以下代码是使用HALCON软件编写的，HALCON是一款用于图像处理、机器视觉和图像分析的高级软件工具。代码的主要功能是创建一个基于组件的机器视觉模型，用于识别和定位图像中的拨动开关（dip switch）

dev_update_off ()
dev_set_draw (‘margin’)
read_image (ModelImage, ‘dip_switch/dip_switch_model’)
get_image_size (ModelImage, Width, Height)
dev_close_window ()
dev_open_window (0, 0, Width, Height, ‘black’, WindowHandle)
set_display_font (WindowHandle, 14, ‘mono’, ‘true’, ‘false’)

• Define the initial components
  gen_rectangle2 (InitialComponents, 265, 138, -0.02, 23, 13)
  gen_rectangle2 (InitialComponent, 342, 286, -0.02, 168, 13)
  concat_obj (InitialComponents, InitialComponent, InitialComponents)
  for I := 0 to 11 by 1
  gen_rectangle2 (InitialComponent, 311 + I * 0.5, 128 + I * 28.2, 0, 10, 12)
  concat_obj (InitialComponents, InitialComponent, InitialComponents)
  endfor
  dev_set_colored (12)
  dev_display (ModelImage)
  dev_display (InitialComponents)
  dev_set_color (‘yellow’)
  disp_message (WindowHandle, ‘Model image and’, ‘image’, 20, 20, ‘yellow’, ‘false’)
  disp_message (WindowHandle, ‘input regions describing the initial components’, ‘image’, 40, 20, ‘yellow’, ‘false’)
  disp_message (WindowHandle, ‘Press mouse button to continue’, ‘window’, 450, 20, ‘yellow’, ‘false’)
  get_mbutton (WindowHandle, Row1, Column1, Button1)
• Get the training images
  gen_empty_obj (TrainingImages)
  for I := 1 to 12 by 1
  read_image (TrainingImage, ‘dip_switch/dip_switch_training_’ + I$‘02’)
  concat_obj (TrainingImages, TrainingImage, TrainingImages)
  dev_display (TrainingImage)
  disp_message (WindowHandle, ‘Training image ’ + I + ’ of 12’, ‘image’, 20, 20, ‘yellow’, ‘false’)
  disp_message (WindowHandle, ‘Press mouse button to continue’, ‘window’, 450, 20, ‘yellow’, ‘false’)
  get_mbutton (WindowHandle, Row2, Column2, Button2)
  endfor
• The component-based matching uses the contours of the components
• as features for the matching. In order to make sure that only significant
• features are involved in the matching the parameters ContrastLow,
• ContrastHigh, and MinSize can be passed to the training. To inspect the
• effect of different values for these three parameters, and hence to find
• the optimum values for a certain application, the following two lines of
• code can be used:
• add_channels (InitialComponents, ModelImage, ModelImageInitComp)
• gen_initial_components (ModelImageInitComp, InitialComponentEdges, 45, 45, 30, ‘connection’, [], [])
• Extract the model components and train the relations
• (This may take a few minutes!)
  dev_display (ModelImage)
  disp_message (WindowHandle, ‘Train the model components…’, ‘image’, 20, 20, ‘yellow’, ‘false’)
  disp_message (WindowHandle, ‘(This may take a few minutes)’, ‘image’, 40, 20, ‘yellow’, ‘false’)
  dev_set_check (‘~give_error’)
  dev_error_var (Error, 1)
  read_training_components (‘train_dip_switch.ct’, ComponentTrainingID)
  dev_error_var (Error, 0)
  dev_set_check (‘give_error’)
  if (Error != H_MSG_TRUE)
  train_model_components (ModelImage, InitialComponents, TrainingImages, ModelComponents, 45, 45, 30, 0.95, -1, -1, rad(20), ‘speed’, ‘rigidity’, 0.2, 0.5, ComponentTrainingID)
  • Ignore writing errors as they are not critical for this example
    dev_set_check (‘~give_error’)
    write_training_components (ComponentTrainingID, ‘train_dip_switch.ct’)
    dev_set_check (‘give_error’)
    endif
    dev_set_color (‘yellow’)
    dev_display (ModelImage)
    disp_message (WindowHandle, ‘Result of the training:’, ‘image’, 20, 20, ‘yellow’, ‘false’)
    disp_message (WindowHandle, ’ 1) Final model components’, ‘image’, 40, 20, ‘yellow’, ‘false’)
    dev_set_colored (12)
    get_training_components (ModelComponents, ComponentTrainingID, ‘model_components’, ‘model_image’, ‘false’, RowRef, ColumnRef, AngleRef, ScoreRef)
    dev_display (ModelComponents)
    disp_message (WindowHandle, ‘Press mouse button to continue’, ‘window’, 450, 20, ‘yellow’, ‘false’)
    get_mbutton (WindowHandle, Row3, Column3, Button3)
    count_obj (ModelComponents, NumComp)
    for I := 0 to NumComp - 1 by 1
    dev_display (ModelImage)
