* add simple synthetic generate function and diagnose to character recognition rate.

accuracy.txt

@@ -1792,3 +1792,38 @@ Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
 Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
 0-error:66.6667%,  1-error:89.2019%,  Chinese error:21.1268%  
 总时间:220秒,  平均执行时间:1.1秒
+2016-06-25 15:25:30
+总图片数:200,  Plates count:238,  未识出图片:25,  定位率:89.4958%  
+Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
+0-error:66.6667%,  1-error:89.2019%,  Chinese error:21.1268%  
+总时间:214秒,  平均执行时间:1.07秒
+2016-06-25 15:41:53
+总图片数:200,  Plates count:238,  未识出图片:25,  定位率:89.4958%  
+Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
+0-error:60.0939%,  1-error:84.507%,  Chinese error:28.6385%  
+总时间:217秒,  平均执行时间:1.085秒
+2016-06-25 16:03:33
+总图片数:200,  Plates count:238,  未识出图片:25,  定位率:89.4958%  
+Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
+0-error:66.6667%,  1-error:89.2019%,  Chinese error:21.1268%  
+总时间:222秒,  平均执行时间:1.11秒
+2016-06-25 16:43:53
+总图片数:200,  Plates count:238,  未识出图片:25,  定位率:89.4958%  
+Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
+0-error:63.3803%,  1-error:86.8545%,  Chinese error:26.7606%  
+总时间:224秒,  平均执行时间:1.12秒
+2016-06-25 16:58:43
+总图片数:200,  Plates count:238,  未识出图片:25,  定位率:89.4958%  
+Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
+0-error:66.6667%,  1-error:89.2019%,  Chinese error:21.1268%  
+总时间:233秒,  平均执行时间:1.165秒
+2016-06-25 17:36:25
+总图片数:200,  Plates count:238,  未识出图片:25,  定位率:89.4958%  
+Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
+0-error:66.6667%,  1-error:89.2019%,  Chinese error:21.1268%  
+总时间:230秒,  平均执行时间:1.15秒
+2016-06-25 18:07:01
+总图片数:200,  Plates count:238,  未识出图片:25,  定位率:89.4958%  
+Recall:83.2581%, Precise:86.2716%, Fscore:84.7381%.
+0-error:66.6667%,  1-error:89.2019%,  Chinese error:21.1268%  
+总时间:245秒,  平均执行时间:1.225秒

include/easypr/core/chars_recognise.h

@@ -16,6 +16,7 @@
 #include "easypr/core/chars_identify.h"
 #include "easypr/core/character.hpp"
 #include "easypr/util/util.h"
+#include "easypr/core/plate.hpp"
 
 namespace easypr {
 
@@ -26,6 +27,7 @@ class CCharsRecognise {
   ~CCharsRecognise();
 
   int charsRecognise(cv::Mat plate, std::string& plateLicense);
+  int charsRecognise(CPlate& plate, std::string& plateLicense);
 
   //! 获得车牌颜色
 
@@ -38,7 +40,7 @@ class CCharsRecognise {
     color = utils::utf8_to_gbk(color.c_str());
 #endif
     return color;
-  }
+  } 
 
   //! 设置变量
 

include/easypr/core/core_func.h

@@ -59,7 +59,6 @@ Color getPlateType(const Mat& src, const bool adaptive_minsv);
 //! 直方图均衡
 
 Mat histeq(Mat in);
-Mat charFeatures(Mat in, int sizeData);
 Rect GetCenterRect(Mat& in);
 Mat CutTheRect(Mat& in, Rect& rect);
 int ThresholdOtsu(Mat mat);
@@ -108,6 +107,12 @@ Rect adaptive_charrect_from_rect(const Rect& rect);
 bool calcSafeRect(const RotatedRect& roi_rect, const Mat& src,
   Rect_<float>& safeBoundRect);
 
+// shift an image
+Mat translateImg(Mat img, int offsetx, int offsety);
+
+// rotate an image
+Mat rotateImg(Mat source, float angle);
+
 } /*! \namespace easypr*/
 
 #endif  // EASYPR_CORE_COREFUNC_H_

include/easypr/core/feature.h

@@ -34,6 +34,9 @@ void getHSVHistFeatures(const cv::Mat& image, cv::Mat& features);
 //! LBP feature
 void getLBPFeatures(const cv::Mat& image, cv::Mat& features);
 
