test_files_count = 0 valid_superpixels = [] validationOriginalImage = [] test_superpixels = [] testOriginalImage = [] train_superpixels = [] for i in xrange(0, num_files): fe = Feature() fe.loadImage(im_file_names[i]) fe.loadSuperpixelImage() #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:
valid_superpixels = [] validationOriginalImage = [] test_superpixels = [] testOriginalImage = [] train_superpixels = [] for i in xrange(0,num_files): fe = Feature() fe.loadImage(im_file_names[i]) fe.loadSuperpixelImage() #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: