图像处理之MLP（多层神经网络）

女爷i 2024-03-25 15:40 20阅读 0赞

#### 一、介绍 ####

在进行颜色识别时，可以利用HSV不同分量具有不同特点，在H或者S通道进行颜色选择，使用过Blob分析：二值化，形态学，形状选择，但是对光照敏感，不稳定。这里另一种方法：MLP分类器的方式来识别颜色。

#### 二、步骤 ####

1、特征提取

*创建空对象，用以保存训练样本 
    gen_empty_obj(Class)  
      
    *绘制ROI选择要训练的颜色
    draw_rectangle2 (WindowHandle, Row, Column, Phi, Length1, Length2)
    gen_rectangle2 (Rectangle, Row, Column, Phi, Length1, Length2)
    concat_obj (Class, Rectangle, Class)

2、创建分类器

create_class_mlp (3, 10, 5, 'softmax', 'normalization', 10, 42, MLPHandle)

3、添加样本

add_samples_image_class_mlp (Image, Class, MLPHandle)

4、训练样本

train_class_mlp (MLPHandle, 200, 0.01, 0.01, Error, ErrorLog)

5、识别

classify_image_class_mlp (Image, ClassRegions, MLPHandle, 0.5)

#### 三、实例 ####

打开用例

![caabb325b11a41d19449c6d7f7bb3883.png][]

识别颜色，原始图像，进行学习

![20896e9e7ca7427e812da54d42145d51.png][]

代码.

* This example demonstrates a completeness check of colored game
    * pieces using MLP classification. The training and application
    * of the classifier is first shown on colored images and then on
    * gray images.
    * 
    *此过程将dev_update_pc、dev_update_var和dev_update_window设置为“关闭”  
    dev_update_off ()
    dev_close_window ()
    dev_open_window (0, 0, 557, 416, 'black', WindowHandle)
    *设置字体样式 
    set_display_font (WindowHandle, 14, 'mono', 'true', 'false')
    dev_set_draw ('margin')
    * 
    * * Initialization
    ImageRootName := 'color/color_pieces_0' *设置图像文件
    *定义一个数组存放字符串 ，代表需要识别区域
    Regions := ['yellow','pink','blue','background'] 
    Highlight := ['goldenrod','magenta','cyan']
    *创建一个空元组Classes  
    gen_empty_obj (Classes)
    * 
    * 训练 并使用 MLP 分类器
    for Mode := 0 to 1 by 1
        dev_set_color ('black')
        read_image (Image, ImageRootName + '0')
        * 
        * Simulate gray image
        if (Mode == 1)
            rgb1_to_gray (Image, GrayImage)
            compose3 (GrayImage, GrayImage, GrayImage, Image)
            dev_display (Image)
            disp_message (WindowHandle, 'Train and apply the classes again on gray images', 'window', 12, 12, 'black', 'false')
            disp_continue_message (WindowHandle, 'black', 'true')
            stop ()
        endif
        * 
        * Colored images
        if (Mode == 0)
            * 
            * Specify color classes
            for I := 1 to 4 by 1
                dev_display (Image)
                dev_display (Classes)
                disp_message (WindowHandle, ['Drag rectangle inside ' + Regions[I - 1] + ' color','Click right mouse button to confirm'], 'window', 24, 12, 'black', 'false')
                *画一个平行于坐标轴的矩形，包含图片中选中的部分  
                draw_rectangle1 (WindowHandle, Row1, Column1, Row2, Column2)
                gen_rectangle1 (Rectangle, Row1, Column1, Row2, Column2)
                *连接两个标志性对象元组 
                concat_obj (Classes, Rectangle, Classes)
            endfor
        endif
        * 
        * Train the specified color classes
    
    * 创建多层感知器，3维特征向量维度，7层神经元的隐藏层，输出4类，softmax分类器（多项式分布），预处理设置为'normalization'，使用主成分分析，特征向量所在的组件数3，随机数初始化种子是42*
    
        create_class_mlp (3, 7, 4, 'softmax', 'normalization', 3, 42, MLPHandle)
    *将图像中的训练样本添加到mlp模型的训练数据中，其中Classes是被训练的区域 
    
        add_samples_image_class_mlp (Image, Classes, MLPHandle)
        disp_message (WindowHandle, 'Training...', 'window', 100, 12, 'black', 'false')
    
