HAlcon例子

气泡思想

* This example shows the use of the operator dyn_threshold for
* the segmentation of the raised dots of braille chharacters.
* The operator dyn_threshold is especially usefull if the
* background is inhomogeneously illuminated. In this example,
* the segmentation with a simple threshold is not possible
* because the brightness of the background increases from
* left to right.
* 
dev_update_off ()
* 
* Preparation
read_image (Image, 'photometric_stereo/embossed_01')
get_image_size (Image, Width, Height)
dev_get_window (WindowHandle)
if (is_handle_elem(WindowHandle) == true)
    * valid window handle
    dev_resize_window_fit_image (Image, 0, 0, -1, -1)
else
    dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle)
endif
set_display_font (WindowHandle, 16, 'mono', 'true', 'false')
dev_set_line_width (2)
* 
dev_display (Image)
Message := 'Original image'
disp_message (WindowHandle, Message, 'window', 12, 12, 'black', 'true')
disp_continue_message (WindowHandle, 'black', 'true')
stop ()
* 
* Use a strong mean filter on the image to blur out the braille points
*使用均值滤镜模糊盲文点，因为图像上的点有这不同光亮的
mean_image (Image, ImageMean, 59, 59)
* 
dev_display (ImageMean)
Message := 'Mean filtered image'
disp_message (WindowHandle, Message, 'window', 12, 12, 'black', 'true')
disp_continue_message (WindowHandle, 'black', 'true')
stop ()
* 
* Compare the original image with the blurred one and determine the
* region where the gray values of the two images differ by more than 15
*因只有小点点的光亮色彩不同，设定一个阈值，跟平均值相比交超出15的进行选中
dyn_threshold (Image, ImageMean, RegionDynThresh, 15, 'not_equal')
* 
dev_display (Image)
dev_display (RegionDynThresh)
Message := 'Regions segmented with dyn_threshold'
disp_message (WindowHandle, Message, 'window', 12, 12, 'black', 'true')
disp_continue_message (WindowHandle, 'black', 'true')
stop ()
* 
* Post-process the found regions
*先膨胀后收缩
closing_circle (RegionDynThresh, RegionClosing, 8.5)
*进行了开运算
opening_circle (RegionClosing, RegionOpening, 6.5)
*进行的打散
connection (RegionOpening, ConnectedRegions)
*在进行外接圆
smallest_circle (ConnectedRegions, Row, Column, Radius)

gen_circle_contour_xld (ContCircle, Row, Column, Radius, 0, 6.28318, 'positive', 1)
* 
* Show the results
dev_display (Image)
dev_set_color ('green')
dev_display (ContCircle)
Message := 'Results of braille segmentation after morphology'
disp_message (WindowHandle, Message, 'window', 12, 12, 'black', 'true')
* 
dev_update_on ()

查找适合的圆及圆的面积和中心点

* ball.hdev: Inspection of Ball Bonding
* 窗口关闭
dev_update_window ('off')
dev_close_window ()
*开窗口
dev_open_window (0, 0, 728, 512, 'black', WindowID)
*读取图片
read_image (Bond, 'die/die_03')
*图像显示
dev_display (Bond)
*设置字体
set_display_font (WindowID, 14, 'mono', 'true', 'false')
*显示字体右下角
disp_continue_message (WindowID, 'black', 'true')
*停止
stop ()
*像素值范围找出来
threshold (Bond, Bright, 100, 255)
*装换成矩形
shape_trans (Bright, Die, 'rectangle2')
*设置一下颜色
dev_set_color ('green')
*设置一下宽度
dev_set_line_width (3)
*边缘
dev_set_draw ('margin')
*
dev_display (Die)
*显示边缘
disp_continue_message (WindowID, 'black', 'true')
stop ()
*减少范围，找出感兴趣区域
reduce_domain (Bond, Die, DieGrey)
*找到的0，到50区域
threshold (DieGrey, Wires, 0, 50)
*进行边缘提取
fill_up_shape (Wires, WiresFilled, 'area', 1, 100)
dev_display (Bond)
dev_set_draw ('fill')
dev_set_color ('red')
dev_display (WiresFilled)
*进行填充
disp_continue_message (WindowID, 'black', 'true')
stop ()
*形态学开运算，范围面积为15.5的圆进行放，放进去的
opening_circle (WiresFilled, Balls, 15.5)
dev_set_color ('green')
dev_display (Balls)
*能放进去的区域保留
disp_continue_message (WindowID, 'black', 'true')
stop ()
*找出圆形的区域
connection (Balls, SingleBalls)
*这些区域是不是特别的圆，不是特别的圆进行去掉
select_shape (SingleBalls, IntermediateBalls, 'circularity', 'and', 0.85, 1.0)
*对区域进行排序，在通过颜色排序
sort_region (IntermediateBalls, FinalBalls, 'first_point', 'true', 'column')
dev_display (Bond)
dev_set_colored (12)
dev_display (FinalBalls)
disp_continue_message (WindowID, 'black', 'true')
stop ()
*经外界圆得到中心点，半径
smallest_circle (FinalBalls, Row, Column, Radius)
*对变量进行赋值
NumBalls := |Radius|
*得到直径
Diameter := 2 * Radius
*所有的和除于个数得到平局
meanDiameter := mean(Diameter)
*找出数值最小的值
minDiameter := min(Diameter)
dev_display (Bond)
*进行显示面积，但是以像素为单位
disp_circle (WindowID, Row, Column, Radius)
dev_set_color ('white')
disp_message (WindowID, 'D: ' + Diameter$'.4', 'image', Row - 2 * Radius, Column, 'white', 'false')
dev_update_window ('on')

