dev_close_window ( )
dev_open_window ( 0 , 0 , 512 , 400 , 'black' , WindowHandle)
list_image_files ( 'C: / Users/ Augustine/ Desktop/ 车牌照图片',' jpg', [ ] , ImageFiles)
for i:= 0 to | ImageFiles| - 1 by 1
read_image ( Image, ImageFiles[ i] )
* 获取三色通道
decompose3 ( Image, r, g, b)
* 将三色通道处理为hsv
trans_from_rgb ( r, g, b, h, s, v, 'hsv' )
* 按照s方式阈值处理
threshold ( s, Regions, 177 , 251 )
* 膨胀运算
dilation_rectangle1 ( Regions, RegionDilation, 1 , 3 )
connection ( RegionDilation, ConnectedRegions)
* 选择特征
select_shape ( ConnectedRegions, SelectedRegions, 'area' , 'and' , 6639.49 , 9284.42 )
* 闭运算，先膨胀在腐蚀
closing_rectangle1 ( SelectedRegions, RegionClosing, 150 , 200 )
* 开运算
opening_rectangle1 ( RegionClosing, RegionOpening, 10 , 10 )
* 获取最小外接矩形
smallest_rectangle2 ( RegionOpening, Row, Column, Phi, Length1, Length2)
* 产生最小外界矩形
gen_rectangle2 ( Rectangle, Row, Column, Phi, Length1, Length2)
* 获取数量
count_obj ( RegionOpening, Number)
* 仿射变换
a:= deg ( Phi)
if ( a<- 90 )
if ( a<= - 180 )
a:= 180 + a
Phi:= rad ( a)
endif
endif
vector_angle_to_rigid ( Row, Column, Phi, Row, Column, Phi, HomMat2D)
affine_trans_region ( Rectangle, RegionAffineTrans, HomMat2D, 'nearest_neighbor')
affine_trans_image ( Image, ImageAffineTrans, HomMat2D, 'constant' , 'false' )
* 裁剪
reduce_domain ( ImageAffineTrans, RegionAffineTrans, ImageReduced)
endfor
* 裁剪
reduce_domain ( ImageAffineTrans, RegionAffineTrans, ImageReduced)
* 做车牌处理，字符分割
decompose3 ( ImageReduced, r, g, b)
* 阈值处理
threshold ( r, Region, 140 , 255 )
* 去除噪声
opening_rectangle1 ( Region, RegionOpening1, 1 , 2 )
connection ( RegionOpening1, ConnectedRegions1)
select_shape ( ConnectedRegions1, SelectedRegions1, 'area' , 'and' , 91 , 306.9 )
* 排序
sort_region ( SelectedRegions1, SortedRegions, 'character' , 'true' , 'column' )
count_obj ( SortedRegions, Number)
if ( Number == 6 )
* 反转图片
invert_image ( r, ImageInvert)
* 分类识别
read_ocr_class_mlp ( 'Industrial_0- 9A- Z_Rej. omc', OCRHandle)
do_ocr_multi_class_mlp ( SortedRegions, ImageInvert, OCRHandle, Class, Confidence)
* 车牌中没有I，将识别的I改成1
dev_clear_window ( )
dev_display ( Image)
set_display_font ( WindowHandle, 50 , 'mono' , 'true' , 'false' )
carNumber:= ''
for Index:= 1 to | Class| - 1 by 1
data:= Class[ Index]
if ( data== 'I' )
data:= 1
endif
carNumber:= carNumber+ data
endfor
endif
dev_set_color ( 'red' )
set_tposition ( WindowHandle, 150 , 100 )
write_string ( WindowHandle, carNumber)
dev_close_window ( )
dev_open_window ( 0 , 0 , 512 , 400 , 'black' , WindowHandle)
list_image_files ( 'C: / Users/ Augustine/ Desktop/ 车牌照图片',' jpg', [ ] , ImageFiles)
for i:= 0 to | ImageFiles| - 1 by 1
read_image ( Image, ImageFiles[ i] )
* 获取三色通道
decompose3 ( Image, r, g, b)
* 将三色通道处理为hsv
trans_from_rgb ( r, g, b, h, s, v, 'hsv' )
* 按照s方式阈值处理
threshold ( s, Regions, 177 , 251 )
* 膨胀运算
dilation_rectangle1 ( Regions, RegionDilation, 1 , 3 )
