class ENV(Constants): ML_PORT = int(get_env('ML_PORT', '3000')) SCANNER = get_env_split('SCANNER', _DEFAULT_SCANNERS)[0] SCANNERS = get_env_split('SCANNER', _DEFAULT_SCANNERS) IMG_LENGTH_LIMIT = int(get_env('IMG_LENGTH_LIMIT', '640')) LOGGING_LEVEL_NAME = get_env('LOGGING_LEVEL_NAME', 'debug').upper() IS_DEV_ENV = get_env('FLASK_ENV', 'production') == 'development' BUILD_VERSION = get_env('APP_VERSION_STRING', 'dev')
class ENV(Constants): USE_REMOTE = get_env_bool('USE_REMOTE') ML_HOST = get_env('ML_HOST', 'localhost') ML_PORT = ENV_MAIN.ML_PORT ML_URL = get_env('ML_URL', f'http://{ML_HOST}:{ML_PORT}') if get_env('IMG_NAMES', '') == '': IMG_NAMES = [i.img_name for i in SAMPLE_IMAGES] elif get_env('IMG_NAMES', '') == '{test_images}': IMG_NAMES = [i.img_name for i in SAMPLE_IMAGES if i.include_to_tests] else: IMG_NAMES = get_env_split('IMG_NAMES') SAVE_IMG_str = get_env('SAVE_IMG', 'true').lower() LOGGING_LEVEL_NAME = ENV_MAIN.LOGGING_LEVEL_NAME
class ENV(Constants): ML_PORT = int(get_env('ML_PORT', '3000')) IMG_LENGTH_LIMIT = int(get_env('IMG_LENGTH_LIMIT', '640')) FACE_DETECTION_PLUGIN = get_env('FACE_DETECTION_PLUGIN', 'facenet.FaceDetector') CALCULATION_PLUGIN = get_env('CALCULATION_PLUGIN', 'facenet.Calculator') EXTRA_PLUGINS = get_env_split( 'EXTRA_PLUGINS', 'facenet.LandmarksDetector,agegender.AgeDetector,agegender.GenderDetector,facenet.facemask.MaskDetector' ) LOGGING_LEVEL_NAME = get_env('LOGGING_LEVEL_NAME', 'debug').upper() IS_DEV_ENV = get_env('FLASK_ENV', 'production') == 'development' BUILD_VERSION = get_env('APP_VERSION_STRING', 'dev') GPU_IDX = int(get_env('GPU_IDX', '-1')) INTEL_OPTIMIZATION = get_env_bool('INTEL_OPTIMIZATION')
class ENV(Constants): LOGGING_LEVEL_NAME = ENV_MAIN.LOGGING_LEVEL_NAME IMG_NAMES = get_env_split('IMG_NAMES', ' '.join([i.img_name for i in SAMPLE_IMAGES]))
class ENV_BENCHMARK(Constants): # NOSONAR SCANNERS = get_env_split('SCANNERS', ' '.join(s.ID for s in TESTED_SCANNERS)) LOGGING_LEVEL_NAME = ENV_MAIN.LOGGING_LEVEL_NAME DRY_RUN = get_env_bool('DRY_RUN')