Halcon 车牌识别-超精细教程

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