+//! get character feature
+cv::Mat charFeatures(cv::Mat in, int sizeData);
+
 } /*! \namespace easypr*/
 
 #endif  // EASYPR_CORE_FEATURE_H_

include/easypr/core/plate.hpp

@@ -39,6 +39,7 @@ namespace easypr {
       m_distVec = plate.m_distVec;
 
       m_mserCharVec = plate.m_mserCharVec;
+      m_reutCharVec = plate.m_reutCharVec;
     }
 
     CPlate& operator=(const CPlate& plate) {
@@ -57,6 +58,7 @@ namespace easypr {
         m_distVec = plate.m_distVec;
 
         m_mserCharVec = plate.m_mserCharVec;
+        m_reutCharVec = plate.m_reutCharVec;
       }     
       return *this;
     }
@@ -94,17 +96,13 @@ namespace easypr {
     inline void setPlatDistVec(Vec2i param) { m_distVec = param; }
     inline Vec2i getPlateDistVec() const { return m_distVec; }
 
-    inline void setMserCharacter(const std::vector<CCharacter>& param) {
-      m_mserCharVec = param;
-    }
+    inline void setMserCharacter(const std::vector<CCharacter>& param) { m_mserCharVec = param; }
+    inline void addMserCharacter(CCharacter param) { m_mserCharVec.push_back(param); }
+    inline std::vector<CCharacter> getCopyOfMserCharacters() { return m_mserCharVec; }
 
-    inline void addMserCharacter(CCharacter param) {
-      m_mserCharVec.push_back(param);
-    }
-
-    inline std::vector<CCharacter> getCopyOfMserCharacters() {
-      return m_mserCharVec;
-    }
+    inline void setReutCharacter(const std::vector<CCharacter>& param) { m_reutCharVec = param; }
+    inline void addReutCharacter(CCharacter param) { m_reutCharVec.push_back(param); }
+    inline std::vector<CCharacter> getCopyOfReutCharacters() { return m_reutCharVec; }
 
     bool operator < (const CPlate& plate) const
     {
@@ -145,6 +143,9 @@ namespace easypr {
     std::vector<CCharacter> m_mserCharVec;
     std::vector<CCharacter> m_slwdCharVec;
 
+    std::vector<CCharacter> m_ostuCharVec;
+    std::vector<CCharacter> m_reutCharVec;
+
     int m_charCount;
 
     //! distVec

include/easypr/train/ann_train.h

@@ -16,6 +16,7 @@ class AnnTrain : public ITrain {
   virtual void test();
 
   std::pair<std::string, std::string> identifyChinese(cv::Mat input);
+  std::pair<std::string, std::string> identify(cv::Mat input);
 
  private:
   virtual cv::Ptr<cv::ml::TrainData> tdata();

src/core/chars_identify.cpp

@@ -2,6 +2,7 @@
 #include "easypr/core/character.hpp"
 #include "easypr/config.h"
 #include "easypr/core/core_func.h"
+#include "easypr/core/feature.h"
 
 namespace easypr {
 

src/core/chars_recognise.cpp

@@ -1,4 +1,5 @@
 #include "easypr/core/chars_recognise.h"
+#include "easypr/core/character.hpp"
 #include "easypr/util/util.h"
 
 namespace easypr {
@@ -22,7 +23,7 @@ int CCharsRecognise::charsRecognise(Mat plate, std::string& plateLicense) {
     int num = matChars.size();
     for (int j = 0; j < num; j++)
     {
-      Mat charMat = matChars[j];
+      Mat charMat = matChars.at(j);
       bool isChinses = false;
       if (j == 0)
         isChinses = true;
@@ -36,6 +37,44 @@ int CCharsRecognise::charsRecognise(Mat plate, std::string& plateLicense) {
   }
 
   return result;
+}
+
+int CCharsRecognise::charsRecognise(CPlate& plate, std::string& plateLicense) {
+  std::vector<Mat> matChars;
+
+  Mat plateMat = plate.getPlateMat();
+
+  int result = m_charsSegment->charsSegment(plateMat, matChars);
+  //std::cout << "charsSegment:" << result << std::endl;
+  if (result == 0) {
+    //for (auto block : matChars) {
+    //  auto character = CharsIdentify::instance()->identify(block);
+    //  plateLicense.append(character.second);
+    //}
+    int num = matChars.size();
+    for (int j = 0; j < num; j++)
+    {
+      Mat charMat = matChars.at(j);
+      bool isChinses = false;
+      if (j == 0)
+        isChinses = true;
+      auto character = CharsIdentify::instance()->identify(charMat, isChinses);
+      plateLicense.append(character.second);
 
