>|hTddlZddlZddlZddlmZmZmZmZmZm Z ddl Z ddl Z ddlZddlmZmZddlmZddlmZmZmZmZmZmZmZddlmZedZej@d d ejBd <ejDZ#e#d k(r*ejHjKejLejNd e(defdZ) d+dee(e jTee+fdee(e jTee+fd e(de(de(de,de,de-de(de,dee(effdZ. d,dee(e jTfdee/e0fde,de(de,de-de,deee(effdZ1 d-dee(e jTfde(d e(de(de,de(de,de-de e+de(de,dee jdfdZ3 d.dee(e jTfd e(de,de(de,de-de(deee(effd Z4 d/de(d e(de(de(d!e,d"ed#e-d$e-ddfd%Z5 d0dee(e jTfd&e ee-e-fde(de,de,de-d'e,deee(effd(Z6d1d)Z7 d2dee(e jTfd&e/de(de,de,dee jTdff d*Z8y)3N)AnyDictListTupleUnionOptional) package_utils folder_utils)Logger)modelingrepresentation verification recognition demography detection streaming) __version__DeepFace)moduleignore3TF_CPP_MIN_LOG_LEVEL model_namereturnc.tj|S)a. This function builds a deepface model Args: model_name (string): face recognition or facial attribute model VGG-Face, Facenet, OpenFace, DeepFace, DeepID for face recognition Age, Gender, Emotion, Race for facial attributes Returns: built_model r)r build_modelrs P/var/www/html/test/engine/venv/lib/python3.12/site-packages/deepface/DeepFace.pyrr*s   : 66 img1_path img2_pathdetector_backenddistance_metricenforce_detectionalignexpand_percentage normalizationsilentc @tj||||||||||  S)a Verify if an image pair represents the same person or different persons. Args: img1_path (str or np.ndarray or List[float]): Path to the first image. Accepts exact image path as a string, numpy array (BGR), base64 encoded images or pre-calculated embeddings. img2_path (str or np.ndarray or List[float]): Path to the second image. Accepts exact image path as a string, numpy array (BGR), base64 encoded images or pre-calculated embeddings. 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' (default is opencv). distance_metric (string): Metric for measuring similarity. Options: 'cosine', 'euclidean', 'euclidean_l2' (default is cosine). 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). 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) silent (boolean): Suppress or allow some log messages for a quieter analysis process (default is False). Returns: result (dict): A dictionary containing verification results with following keys. - 'verified' (bool): Indicates whether the images represent the same person (True) or different persons (False). - 'distance' (float): The distance measure between the face vectors. A lower distance indicates higher similarity. - 'max_threshold_to_verify' (float): The maximum threshold used for verification. If the distance is below this threshold, the images are considered a match. - 'model' (str): The chosen face recognition model. - 'distance_metric' (str): The chosen similarity metric for measuring distances. - 'facial_areas' (dict): Rectangular regions of interest for faces in both images. - 'img1': {'x': int, 'y': int, 'w': int, 'h': int} Region of interest for the first image. - 'img2': {'x': int, 'y': int, 'w': int, 'h': int} Region of interest for the second image. - 'time' (float): Time taken for the verification process in seconds. r!r"rr#r$r%r&r'r(r))rverifyr+s rr,r,7s6L   )'++#  r img_pathactionsc :tj|||||||S)a Analyze facial attributes such as age, gender, emotion, and race in the provided image. 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. actions (tuple): Attributes to analyze. The default is ('age', 'gender', 'emotion', 'race'). You can exclude some of these attributes from the analysis if needed. 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). detector_backend (string): face detector backend. Options: 'opencv', 'retinaface', 'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv). distance_metric (string): Metric for measuring similarity. Options: 'cosine', 'euclidean', 'euclidean_l2' (default is cosine). 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). Returns: results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary represents the analysis results for a detected face. Each dictionary in the list contains the following keys: - 'region' (dict): Represents the rectangular region of the detected face in the image. - 'x': x-coordinate of the top-left corner of the face. - 'y': y-coordinate of the top-left corner of the face. - 'w': Width of the detected face region. - 'h': Height of the detected face region. - 'age' (float): Estimated age of the detected face. - 'face_confidence' (float): Confidence score for the detected face. Indicates the reliability of the face detection. - 'dominant_gender' (str): The dominant gender in the detected face. Either "Man" or "Woman". - 'gender' (dict): Confidence scores for each gender category. - 'Man': Confidence score for the male gender. - 'Woman': Confidence score for the female gender. - 'dominant_emotion' (str): The dominant emotion in the detected face. Possible values include "sad," "angry," "surprise," "fear," "happy," "disgust," and "neutral" - 'emotion' (dict): Confidence scores for each emotion category. - 'sad': Confidence score for sadness. - 'angry': Confidence score for anger. - 'surprise': Confidence score for surprise. - 'fear': Confidence score for fear. - 'happy': Confidence score for happiness. - 'disgust': Confidence score for disgust. - 'neutral': Confidence score for neutrality. - 'dominant_race' (str): The dominant race in the detected face. Possible values include "indian," "asian," "latino hispanic," "black," "middle eastern," and "white." - 'race' (dict): Confidence scores for each race category. - 'indian': Confidence score for Indian ethnicity. - 'asian': Confidence score for Asian ethnicity. - 'latino hispanic': Confidence score for Latino/Hispanic ethnicity. - 'black': Confidence score for Black ethnicity. - 'middle eastern': Confidence score for Middle Eastern ethnicity. - 'white': Confidence score for White ethnicity. r-r.r%r#r&r'r))ranalyzer0s rr1r1s-f   +)+ r db_path thresholdc Btj|||||||||| |  S)a Identify individuals in a database 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. db_path (string): Path to the folder containing image files. All detected faces 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). distance_metric (string): Metric for measuring similarity. Options: 'cosine', 'euclidean', 'euclidean_l2' (default is cosine). 