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adding expand percentage argument for detection

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This commit is contained in:
Sefik Ilkin Serengil 2024-01-31 23:43:30 +00:00
parent 9494d47e31
commit 96d29ab069
17 changed files with 314 additions and 79 deletions
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20
deepface/DeepFace.py
View File

@ -58,6 +58,7 @@ def verify(
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
) -> Dict[str, Any]:
"""
@ -83,6 +84,8 @@ def verify(
align (bool): Flag to enable face alignment (default is True).
expand_percentage (int): expand detected facial area with a percentage (default is 0).
normalization (string): Normalize the input image before feeding it to the model.
Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base)
@ -119,6 +122,7 @@ def verify(
distance_metric=distance_metric,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
normalization=normalization,
)
@ -129,6 +133,7 @@ def analyze(
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
) -> List[Dict[str, Any]]:
"""
@ -152,6 +157,8 @@ def analyze(
align (boolean): Perform alignment based on the eye positions (default is True).
expand_percentage (int): expand detected facial area with a percentage (default is 0).
silent (boolean): Suppress or allow some log messages for a quieter analysis process
(default is False).
@ -209,6 +216,7 @@ def analyze(
enforce_detection=enforce_detection,
detector_backend=detector_backend,
align=align,
expand_percentage=expand_percentage,
silent=silent,
)
@ -221,6 +229,7 @@ def find(
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,
@ -249,6 +258,8 @@ def find(
align (boolean): Perform alignment based on the eye positions (default is True).
expand_percentage (int): expand detected facial area with a percentage (default is 0).
threshold (float): Specify a threshold to determine whether a pair represents the same
person or different individuals. This threshold is used for comparing distances.
If left unset, default pre-tuned threshold values will be applied based on the specified
@ -286,6 +297,7 @@ def find(
enforce_detection=enforce_detection,
detector_backend=detector_backend,
align=align,
expand_percentage=expand_percentage,
threshold=threshold,
normalization=normalization,
silent=silent,
@ -298,6 +310,7 @@ def represent(
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
) -> List[Dict[str, Any]]:
"""
@ -320,6 +333,8 @@ def represent(
align (boolean): Perform alignment based on the eye positions (default is True).
expand_percentage (int): expand detected facial area with a percentage (default is 0).
normalization (string): Normalize the input image before feeding it to the model.
Default is base. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace
(default is base).
@ -346,6 +361,7 @@ def represent(
enforce_detection=enforce_detection,
detector_backend=detector_backend,
align=align,
expand_percentage=expand_percentage,
normalization=normalization,
)
@ -409,6 +425,7 @@ def extract_faces(
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
) -> List[Dict[str, Any]]:
"""
@ -429,6 +446,8 @@ def extract_faces(
align (bool): Flag to enable face alignment (default is True).
expand_percentage (int): expand detected facial area with a percentage (default is 0).
grayscale (boolean): Flag to convert the image to grayscale before
processing (default is False).
@ -448,6 +467,7 @@ def extract_faces(
detector_backend=detector_backend,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
grayscale=grayscale,
human_readable=True,
)

31
deepface/detectors/DetectorWrapper.py
View File

@ -12,6 +12,9 @@ from deepface.detectors import (
Yolo,
YuNet,
)
from deepface.commons.logger import Logger
logger = Logger(module="deepface/detectors/DetectorWrapper.py")
def build_model(detector_backend: str) -> Any:
@ -52,19 +55,35 @@ def build_model(detector_backend: str) -> Any:
return face_detector_obj[detector_backend]
def detect_faces(detector_backend: str, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
detector_backend: str, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect face(s) from a given image
Args:
detector_backend (str): detector name
img (np.ndarray): pre-loaded image
alig (bool): enable or disable alignment after detection
align (bool): enable or disable alignment after detection
expand_percentage (int): expand detected facial area with a percentage (default is 0).
Returns:
results (List[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
face_detector: Detector = build_model(detector_backend)
return face_detector.detect_faces(img=img, align=align)
if expand_percentage < 0:
logger.warn(
f"Expand percentage cannot be negative but you set it to {expand_percentage}."
"Overwritten it to 0."
)
expand_percentage = 0
return face_detector.detect_faces(img=img, align=align, expand_percentage=expand_percentage)