+      CCharacter charResult;
+      charResult.setCharacterMat(charMat);
+      charResult.setCharacterStr(character.second);
+
+      plate.addReutCharacter(charResult);
+    }
+
+  }
+  if (plateLicense.size() < 7) {
+    return -1;
+  }
+
+  return result;
 }
+
+
 }

src/core/core_func.cpp

@@ -685,50 +685,6 @@ Rect GetCenterRect(Mat &in) {
   return _rect;
 }
 
-Mat charFeatures(Mat in, int sizeData) {
-
-  //抠取中间区域
-  Rect _rect = GetCenterRect(in);
-
-  Mat tmpIn = CutTheRect(in, _rect);
-  // Mat tmpIn = in.clone();
-  // Low data feature
-  Mat lowData;
-  resize(tmpIn, lowData, Size(sizeData, sizeData));
-
-  // Histogram features
-  Mat vhist = ProjectedHistogram(lowData, VERTICAL);
-  Mat hhist = ProjectedHistogram(lowData, HORIZONTAL);
-
-  // Last 10 is the number of moments components
-  int numCols = vhist.cols + hhist.cols + lowData.cols * lowData.cols;
-
-  Mat out = Mat::zeros(1, numCols, CV_32F);
-  // Asign values to
-
-  // feature,ANN的样本特征为水平、垂直直方图和低分辨率图像所组成的矢量
-
-  int j = 0;
-  for (int i = 0; i < vhist.cols; i++) {
-    out.at<float>(j) = vhist.at<float>(i);
-    j++;
-  }
-  for (int i = 0; i < hhist.cols; i++) {
-    out.at<float>(j) = hhist.at<float>(i);
-    j++;
-  }
-  for (int x = 0; x < lowData.cols; x++) {
-    for (int y = 0; y < lowData.rows; y++) {
-      out.at<float>(j) += (float) lowData.at <unsigned char> (x, y);
-      j++;
-    }
-  }
-
-  //std::cout << out << std::endl;
-
-  return out;
-}
-
 float countOfBigValue(Mat &mat, int iValue) {
   float iCount = 0.0;
   if (mat.rows > 1) {
@@ -1894,6 +1850,23 @@ Mat adaptive_image_from_points(const std::vector<Point>& points,
   return result;
 }
 
+// shift an image
+Mat translateImg(Mat img, int offsetx, int offsety){
+  Mat dst;
+  Mat trans_mat = (Mat_<double>(2, 3) << 1, 0, offsetx, 0, 1, offsety);
+  warpAffine(img, dst, trans_mat, img.size());
+  return dst;
+}
+
+// rotate an image
+Mat rotateImg(Mat source, float angle){
+  Point2f src_center(source.cols / 2.0F, source.rows / 2.0F);
+  Mat rot_mat = getRotationMatrix2D(src_center, angle, 1.0);
+  Mat dst;
+  warpAffine(source, dst, rot_mat, source.size());
+  return dst;
+}
+
 //! 计算一个安全的Rect
 //! 如果不存在，返回false
 

src/core/feature.cpp

@@ -91,4 +91,51 @@ void getLBPFeatures(const Mat& image, Mat& features) {
   features = lbp_hist;
 }
 
