>|hU8ddlZddlmZmZmZmZmZddlZddl m Z m Z m Z ddl mZddlmZedZ d deeej&eefd eeej&eefd ed ed ed edededededeeeffdZ d!deeej&fd ed ed ededededeeeeeeffdZdeej&efdeej&efdej6fdZdeej&efdeej&efdej6fdZdeej&efdej&fdZdeej&efdeej&efd edej6fdZd ed edefdZ y)"N)AnyDictUnionListTuple)representation detectionmodeling)FacialRecognition)Loggerz deepface/modules/verification.py)module img1_path img2_path model_namedetector_backenddistance_metricenforce_detectionalignexpand_percentage normalizationsilentreturnc tj} tj|} | j} t |t rmt d|Ds td| durtjd|dt|| k7rtd|d| dt|d |g} d g}n t||||||| \} }t |t rmt d |Ds td| durtjd|dt|| k7rtd|d| dt|d |g}d g}n t||||||| \}}d d d d d d d}g}g}t| D]W\}}t|D]D\}}t|||}|j||j||xs|||xs|fFYt||}t!t#|}|t%j&|}tj}||k||||||d|ddt)|| z dd}|S#t$r}td |d }~wwxYw#t$r}td|d }~wwxYw)a Verify if an image pair represents the same person or different persons. The verification function converts facial images to vectors and calculates the similarity between those vectors. Vectors of images of the same person should exhibit higher similarity (or lower distance) than vectors of images of 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 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. - '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. - 'similarity_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. c3<K|]}t|tywN isinstancefloat.0dims \/var/www/html/test/engine/venv/lib/python3.12/site-packages/deepface/modules/verification.py zverify..c?c:c5)?zNWhen passing img1_path as a list, ensure that all its items are of type float.FzmYou passed 1st image as pre-calculated embeddings.Please ensure that embeddings have been calculated for the z model.zembeddings of z should have z dimensions, but it has z dimensions inputN)img_pathrrrrrrz$Exception while processing img1_pathc3<K|]}t|tywrrrs r"r#zverify..r$r%zNWhen passing img2_path as a list, ensure that all its items are of type float.zmYou passed 2nd image as pre-calculated embeddings.Please ensure that embeddings have been calculated for the z$Exception while processing img2_path)xywhleft_eye right_eyer)img1img2)verifieddistance thresholdmodelrsimilarity_metric facial_areastime)r8r build_model output_shaperlistall ValueErrorloggerwarnlen__extract_faces_and_embeddings enumerate find_distanceappendfind_thresholdrminnpargminround)rrrrrrrrrrticr5dimsimg1_embeddingsimg1_facial_areaserrimg2_embeddingsimg2_facial_areasno_facial_area distancesr7idximg1_embeddingidyimg2_embeddingr3r4tocresp_objs r"verifyrYs0V ))+C'33J?E   D)T"?Y??`  U? KKNNX\Y`b  y>T !  M$@"9~..?A  %+!F N1O"%!1"3"3+2 .O.)T"?Y??`  U? KKNNX\Y`b  y>T !  M$@"9~..?A  %+!F N1O"%!1"3"3+2 .O.    NIL(9^#,_#=  C$^^_UH   X &   "3'9>;LS;Q;cUcd  z?;IS^$H ) 45L ))+C ),,!-a,q/JcCi# H OY NCD# M NH NCD# M Ns04II I II I7& I22I7r&c 0g}g}tj|} | j} tj|| |d|||} | D]M} t j | d||d||} | dd}|j||j| dO||fS) z Extract facial areas and find corresponding embeddings for given image Returns: embeddings (List[float]) facial areas (List[dict]) F)r& target_sizer grayscalerrrfaceskip)r&rrrrrr embedding facial_area)r r9 input_shaper extract_facesr representrD)r&rrrrrr embeddingsr7r5r[img_objsimg_objimg_embedding_obj img_embeddings r"rArAsJL'33J?E##K&&)++H 4*44V_!/#'  *!,[9 -(GM23 4 | ##source_representationtest_representationct|trtj|}t|trtj|}tjtj ||}tj tj||}tj tj||}d|tj|tj|zz z S)a Find cosine distance between two given vectors Args: source_representation (np.ndarray or list): 1st vector test_representation (np.ndarray or list): 2nd vector Returns distance (np.float64): calculated cosine distance r.) rr;rGarraymatmul transposesummultiplysqrt)rjrkabcs r"find_cosine_distancervs'. ")> ?%t, hh':; ",,457JKA r{{02GHIA r{{.0CDEA RWWQZ"''!*,- ..ric t|trtj|}t|trtj|}||z }tjtj ||}tj |}|S)a  Find euclidean distance between two given vectors Args: source_representation (np.ndarray or list): 1st vector test_representation (np.ndarray or list): 2nd vector Returns distance (np.float64): calculated euclidean distance )rr;rGrmrprqrr)rjrkeuclidean_distances r"find_euclidean_distancerysx'. ")> ?%t, hh':;.1DD ,>@R ST!34 rir(c t|trtj|}|tjtj tj ||z S)z Normalize input vector with l2 Args: x (np.ndarray or list): given vector Returns: y (np.ndarray): l2 normalized vector )rr;rGrmrrrprq)r(s r" l2_normalizer{/sC!T HHQK rwwrvvbkk!Q/01 11rialpha_embeddingbeta_embeddingc|dk(rt||}|S|dk(rt||}|S|dk(r tt|t|}|Std|)a% Wrapper to find distance between vectors according to the given distance metric Args: source_representation (np.ndarray or list): 1st vector test_representation (np.ndarray or list): 2nd vector Returns distance (np.float64): calculated cosine distance cosine euclidean euclidean_l2z!Invalid distance_metric passed - )rvryr{r=)r|r}rr3s r"rCrC<sv("'H O K '*?NK O N **  )<+G  O:>>PSTI ri)ropencvrTTrbaseF)rrTTrr)!r8typingrrrrrnumpyrGdeepface.modulesrr r !deepface.models.FacialRecognitionr deepface.commons.loggerr r>strndarrayrboolintrYdictrAr;float64rvryr{rCrErir"rs 00A@?* 9 : !$#"}S"**d5k12}S"**d5k12}} }  }  } }}} } #s(^}D!$".$CO$.$.$.$ .$  .$  .$.$ 4U d4j ().$b/ T!12/INrzz[_O_I`/ZZ/. 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