Exemplo n.º 1
0
    #fe.loadSuperpixelFromFile(sp_file_names[i])
    fe.loadLabelImage(label_file_names[i])

    featureVectors = fe.getFeaturesVectors()
    labels = fe.getSuperPixelLabels()

    #Test purposes
    edges, edgeFeatures1, edgeFeatures2 = fe.getEdges()
    if file_labels[i] != TESTING_LABEL:
        # store data
        if file_labels[i] == TRAINING_LABEL:
            train_edges.append(edges)
            train_edgesFeatures1.append(edgeFeatures1)
            train_edgesFeatures2.append(edgeFeatures2)
            train_superpixels.append(fe.getSuperpixelImage())
            train_labels = np.append(train_labels, labels, 0)
            if train_data == []:
                train_data = featureVectors
            else:
                train_data = np.vstack((train_data, featureVectors))
        else:
            # get superpixel valid files
            valid_edges.append(edges)
            valid_edgesFeatures1.append(edgeFeatures1)
            valid_edgesFeatures2.append(edgeFeatures2)
            valid_superpixels.append(fe.getSuperpixelImage())
            validationOriginalImage.append(im_file_names[i])
            # these two lines need to be added into featureExtraction class
            valid_files = sp.getSuperValidFiles(fe.getSuperpixelImage(),
                                                valid_files_count, valid_files)
Exemplo n.º 2
0
    #fe.loadSuperpixelFromFile(sp_file_names[i])
    fe.loadLabelImage(label_file_names[i])

    featureVectors = fe.getFeaturesVectors()
    labels = fe.getSuperPixelLabels()

    #Test purposes
    edges, edgeFeatures1, edgeFeatures2 = fe.getEdges()
    if file_labels[i] != TESTING_LABEL:   
        # store data
        if file_labels[i] == TRAINING_LABEL:
            train_edges.append(edges)
            train_edgesFeatures1.append(edgeFeatures1)
            train_edgesFeatures2.append(edgeFeatures2)
            train_superpixels.append(fe.getSuperpixelImage())
            train_labels = np.append(train_labels, labels, 0)
            if train_data==[]:
                train_data = featureVectors
            else:
                train_data = np.vstack((train_data,featureVectors))
        else:
            # get superpixel valid files
            valid_edges.append(edges)
            valid_edgesFeatures1.append(edgeFeatures1)
            valid_edgesFeatures2.append(edgeFeatures2)
            valid_superpixels.append(fe.getSuperpixelImage())
            validationOriginalImage.append(im_file_names[i])
            # these two lines need to be added into featureExtraction class
            valid_files = sp.getSuperValidFiles(fe.getSuperpixelImage(), valid_files_count, valid_files)
            valid_pixels_labels.append(sp.getPixelLabel(fe.getLabelImage()))