一次halcon翻译成python的过程记录_halcon python

距离上次发出记录halcon和python联合的文章已经半年过去,为了弥补上次疏漏这次做个入门级的.   详见:>https://blog.csdn.net/u014584014/article/details/127500490

众所周知halcon从20.11版本后提供了python接口,但是没有自动导出python的功能,这使人非常苦恼.

于是手工翻译一下:基本上都是语法差异:

首先说一下环境:halcon20.11是第一个支持python的版本.(同时它不支持32位操作系统||不是python不支持,而是halcon这个版本自身就不支持32位.)(python解释器版本要求3.8以及以上且必须是64位)(只有64位操作系统才支持64位的python解释器):是不是很啰嗦?没关系再总结一遍.省的有人拿别的版本折腾不到位...(在终端输入pip install mvtec-halcon==0:这时pip找不到0版本的安装包,就会报错并且返回可用包的版本号列表,)

C:\Users\Administrator>pip install mvtec-halcon==0
ERROR: Could not find a version that satisfies the 
requirement mvtec-halcon==0 (from versions: 
20110.0.0, 20110.0.1, 20111.0.0, 20111.0.1, 
20112.0.0, 20113.0.0, 21050.0.0, 21110.0.0, 
22050.0.0, 22110.0.0, 22111.0.0, 23050.0.0)
ERROR: No matching distribution 
found for mvtec-halcon==0

由此可见如今halcon已经有12个版本支持python了.

1.操作系统必须是windows，X64; win7_64; win10_64 都可以。
2.软件版本:halcon20.11(这个只支持64位的windows,32位低版本虽然可以pythonnet勉强写出来,但是风格非常C#,低版本还是考虑原生C#走起.
3.python版本:最低3.8.8也得是64位解释器;我用的是python-3.8.8-amd64.exe;
4.文本编辑器:我用的Notepad++编辑;调试用自带的IDLE; (根据自己喜好)vscode有微弱的自动提示.装了但用不习惯.pycharm..太专业不会配置.庞大臃肿.不想装..
如果没有安装用以下命令安装..

pip install mvtec-halcon==20111

 下来就是找一个文件夹把C/C++de那些dll拿进来,新建.py文件也放进来作为pyhalcon的工作空间:

 就是这么一张图,"display.jpg"也丢进刚才的文件夹.

先看halcon 的.hdev..

dev_close_window ()
read_image (Image_display, 'display.jpg')
rgb1_to_gray (Image_display, GrayImage)
get_image_size(Image_display,imageWidth, imageHeight)
dev_open_window (0, 0, imageWidth, imageHeight, 'black', WindowHandle1)
dev_display (GrayImage)
XCoordCorners := []
YCoordCorners := []
threshold(GrayImage,DarkRegion,0, 80)
connection (DarkRegion, ConnectedRegions)
select_shape_std (ConnectedRegions, displayRegion, 'max_area', 70)
reduce_domain (GrayImage, displayRegion, displayImage)
gen_contour_region_xld (displayRegion, Contours, 'border')
segment_contours_xld (Contours, ContoursSplit, 'lines', 5, 4, 2)
count_obj (ContoursSplit, Number)
for index:=1 to Number by 1
   select_obj(ContoursSplit, ObjectCurrent, index)
   fit_line_contour_xld (ObjectCurrent, 'tukey', -1, 0, 5, 2, RowBegin, ColBegin, RowEnd, ColEnd, Nr, Nc, Dist)
   tuple_concat (XCoordCorners, RowBegin, XCoordCorners)
   tuple_concat (YCoordCorners, ColBegin, YCoordCorners)
endfor
XOff:= 100
YOff:= 100*imageHeight/imageWidth
hom_vector_to_proj_hom_mat2d (XCoordCorners, YCoordCorners, [1,1,1,1], [YOff,YOff,imageHeight-YOff,imageHeight-YOff], [XOff,imageWidth-XOff,imageWidth-XOff,XOff], [1,1,1,1], 'normalized_dlt', HomMat2D)
projective_trans_image (Image_display, Image_rectified, HomMat2D, 'bilinear', 'false', 'false')
dev_display (Image_rectified)

注释就不用写了吧,代码具有自解释性.然后上python (其实几乎一样,这里主要是感受下语法差异).

import os
import halcon as ha 
def cmd(s="pause"):
    os.system(s)
def open_window(width, height):
    if os.name == 'nt':
        ha.set_system('use_window_thread', 'true')
    return ha.open_window(
        row=0,
        column=0,
        width=width,
        height=height,
        father_window=0,
        mode='visible',
        machine=''
    )
Image_display = ha.read_image('display.jpg')
Width, Height = ha.get_image_size(Image_display)
WindowHandle1 =open_window(Width[0], Height[0])
WindowHandle2 =open_window(Width[0], Height[0])
ha.disp_obj(Image_display, WindowHandle1);
GrayImage = ha.rgb1_to_gray(Image_display) 
XCoordCorners = []
YCoordCorners = []
DarkRegion=ha.threshold(GrayImage,0, 80)
ConnectedRegions=ha.connection (DarkRegion)
displayRegion=ha.select_shape_std (ConnectedRegions,'max_area', 70)
displayImage=ha.reduce_domain(GrayImage, displayRegion)
Contours =ha.gen_contour_region_xld (displayRegion,'border')
ContoursSplit=ha.segment_contours_xld (Contours, 'lines', 5, 4, 2)
Number=ha.count_obj (ContoursSplit)
for i in range(Number):
    print(i+1)
    ObjectCurrent=ha.select_obj(ContoursSplit, i+1)
    RowBegin, ColBegin, RowEnd, ColEnd, Nr, Nc, Dist=ha.fit_line_contour_xld(ObjectCurrent, 'tukey', -1, 0, 5, 2)
    XCoordCorners=ha.tuple_concat (XCoordCorners,RowBegin)
    YCoordCorners=ha.tuple_concat (YCoordCorners,ColBegin)
    pass    
XOff=100
YOff=100*Height[0]/Width[0]  
imageHeight=Height[0]
imageWidth=Width[0]
HomMat2D=ha.hom_vector_to_proj_hom_mat2d (XCoordCorners, YCoordCorners, (1,1,1,1), [YOff,YOff,imageHeight-YOff,imageHeight-YOff], (XOff,imageWidth-XOff,imageWidth-XOff,XOff), (1,1,1,1), 'normalized_dlt')
#方括号 圆形括号都可以 在python中并没有影响.
Image_rectified=ha.projective_trans_image (Image_display,  HomMat2D, 'bilinear', 'false', 'false')
ha.disp_obj(Image_rectified, WindowHandle2);
ha.write_image (Image_rectified, 'jpg', 0, "GG")
cmd()

可以看到显示原图和透视变换后的图 左后输出"GG.jpg"

 主要语法差异,不管有多少个参数python的输出总是在等号左边,输入在括号里面...而其他语言全部在括号里面....当然这是本人粗俗的认知...

结束.