HALCON示例程序classify_citrus_fruits.hdev应用常规gmm分类器进行水果分类

示例程序源码（加注释）

读入图片、显示与显示相关设置
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’)
定义变量数组
FeaturesArea := []
FeaturesCircularity := []
ClassName := [‘orange’,‘lemon’]
创建一个GMM分类器，这个函数之前的帖子介绍过
create_class_gmm (2, 2, 1, ‘spherical’, ‘normalization’, 10, 42, GMMHandle)
为分类器增加例子
for i := 1 to 4 by 1
read_image (Image, ‘color/citrus_fruits_’ + i$’.2d’)
dev_display (Image)
- 自定义函数
  get_regions (Image, SelectedRegions)
  将彩色图像分割成RGB三个通道，并使用简单bolo方法进行分析
  decompose3 (Image, ImageRed, ImageGreen, ImageBlue)
  dev_set_color (‘white’)
  threshold (ImageRed, Region, 50, 255)
  fill_up (Region, RegionFillUp)
  connection (RegionFillUp, ConnectedRegions)
  select_shape (ConnectedRegions, SelectedRegions, ‘area’, ‘and’, 50, 999999)
  return ()
- 显示SelectedRegions区域
  dev_display (SelectedRegions)
- 对区域SelectedRegions进行计数
  count_obj (SelectedRegions, NumberObjects)
  for j := 1 to NumberObjects by 1
  - 选取数组当中的指定元素
    select_obj (SelectedRegions, ObjectSelected, j)
  - 自定义函数
    get_features (ObjectSelected, WindowHandle, Circularity, Area, RowRegionCenter, ColumnRegionCenter)
    得到ObjectSelected区域的圆度值
    circularity (ObjectSelected, Circularity)
    求取ObjectSelected的面积与中心坐标
    area_center (ObjectSelected, Area, Row, Column)
    dev_set_color (‘white’)
    return ()
    FeaturesArea := [FeaturesArea,Area]
    FeaturesCircularity := [FeaturesCircularity,Circularity]
  - real将输入数组元素转换为浮点数形式
    FeatureVector := real([Circularity,Area])
    if (i <= 2)
    - add_sample_class_gmm - 将训练样本添加到高斯混合模型的训练数据中
    - add_sample_class_gmm（:: GMM句柄，样本要素向量，类别ID，随机噪声：）
      add_sample_class_gmm (GMMHandle, FeatureVector, 0, 0)
    - 显示信息
      disp_message (WindowHandle, ‘Add to Class:’ + ClassName[0], ‘window’, RowRegionCenter, ColumnRegionCenter - 100, ‘black’, ‘true’)
      else
      add_sample_class_gmm (GMMHandle, FeatureVector, 1, 0)
      disp_message (WindowHandle, ‘Add to Class:’ + ClassName[1], ‘window’, RowRegionCenter, ColumnRegionCenter - 100, ‘black’, ‘true’)
      endif
      endfor
- 显示按F5继续
  disp_continue_message (WindowHandle, ‘black’, ‘true’)
  stop ()
  endfor
  dev_clear_window ()
构建可视化空间，即二维坐标系
visualize_2D_feature_space (Cross, Height, Width, WindowHandle, FeaturesArea[0:5], FeaturesCircularity[0:5], ‘dim gray’, 18)
dev_set_color (‘black’)
OriginOfGraph := [Height - 0.1 * Height,0.1 * Width]
画箭头
disp_arrow (WindowID, OriginOfGraph[0], OriginOfGraph[1], OriginOfGraph[0], Width - 0.2 * Width, 2)
disp_arrow (WindowID, OriginOfGraph[0], OriginOfGraph[1], 0.1 * Height, OriginOfGraph[1], 2)
显示横纵坐标的名称
set_tposition (WindowID, OriginOfGraph[0], Width - 0.2 * Width)
write_string (WindowID, ‘Area’)
set_tposition (WindowID, 0.07 * Height, OriginOfGraph[1])
write_string (WindowID, ‘Circularity’)
dev_set_color (ColorFeatureVector)
ExtentOfGraph := Height - 0.3 * Height
RangeC := 0.5
RangeA := 24000
ScaleC := ExtentOfGraph / RangeC
ScaleA := ExtentOfGraph / RangeA
MinC := 0.5
MinA := 20000
NumberFeatureVectors := |FeaturesA|
for i := 0 to NumberFeatureVectors - 1 by 1
DiffC := ScaleC * (FeaturesC[i] - MinC)
DiffA := ScaleA * (FeaturesA[i] - MinA)
RowFeature := OriginOfGraph[0] - DiffC
ColumnFeature := OriginOfGraph[1] + DiffA
画X号
gen_cross_contour_xld (Cross, RowFeature, ColumnFeature, CrossSize, 0.785398)
dev_display (Cross)
endfor
return ()
柠檬的自定义函数
visualize_2D_feature_space (Cross, Height, Width, WindowHandle, FeaturesArea[6:11], FeaturesCircularity[6:11], ‘light gray’, 18)
disp_continue_message (WindowHandle, ‘black’, ‘true’)
stop ()
训练分类器train_class_gmm（:: 分类器句柄，最大迭代次数，阈值，计算方法，规范单一协方差矩阵：类中心数量，每个类的迭代次数）
train_class_gmm (GMMHandle, 100, 0.001, ‘training’, 0.0001, Centers, Iter)
分类
for i := 1 to 15 by 1
read_image (Image, ‘color/citrus_fruits_’ + i$’.2d’)
dev_display (Image)
- 和上文get_regions含义一致
  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 - 通过高斯混合模型计算特征向量的类。
  - 函数原型classify_class_gmm（:: 分类器句柄，特征向量，确定的最佳类数：分类结果，类的后验概率，概率密度，归一化概率）
    classify_class_gmm (GMMHandle, FeatureVector, 1, ClassID, ClassProb, Density, KSigmaProb)
  - 显示
    disp_message (WindowHandle, 'Class: ’ + ClassName[ClassID], ‘window’, RowRegionCenter, ColumnRegionCenter - 100, ‘black’, ‘true’)
    disp_message (WindowHandle, 'KSigmaProb: ’ + KSigmaProb, ‘window’, RowRegionCenter + 30, ColumnRegionCenter - 100, ‘black’, ‘true’)
    endfor
    if (i != 15)
    disp_continue_message (WindowHandle, ‘black’, ‘true’)
    endif
    stop ()
    endfor
清除训练句柄
clear_class_gmm (GMMHandle)

处理思路

这个例子介绍了GMM分类器的使用方法，步骤是create_class_gmm 、add_sample_class_gmm、train_class_gmm、classify_class_gmm 先创建分类器，为分类器添加样本，训练分类器，使用分类器对进行分类。一个全面介绍GMM分类器使用的一个例子。通过GMM分类器实现了橙子和柠檬的分类。