all_accuracies_list = [] all_labels = [] for current_classifier in selected_classifiers: print( "~ Running program with {} classifier.".format(current_classifier)) classifier = Classifier(current_classifier) accuracy_list = [] start_time = time.time() for i, (train, test) in enumerate( kfold_cross_validation(data, k=int(args["kfold"]))): samples, labels = parse_samples_labels(train) classifier.fit(samples, labels) samples, labels = parse_samples_labels(test) accuracy = classifier.accuracy(samples, labels) if args["verbose"]: print("+ [{:02d}/10] accuracy: {:3d}%".format( i + 1, int(accuracy * 100))) if draw_confusion_matrix: confusion_matrix = classifier.confusion_matrix(samples, labels) accuracy_list.append(accuracy) all_accuracies_list.append(accuracy_list) all_labels.append(current_classifier) if args["verbose"]: print("- Took {:.2f} seconds.".format(time.time() - start_time))
data = DataProvider(cars_paths, non_cars_paths, car_id=car_id, non_car_id=non_car_id, test_size=0.2) data.save_labels_info_graph() data.save_random_images(car_id, "../training_results/random_car_images.png") data.save_random_images(non_car_id, "../training_results/random_non_car_images.png") print("Training is in progress...") clf.train(data.train.features, data.train.labels) print("Training time: {:.2f} min.".format(clf.training_time / 60)) accuracy = clf.accuracy(data.test.features, data.test.labels) print("Test accuracy: {:.3f}%.".format(accuracy * 100)) space = "-" * 50 info = [ "Accuracy: {:.4f}%".format(accuracy * 100), space, "\t- Features vector compound parts -", "features_providers: [{}]".format( ", ".join([type(i).__name__ for i in features_providers ])), "color_space: {}".format(color_space), "classifier_image_size: {}".format(classifier_image_size), space, "\t- Spatial binning of color parameters -", "spatial_binning_size: {}".format(spatial_binning_size), space, "\t- Color histogram parameters -", "color_histogram_n_bins: {}".format(color_histogram_n_bins), "bins_range: {}".format(bins_range), space, "\t- Histogram of oriented gradients parameters -",