#include <opencv2/core/core.hpp>
#include <opencv2/ml/ml.hpp>
+#include <opencv2/highgui/highgui.hpp>
+
using namespace std;
using namespace cv;
{
Mat data_temp, labels_temp;
Mat data, labels;
+
+ Mat data_train, data_test;
+ Mat labels_train, labels_test;
+
Mat responses, result;
FileStorage f;
data_temp.convertTo(data, CV_32F);
labels_temp.convertTo(labels, CV_32F);
+ for(int i =0;i<data.rows;i++)
+ {
+ if(i%2 ==0)
+ {
+ data_train.push_back(data.row(i));
+ labels_train.push_back(labels.row(i));
+ }
+ else
+ {
+ data_test.push_back(data.row(i));
+ labels_test.push_back(labels.row(i));
+ }
+ }
+
+ cout<<"training samples per class: "<<data_train.rows/2<<endl;
+ cout<<"testing samples per class: "<<data_test.rows/2<<endl;
+
+ // display sample image
+ Mat img_disp1 = data_train.row(2).reshape(0,28).t();
+ Mat img_disp2 = data_train.row(18).reshape(0,28).t();
+
+ imshow("digit 0", img_disp1);
+ imshow("digit 1", img_disp2);
+
+
+
cout<<"initializing Logisitc Regression Parameters\n"<<endl;
CvLR_TrainParams params = CvLR_TrainParams();
cout<<"training Logisitc Regression classifier\n"<<endl;
- CvLR lr_(data, labels, params);
-
- cout<<"predicting the trained dataset\n"<<endl;
-
- lr_.predict(data, responses);
-
- labels.convertTo(labels, CV_32S);
-
+ CvLR lr_(data_train, labels_train, params);
+ lr_.predict(data_test, responses);
+ labels_test.convertTo(labels_test, CV_32S);
+
cout<<"Original Label :: Predicted Label"<<endl;
- result = (labels == responses)/255;
- for(int i=0;i<labels.rows;i++)
+ result = (labels_test == responses)/255;
+
+ for(int i=0;i<labels_test.rows;i++)
{
- cout<<labels.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl;
+ cout<<labels_test.at<int>(i,0)<<" :: "<< responses.at<int>(i,0)<<endl;
}
+
// calculate accuracy
cout<<"accuracy: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
+ cout<<"saving the classifier"<<endl;
// save the classfier
lr_.save("NewLR_Trained.xml");
// predict using loaded classifier
cout<<"predicting the dataset using the loaded classfier\n"<<endl;
- lr2.predict(data, responses2);
+ lr2.predict(data_test, responses2);
// calculate accuracy
- result = (labels == responses2)/255;
+ result = (labels_test == responses2)/255;
cout<<"accuracy using loaded classifier: "<<((double)cv::sum(result)[0]/result.rows)*100<<"%\n";
+ waitKey(0);
return 0;
}