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). detector_backend (string): face detector backend. Options: 'opencv', 'retinaface', 'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv). 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 model name and distance metric (default is None). normalization (string): Normalize the input image before feeding it to the model. Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base). silent (boolean): Suppress or allow some log messages for a quieter analysis process (default is False). Returns: results (List[pd.DataFrame]): A list of pandas dataframes. Each dataframe corresponds to the identity information for an individual detected in the source image. The DataFrame columns include: - 'identity': Identity label of the detected individual. - 'target_x', 'target_y', 'target_w', 'target_h': Bounding box coordinates of the target face in the database. - 'source_x', 'source_y', 'source_w', 'source_h': Bounding box coordinates of the detected face in the source image. - 'threshold': threshold to determine a pair whether same person or different persons - 'distance': Similarity score between the faces based on the specified model and distance metric r-r2rr$r%r#r&r'r3r(r))rfindr5s rr6r6s9H   '+)+#  r c :tj|||||||S)a 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 (default is VGG-Face.). 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 (default is True). detector_backend (string): face detector backend. Options: 'opencv', 'retinaface', 'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv). 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). 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. r-rr%r#r&r'r()r representr8s rr9r9<s-h  # #+)+# r enable_face_analysissourcetime_thresholdframe_thresholdc nt|d}t|d}tj||||||||y)a Run real time face recognition and facial attribute analysis Args: db_path (string): Path to the folder containing image files. All detected faces 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). detector_backend (string): face detector backend. Options: 'opencv', 'retinaface', 'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (default is opencv). distance_metric (string): Metric for measuring similarity. Options: 'cosine', 'euclidean', 'euclidean_l2' (default is cosine). enable_face_analysis (bool): Flag to enable face analysis (default is True). source (Any): The source for the video stream (default is 0, which represents the default camera). time_threshold (int): The time threshold (in seconds) for face recognition (default is 5). frame_threshold (int): The frame threshold for face recognition (default is 5). Returns: None r2rr#r$r:r;r<r=N)maxranalysisr@s rstreamrC{sCL+N/1-O )'1%' r target_size grayscalec <tj|||||||dS)a Extract faces from a given image Args: img_path (str or np.ndarray): Path to the first image. Accepts exact image path as a string, numpy array (BGR), or base64 encoded images. target_size (tuple): final shape of facial image. black pixels will be added to resize the image (default is (224, 224)). detector_backend (string): face detector backend. Options: 'opencv', 'retinaface', 'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (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). 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). Returns: results (List[Dict[str, Any]]): A list of dictionaries, where each dictionary contains: - "face" (np.ndarray): The detected face as a NumPy array. - "facial_area" (Dict[str, Any]): The detected face's regions as a dictionary containing: - keys 'x', 'y', 'w', 'h' with int values - keys 'left_eye', 'right_eye' with a tuple of 2 ints as values - "confidence" (float): The confidence score associated with the detected face. T)r-rDr#r%r&r'rEhuman_readable)r extract_faces)r-rDr#r%r&r'rEs rrHrHs0X  " ")++  r c,ddl}|jy)zG command line interface function will be offered in this block rN)fireFire)rJs rclirLsIIKr ctjdt|||||d}d}t|dkDr|dd}|S)a Deprecated face detection function. Use extract_faces for same functionality. Args: img_path (str or np.ndarray): Path to the first image. Accepts exact image path as a string, numpy array (BGR), or base64 encoded images. target_size (tuple): final shape of facial image. black pixels will be added to resize the image (default is (224, 224)). detector_backend (string): face detector backend. Options: 'opencv', 'retinaface', 'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8' (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). align (bool): Flag to enable face alignment (default is True). Returns: img (np.ndarray): detected (and aligned) facial area image as numpy array z=Function detectFace is deprecated. Use extract_faces instead.F)r-rDr#r%r&rENrface)loggerwarnrHlen)r-rDr#r%r& face_objsextracted_faces r detectFacerTsT8 KKOP)+ IN 9~"1f- r )VGG-FaceopencvcosineTTrbaseF))emotionagegenderraceTrVTrF) rUrWTrVTrNrXF)rUTrVTrrX)rUrVrWTrr^)r`rVTTrF)rN)r_rVTT)9oswarningsloggingtypingrrrrrrnumpynppandaspd tensorflowtfdeepface.commonsr r deepface.commons.loggerr deepface.modulesr r rrrrrdeepfacerrOfilterwarningsenvironget_tf_major_version tf_version get_loggersetLevelERRORinitialize_folderstrrndarrayfloatboolintr,tuplelistr1 DataFramer6r9rCrHrLrTr rrs ::9*! z " !%( !" /] / / 1  ?BMMOW]]+   7C 7C 7 !$#"QS"**d5k12QS"**d5k12QQ Q  Q  Q QQQ Q #s(^Ql#G"$[CO$[ 5$; [[ [  [  [ [ $sCx.[B!#"$!%PCO$P PP P  P  P PPPP P ",,Pj!"$<CO$<<< <  <  << $sCx.<@ $#!%2 222 2  2  222 2n.8$"5CO$5%S/*55 5  5  55 $sCx.5p$$" (CO$((( (  (  2::t  (r