TrainingImageIndices := [1,2]
TextureModelFilename := \'texture_model_carpet\'
dev_open_window_fit_size (0, 0, Width, Height, -1, -1, WindowHandle1)
dev_display (Image)
*创建模型
 create_texture_inspection_model (\'basic\', TextureInspectionModel)
 for Index := 0 to |TrainingImageIndices| - 1 by 1
read_image (Image, \'carpet/carpet_\' + TrainingImageIndices[Index]$\'02\')
dev_display (Image)
Message := \'添加图片 \' + (Index + 1) + \' of \' + |TrainingImageIndices| + \'训练准备\'
dev_disp_text (Message, \'window\', 12, 12, \'black\', [], [])
 *加载训练样本（两张）
 add_texture_inspection_model_image (Image, TextureInspectionModel, Indices)
 endfor

*参数设定\'patch_normalization\'：\'weber\'对亮度鲁棒，‘none’需要亮度作为评判（默认）
set_texture_inspection_model_param (TextureInspectionModel, \'patch_normalization\', \'weber\')
Levels := [2,3,4]
* \'levels\'：设置具体的金字塔层参与训练，纹理越粗糙，则较低的金字塔层级越可省略。默认auto。
set_texture_inspection_model_param (TextureInspectionModel, \'levels\', Levels)
* 开始训练
train_texture_inspection_model (TextureInspectionModel)
*查看样本参数\'novelty_threshold\'，阈值，自动计算得到，若结果不理想，可以手动微调。
get_texture_inspection_model_param (TextureInspectionModel, \'novelty_threshold\', NoveltyThreshold)
* 查看各个金字塔等级的新颖性得分图像和新颖性区域，可以把\'gen_result_handle\'设置为\'true\'，
 *之后get_texture_inspection_result_object读取\'novelty_score_image\'和\'novelty_region\'。
set_texture_inspection_model_param (TextureInspectionModel, \'gen_result_handle\', \'true\')

 *设置窗口，用于显示各个金字塔层图像
WindowWidth := 320
WindowHeight := 280
dev_open_window (0, 0, WindowWidth, WindowHeight, \'black\', WindowHandle1)
set_display_font (WindowHandle1, 16, \'mono\', \'true\', \'false\')
dev_open_window (0, WindowWidth + 8, WindowWidth, WindowHeight, \'black\', WindowHandle2)
set_display_font (WindowHandle2, 16, \'mono\', \'true\', \'false\')
dev_open_window (0, 2 * WindowWidth + 16, WindowWidth, WindowHeight, \'black\', WindowHandle3)
set_display_font (WindowHandle3, 16, \'mono\', \'true\', \'false\')
dev_open_window (WindowHeight + 50, WindowWidth / 2 + 8, 2 * WindowWidth, 2 * WindowHeight, \'black\', WindowHandle4)
set_display_font (WindowHandle4, 16, \'mono\', \'true\', \'false\')
 WindowHandles := [WindowHandle1,WindowHandle2,WindowHandle3]
  ** 检测第一张训练图像上的纹理缺陷以微调参数。
  for Index := 1 to 3 by 1
    ImageIndex := 5
        read_image (TestImage, \'carpet/carpet_\' + ImageIndex$\'02\')
        *测试当前图像
    apply_texture_inspection_model (TestImage, NoveltyRegion, TextureInspectionModel, TextureInspectionResultID)
* 检查调试信息。
    *查看各个金字塔等级的新颖性得分图像（NovScoreImage)和新颖性区域(NovRegionL)
     * 新颖性评分图像可用于单独微调新颖性阈值。
    get_texture_inspection_result_object (NovScoreImage, TextureInspectionResultID, \'novelty_score_image\')
    get_texture_inspection_result_object (NovRegion, TextureInspectionResultID, \'novelty_region\')
    * 显示每层（金字塔）的结果
        count_obj (NovScoreImage, Number)
         for Level := 1 to Number by 1
                     CurrentWindow := WindowHandles[Level - 1]
                             dev_set_window (CurrentWindow)
                             dev_clear_window ()
        select_obj (NovScoreImage, NovScoreImageL, Level)
        select_obj (NovRegion, NovRegionL, Level)
        get_image_size (NovScoreImageL, Width, Height)
        dev_set_part (0, 0, Height - 1, Width - 1)
        dev_display (NovScoreImageL)
        Legend := \'Novelty region (level \' + Levels[Level - 1] + \')\'
        dev_set_color (\'red\')
        dev_set_line_width (2)
        * 
        dev_display (NovRegionL)
        dev_disp_text ([\'Novelty score image (level \' + Levels[Level - 1] + \')\',\'Novelty threshold: \' + NoveltyThreshold[Level - 1]$\'.1f\'], \'window\', 12, 12, \'black\', [], [])
        dev_disp_text (Legend, \'window\', WindowHeight - 30, 12, \'white\', [\'box_color\',\'shadow\'], [\'black\',\'false\'])
         endfor
 *显示结果
    dev_set_window (WindowHandle4)
    dev_display (TestImage)
    dev_set_line_width (2)
    dev_set_color (\'red\')
    dev_display (NoveltyRegion)
    area_center (NoveltyRegion, Area, Row, Column)
    if (Index < 3)
        dev_disp_text (\'Result\', \'window\', 12, 12, \'black\', [], [])
    else
        dev_disp_text (\'Final result\', \'window\', 12, 12, \'black\', [], [])
    endif

