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做动态网站用哪个程序软件比较简单?,软文,烟台网站搭建,国家税务总局网上开票车牌示例流程: 读取图片转灰度图阈值分割,找车牌内容将车牌位置设置变换区域形状找到中心点和弧度利用仿射变换,斜切车牌旋转转正,把车牌抠出来利用形态学操作拼接车牌号数字训练ocr开始识别中文车牌本文章用到的算子(解析) Halcon 算子-承接车牌识别-CSDN博客 rgb1_to_gray…

车牌示例

流程:

读取图片转灰度图
阈值分割,找车牌内容
将车牌位置设置变换区域形状
找到中心点和弧度
利用仿射变换,斜切
车牌旋转转正,把车牌抠出来
利用形态学操作
拼接车牌号数字
训练ocr开始识别中文车牌

本文章用到的算子(解析)

Halcon 算子-承接车牌识别-CSDN博客

rgb1_to_gray 转灰度图
threshold 阈值分割

connection 将图像进行分割多张

select_shape 特征阈值

shape_trans 变换区域形状

area_center 取区域面积和中心

orientation_region 区域方向

vector_angle_to_rigid 计算平移和旋转仿射变换关系的变换矩阵

hom_mat2d_slant 斜切

affine_trans_region 仿射变换区域

affine_trans_Image 图像仿射变换

reduce_domain 取域图像

opening_circle 使用圆形结构的开运算

sort_region 排序区域

select_obj 选中组中对象

union2 计算两区域并集

gen_empty_oj 创建空对象

concat_obj 合并元组

write_ocr_trainf 写OCR训练文件

read_ocr_trainf_names 读OCR训练文件名

create_ocr_class_mlp 创建OCR多层感知器

trainf_ocr_class_mlp 从文件训练OCR多层感知器

write_ocr_class_mlp 写OCR多层感知器

read_ocr_class_mlp 读OCR多层感知器

do_ocr_multi_class_mlp 执行OCR多层感知器多分类

set_tposition 设置文本光标位置

1、关闭数据、窗体

dev_update_off ()
dev_close_window ()

2、读取图片、打开窗体

read_image (Image, 'F:/Halcon/‫Image/车牌.jpg')
dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle)
dev_display (Image)

3、处理图片-find车牌

rgb1_to_gray (Image, GrayImage)
threshold (GrayImage, Regions, 76, 100)
connection (Regions, ConnectedRegions)
select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 8600, 10000)

效果 threshold connection select_shape

4、处理图片-变换区域形状

shape_trans (SelectedRegions, RegionTrans, 'rectangle2')

5、取区域面积和中心

area_center (RegionTrans, Area, Row, Column)

6、旋转仿射变换、斜切、旋转、抠图

orientation_region (RegionTrans, Phi)
vector_angle_to_rigid (Row, Column, Phi, Row, Column, rad(0), HomMat2D)
hom_mat2d_slant (HomMat2D, rad(15), 'x', Column, Row, HomMat2DSlant)
affine_trans_region (RegionTrans, RegionAffineTrans, HomMat2DSlant, 'nearest_neighbor')
affine_trans_image (Image, ImageAffineTrans, HomMat2DSlant, 'constant', 'false')
reduce_domain (ImageAffineTrans, RegionAffineTrans, ImageReduced)

效果：斜切区域斜切图像旋转纠正后区域

7、转灰度图，进行形态学操作，阈值操作，进行排序

rgb1_to_gray (ImageReduced, GrayImage1)
threshold (GrayImage1, Regions1, 172, 255)
opening_circle (Regions1, RegionOpening, 1.5)
closing_circle (Regions, RegionClosing, 1.7) 注意这个知识做个对比
connection (RegionOpening, ConnectedRegions1)
select_shape (ConnectedRegions1, SelectedRegions1, 'area', 'and', 19.97, 600)
sort_region (SelectedRegions1, SortedRegions, 'character', 'true', 'column')

这是分成多区域的苏字也被分割多个了，所以下面要进行合并

8、组装车牌号苏字

select_obj (SortedRegions, ObjectSelected1, 1)
select_obj (SortedRegions, ObjectSelected2, 2)
select_obj (SortedRegions, ObjectSelected3, 3)
union2 (ObjectSelected1, ObjectSelected2, RegionUnion)
union2 (RegionUnion, ObjectSelected3, RegionUnion1)
select_obj (SortedRegions, ObjectSelected4, 4)
select_obj (SortedRegions, ObjectSelected5, 5)
select_obj (SortedRegions, ObjectSelected6, 6)
select_obj (SortedRegions, ObjectSelected7, 7)
select_obj (SortedRegions, ObjectSelected8, 8)
select_obj (SortedRegions, ObjectSelected9, 9)

9、将上面零散的车牌号进行拼接

gen_empty_obj (EmptyObject)
concat_obj (EmptyObject, RegionUnion1, EmptyObject)
concat_obj (EmptyObject, ObjectSelected4, EmptyObject)
concat_obj (EmptyObject, ObjectSelected5, EmptyObject)
concat_obj (EmptyObject, ObjectSelected6, EmptyObject)
concat_obj (EmptyObject, ObjectSelected7, EmptyObject)
concat_obj (EmptyObject, ObjectSelected8, EmptyObject)
concat_obj (EmptyObject, ObjectSelected9, EmptyObject)

10、创建训练文件并读取

TrainFile:='./Charactor.trf'
Words:=['苏','E','C','6','2','N','8']
write_ocr_trainf (EmptyObject, GrayImage1, Words, TrainFile)
read_ocr_trainf_names (TrainFile, CharacterNames, CharacterCount)
create_ocr_class_mlp (8, 10, 'constant', 'default',  CharacterNames, 80, 'none', 10, 42, OCRHandle)
trainf_ocr_class_mlp (OCRHandle, TrainFile, 200, 1, 0.01, Error, ErrorLog)

11、训练omc开始识别

read_ocr_class_mlp ('./Charactor.omc', OCRHandle1)
do_ocr_multi_class_mlp (EmptyObject, GrayImage1, OCRHandle1, Class, Confidence)
dev_clear_window ()
dev_set_color ('red')
set_display_font (WindowHandle,30, 'mono', 'true', 'false')
for Index:=0 to |Class|-1 by 1set_tposition (WindowHandle, 30, 120+40*Index)write_string (WindowHandle, Class[Index]) 
endfor

全部代码

dev_update_off ()
dev_close_window ()
*读取图片
read_image (Image, 'F:/Halcon/‫Image/车牌.jpg')
dev_open_window_fit_image (Image, 0, 0, -1, -1, WindowHandle)
dev_display (Image)
* 处理图片 - 定位车牌
rgb1_to_gray (Image, GrayImage)
threshold (GrayImage, Regions, 76, 100)
connection (Regions, ConnectedRegions)
select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 8600, 10000)
* 处理图像-转正
shape_trans (SelectedRegions, RegionTrans, 'rectangle2')
* 找到中心点
area_center (RegionTrans, Area, Row, Column)
*找弧度
orientation_region (RegionTrans, Phi)
vector_angle_to_rigid (Row, Column, Phi, Row, Column, rad(0), HomMat2D)
hom_mat2d_slant (HomMat2D, rad(15), 'x', Column, Row, HomMat2DSlant)
affine_trans_region (RegionTrans, RegionAffineTrans, HomMat2DSlant, 'nearest_neighbor')
affine_trans_image (Image, ImageAffineTrans, HomMat2DSlant, 'constant', 'false')
reduce_domain (ImageAffineTrans, RegionAffineTrans, ImageReduced)
* 开始识别 图片处理    苏字拼接
rgb1_to_gray (ImageReduced, GrayImage1)
threshold (GrayImage1, Regions1, 172, 255)
opening_circle (Regions1, RegionOpening, 1.5)
closing_circle (Regions, RegionClosing, 1.7)
connection (RegionOpening, ConnectedRegions1)
select_shape (ConnectedRegions1, SelectedRegions1, 'area', 'and', 19.97, 600)
sort_region (SelectedRegions1, SortedRegions, 'character', 'true', 'column')
* 组装苏字区域
select_obj (SortedRegions, ObjectSelected1, 1)
select_obj (SortedRegions, ObjectSelected2, 2)
select_obj (SortedRegions, ObjectSelected3, 3)
union2 (ObjectSelected1, ObjectSelected2, RegionUnion)
union2 (RegionUnion, ObjectSelected3, RegionUnion1)
select_obj (SortedRegions, ObjectSelected4, 4)
select_obj (SortedRegions, ObjectSelected5, 5)
select_obj (SortedRegions, ObjectSelected6, 6)
select_obj (SortedRegions, ObjectSelected7, 7)
select_obj (SortedRegions, ObjectSelected8, 8)
select_obj (SortedRegions, ObjectSelected9, 9)
**把所有区域保存一个对象
gen_empty_obj (EmptyObject)
concat_obj (EmptyObject, RegionUnion1, EmptyObject)
concat_obj (EmptyObject, ObjectSelected4, EmptyObject)
concat_obj (EmptyObject, ObjectSelected5, EmptyObject)
concat_obj (EmptyObject, ObjectSelected6, EmptyObject)
concat_obj (EmptyObject, ObjectSelected7, EmptyObject)
concat_obj (EmptyObject, ObjectSelected8, EmptyObject)
concat_obj (EmptyObject, ObjectSelected9, EmptyObject)
****创建训练文件
TrainFile:='./Charactor.trf'
Words:=['苏','E','C','6','2','N','8']
* 完成图像与字符训练对应关系
write_ocr_trainf (EmptyObject, GrayImage1, Words, TrainFile)
* 读取训练文件
read_ocr_trainf_names (TrainFile, CharacterNames, CharacterCount)
* 创建一个分类识别器
create_ocr_class_mlp (8, 10, 'constant', 'default',  CharacterNames, 80, 'none', 10, 42, OCRHandle)
* 训练分类识别器
trainf_ocr_class_mlp (OCRHandle, TrainFile, 200, 1, 0.01, Error, ErrorLog)
* 保存分类文件
*write_ocr_class_mlp (OCRHandle, './Charactor.omc')
**训练omc开始识别带中文车牌
read_ocr_class_mlp ('./Charactor.omc', OCRHandle1)
do_ocr_multi_class_mlp (EmptyObject, GrayImage1, OCRHandle1, Class, Confidence)
dev_clear_window ()
dev_set_color ('red')
set_display_font (WindowHandle,30, 'mono', 'true', 'false')
for Index:=0 to |Class|-1 by 1set_tposition (WindowHandle, 30, 120+40*Index)write_string (WindowHandle, Class[Index]) 
endfor

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