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Update master branch. Merge branch 'master' into feat/batch-predict-age-and-gender

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5
README.md
View File

@ -121,7 +121,7 @@ models = [
"ArcFace",
"Dlib",
"SFace",
"GhostFaceNet",
"GhostFaceNet"
]
#face verification
@ -223,6 +223,9 @@ backends = [
'retinaface',
'mediapipe',
'yolov8',
'yolov11s',
'yolov11n',
'yolov11m',
'yunet',
'centerface',
]

166
deepface/DeepFace.py
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@ -54,10 +54,10 @@ 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 for face recognition
ArcFace, SFace and GhostFaceNet for face recognition
- Age, Gender, Emotion, Race for facial attributes
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, 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 +68,18 @@ 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.
@ -96,8 +96,8 @@ def verify(
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', '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).
@ -164,14 +164,14 @@ 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 +187,8 @@ 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', '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 +263,20 @@ 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
@ -298,8 +298,8 @@ 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', '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 +369,15 @@ 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.
@ -396,8 +396,8 @@ def represent(
(default is True).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', '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 +441,15 @@ 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
@ -462,8 +462,8 @@ def stream(
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', '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 +499,15 @@ 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 +517,8 @@ 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', '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 +584,11 @@ 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 +601,8 @@ 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', '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).

12
deepface/api/src/modules/core/routes.py
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@ -72,7 +72,9 @@ def extract_image_from_request(img_key: str) -> Union[str, np.ndarray]:
@blueprint.route("/represent", methods=["POST"])
def represent():
input_args = request.get_json() or request.form.to_dict()
input_args = (request.is_json and request.get_json()) or (
request.form and request.form.to_dict()
)
try:
img = extract_image_from_request("img")
@ -96,7 +98,9 @@ def represent():
@blueprint.route("/verify", methods=["POST"])
def verify():
input_args = request.get_json() or request.form.to_dict()
input_args = (request.is_json and request.get_json()) or (
request.form and request.form.to_dict()
)
try:
img1 = extract_image_from_request("img1")
@ -126,7 +130,9 @@ def verify():
@blueprint.route("/analyze", methods=["POST"])
def analyze():
input_args = request.get_json() or request.form.to_dict()
input_args = (request.is_json and request.get_json()) or (
request.form and request.form.to_dict()
)
try:
img = extract_image_from_request("img")

21
deepface/commons/weight_utils.py
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@ -128,8 +128,9 @@ def download_all_models_in_one_shot() -> None:
WEIGHTS_URL as SSD_WEIGHTS,
)
from deepface.models.face_detection.Yolo import (
WEIGHT_URL as YOLOV8_WEIGHTS,
WEIGHT_NAME as YOLOV8_WEIGHT_NAME,
WEIGHT_URLS as YOLO_WEIGHTS,
WEIGHT_NAMES as YOLO_WEIGHT_NAMES,
YoloModel
)
from deepface.models.face_detection.YuNet import WEIGHTS_URL as YUNET_WEIGHTS
from deepface.models.face_detection.Dlib import WEIGHTS_URL as DLIB_FD_WEIGHTS
@ -162,8 +163,20 @@ def download_all_models_in_one_shot() -> None:
SSD_MODEL,
SSD_WEIGHTS,
{
"filename": YOLOV8_WEIGHT_NAME,
"url": YOLOV8_WEIGHTS,
"filename": YOLO_WEIGHT_NAMES[YoloModel.V8N.value],
"url": YOLO_WEIGHTS[YoloModel.V8N.value],
},
{
"filename": YOLO_WEIGHT_NAMES[YoloModel.V11N.value],
"url": YOLO_WEIGHTS[YoloModel.V11N.value],
},
{
"filename": YOLO_WEIGHT_NAMES[YoloModel.V11S.value],
"url": YOLO_WEIGHTS[YoloModel.V11S.value],
},
{
"filename": YOLO_WEIGHT_NAMES[YoloModel.V11M.value],
"url": YOLO_WEIGHTS[YoloModel.V11M.value],
},
YUNET_WEIGHTS,
DLIB_FD_WEIGHTS,

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

@ -46,7 +46,7 @@ class CenterFaceClient(Detector):
"""
resp = []
threshold = float(os.getenv("CENTERFACE_THRESHOLD", "0.80"))
threshold = float(os.getenv("CENTERFACE_THRESHOLD", "0.35"))
# BUG: model causes problematic results from 2nd call if it is not flushed
# detections, landmarks = self.model.forward(

