?第一步 ??創建支持向量機分類器

create_class_svm (7, 'rbf', KernelParam, Nu, |ClassNames|, 'one-versus-one', 'principal_components', 5, SVMHandle)

第二步 ?添加樣本到分類器里

for ClassNumber := 0 to |ClassNames| - 1 by 1

????*列出目錄下的所有文件

????list_files (ReadPath + ClassNames[ClassNumber], 'files', Files)

????*獲取后綴為PNG的所有文件

????Selection := regexp_select(Files,'.*[.]png')

????for Index := 0 to |Selection| - 1 by 1

????????*讀取圖像

????????read_image (Image, Selection[Index])

????????dev_display (Image)

????????* 'Add Samples...', -1

????????*圖像閾值，獲取黑色區域

????????threshold (Image, Region, 0, 40)

* ??????calculate_features_define (Region, Features)

????????calculate_features_define (Region, Features)

????????add_sample_class_svm (SVMHandle, Features, ClassNumber)

????endfor

endfor

* add_samples_to_svm_define(ClassNames, SVMHandle, WindowHandle, ReadPath)

* 關閉窗口

dev_clear_window ()

*第三步 ?訓練支持向量機分類器

disp_message (WindowHandle, 'Training...', 'window', -1, -1, 'black', 'true')

train_class_svm (SVMHandle, 0.001, 'default')

disp_message (WindowHandle, 'Training completed', 'window', -1, -1, 'black', 'true')

disp_continue_message (WindowHandle, 'black', 'true')

stop ()

*第四步 用SVM分類器進行分類

????*讀取圖像

????read_image (Image, Selection[Index])

????*對圖像閾值

????threshold (Image, Region, 0, 40)

????*計算區域的特征值

????calculate_features_define (Region, Features)

????*用SVM分類器進行分類

????classify_class_svm (SVMHandle, Features, 1, Class)

第五步 ?顯示和輸出結果

????dev_display (Region)

????disp_message (WindowHandle, 'Classified as:' + ClassNames[Class], 'window', -1, -1, 'black', 'true')

????disp_continue_message (WindowHandle, 'black', 'true')?