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')