设置ROI金属凸轮正反位置判断

read_image(Image, 'D:/c++/image/6')

*画ROI感兴趣区域
dev_open_window (0, 0, 512, 512, 'black', WindowHandle)
dev_display (Image)
Row1:=30
Column1:=31
Row2:=210
Column2:=212
draw_rectangle1_mod (WindowHandle, 100, 100, 200, 200, Row1, Column1, Row2, Column2)
*显示ROI区域 
gen_rectangle1(Rectangle, Row1, Column1, Row2, Column2)
*截取了ROI区域的
reduce_domain(Image, Rectangle, ImageReduced)
*提取亮度阈值
binary_threshold(ImageReduced, Region, 'max_separability', 'light', UsedThreshold)
*做开运算吧小的去掉
opening_rectangle1(Region, RegionOpening, 10, 10)
*有两个连到一起，运用腐蚀进行分开
erosion_circle(RegionOpening, RegionErosion, 7.5)
*进行打散
connection(RegionErosion, ConnectedRegions)


dilation_circle(ConnectedRegions, ObjectSelected, 5.5)



*设置字体
set_display_font ( WindowHandle, 44, 'mono', 'true', 'false')


connection(ObjectSelected, ConnectedRegions1)
*得到roi区域面积，宽和高
area_center(Rectangle, Area, Row, Column)
*只找到上面的分割图，就可以
select_shape(ConnectedRegions1, SelectedRegions, 'row', 'and', 0, Row)
*找到一整个原件的宽度符合阈值
select_shape(SelectedRegions, SelectedRegions1, 'width', 'and', 100, 99999)
*查看有几个分割区域
count_obj(SelectedRegions1, Number)

*循环判断是否有正反
for Index := 1 to Number  by 1
    *选中SelectedRegions1中的循环第几个 
    select_obj(SelectedRegions1, ObjectSelected, Index)
    
    *进行类接矩形，得到的矩形的左上和右下坐标位置
    inner_rectangle1(ObjectSelected, Row11, Column11, Row21, Column21)
    *inner_rectangle1(RegionDilation, Row11, Column11, Row21, Column21)
    
    *进行画图填充
    gen_rectangle1(Rectangle1, Row11, Column11, Row21, Column21)
    
    *进行了开运算
    opening_rectangle1(ObjectSelected, RegionOpeningTOU, 10,Row21-Row11+10 )
    *进行打散
    connection(RegionOpeningTOU, ConnectedRegionsT)
    *找到面积最大的
    select_shape_std(ConnectedRegionsT, SelectedRegionsT, 'max_area', 1)
    
    
    *把大的减去就是小头
    difference(ObjectSelected, SelectedRegionsT, RegionDifference)
    *进行打散
    connection(RegionDifference, ConnectedRegionsW)
    *找到最大的那个
    select_shape_std(ConnectedRegionsW, SelectedRegions2, 'max_area', 1)
    
    *找到了两头
    area_center(SelectedRegionsT, Area1, Row3, Column3)
    area_center(SelectedRegions2, Area2, Row4, Column4)
    
