class FilesDownloader():
def __init__(self):
self.pathsProvider = PathsProvider()
self.dropbox = DropboxClient()
self.directory = DirectoryManager()
self.logger = LoggerFactory()
def download_detection_input(self, request_id: int):
dropbox_request_path = os.path.join(
self.pathsProvider.dropbox_detection_image_path(),
str(request_id)).replace("\\", "/")
local_save_path = os.path.join(
self.pathsProvider.local_detection_image_path(), str(request_id))
self.__single_file_download__(dropbox_request_path, local_save_path)
self.logger.info(
F"Finished downloading input for detection with id :{request_id}")
def download_person(self, person_id: int):
dropbox_person_path = os.path.join(
self.pathsProvider.dropbox_person_image_path(),
str(person_id)).replace("\\", "/")
local_save_path = os.path.join(
self.pathsProvider.local_person_image_path(), str(person_id))
self.__multiple_file_download__(dropbox_person_path, local_save_path)
self.logger.info(F"Finished downloading person with id :{person_id}")
def download_recognition_input(self, request_id: int):
dropbox_request_path = os.path.join(
self.pathsProvider.dropbox_recognition_image_path(),
str(request_id)).replace("\\", "/")
local_save_path = os.path.join(
self.pathsProvider.local_recognition_image_path(), str(request_id))
self.__single_file_download__(dropbox_request_path, local_save_path)
self.logger.info(
F"Finished downloading input for recognition with id :{request_id}"
)
def download_neural_network(self, nn_id: int):
dropbox_neural_network_path = os.path.join(
self.pathsProvider.dropbox_neural_network_path(),
str(nn_id)).replace("\\", "/")
local_save_path = os.path.join(
self.pathsProvider.local_neural_network_path(), str(nn_id))
self.__multiple_file_download__(dropbox_neural_network_path,
local_save_path)
self.logger.info(
F"Finished downloading neural network with id :{nn_id}")
def __single_file_download__(self, dropbox_request_path, local_save_path):
self.directory.create_directory_if_doesnt_exist(local_save_path)
self.dropbox.download_single_file(dropbox_request_path,
local_save_path)
def __multiple_file_download__(self, dropbox_folder_path, local_save_path):
self.directory.clean_directory(local_save_path)
self.directory.create_directory_if_doesnt_exist(local_save_path)
self.dropbox.download_folder(dropbox_folder_path, local_save_path)
class FaceRecognizerProvider():
def __init__(self):
self.pathsProvider = PathsProvider()
self.neuralNetworkProvider = NeuralNetworkProvider()
self.neuralNetworkFilesRepo = NeuralNetworkFileRepository()
def create_open_cv_face_recognizers_for_request(self, request_id):
nn_files = self.neuralNetworkFilesRepo.get_all_open_cv_files_connected_to_neural_network(
request_id)
face_recognizers = []
for file in nn_files:
nn_path = os.path.join(
self.pathsProvider.local_neural_network_path(),
str(file.neuralNetworkId), file.name)
recognizer = self.neuralNetworkProvider.create_neural_network_by_type_id(
nn_path, file.neuralNetworkTypeId)
face_recognizers.append([recognizer, file.id])
return face_recognizers
def create_open_cv_face_recognizers_with_type(self, request_id):
nn_files = self.neuralNetworkFilesRepo.get_all_open_cv_files_connected_to_neural_network(
request_id)
eigen = []
fisher = []
lbph = []
for file in nn_files:
nn_path = os.path.join(
self.pathsProvider.local_neural_network_path(),
str(file.neuralNetworkId), file.name)
recognizer = self.neuralNetworkProvider.create_neural_network_by_type_id(
nn_path, file.neuralNetworkTypeId)
if file.neuralNetworkTypeId is NeuralNetworkTypes().fisher_id:
fisher = recognizer
elif file.neuralNetworkTypeId is NeuralNetworkTypes().eigen_id:
eigen = recognizer
elif file.neuralNetworkTypeId is NeuralNetworkTypes().lbph_id:
lbph = recognizer
return fisher, eigen, lbph
class NeuralNetworkResultsSaver():
def __init__(self):
self.logger = LoggerFactory()
self.pathsProvider = PathsProvider()
self.filesUploader = FilesUploader()
self.nnFilesRepo = NeuralNetworkFileRepository()
self.nnTypes = NeuralNetworkTypes()
self.stringOperator = StringOperator()
def save_result_files(self, neural_network_id, training_times,
data_preparation_time):
base_path = path.join(self.pathsProvider.local_neural_network_path(),
str(neural_network_id))
file_paths = [path.join(base_path, f) for f in listdir(base_path)]
for file_path in file_paths:
file_name, nn_type_id = self.__get_file_name_and_file_type_id(
file_path)
training_time_of_nn = training_times[nn_type_id]
self.__save_data__(file_name, neural_network_id, nn_type_id,
file_path,
training_time_of_nn + data_preparation_time,
training_time_of_nn)
# commented because files are now too big
# self.__upload_result_file__(file_name, file_path, neural_network_id)
def __save_data__(self, file_name, neural_network_id, nn_type_id,
file_path, process_time, training_time):
file_size = get_file_size(file_path)
neural_network_file_entity = NeuralNetworkFile(file_name,
neural_network_id,
nn_type_id,
str(process_time),
str(training_time),
file_size)
self.nnFilesRepo.add_neural_network_file(neural_network_file_entity)
self.logger.info(f"Upload of file {file_name} FINISHED")
def __upload_result_file__(self, file_name, file_path, neural_network_id):
self.logger.info(
f"Upload of file {file_name} STARTED (possible timeout error on weak network and big file size)"
)
opened_file = open(file_path, 'rb')
self.filesUploader.upload_neural_network(neural_network_id,
opened_file.read(), file_name)
def __get_file_name_and_file_type_id(self, file_path):
file_name = self.stringOperator.get_file_name_from_path(file_path)
nn_type_name = self.stringOperator.find_between(file_name, '_', '.')
nn_type_id = self.nnTypes.get_type_id(nn_type_name)
return file_name, nn_type_id
class NeuralNetworksProvider():
def __init__(self):
self.pathProvider = PathsProvider()
self.logger = LoggerFactory()
self.filesDownloader = FilesDownloader()
self.nnRepo = NeuralNetworkRepository()
self.directoryManager = DirectoryManager()
def download_neural_networks_to_local(self):
nn_path = self.pathProvider.local_neural_network_path()
directories = self.directoryManager.get_subdirectories_with_files_count(
nn_path)
neural_networks = self.nnRepo.get_completed_neural_networks_ids_with_downloadable_files_count(
)
nns_to_download = [x for x in neural_networks if x not in directories]
self.logger.info(f"directories {directories} "
f"\nneural_networks: {neural_networks}"
f"\nneural_networks_to_download: {nns_to_download}")
for nn in nns_to_download:
self.filesDownloader.download_neural_network(nn[0])