HALCON示例程序classify_image_class_mlp.hdev如何使用MLP分类器分割RGB图像

示例程序源码（加注释）

关于显示相关显示在其他帖子有介绍。
dev_update_off ()
dev_close_window ()
dev_open_window (0, 0, 735, 485, ‘black’, WindowHandle)
set_display_font (WindowHandle, 14, ‘mono’, ‘true’, ‘false’)
dev_set_draw (‘margin’)
dev_set_colored (6)
dev_set_line_width (3)
read_image (Image, ‘patras’)
dev_display (Image)
Color := [‘indian red’,‘cornflower blue’,‘white’,‘black’,‘yellow’]

创建分割不同类别的区域
gen_rectangle1 (Sea, 10, 10, 120, 270)
gen_rectangle2 (Deck, [170,400], [350,375], [-0.56,-0.75], [64,104], [26,11])
union1 (Deck, Deck)
gen_rectangle1 (Walls, 355, 623, 420, 702)
gen_rectangle2 (Chimney, 286, 623, -0.56, 64, 33)
concat_obj介绍
concat_obj (Sea, Deck, Classes)
concat_obj (Classes, Walls, Classes)
concat_obj (Classes, Chimney, Classes)
显示
dev_set_color (Color[0])
dev_display (Deck)
dev_set_color (Color[1])
dev_display (Sea)
dev_set_color (Color[2])
dev_display (Walls)
dev_set_color (Color[3])
dev_display (Chimney)
Message := ‘Training regions for the color classifier’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’)
disp_continue_message (WindowHandle, ‘black’, ‘true’)
程序停止运行，便于观察
stop ()
创建MLP分类器，并添加分类样本
create_class_mlp( : : 输入维度数量, 隐藏单位数, 输出类别数, 输出函数类型, 预处理类型, 预处理参数, 迭代次数 : MLP句柄)
create_class_mlp (3, 3, 4, ‘softmax’, ‘principal_components’, 3, 42, MLPHandle)
为MLP添加训练样本
add_samples_image_class_mlp(图像, 不同类的区域: : MLP句柄 : )
add_samples_image_class_mlp (Image, Classes, MLPHandle)
dev_display (Image)
Message := ‘Training …’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’)
训练分类器
train_class_mlp( : : MLP句柄, 最大迭代次数, 权重差异阈值, 误差差异阈值: 平均误差, 平均误差Log)
train_class_mlp (MLPHandle, 200, 1, 0.01, Error, ErrorLog)
Message := Message + ’ ready.’
Message[1] := ‘Segment image using the classifier …’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’)
分割图像
classify_image_class_mlp(图像 : 分割结果: MLP句柄, 拒绝分类区域阈值: )
classify_image_class_mlp (Image, ClassRegions, MLPHandle, 0.5)
使用区域的平均灰度值填充图像
region_to_mean (ClassRegions, Image, ImageClass)
dev_display (ImageClass)
Message[1] := Message[1] + ’ ready.’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’)
清除句柄
clear_class_mlp (MLPHandle)
disp_continue_message (WindowHandle, ‘black’, ‘true’)
stop ()
下边分割了另几种颜色和上文一样
gen_rectangle2 (Rejection, [193,66,261], [235,332,328], [-0.32,-1.45,-1.51], [33,34,60], [4,3,3])
union1 (Rejection, Rejection)
concat_obj (Classes, Rejection, Classes)
dev_display (Image)
dev_set_color (Color[0])
dev_display (Deck)
dev_set_color (Color[1])
dev_display (Sea)
dev_set_color (Color[2])
dev_display (Walls)
dev_set_color (Color[3])
dev_display (Chimney)
dev_set_color (Color[4])
dev_display (Rejection)
disp_message (WindowHandle, ‘Add a rejection class to improve the robustness of the classifier’, ‘window’, 12, 12, ‘black’, ‘true’)
disp_continue_message (WindowHandle, ‘black’, ‘true’)
stop ()
dev_display (Image)
Create the classifier and add the samples
create_class_mlp (3, 4, 5, ‘softmax’, ‘principal_components’, 3, 42, MLPHandle)
add_samples_image_class_mlp (Image, Classes, MLPHandle)
Train the classifier
Message := ‘Training the classifier with rejection class…’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’)
train_class_mlp (MLPHandle, 200, 1, 0.01, Error, ErrorLog)
Message := Message + ’ ready.’
Message[1] := ‘Segment image using the classifier …’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’)
Segment (classify) the image
classify_image_class_mlp (Image, ClassRegionsNotRejected, MLPHandle, 0.5)
Select every class except the rejection class.
copy_obj (ClassRegionsNotRejected, ClassRegionsNotRejected, 1, 4)
Note that the black areas in the mean image correspond to the rejected pixels
region_to_mean (ClassRegionsNotRejected, Image, ImageClassNotRejected)
dev_display (ImageClassNotRejected)
Message[1] := Message[1] + ’ ready.’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’)
clear_class_mlp (MLPHandle)