 * 新奇阈值的微调。
     if (Index == 1)
        Message[0] := \'图像中有很多小错误.\'
        Message[1] := \'可以通过改变 novelty thresholds的值来调整灵敏度（sensitivity—）\'
        Message[2] := \'例如减少灵敏度参数的值\'
        dev_disp_text (Message, \'window\', 12, 12, \'black\', [], [])

         * 设置阈值计算的灵敏度。 负值导致更高的阈值，因此检测到的缺陷更少。
        * \'sensitivity\'：灵敏度，影响\'novelty_threshold\'的计算结果。负值会导致更高的阈值，从而更少的发现缺陷。默认0。
          set_texture_inspection_model_param (TextureInspectionModel, \'sensitivity\', -10)
        get_texture_inspection_model_param (TextureInspectionModel, \'novelty_threshold\', NoveltyThreshold)
     endif
 if (Index == 2)
        Message := \'也可以通过直接操纵新颖性边界来单独调整单个级别的敏感度\'
        dev_disp_text (Message, \'window\', 12, 12, \'black\', [], [])
* 新奇阈值的微调。
         *
         * 从纹理中获取（自动确定的）新奇阈值
         * 检查模型并将适当修改的值设置为新的新颖性阈值。
         *
         *如果我们明确设置新颖性边界，则忽略敏感性。
         * 我们在这里将其重新设置为 0 以避免混淆
        set_texture_inspection_model_param (TextureInspectionModel, \'sensitivity\', 0)
        * 
        Offset := [25,10,30]
        get_texture_inspection_model_param (TextureInspectionModel, \'novelty_threshold\', NoveltyThreshold)
        set_texture_inspection_model_param (TextureInspectionModel, \'novelty_threshold\', Offset + NoveltyThreshold)
        get_texture_inspection_model_param (TextureInspectionModel, \'novelty_threshold\', NoveltyThreshold)
    endif
  endfor

for Level := 1 to |WindowHandles| by 1
    dev_set_window (WindowHandles[Level - 1])
    dev_clear_window ()
endfor
dev_set_window (WindowHandle4)
dev_clear_window ()

*检测所有测试图像上的纹理缺陷。
NumImages := 7
for Index := 1 to NumImages by 1
    read_image (TestImage, \'carpet/carpet_\' + Index$\'02\')
    * 
    *检测当前图像
    apply_texture_inspection_model (TestImage, NoveltyRegion, TextureInspectionModel, TextureInspectionResultID)
    *得到新颖性图像和区域
    get_texture_inspection_result_object (NovScoreImage, TextureInspectionResultID, \'novelty_score_image\')
    get_texture_inspection_result_object (NovRegion, TextureInspectionResultID, \'novelty_region\')
    * 显示单个金字塔层数的结果
    count_obj (NovScoreImage, Number)
    for Level := 1 to Number by 1
        CurrentWindow := WindowHandles[Level - 1]
        dev_set_window (CurrentWindow)
        dev_clear_window ()
        select_obj (NovScoreImage, NovScoreImageL, Level)
        select_obj (NovRegion, NovRegionL, Level)
        get_image_size (NovScoreImageL, Width, Height)
        dev_set_part (0, 0, Height - 1, Width - 1)
        dev_display (NovScoreImageL)
         Legend := \'Novelty region (level \' + Levels[Level - 1] + \')\'
        dev_set_color (\'red\')
        dev_set_line_width (2)
        * 
        dev_display (NovRegionL)
        dev_disp_text ([\'Novelty score image (level \' + Levels[Level - 1] + \')\',\'Novelty threshold: \' + NoveltyThreshold[Level - 1]$\'.1f\'], \'window\', 12, 12, \'black\', [], [])
        dev_disp_text (Legend, \'window\', WindowHeight - 50, 12, [\'red\',\'white\'], [\'box_color\',\'shadow\'], [\'black\',\'false\'])
        endfor
            * 显示结果
             dev_set_window (WindowHandle4)
    dev_display (TestImage)
    dev_set_line_width (2)
    dev_set_color (\'red\')
    dev_display (NoveltyRegion)
    area_center (NoveltyRegion, Area, Row, Column)
    if (Area > 100)
        dev_disp_text (\'Not OK\', \'window\', 12, 12, \'white\', \'box_color\', \'red\')
    else
        dev_disp_text (\'OK\', \'window\', 12, 12, \'white\', \'box_color\', \'forest green\')
    endif
 if (Index < NumImages)
        dev_disp_text (\'Press Run (F5) to continue\', \'window\', \'bottom\', \'right\', \'black\', [], [])
        stop ()
    endif
endfor