#%h(ddlZddlZddlZddlmZ ddlZde_ ejZ ejZejeZej"ZejeZej(Zej,eZej0Zej4eZdZdZdZd Zdd Z dd Z!dd Z"dd Z#ddZ$ddZ%ddZ&ddZ'ddZ(y#e$redede YwxYw)N) ImageFilez\Please install `face_recognition_models` with this command before using `face_recognition`: zCpip install git+https://github.com/ageitgey/face_recognition_modelsTc~|j|j|j|jfS)z Convert a dlib 'rect' object to a plain tuple in (top, right, bottom, left) order :param rect: a dlib 'rect' object :return: a plain tuple representation of the rect in (top, right, bottom, left) order )toprightbottomleft)rects R/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/face_recognition/api.py _rect_to_cssr s+ 88:tzz|T[[]DIIK ??cJtj|d|d|d|dS)z Convert a tuple in (top, right, bottom, left) order to a dlib `rect` object :param css: plain tuple representation of the rect in (top, right, bottom, left) order :return: a dlib `rect` object r)dlib rectangle)csss r _css_to_rectr*s) >>#a&#a&#a&#a& 99r ct|ddt|d|dt|d|dt|ddfS)aX Make sure a tuple in (top, right, bottom, left) order is within the bounds of the image. :param css: plain tuple representation of the rect in (top, right, bottom, left) order :param image_shape: numpy shape of the image array :return: a trimmed plain tuple representation of the rect in (top, right, bottom, left) order rrrr)maxmin)r image_shapes r _trim_css_to_boundsr4sL s1vq>3s1v{1~6CFKPQN8SUXY\]^Y_abUc ccr ct|dk(rtjdStjj ||z dS)a Given a list of face encodings, compare them to a known face encoding and get a euclidean distance for each comparison face. The distance tells you how similar the faces are. :param faces: List of face encodings to compare :param face_to_compare: A face encoding to compare against :return: A numpy ndarray with the distance for each face in the same order as the 'faces' array rr)axis)lennpemptylinalgnorm)face_encodingsface_to_compares r face_distancer#?s; >axx} 99>>.?:> CCr ctjj|}|r|j|}t j |S)a# Loads an image file (.jpg, .png, etc) into a numpy array :param file: image file name or file object to load :param mode: format to convert the image to. Only 'RGB' (8-bit RGB, 3 channels) and 'L' (black and white) are supported. :return: image contents as numpy array )PILImageopenconvertrarray)filemodeims r load_image_filer-Ns5  B ZZ  88B<r c<|dk(r t||St||S)a Returns an array of bounding boxes of human faces in a image :param img: An image (as a numpy array) :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog". :return: A list of dlib 'rect' objects of found face locations cnn)cnn_face_detector face_detector)imgnumber_of_times_to_upsamplemodels r _raw_face_locationsr5\s' ~ &ABBS"=>>r c|dk(rBt||dDcgc]+}tt|j|j-c}St|||Dcgc]!}tt||j#c}Scc}wcc}w)a7 Returns an array of bounding boxes of human faces in a image :param img: An image (as a numpy array) :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog". :return: A list of tuples of found face locations in css (top, right, bottom, left) order r/)r5rr r shape)r2r3r4faces r face_locationsr9ls ~TghknIKPUQRD#L$;SYYGR RObcfiDFKPLMt#L$6 BM MRMs 0B&Bct|||S)aa Returns an 2d array of dlib rects of human faces in a image using the cnn face detector :param img: A list of images (each as a numpy array) :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. :return: A list of dlib 'rect' objects of found face locations ) batch_size)r0)imagesr3r;s r _raw_face_locations_batchedr=|s V%@Z XXr cRfd}t||}tt||S)a Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector If you are using a GPU, this can give you much faster results since the GPU can process batches of images at once. If you aren't using a GPU, you don't need this function. :param images: A list of images (each as a numpy array) :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. :param batch_size: How many images to include in each GPU processing batch. :return: A list of tuples of found face locations in css (top, right, bottom, left) order c|Dcgc].