程式狂想曲

 
建立校正資料開始:
create_calib_data( : : CalibSetup, NumCameras, NumCalibObjects : CalibDataID)
  CalibSetup：校正模式
  NumCameras：相機數量
  NumCalibObjects：校正版數量
  CalibDataID：校正資料ID

適用  Area Camera, 使用 CCTV 鏡頭

CalPath := 'C:/Program Files/MVTec/HALCON-12.0/calib/自建/calplate_big.cpd' '校正版檔案路徑
StartCamPar := [0.016,0,0.0000074,0.0000074,320,240,640,480] '相機初始參數

Halcon 在3D 模組使用前需要將相機做參數校正
首先 需要設定 要使用的相機組成(CameraType)  與 相機與鏡頭的參數(CameraPAram)
 
CameraType 這邊參考 Halcon 的文件 選擇 合適的 Type

* Image Acquisition 01: Code generated by Image Acquisition 01
list_files ('D:/Study/鐵片方型尋找', ['files','follow_links'], ImageFiles)
tuple_regexp_select (ImageFiles, ['\\.(tif|tiff|gif|bmp|jpg|jpeg|jp2|png|pcx|pgm|ppm|pbm|xwd|ima|hobj)$','ignore_case'], ImageFiles)
for Index := 0 to |ImageFiles| - 1 by 1
    read_image (Image, ImageFiles[Index])
    rgb1_to_gray(Image, GrayImage)
    intensity(Image,GrayImage,Mean, Deviation)
    threshold(GrayImage,Region,0,Mean+Deviation*0.5)
    difference(GrayImage,Region,diffReg)
    fill_up(diffReg, RegionFillUp)
    reduce_domain(GrayImage, RegionFillUp, ImageReduced)
    threshold(ImageReduced,Region2,0,Mean)
    fill_up(Region2, RegionFillUp1)
    boundary(RegionFillUp1, RegionBorder, 'inner')
    connection(RegionBorder, ConnectedRegions)
    select_shape(ConnectedRegions,SelectedRegions,'contlength','and',190, 250)
    count_obj(SelectedRegions, Number)
    if (Number > 0)
        dev_display(GrayImage)
        for index := 1 to Number by 1
              select_obj(SelectedRegions, ObjectSelected, index)
              gen_contour_region_xld(ObjectSelected,Contours, 'border')
              shape_trans_xld(Contours,XLDTrans,'rectangle2')
              smallest_rectangle2_xld (XLDTrans, Row, Col, Phi, Len1, Len2)
              tuple_sin (Phi, Sin)
              tuple_cos (Phi, Cos)
              * 四点中心坐标 左，右，上，下
              L_Row := Row - (Len2 * Cos)
              L_Col := Col - (Len2 * Sin)
              B_Row := Row + (Len1 * Sin)
              B_Col := Col - (Len1 * Cos)
              T_Row := Row - (Len1 * Sin)
              T_Col := Col + (Len1 * Cos)
              R_Row := Row + (Len2 * Cos)
              R_Col := Col + (Len2 * Sin)
              gen_arrow_contour_xld (Arrow1, L_Row, L_Col, R_Row, R_Col, 1, 1)
              gen_arrow_contour_xld (Arrow2, B_Row, B_Col, T_Row, T_Col, 1, 1)
              gen_contour_polygon_xld (ContourL, [L_Row + 10 * Sin/2, L_Row - 10 * Sin/2], [L_Col - 10 * Cos/2, L_Col + 10 * Cos/2])
              gen_contour_polygon_xld (ContourR, [R_Row + 10 * Sin/2, R_Row - 10 * Sin/2], [R_Col - 10 * Cos/2, R_Col + 10 * Cos/2])
              gen_contour_polygon_xld (ContourT, [T_Row - 10 * Cos/2, T_Row + 10 * Cos/2], [T_Col - 10 * Sin/2, T_Col + 10 * Sin/2])
              gen_contour_polygon_xld (ContourB, [B_Row - 10 * Cos/2, B_Row + 10 * Cos/2], [B_Col - 10 * Sin/2, B_Col + 10 * Sin/2])
              dev_set_color ('blue')
              dev_display (Arrow1)
              dev_display (ContourL)
              dev_display (ContourR)
              dev_set_color ('green')
              dev_display (Arrow2)
              dev_display (ContourT)
              dev_display (ContourB)
              disp_message (3600, Len1 * 2, 'image', T_Row, T_Col, 'red', 'false')
              disp_message (3600, Len2 * 2, 'image', R_Row, R_Col, 'magenta', 'false')
             
