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updating docstrings to appease linter

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