github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-06 11:35:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix: documentation

Browse Source
This commit is contained in:
roberto-corno-nttdata 2024-12-09 16:00:27 +01:00
parent 79dedc08c1
commit d3a3f2b65f
9 changed files with 128 additions and 114 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

176
deepface/DeepFace.py
View File

@ -54,10 +54,11 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
Args:
model_name (str): model identifier
- VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
ArcFace, SFace, GhostFaceNet, Yolo-Face for face recognition
ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n, Yolov11s and
Yolov11m for face recognition
- Age, Gender, Emotion, Race for facial attributes
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, 'yolov11n', 'yolov11s','yolov11m', yunet,
fastmtcnn or centerface for face detectors
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, yolov11n,
yolov11s, yolov11m, yunet, fastmtcnn or centerface for face detectors
- Fasnet for spoofing
task (str): facial_recognition, facial_attribute, face_detector, spoofing
default is facial_recognition
@ -68,18 +69,18 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
def verify(
img1_path: Union[str, np.ndarray, List[float]],
img2_path: Union[str, np.ndarray, List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
silent: bool = False,
threshold: Optional[float] = None,
anti_spoofing: bool = False,
img1_path: Union[str, np.ndarray, List[float]],
img2_path: Union[str, np.ndarray, List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
silent: bool = False,
threshold: Optional[float] = None,
anti_spoofing: bool = False,
) -> Dict[str, Any]:
"""
Verify if an image pair represents the same person or different persons.
@ -93,7 +94,8 @@ def verify(
or pre-calculated embeddings.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet and Yolo-Face (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
@ -164,14 +166,14 @@ def verify(
def analyze(
img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
anti_spoofing: bool = False,
img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
anti_spoofing: bool = False,
) -> List[Dict[str, Any]]:
"""
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
@ -187,8 +189,8 @@ def analyze(
Set to False to avoid the exception for low-resolution images (default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
@ -263,20 +265,20 @@ def analyze(
def find(
img_path: Union[str, np.ndarray],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
refresh_database: bool = True,
anti_spoofing: bool = False,
batched: bool = False,
img_path: Union[str, np.ndarray],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
refresh_database: bool = True,
anti_spoofing: bool = False,
batched: bool = False,
) -> Union[List[pd.DataFrame], List[List[Dict[str, Any]]]]:
"""
Identify individuals in a database
@ -289,7 +291,8 @@ def find(
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet and Yolo-Face (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
@ -298,8 +301,8 @@ def find(
Set to False to avoid the exception for low-resolution images (default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
align (boolean): Perform alignment based on the eye positions (default is True).
@ -369,15 +372,15 @@ def find(
def represent(
img_path: Union[str, np.ndarray],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
anti_spoofing: bool = False,
max_faces: Optional[int] = None,
img_path: Union[str, np.ndarray],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
anti_spoofing: bool = False,
max_faces: Optional[int] = None,
) -> List[Dict[str, Any]]:
"""
Represent facial images as multi-dimensional vector embeddings.
@ -388,16 +391,16 @@ def represent(
include information for each detected face.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet and Yolo-Face
(default is VGG-Face.).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face.).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Default is True. Set to False to avoid the exception for low-resolution images
(default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
align (boolean): Perform alignment based on the eye positions (default is True).
@ -441,15 +444,15 @@ def represent(
def stream(
db_path: str = "",
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enable_face_analysis: bool = True,
source: Any = 0,
time_threshold: int = 5,
frame_threshold: int = 5,
anti_spoofing: bool = False,
db_path: str = "",
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enable_face_analysis: bool = True,
source: Any = 0,
time_threshold: int = 5,
frame_threshold: int = 5,
anti_spoofing: bool = False,
) -> None:
"""
Run real time face recognition and facial attribute analysis
@ -459,11 +462,12 @@ def stream(
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet and Yolo-Face (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
@ -499,15 +503,15 @@ def stream(
def extract_faces(
img_path: Union[str, np.ndarray],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
color_face: str = "rgb",
normalize_face: bool = True,
anti_spoofing: bool = False,
img_path: Union[str, np.ndarray],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
color_face: str = "rgb",
normalize_face: bool = True,
anti_spoofing: bool = False,
) -> List[Dict[str, Any]]:
"""
Extract faces from a given image
@ -517,8 +521,8 @@ def extract_faces(
as a string, numpy array (BGR), or base64 encoded images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Set to False to avoid the exception for low-resolution images (default is True).
@ -584,11 +588,11 @@ def cli() -> None:
def detectFace(
img_path: Union[str, np.ndarray],
target_size: tuple = (224, 224),
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
img_path: Union[str, np.ndarray],
target_size: tuple = (224, 224),
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
) -> Union[np.ndarray, None]:
"""
Deprecated face detection function. Use extract_faces for same functionality.
@ -601,8 +605,8 @@ def detectFace(
added to resize the image (default is (224, 224)).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Set to False to avoid the exception for low-resolution images (default is True).

