HALCON示例程序classify_image_class_svm.hdev使用SVM分類器對多通道圖像進行分類
示例程序源碼(加注釋)
由于關于SVM分類器的使用之前的貼子介紹過,所以關于SVM的函數,直接點擊函數可跳轉到之前的介紹貼子。
- 顯示函數解釋
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’] - 為將要分類的4個類別創建區域
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 (Sea, Deck, Classes)
concat_obj (Classes, Walls, Classes)
concat_obj (Classes, Chimney, Classes)
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’)
stop () - 創建分類器并且為分類器添加訓練樣本
create_class_svm (3, ‘rbf’, 0.1, 0.0005, 4, ‘one-versus-all’, ‘normalization’, 3, SVMHandle)
add_samples_image_class_svm (Image, Classes, SVMHandle)
dev_display (Image)
Message := ‘Training …’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’) - 訓練分類器
train_class_svm (SVMHandle, 0.001, ‘default’)
Message := Message + ’ ready.’
Message[1] := ‘Segment image using the classifier …’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’) - 通過分類器分割圖像
clear_samples_class_svm (SVMHandle)
classify_image_class_svm (Image, ClassRegions, SVMHandle)
region_to_mean (ClassRegions, Image, ImageClass)
dev_display (ImageClass)
Message[1] := Message[1] + ’ ready.’
disp_message (WindowHandle, Message, ‘window’, 12, 12, ‘black’, ‘true’)
clear_class_svm (SVMHandle)
處理思路
這個例子介紹了create_class_svm 、add_sample_class_svm、train_class_svm、classify_class_svm 的使用方法,使用SVM分類器對元素的凸度與不變的結構向量進行分類。完成了對鹵素燈泡的完整性的檢測。
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。