}tt|jdj0c}Scc}w)Nr)rr r r7) detectionsr8r<s r convert_cnn_detections_to_cssz;batch_face_locations..convert_cnn_detections_to_csss1ZdeRV#L$;VAY__Meees3<)r=listmap)r<r3r;rAraw_detections_batcheds` r batch_face_locationsrEs0f9A\^hi 13IJ KKr c| t|}n|Dcgc] }t|}}t}|dk(rt}|Dcgc] }||| c}Scc}wcc}w)Nsmall)r5rpose_predictor_68_pointpose_predictor_5_point) face_imager9r4 face_locationpose_predictors r _raw_face_landmarksrMs`,Z8KYZ-,}5ZZ,N /KY Z-N:} 5 ZZ[ [s AAc6t|||}|Dcgc]6}|jDcgc]}|j|jfc}8}}}|dk(r|Dcgc]v}|dd|dd|dd|dd|dd|dd|dd |d d |d gz|d gz|d gz|dgz|dgz|dd|d gz|dgz|dgz|dgz|dgz|d gzd xc}S|dk(r|Dcgc]}|dg|dd|dddc}St dcc}wcc}}wcc}wcc}w)a Given an image, returns a dict of face feature locations (eyes, nose, etc) for each face in the image :param face_image: image to search :param face_locations: Optionally provide a list of face locations to check. :param model: Optional - which model to use. "large" (default) or "small" which only returns 5 points but is faster. :return: A list of dicts of face feature locations (eyes, nose, etc) larger$*07@?>=<6CBA) chin left_eyebrow right_eyebrow nose_bridgenose_tipleft_eye right_eyetop_lip bottom_liprGr)rerfrgzFInvalid landmarks model type. Supported models are ['small', 'large'].)rMpartsxy ValueError)rJr9r4 landmarkslandmarkplandmarks_as_tuplespointss r face_landmarksrts$JFIR[\h0@A1QSS!##JA\\ , -1RL"2bM#Br]!"R=r" r" 2b}r |3vbzlBfRj\QU[\^U_T``djkmdncoo B-6":,6&*EPR TX^_aXbWccgmnpgqfrrv|}wAvBB  - - '  , -  q ! - - abb-B\ --s#D D D A;D!DD c t|||}|Dcgc],}tjtj |||.c}Scc}w)aj Given an image, return the 128-dimension face encoding for each face in the image. :param face_image: The image that contains one or more faces :param known_face_locations: Optional - the bounding boxes of each face if you already know them. :param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower) :param model: Optional - which model to use. "large" (default) or "small" which only returns 5 points but is faster. :return: A list of 128-dimensional face encodings (one for each face in the image) )rMrr) face_encodercompute_face_descriptor)rJknown_face_locations num_jittersr4 raw_landmarksraw_landmark_sets r r!r!sR( 4H%PML MjzBHH\99*FVXcd e MM Ms1Ac2tt|||kS)a Compare a list of face encodings against a candidate encoding to see if they match. :param known_face_encodings: A list of known face encodings :param face_encoding_to_check: A single face encoding to compare against the list :param tolerance: How much distance between faces to consider it a match. Lower is more strict. 0.6 is typical best performance. :return: A list of True/False values indicating which known_face_encodings match the face encoding to check )rBr#)known_face_encodingsface_encoding_to_check tolerances r compare_facesrs  24JKyX YYr )RGB)rhog)r)NrO)NrrG)g333333?)) PIL.Imager%rnumpyrrface_recognition_models ExceptionprintquitLOAD_TRUNCATED_IMAGESget_frontal_face_detectorr1pose_predictor_model_locationpredictor_68_point_modelshape_predictorrH(pose_predictor_five_point_model_locationpredictor_5_point_modelrI cnn_face_detector_model_locationcnn_face_detection_modelcnn_face_detection_model_v1r0face_recognition_model_locationface_recognition_modelface_recognition_model_v1rvr rrr#r-r5r9r=rErMrtr!rr r rsA  " #' ...0 P2PPR.$../GHZ1ZZ\---.EFS2SSU4D445MNP0PPR-t--.DE @:d D  ? M YL& [ cF M Z_  ij OPF sC--D  D