VS2017+OpenCV+Halcon实现包装袋日期识别（二）——OCR字符识别_mfc+halcon+opencv字符识别

本项目使用到Halcon的OCR识别，也可使用Opencv训练数据集来识别日期。使用Halcon主要是因为用OpenCV训练数据集较为麻烦（对小白不太友好），而Halcon本身便自带许多已训练好的数据集，并且使用Halcon的ocr助手也是相当容易的。

下面介绍如何使用Halcon的OCR助手

第一步：打开新的OCR

第二步 加载一个示例图像，再标记出需要识别的文本位置，右键确定，点击分割

第三步：调整参数，若是暗背景亮文本，记得勾选第一个方框

第四步： 选择“训练文件”，点击学习下方的输入框，根据右侧图片输入学习内容，接着点击“加入训练数据”。接下来，为保证训练数据集的准确性，可重复第一步操作，多次加入训练数据。最后，点击保存即可。

训练好的ocr文件可点击 可视化->工具->ocr训练文件浏览器 预览。当然，也可以直接使用第四步中 预训练分类器 的文件。

下面介绍本项目使用的ocr识别日期代码

 本项目参考Hanlcon的实例程序

下面是根据上面的实例程序所更改的代码

dev_update_window ('off')
//加载OpenCVc处理后的图像
read_image (Image01, 'C:/Users/86133/source/repos/main/main/01.jpg')
get_image_size (Image01, Width, Height)
 
//预处理（也可在OpenCV中提前处理）
invert_image (Image01, ImggrayInvert)
threshold (Image01, Region, 130, 600)
connection (Region, ConnectedRegions)
select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 50, 500)
sort_region (SelectedRegions, SortedRegions, 'first_point', 'true', 'column')
shape_trans (SortedRegions, RegionTrans, 'rectangle1')
area_center (RegionTrans, Area, Row, Column)
MeanRow := mean(Row)
count_obj (SortedRegions, Number)
 
//OCR识别
read_ocr_class_mlp ('C:/Users/86133/Desktop/学习资料/halcon/1.omc', OCRHandle)
for I := 1 to Number by 1
    select_obj (SortedRegions, ObjectSelected, I)
    do_ocr_single_class_mlp (ObjectSelected, ImggrayInvert, OCRHandle, 1, Class, Confidence)
    dev_clear_window()
    dev_display (ObjectSelected)
 
    ocrNumbers[I-1] := Class
    
    //识别结果进行对比，判断日期是否正确，若equal==1,则正确
    Array1:=['2','0','2','1','/','0','5','/','2','3','/','A','-','Y']
    tuple_equal(Array1,ocrNumbers,Equal)
 
endfor

最后，选择 文件->导出 ->c++->导出

 下面是我导出后删减的代码

#  include "HalconCpp.h"
#  include "HDevThread.h"
 
using namespace HalconCpp;
 
 
#ifndef NO_EXPORT_MAIN
// Main procedure 
void action()
{
 
  // Local iconic variables
  HObject  ho_Image01, ho_ImggrayInvert, ho_Region;
  HObject  ho_ConnectedRegions, ho_SelectedRegions, ho_SortedRegions;
  HObject  ho_RegionTrans, ho_ObjectSelected;
 
  // Local control variables
  HTuple  hv_Width, hv_Height, hv_Area, hv_Row;
  HTuple  hv_Column, hv_MeanRow, hv_Number, hv_OCRHandle;
  HTuple  hv_I, hv_Class, hv_Confidence, hv_ocrNumbers, hv_Array1;
  HTuple  hv_Equal;
 
  // dev_update_window(...); only in hdevelop
  ReadImage(&ho_Image01, "C:/Users/86133/source/repos/main/main/01.jpg");
  GetImageSize(ho_Image01, &hv_Width, &hv_Height);
 
  InvertImage(ho_Image01, &ho_ImggrayInvert);
  Threshold(ho_Image01, &ho_Region, 130, 600);
  Connection(ho_Region, &ho_ConnectedRegions);
  SelectShape(ho_ConnectedRegions, &ho_SelectedRegions, "area", "and", 50, 500);
  SortRegion(ho_SelectedRegions, &ho_SortedRegions, "first_point", "true", "column");
 
