’表示类发生的概率是依据样本中的概率推断。
Regularize:调整值，为了防止计算中出现奇异协方差矩阵。增加到协方差矩阵的对角线上的一个很小的值
输出参数：Centers:每个类的中心个数
Iter:每个类的迭代次数
train_class_gmm (GMMHandle, 100, 0.001, ‘training’, 0.0001, Centers, Iter)
检测胶囊
Count := 12
for FileIndex := 1 to Count by 1
- 读入图片
  read_image (Image, ‘blister/blister_mixed_’ + FileIndex$‘02’)
- 阈值分割
  threshold (Image, Region, 90, 255)
- 分割连通域
  connection (Region, ConnectedRegions)
- 面积对区域筛选
  select_shape (ConnectedRegions, SelectedRegions, ‘area’, ‘and’, 5000, 9999999)
- 求取最大区域边缘
  shape_trans (SelectedRegions, RegionTrans, ‘convex’)
- 利用椭圆变换，得出区域角度
  orientation_region (RegionTrans, Phi)
  if (abs(Phi) > rad(90))
  Phi := rad(180) + Phi
  endif
- 求取区域面积与中心坐标
  area_center (RegionTrans, Area1, Row, Column)
- 创建平移旋转变换矩阵
  vector_angle_to_rigid (Row, Column, Phi, RowRef, ColumnRef, PhiRef, HomMat2D)
- 使用仿射变换矩阵进行任意仿射变换
  affine_trans_image (Image, ImageAffinTrans, HomMat2D, ‘constant’, ‘false’)
- 减少图像定义域
  reduce_domain (ImageAffinTrans, ChambersUnion, ImageReduced)
- 将图片分为rgb三通道图像
  decompose3 (ImageAffinTrans, ImageR, ImageG, ImageB)
- 对每个胶囊进行识别检测
- 含义：使用GMM分类一个图像
- 输入参数：Image:训练的图像（多通道）；GMMHandle:GMM模型；
- RejectionThreshold:分类拒绝阈值，如果图像所有的像素概率低于此值将不会分类任何类。
- 输出参数：ClassRegions:分类的各个区域
  classify_image_class_gmm (ImageReduced, ClassRegions, GMMHandle, 0.005)
- 对ClassRegions进行计数
  count_obj (ClassRegions, Number)
- 生成空区域
  gen_empty_obj (FinalClasses)
- 分割连通域
  connection (Chambers, ChambersRemaining)
  for Index := Number to 1 by -1
  dev_clear_window ()
  - 选择ClassRegions区域中的第Index个对象
    select_obj (ClassRegions, Region, Index)
  - 求交集
    intersection (ChambersRemaining, Region, Region)
  - 使用面积与宽度进行区域筛选
    select_shape (Region, PillsOfOneType, [‘area’,‘width’], ‘and’, [200,40], [3000,68])
  - 求取两个区域的差集
    difference (ChambersUnion, PillsOfOneType, RegionDifference)
  - 分割连通域
    connection (RegionDifference, ConnectedRegions)
  - 使用面积进行区域筛选
    select_shape (ConnectedRegions, SelectedRegions, ‘area’, ‘and’, 0, 7868)
  - 求取区域的最大边缘
    shape_trans (SelectedRegions, SelectedRegions, ‘convex’)
  - 联合区域
    union1 (SelectedRegions, SelectedRegions)
  - 求取区域差集
    difference (ChambersRemaining, SelectedRegions, ChambersRemaining)
  - 联合两个区域
    concat_obj (SelectedRegions, FinalClasses, FinalClasses)
    endfor
- 得出结果进行处理得出结论
  gen_empty_obj (MissingPills)
  gen_empty_obj (WrongPills)
  gen_empty_obj (WrongNumberOfPills)
  difference (ChambersUnion, FinalClasses, LeftOvers)
  area_center (LeftOvers, Area, Row1, Column1)
  if (Area > 0)
  connection (LeftOvers, LeftOvers)
  count_obj (LeftOvers, Number)
  for Index := 1 to Number by 1
  select_obj (LeftOvers, ObjectSelected, Index)
  intensity (ObjectSelected, ImageB, Mean, Deviation)
  if (Deviation > 40)
  concat_obj (WrongPills, ObjectSelected, WrongPills)
  else
  concat_obj (MissingPills, ObjectSelected, MissingPills)
  endif
  endfor
  endif
- Compute histogram
  CountFinalClass := []
  for Index := 1 to NumClasses by 1
  select_obj (FinalClasses, ObjectSelected, Index)
  connection (ObjectSelected, ObjectSelected)
  count_obj (ObjectSelected, Size)
  CountFinalClass := [CountFinalClass,Size]
  endfor
- Display classification results and output allover statistic
  display_results (ImageAffinTrans, LeftOvers, FinalClasses, WrongPills, CountFinalClass, PillTypeCount, WindowHandle)
  if (FileIndex < Count)
  disp_continue_message (WindowHandle, ‘black’, ‘true’)
  endif
  stop ()
  endfor
Clear classifier handle
clear_class_gmm (GMMHandle)