HALCON示例程序classify_citrus_fruits.hdev應用常規gmm分類器進行水果分類

示例程序源碼（加注釋）

• 讀入圖片、顯示與顯示相關設置
  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’)

• 定義變量數組
  FeaturesArea := []
  FeaturesCircularity := []
  ClassName := [‘orange’,‘lemon’]

• 創建一個GMM分類器，這個函數之前的帖子介紹過
  create_class_gmm (2, 2, 1, ‘spherical’, ‘normalization’, 10, 42, GMMHandle)

• 為分類器增加例子
  for i := 1 to 4 by 1
  read_image (Image, ‘color/citrus_fruits_’ + i$’.2d’)
  dev_display (Image)

  • 自定義函數
    get_regions (Image, SelectedRegions)
    將彩色圖像分割成RGB三個通道，并使用簡單bolo方法進行分析
    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 ()
  • 顯示SelectedRegions區域
    dev_display (SelectedRegions)
  • 對區域SelectedRegions進行計數
    count_obj (SelectedRegions, NumberObjects)
    for j := 1 to NumberObjects by 1
    • 選取數組當中的指定元素
      select_obj (SelectedRegions, ObjectSelected, j)
    • 自定義函數
      get_features (ObjectSelected, WindowHandle, Circularity, Area, RowRegionCenter, ColumnRegionCenter)
      得到ObjectSelected區域的圓度值
      circularity (ObjectSelected, Circularity)
      求取ObjectSelected的面積與中心坐標
      area_center (ObjectSelected, Area, Row, Column)
      dev_set_color (‘white’)
      return ()
      FeaturesArea := [FeaturesArea,Area]
      FeaturesCircularity := [FeaturesCircularity,Circularity]
    • real將輸入數組元素轉換為浮點數形式
      FeatureVector := real([Circularity,Area])
      if (i <= 2)
      • add_sample_class_gmm - 將訓練樣本添加到高斯混合模型的訓練數據中
      • add_sample_class_gmm（:: GMM句柄，樣本要素向量，類別ID，隨機噪聲：）
        add_sample_class_gmm (GMMHandle, FeatureVector, 0, 0)
      • 顯示信息
        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
  • 顯示按F5繼續
    disp_continue_message (WindowHandle, ‘black’, ‘true’)
    stop ()
    endfor
    dev_clear_window ()

• 構建可視化空間，即二維坐標系
  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]
  畫箭頭
  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)
  顯示橫縱坐標的名稱
  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
  畫X號
  gen_cross_contour_xld (Cross, RowFeature, ColumnFeature, CrossSize, 0.785398)
  dev_display (Cross)
  endfor
  return ()

• 檸檬的自定義函數
  visualize_2D_feature_space (Cross, Height, Width, WindowHandle, FeaturesArea[6:11], FeaturesCircularity[6:11], ‘light gray’, 18)
  disp_continue_message (WindowHandle, ‘black’, ‘true’)
  stop ()

• 訓練分類器train_class_gmm（:: 分類器句柄，最大迭代次數，閾值，計算方法，規范單一協方差矩陣：類中心數量，每個類的迭代次數）
  train_class_gmm (GMMHandle, 100, 0.001, ‘training’, 0.0001, Centers, Iter)

• 分類
  for i := 1 to 15 by 1
  read_image (Image, ‘color/citrus_fruits_’ + i$’.2d’)
  dev_display (Image)

  • 和上文get_regions含義一致
    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 - 通過高斯混合模型計算特征向量的類。
    • 函數原型classify_class_gmm（:: 分類器句柄，特征向量，確定的最佳類數：分類結果，類的后驗概率，概率密度，歸一化概率）
      classify_class_gmm (GMMHandle, FeatureVector, 1, ClassID, ClassProb, Density, KSigmaProb)
    • 顯示
      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

• 清除訓練句柄
  clear_class_gmm (GMMHandle)

處理思路

這個例子介紹了GMM分類器的使用方法，步驟是create_class_gmm 、add_sample_class_gmm、train_class_gmm、classify_class_gmm 先創建分類器，為分類器添加樣本，訓練分類器，使用分類器對進行分類。一個全面介紹GMM分類器使用的一個例子。通過GMM分類器實現了橙子和檸檬 的分類。

后記

大家有什么問題可以向我提問哈，我看到了第一時間回復，希望在學習的路上多多結交良師益友。