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examples explained in docstrings
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Sefik Ilkin Serengil
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d429d71c93
@ -75,37 +75,45 @@ def verify(
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as a string, numpy array (BGR), or base64 encoded images.
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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 and SFace
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OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
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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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'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv)
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distance_metric (string): Metric for measuring similarity. Options: 'cosine',
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'euclidean', 'euclidean_l2'.
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'euclidean', 'euclidean_l2' (default is cosine).
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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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Set to False to avoid the exception for low-resolution images (default is True).
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align (bool): Flag to enable face alignment (default is True).
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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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Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base)
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Returns:
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result (dict): A dictionary containing verification results.
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{
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"verified": True
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, "distance": 0.2563
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, "max_threshold_to_verify": 0.40
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, "model": "VGG-Face"
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, "similarity_metric": "cosine"
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, 'facial_areas': {
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'img1': {'x': 345, 'y': 211, 'w': 769, 'h': 769},
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'img2': {'x': 318, 'y': 534, 'w': 779, 'h': 779}
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}
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, "time": 2
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}
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- 'verified' (bool): Indicates whether the images represent the same person (True)
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or different persons (False).
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- 'distance' (float): The distance measure between the face vectors.
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A lower distance indicates higher similarity.
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- 'max_threshold_to_verify' (float): The maximum threshold used for verification.
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If the distance is below this threshold, the images are considered a match.
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- 'model' (str): The chosen face recognition model.
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- 'similarity_metric' (str): The chosen similarity metric for measuring distances.
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- 'facial_areas' (dict): Rectangular regions of interest for faces in both images.
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- 'img1': {'x': int, 'y': int, 'w': int, 'h': int}
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Region of interest for the first image.
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- 'img2': {'x': int, 'y': int, 'w': int, 'h': int}
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Region of interest for the second image.
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- 'time' (float): Time taken for the verification process in seconds.
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"""
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return verification.verify(
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@ -140,53 +148,67 @@ def analyze(
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You can exclude some of these attributes from the analysis if needed.
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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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Set to False to avoid the exception for low-resolution images (default is True).
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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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'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
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distance_metric (string): Metric for measuring similarity. Options: 'cosine',
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'euclidean', 'euclidean_l2'.
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'euclidean', 'euclidean_l2' (default is cosine).
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align (boolean): Perform alignment based on the eye positions.
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align (boolean): Perform alignment based on the eye positions (default is True).
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silent (boolean): Suppress or allow some log messages for a quieter analysis process.
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silent (boolean): Suppress or allow some log messages for a quieter analysis process
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(default is False).
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Returns:
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results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents
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the analysis results for a detected face. Example:
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the analysis results for a detected face.
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[
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{
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"region": {'x': 230, 'y': 120, 'w': 36, 'h': 45},
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"age": 28.66,
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'face_confidence': 0.9993908405303955,
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"dominant_gender": "Woman",
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"gender": {
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'Woman': 99.99407529830933,
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'Man': 0.005928758764639497,
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}
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"dominant_emotion": "neutral",
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"emotion": {
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'sad': 37.65260875225067,
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'angry': 0.15512987738475204,
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'surprise': 0.0022171278033056296,
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'fear': 1.2489334680140018,
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'happy': 4.609785228967667,
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'disgust': 9.698561953541684e-07,
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'neutral': 56.33133053779602
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}
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"dominant_race": "white",
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"race": {
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'indian': 0.5480832420289516,
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'asian': 0.7830780930817127,
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'latino hispanic': 2.0677512511610985,
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'black': 0.06337375962175429,
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'middle eastern': 3.088453598320484,
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'white': 93.44925880432129
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}
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}
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]
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Each dictionary in the list contains the following keys:
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- 'region' (dict): Represents the rectangular region of the detected face in the image.
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- 'x': x-coordinate of the top-left corner of the face.
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- 'y': y-coordinate of the top-left corner of the face.
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- 'w': Width of the detected face region.
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- 'h': Height of the detected face region.
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- 'age' (float): Estimated age of the detected face.
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- 'face_confidence' (float): Confidence score for the detected face.
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Indicates the reliability of the face detection.
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- 'dominant_gender' (str): The dominant gender in the detected face.
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Either "Man" or "Woman."
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- 'gender' (dict): Confidence scores for each gender category.
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- 'Man': Confidence score for the male gender.
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- 'Woman': Confidence score for the female gender.
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- 'dominant_emotion' (str): The dominant emotion in the detected face.
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Possible values include "sad," "angry," "surprise," "fear," "happy,"
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"disgust," and "neutral."
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- 'emotion' (dict): Confidence scores for each emotion category.
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- 'sad': Confidence score for sadness.
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- 'angry': Confidence score for anger.
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- 'surprise': Confidence score for surprise.
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- 'fear': Confidence score for fear.
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- 'happy': Confidence score for happiness.
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- 'disgust': Confidence score for disgust.
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- 'neutral': Confidence score for neutrality.
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- 'dominant_race' (str): The dominant race in the detected face.
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Possible values include "indian," "asian," "latino hispanic,"
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"black," "middle eastern," and "white."
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- 'race' (dict): Confidence scores for each race category.
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- 'indian': Confidence score for Indian ethnicity.
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- 'asian': Confidence score for Asian ethnicity.
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- 'latino hispanic': Confidence score for Latino/Hispanic ethnicity.
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- 'black': Confidence score for Black ethnicity.
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- 'middle eastern': Confidence score for Middle Eastern ethnicity.
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- 'white': Confidence score for White ethnicity.
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"""
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return demography.analyze(
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img_path=img_path,
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@ -31,55 +31,69 @@ def analyze(
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You can exclude some of these attributes from the analysis if needed.
