HALCON示例程序classify_image_class_mlp.hdev如何使用MLP分類器分割RGB圖像

示例程序源碼（加注釋）

關于顯示相關顯示在其他帖子有介紹。
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’]

• 創建分割不同類別的區域
  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介紹
  concat_obj (Sea, Deck, Classes)
  concat_obj (Classes, Walls, Classes)
  concat_obj (Classes, Chimney, Classes)
• 顯示
  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’)
• 程序停止運行，便于觀察
  stop ()
• 創建MLP分類器，并添加分類樣本
• create_class_mlp( : : 輸入維度數量, 隱藏單位數, 輸出類別數, 輸出函數類型, 預處理類型, 預處理參數, 迭代次數 : MLP句柄)
  create_class_mlp (3, 3, 4, ‘softmax’, ‘principal_components’, 3, 42, MLPHandle)
• 為MLP添加訓練樣本
• add_samples_image_class_mlp(圖像, 不同類的區域: : 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’)
• 訓練分類器
• train_class_mlp( : : MLP句柄, 最大迭代次數, 權重差異閾值, 誤差差異閾值: 平均誤差, 平均誤差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’)
• 分割圖像
• classify_image_class_mlp(圖像 : 分割結果: MLP句柄, 拒絕分類區域閾值: )
  classify_image_class_mlp (Image, ClassRegions, MLPHandle, 0.5)
• 使用區域的平均灰度值填充圖像
  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 ()
• 下邊分割了另幾種顏色和上文一樣
  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)

處理思路

這個例子介紹了使用MLP分類器對彩色圖片進行分類的例子，選取多通道圖片的感興趣區域與背景可以對多通道圖片進行快速分類。

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。