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()
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! ==========================================="
)