程式練習5 MLP的紋理識別程式

*********************************木板的紋理識別程式

**********************************本程式知識量太大，需要多多研究！
***定義變數
feature***tended:=
feature***tended1:=
numberclasses := |classes|
**載入影象檔案
list_files ('f:/7.機器視覺/halcon/halcon學習/紋理識別專題', ['files','follow_links'], imagefiles)
**初始化視窗
dev_close_window ()
read_image (image, imagefiles[0])
get_image_size (image, width, height)
dev_open_window (0, 0, width, height, 'black', windowhandle)
for i := 0 to |imagefiles| - 1 by 1
**1.提取特徵
read_image (image, imagefiles[i])
rgb1_to_gray (image, grayimage)
threshold (grayimage, region, 31, 254)
connection (region, connectedregions)    
select_shape (connectedregions, selectedregions, 'area', 'and', 150, 99999)       
**灰度共生矩陣，目的是提取紋理特徵後面四個變數
cooc_feature_image (image, image, 6, 90, energy, correlation, homogeneity, contrast)
*sobel邊緣檢測
sobel_amp (image, edgeamplitude, 'sum_abs', 3)
**獲得灰度直方圖，8是量化因子
gray_histo_abs (edgeamplitude, edgeamplitude, 8, absolutehisto)
feature***tended:=[energy, correlation, homogeneity, contrast]
**向特徵陣列中新增乙個特徵absolutehisto
feature***tended:=[feature***tended,absolutehisto]
cooc_feature_image (image, image, 6, 90, energy, correlation, homogeneity, contrast)
*sobel邊緣檢測
sobel_amp (image, edgeamplitude, 'sum_abs', 3)
**獲得灰度直方圖，8是量化因子
gray_histo_abs (edgeamplitude, edgeamplitude, 8, absolutehisto)
feature***tended1 := [feature***tended,energy, correlation, homogeneity, contrast]
**向特徵陣列中新增乙個特徵absolutehisto
feature***tended1 := [feature***tended1,absolutehisto]  
**生成特徵向量
featurevector := real(feature***tended1)
**如果是第一張影象，則建立分類器。
if(i == 0)
***2.建立分類器
numberfeatures := |feature***tended1|
create_class_mlp (numberfeatures, 15, 5, 'softmax', 'normalization', 10, 42, mlphandle)
endif
**3.新增樣本到分類器中
add_sample_class_mlp (mlphandle, featurevector, i)
stop()
endfor
**4.訓練分類器
train_class_mlp (mlphandle, 200, 1, 000.01, error, errorlog)
stop()
**寫入分類器
write_class_mlp (mlphandle, 'd:')
**5.分類器識別特徵向量
for i := 0 to |imagefiles|-1 by 1
**提取測試樣本的特徵向量
read_image (image, imagefiles[i])
rgb1_to_gray (image, grayimage)
threshold (grayimage, region, 31, 254)
connection (region, connectedregions)
select_shape (connectedregions, selectedregions, 'area', 'and', 150, 99999)
**灰度共生矩陣，目的是提取紋理特徵後面四個變數
cooc_feature_image (image, image, 6, 90, energy, correlation, homogeneity, contrast)
*sobel邊緣檢測
sobel_amp (image, edgeamplitude, 'sum_abs', 3)
**獲得灰度直方圖，8是量化因子
gray_histo_abs (edgeamplitude, edgeamplitude, 8, absolutehisto)
feature***tended:=[energy, correlation, homogeneity, contrast]
**向特徵向量中新增乙個特徵absolutehisto
feature***tended:=[feature***tended,absolutehisto]
cooc_feature_image (image, image, 6, 90, energy, correlation, homogeneity, contrast)
*sobel邊緣檢測
sobel_amp (image, edgeamplitude, 'sum_abs', 3)
**獲得灰度直方圖，8是量化因子
gray_histo_abs (edgeamplitude, edgeamplitude, 8, absolutehisto)
feature***tended1 := [feature***tended, energy, correlation, homogeneity, contrast]
**向特徵向量中新增乙個特徵absolutehisto
feature***tended1 := [feature***tended1,absolutehisto]        
**得到測試樣本的特徵向量
featurevector := real(feature***tended1)
**識別測試樣本的特徵向量
classify_class_mlp (mlphandle, featurevector, 1, classids, confidence)
dev_display (image)
imagefiles1 :='founde classes ' + classes[classids[0]]
disp_message (windowhandle, imagefiles1, 'image', 12, 12, 'black', 'true')
stop()
endfor
disp_message (windowhandle, '所有測試樣本都已經識別完成...', 'window', 12, 12, 'black', 'true')
stop()
disp_message (windowhandle, '所有工作完成...', 'window', 12, 12, 'black', 'true')
這個程式雖然和之前的程式一樣使用了mlp，但是這個程式涉及到了乙個概念：特徵向量。特徵提取和特徵識別始終貫穿了機器視覺的學習和使用，是核心中的核心。
**灰度共生矩陣，目的是提取紋理特徵後面四個變數

cooc_feature_image (image, image, 6, 90, energy, correlation, homogeneity, contrast)
特徵相加，相當於陣列的加法：
feature***tended:=[feature***tended,absolutehisto]
				
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