药品包装或质量控制

下述代码是一个图像处理和机器学习分类的脚本，用于识别和分类药片。它使用了HALCON软件的命令集，HALCON是一个用于机器视觉和图像分析的高级编程语言和开发环境。

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’)
*

• First, we extract the content of the blister and pass
• this information on to the gmm classifier disp_message (WindowHandle, ‘Train gmm classifier on pill types’, ‘window’, -1, -1, ‘black’, ‘true’) extract_pill_types (Image, Chambers, ChambersUnion, Classes, PhiRef, RowRef, ColumnRef, PillTypeCount) NumClasses := |PillTypeCount| create_class_gmm (3, 3, [1,5], ‘spherical’, ‘normalization’, 10, 42, GMMHandle) add_samples_image_class_gmm (Image, Classes, GMMHandle, 0) train_class_gmm (GMMHandle, 100, 0.001, ‘training’, 0.0001, Centers, Iter)
• Then, the subsequent blisters are tested for their right combination Count := 12 for FileIndex := 1 to Count by 1 * - Align image read read_image (Image, ‘blister/blister_mixed_’ + FileIndex$‘02’) threshold (Image, Region, 90, 255) connection (Region, ConnectedRegions) select_shape (ConnectedRegions, SelectedRegions, ‘area’, ‘and’, 5000, 9999999) shape_trans (SelectedRegions, RegionTrans, ‘convex’) orientation_region (RegionTrans, Phi) if (abs(Phi) > rad(90)) Phi := rad(180) + Phi endif area_center (RegionTrans, Area1, Row, Column) vector_angle_to_rigid (Row, Column, Phi, RowRef, ColumnRef, PhiRef, HomMat2D) affine_trans_image (Image, ImageAffinTrans, HomMat2D, ‘constant’, ‘false’) reduce_domain (ImageAffinTrans, ChambersUnion, ImageReduced) decompose3 (ImageAffinTrans, ImageR, ImageG, ImageB)- Classify pill type for each chamber classify_image_class_gmm (ImageReduced, ClassRegions, GMMHandle, 0.005) count_obj (ClassRegions, Number) gen_empty_obj (FinalClasses) connection (Chambers, ChambersRemaining) for Index := Number to 1 by -1 dev_clear_window () select_obj (ClassRegions, Region, Index) intersection (ChambersRemaining, Region, Region) select_shape (Region, PillsOfOneType, [‘area’,‘width’], ‘and’, [200,40], [3000,68]) difference (ChambersUnion, PillsOfOneType, RegionDifference) connection (RegionDifference, ConnectedRegions) select_shape (ConnectedRegions, SelectedRegions, ‘area’, ‘and’, 0, 7868) shape_trans (SelectedRegions, SelectedRegions, ‘convex’) union1 (SelectedRegions, SelectedRegions) difference (ChambersRemaining, SelectedRegions, ChambersRemaining) concat_obj (SelectedRegions, FinalClasses, FinalClasses) endfor * * - Check for right combination 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)

blister_mixed_reference.png

blister_mixed_01.png

blister_mixed_02.png

blister_mixed_03.png

blister_mixed_04.png

blister_mixed_05.png

blister_mixed_06.png

blister_mixed_07.png

blister_mixed_08.png

blister_mixed_09.png

blister_mixed_010.png

blister_mixed_011.png

blister_mixed_012.png

以下是程序运行结果：












以下是代码的大致流程和功能解释：

初始化和显示设置：

dev_close_window()：关闭当前窗口。
dev_open_window_fit_image()：打开一个窗口以适应图像大小。
set_display_font()：设置显示字体。
图像读取和预处理：

read_image()：读取图像。
threshold()：对图像进行阈值处理，提取感兴趣的区域。
connection()：连接相邻的区域。
select_shape()：根据面积筛选区域。
shape_trans()：转换区域形状，例如转换为凸包。
orientation_region()：计算区域的方向。
图像变换：

area_center()：计算区域的中心点。
vector_angle_to_rigid()：根据中心点和方向生成刚体变换矩阵。
affine_trans_image()：对图像进行仿射变换。
颜色分解：

decompose3()：将图像分解为红绿蓝三个颜色通道。
GMM分类器训练：

create_class_gmm()：创建高斯混合模型（GMM）分类器。
add_samples_image_class_gmm()：向GMM分类器添加图像样本。
train_class_gmm()：训练GMM分类器。
药片类型分类：

classify_image_class_gmm()：使用训练好的GMM分类器对图像进行分类。
后处理和统计：

count_obj()：计算对象的数量。
gen_empty_obj()：生成空的对象。
difference()：计算差集。
union1()：计算并集。
concat_obj()：连接对象。
结果展示和统计输出：

display_results()：展示分类结果和统计信息。
循环处理多个图像：

通过一个循环读取和处理多个图像文件。
清理资源：

clear_class_gmm()：清理GMM分类器资源。
这段代码是一个完整的工作流程，从图像的读取、预处理、特征提取、分类器训练、分类、后处理到结果展示。它适用于自动化药片识别和分类的场景，例如在药品包装或质量控制中。