    *判断如果是前面的矩形大于后面的矩形就是NG，否则不是
    if(Column3 < Column4)
        *设置光标位置set_tposition（:: 窗口句柄，行坐标，列坐标:)。
        set_tposition(WindowHandle, Row4, Column4)
        *在窗口打印字符串write_string( : : 窗口句柄, 字符串: )。
        write_string(WindowHandle, 'NG')
        stop()
    else
        set_tposition(WindowHandle, Row4, Column4)
        write_string(WindowHandle, 'OK')
        stop()
    endif
endfor

read_image(Image, 'D:/c++/image/5')

*读取图像大小
get_image_size(Image, Width, Height)
*使用窗口
dev_clear_window()
dev_open_window(0, 0, Width/2, Height/2, 'black', WindowHandle)
dev_display(Image)

*创建roi区域
draw_rectangle1(WindowHandle, Row1, Column1, Row2, Column2)

*显示出来
gen_rectangle1(Rectangle, Row1, Column1, Row2, Column2)

*roi区域剪辑出来
reduce_domain(Image, Rectangle, ImageReduced)

*动态阈值处理，均值滤波
mean_image(ImageReduced, ImageMean, 3, 3)
dyn_threshold(ImageReduced, ImageMean, RegionDynThresh, 2, 'light')
*进行打散操作
connection(RegionDynThresh, ConnectedRegions)
*面积筛选
select_shape(ConnectedRegions, SelectedRegions, 'area', 'and', 350, 99999)
*进行找出边缘
fill_up(SelectedRegions, RegionFillUp)

*进行开运算
opening_circle(RegionFillUp, RegionOpening, 40.5)
*进行外接圆过滤
select_shape(RegionOpening, SelectedRegions1, 'outer_radius', 'and', 80, 99999)
*有几个分割区域
count_obj(SelectedRegions1, Number)
*进行判断，有一个区域正常，否者不是
if(Number # 1)
    stop()
endif

*进行膨胀变大
dilation_circle(SelectedRegions1, RegionDilation, 17.5)
*在进行裁剪得到想要的特征位置
reduce_domain(ImageMean, RegionDilation, ImageReduced1)
*显示边缘检测
binary_threshold(ImageReduced1, Region, 'max_separability', 'light', UsedThreshold)
*进行打散
connection(Region, ConnectedRegions1)

count_obj(ConnectedRegions1, Number1)
*外界圆半径
select_shape(ConnectedRegions1, SelectedRegions2, 'outer_radius', 'and', 80, 99999)

count_obj(SelectedRegions2, Number2)

if(Number2 # 1)
    stop()
endif

*用面积,和圆度进行区分筛选
select_shape(ConnectedRegions1, SelectedRegions3, ['area','circularity'], 'and', [180,0.8 ], [220,1])

count_obj(SelectedRegions3, Number3)

if(Number3 # 1)
    stop()
endif

dev_display(SelectedRegions3)
dev_display(SelectedRegions2)
*找出圆的中心点
smallest_circle(SelectedRegions3, Row, Column, Radius)
*用中心点进行画圆
gen_circle(Circle, Row, Column, Radius)
*外边
fill_up(SelectedRegions2, RegionFillUp1)
*生成矩形
boundary(RegionFillUp1, RegionBorder, 'inner')
*外接矩形的参数
smallest_rectangle2(RegionFillUp1, Row3, Column3, Phi, Length1, Length2)
*生成一个矩形
gen_rectangle2(Rectangle1, Row, Column, Phi,  Radius,40)

*一个矩形和第二生成的矩形进行相交的得到外面的矩形短线
intersection(Rectangle1, RegionBorder, RegionIntersection)


*求出圆的中心点到矩形的位置
distance_pr(RegionIntersection, Row, Column, DistanceMin, DistanceMax)

*第二种求出中心点算法
*画出中心点
gen_region_points(Region1, Row, Column)

*求出中心点到直线的距离
distance_rr_min(RegionIntersection, Region1, MinDistance, Row11, Column11, Row21, Column21)

*生成点到直线的距离
gen_region_line(RegionLines, Row11, Column11,Row21, Column21)

