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Refine type hints as per PR review
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@ -5,7 +5,9 @@ import warnings
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import time
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import pickle
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import logging
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from typing import Any, Dict, List, Tuple
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from typing import Any, Dict, List, Tuple, Union
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from keras.engine.functional import Functional
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from deepface.basemodels import DlibResNet, SFace
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# 3rd party dependencies
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import numpy as np
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@ -46,7 +48,7 @@ if tf_version == 2:
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# -----------------------------------
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def build_model(model_name: str) -> Any:
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def build_model(model_name: str) -> Union[Functional, DlibResNet.DlibResNet, SFace.SFaceModel]:
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"""
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This function builds a deepface model
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Parameters:
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@ -92,8 +94,8 @@ def build_model(model_name: str) -> Any:
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def verify(
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img1_path: str,
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img2_path: str,
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img1_path: Union[str, np.ndarray],
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img2_path: Union[str, np.ndarray],
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model_name: str = "VGG-Face",
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detector_backend: str = "opencv",
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distance_metric: str = "cosine",
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@ -232,7 +234,7 @@ def verify(
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def analyze(
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img_path: str,
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img_path: Union[str, np.ndarray],,
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actions: Tuple[str, ...] = ("emotion", "age", "gender", "race"),
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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@ -410,7 +412,7 @@ def analyze(
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def find(
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img_path: str,
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img_path: Union[str, np.ndarray],
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db_path : str,
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model_name : str ="VGG-Face",
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distance_metric : str ="cosine",
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@ -651,7 +653,7 @@ def find(
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def represent(
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img_path: str,
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img_path: Union[str, np.ndarray],
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model_name: str = "VGG-Face",
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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@ -817,7 +819,7 @@ def stream(
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def extract_faces(
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img_path: str,
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img_path: Union[str, np.ndarray],
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target_size: Tuple[int, int] = (224, 224),
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detector_backend: str = "opencv",
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enforce_detection: bool = True,
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