25
deepface/detectors/Dlib.py
View File

@ -56,18 +56,27 @@ class DlibClient(Detector):
detector["sp"] = sp
return detector
def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with dlib
Args:
face_detector (Any): dlib face detector object
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[DetectedFace]): A list of DetectedFace objects
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
# this is not a must dependency. do not import it in the global level.
@ -79,6 +88,12 @@ class DlibClient(Detector):
"Please install using 'pip install dlib' "
) from e
if expand_percentage != 0:
logger.warn(
f"You set expand_percentage argument to {expand_percentage},"
"but dlib hog handles detection by itself"
)
resp = []
sp = self.model["sp"]

29
deepface/detectors/FastMtCnn.py
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@ -12,17 +12,27 @@ class FastMtCnnClient(Detector):
def __init__(self):
self.model = self.build_model()
def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with mtcnn
Args:
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[DetectedFace]): A list of DetectedFace objects
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
resp = []
@ -37,7 +47,16 @@ class FastMtCnnClient(Detector):
for current_detection in zip(*detections):
x, y, w, h = xyxy_to_xywh(current_detection[0])
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * expand_percentage) / 100)) # expand bottom
# detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
confidence = current_detection[1]

29
deepface/detectors/MediaPipe.py
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@ -29,17 +29,27 @@ class MediaPipeClient(Detector):
face_detection = mp_face_detection.FaceDetection(min_detection_confidence=0.7)
return face_detection
def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with mediapipe
Args:
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[DetectedFace): A list of DetectedFace objects
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
resp = []
@ -74,7 +84,16 @@ class MediaPipeClient(Detector):
# left_ear = (int(landmarks[5].x * img_width), int(landmarks[5].y * img_height))
if x > 0 and y > 0:
detected_face = img[y : y + h, x : x + w]
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * expand_percentage) / 100)) # expand bottom
# detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
if align:

29
deepface/detectors/MtCnn.py
View File

@ -13,17 +13,27 @@ class MtCnnClient(Detector):
def __init__(self):
self.model = MTCNN()
def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with mtcnn
Args:
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[DetectedFace]): A list of DetectedFace objects
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
@ -40,7 +50,16 @@ class MtCnnClient(Detector):
for current_detection in detections:
x, y, w, h = current_detection["box"]
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * expand_percentage) / 100)) # expand bottom
# detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
confidence = current_detection["confidence"]

27
deepface/detectors/OpenCv.py
View File

@ -25,18 +25,27 @@ class OpenCvClient(Detector):
detector["eye_detector"] = self.__build_cascade("haarcascade_eye")
return detector
def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with opencv
Args:
face_detector (Any): opencv face detector object
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
resp = []
@ -56,7 +65,15 @@ class OpenCvClient(Detector):
if len(faces) > 0:
for (x, y, w, h), confidence in zip(faces, scores):
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * expand_percentage) / 100)) # expand bottom
# detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
if align:
left_eye, right_eye = self.find_eyes(img=detected_face)

30
deepface/detectors/RetinaFace.py
View File

@ -9,17 +9,27 @@ class RetinaFaceClient(Detector):
def __init__(self):
self.model = rf.build_model()
def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with retinaface
Args:
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[DetectedFace]): A list of DetectedFace object
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
resp = []
@ -38,10 +48,14 @@ class RetinaFaceClient(Detector):
img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
confidence = identity["score"]
# detected_face = img[int(y):int(y+h), int(x):int(x+w)] #opencv
detected_face = img[
facial_area[1] : facial_area[3], facial_area[0] : facial_area[2]
]
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * expand_percentage) / 100)) # expand bottom
# detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
if align:
landmarks = identity["landmarks"]

