分类器——高斯混合模型之查找表分类器（保险丝分类）

合集 - Halcon(58)

52.分类器——高斯混合模型之查找表分类器（保险丝分类）2024-03-08

53.模版匹配——set_shape_model_param2024-03-13 54.模板匹配——set_shape_model_clutter2024-03-13 55.模板匹配——金字塔图像计算gen_gauss_pyramid2024-03-13 56.模板匹配——determine_shape_model_params2024-03-14 57.模板匹配——create_shape_model2024-03-14 58.模版匹配——inspect_shape_model2024-03-15


* In this example five different color fuses are segmented with
* a look-up table classifier (LUT) based on a Gaussian Mixture
* Model (GMM).
*基于高斯混合模型查找表分类器，用于颜色分割匹配
* 
dev_update_off ()
dev_close_window ()
dev_open_window (0, 0, 800, 600, 'white', WindowHandle)
dev_set_draw ('margin')
ImageRootName := 'color/color_fuses_0'
*保险丝类型  橘色5A    红色10A    蓝色15A   黄色20A     绿色30A
FuseTypes := [5,10,15,20,30]
*保险丝颜色
FuseColors := ['橘色','红色','蓝色','黄色','绿色']
FuseHighlight := ['orange','red','blue','goldenrod','forest green']
DisplayTextShift := [85,65,75,85,85]
dev_set_color ('white')
dev_set_line_width (2)
read_image (Image, ImageRootName + '0')
dev_display (Image)
* 
* Define ROIs for the training data of the classifier
*获取(标记)分类器训练数据区域
set_display_font (WindowHandle, 14, 'mono', 'true', 'false')
gen_rectangle1 (FuseOrange, 195, 90, 230, 120)
dev_display (FuseOrange)
disp_message (WindowHandle, '橘色保险丝', 'image', 160, 90 - 65, 'black', 'false')
gen_rectangle1 (FuseRed, 191, 280, 226, 310)
dev_display (FuseRed)
disp_message (WindowHandle, '红色保险丝', 'image', 160, 280 - 55, 'black', 'false')
gen_rectangle1 (FuseBlue, 190, 470, 225, 500)
dev_display (FuseBlue)
disp_message (WindowHandle, '蓝色保险丝', 'image', 160, 470 - 60, 'black', 'false')
gen_rectangle1 (FuseYellow, 192, 672, 227, 702)
dev_display (FuseYellow)
disp_message (WindowHandle, '黄色保险丝', 'image', 160, 672 - 70, 'black', 'false')
gen_rectangle1 (FuseGreen, 197, 880, 232, 910)
dev_display (FuseGreen)
disp_message (WindowHandle, '绿色保险丝', 'image', 160, 880 - 65, 'black', 'false')
*创建分类器中类别
gen_empty_obj (Classes)
concat_obj (FuseOrange, FuseRed, Classes)
concat_obj (Classes, FuseBlue, Classes)
concat_obj (Classes, FuseYellow, Classes)
concat_obj (Classes, FuseGreen, Classes)
disp_message (WindowHandle, '训练数据标记完成', 'window', 12, 12, 'orange', 'false')
disp_continue_message (WindowHandle, 'black', 'false')
stop ()
* 
* Create the GMM classifier, add the samples, and train it
*创建高斯混合模型，添加训练样本以及训练
*创建含5个类的高斯混合模型分类器
create_class_gmm (3, 5, 1, 'full', 'none', 3, 42, GMMHandle)
add_samples_image_class_gmm (Image, Classes, GMMHandle, 0)
disp_message (WindowHandle, 'GMM分类器训练中……', 'window', 48, 12, 'black', 'true')
train_class_gmm (GMMHandle, 100, 0.001, 'training', 0.001, Centers, Iter)
* 
* 创建基于高斯混合模型的查找表分类器
disp_message (WindowHandle, '创建基于高斯混合模型的查找表分类器...', 'window', 84, 12, 'black', 'true')
create_class_lut_gmm (GMMHandle, ['bit_depth','rejection_threshold'], [6,0.03], ClassLUTHandle)
* 
* Segment images with LUT classifier
*基于GMM查找表分类器分割图像
for Img := 0 to 3 by 1
    read_image (Image, ImageRootName + Img)
    count_seconds (T1)
    classify_image_class_lut (Image, ClassRegions, ClassLUTHandle)
    count_seconds (T2)
    TimeToClassify := (T2 - T1) * 1000
    dev_display (Image)
    dev_set_line_width (3)
    for Fuse := 1 to 5 by 1
        * 
        * Perform post-processing on returned classes
        *返回结果进行后处理
        copy_obj (ClassRegions, ObjectsSelected, Fuse, 1)
        closing_circle (ObjectsSelected, RegionClosing, 3.5)
        connection (RegionClosing, ConnectedRegions)
        select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 2500, 99999)
        fill_up (SelectedRegions, RegionFillUp)
        area_center (RegionFillUp, Area, Row, Column)
        shape_trans (RegionFillUp, RegionTrans, 'convex')
        dev_set_color (FuseHighlight[Fuse - 1])
        dev_display (RegionTrans)
        * 
        disp_message (WindowHandle, FuseColors[Fuse - 1] + ' ' + FuseTypes[Fuse - 1] + ' A', 'image', Row - 10, Column - DisplayTextShift[Fuse - 1], FuseHighlight[Fuse - 1], 'white')
    endfor
    disp_message (WindowHandle,'用时'+TimeToClassify$'.1f' + ' ms', 'window', 12, 12, 'black', 'false')
    if (Img < 3)
        disp_continue_message (WindowHandle, 'black', 'false')
        stop ()
    endif
endfor
* 
disp_message (WindowHandle, '执行完成', 'window', 50, 12, 'black', 'false')

1.样本标注

2.高斯混合模型训练

3.创建基于GMM的查找表分类器

4.分类结果

>>>classify_image_class_lut和classify_class_gmm比较：

　　　　由于它取代了运算符classify_class_gmm，因此 NumComponents <= 3 必须成立。如果没有减少图像通道数的预处理（NumDim = NumComponents），则字节图像中可能出现的所有可能的像素值都用classify_class_gmm进行分类。返回的类存储在 LUT 中。如果存在减少图像通道数的预处理（NumDim > NumComponents），则 GMM 的预处理参数将存储在 LUT 的单独结构中。要创建 LUT，所有转换后的像素值都用classify_class_gmm进行分类。返回的类存储在 LUT 中。由于 LUT 的离散化，LUT 分类器的精度可能会低于 classify_image_class_gmm 的精度。使用“bit_depth”和“class_selection”，可以控制分类的准确性、所需的存储和创建 LUT 所需的运行时间。

>>create_class_lut_gmm参数说明　　