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# common dependencies
import os
import warnings
import logging
from typing import Any, Dict, List, Tuple, Union, Optional
# this has to be set before importing tensorflow
os.environ["TF_USE_LEGACY_KERAS"] = "1"
# pylint: disable=wrong-import-position
# 3rd party dependencies
import numpy as np
import pandas as pd
import tensorflow as tf
# package dependencies
from deepface.commons import package_utils, folder_utils
from deepface.modules import (
recognition,
demography,
)
# users should install tf_keras package if they are using tf 2.16 or later versions
package_utils.validate_for_keras3()
# create required folders if necessary to store model weights
folder_utils.initialize_folder()
def analyze(
img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
) -> List[Dict[str, Any]]:
return demography.analyze(
img_path=img_path,
actions=actions,
enforce_detection=enforce_detection,
detector_backend=detector_backend,
align=align,
expand_percentage=expand_percentage,
silent=silent,
)
def find(
img_path: Union[str, np.ndarray],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
) -> List[pd.DataFrame]:
return recognition.find(
img_path=img_path,
db_path=db_path,
model_name=model_name,
distance_metric=distance_metric,
enforce_detection=enforce_detection,
detector_backend=detector_backend,
align=align,
expand_percentage=expand_percentage,
threshold=threshold,
normalization=normalization,
silent=silent,
)
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