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List->Sequence typing

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galthran-wq 2025-02-11 17:03:00 +00:00
parent 035d3c8ba8
commit 3a9385fad8
2 changed files with 8 additions and 8 deletions
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8
deepface/DeepFace.py
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@ -2,7 +2,7 @@
import os
import warnings
import logging
from typing import Any, Dict, IO, List, Union, Optional
from typing import Any, Dict, IO, List, Union, Optional, Sequence
# this has to be set before importing tensorflow
os.environ["TF_USE_LEGACY_KERAS"] = "1"
@ -373,7 +373,7 @@ def find(
def represent(
img_path: Union[str, np.ndarray, IO[bytes], List[Union[str, np.ndarray]]],
img_path: Union[str, np.ndarray, IO[bytes], Sequence[Union[str, np.ndarray, IO[bytes]]]],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
@ -387,10 +387,10 @@ def represent(
Represent facial images as multi-dimensional vector embeddings.
Args:
img_path (str, np.ndarray, IO[bytes], or List[Union[str, np.ndarray]]): The exact path to the image, a numpy array
img_path (str, np.ndarray, IO[bytes], or Sequence[Union[str, np.ndarray, IO[bytes]]]): The exact path to the image, a numpy array
in BGR format, a file object that supports at least `.read` and is opened in binary
mode, or a base64 encoded image. If the source image contains multiple faces,
the result will include information for each detected face. If a list is provided,
the result will include information for each detected face. If a sequence is provided,
each element should be a string or numpy array representing an image, and the function
will process images in batch.

8
deepface/modules/representation.py
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@ -1,5 +1,5 @@
# built-in dependencies
from typing import Any, Dict, List, Union, Optional
from typing import Any, Dict, List, Union, Optional, Sequence, IO
# 3rd party dependencies
import numpy as np
@ -11,7 +11,7 @@ from deepface.models.FacialRecognition import FacialRecognition
def represent(
img_path: Union[str, np.ndarray, List[Union[str, np.ndarray]]],
img_path: Union[str, IO[bytes], np.ndarray, Sequence[Union[str, np.ndarray, IO[bytes]]]],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
@ -25,8 +25,8 @@ def represent(
Represent facial images as multi-dimensional vector embeddings.
Args:
img_path (str, np.ndarray, or list): The exact path to the image, a numpy array in BGR format,
a base64 encoded image, or a list of these. If the source image contains multiple faces,
img_path (str, np.ndarray, or Sequence[Union[str, np.ndarray]]): The exact path to the image, a numpy array in BGR format,
a base64 encoded image, or a sequence of these. If the source image contains multiple faces,
the result will include information for each detected face.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,