少有人走的路
*重要理解一下这个几个算子
* dev_update_off ()
* dev_update_on ()
* dev_update_pc ('off')
* 计算区域的总数，使用count_obj（）算子
* 选择访问第i个区域的，使用select_obj（）算子
****************第1步：定位矫正，分割，形成单个汉字区域，排序
*关闭窗口
dev_close_window ()
*开窗口
dev_open_window (0, 0, 512, 512, 'black', WindowHandle)
*读取图像
read_image (Image, 'E:/BaiduNetdiskDownload/Halcon采用分类器实现OCR识别/汉字识别 .jpg')
*灰度化
rgb1_to_gray (Image, GrayImage)
*线性灰度变换
scale_image (GrayImage, ImageScaled, 1.34921, -89)
*二值化
threshold (ImageScaled, Regions, 152, 226)
*断开连通域
connection (Regions, ConnectedRegions)
*特征选择，把想要的目标显示提取
select_shape (ConnectedRegions, SelectedRegions, ['area','row'], 'and', [0,243.86], [22589.8,599.24])
*联合成一个连通域
union1 (SelectedRegions, RegionUnion)
*形状凸性变换
shape_trans (RegionUnion, RegionTrans, 'rectangle2')
*求角度
orientation_region (RegionTrans, Phi)
*求面积和行列坐标
area_center (RegionTrans, Area, Row, Column)
*仿射变换
vector_angle_to_rigid (Row, Column, Phi, Row, Column, rad(0), HomMat2D)
*作用到图像上
affine_trans_image (GrayImage, ImageAffinTrans, HomMat2D, 'constant', 'false')
*作用到区域上
affine_trans_region (RegionTrans, RegionAffineTrans, HomMat2D, 'nearest_neighbor')
*剪切
reduce_domain (ImageAffinTrans, RegionAffineTrans, ImageReduced)
*灰度化
rgb1_to_gray (ImageReduced, GrayImage1)
*二值化
threshold (GrayImage1, Regions1, 194, 230)
*采用矩形进行闭运算
closing_rectangle1 (Regions1, RegionClosing, 7,7)
*断开连通域
connection (RegionClosing, ConnectedRegions1)
*闭运算
closing_circle (ConnectedRegions1, RegionClosing1,125.5)
*联合连通域
union1 (RegionClosing1, RegionUnion1)
*断开连通域
connection (RegionUnion1, ConnectedRegions2)
*求出分割单个字体
intersection (ConnectedRegions2, RegionClosing, RegionIntersection)
*计算有几个独立的区域
count_obj (RegionIntersection, Number)
*排序
sort_region (RegionIntersection, SortedRegions, 'character', 'true', 'row')
*验证排序是否正确
for Index := 1 to Number by 1
select_obj (SortedRegions, SingleWord, Index)
endfor
*************************第2步 形成训练文件
*创建trf训练文件，即将字符图像和字符关联起来
*如果这些字符图像在一幅图像中，一个一个添加用append_ocr_trainf，
*可以直接用write_ocr_trainf把所有字符图像全部写进去
*形成汉字文件
words:=['数','字','图','像','处','理','与','机','器','视','觉']
*赋值路径
TrainFile:='E:/BaiduNetdiskDownload/Halcon采用分类器实现OCR识别/word.trf'
*索引每一个文字，把
for Index := 1 to Number by 1
select_obj (SortedRegions, SingleWord, Index)
append_ocr_trainf(SingleWord,ImageAffinTrans,words[Index-1],TrainFile)
endfor
FontFile:='E:/BaiduNetdiskDownload/Halcon采用分类器实现OCR识别/words.omc'
*读取训练文件
read_ocr_trainf_names (TrainFile, CharacterNames, CharacterCount)
NumHidden:=20
*创建模型，注意第五个参数为识别结果的所有类别
create_ocr_class_mlp (8, 10, 'constant', 'default',CharacterNames, 80, 'none', 10, 42, OCRHandle)
*训练模型
trainf_ocr_class_mlp(OCRHandle,TrainFile,200,1,0.01,Error,ErrorLog)
*保存训练好的模型，omc文件
write_ocr_class_mlp(OCRHandle,FontFile)
*清除句柄
clear_ocr_class_mlp (OCRHandle)
*****************第3步 读取显示
*读取训练好的模型
read_ocr_class_mlp ('E:/BaiduNetdiskDownload/Halcon采用分类器实现OCR识别/words.omc', OCRHandle1)
*识别
*这里面可以用其他的文字信息
do_ocr_multi_class_mlp (SortedRegions, ImageReduced, OCRHandle1, Class, Confidence)
*显示图像
dev_display (Image)
*设置显示字体的大小，是否倾斜，是否有背景，什么颜色
set_display_font (WindowHandle, 26, 'mono', 'true', 'false')
xinxi:='www.skcircle.com'
disp_message (WindowHandle, xinxi, 'window', 400, 100, 'black', 'true')
**********另外一种显示的方式，在C#中可以直接用，不用写disp_message的类
**
set_tposition (WindowHandle, 10, 10)
write_string (WindowHandle, xinxi)
*显示方法1
result:=''
for Index1 := 0 to |Class|-1 by 1
result:=result+Class[Index1]
disp_message (WindowHandle, result, 'window',30,50, 'red', 'true')
endfor
*显示方法2
for i := 1 to Number by 1
select_obj (SortedRegions, SingleWord, i)
area_center (SingleWord, Area1, Row1, Column1)
disp_message (WindowHandle, Class[i-1], 'Image', Row1+300, Column1, 'green', 'true')
endfor本文出自勇哥的网站《少有人走的路》wwww.skcircle.com，转载请注明出处！讨论可扫码加群：
