mirror of
https://github.com/serengil/deepface.git
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6cc36922d4
- standard FR models are now using find embeddings method from its super FacialRecognition - find embeddings renamed to forward.
116 lines
4.6 KiB
Python
116 lines
4.6 KiB
Python
# built-in dependencies
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from typing import Any, Dict, List, Union
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# 3rd party dependencies
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import numpy as np
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import cv2
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# project dependencies
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from deepface.modules import modeling, detection, preprocessing
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from deepface.models.FacialRecognition import FacialRecognition
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def represent(
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img_path: Union[str, np.ndarray],
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model_name: str = "VGG-Face",
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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align: bool = True,
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expand_percentage: int = 0,
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normalization: str = "base",
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) -> List[Dict[str, Any]]:
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"""
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Represent facial images as multi-dimensional vector embeddings.
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Args:
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img_path (str or np.ndarray): The exact path to the image, a numpy array in BGR format,
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or a base64 encoded image. If the source image contains multiple faces, the result will
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include information for each detected face.
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model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
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OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet
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enforce_detection (boolean): If no face is detected in an image, raise an exception.
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Default is True. Set to False to avoid the exception for low-resolution images.
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detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
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'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8'.
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align (boolean): Perform alignment based on the eye positions.
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expand_percentage (int): expand detected facial area with a percentage (default is 0).
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normalization (string): Normalize the input image before feeding it to the model.
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Default is base. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace
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Returns:
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results (List[Dict[str, Any]]): A list of dictionaries, each containing the
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following fields:
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- embedding (List[float]): Multidimensional vector representing facial features.
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The number of dimensions varies based on the reference model
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(e.g., FaceNet returns 128 dimensions, VGG-Face returns 4096 dimensions).
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- facial_area (dict): Detected facial area by face detection in dictionary format.
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Contains 'x' and 'y' as the left-corner point, and 'w' and 'h'
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as the width and height. If `detector_backend` is set to 'skip', it represents
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the full image area and is nonsensical.
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- face_confidence (float): Confidence score of face detection. If `detector_backend` is set
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to 'skip', the confidence will be 0 and is nonsensical.
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"""
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resp_objs = []
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model: FacialRecognition = modeling.build_model(model_name)
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# ---------------------------------
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# we have run pre-process in verification. so, this can be skipped if it is coming from verify.
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target_size = model.input_shape
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if detector_backend != "skip":
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img_objs = detection.extract_faces(
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img_path=img_path,
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target_size=(target_size[1], target_size[0]),
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detector_backend=detector_backend,
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grayscale=False,
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enforce_detection=enforce_detection,
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align=align,
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expand_percentage=expand_percentage,
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)
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else: # skip
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# Try load. If load error, will raise exception internal
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img, _ = preprocessing.load_image(img_path)
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# --------------------------------
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if len(img.shape) == 4:
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img = img[0] # e.g. (1, 224, 224, 3) to (224, 224, 3)
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if len(img.shape) == 3:
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img = cv2.resize(img, target_size)
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img = np.expand_dims(img, axis=0)
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# when called from verify, this is already normalized. But needed when user given.
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if img.max() > 1:
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img = (img.astype(np.float32) / 255.0).astype(np.float32)
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# --------------------------------
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# make dummy region and confidence to keep compatibility with `extract_faces`
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img_objs = [
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{
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"face": img,
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"facial_area": {"x": 0, "y": 0, "w": img.shape[1], "h": img.shape[2]},
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"confidence": 0,
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}
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]
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# ---------------------------------
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for img_obj in img_objs:
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img = img_obj["face"]
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region = img_obj["facial_area"]
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confidence = img_obj["confidence"]
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# custom normalization
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img = preprocessing.normalize_input(img=img, normalization=normalization)
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embedding = model.forward(img)
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resp_obj = {}
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resp_obj["embedding"] = embedding
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resp_obj["facial_area"] = region
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resp_obj["face_confidence"] = confidence
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resp_objs.append(resp_obj)
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return resp_objs
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