分类器——高斯混合模型之水果分类

合集 - Halcon(58)

1.halcon-轮廓拟合圆fit_circle_contour_xld2023-12-252.Halcon 标定例程（calibration-adjust_mosaic_images.hdev）2023-12-283.Halcon 相机外部参数（camera_calibration_external.hdev）2023-12-284.halcon 标定2023-12-285.Halcon 相机内部参数（camera_calibration_internal.hdev）2023-12-286.halcon 标定算子汇总2024-04-197.halcon 拼接参考2024-04-198.halcon 双目相机标定2024-01-199.HALCON 杂记2024-01-2310.dump_window & dump_window_image 区别2024-02-2911.拟合——直线2024-02-2912.halcon崩溃，未及时保存文件临时存储路径2024-02-2913.halcon-——显示颜色对照表2024-02-2914.矩阵基本操作2024-02-2915.通过高斯的导数获取法向量2024-02-2916.halcon ——从几个不同照明的图像重建表面2024-02-2917.脱离halcon环境运行2024-02-2918.Halcon 一维测量/1D-Measuring2024-03-0119.Halcon——矩阵/Matrix2024-03-0120.dev_set_lut显示效果2024-03-0121.基础设置之——dev_set_check2024-03-0122.基础设置之——dev_open_file_dialog2024-03-0123.基础设置之——dev_open_dialog2024-03-0124.基础设置之——dev_disp_text2024-03-0225.基础操作之——生成一张背景图像2024-03-0226.基础设置之——dev_open_tool2024-03-0427.基本设置之——算术运算2024-03-0428.基础设置之——字符显示格式设置2024-03-0429.基础设置之——条件判断(一)2024-03-0430.基础操作之——tuple基本操作2024-03-0431.基本操作之——位运算2024-03-0532.基本操作之——正弦函数绘制2024-03-0533.基本操作之——多维空间欧几里得距离距离计算及标量积计算2024-03-0534.基本操作之——集合基本操作2024-03-0535.基本操作之——字符截取操作2024-03-0536.基本操作之——Unicode编码2024-03-0537.基本操作之——正则表达式2024-03-0538.基本操作之——字典2024-03-0639.基础设置之——条件判断(二)2024-03-0640.基本操作之——tuple类型2024-03-0641.基本操作之——tuple值分布范围获取2024-03-0642.基本操作之——tuple增删查改操作2024-03-0643.基本操作之——Object比较2024-03-0744.基本操作之——Object插入2024-03-0745.基本操作之——Object差异2024-03-0746.基本操作之——Object移除操作2024-03-0747.基本操作之——Object替换2024-03-0748.分类器——高斯混合模型/Gaussian-Mixture-Models(GMM)之图像分割2024-03-07

49.分类器——高斯混合模型之水果分类2024-03-07

50.分类器——高斯混合模型之缺陷检测（纹理缺陷检测）2024-03-0751.基本操作之——图像纹理处理texture_laws2024-04-1952.分类器——高斯混合模型之查找表分类器（保险丝分类）2024-03-0853.模版匹配——set_shape_model_param2024-03-1354.模板匹配——set_shape_model_clutter2024-03-1355.模板匹配——金字塔图像计算gen_gauss_pyramid2024-03-1356.模板匹配——determine_shape_model_params2024-03-1457.模板匹配——create_shape_model2024-03-1458.模版匹配——inspect_shape_model2024-03-15