    disp_message (WindowHandle, ‘Result of the training:’, ‘image’, 20, 20, ‘yellow’, ‘false’)
    disp_message (WindowHandle, ’ 1) Final model components’, ‘image’, 40, 20, ‘yellow’, ‘false’)
    disp_message (WindowHandle, ’ 2) Relations with respect to component ’ + I, ‘image’, 60, 20, ‘yellow’, ‘false’)
    dev_set_colored (12)
    get_component_relations (Relations, ComponentTrainingID, I, ‘model_image’, Row, Column, Phi, Length1, Length2, AngleStart, AngleExtent)
    dev_display (Relations)
    disp_message (WindowHandle, ‘Press mouse button to continue’, ‘window’, 450, 20, ‘yellow’, ‘false’)
    get_mbutton (WindowHandle, Row4, Column4, Button4)
    endfor
• Add small tolerances to the automatically derived relations in order
• to receive a higher robustness.
  modify_component_relations (ComponentTrainingID, ‘all’, ‘all’, 0, rad(4))
• Create the component model based on the training result.
• The minimum score for the model components should be set individually.
• On the one hand the print on the module may be occluded, which requires
• a low minimum score. On the other hand the switches must be distinguished
• from from the background, which requires to set the corresponding minimum
• score values to higher values.
  MinScoreComp := gen_tuple_const(13,0.8)
  MinScoreComp[0] := 0.4
  dev_display (ModelImage)
  disp_message (WindowHandle, ‘Create the component model…’, ‘image’, 20, 20, ‘yellow’, ‘false’)
• Automatically determine the optimum number of pyramid levels for the
• switches. However, for the print on the module 4 pyramid levels
• instead of the automatically determined 3 levels are used to speed up
• the search.
  NumLevelsComp := gen_tuple_const(13,0)
  NumLevelsComp[0] := 4
  create_trained_component_model (ComponentTrainingID, 0, rad(360), 10, MinScoreComp, NumLevelsComp, ‘auto’, ‘none’, ‘use_polarity’, ‘false’, ComponentModelID, RootRanking)
  get_component_model_tree (Tree, Relations, ComponentModelID, RootRanking, ‘model_image’, StartNode, EndNode, Row, Column, Phi, Length1, Length2, AngleStart, AngleExtent)
  dev_display (ModelImage)
  disp_message (WindowHandle, ‘Component model’, ‘image’, 20, 20, ‘yellow’, ‘false’)
  dev_set_colored (12)
  dev_display (Tree)
  dev_display (Relations)
  disp_message (WindowHandle, ‘Press mouse button to continue’, ‘window’, 450, 20, ‘yellow’, ‘false’)
  get_mbutton (WindowHandle, Row5, Column5, Button5)
  wait_seconds (0.5)
  clear_training_components (ComponentTrainingID)
• Find the component model in a run-time image
  ImgNo := 1
  Button := 0
  while (Button != 1)
  read_image (SearchImage, ‘dip_switch/dip_switch_’ + ImgNo${}^{\prime }02d^{\prime })coun{t}_{s}econds(Seconds1)fin{d}_{c}omponen{t}_{m}odel(SearchImage,ComponentModelID,RootRanking,0,rad(360),0,0,0.5{,}^{\prime }sto{p}_{s}earc{h}^{\prime }{,}^{\prime }prun{e}_{b}ranc{h}^{\prime }{,}^{\prime }non{e}^{\prime },MinScoreComp{,}^{\prime }leas{t}_{s}quare{s}^{\prime },0,0.9,ModelStart,ModelEnd,Score,RowComp,ColumnComp,AngleComp,ScoreComp,ModelComp)coun{t}_{s}econds(Seconds2)de{v}_{d}isplay(SearchImage)NumFound:=|ModelStart|Time:=(Seconds2-Seconds1)\ast 1000dis{p}_{m}essage(WindowHandle,NumFound{+}^{\prime }object(s)foundi{n}^{\prime }+Time$’4.2f’ + ’ ms’, ‘image’, 20, 20, ‘yellow’, ‘false’)
  for Match := 0 to |ModelStart| - 1 by 1
  get_found_component_model (FoundComponents, ComponentModelID, ModelStart, ModelEnd, RowComp, ColumnComp, AngleComp, ScoreComp, ModelComp, Match, ‘false’, RowCompInst, ColumnCompInst, AngleCompInst, ScoreCompInst)
  dev_set_colored (12)
  dev_display (FoundComponents)
  visualize_dip_switch_match (RowCompInst, ColumnCompInst, AngleCompInst, RowRef, ColumnRef, AngleRef, WindowHandle, Match)
  endfor
  disp_message (WindowHandle, ‘Press mouse button to stop’, ‘window’, 450, 20, ‘yellow’, ‘false’)
  ImgNo := ImgNo + 1
  if (ImgNo > 13)
  ImgNo := 1
  endif
  dev_error_var (Error, 1)
  dev_set_check (‘~give_error’)
  get_mposition (WindowHandle, R, C, Button)
  dev_error_var (Error, 0)
  dev_set_check (‘give_error’)
  if (Error != H_MSG_TRUE)
  Button := 0
  endif