48
deepface/detectors/Ssd.py
View File

@ -71,17 +71,27 @@ class SsdClient(Detector):
return detector
def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with ssd
Args:
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[DetectedFace]): A list of DetectedFace object
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
resp = []
@ -92,16 +102,14 @@ class SsdClient(Detector):
target_size = (300, 300)
base_img = img.copy() # we will restore base_img to img later
original_size = img.shape
img = cv2.resize(img, target_size)
current_img = cv2.resize(img, target_size)
aspect_ratio_x = original_size[1] / target_size[1]
aspect_ratio_y = original_size[0] / target_size[0]
imageBlob = cv2.dnn.blobFromImage(image=img)
imageBlob = cv2.dnn.blobFromImage(image=current_img)
face_detector = self.model["face_detector"]
face_detector.setInput(imageBlob)
@ -126,17 +134,21 @@ class SsdClient(Detector):
bottom = instance["bottom"]
top = instance["top"]
detected_face = base_img[
int(top * aspect_ratio_y) : int(bottom * aspect_ratio_y),
int(left * aspect_ratio_x) : int(right * aspect_ratio_x),
]
x = int(left * aspect_ratio_x)
y = int(top * aspect_ratio_y)
w = int(right * aspect_ratio_x) - int(left * aspect_ratio_x)
h = int(bottom * aspect_ratio_y) - int(top * aspect_ratio_y)
face_region = FacialAreaRegion(
x=int(left * aspect_ratio_x),
y=int(top * aspect_ratio_y),
w=int(right * aspect_ratio_x) - int(left * aspect_ratio_x),
h=int(bottom * aspect_ratio_y) - int(top * aspect_ratio_y),
)
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * expand_percentage) / 100)) # expand bottom
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
face_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
confidence = instance["confidence"]

27
deepface/detectors/Yolo.py
View File

@ -51,18 +51,27 @@ class YoloClient(Detector):
# Return face_detector
return YOLO(weight_path)
def detect_faces(self, img: np.ndarray, align: bool = False) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = False, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with yolo
Args:
face_detector (Any): yolo face detector object
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
resp = []
@ -78,7 +87,15 @@ class YoloClient(Detector):
x, y, w, h = int(x - w / 2), int(y - h / 2), int(w), int(h)
region = FacialAreaRegion(x=x, y=y, w=w, h=h)
detected_face = img[y : y + h, x : x + w].copy()
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * expand_percentage) / 100)) # expand bottom
# detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
if align:
# Tuple of x,y and confidence for left eye

29
deepface/detectors/YuNet.py
View File

@ -49,17 +49,27 @@ class YuNetClient(Detector):
) from err
return face_detector
def detect_faces(self, img: np.ndarray, align: bool = True) -> List[DetectedFace]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List[DetectedFace]:
"""
Detect and align face with yunet
Args:
img (np.ndarray): pre-loaded image
align (bool): default is true
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[DetectedFace]): A list of DetectedFace objects
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
# FaceDetector.detect_faces does not support score_threshold parameter.
@ -115,7 +125,16 @@ class YuNetClient(Detector):
)
confidence = face[-1]
confidence = f"{confidence:.2f}"
detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
# expand the facial area to be extracted and stay within img.shape limits
x2 = max(0, x - int((w * expand_percentage) / 100)) # expand left
y2 = max(0, y - int((h * expand_percentage) / 100)) # expand top
w2 = min(img.shape[1], w + int((w * expand_percentage) / 100)) # expand right
h2 = min(img.shape[0], h + int((h * expand_percentage) / 100)) # expand bottom
# detected_face = img[int(y) : int(y + h), int(x) : int(x + w)]
detected_face = img[int(y2) : int(y2 + h2), int(x2) : int(x2 + w2)]
img_region = FacialAreaRegion(x=x, y=y, w=w, h=h)
if align:
detected_face = detection.align_face(detected_face, (x_re, y_re), (x_le, y_le))

27
deepface/models/Detector.py
View File

@ -8,19 +8,28 @@ import numpy as np
# pylint: disable=unnecessary-pass, too-few-public-methods
class Detector(ABC):
@abstractmethod
def detect_faces(self, img: np.ndarray, align: bool = True) -> List["DetectedFace"]:
def detect_faces(
self, img: np.ndarray, align: bool = True, expand_percentage: int = 0
) -> List["DetectedFace"]:
"""
Detect faces from a given image
Interface for detect and align face
Args:
img (np.ndarray): pre-loaded image as a NumPy array
align (bool): enable or disable alignment after face detection
img (np.ndarray): pre-loaded image as numpy array
align (bool): flag to enable or disable alignment after detection (default is True)
expand_percentage (int): expand detected facial area with a percentage
Returns:
results (List[DetectedFace]): A list of DetectedFace object
results (List[Tuple[DetectedFace]): A list of DetectedFace objects
where each object contains:
- face (np.ndarray): The detected face as a NumPy array.
- face_region (List[float]): The image region represented as
a list of floats e.g. [x, y, w, h]
- confidence (float): The confidence score associated with the detected face.
- img (np.ndarray): The detected face as a NumPy array.
- facial_area (FacialAreaRegion): The facial area region represented as x, y, w, h
- confidence (float): The confidence score associated with the detected face.
"""
pass

