void saveModel(int _predictsize, int _neurons)
{
FileStorage fs;
fs.open("train/ann_data.xml", FileStorage::READ);
Mat TrainingData;
Mat Classes;
string training;
if(1)
{
stringstream ss(stringstream::in | stringstream::out);
ss << "TrainingDataF" << _predictsize;
training = ss.str();
}
fs[training] >> TrainingData;
fs["classes"] >> Classes;
//train the Ann
cout << "Begin to saveModelChar predictSize:" << _predictsize
<< " neurons:" << _neurons << endl;
double start = cv::getTickCount();
annTrain(TrainingData, Classes, _neurons);
double end = cv::getTickCount();
cout << "GetTickCount:" << (end-start)/1000 << endl;
cout << "End the saveModelChar" << endl;
string model_name = "train/ann.xml";
//if(1)
//{
// stringstream ss(stringstream::in | stringstream::out);
// ss << "ann_prd" << _predictsize << "_neu"<< _neurons << ".xml";
// model_name = ss.str();
//}
FileStorage fsTo(model_name, cv::FileStorage::WRITE);
ann.write(*fsTo, "ann");
}
void MainWindow::on_pushButton_test_clicked()
{
QString str = QFileDialog::getExistingDirectory();
QByteArray ba = str.toLocal8Bit();
char *c_str = ba.data();
string slash = "/";
Mat training;
Mat response;
read_num_class_data("train.txt", 4, training, response);
cout<<training.rows<<endl;
cout<<response.rows<<endl;
ofstream output_file;
output_file.open("Ratio.txt");
Mat layers = Mat(3,1,CV_32SC1);
int sz = training.cols ;
layers.row(0) = Scalar(sz);
layers.row(1) = Scalar(16);
layers.row(2) = Scalar(1);
CvANN_MLP mlp;
CvANN_MLP_TrainParams params;
CvTermCriteria criteria;
criteria.max_iter = 1000;
criteria.epsilon = 0.00001f;
criteria.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = 0.1f;
params.bp_moment_scale = 0.1f;
params.term_crit = criteria;
mlp.create(layers,CvANN_MLP::SIGMOID_SYM);
int i = mlp.train(training, response, Mat(),Mat(),params); // Train dataset
FileStorage fs("mlp.xml", FileStorage::WRITE); // or xml
mlp.write(*fs, "mlp"); // don't think too much about the deref, it casts to a FileNode
ui->label_training->setText("Training finish");
//mlp.load("mlp.xml","mlp"); //Load ANN weights for each layer
vector<string> img_name;
string output_directory = "output_img/";
img_name = listFile(c_str);
Mat testing(1, 3, CV_32FC1);
Mat predict (1 , 1, CV_32F );
int file_num = 0;
for(int i = 0; i < img_name.size(); i++) //size of the img_name
{
ui->progressBar->setValue(i*100/img_name.size());
string file_name = c_str + slash + img_name[i];
Mat img_test = imread(file_name);
Mat img_test_clone = img_test.clone();
Mat img_thresh, img_thresh_copy, img_HSV, img_gray;
vector<Mat> img_split;
cvtColor(img_test_clone, img_HSV, CV_RGB2HSV);
cvtColor(img_test_clone, img_gray, CV_RGB2GRAY);
split(img_HSV, img_split);
threshold(img_split[0], img_thresh, 75, 255, CV_THRESH_BINARY);
img_thresh_copy = img_thresh.clone();
Mat hole = img_thresh_copy.clone();
floodFill(hole, Point(0,0), Scalar(255));
bitwise_not(hole, hole);
img_thresh_copy = (img_thresh_copy | hole);
Mat element = getStructuringElement(MORPH_RECT, Size(15, 15));
Mat open_result;
morphologyEx(img_thresh, open_result, MORPH_CLOSE, element );
int infected_num = 0;
int total_pixels = 0;
if(img_test.data)
{
file_num++;
for (int m = 0; m < img_test.rows; m++)
{
for (int n = 0; n < img_test.cols; n++)
{
if (img_thresh_copy.at<uchar>(m, n) == 255)
{
total_pixels++;
testing.at<float>(0, 0) = (float)img_test.at<Vec3b>(m, n)[0];
testing.at<float>(0, 1) = (float)img_test.at<Vec3b>(m, n)[1];
testing.at<float>(0, 2) = (float)img_test.at<Vec3b>(m, n)[2];
mlp.predict(testing,predict);
float a = predict.at<float>(0,0);
if (a < 0.4) //0.4
{
img_test.at<Vec3b>(m, n)[0] = 0;
img_test.at<Vec3b>(m, n)[1] = 0;
img_test.at<Vec3b>(m, n)[2] = 255;
infected_num++;
}
}
}
}
float ratio = (float)infected_num / total_pixels * 100;
output_file<<img_name[i]<<" "<<(ratio)<<endl;
string output_file_name = output_directory + img_name[i];
cout<<output_file_name<<endl;
imwrite(output_file_name, img_test);
QImage img_qt = QImage((const unsigned char*)(img_test_clone.data), img_test_clone.cols, img_test_clone.rows, QImage::Format_RGB888);
QImage img_qt_result = QImage((const unsigned char*)(img_test.data), img_test.cols, img_test.rows, QImage::Format_RGB888);
//ui->label_original->setPixmap(QPixmap::fromImage(img_qt.rgbSwapped()));
//ui->label_resulting->setPixmap(QPixmap::fromImage((img_qt_result.rgbSwapped())));
// imshow("Ori", img_thresh_copy);
imshow("split", img_test);
waitKey(0);
QThread::msleep(100);
}
else
{
continue;
}
}
ui->progressBar->setValue(100);
output_file<<endl<<endl<<"Number of processed images: "<<file_num<<endl;
cout<<"Test finished!";
}