        *训练mlp模型 
        train_class_mlp (MLPHandle, 400, 0.5, 0.01, Error, ErrorLog)
        * 
        * Use the trained MLP classifier to test if each image
        * contains four game pieces of each color
        for J := 0 to 3 by 1
            read_image (Image, ImageRootName + J)*读取不同的图像文件
            if (Mode == 1)
                rgb1_to_gray (Image, GrayImage)*模拟灰度图像
                compose3 (GrayImage, GrayImage, GrayImage, Image)
            endif
            * 
            * Apply the trained classes
            *开始分类识别物体 
            classify_image_class_mlp (Image, ClassRegions, MLPHandle, 0.5)
            dev_display (Image)
            disp_message (WindowHandle, 'Looking for 4 game pieces of each color ...', 'window', 24, 12, 'black', 'false')
            dev_set_line_width (2)
            * 
            * Count the number of game pieces for each color class
            for Figure := 1 to 3 by 1
                copy_obj (ClassRegions, ObjectsSelected, Figure, 1)
                connection (ObjectsSelected, ConnectedRegions)
                select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 400, 99999)
                count_obj (SelectedRegions, Number)
                dev_set_color (Highlight[Figure - 1])
                dev_display (SelectedRegions)
                OutString := Regions[Figure - 1] + ': ' + Number + '   '
                dev_set_color ('green')
                disp_message (WindowHandle, OutString, 'window', 24 + 30 * Figure, 12, 'black', 'false')
                if (Number != 4)
                    disp_message (WindowHandle, 'Not OK', 'window', 24 + 30 * Figure, 120, 'red', 'false')
                else
                    disp_message (WindowHandle, 'OK', 'window', 24 + 30 * Figure, 120, 'green', 'false')
                endif
            endfor
            if (J < 3)
                disp_continue_message (WindowHandle, 'black', 'true')
                stop ()
            endif
        endfor
    endfor
    dev_clear_window ()
    dev_display (Image)
    Message := 'The game pieces cannot be classified reliable on'
    Message[1] := 'gray images because the gray values of the'
    Message[2] := 'game pieces cannot always be distinguished from'
    Message[3] := 'the gray values of the background.'
    disp_message (WindowHandle, Message, 'window', 12, 12, 'black', 'true')

识别效果

![a874fd3551e4406b8a326b6dfe65d255.png][]

![8453655a98984c3d9ca488aee2b03bb1.png][]

参考：

[(机器视觉)Halcon下颜色识别与联合C\#撸代码!1 | 码农家园][Halcon_C_1 _]

[网格缺陷检测（MLP（多层神经网络））\_create\_class\_mlp参数\_w3071206219的博客-CSDN博客][MLP_create_class_mlp_w3071206219_-CSDN]

[https://www.cnblogs.com/xingyuanzier/p/12951150.html][https_www.cnblogs.com_xingyuanzier_p_12951150.html]

[(机器视觉)Halcon下颜色识别与联合C\#撸代码!1 | 码农家园][Halcon_C_1 _]

[caabb325b11a41d19449c6d7f7bb3883.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/25/f956887cde704afab0435eb8ec2ac984.png
[20896e9e7ca7427e812da54d42145d51.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/25/ce8fcb7eb0ce4e0e9e8e2211cb4ced5e.png
[a874fd3551e4406b8a326b6dfe65d255.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/25/00b7788a8fa34eea99e376caab8296bd.png
[8453655a98984c3d9ca488aee2b03bb1.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/25/91a9214d1c734bf58f9a4a439ab5bd75.png
[Halcon_C_1 _]: https://www.codenong.com/cs105377012/
[MLP_create_class_mlp_w3071206219_-CSDN]: https://blog.csdn.net/w3071206219/article/details/85273139
[https_www.cnblogs.com_xingyuanzier_p_12951150.html]: https://www.cnblogs.com/xingyuanzier/p/12951150.html