             * dev_display(ObjectSelected)
              wait_seconds(1)
        endfor            
    endif    
endfor

Hard link 就是 建立A目錄 連接到  B目錄
例：
D:\ 沒有temp 的資料夾    
mklink /j  D:\temp C:\temp

 
Paste your text here.read_image (Image, 'CutImage.png')
rgb1_to_gray (Image, GrayImage)
get_image_size (GrayImage, Width, Height)
dev_close_window ()
dev_open_window (0, 0, Width, Height, 'black', WindowHandle)
dev_display (Image)
dev_set_draw ('margin')

*排除塗黑的區塊
mean_image (GrayImage, ImageMean, 50, 50)
threshold (ImageMean, RegionBlacked, 20, 255)
erosion_circle (RegionBlacked, RegionBlackedDilation, 20.5)
median_rect (GrayImage, ImageMedian, 1, 5)

*方法:提取線
lines_gauss (ImageMedian, Lines, 1, 2, 6, 'dark', 'true', 'bar-shaped', 'true')
union_collinear_contours_xld (Lines, UnionContours, 10, 1, 5, 0.4, 'attr_keep')
select_shape_xld (UnionContours, SelectedXLD, 'contlength', 'and', 40, 99999)
select_shape_xld (SelectedXLD, SelectedXLD1, 'anisometry_points', 'and', 10, 99999)
gen_region_contour_xld (SelectedXLD1, RegionLines, 'filled')
intersection (RegionLines, RegionBlackedDilation, RegionIntersection)
union1 (RegionIntersection, RegionUnion1)
closing_circle (RegionUnion1, RegionClosing, 3.5)
connection (RegionClosing, ConnectedRegions)
select_shape (ConnectedRegions, SelectedRegionsFinal, 'area', 'and', 75, 9999999)
union1 (SelectedRegionsFinal, RegionUnionFinal)

*方法:邊緣檢測後提取線
derivate_gauss (ImageMedian, DerivGauss, 1, 'gradient')
sobel_amp (ImageMedian, EdgeAmplitude, 'sum_abs', 5)
lines_gauss (EdgeAmplitude, Lines1, 1, 6, 15, 'light', 'true', 'bar-shaped', 'true')
union_collinear_contours_xld (Lines1, UnionContours1, 10, 1, 5, 0.4, 'attr_keep')
select_shape_xld (UnionContours1, SelectedXLD2, 'contlength', 'and', 40, 99999)
select_shape_xld (SelectedXLD2, SelectedXLD3, 'anisometry_points', 'and', 10, 99999)
gen_region_contour_xld (SelectedXLD3, RegionLines1, 'filled')
intersection (RegionLines1, RegionBlackedDilation, RegionIntersection1)
union1 (RegionIntersection1, RegionUnion2)
closing_circle (RegionUnion2, RegionClosing1, 3.5)
connection (RegionClosing1, ConnectedRegions1)
select_shape (ConnectedRegions1, SelectedRegionsFinal1, 'area', 'and', 75, 9999999)
union1 (SelectedRegionsFinal1, RegionUnionFinal1)

*結合兩種方法
union2 (RegionUnionFinal, RegionUnionFinal1, AllRegions)
dilation_circle (AllRegions, Defects, 6.5)

*可視化
dev_set_color ('red')
dev_display (Image)
dev_display (Defects)

一直以來  進到一個誤區，為了讓VM可以跑得很順利 升級電腦  加大記憶體  到最近換了 SSD
的確，花了錢  開 VM的確順很多
但雙開 VM 還是會卡卡的。
最後發現  給予VM適當的RAM 大小很重要

如果只是要消除  顯示的 三角形
 padding的參數中 設個數值就好
 datagridview1.RowHeadersDefaultCellStyle.Padding = New Padding(50)
 

在自定義物件的操作，當要在物件被回收時 同時啟用 某個相關 操作
可複寫 該物件的 Finalize
 
  Protected Overrides Sub Finalize()
     ..............

這張圖是今天抽空把過去的一個想法 用成圖來表示
橘色的線代表 當年 25 ~50歲的人數
藍色的線代表 當年 結婚對數
(PS1. 這邊會抓 25~50歲 因為這年齡層是最多結婚 與 需要買房子的人)
(PS2. 這邊會抓50歲做一個分隔，因為銀行不喜歡貸款給50歲以上的人)
(PS3. 2018 2023 2028 是由 2013年的人口統計資料繼續統計出來)