Simplify SVM training.

include/easypr/train/svm_train.h

@@ -1,48 +1,37 @@
-#ifndef EASYPR_SVM_TRAIN_H
+#ifndef EASYPR_TRAIN_SVMTRAIN_H_
-#define EASYPR_SVM_TRAIN_H
+#define EASYPR_TRAIN_SVMTRAIN_H_
 
+#include "easypr/train/train.h"
 #include <vector>
-#include <opencv2/opencv.hpp>
 #include "easypr/config.h"
 
 namespace easypr {
 
-  class SvmTrain {
+class SvmTrain : public ITrain {
-  public:
+ public:
-    typedef enum {
+   typedef struct {
-      kForward = 1, // correspond to "has plate"
+     std::string file;
-      kInverse = 0  // correspond to "no plate"
+     SvmLabel    label;
-    } Label;
+   } TrainItem;
 
-    SvmTrain(const char* forward_data_folder, const char* inverse_data_folder);
+    SvmTrain(const char* plates_folder, const char* xml);
 
-    void train(bool divide = true, float divide_percentage = 0.7,
+    virtual void train();
-      const char* out_svm_folder = kDefaultSvmPath);
 
-    void runTest(const char* svm_path = kDefaultSvmPath);
+    virtual void test();
 
-  private:
+ private:
-    /*
+    void prepare();
-     * divide images into train part and test part by percentage
-     */
-    void divide(const char* images_folder, float percentage = 0.7);
 
-    void getTrain();
+    virtual cv::Ptr<cv::ml::TrainData> tdata();
 
-    void getTest();
+    cv::Ptr<cv::ml::SVM> svm_;
-
+    const char* plates_folder_;
-    const char* forward_;
+    const char* svm_xml_;
-    const char* inverse_;
+    std::vector<TrainItem> train_file_list_;
-
+    std::vector<TrainItem> test_file_list_;
-    // these two variables are used for cv::CvSVM::train_auto()
+};
-    cv::Mat classes_;
-    cv::Ptr<cv::ml::TrainData> trainingData_;
-
-    // these two variables are used for cv::CvSVM::predict()
-    std::vector<cv::Mat> test_imgaes_;
-    std::vector<Label> test_labels_;
-  };
 
 }
 
-#endif //EASYPR_SVM_TRAIN_H
+#endif // EASYPR_TRAIN_SVMTRAIN_H_

test/main.cpp

@@ -130,43 +130,15 @@ void command_line_handler(int argc, const char* argv[]) {
      | SVM Training operations
      | ------------------------------------------
      |
-     | $ demo svm --[create|tag|train] options
+     | $ demo svm --plates=path/to/plates/ [--test] --svm=save/to/svm.xml
      |
      | ------------------------------------------
      */
     options("h,help", "show help information");
-    options(",svm", "resources/model/svm.xml",
+    options(",plates", "", 
-            "the svm model file,"
+      "a folder contains both forward data and inverse data in the separated subfolders");
-                    " this option is used for '--tag'(required)"
+    options(",svm", easypr::kDefaultSvmPath, "the svm model file");
-                    " and '--train'(save svm model to) functions");
+    options("t,test", "run tests in --plates");
-    // create
-    options(
-            ",create",
-            "create learn data, this function "
-                    "will intercept (--max) raw images (--in) and preprocess into (--out)");
-    options("i,in", "", "where is the raw images");
-    options("o,out", "", "where to put the preprocessed images");
-    options("m,max", "5000", "how many learn data would you want to create");
-    // tag
-    options(",tag",
-            "tag learn data, this function "
-                    "will find plate blocks in your images(--source) "
-                    "as well as classify them into (--has) and (--no)");
-    options("s,source", "", "where is your images to be classified");
-    options(",has", "", "put plates in this folder");
-    options(",no", "", "put images without plate in this folder");
-    // train
-    options(",train",
-            "train given data, "
-                    "including the forward(has plate) and the inverse(no plate).");
-    options(",has-plate", "", "where is the forward data");
-    options(",no-plate", "", "where is the inverse data");
-    options(",divide",
-            "whether divide train data into two parts by --percentage or not");
-    options(",percentage", "0.7",
-            "70% train data will be used for training,"
-                    " others will be used for testing");
-    options(",test", "don't train again, run testing directly");
   }
 
   options.add_subroutine("ann", "ann operation").make_usage("Usages:");
@@ -259,57 +231,17 @@ void command_line_handler(int argc, const char* argv[]) {
               std::cout << options("svm");
               return;
             }
-
+            
-            if (parser->has("create")) {
+            easypr::SvmTrain svm(
-              assert(parser->has("in"));
+              parser->get("plates")->c_str(),
-              assert(parser->has("out"));
+              parser->get("svm")->c_str()
-              assert(parser->has("max"));
+            );
-
+
-              auto in = parser->get("in")->val();
+            if (parser->has("test")) {
-              auto out = parser->get("out")->val();
+              svm.test();
-              auto max = parser->get("max")->as<int>();
-              easypr::preprocess::create_learn_data(in.c_str(), out.c_str(),
-                                                    max);
-            }
-
-            if (parser->has("tag")) {
-              assert(parser->has("source"));
-              assert(parser->has("has"));
-              assert(parser->has("no"));
-              assert(parser->has("svm"));
-
-              auto source = parser->get("source")->val();
-              auto has_path = parser->get("has")->val();
-              auto no_path = parser->get("no")->val();
-              auto svm = parser->get("svm")->val();
-              easypr::preprocess::tag_data(source.c_str(), has_path.c_str(),
-                                           no_path.c_str(), svm.c_str());
-              std::cout << "Tagging finished, check out output images "
-              << "and classify the wrong images manually." << std::endl;
             }
-
+            else {
-            if (parser->has("train")) {
+              svm.train();
-              assert(parser->has("has-plate"));
-              assert(parser->has("no-plate"));
-              assert(parser->has("percentage"));
-              assert(parser->has("svm"));
-
-              auto forward_data_path = parser->get("has-plate")->val();
-              auto inverse_data_path = parser->get("no-plate")->val();
-              bool divide = parser->has("divide");
-              auto percentage = parser->get("percentage")->as<float>();
-              auto svm_model = parser->get("svm")->val();
-              bool test = parser->has("test");
-
-              easypr::SvmTrain svm(forward_data_path.c_str(),
-                                   inverse_data_path.c_str());
-
-              if (test) {
-                svm.runTest(svm_model.c_str());
-              }
-              else {
-                svm.train(divide, percentage, svm_model.c_str());
-              }
             }
           })
           .found("ann", [&]() {