2
deepface/models/face_detection/FastMtCnn.py
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@ -92,4 +92,4 @@ def xyxy_to_xywh(regions: Union[list, tuple]) -> tuple:
x, y, x_plus_w, y_plus_h = regions[0], regions[1], regions[2], regions[3]
w = x_plus_w - x
h = y_plus_h - y
return (x, y, w, h)
return (int(x), int(y), int(w), int(h))

76
deepface/models/face_detection/Yolo.py
View File

@ -1,29 +1,45 @@
# built-in dependencies
import os
from typing import Any, List
from typing import List, Any
from enum import Enum
# 3rd party dependencies
import numpy as np
# project dependencies
from deepface.models.Detector import Detector, FacialAreaRegion
from deepface.commons import weight_utils
from deepface.commons.logger import Logger
from deepface.commons import weight_utils
logger = Logger()
class YoloModel(Enum):
V8N = 0
V11N = 1
V11S = 2
V11M = 3
# Model's weights paths
WEIGHT_NAME = "yolov8n-face.pt"
WEIGHT_NAMES = ["yolov8n-face.pt",
"yolov11n-face.pt",
"yolov11s-face.pt",
"yolov11m-face.pt"]
# Google Drive URL from repo (https://github.com/derronqi/yolov8-face) ~6MB
WEIGHT_URL = "https://drive.google.com/uc?id=1qcr9DbgsX3ryrz2uU8w4Xm3cOrRywXqb"
WEIGHT_URLS = ["https://drive.google.com/uc?id=1qcr9DbgsX3ryrz2uU8w4Xm3cOrRywXqb",
"https://github.com/akanametov/yolo-face/releases/download/v0.0.0/yolov11n-face.pt",
"https://github.com/akanametov/yolo-face/releases/download/v0.0.0/yolov11s-face.pt",
"https://github.com/akanametov/yolo-face/releases/download/v0.0.0/yolov11m-face.pt"]
class YoloClient(Detector):
def __init__(self):
self.model = self.build_model()
class YoloDetectorClient(Detector):
def __init__(self, model: YoloModel):
super().__init__()
self.model = self.build_model(model)
def build_model(self) -> Any:
def build_model(self, model: YoloModel) -> Any:
"""
Build a yolo detector model
Returns:
@ -40,7 +56,7 @@ class YoloClient(Detector):
) from e
weight_file = weight_utils.download_weights_if_necessary(
file_name=WEIGHT_NAME, source_url=WEIGHT_URL
file_name=WEIGHT_NAMES[model.value], source_url=WEIGHT_URLS[model.value]
)
# Return face_detector
@ -69,21 +85,27 @@ class YoloClient(Detector):
# For each face, extract the bounding box, the landmarks and confidence
for result in results:
if result.boxes is None or result.keypoints is None:
if result.boxes is None:
continue
# Extract the bounding box and the confidence
x, y, w, h = result.boxes.xywh.tolist()[0]
confidence = result.boxes.conf.tolist()[0]
# right_eye_conf = result.keypoints.conf[0][0]
# left_eye_conf = result.keypoints.conf[0][1]
right_eye = result.keypoints.xy[0][0].tolist()
left_eye = result.keypoints.xy[0][1].tolist()
right_eye = None
left_eye = None
# eyes are list of float, need to cast them tuple of int
left_eye = tuple(int(i) for i in left_eye)
right_eye = tuple(int(i) for i in right_eye)
# 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]
right_eye = result.keypoints.xy[0][0].tolist()
left_eye = result.keypoints.xy[0][1].tolist()
# eyes are list of float, need to cast them tuple of int
left_eye = tuple(int(i) for i in left_eye)
right_eye = tuple(int(i) for i in right_eye)
x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
facial_area = FacialAreaRegion(
@ -98,3 +120,23 @@ class YoloClient(Detector):
resp.append(facial_area)
return resp
class YoloDetectorClientV8n(YoloDetectorClient):
def __init__(self):
super().__init__(YoloModel.V8N)
class YoloDetectorClientV11n(YoloDetectorClient):
def __init__(self):
super().__init__(YoloModel.V11N)
class YoloDetectorClientV11s(YoloDetectorClient):
def __init__(self):
super().__init__(YoloModel.V11S)
class YoloDetectorClientV11m(YoloDetectorClient):
def __init__(self):
super().__init__(YoloModel.V11M)

2
deepface/models/facial_recognition/Facenet.py
View File

@ -64,7 +64,7 @@ class FaceNet128dClient(FacialRecognition):
class FaceNet512dClient(FacialRecognition):
"""
FaceNet-1512d model class
FaceNet-512d model class
"""
def __init__(self):

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', '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).