笛卡尔和条形码

* Read circularly printed bar codes.
* 
dev_update_off ()
get_system ('clip_region', Information)
set_system ('clip_region', 'true')
read_image (Image, 'circular_barcode')
get_image_size (Image, Width, Height)
dev_close_window ()
dev_open_window (0, 0, Width / 2, Height / 2, 'black', WindowHandle)
dev_set_colored (12)
dev_display (Image)
set_display_font (WindowHandle, 14, 'mono', 'true', 'false')
stop ()
* 
* Segment the ring on the CD that contains the bar code.
*找出0到100内的
threshold (Image, Region, 0, 100)
*做闭区间
closing_circle (Region, Region, 3.5) 
*进行打散
connection (Region, ConnectedRegions)
*进行圆的宽和高进行过滤
select_shape (ConnectedRegions, Ring, ['width','height'], 'and', [550,550], [750,750])
*进行变成圆
shape_trans (Ring, OuterCircle, 'outer_circle')
*补齐，除去之前的圆环
complement (Ring, RegionComplement)
*进行打散
connection (RegionComplement, ConnectedRegions)
*在进行过滤
select_shape (ConnectedRegions, InnerCircle, ['width','height'], 'and', [450,450], [650,650])


* Determine the parameters of the ring that contains the bar code.
*得到外圆的中心和半径
smallest_circle (Ring, Row, Column, OuterRadius)

*得到内圆的中心和半径
smallest_circle (InnerCircle, InnerRow, InnerColumn, InnerRadius)
dev_set_color ('green')
dev_set_draw ('margin')
dev_set_line_width (3)
dev_display (Image)
dev_display (OuterCircle)
dev_display (InnerCircle)
stop ()
* 
* Now read the bar code. This is done by computing the polar transformation
* of the ring in the image that contains the bar code.
*自己设置的宽度
WidthPolar := 1440

*高度是外圆减去内圆，再减去10
HeightPolar := round(OuterRadius - InnerRadius - 10)

*进行笛卡尔转换，前面描述的是圆的坐标宽和高，后面描述的就是笛卡尔
polar_trans_image_ext (Image, PolarTransImage, Row, Column, rad(360), 0, OuterRadius - 5, InnerRadius + 5, WidthPolar, HeightPolar, 'bilinear')

*黑变白，反转
invert_image (PolarTransImage, ImageInvert)
* 
* Since the bar code region is quite flat the image height is doubled.
*按给定因子缩放图像
zoom_image_factor (ImageInvert, ImageZoomed, 1, 2, 'weighted')

*创建bar conde reder的模型
create_bar_code_model ([], [], BarCodeHandle)
* 
* Bars are small and the contrast is low; therefore the threshold is raised from 0.05 to 0.1.
*设置解码参数
set_bar_code_param (BarCodeHandle, 'element_size_min', 1.5)
set_bar_code_param (BarCodeHandle, 'meas_thresh', 0.3)

*查找条码，进行解成128
find_bar_code (ImageZoomed, SymbolRegions, BarCodeHandle, 'Code 128', DecodedDataStrings)
dev_set_window_extents (-1, -1, WidthPolar / 2, HeightPolar)
dev_display (ImageZoomed)
dev_display (SymbolRegions)
set_system ('clip_region', Information)

*设置文字
disp_message (WindowHandle, DecodedDataStrings, 'image', 10, 180, 'black', 'true')
stop ()
* 
* Transform the code region back to the original image and display it.
zoom_region (SymbolRegions, SymbolRegions, 1, 0.5)

*将极坐标中的一个区域变换回笛卡尔坐标。
polar_trans_region_inv (SymbolRegions, CodeRegionCircular, Row, Column, rad(360), 0, OuterRadius - 5, InnerRadius + 5, WidthPolar, HeightPolar, Width, Height, 'nearest_neighbor')
dev_set_window_extents (-1, -1, Width / 2, Height / 2)
dev_display (Image)
dev_display (CodeRegionCircular)
disp_message (WindowHandle, DecodedDataStrings, 'window', 12, 12, 'black', 'true')

