Halcon 学习笔记3 仿射变换

像素的减少

开运算（较少）

腐蚀（去除更多）

对灰度图像的开运算或腐蚀 相当于将灰度图像变暗

像素增加

闭运算（较少）

膨胀（较多）

对灰度图像的闭运算或膨胀 相当于将灰度图像变亮

仿射变换

另外一种仿射变换

* 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)
threshold (Image1, Region, 90, 255)
shape_trans (Region, Blister, ‘convex‘)
orientation_region (Blister, Phi)//计算角度Phi
area_center (Blister, Area1, Row, Column)//获取面积和中心点坐标
*将原来的（Row, Column, Phi）表示为（Row, Column, 0），也就是将角度调正
*输出变换矩阵HomMat2D
vector_angle_to_rigid (Row, Column, Phi, Row, Column, 0, HomMat2D)
affine_trans_image (ImageOrig, Image2, HomMat2D, ‘constant‘, ‘false‘)
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‘)
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

仿射变换 理论

https://www.cnblogs.com/liekkas0626/p/5238564.html　

原文地址：https://www.cnblogs.com/my-cat/p/8206128.html