connection ( RegionDilation, ConnectedRegions)
* 选择特征
select_shape ( ConnectedRegions, SelectedRegions, 'area' , 'and' , 6639.49 , 9284.42 )
* 闭运算，先膨胀在腐蚀
closing_rectangle1 ( SelectedRegions, RegionClosing, 150 , 200 )
* 开运算
opening_rectangle1 ( RegionClosing, RegionOpening, 10 , 10 )
* 获取数量
count_obj ( RegionOpening, Number)
if ( Number!= 1 )
if ( Number == 0 )
* 换通道定位车牌，处理第二张
threshold ( s, Regions1, 108 , 188 )
connection ( Regions1, ConnectedRegions2)
select_shape ( ConnectedRegions2, SelectedRegions2, [ 'column' , 'row' , 'area' ] , 'and' , [ 145.61 , 385.75 , 81.52 ] , [ 335.57 , 411.74 , 10000 ] )
opening_rectangle1 ( SelectedRegions2, RegionOpening2, 2 , 3 )
connection ( RegionOpening2, ConnectedRegions3)
select_shape ( ConnectedRegions3, SelectedRegions3, [ 'row' , 'column' ] , 'and' , [ 376.792 , 145.61 ] , [ 414.427 , 380.38 ] )
closing_circle ( SelectedRegions3, RegionClosing1, 3 )
closing_rectangle1 ( RegionClosing1, RegionClosing2, 15 , 30 )
connection ( RegionClosing2, ConnectedRegions4)
select_shape ( ConnectedRegions4, SelectedRegions4, 'area' , 'and' , 31.36 , 3920.25 )
opening_rectangle1 ( SelectedRegions4, RegionOpening, 3 , 2 )
erosion_circle ( RegionOpening, RegionOpening, 1.5 )
endif
endif
* 获取最小外接矩形
smallest_rectangle2 ( RegionOpening, Row, Column, Phi, Length1, Length2)
* 产生最小外界矩形
gen_rectangle2 ( Rectangle, Row, Column, Phi, Length1, Length2)
* 仿射变换
a:= deg ( Phi)
if ( a<- 90 )
if ( a<= - 180 )
a:= 180 + a
Phi:= rad ( a)
endif
endif
vector_angle_to_rigid ( Row, Column, Phi, Row, Column, Phi, HomMat2D)
affine_trans_region ( Rectangle, RegionAffineTrans, HomMat2D, 'nearest_neighbor')
affine_trans_image ( Image, ImageAffineTrans, HomMat2D, 'constant' , 'false' )
* 裁剪
reduce_domain ( ImageAffineTrans, RegionAffineTrans, ImageReduced)
* 做车牌处理，字符分割
decompose3 ( ImageReduced, r, g, b)
* 阈值处理
threshold ( r, Region, 140 , 255 )
* 去除噪声
opening_rectangle1 ( Region, RegionOpening1, 1 , 2 )
connection ( RegionOpening1, ConnectedRegions1)
select_shape ( ConnectedRegions1, SelectedRegions1, 'area' , 'and' , 91 , 306.9 )
* 排序
sort_region ( SelectedRegions1, SortedRegions, 'character' , 'true' , 'column' )
count_obj ( SortedRegions, Number)
if ( Number!= 6 )
* 第二张图片处理
endif
if ( Number == 6 )
* 反转图片
invert_image ( r, ImageInvert)
* 分类识别
read_ocr_class_mlp ( 'Industrial_0- 9A- Z_Rej. omc', OCRHandle)
do_ocr_multi_class_mlp ( SortedRegions, ImageInvert, OCRHandle, Class, Confidence)
* 车牌中没有I，将识别的I改成1
dev_clear_window ( )
dev_display ( Image)
set_display_font ( WindowHandle, 50 , 'mono' , 'true' , 'false' )
carNumber:= ''
for Index:= 1 to | Class| - 1 by 1
data:= Class[ Index]
if ( data== 'I' )
data:= 1
endif
carNumber:= carNumber+ data
endfor
endif
dev_set_color ( 'red' )
set_tposition ( WindowHandle, 150 , 100 )
write_string ( WindowHandle, carNumber)
endfor