• stop ()
  

endwhile
clear_component_model (ComponentModelID)

以下是代码的详细解释：

初始化和显示模型图像以及输入区域：

dev_update_off()：关闭设备更新，以提高性能。
dev_set_draw(‘margin’)：设置绘图边界。
read_image(ModelImage, …)：读取拨动开关的模型图像。
get_image_size(…)：获取模型图像的尺寸。
dev_open_window(…)：打开一个窗口来显示图像。
set_display_font(…)：设置显示字体。
定义初始组件：

使用gen_rectangle2函数生成初始组件的矩形区域。
使用concat_obj函数将这些矩形区域合并为一个对象。
读取训练图像并显示：

使用循环读取12张拨动开关的训练图像。
使用dev_display函数显示每张训练图像。
训练模型组件：

使用read_training_components函数读取训练组件。
使用train_model_components函数训练模型组件，这可能需要几分钟时间。
显示训练结果：

使用get_training_components函数获取训练后的组件。
使用dev_display函数显示训练后的组件。
显示组件关系：

使用循环和get_component_relations函数获取并显示每个组件的关系。
修改组件关系以增加鲁棒性：

使用modify_component_relations函数修改关系，增加容忍度。
创建经过训练的组件模型：

使用create_trained_component_model函数创建组件模型。
使用get_component_model_tree函数获取组件模型树。
显示组件模型：

使用dev_display函数显示组件模型树和关系。
在运行时图像中查找组件模型：

使用循环和find_component_model函数在一系列图像中查找组件模型。
使用dev_display函数显示搜索图像和找到的组件模型。
使用visualize_dip_switch_match函数可视化匹配结果。
清理：

使用clear_training_components和clear_component_model函数清理训练组件和组件模型。
总的来说，这段代码实现了一个完整的机器视觉工作流程，包括图像读取、组件定义、训练、显示结果、关系获取、模型创建和模型匹配。这可以用于自动化的质量控制、装配线检查或任何需要识别和定位特定组件的场景。