def RandomTrees_LBGLCM(self):
     global accuracies, all_classifiers, labels
     lbglcm_feat = self.compute_LBGLCM()
     n_trees = self.notreesRF.text()
     max_feats = self.FeaturesRF.currentText()
     clf, x_rf2, y_rf2, dict2 = Classifiers.RF_train(
         lbglcm_feat, n_trees, max_feats
     )  #Collecting the trained classifier, x_test, y_test and labels
     Y_pred_rf2 = Classifiers.pred(clf, x_rf2)
     acc_test = Classifiers.display_results(Y_pred_rf2, y_rf2)
     all_classifiers.append(clf)
     accuracies.append(acc_test)
     labels.append(dict2)
 def RandomTrees_GLCM(self):
     global accuracies, all_classifiers, labels
     glcm_feat = self.compute_GLCM()
     n_trees = self.notreesRF.text()
     max_feats = self.FeaturesRF.currentText()
     clf, x_rf1, y_rf1, dict1 = Classifiers.RF_train(
         glcm_feat, n_trees, max_feats
     )  #Collecting the trained classifier, x_test, y_test and labels
     Y_pred_rf1 = Classifiers.pred(clf,
                                   x_rf1)  #Predicting the x_test labels
     acc_test = Classifiers.display_results(Y_pred_rf1,
                                            y_rf1)  #accuracy of prediction
     all_classifiers.append(clf)
     accuracies.append(acc_test)
     labels.append(dict1)