+
+Mat charFeatures(Mat in, int sizeData) {
+  const int VERTICAL = 0;
+  const int HORIZONTAL = 1;
+
+  // cut the cetner, will afect 5% perices.
+  Rect _rect = GetCenterRect(in);
+  Mat tmpIn = CutTheRect(in, _rect);
+  //Mat tmpIn = in.clone();
+
+  // Low data feature
+  Mat lowData;
+  resize(tmpIn, lowData, Size(sizeData, sizeData));
+
+  // Histogram features
+  Mat vhist = ProjectedHistogram(lowData, VERTICAL);
+  Mat hhist = ProjectedHistogram(lowData, HORIZONTAL);
+
+  // Last 10 is the number of moments components
+  int numCols = vhist.cols + hhist.cols + lowData.cols * lowData.cols;
+
+  Mat out = Mat::zeros(1, numCols, CV_32F);
+  // Asign values to
+
+  // feature,ANN的样本特征为水平、垂直直方图和低分辨率图像所组成的矢量
+
+  int j = 0;
+  for (int i = 0; i < vhist.cols; i++) {
+    out.at<float>(j) = vhist.at<float>(i);
+    j++;
+  }
+  for (int i = 0; i < hhist.cols; i++) {
+    out.at<float>(j) = hhist.at<float>(i);
+    j++;
+  }
+  for (int x = 0; x < lowData.cols; x++) {
+    for (int y = 0; y < lowData.rows; y++) {
+      out.at<float>(j) += (float)lowData.at <unsigned char>(x, y);
+      j++;
+    }
+  }
+
+  //std::cout << out << std::endl;
+
+  return out;
+}
+
 }

src/core/plate_detect.cpp

@@ -85,7 +85,7 @@ namespace easypr {
           cv::rectangle(result, outputRect, Scalar(0, 0, 255));
         }
 
-        if (1)
+        if (0)
         {
           std::stringstream ss(std::stringstream::in | std::stringstream::out);
           ss << "resources/image/tmp/plate_" << index << "_" << i << ".jpg";

src/core/plate_judge.cpp

@@ -109,6 +109,7 @@ namespace easypr {
   int PlateJudge::plateJudgeUsingNMS(const std::vector<CPlate> &inVec, std::vector<CPlate> &resultVec, int maxPlates) {
     std::vector<CPlate> plateVec;
     int num = inVec.size();
+    bool outputResult = false;
 
     for (int j = 0; j < num; j++) {
       CPlate plate = inVec[j];
@@ -123,6 +124,13 @@ namespace easypr {
       int result = plateSetScore(plate);
       if (result == 0) {
         plateVec.push_back(plate);
+        if (outputResult) {
+          std::stringstream ss(std::stringstream::in | std::stringstream::out);
+          ss << "resources/image/tmp/plate/has" << "/" << plate.getPlatePos().center << "_"
+            << plate.getPlatePos().size << "_" << plate.getPlatePos().angle << "_"
+            << plate.getPlateScore() << ".jpg";
+          imwrite(ss.str(), inMat);
+        }
       }
       else {
         int w = inMat.cols;
@@ -140,6 +148,22 @@ namespace easypr {
 
         if (resultCascade == 0) {
           plateVec.push_back(plate);
+          if (outputResult) {
+            std::stringstream ss(std::stringstream::in | std::stringstream::out);
+            ss << "resources/image/tmp/plate/has" << "/" << plate.getPlatePos().center << "_" 
+              << plate.getPlatePos().size << "_" << plate.getPlatePos().angle << "_" 
+              << plate.getPlateScore() << ".jpg";
+            imwrite(ss.str(), tmpDes);
+          }
+        }
+        else {
+          if (outputResult) {
+            std::stringstream ss(std::stringstream::in | std::stringstream::out);
+            ss << "resources/image/tmp/plate/no" << "/" << plate.getPlatePos().center << "_"
+              << plate.getPlatePos().size << "_" << plate.getPlatePos().angle << "_"
+              << plate.getPlateScore() << ".jpg";
+            imwrite(ss.str(), tmpDes);
+          }
         }
       }
     }

src/core/plate_recognize.cpp

@@ -296,12 +296,9 @@ int CPlateRecognize::plateRecognizeAsText(Mat src, std::vector<CPlate> &licenseV
 
 
 // !车牌识别模块
-
-int CPlateRecognize::plateRecognize(Mat src,
-                                    std::vector<CPlate> &licenseVec, int index) {
+int CPlateRecognize::plateRecognize(Mat src, std::vector<CPlate> &licenseVec, int index) {
 
   // 车牌集合
-
   std::vector<CPlate> plateVec;
 