4
deepface/modules/demography.py
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@ -16,6 +16,7 @@ def analyze(
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
) -> List[Dict[str, Any]]:
"""
@ -40,6 +41,8 @@ def analyze(
align (boolean): Perform alignment based on the eye positions (default is True).
expand_percentage (int): expand detected facial area with a percentage (default is 0).
silent (boolean): Suppress or allow some log messages for a quieter analysis process
(default is False).
@ -120,6 +123,7 @@ def analyze(
grayscale=False,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
)
for img_obj in img_objs:

10
deepface/modules/detection.py
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@ -31,6 +31,7 @@ def extract_faces(
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
human_readable=False,
) -> List[Dict[str, Any]]:
@ -52,6 +53,8 @@ def extract_faces(
align (bool): Flag to enable face alignment (default is True).
expand_percentage (int): expand detected facial area with a percentage
grayscale (boolean): Flag to convert the image to grayscale before
processing (default is False).
@ -75,7 +78,12 @@ def extract_faces(
if detector_backend == "skip":
face_objs = [DetectedFace(img=img, facial_area=base_region, confidence=0)]
else:
face_objs = DetectorWrapper.detect_faces(detector_backend, img, align)
face_objs = DetectorWrapper.detect_faces(
detector_backend=detector_backend,
img=img,
align=align,
expand_percentage=expand_percentage,
)
# in case of no face found
if len(face_objs) == 0 and enforce_detection is True:

19
deepface/modules/recognition.py
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@ -26,6 +26,7 @@ def find(
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,
@ -55,6 +56,8 @@ def find(
align (boolean): Perform alignment based on the eye positions.
expand_percentage (int): expand detected facial area with a percentage (default is 0).
threshold (float): Specify a threshold to determine whether a pair represents the same
person or different individuals. This threshold is used for comparing distances.
If left unset, default pre-tuned threshold values will be applied based on the specified
@ -211,6 +214,7 @@ def find(
grayscale=False,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
)
resp_obj = []
@ -309,6 +313,7 @@ def __find_bulk_embeddings(
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
silent: bool = False,
):
@ -317,15 +322,24 @@ def __find_bulk_embeddings(
Args:
employees (list): list of exact image paths
model_name (str): facial recognition model name
target_size (tuple): expected input shape of facial
recognition model
target_size (tuple): expected input shape of facial recognition model
detector_backend (str): face detector model name
enforce_detection (bool): set this to False if you
want to proceed when you cannot detect any face
align (bool): enable or disable alignment of image
before feeding to facial recognition model
expand_percentage (int): expand detected facial area with a
percentage (default is 0).
normalization (bool): normalization technique
silent (bool): enable or disable informative logging
Returns:
representations (list): pivot list of embeddings with
@ -344,6 +358,7 @@ def __find_bulk_embeddings(
grayscale=False,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
)
for img_obj in img_objs:

4
deepface/modules/representation.py
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@ -16,6 +16,7 @@ def represent(
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
) -> List[Dict[str, Any]]:
"""
@ -37,6 +38,8 @@ def represent(
align (boolean): Perform alignment based on the eye positions.
expand_percentage (int): expand detected facial area with a percentage (default is 0).
normalization (string): Normalize the input image before feeding it to the model.
Default is base. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace
@ -69,6 +72,7 @@ def represent(
grayscale=False,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
)
else: # skip
# Try load. If load error, will raise exception internal

5
deepface/modules/verification.py
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@ -19,6 +19,7 @@ def verify(
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
) -> Dict[str, Any]:
"""
@ -49,6 +50,8 @@ def verify(
align (bool): Flag to enable face alignment (default is True).
expand_percentage (int): expand detected facial area with a percentage (default is 0).
normalization (string): Normalize the input image before feeding it to the model.
Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base)
@ -91,6 +94,7 @@ def verify(
grayscale=False,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
)
img2_objs = detection.extract_faces(
@ -100,6 +104,7 @@ def verify(
grayscale=False,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
)
# --------------------------------
distances = []