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