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list_image_files (train_dir, 'default', [], ImageFiles)
train_samples := []
image_width := image_size[0]
image_height := image_size[1]
for Index := 0 to |ImageFiles|-1 by 1
        image_path := ImageFiles[Index]
    create_dict (sample_info)
    set_dict_tuple (sample_info, 'image_id', Index)
    set_dict_tuple (sample_info, 'anomaly_label', 'ok')
    set_dict_tuple (sample_info, 'anomaly_label_id', 0)
    read_image (Image, image_path)
    convert_image_type (Image, Image, 'real')
        if (Index < 1)
        image_sum := Image
    else
        add_image (Image, image_sum, image_sum, 1, 0)
    endif
endfor
sub_image (image_sum, image_sum, Imagezero, 1, |ImageFiles|)
div_image (image_sum, Imagezero, ImageResult, 1, 0)
count_channels (ImageResult, Channels)
* 初始化均值和方差列表
MeanValues := []
DeviationValues := []
if (Channels == 3)
* 处理彩色图像
decompose3(ImageResult, Image1, Image2, Image3)
else
    Image1 := ImageResult
    Image2 := ImageResult
    Image3 := ImageResult
endif
gen_empty_obj (EmptyObject)
concat_obj (EmptyObject, Image1, EmptyObject)
concat_obj (EmptyObject, Image2, EmptyObject)
concat_obj (EmptyObject, Image3, EmptyObject)
* 计算每个通道的均值和标准差
for ChannelIndex := 1 to 3 by 1
    select_obj (EmptyObject, ObjectSelected, ChannelIndex)
    intensity(ObjectSelected, ObjectSelected, Mean, Deviation)
    MeanValues := [MeanValues, Mean]
    DeviationValues := [DeviationValues, Deviation]
endfor
create_dict (data_param)
set_dict_tuple (data_param, 'DeviationValues', DeviationValues)
set_dict_tuple (data_param, 'MeanValues', MeanValues)
for Index := 0 to |ImageFiles|-1 by 1
        image_path := ImageFiles[Index]
    create_dict (sample_info)
    set_dict_tuple (sample_info, 'image_id', Index)
    set_dict_tuple (sample_info, 'anomaly_label', 'ok')
    set_dict_tuple (sample_info, 'anomaly_label_id', 0)
        read_image (Image, image_path)
    convert_image_type (Image, Image, 'real')
    zoom_image_size (Image, Image, image_width, image_height, 'bilinear')
        count_channels (Image, Channels)
        if (Channels == 3)
        * 处理彩色图像
        decompose3(Image, Image1, Image2, Image3)
    else
        Image1 := Image
        Image2 := Image
        Image3 := Image
    endif
        gen_empty_obj (ImageScaled)
        Scale1 := 1.0 / DeviationValues[0]
    Shift1 := - Scale1 * MeanValues[0]
    scale_image (Image1, ImageScaled1, Scale1, Shift1)
    append_channel (ImageScaled, ImageScaled1, ImageScaled)
        Scale2 := 1.0 / DeviationValues[1]
    Shift2 := - Scale1 * MeanValues[1]
    scale_image (Image2, ImageScaled2, Scale2, Shift2)
    append_channel (ImageScaled, ImageScaled2, ImageScaled)
        Scale3 := 1.0 / DeviationValues[2]
    Shift3 := - Scale3 * MeanValues[2]
    scale_image (Image3, ImageScaled3, Scale3, Shift3)
    append_channel (ImageScaled, ImageScaled3, ImageScaled)
        gen_empty_obj (ImagesScaled)
    concat_obj (ImagesScaled, ImageScaled, ImagesScaled)
        set_dict_object (ImagesScaled, sample_info, 'image')
        train_samples :=[train_samples, sample_info]
endfor
return ()