极坐标，光度立体，ocr

* This program demonstrates the use of the photometric stereo technique
* to read imprinted letters on a tire with a low cost setup.
* Even if the light sources are not very homogeneous and their
* orientation is only very roughly known, the text can be
* read robustly.
* 
* Initialization
PolarTransWidth := 512
PolarTransHeight := 80
* 
dev_close_window ()
dev_update_off ()
read_image (Images, 'photometric_stereo/tire_0' + [1:4])
select_obj (Images, ObjectSelected, 1)
dev_open_window_fit_image (ObjectSelected, 0, 0, -1, -1, WindowHandle)
set_display_font (WindowHandle, 16, 'mono', 'true', 'false')
for Index := 1 to 4 by 1
    select_obj (Images, ObjectSelected, Index)
    dev_display (ObjectSelected)
    disp_message (WindowHandle, 'Image ' + Index + '/4', 'window', 12, 12, 'black', 'true')
    wait_seconds (0.5)
endfor
* 
* We do not know the exact orientations of the light sources
* and therefore use only very rough estimates.
Tilts := [180,270,0,90]
Slants := [45,45,45,45]
* 
* Apply photometric stereo to determine the surface gradient
*用不同的光拍一张照片，使用光度立体
photometric_stereo (Images, HeightField, Gradient, Albedo, Slants, Tilts, 'gradient', 'poisson', [], [])
* 
* Calculate the surface curvature for easy segmentation of the letters
*计算曲面曲率以便于字母分割 
derivate_vector_field (Gradient, Result, 3, 'mean_curvature')

* Display surface curvature，显示曲面曲率
dev_display (Result)
disp_message (WindowHandle, 'Surface curvature image', 'window', 12, 12, 'black', 'true')
disp_continue_message (WindowHandle, 'black', 'true')
stop ()
* 



* Segment text and base line
* The base line of the text is used to align
* the text horizontally (to simplify the OCR)


*分段文本和基线

*文本的基线用于对齐

*文本水平显示（简化OCR
*基本的阈值处理
threshold (Result, Region, -0.2, -0.01)
connection (Region, ConnectedRegions)
*宽度和行进行过滤
select_shape (ConnectedRegions, BaseLine, ['width','row'], 'and', [600,280], [99999,350])


* The base line is converted to an XLD contour
* to determine the radius of the line by fitting
* a circle to the contour
*基线将转换为XLD轮廓
*通过拟合确定直线半径的步骤
*与等高线成圆
gen_contour_region_xld (BaseLine, Contours, 'center')
*轮廓分割
segment_contours_xld (Contours, ContoursSplit, 'lines_circles', 8, 14, 12)

*选取多种特征要求的XLD轮廓或多边形
select_contours_xld (ContoursSplit, SelectedContours, 'contour_length', 500, 1e10, -0.5, 0.5)

*用圆近似XLD轮廓；
fit_circle_contour_xld (ContoursSplit, 'ahuber', -1, 0, 0, 3, 1, Row, Column, Radius, StartPhi, EndPhi, PointOrder)
* Generate circle contours for debugging (not used)
* gen_circle_contour_xld (ContCircle, Row, Column, Radius, StartPhi, EndPhi, PointOrder, 1)
* If more than 1 contour have been selected,
* choose the one with the largest radius
I := sort_index(Radius)[|Radius| - 1]
* If the point order of the fitted circle is 'positive',
* start and end angle have to be swapped for the polar transform
if (PointOrder[I] == 'positive')
    Tmp := StartPhi[I]
    StartPhi[I] := EndPhi[I]
    EndPhi[I] := Tmp
endif
* Perform a polar transform on the region
* to align the text with the base line
*极坐标通过圆进行拉直
polar_trans_region (Region, RegionPolarTrans, Row[I], Column[I], StartPhi[I], EndPhi[I], Radius[I] + PolarTransHeight, Radius[I], PolarTransWidth, PolarTransHeight, 'nearest_neighbor')
* Select characters from the transformed region
connection (RegionPolarTrans, ConnectedTransRegions)
select_shape (ConnectedTransRegions, Letters, ['height','width'], 'and', [40,20], [60,50])

*进行相关位置排序
sort_region (Letters, SortedLetters, 'character', 'true', 'row')
* Create a black & white image from the region for the OCR，从OCR区域创建黑白图像

* 将区域转换为二进制字节图像。
region_to_bin (SortedLetters, BinImage, 0, 255, 512, 100)
* Read text using a pre-trained classifier

*从文件中读取OCR的.omc分类器，链接：https://www.cnblogs.com/xh6300/p/13392610.html
read_ocr_class_mlp ('Industrial_NoRej', OCRHandle)

