一、流程：
车牌识别的流程一般是：
1） 采集
2）预处理：
- 增强对比度：有scale_image（灰度变换）、emphasize、equ_histo_image（直方图均衡化）、灰度图进行形态学等方法
- 滤波降噪
3） 定位、矫正车牌
4）字符分割
5）字符提取
6） 字符识别
7）显示
一、实现：
dev_close_window () dev_open_window (0, 0, 680, 320, 'black', WindowHandle) read_image (Image, 'C:/Users/Administrator/Desktop/车牌.jpg') *1.预处理 rgb1_to_gray (Image, GrayImage1) scale_image (GrayImage1, ImageScaled, 6.71053, -1456) *decompose3 (Image, Red, Green, Blue) *trans_from_rgb (Red, Green, Blue, Hue, Saturation, Intensity, 'hsv') *2.基于颜色的blob分析，定位、矫正车牌 threshold (ImageScaled, Regions, 73, 255) closing_circle (Regions, RegionOpening1,1) connection (RegionOpening1, ConnectedRegions) select_shape (ConnectedRegions, SelectedRegions, ['row','height','area'], 'and', [140.31,28.622,131.63], [500,50,1000]) union1 (SelectedRegions, RegionUnion) shape_trans (RegionUnion, RegionTrans, 'rectangle2') *图像矫正 orientation_region (RegionTrans, Phi) area_center (RegionTrans, Area, Row, Column) vector_angle_to_rigid (Row, Column, Phi, Row, Column,0, HomMat2D) affine_trans_image (Image, ImageAffinTrans, HomMat2D, 'constant', 'false') affine_trans_region (RegionTrans, RegionAffineTrans, HomMat2D, 'nearest_neighbor') reduce_domain (ImageAffinTrans, RegionAffineTrans, ImageReduced) invert_image (ImageReduced, ImageInvert) *3.字符分割 rgb1_to_gray (ImageReduced, GrayImage) scale_image (GrayImage, ImageScaled1, 6.71053, -1456) threshold (ImageScaled1, Regions1, 73, 255) closing_circle (Regions1, RegionOpening2,1.5) connection (RegionOpening2, ConnectedRegions1) *4.字符提取 select_shape (ConnectedRegions1, SelectedRegions1, ['row','height','area'], 'and', [144.286,28.622,113.78], [200,50,500]) *'character'：按照 字母排序 Column:按照列的方式 sort_region (SelectedRegions1, SortedRegions, 'character', 'true', 'column') count_obj (SortedRegions, Number) for i := 1 to Number by 1 select_obj (SortedRegions, ImageAffinTrans, i) endfor *4.1训练：形成trf文件，字符图像和字符相关联 words:=['陕','K','7','5','5','5','5'] TrainFile:='C:/Users/Administrator/Desktop/训练文件/word.trf' for i := 1 to Number by 1 select_obj (SortedRegions, ObjectSelected, i) append_ocr_trainf (ObjectSelected, ImageReduced, words[i-1], TrainFile) endfor FontFile:='C:/Users/Administrator/Desktop/训练文件/word.ocr' read_ocr_trainf_names (TrainFile, CharacterNames, CharacterCount) NumHidden:=20 *创建神经网络分类器 create_ocr_class_mlp (8, 10, 'constant', 'default', CharacterNames, 80, 'none', 10, 42, OCRHandle1) *训练分类器 trainf_ocr_class_mlp (OCRHandle1, TrainFile, 200, 1, 0.01, Error, ErrorLog) *保存omc文件 write_ocr_class_mlp (OCRHandle1, FontFile) *清除句柄 clear_ocr_class_mlp (OCRHandle1) *TrainFile: *5.字符识别 read_ocr_class_mlp ('C:/Users/Administrator/Desktop/训练文件/word.ocr', OCRHandle) do_ocr_multi_class_mlp (SortedRegions, ImageReduced, OCRHandle, Class, Confidence) dev_display (ImageAffinTrans) *6.显示 smallest_rectangle1 (SortedRegions, Row11, Column1, Row2, Column21) for i := 1 to Number by 1 disp_message (WindowHandle, Class[i - 1], 'image', Row2[i - 1], Column1[i - 1], 'blue', 'false') endfor
注：字符识别时我用的是自己训练生成的.ocr文件，如果要用halcon自带的.ocm文件识别字符时，需要将do_ocr_multi_class_mlp算子中第二个输入图片里的字体背景转换成暗字体（用invert_image ）
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