def test_predict(self, mock_load, mock_predict, mock_preprocess_ds):
mock_load.return_value = ParallelPostFit()
mock_preprocess_ds.return_value = [1, 2, 3]
testable = SKLinearImageModel(pkl_file='trained_models/hog_sklearn.pkl')
testable.predict(self.X)
mock_load.assert_called_once()
mock_predict.assert_called_once_with([1, 2, 3])
help='Limit to accept probability to predict a class.')
arg_parser.add_argument('dir',
help='Path to the input directory with image files.')
parsed_args = arg_parser.parse_args(sys.argv[1:])
input_directory = parsed_args.dir
pkl_file = parsed_args.pkl_file
probability_threshold = float(parsed_args.probability_threshold)
try:
dir_parser = DirectoryParser(input_directory)
except ValueError as e:
print(*e.args)
sys.exit(1)
print(f"Data dir: {dir_parser}")
feature_extractor = ImageFeatureExtractor()
if parsed_args.model_type == ModelUsageType.sk:
model = SKLinearImageModel(pkl_file=pkl_file,
probability_threshold=probability_threshold)
X, _ = feature_extractor.transform_image_to_dataset(dir_parser.full_path_image_files)[0]
else:
model = TFModel(pre_train=pkl_file,
probability_threshold=probability_threshold)
X, _ = feature_extractor.transform_image_to_dataset(dir_parser.full_path_image_files,
image_size=model.image_shape[:-1])[0]
prediction = model.predict(X)
for filepath, prediction in zip(dir_parser.full_path_image_files, prediction):
print(f"{filepath}: {prediction.value}")