*使用 OCR 分类器对多个字符进行分类。 
do_ocr_multi_class_mlp (SortedLetters, BinImage, OCRHandle, Class, Confidence)
* 
* Display results
dev_display (ObjectSelected)
area_center (SortedLetters, Area, Row, Column)
dev_open_window (60, 12, PolarTransWidth, PolarTransHeight + 50, 'black', WindowHandle1)
set_display_font (WindowHandle1, 16, 'mono', 'true', 'false')
dev_set_part (-30, 0, PolarTransHeight - 1 + 20, PolarTransWidth - 1)
dev_display (SortedLetters)
DispRow := mean(Row) + 30
disp_message (WindowHandle1, 'OCR result after polar transform', 'window', 12, 12, 'black', 'true')
disp_message (WindowHandle1, Class, 'image', DispRow, Column - 18, 'black', 'true')

使用另一种能过滤区域

* ball_seq.hdev: Inspection of Ball Bonding
* 
dev_update_off ()

*4张图
ImageNames := 'die/' + ['die_02','die_03','die_04','die_07']
dev_set_colored (12)
read_image (Bond, ImageNames[0])
get_image_size (Bond, Width, Height)
dev_close_window ()
dev_open_window (0, 0, Width, Height, 'black', WindowHandle)
set_display_font (WindowHandle, 16, 'mono', 'true', 'false')
dev_set_draw ('margin')
dev_set_line_width (3)
NumImages := |ImageNames|
for I := 0 to NumImages - 1 by 1
    read_image (Bond, ImageNames[I])
    dev_display (Bond)
    *找出灰色的最小和最大的，
    min_max_gray (Bond, Bond, 0, Min, Max, Range)
    
    *用最大的灰度值减去80
    threshold (Bond, Bright, Max - 80, 255)
    shape_trans (Bright, Die, 'rectangle2')
    dev_display (Die)
    reduce_domain (Bond, Die, DieGrey)
    min_max_gray (Die, Bond, 0, Min, Max, Range)
    threshold (DieGrey, Wires, 0, Min + 30)
    *1到100的进行填充
    fill_up_shape (Wires, WiresFilled, 'area', 1, 100)
 *进行开运算
    opening_circle (WiresFilled, Balls, 9.5)
    
    connection (Balls, SingleBalls)
    
    *选出矩形
    select_shape_std (SingleBalls, Rect, 'rectangle1', 90)
    *在把矩形给去掉
    difference (SingleBalls, Rect, IntermediateBalls)
    gen_empty_region (Forbidden)
    expand_gray (IntermediateBalls, Bond, Forbidden, RegionExpand, 4, 'image', 6)
    
    *开运算变成了圆
    opening_circle (RegionExpand, RoundBalls, 15.5)
    sort_region (RoundBalls, FinalBalls, 'first_point', 'true', 'column')
    smallest_circle (FinalBalls, Row, Column, Radius)
    NumBalls := |Radius|
    Diameter := 2 * Radius
    meanDiameter := sum(Diameter) / NumBalls
    mimDiameter := min(Diameter)
    dev_display (RoundBalls)
    if (I != NumImages)
        disp_continue_message (WindowHandle, 'black', 'true')
    endif
    stop ()
endfor

使用放射变换让图片确定位置，在进行，面积和灰度进行判断

* This example demonstrates an application from the pharmaceutical
* industry. The task is to check the content of automatically filled
* blisters. The first image (reference) is used to locate the chambers
* within a blister shape as a reference model, which is then used to
* realign the subsequent images along to this reference shape. Using
* blob analysis the content of each chamber is segmented and finally
* classified by a few shape features.
* 
dev_close_window ()
dev_update_off ()
read_image (ImageOrig, 'blister/blister_reference')
dev_open_window_fit_image (ImageOrig, 0, 0, -1, -1, WindowHandle)
set_display_font (WindowHandle, 14, 'mono', 'true', 'false')
dev_set_draw ('margin')
dev_set_line_width (3)
* 
* In the first step, we create a pattern to cut out the chambers in the
* subsequent blister images easily.

access_channel (ImageOrig, Image1, 1)

*阈值分割 
threshold (Image1, Region, 90, 255)
shape_trans (Region, Blister, 'convex')

*角度，用于计算区域的方位信息 
orientation_region (Blister, Phi)

*找出中心点，和角度
area_center (Blister, Area1, Row, Column)
*要放射变换，
vector_angle_to_rigid (Row, Column, Phi, Row, Column, 0, HomMat2D)
affine_trans_image (ImageOrig, Image2, HomMat2D, 'constant', 'false')

