def perform_feature_model(self, feature):
if feature == 'sift':
sift = SIFT(self.DATABASE_IMAGES_PATH)
sift.read_and_clusterize(num_cluster=150)
feature_vectors = sift.calculate_centroids_histogram()
else:
histogram_of_gradients = HistogramOfGradients(self.DATABASE_IMAGES_PATH)
feature_vectors = histogram_of_gradients.get_image_vectors()
self.database_connection.create_feature_model_table(feature)
self.database_connection.insert_feature_data(feature, feature_vectors)
def perform_classification_feature_model(self, feature, path, cluster_count):
self.database_connection.create_feature_model_table(feature)
if "histogram_of_gradients" in feature:
histogram_of_gradients = HistogramOfGradients(path)
feature_vectors = histogram_of_gradients.get_image_vectors()
elif "local_binary_pattern" in feature:
local_binary_pattern = LocalBinaryPattern(path)
feature_vectors = local_binary_pattern.get_image_vectors()
elif "sift" in feature:
sift = SIFT(path)
sift.read_and_clusterize(num_cluster=int(cluster_count))
feature_vectors = sift.calculate_centroids_histogram()
self.database_connection.insert_feature_data(feature, feature_vectors)