89
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', '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.
@ -255,7 +255,7 @@ def detect_faces(
)
return [
expand_and_align_face(
extract_face(
facial_area=facial_area,
img=img,
align=align,
@ -267,7 +267,7 @@ def detect_faces(
]
def expand_and_align_face(
def extract_face(
facial_area: FacialAreaRegion,
img: np.ndarray,
align: bool,
@ -301,15 +301,32 @@ def expand_and_align_face(
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
# align original image, then find projection of detected face area after alignment
if align is True: # and left_eye is not None and right_eye is not None:
aligned_img, angle = align_img_wrt_eyes(img=img, left_eye=left_eye, right_eye=right_eye)
# we were aligning the original image before, but this comes with an extra cost
# instead we now focus on the facial area with a margin
# and align it instead of original image to decrese the cost
sub_img, relative_x, relative_y = extract_sub_image(img=img, facial_area=(x, y, w, h))
aligned_sub_img, angle = align_img_wrt_eyes(
img=sub_img, left_eye=left_eye, right_eye=right_eye
)
rotated_x1, rotated_y1, rotated_x2, rotated_y2 = project_facial_area(
facial_area=(x, y, x + w, y + h), angle=angle, size=(img.shape[0], img.shape[1])
facial_area=(
relative_x,
relative_y,
relative_x + w,
relative_y + h,
),
angle=angle,
size=(sub_img.shape[0], sub_img.shape[1]),
)
detected_face = aligned_img[
detected_face = aligned_sub_img[
int(rotated_y1) : int(rotated_y2), int(rotated_x1) : int(rotated_x2)
]
# do not spend memory for these temporary variables anymore
del aligned_sub_img, sub_img
# restore x, y, le and re before border added
x = x - width_border
y = y - height_border
@ -339,14 +356,66 @@ def expand_and_align_face(
mouth_left=mouth_left,
mouth_right=mouth_right,
),
confidence=confidence,
confidence=confidence or 0,
)
def extract_sub_image(
img: np.ndarray, facial_area: Tuple[int, int, int, int]
) -> Tuple[np.ndarray, int, int]:
"""
Get the sub image with given facial area while expanding the facial region
to ensure alignment does not shift the face outside the image.
This function doubles the height and width of the face region,
and adds black pixels if necessary.
Args:
- img (np.ndarray): pre-loaded image with detected face
- facial_area (tuple of int): Representing the (x, y, w, h) of the facial area.
Returns:
- extracted_face (np.ndarray): expanded facial image
- relative_x (int): adjusted x-coordinates relative to the expanded region
- relative_y (int): adjusted y-coordinates relative to the expanded region
"""
x, y, w, h = facial_area
relative_x = int(0.5 * w)
relative_y = int(0.5 * h)
# calculate expanded coordinates
x1, y1 = x - relative_x, y - relative_y
x2, y2 = x + w + relative_x, y + h + relative_y
# most of the time, the expanded region fits inside the image
if x1 >= 0 and y1 >= 0 and x2 <= img.shape[1] and y2 <= img.shape[0]:
return img[y1:y2, x1:x2], relative_x, relative_y
# but sometimes, we need to add black pixels
# ensure the coordinates are within bounds
x1, y1 = max(0, x1), max(0, y1)
x2, y2 = min(img.shape[1], x2), min(img.shape[0], y2)
cropped_region = img[y1:y2, x1:x2]
# create a black image
extracted_face = np.zeros(
(h + 2 * relative_y, w + 2 * relative_x, img.shape[2]), dtype=img.dtype
)
# map the cropped region
start_x = max(0, relative_x - x)
start_y = max(0, relative_y - y)
extracted_face[
start_y : start_y + cropped_region.shape[0], start_x : start_x + cropped_region.shape[1]
] = cropped_region
return extracted_face, relative_x, relative_y
def align_img_wrt_eyes(
img: np.ndarray,
left_eye: Union[list, tuple],
right_eye: Union[list, tuple],
left_eye: Optional[Union[list, tuple]],
right_eye: Optional[Union[list, tuple]],
) -> Tuple[np.ndarray, float]:
"""
Align a given image horizantally with respect to their left and right eye locations