2
deepface/models/face_detection/Yolo.py
View File

@ -94,6 +94,8 @@ class YoloDetectorClient(Detector):
right_eye = None
left_eye = None
# yolo-facev8 is detecting eyes through keypoints, while for v11 keypoints are always None
if result.keypoints is not None:
# right_eye_conf = result.keypoints.conf[0][0]
# left_eye_conf = result.keypoints.conf[0][1]

4
deepface/modules/demography.py
View File

@ -35,8 +35,8 @@ def analyze(
Set to False to avoid the exception for low-resolution images (default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).

4
deepface/modules/detection.py
View File

@ -38,8 +38,8 @@ def extract_faces(
as a string, numpy array (BGR), or base64 encoded images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv)
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv)
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Default is True. Set to False to avoid the exception for low-resolution images.

10
deepface/modules/modeling.py
View File

@ -37,7 +37,7 @@ def build_model(task: str, model_name: str) -> Any:
task (str): facial_recognition, facial_attribute, face_detector, spoofing
model_name (str): model identifier
- VGG-Face, Facenet, Facenet512, OpenFace, DeepFace, DeepID, Dlib,
ArcFace, SFace, GhostFaceNet for face recognition
ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n, Yolov11s and Yolov11m for face recognition
- Age, Gender, Emotion, Race for facial attributes
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, 'yolov11n', 'yolov11s', 'yolov11m', yunet,
fastmtcnn or centerface for face detectors
@ -61,10 +61,10 @@ def build_model(task: str, model_name: str) -> Any:
"ArcFace": ArcFace.ArcFaceClient,
"SFace": SFace.SFaceClient,
"GhostFaceNet": GhostFaceNet.GhostFaceNetClient,
"yolov8": YoloFacialRecognition.YoloFacialRecognitionClientV8n,
"yolov11n": YoloFacialRecognition.YoloFacialRecognitionClientV11n,
"yolov11s": YoloFacialRecognition.YoloFacialRecognitionClientV11s,
"yolov11m": YoloFacialRecognition.YoloFacialRecognitionClientV11m
"Yolov8": YoloFacialRecognition.YoloFacialRecognitionClientV8n,
"Yolov11n": YoloFacialRecognition.YoloFacialRecognitionClientV11n,
"Yolov11s": YoloFacialRecognition.YoloFacialRecognitionClientV11s,
"Yolov11m": YoloFacialRecognition.YoloFacialRecognitionClientV11m
},
"spoofing": {
"Fasnet": FasNet.Fasnet,

15
deepface/modules/recognition.py
View File

@ -45,7 +45,8 @@ def find(
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2'.
@ -54,7 +55,8 @@ def find(
Default is True. Set to False to avoid the exception for low-resolution images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8','yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'.
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8','yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip'.
align (boolean): Perform alignment based on the eye positions.
@ -359,7 +361,8 @@ def __find_bulk_embeddings(
employees (list): list of exact image paths
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
detector_backend (str): face detector model name
@ -474,7 +477,8 @@ def find_batched(
(used for anti-spoofing).
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2'.
@ -483,7 +487,8 @@ def find_batched(
Default is True. Set to False to avoid the exception for low-resolution images.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'.
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip'.
align (boolean): Perform alignment based on the eye positions.

2
deepface/modules/representation.py
View File

@ -30,7 +30,7 @@ def represent(
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
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n, Yolov11s and Yolov11m
enforce_detection (boolean): If no face is detected in an image, raise an exception.
Default is True. Set to False to avoid the exception for low-resolution images.

22
deepface/modules/streaming.py
View File

@ -42,11 +42,12 @@ def analysis(
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
@ -190,10 +191,11 @@ def search_identity(
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
Returns:
@ -374,8 +376,8 @@ def grab_facial_areas(
Args:
img (np.ndarray): image itself
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv).
threshold (int): threshold for facial area, discard smaller ones
Returns
result (list): list of tuple with x, y, w and h coordinates
@ -443,8 +445,8 @@ def perform_facial_recognition(
db_path (string): Path to the folder containing image files. All detected faces
in the database will be considered in the decision-making process.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv).
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip' (default is opencv).
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,

7
deepface/modules/verification.py
View File

@ -44,11 +44,12 @@ def verify(
or pre-calculated embeddings.
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace, GhostFaceNet, Yolov8, Yolov11n,
Yolov11s and Yolov11m (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m', 'centerface' or 'skip'
(default is opencv)
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
'centerface' or 'skip' (default is opencv)
distance_metric (string): Metric for measuring similarity. Options: 'cosine',
'euclidean', 'euclidean_l2' (default is cosine).