def test_extraction_setup(self, method: str):
extractor = BoWExtractor(base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64)
extractor.setup()
descriptors = extractor.bow_kmeans.getDescriptors()
assert (len(descriptors) > 0)
assert (len(descriptors) <= len(extractor.image_keypoints.keys()))
def test_keypoints_and_descriptors_detection(self, method: str):
extractor = BoWExtractor(base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64)
descriptors, keypoints = extractor._process_image(
image_route=test_utils.get_test_image_route())
assert (len(descriptors) > 0)
assert (keypoints.shape[0] == len(descriptors))
assert (keypoints.shape[1] == extractor.detector.descriptorSize())
def test_feature_detection_and_addition(self, method: str):
image_route = test_utils.get_test_image_route()
extractor = BoWExtractor(base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64)
extractor._extract_and_add_image(image_route)
descriptors = extractor.bow_kmeans.getDescriptors()
expected_keypoints, expected_descriptors = extractor._process_image(
image_route)
assert (len(descriptors[0]) == len(expected_descriptors))
assert (len(descriptors[0]) == len(expected_keypoints))
def test_kmeans_model_is_exported(self, method: str):
vocabulary_file = test_utils.get_test_output_ndarray_route()
extractor = BoWExtractor(base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64,
cluster_mode='manual')
extractor.setup()
extractor.fit()
extractor.export(vocabulary_file)
assert (os.path.exists(vocabulary_file))
vocabulary = np.load(vocabulary_file)
assert (vocabulary.shape[0] == extractor.k)
assert (np.array_equal(vocabulary,
extractor.bow_extractor.getVocabulary()))
def test_features_are_extracted_from_a_given_image_route_in_manual_mode(
self, method: str):
output_file = test_utils.get_test_output_csv_route()
extractor = BoWExtractor(base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64,
cluster_mode='manual')
extractor.setup()
extractor.fit()
extractor.extract_and_save(output_file)
features = test_utils.load_csv_from_route(output_file)
expected_width = extractor.k + 1
expected_height = test_utils.count_test_images()
assert (features.shape == (expected_height, expected_width))
def test_features_are_extracted_from_a_given_image_route_in_automatic_mode(
self, method: str):
output_file = test_utils.get_test_output_csv_route()
extractor = BoWExtractor(base_route=test_utils.get_test_base_route(),
method=method,
size=64,
cluster_mode='auto',
min_k=2,
max_k=100,
step=1,
threshold=0.95)
extractor.setup()
extractor.fit()
extractor.extract_and_save(output_file)
features = test_utils.load_csv_from_route(output_file)
expected_width = extractor.k + 1
expected_height = test_utils.count_test_images()
assert (features.shape == (expected_height, expected_width))
def test_kmeans_model_is_loaded_from_file(self, method: str):
vocabulary_file = test_utils.get_test_output_ndarray_route()
extractor = BoWExtractor(base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64,
cluster_mode='manual')
extractor.setup()
extractor.fit()
extractor.export(vocabulary_file)
new_extractor = BoWExtractor(
base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64,
cluster_mode='manual')
new_extractor.setup()
new_extractor.load(vocabulary_file)
assert (np.array_equal(new_extractor.bow_extractor.getVocabulary(),
extractor.bow_extractor.getVocabulary()))
def test_features_are_extracted_using_a_loaded_model(self, method: str):
output_file = test_utils.get_test_output_csv_route()
vocabulary_file = test_utils.get_test_output_ndarray_route()
extractor = BoWExtractor(base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64,
cluster_mode='manual')
extractor.setup()
extractor.fit()
extractor.export(vocabulary_file)
new_extractor = BoWExtractor(
base_route=test_utils.get_test_base_route(),
k=3,
method=method,
size=64,
cluster_mode='manual')
new_extractor.setup()
new_extractor.load(vocabulary_file)
new_extractor.extract_and_save(output_file)
features = test_utils.load_csv_from_route(output_file)
expected_width = extractor.k + 1
expected_height = test_utils.count_test_images()
assert (features.shape == (expected_height, expected_width))