   // 对图像进行统一缩放，确保图像不要过大
@@ -321,19 +318,17 @@ int CPlateRecognize::plateRecognize(Mat src,
     int index = 0;
 
     //依次识别每个车牌内的符号
-
     for (size_t j = 0; j < num; j++) {
-      CPlate item = plateVec[j];
+      CPlate item = plateVec.at(j);
       Mat plate = item.getPlateMat();
 
       //获取车牌颜色
-
       std::string plateType = getPlateColor(plate);
 
       //获取车牌号
-
       std::string plateIdentify = "";
-      int resultCR = charsRecognise(plate, plateIdentify);
+      int resultCR = charsRecognise(item, plateIdentify);
+           
       if (resultCR == 0) {
         std::string license = plateType + ":" + plateIdentify;
         item.setPlateStr(license);

src/train/ann_train.cpp

@@ -1,6 +1,7 @@
 #include "easypr/train/ann_train.h"
 #include "easypr/config.h"
 #include "easypr/core/chars_identify.h"
+#include "easypr/core/feature.h"
 #include "easypr/core/core_func.h"
 #include "easypr/util/util.h"
 #include <numeric>
@@ -64,7 +65,6 @@ void AnnTrain::train() {
     layers.at<int>(3) = output_number;
   }
 
-
   ann_->setLayerSizes(layers);
   ann_->setActivationFunction(cv::ml::ANN_MLP::SIGMOID_SYM, 1, 1);
   ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::BACKPROP);
@@ -74,13 +74,13 @@ void AnnTrain::train() {
 
   //using raw data or raw + synthic data.
   //auto traindata = tdata();
-  auto traindata = sdata();
+  auto traindata = sdata(100);
 
   std::cout << "Training ANN model, please wait..." << std::endl;
   long start = utils::getTimestamp();
   ann_->train(traindata);
   long end = utils::getTimestamp();
-  std::cout << "Training done. Time elapse: " << (end - start) << "ms"
+  std::cout << "Training done. Time elapse: " << (end - start) / (1000 * 60) << "minute"
             << std::endl;
 
   ann_->save(ann_xml_);
@@ -92,7 +92,6 @@ void AnnTrain::train() {
 std::pair<std::string, std::string> AnnTrain::identifyChinese(cv::Mat input) {
   cv::Mat feature = charFeatures(input, kPredictSize);
   float maxVal = -2;
-
   int result = -1;
 
   cv::Mat output(1, kChineseNumber, CV_32FC1);
@@ -115,6 +114,36 @@ std::pair<std::string, std::string> AnnTrain::identifyChinese(cv::Mat input) {
   return std::make_pair(s, province);
 }
 
+
+std::pair<std::string, std::string> AnnTrain::identify(cv::Mat input) {
+  cv::Mat feature = charFeatures(input, kPredictSize);
+  float maxVal = -2;
+  int result = -1;
+
+  cv::Mat output(1, kCharsTotalNumber, CV_32FC1);
+  ann_->predict(feature, output);
+
+  for (int j = 0; j < kCharsTotalNumber; j++) {
+    float val = output.at<float>(j);
+    // std::cout << "j:" << j << "val:" << val << std::endl;
+    if (val > maxVal) {
+      maxVal = val;
+      result = j;
+    }
+  }
+
+  auto index = result;
+  if (index < kCharactersNumber) {
+    return std::make_pair(kChars[index], kChars[index]);
+  }
+  else {
+    const char* key = kChars[index];
+    std::string s = key;
+    std::string province = kv_->get(s);
+    return std::make_pair(s, province);
+  }
+}
+
 void AnnTrain::test() {
   assert(chars_folder_);
 
@@ -137,7 +166,11 @@ void AnnTrain::test() {
 
     for (auto file : chars_files) {
       auto img = cv::imread(file, 0);  // a grayscale image
-      std::pair<std::string, std::string> ch = identifyChinese(img);
+      std::pair<std::string, std::string> ch;
+
+      if (type == 0) ch = identify(img);
+      if (type == 1) ch = identifyChinese(img);
+
       if (ch.first == char_key) {
         ++corrects;
         ++corrects_all;
@@ -179,7 +212,21 @@ void AnnTrain::test() {
 }
 
 cv::Mat getSyntheticImage(const Mat& image) {
+  int rand_type = rand();
   Mat result = image.clone();
+
+  if (rand_type % 2 == 0) {
+    int ran_x = rand() % 7 - 3;
+    int ran_y = rand() % 7 - 3;
+
+    result = translateImg(result, ran_x, ran_y);
+  }
+  else if (rand_type % 2 != 0) {
+    float angle = float(rand() % 15 - 7);
+
+    result = rotateImg(result, angle);
+  }
+  
   return result;
 }
 
