一、概述
训练数据
二、算子解释
compactness
Halcon 算子 compactness_halcon compactness-CSDN博客
*计算输入区域的紧凑度
compactness (Region, Compactness) 原理解释
convexity 每个输入区域的凸度
Halcon 算子 convexity_halcon convexity-CSDN博客
*计算每个输入区域的凸度
convexity (Region, Convexity) 原理解释
moments_region_central_invar 计算区域的四个矩
三、halcon 代码
get_system ('image_dir', HalconImages)
get_system ('operating_system', OS)
if (OS{0:2} == 'Win')
tuple_split (HalconImages, ';', HalconImages)
else
tuple_split (HalconImages, ':', HalconImages)
endif
ReadOK := false
for k := 0 to |HalconImages| - 1 by 1
try
read_image (Image, HalconImages[k] + '/halogen_bulb/halogen_bulb_01.png')
ReadPath := HalconImages[k] + '/halogen_bulb/'
ReadOK := true
break
catch (Exception)
endtry
endfor
if (not ReadOK)
disp_message (WindowHandle, 'Could not find the images in $HALCONIMAGES', 'window', 12, 12, 'black', 'true')
stop ()
endif
read_image (Image, 'halogen_bulb/halogen_bulb_01.png')
get_image_pointer1 (Image, Pointer, Type, Width, Height)
dev_close_window ()
dev_open_window (0, 0, Width / 2, Height / 2, 'black', WindowHandle)
set_display_font (WindowHandle, 14, 'mono', 'true', 'false')
*
ClassNames := ['good','bad','none']
Colors := ['forest green','red','red']
Nu := 0.05
KernelParam := 0.02
*
* Create an SVM classifier
*NumFeatures 输入变量数
*KernelType :内核类型 线性”，“多项式均匀”，“多项式不均匀”，“ rbf”
*KernelParam :内核参数 内核函数的附加参数。如果是RBF内核，则的值 。对于多项式核，度
*Nu SVM的正则化常数。
*NumClasses 类数。
*Mode SVM的模式 “新颖性检测”，“一个对所有”，“一个对一个”
*Preprocessing 用于转换特征向量的预处理类型。
*NumComponents 预处理参数：变换后的特征数量
*SVMHandle SVM句柄。
create_class_svm (7, 'rbf', KernelParam, Nu, |ClassNames|, 'one-versus-one', 'principal_components', 7, SVMHandle)
*
* Add samples
* 这里是只训练好的 还有 坏的的以及那个none 的
add_samples_to_svm (ClassNames, SVMHandle, WindowHandle, ReadPath)
*add_samples_to_svm 函数实现
* for ClassNumber := 0 to |ClassNames| - 1 by 1
* list_files (ReadPath + ClassNames[ClassNumber], 'files', Files)
* Selection := regexp_select(Files,'.*[.]png')
* for Index := 0 to |Selection| - 1 by 1
* read_image (Image, Selection[Index])
* dev_display (Image)
* 'Add Samples...', -1
* threshold (Image, Region, 0, 40)
* calculate_features (Region, Features)
* add_sample_class_svm (SVMHandle, Features, ClassNumber)
* endfor
* endfor
*==========================================
*calculate_features 的实现
* area_center (Region, Area, Row, Column)
* 计算输入区域的紧凑度
* compactness (Region, Compactness)
* 四个不变的矩
* moments_region_central_invar (Region, PSI1, PSI2, PSI3, PSI4)
*计算每个输入区域的凸度
* convexity (Region, Convexity)
* Features := real([Area,Compactness,PSI1,PSI2,PSI3,PSI4,Convexity])
* return ()
return ()
dev_clear_window ()
*
* Train the classifier
disp_message (WindowHandle, 'Training...', 'window', 12, 12, 'black', 'true')
*训练
*Epsilon (input_control) 精度
*TrainMode 'add_sv_to_train_set', 'default'
train_class_svm (SVMHandle, 0.001, 'default')
disp_message (WindowHandle, 'Training completed', 'window', 12, 12, 'black', 'true')
disp_continue_message (WindowHandle, 'black', 'true')
stop ()
*
* Classify halogen bulbs
classify_regions_with_svm (SVMHandle, Colors, ClassNames, ReadPath)
*
*list_files (ReadPath, ['files','recursive'], Files)
*Selection := regexp_select(Files,'.*[.]png')
*read_image (Image, Selection[0])
*dev_close_window ()
*get_image_size (Image, Width, Height)
*dev_open_window (0, 0, Width / 2, Height / 2, 'black', WindowHandle)
*set_display_font (WindowHandle, 14, 'mono', 'true', 'false')
*for Index := 0 to |Selection| - 1 by 1
* read_image (Image, Selection[Index])
* threshold (Image, Region, 0, 40)
* calculate_features (Region, Features)
* *特征
* classify_class_svm (SVMHandle, Features, 1, Class)
* dev_display (Image)
* dev_set_color (Colors[Class])
* dev_display (Region)
* disp_message (WindowHandle, 'Classified as:' + ClassNames[Class], 'window', 12, 12, 'black', 'true')
* disp_continue_message (WindowHandle, 'black', 'true')
* stop ()
*endfor
*dev_display (Image)
*return ()
* Clear the classifier from memory
clear_class_svm (SVMHandle)
