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# built-in dependencies
from typing import Any, Dict, List, Union
# 3rd party dependencies
import numpy as np
# project dependencies
from deepface.commons import image_utils
from deepface.modules import modeling, detection, preprocessing
from deepface.models.FacialRecognition import FacialRecognition
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",
) -> List[Dict[str, Any]]:
"""
Represent facial images as multi-dimensional vector embeddings.
Args:
img_path (str or np.ndarray): 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
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.
detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'centerface' or 'skip'.
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
Returns:
results (List[Dict[str, Any]]): A list of dictionaries, each containing the
following fields:
- embedding (List[float]): Multidimensional vector representing facial features.
The number of dimensions varies based on the reference model
(e.g., FaceNet returns 128 dimensions, VGG-Face returns 4096 dimensions).
- facial_area (dict): Detected facial area by face detection in dictionary format.
Contains 'x' and 'y' as the left-corner point, and 'w' and 'h'
as the width and height. If `detector_backend` is set to 'skip', it represents
the full image area and is nonsensical.
- face_confidence (float): Confidence score of face detection. If `detector_backend` is set
to 'skip', the confidence will be 0 and is nonsensical.
"""
resp_objs = []
model: FacialRecognition = modeling.build_model(model_name)
# ---------------------------------
# we have run pre-process in verification. so, this can be skipped if it is coming from verify.
target_size = model.input_shape
if detector_backend != "skip":
img_objs = detection.extract_faces(
img_path=img_path,
detector_backend=detector_backend,
grayscale=False,
enforce_detection=enforce_detection,
align=align,
expand_percentage=expand_percentage,
)
else: # skip
# Try load. If load error, will raise exception internal
img, _ = image_utils.load_image(img_path)
if len(img.shape) != 3:
raise ValueError(f"Input img must be 3 dimensional but it is {img.shape}")
# make dummy region and confidence to keep compatibility with `extract_faces`
img_objs = [
{
"face": img,
"facial_area": {"x": 0, "y": 0, "w": img.shape[1], "h": img.shape[2]},
"confidence": 0,
}
]
# ---------------------------------
for img_obj in img_objs:
img = img_obj["face"]
# rgb to bgr
img = img[:, :, ::-1]
region = img_obj["facial_area"]
confidence = img_obj["confidence"]
# resize to expected shape of ml model
img = preprocessing.resize_image(
img=img,
# thanks to DeepId (!)
target_size=(target_size[1], target_size[0]),
)
# custom normalization
img = preprocessing.normalize_input(img=img, normalization=normalization)
embedding = model.forward(img)
resp_obj = {}
resp_obj["embedding"] = embedding
resp_obj["facial_area"] = region
resp_obj["face_confidence"] = confidence
resp_objs.append(resp_obj)
return resp_objs
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