Fixed SVM train.

src/train/svm_train.cpp

@@ -1,7 +1,6 @@
 #include "easypr/train/svm_train.h"
 #include "easypr/config.h"
-#include "easypr/core/core_func.h"
-#include "easypr/core/plate_judge.h"
+#include "easypr/core/feature.h"
 #include "easypr/util/util.h"
 
 #ifdef OS_WINDOWS
@@ -16,11 +15,10 @@ SvmTrain::SvmTrain(const char* plates_folder, const char* xml)
         : plates_folder_(plates_folder), svm_xml_(xml) {
   assert(plates_folder);
   assert(xml);
-  srand(unsigned(time(NULL)));
-  svm_ = cv::ml::SVM::create();
 }
 
 void SvmTrain::train() {
+  svm_ = cv::ml::SVM::create();
   svm_->setType(cv::ml::SVM::C_SVC);
   svm_->setKernel(cv::ml::SVM::RBF);
   svm_->setDegree(0.1);
@@ -33,25 +31,32 @@ void SvmTrain::train() {
 
   auto train_data = tdata();
 
-  fprintf(stdout, "Training SVM model, please wait...\n");
+  fprintf(stdout, ">> Training SVM model, please wait...\n");
   long start = utils::getTimestamp();
   svm_->trainAuto(train_data, 10,
-                  SVM::getDefaultGrid(SVM::C),
-                  SVM::getDefaultGrid(SVM::GAMMA),
-                  SVM::getDefaultGrid(SVM::P),
-                  SVM::getDefaultGrid(SVM::NU),
-                  SVM::getDefaultGrid(SVM::COEF),
+                  SVM::getDefaultGrid(SVM::C),
+                  SVM::getDefaultGrid(SVM::GAMMA),
+                  SVM::getDefaultGrid(SVM::P),
+                  SVM::getDefaultGrid(SVM::NU),
+                  SVM::getDefaultGrid(SVM::COEF),
                   SVM::getDefaultGrid(SVM::DEGREE),
                   true
   );
   long end = utils::getTimestamp();
-  fprintf(stdout, "Training done. Time elapse: %ldms\n", end - start);
+  fprintf(stdout, ">> Training done. Time elapse: %ldms\n", end - start);
+  fprintf(stdout, ">> Saving model file...\n");
   svm_->save(svm_xml_);
-  fprintf(stdout, "Your ANN Model was saved to %s\n", svm_xml_);
+  fprintf(stdout, ">> Your ANN Model was saved to %s\n", svm_xml_);
+  fprintf(stdout, ">> Testing...\n");
+  this->test();
 }
 
 void SvmTrain::test() {
-  this->prepare();
+  svm_ = cv::ml::SVM::load<cv::ml::SVM>(svm_xml_);
+
+  if (test_file_list_.empty()) {
+    this->prepare();
+  }
 
   double count_all = test_file_list_.size();
   double ptrue_rtrue = 0;
@@ -59,16 +64,17 @@ void SvmTrain::test() {
   double pfalse_rtrue = 0;
   double pfalse_rfalse = 0;
 
-  size_t label_index = 0;
   for (auto item : test_file_list_) {
     auto image = cv::imread(item.file);
-    auto features = easypr::histeq(image);
-    features = features.reshape(1, 1);
+    if (!image.data){
+      continue;
+    }
+    cv::Mat feature;
+    getHistogramFeatures(image, feature);
+    feature.reshape(1, 1).convertTo(feature, CV_32F);
 
-    int predict;
-    PlateJudge::instance()->plateJudge(features, predict);
+    auto predict = static_cast<int>(svm_->predict(feature));
     auto real = item.label;
-
     if (predict == kForward && real == kForward) ptrue_rtrue++;
     if (predict == kForward && real == kInverse) ptrue_rfalse++;
     if (predict == kInverse && real == kForward) pfalse_rtrue++;
@@ -87,8 +93,7 @@ void SvmTrain::test() {
     std::cout << "precise: " << precise << std::endl;
   }
   else {
-    std::cout << "precise: "
-    << "NA" << std::endl;
+    std::cout << "precise: " << "NA" << std::endl;
   }
 
   double recall = 0;
@@ -97,8 +102,7 @@ void SvmTrain::test() {
     std::cout << "recall: " << recall << std::endl;
   }
   else {
-    std::cout << "recall: "
-    << "NA" << std::endl;
+    std::cout << "recall: " << "NA" << std::endl;
   }
 
   double Fsocre = 0;
@@ -107,12 +111,13 @@ void SvmTrain::test() {
     std::cout << "Fsocre: " << Fsocre << std::endl;
   }
   else {
-    std::cout << "Fsocre: "
-    << "NA" << std::endl;
+    std::cout << "Fsocre: " << "NA" << std::endl;
   }
 }
 
 void SvmTrain::prepare() {
+  srand(unsigned(time(NULL)));
+
   char buffer[260] = { 0 };
 
   sprintf(buffer, "%s/has", plates_folder_);
@@ -155,7 +160,7 @@ void SvmTrain::prepare() {
   // copy the rest of has_file_list to the test_file_list_
   test_file_list_.reserve(has_for_test + no_for_test);
   for (auto i = has_for_test; i < has_num; i++) {
-    train_file_list_.push_back({
+    test_file_list_.push_back({
       has_file_list[i],
       kForward
     });
@@ -163,7 +168,7 @@ void SvmTrain::prepare() {
 
   // copy the rest of no_file_list to the end of the test_file_list_
   for (auto i = no_for_test; i < no_num; i++) {
-    train_file_list_.push_back({
+    test_file_list_.push_back({
       no_file_list[i],
       kInverse
     });
@@ -179,21 +184,22 @@ cv::Ptr<cv::ml::TrainData> SvmTrain::tdata() {
   for (auto f : train_file_list_) {
     auto image = cv::imread(f.file);
     if (!image.data) {
-      fprintf(stdout, ">> Invalid image: %s\n", f.file.c_str());
+      fprintf(stdout, ">> Invalid image: %s  ignore.\n", f.file.c_str());
       continue;
     }
-    auto features = easypr::histeq(image);
-    features = features.reshape(1, 1);
+    cv::Mat feature;
+    getHistogramFeatures(image, feature);
+    feature = feature.reshape(1, 1);
 
-    samples.push_back(features);
+    samples.push_back(feature);
     responses.push_back(f.label);
   }
 
-  cv::Mat samples_;
+  cv::Mat samples_, responses_;
   samples.convertTo(samples_, CV_32F);
+  cv::Mat(responses).reshape(0, 1).copyTo(responses_);
 
-  return cv::ml::TrainData::create(samples_, ml::SampleTypes::ROW_SAMPLE,
-    cv::Mat(responses));
+  return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE, responses_);
 }
 
 }  // namespace easypr