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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 os
import warnings import warnings
import logging 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 # this has to be set before importing tensorflow
os.environ["TF_USE_LEGACY_KERAS"] = "1" os.environ["TF_USE_LEGACY_KERAS"] = "1"
@ -373,7 +373,7 @@ def find(
def represent( 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", model_name: str = "VGG-Face",
enforce_detection: bool = True, enforce_detection: bool = True,
detector_backend: str = "opencv", detector_backend: str = "opencv",
@ -387,10 +387,10 @@ def represent(
Represent facial images as multi-dimensional vector embeddings. Represent facial images as multi-dimensional vector embeddings.
Args: 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 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, 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 each element should be a string or numpy array representing an image, and the function
will process images in batch. will process images in batch.

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