  ShapeTrans(ho_SortedRegions, &ho_RegionTrans, "rectangle1");
  AreaCenter(ho_RegionTrans, &hv_Area, &hv_Row, &hv_Column);
  hv_MeanRow = hv_Row.TupleMean();
  CountObj(ho_SortedRegions, &hv_Number);
  ReadOcrClassMlp(//'C:/Users/86133/Desktop/学习资料/halcon/1.omc'
      "C:/Users/86133/Desktop/\321\247\317\260\327\312\301\317/halcon/1.omc", 
      &hv_OCRHandle);
  {
  HTuple end_val15 = hv_Number;
  HTuple step_val15 = 1;
  for (hv_I=1; hv_I.Continue(end_val15, step_val15); hv_I += step_val15)
  {
    SelectObj(ho_SortedRegions, &ho_ObjectSelected, hv_I);
    DoOcrSingleClassMlp(ho_ObjectSelected, ho_ImggrayInvert, hv_OCRHandle, 1, &hv_Class, 
        &hv_Confidence);
    if (HDevWindowStack::IsOpen())
      ClearWindow(HDevWindowStack::GetActive());
    if (HDevWindowStack::IsOpen())
      DispObj(ho_ObjectSelected, HDevWindowStack::GetActive());
	cout << (HString)hv_Class;//这里能打印出ocr识别内容，需要数据类型转换
    hv_ocrNumbers[hv_I-1] = hv_Class;
 
    hv_Array1.Clear();
    hv_Array1[0] = "2";
    hv_Array1[1] = "0";
    hv_Array1[2] = "2";
    hv_Array1[3] = "1";
    hv_Array1[4] = "/";
    hv_Array1[5] = "0";
    hv_Array1[6] = "5";
    hv_Array1[7] = "/";
    hv_Array1[8] = "2";
    hv_Array1[9] = "3";
    hv_Array1[10] = "/";
    hv_Array1[11] = "A";
    hv_Array1[12] = "-";
    hv_Array1[13] = "Y";
    TupleEqual(hv_Array1, hv_ocrNumbers, &hv_Equal);
 
  }
  if (hv_Equal == 1) {
	  cout << "1" << endl;
	  putText(img, "OK", Point(50, 50), FONT_HERSHEY_COMPLEX_SMALL, 2, Scalar(255, 69, 255), 2);
  }
  else if (hv_Equal == 0) {
	  cout << "0" << endl;
	  putText(img, "NO", Point(50, 50), FONT_HERSHEY_COMPLEX_SMALL, 2, Scalar(255, 69, 255), 2);
  }
  }
}
 
 
#ifndef NO_EXPORT_APP_MAIN
 
 
int main(int argc, char *argv[])
{
  int ret = 0;
 
  try
  {
#if defined(_WIN32)
    SetSystem("use_window_thread", "true");
#elif defined(__linux__) && (defined(__i386__) || defined(__x86_64__))
    XInitThreads();
#endif
 
    // file was stored with local-8-bit encoding
    //   -> set the interface encoding accordingly
    SetHcppInterfaceStringEncodingIsUtf8(false);
 
    // Default settings used in HDevelop (can be omitted)
    SetSystem("width", 512);
    SetSystem("height", 512);
 
#ifndef __APPLE__
    action();
#else
    ret = apple_main(argc,argv);
#endif
  }
  catch (HException &exception)
  {
    fprintf(stderr,"  Error #%u in %s: %s\n", exception.ErrorCode(),
            (const char *)exception.ProcName(),
            (const char *)exception.ErrorMessage());
    ret = 1;
  }
  return ret;
}
 
#endif
 
 
 
 

        这一节内容其实完全可以用OpenCV来完成，但为何使用Halcon，前文已经说了。当然，这个项目只用Halcon做，或许会更简单，但毕竟Halcon并不像OpenCV一样开源，而且本人在机器视觉学习的主要是使用OpenCV。

        下一节，将介绍如何将Halcon导出的代码嵌入到OpenCV程序中。

下一篇链接：

VS2017+OpenCV+Halcon实现包装袋日期识别（三）——多平台联合_栖玖的博客-CSDN博客