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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