Ejemplo n.º 1
0
def initialise_everything():
    print('''This procedure can take up to hours to finish.
    The program will now run:
     - Normalisation pipeline over the shape database. (~3hrs)
     - Feature extraction over shape database. (~2hrs)\n
     Are you sure you want to continue (y/n)?\n
    ''')
    choice = input(">> ")
    if choice == "n" or choice == "no":
        return

    with open('config.json') as f:
        data = json.load(f)
    path_psd = data["DATA_PATH_PSB"]
    path_normed = data["DATA_PATH_NORMED"]
    path_feature = data["FEATURE_DATA_FILE"]
    db = PSBDataset()
    if len(os.listdir(path_psd)) == 0:
        print("No valid dataset found.\nPoint to a valid dataset.")
        return
    else:
        prompt_for_class_files(path_psd)
        choice = input(
            "Do you wish to go back to the menu to change the current classification settings? (y/n)\n>> "
        )
        if choice == "n":
            return
    if not os.path.isfile(path_normed):
        print("No valid normalised dataset found.\nRunning normalisation.")
        norm = Normalizer(db)
        norm.run_full_pipeline()
    if not os.path.isfile(path_feature):
        print("No valid feature file found.\nRun feature extraction.")
        FE = FeatureExtractor(db)
        FE.run_full_pipeline()
Ejemplo n.º 2
0
    print("=" * 10 + "Testing full pipeline for dataset reader" + "=" * 10)
    dataset = PSBDataset(DATA_PATH_DEBUG, class_file_path=CLASS_FILE)
    dataset.run_full_pipeline()
    dataset.compute_shape_statistics()
    dataset.detect_outliers()
    dataset.convert_all_to_polydata()
    dataset.save_statistics("./trash", "stats_test.csv")
    print(
        "======================================= Done! ==========================================="
    )

    print("=" * 10 + "Testing full pipeline for normalizer" + "=" * 10)
    init_dataset = PSBDataset(DATA_PATH_DEBUG, class_file_path=CLASS_FILE)
    norm = Normalizer(init_dataset)
    norm.target_path = DATA_PATH_NORMED_SUBSET
    normed_data = norm.run_full_pipeline()
    print(
        "======================================= Done! ==========================================="
    )

    print("=" * 10 + "Testing full pipeline for feature extractor" + "=" * 10)
    normed_dataset = PSBDataset(search_path=DATA_PATH_NORMED_SUBSET,
                                class_file_path=CLASS_FILE)
    FE = FeatureExtractor(normed_dataset,
                          target_file="./trash/feat_test.jsonl")
    features = FE.run_full_pipeline()
    print(
        "======================================= Done! ==========================================="
    )