*根据得到的中心点和角度对其进行画框
gen_empty_obj (Chambers)
for I := 0 to 4 by 1
    Row := 88 + I * 70
    for J := 0 to 2 by 1
        Column := 163 + J * 150
        gen_rectangle2 (Rectangle, Row, Column, 0, 64, 30)
        concat_obj (Chambers, Rectangle, Chambers)
    endfor
endfor
affine_trans_region (Blister, Blister, HomMat2D, 'nearest_neighbor')
*与原的矩形进行相减得到，
difference (Blister, Chambers, Pattern)
union1 (Chambers, ChambersUnion)
orientation_region (Blister, PhiRef)
PhiRef := rad(180) + PhiRef
area_center (Blister, Area2, RowRef, ColumnRef)
* 
* 
* Each image read will be aligned to this pattern and reduced to the area of interest,
* which is the chambers of the blister
Count := 6
for Index := 1 to Count by 1
    read_image (Image, 'blister/blister_' + Index$'02')
    threshold (Image, Region, 90, 255)
    connection (Region, ConnectedRegions)
    select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 5000, 9999999)
    shape_trans (SelectedRegions, RegionTrans, 'convex')
    * 
    * Align pattern along blister of image
    orientation_region (RegionTrans, Phi)
    area_center (RegionTrans, Area3, Row, Column)
    vector_angle_to_rigid (Row, Column, Phi, RowRef, ColumnRef, PhiRef, HomMat2D)
    affine_trans_image (Image, ImageAffineTrans, HomMat2D, 'constant', 'false')
    * 
    * Segment pills
    reduce_domain (ImageAffineTrans, ChambersUnion, ImageReduced)
    decompose3 (ImageReduced, ImageR, ImageG, ImageB)
    var_threshold (ImageB, Region, 7, 7, 0.2, 2, 'dark')
    connection (Region, ConnectedRegions0)
    closing_rectangle1 (ConnectedRegions0, ConnectedRegions, 3, 3)
    fill_up (ConnectedRegions, RegionFillUp)
    select_shape (RegionFillUp, SelectedRegions, 'area', 'and', 1000, 99999)
    opening_circle (SelectedRegions, RegionOpening, 4.5)
    connection (RegionOpening, ConnectedRegions)
    select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 1000, 99999)
    shape_trans (SelectedRegions, Pills, 'convex')
    * 
    * Classify segmentation results and display statistics
    count_obj (Chambers, Number)
    gen_empty_obj (WrongPill)
    gen_empty_obj (MissingPill)
    for I := 1 to Number by 1
        select_obj (Chambers, Chamber, I)
        intersection (Chamber, Pills, Pill)
        area_center (Pill, Area, Row1, Column1)
        if (Area > 0)
            min_max_gray (Pill, ImageB, 0, Min, Max, Range)
            if (Area < 3800 or Min < 60)
                concat_obj (WrongPill, Pill, WrongPill)
            endif
        else
            concat_obj (MissingPill, Chamber, MissingPill)
        endif
    endfor
    * 
    dev_clear_window ()
    dev_display (ImageAffineTrans)
    dev_set_color ('forest green')
    count_obj (Pills, NumberP)
    count_obj (WrongPill, NumberWP)
    count_obj (MissingPill, NumberMP)
    dev_display (Pills)
    if (NumberMP > 0 or NumberWP > 0)
        disp_message (WindowHandle, 'Not OK', 'window', 12, 12 + 600, 'red', 'true')
    else
        disp_message (WindowHandle, 'OK', 'window', 12, 12 + 600, 'forest green', 'true')
    endif
    * 
    Message := '# Correct pills: ' + (NumberP - NumberWP)
    Message[1] := '# Wrong pills  :  ' + NumberWP
    Message[2] := '# Missing pills:  ' + NumberMP
    * 
    Colors := gen_tuple_const(3,'black')
    if (NumberWP > 0)
        Colors[1] := 'red'
    endif
    if (NumberMP > 0)
        Colors[2] := 'red'
    endif
    disp_message (WindowHandle, Message, 'window', 12, 12, Colors, 'true')
    dev_set_color ('red')
    dev_display (WrongPill)
    dev_display (MissingPill)
    if (Index < Count)
        disp_continue_message (WindowHandle, 'black', 'true')
    endif
    stop ()
endfor