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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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Set to False to avoid the exception for low-resolution images (default is True).
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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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'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv).
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distance_metric (string): Metric for measuring similarity. Options: 'cosine',
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'euclidean', 'euclidean_l2'.
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'euclidean', 'euclidean_l2' (default is cosine).
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align (boolean): Perform alignment based on the eye positions.
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align (boolean): Perform alignment based on the eye positions (default is True).
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silent (boolean): Suppress or allow some log messages for a quieter analysis process.
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silent (boolean): Suppress or allow some log messages for a quieter analysis process
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(default is False).
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Returns:
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results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents
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the analysis results for a detected face. Example:
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the analysis results for a detected face.
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[
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{
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"region": {'x': 230, 'y': 120, 'w': 36, 'h': 45},
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"age": 28.66,
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'face_confidence': 0.9993908405303955,
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"dominant_gender": "Woman",
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"gender": {
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'Woman': 99.99407529830933,
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'Man': 0.005928758764639497,
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}
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"dominant_emotion": "neutral",
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"emotion": {
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'sad': 37.65260875225067,
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'angry': 0.15512987738475204,
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'surprise': 0.0022171278033056296,
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'fear': 1.2489334680140018,
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'happy': 4.609785228967667,
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'disgust': 9.698561953541684e-07,
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'neutral': 56.33133053779602
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}
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"dominant_race": "white",
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"race": {
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'indian': 0.5480832420289516,
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'asian': 0.7830780930817127,
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'latino hispanic': 2.0677512511610985,
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'black': 0.06337375962175429,
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'middle eastern': 3.088453598320484,
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'white': 93.44925880432129
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}
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}
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]
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Each dictionary in the list contains the following keys:
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- 'region' (dict): Represents the rectangular region of the detected face in the image.
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- 'x': x-coordinate of the top-left corner of the face.
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- 'y': y-coordinate of the top-left corner of the face.
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- 'w': Width of the detected face region.
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- 'h': Height of the detected face region.
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- 'age' (float): Estimated age of the detected face.
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- 'face_confidence' (float): Confidence score for the detected face.
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Indicates the reliability of the face detection.
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- 'dominant_gender' (str): The dominant gender in the detected face.
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Either "Man" or "Woman."
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- 'gender' (dict): Confidence scores for each gender category.
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- 'Man': Confidence score for the male gender.
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- 'Woman': Confidence score for the female gender.
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- 'dominant_emotion' (str): The dominant emotion in the detected face.
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Possible values include "sad," "angry," "surprise," "fear," "happy,"
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"disgust," and "neutral."
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- 'emotion' (dict): Confidence scores for each emotion category.
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- 'sad': Confidence score for sadness.
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- 'angry': Confidence score for anger.
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- 'surprise': Confidence score for surprise.
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- 'fear': Confidence score for fear.
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- 'happy': Confidence score for happiness.
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- 'disgust': Confidence score for disgust.
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- 'neutral': Confidence score for neutrality.
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- 'dominant_race' (str): The dominant race in the detected face.
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Possible values include "indian," "asian," "latino hispanic,"
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"black," "middle eastern," and "white."
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- 'race' (dict): Confidence scores for each race category.
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- 'indian': Confidence score for Indian ethnicity.
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- 'asian': Confidence score for Asian ethnicity.
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- 'latino hispanic': Confidence score for Latino/Hispanic ethnicity.
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- 'black': Confidence score for Black ethnicity.
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- 'middle eastern': Confidence score for Middle Eastern ethnicity.
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- 'white': Confidence score for White ethnicity.
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"""
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# ---------------------------------
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# validate actions
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if isinstance(actions, str):
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actions = (actions,)
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@ -35,37 +35,45 @@ def verify(
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as a string, numpy array (BGR), or base64 encoded images.
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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 and SFace
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OpenFace, DeepFace, DeepID, Dlib, ArcFace and SFace (default is VGG-Face).
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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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'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv)
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distance_metric (string): Metric for measuring similarity. Options: 'cosine',
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'euclidean', 'euclidean_l2'.
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'euclidean', 'euclidean_l2' (default is cosine).
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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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Set to False to avoid the exception for low-resolution images (default is True).
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align (bool): Flag to enable face alignment (default is True).
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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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Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base)
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Returns:
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result (dict): A dictionary containing verification results.
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{
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"verified": True
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, "distance": 0.2563
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, "max_threshold_to_verify": 0.40
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, "model": "VGG-Face"
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, "similarity_metric": "cosine"
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, 'facial_areas': {
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'img1': {'x': 345, 'y': 211, 'w': 769, 'h': 769},
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'img2': {'x': 318, 'y': 534, 'w': 779, 'h': 779}
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}
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, "time": 2
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}
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- 'verified' (bool): Indicates whether the images represent the same person (True)
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or different persons (False).
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- 'distance' (float): The distance measure between the face vectors.
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A lower distance indicates higher similarity.
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- 'max_threshold_to_verify' (float): The maximum threshold used for verification.
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If the distance is below this threshold, the images are considered a match.
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- 'model' (str): The chosen face recognition model.
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- 'similarity_metric' (str): The chosen similarity metric for measuring distances.
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- 'facial_areas' (dict): Rectangular regions of interest for faces in both images.
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- 'img1': {'x': int, 'y': int, 'w': int, 'h': int}
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Region of interest for the first image.
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- 'img2': {'x': int, 'y': int, 'w': int, 'h': int}
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Region of interest for the second image.
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- 'time' (float): Time taken for the verification process in seconds.
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"""
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tic = time.time()
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