* fix ann_train test bug, make the result initial to 0 not -1

resources/result/accuracy.txt

@@ -3278,3 +3278,8 @@ Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%.
 Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%.
 0-error:70.6122%,  1-error:82.8571%,  Chinese-precise:86.5306%  
 总共时间: 152秒,  平均执行时间:0.59375秒
+2017-05-07 11:06:01
+总图片数:256,  Plates count:297,  未定位车牌:23,  检出率:92.2559%  
+Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%.
+0-error:70.6122%,  1-error:82.8571%,  Chinese-precise:86.5306%  
+总共时间: 156秒,  平均执行时间:0.609375秒

resources/result/plateGroundTruth.txt

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src/core/chars_identify.cpp

@@ -67,7 +67,7 @@ void CharsIdentify::classify(cv::Mat featureRows, std::vector<int>& out_maxIndex
 
   for (int output_index = 0; output_index < rowNum; output_index++) {
     Mat output_row = output.row(output_index);
-    int result = -1;
+    int result = 0;
     float maxVal = -2.f;
     bool isChinses = isChineseVec[output_index];
     if (!isChinses) {
@@ -118,7 +118,7 @@ void CharsIdentify::classify(std::vector<CCharacter>& charVec){
     CCharacter& character = charVec[output_index];
     Mat output_row = output.row(output_index);
 
-    int result = -1;
+    int result = 0;
     float maxVal = -2.f;
     std::string label = "";
 
@@ -180,7 +180,7 @@ void CharsIdentify::classifyChineseGray(std::vector<CCharacter>& charVec){
     bool isChinese = true;
 
     float maxVal = -2;
-    int result = -1;
+    int result = 0;
 
     for (int j = 0; j < kChineseNumber; j++) {
       float val = output_row.at<float>(j);
@@ -234,7 +234,7 @@ void CharsIdentify::classifyChinese(std::vector<CCharacter>& charVec){
     bool isChinese = true;
 
     float maxVal = -2;
-    int result = -1;
+    int result = 0;
 
     for (int j = 0; j < kChineseNumber; j++) {
       float val = output_row.at<float>(j);
@@ -267,7 +267,7 @@ void CharsIdentify::classifyChinese(std::vector<CCharacter>& charVec){
 }
 
 int CharsIdentify::classify(cv::Mat f, float& maxVal, bool isChinses, bool isAlphabet){
-  int result = -1;
+  int result = 0;
 
   cv::Mat output(1, kCharsTotalNumber, CV_32FC1);
   ann_->predict(f, output);
@@ -342,7 +342,7 @@ bool CharsIdentify::isCharacter(cv::Mat input, std::string& label, float& maxVal
 std::pair<std::string, std::string> CharsIdentify::identifyChinese(cv::Mat input, float& out, bool& isChinese) {
   cv::Mat feature = charFeatures(input, kChineseSize);
   float maxVal = -2;
-  int result = -1;
+  int result = 0;
 
   cv::Mat output(1, kChineseNumber, CV_32FC1);
   annChinese_->predict(feature, output);
@@ -379,7 +379,7 @@ std::pair<std::string, std::string> CharsIdentify::identifyChineseGray(cv::Mat i
   cv::Mat feature;
   extractFeature(input, feature);
   float maxVal = -2;
-  int result = -1;
+  int result = 0;
   cv::Mat output(1, kChineseNumber, CV_32FC1);
   annGray_->predict(feature, output);
 

src/train/annCh_train.cpp

@@ -91,7 +91,7 @@ std::pair<std::string, std::string> AnnChTrain::identifyGrayChinese(cv::Mat inpu
   Mat feature;
   extractFeature(input, feature);
   float maxVal = -2;
-  int result = -1;
+  int result = 0;
 
   cv::Mat output(1, kChineseNumber, CV_32FC1);
   ann_->predict(feature, output);

src/train/ann_train.cpp

@@ -22,6 +22,7 @@ AnnTrain::AnnTrain(const char* chars_folder, const char* xml)
 }
 
 void AnnTrain::train() {
+  
   int classNumber = 0;
 
   cv::Mat layers;
@@ -93,17 +94,16 @@ void AnnTrain::train() {
   ann_->train(traindata);
   long end = utils::getTimestamp();
   ann_->save(ann_xml_);
- 
+  
   test();
   std::cout << "Your ANN Model was saved to " << ann_xml_ << std::endl;
-  std::cout << "Training done. Time elapse: " << (end - start) / (1000 * 60) << "minute"
-    << std::endl;
+  std::cout << "Training done. Time elapse: " << (end - start) / (1000 * 60) << "minute" << std::endl;
 }
 
 std::pair<std::string, std::string> AnnTrain::identifyChinese(cv::Mat input) {
   cv::Mat feature = charFeatures2(input, kPredictSize);
   float maxVal = -2;
-  int result = -1;
+  int result = 0;
 
   cv::Mat output(1, kChineseNumber, CV_32FC1);
   ann_->predict(feature, output);
@@ -129,14 +129,15 @@ std::pair<std::string, std::string> AnnTrain::identifyChinese(cv::Mat input) {
 std::pair<std::string, std::string> AnnTrain::identify(cv::Mat input) {
   cv::Mat feature = charFeatures2(input, kPredictSize);
   float maxVal = -2;
-  int result = -1;
+  int result = 0;
 
+  //std::cout << feature << std::endl;
   cv::Mat output(1, kCharsTotalNumber, CV_32FC1);
   ann_->predict(feature, output);
-
+  //std::cout << output << std::endl;
   for (int j = 0; j < kCharsTotalNumber; j++) {
     float val = output.at<float>(j);
-    // std::cout << "j:" << j << "val:" << val << std::endl;
+    //std::cout << "j:" << j << "val:" << val << std::endl;
     if (val > maxVal) {
       maxVal = val;
       result = j;
@@ -177,6 +178,10 @@ void AnnTrain::test() {
 
     for (auto file : chars_files) {
       auto img = cv::imread(file, 0);  // a grayscale image
+      if (!img.data) {
+        //cout << "Null pointer!" << endl;
+        continue;
+      }
       std::pair<std::string, std::string> ch;
 
       if (type == 0) ch = identify(img);
@@ -186,8 +191,7 @@ void AnnTrain::test() {
         ++corrects;
         ++corrects_all;
       } else {
-        error_files.push_back(
-            std::make_pair(utils::getFileName(file), ch.second));
+        error_files.push_back(std::make_pair(utils::getFileName(file), ch.second));
       }
       ++sum;
       ++sum_all;