收起

* 此示例程序演示如何应用通用 GMM
* 使用“面积”和“圆度”特征区分柑橘类水果的分类。此外，还对提取的水果的 2D 特征空间进行了可视化。
* 
read_image (Image, 'color/citrus_fruits_01')
get_image_pointer1 (Image, Pointer, Type, Width, Height)
dev_close_window ()
dev_open_window (0, 0, Width, Height, 'white', WindowHandle)
set_display_font (WindowHandle, 12, 'mono', 'true', 'false')
dev_set_draw ('margin')
dev_set_line_width (2)
dev_display (Image)
dev_update_window ('off')
dev_update_pc ('off')
dev_update_var ('off')
* 
FeaturesArea := []
FeaturesCircularity := []
ClassName := ['橘子','柠檬']
* 
* 创建一个高斯混合模型分类器
create_class_gmm (2, 2, 1, 'spherical', 'normalization', 10, 42, GMMHandle)
* 
* 增加训练样本
for I := 1 to 4 by 1
    read_image (Image, 'color/citrus_fruits_' + I$'.2d')
    dev_display (Image)
    * 获取样本区域
    get_regions (Image, SelectedRegions)
    dev_display (SelectedRegions)
    count_obj (SelectedRegions, NumberObjects)
    for J := 1 to NumberObjects by 1
        select_obj (SelectedRegions, ObjectSelected, J)
        get_features (ObjectSelected, WindowHandle, Circularity, Area, RowRegionCenter, ColumnRegionCenter)
        FeaturesArea := [FeaturesArea,Area]
        FeaturesCircularity := [FeaturesCircularity,Circularity]
        *样本圆度和面积特征用于，训练GMM模型
        FeatureVector := real([Circularity,Area])
        if (I <= 2)
            *前两张图像为橘子的样本
            add_sample_class_gmm (GMMHandle, FeatureVector, 0, 0)
            disp_message (WindowHandle, '至样本增加:' + ClassName[0], 'window', RowRegionCenter, ColumnRegionCenter - 100, 'white', 'false')
        else
            *从第三张图像为柠檬的样本
            add_sample_class_gmm (GMMHandle, FeatureVector, 1, 0)
            disp_message (WindowHandle, '至样本增加:' + ClassName[1], 'window', RowRegionCenter, ColumnRegionCenter - 100, 'white', 'false')
        endif
    endfor
    disp_continue_message (WindowHandle, 'white', 'false')
    stop ()
endfor
dev_clear_window ()
* 
*可视化样本特征空间
visualize_2D_feature_space (Cross, Height, Width, WindowHandle, FeaturesArea[0:5], FeaturesCircularity[0:5], 'orange', 18)
* 'oranges', 40, 440
visualize_2D_feature_space (Cross, Height, Width, WindowHandle, FeaturesArea[6:11], FeaturesCircularity[6:11], 'green', 18)
* 'lemons', 70, 440
disp_continue_message (WindowHandle, 'white', 'false')
stop ()
* 
* 训练分类器
train_class_gmm (GMMHandle, 100, 0.001, 'training', 0.0001, Centers, Iter)
* 
* 开始分类
for I := 1 to 15 by 1
    read_image (Image, 'color/citrus_fruits_' + I$'.2d')
    dev_display (Image)
    * 'Classify Image', 10, 10
    get_regions (Image, SelectedRegions)
    dev_display (SelectedRegions)
    count_obj (SelectedRegions, NumberObjects)
    for J := 1 to NumberObjects by 1
        select_obj (SelectedRegions, ObjectSelected, J)
        get_features (ObjectSelected, WindowHandle, Circularity, Area, RowRegionCenter, ColumnRegionCenter)
        FeaturesArea := [FeaturesArea,Area]
        FeaturesCircularity := [FeaturesCircularity,Circularity]
        FeatureVector := real([Circularity,Area])
        classify_class_gmm (GMMHandle, FeatureVector, 1, ClassID, ClassProb, Density, KSigmaProb)
        disp_message (WindowHandle, '得分最高类: ' + ClassName[ClassID], 'window', RowRegionCenter, ColumnRegionCenter - 100,'white', 'false')
        disp_message (WindowHandle, '得分: ' + KSigmaProb, 'window', RowRegionCenter + 30, ColumnRegionCenter - 100, 'white', 'false')
    endfor
    if (I != 15)
        disp_continue_message (WindowHandle,'white', 'false')
    endif
    stop ()
endfor
* 
* 释放分类器内存
clear_class_gmm (GMMHandle)

1.样本训练

 2.样本空间特征统计

 3.分类结果