17
deepface/modules/modeling.py
View File

@ -11,7 +11,7 @@ from deepface.models.facial_recognition import (
SFace,
Dlib,
Facenet,
GhostFaceNet,
GhostFaceNet
)
from deepface.models.face_detection import (
FastMtCnn,
@ -21,7 +21,7 @@ from deepface.models.face_detection import (
Dlib as DlibDetector,
RetinaFace,
Ssd,
Yolo,
Yolo as YoloFaceDetector,
YuNet,
CenterFace,
)
@ -36,10 +36,10 @@ 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 and GhostFaceNet for face recognition
- Age, Gender, Emotion, Race for facial attributes
- opencv, mtcnn, ssd, dlib, retinaface, mediapipe, yolov8, 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
Returns:
built model class
@ -59,7 +59,7 @@ def build_model(task: str, model_name: str) -> Any:
"Dlib": Dlib.DlibClient,
"ArcFace": ArcFace.ArcFaceClient,
"SFace": SFace.SFaceClient,
"GhostFaceNet": GhostFaceNet.GhostFaceNetClient,
"GhostFaceNet": GhostFaceNet.GhostFaceNetClient
},
"spoofing": {
"Fasnet": FasNet.Fasnet,
@ -77,7 +77,10 @@ def build_model(task: str, model_name: str) -> Any:
"dlib": DlibDetector.DlibClient,
"retinaface": RetinaFace.RetinaFaceClient,
"mediapipe": MediaPipe.MediaPipeClient,
"yolov8": Yolo.YoloClient,
"yolov8": YoloFaceDetector.YoloDetectorClientV8n,
"yolov11n": YoloFaceDetector.YoloDetectorClientV11n,
"yolov11s": YoloFaceDetector.YoloDetectorClientV11s,
"yolov11m": YoloFaceDetector.YoloDetectorClientV11m,
"yunet": YuNet.YuNetClient,
"fastmtcnn": FastMtCnn.FastMtCnnClient,
"centerface": CenterFace.CenterFaceClient,

6
deepface/modules/recognition.py
View File

@ -54,7 +54,8 @@ 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', 'centerface' or 'skip'.
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8','yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip'.
align (boolean): Perform alignment based on the eye positions.
@ -483,7 +484,8 @@ 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', 'centerface' or 'skip'.
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip'.
align (boolean): Perform alignment based on the eye positions.

5
deepface/modules/representation.py
View File

@ -36,7 +36,8 @@ def represent(
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', 'centerface' or 'skip'.
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s',
'yolov11m', 'centerface' or 'skip'.
align (boolean): Perform alignment based on the eye positions.
@ -115,7 +116,7 @@ def represent(
raise ValueError("Spoof detected in the given image.")
img = img_obj["face"]
# rgb to bgr
# bgr to rgb
img = img[:, :, ::-1]
region = img_obj["facial_area"]

18
deepface/modules/streaming.py
View File

@ -42,11 +42,11 @@ 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 and GhostFaceNet (default is VGG-Face)
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', '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).
@ -192,8 +192,8 @@ def search_identity(
model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', '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 +374,8 @@ def grab_facial_areas(
Args:
img (np.ndarray): image itself
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', '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 +443,8 @@ 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', '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,

4
deepface/modules/verification.py
View File

@ -47,8 +47,8 @@ def verify(
OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', '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).

32
tests/test_extract_faces.py
View File

@ -119,25 +119,31 @@ def image_to_base64(image_path):
def test_facial_coordinates_are_in_borders():
detectors = ["retinaface", "mtcnn"]
expected_faces = [7, 6]
img_path = "dataset/selfie-many-people.jpg"
img = cv2.imread(img_path)
height, width, _ = img.shape
results = DeepFace.extract_faces(img_path=img_path)
for i, detector_backend in enumerate(detectors):
results = DeepFace.extract_faces(img_path=img_path, detector_backend=detector_backend)
assert len(results) > 0
# this is a hard example, mtcnn can detect 6 and retinaface can detect 7 faces
# be sure all those faces detected. any change in detection module can break this.
assert len(results) == expected_faces[i]
for result in results:
facial_area = result["facial_area"]
for result in results:
facial_area = result["facial_area"]
x = facial_area["x"]
y = facial_area["y"]
w = facial_area["w"]
h = facial_area["h"]
x = facial_area["x"]
y = facial_area["y"]
w = facial_area["w"]
h = facial_area["h"]
assert x >= 0
assert y >= 0
assert x + w < width
assert y + h < height
assert x >= 0
assert y >= 0
assert x + w < width
assert y + h < height
logger.info("✅ facial area coordinates are all in image borders")
logger.info(f"✅ facial area coordinates are all in image borders for {detector_backend}")

5
tests/visual-test.py
View File

@ -21,7 +21,7 @@ model_names = [
"Dlib",
"ArcFace",
"SFace",
"GhostFaceNet",
"GhostFaceNet"
]
detector_backends = [
@ -34,6 +34,9 @@ detector_backends = [
"retinaface",
"yunet",
"yolov8",
"yolov11n",
"yolov11s",
"yolov11m",
"centerface",
]