@@ -193,9 +240,9 @@ cv::Ptr<cv::ml::TrainData> AnnTrain::sdata(size_t number_for_count) {
   if (type == 0) classNumber = kCharsTotalNumber;
   if (type == 1) classNumber = kChineseNumber;
   
+  srand((unsigned)time(0));
   for (int i = 0; i < classNumber; ++i) {
-    srand((unsigned)time(0));
-
+   
     auto char_key = kChars[i + kCharsTotalNumber - classNumber];
     char sub_folder[512] = { 0 };
 
@@ -219,6 +266,11 @@ cv::Ptr<cv::ml::TrainData> AnnTrain::sdata(size_t number_for_count) {
       auto img = matVec.at(ran_num);
       auto simg = getSyntheticImage(img);
       matVec.push_back(simg);
+      if (1) {
+        std::stringstream ss(std::stringstream::in | std::stringstream::out);
+        ss << sub_folder << "/" << i << "_" << t << "_" << ran_num << ".jpg";
+        imwrite(ss.str(), simg);
+      }
     }
 
     fprintf(stdout, ">> Characters count: %d \n", matVec.size());

test/accuracy.hpp

@@ -56,15 +56,11 @@ namespace easypr {
           plate.setPlatePos(rr);
           plateVec.push_back(plate);
         }
-
         xmlMap[imageName] = plateVec;
       }
-
       return 0;
     }
 
-
-
     int accuracyTest(const char* test_path) {
       std::shared_ptr<easypr::Kv> kv(new easypr::Kv);
       kv->load("etc/chinese_mapping");
@@ -90,12 +86,10 @@ namespace easypr {
       CPlateRecognize pr;
 
       // 设置Debug模式
-
       pr.setDebug(false);
       pr.setLifemode(true);
 
       // 设置要处理的一张图片中最多有多少车牌
-
       pr.setMaxPlates(4);
       pr.setDetectType(PR_DETECT_COLOR | PR_DETECT_SOBEL);
       //pr.setDetectType(PR_DETECT_CMSER);
@@ -267,6 +261,14 @@ namespace easypr {
                 if (diff == 2) {
                   one_error_count++;
                 }
+
+                vector<CCharacter> charVec = matchPlate->getCopyOfReutCharacters();
+                CCharacter character = charVec.at(0);
+                if (1) {
+                  std::stringstream ss(std::stringstream::in | std::stringstream::out);
+                  ss << "resources/image/tmp/chinese" << "/" << i << "_" << t << "_" << character.getCharacterStr() << ".jpg";
+                  imwrite(ss.str(), character.getCharacterMat());
+                }
               }
               cout << "  chineseError:" << chineseError << endl;
             } 
@@ -282,7 +284,8 @@ namespace easypr {
             }
           }
 
-          /*if (1)
+          /* REMAIN
+          if (1)
           {
             std::stringstream ss(std::stringstream::in | std::stringstream::out);
             ss << "resources/image/tmp/plate_" << license << ".jpg";
@@ -398,12 +401,14 @@ namespace easypr {
       cout << kv->get("seconds") << ":" << seconds << kv->get("sec") << ",  ";
       cout << kv->get("seconds_average") << ":" << avgsec << kv->get("sec") << endl;
 
-      /*cout << kv->get("unrecognized") << ":" << endl;
+      /* REMAIN
+      cout << kv->get("unrecognized") << ":" << endl;
       for (auto it = not_recognized_files.begin(); it != not_recognized_files.end();
         ++it) {
         cout << *it << endl;
       }
-      cout << endl;*/
+      cout << endl;
+      */
       cout << "------------------" << endl;
 
       ofstream myfile("accuracy.txt", ios::app);