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updating docstrings to appease linter

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Samuel J. Woodward 2025-01-10 11:46:28 -05:00
parent 7112766966
commit 86fa2dfa83
1 changed files with 9 additions and 9 deletions
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deepface/DeepFace.py
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@ -176,9 +176,9 @@ def analyze(
"""
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
Args:
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array in BGR format,
or a base64 encoded image. If the source image contains multiple faces, the result will
include information for each detected face.
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
in BGR format, or a base64 encoded image. If the source image contains multiple faces,
the result will include information for each detected face.
actions (tuple): Attributes to analyze. The default is ('age', 'gender', 'emotion', 'race').
You can exclude some of these attributes from the analysis if needed.
@ -281,9 +281,9 @@ def find(
"""
Identify individuals in a database
Args:
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array in BGR format,
or a base64 encoded image. If the source image contains multiple faces, the result will
include information for each detected face.
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
in BGR format, or a base64 encoded image. If the source image contains multiple
faces, the result will include information for each detected face.
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
@ -383,9 +383,9 @@ def represent(
Represent facial images as multi-dimensional vector embeddings.
Args:
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array in BGR format,
or a base64 encoded image. If the source image contains multiple faces, the result will
include information for each detected face.
img_path (str or np.ndarray or IO[bytes]): The exact path to the image, a numpy array
in BGR format, or a base64 encoded image. 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,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet