I/Yh(ddlZddlZddlZddlmZ ddlZn+#e$r#edede YnwxYwde_ ej Z ej ZejeZejZejeZejZejeZejZejeZdZdZdZd Zdd Z ddZ!ddZ"ddZ#ddZ$ddZ%ddZ&ddZ'ddZ(dS) N) ImageFilez\Please install `face_recognition_models` with this command before using `face_recognition`: zCpip install git+https://github.com/ageitgey/face_recognition_modelsTc||||fS)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 \D:\DownLoads\facial_attendance_system-main\venv311\Lib\site-packages\face_recognition/api.py _rect_to_cssr s3 88::tzz||T[[]]DIIKK ??c`tj|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& 9 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_boundsr4sW s1vq>>3s1v{1~66CFKPQN8S8SUXY\]^Y_abUcUc ccr ct|dkrtjdStj||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#?sB >ax}} 9>>.?:> C CCr RGBctj|}|r||}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.Ns>   B  ZZ   8B<<r rhogcN|dkrt||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_detectorimgnumber_of_times_to_upsamplemodels r _raw_face_locationsr8\s/ ~~ &ABBBS"=>>>r c|dkrfdt|dDSfdt||DS)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 r1c^g|])}tt|jj*Srr r shape.0facer5s r z"face_locations..wsBRRRD#L$;$;SYGGRRRr cTg|]$}tt|j%Sr;)rr r=r>s r rAz"face_locations..ys@MMMt#L$6$6 BBMMMr )r8r4s` r face_locationsrCls ~~RRRRTghknIKPUQUQRRR RMMMMObcfiDFKPLPLMMM Mr c&t|||S)aa Returns an 2d array of dlib rects of human faces in a image using the cnn face detector :param img: A list of images (each as a numpy array) :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. :return: A list of dlib 'rect' objects of found face locations ) batch_size)r2)imagesr6rFs r _raw_face_locations_batchedrH|s V%@Z X X XXr cjfd}t||}tt||S)a Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector If you are using a GPU, this can give you much faster results since the GPU can process batches of images at once. If you aren't using a GPU, you don't need this function. :param images: A list of images (each as a numpy array) :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. :param batch_size: How many images to include in each GPU processing batch. :return: A list of tuples of found face locations in css (top, right, bottom, left) order c fd|DS)Ncjg|]/}tt|jdj0S)rr<)r?r@rGs r rAzObatch_face_locations..convert_cnn_detections_to_css..s5eeeRV#L$;$;VAY_MMeeer r;) detectionsrGs r convert_cnn_detections_to_cssz;batch_face_locations..convert_cnn_detections_to_cssseeeeZdeeeer )rHlistmap)rGr6rFrMraw_detections_batcheds` r batch_face_locationsrQsQfffff9A\^hii 13IJJ K KKr largec|t}n d|D}t|dkrtfd|DS)Nc,g|]}t|Sr;)r)r? face_locations r rAz'_raw_face_landmarks..s ZZZ-,}55ZZZr smallc(g|]}|Sr;r;)r?rU face_imagepose_predictors r rAz'_raw_face_landmarks..s% Z Z Z-NN:} 5 5 Z Z Zr )r8pose_predictor_68_pointpose_predictor_5_point)rXrCr7rYs` @r _raw_face_landmarksr\s^,Z88ZZ>ZZZ,N / Z Z Z Z Z> Z Z ZZr ct|||}d|D}|dkr d|DS|dkr d|DStd)a Given an image, returns a dict of face feature locations (eyes, nose, etc) for each face in the image :param face_image: image to search :param face_locations: Optionally provide a list of face locations to check. :param model: Optional - which model to use. "large" (default) or "small" which only returns 5 points but is faster. :return: A list of dicts of face feature locations (eyes, nose, etc) cJg|] }d|D!S)c*g|]}|j|jfSr;)xy)r?ps r rAz-face_landmarks...s AAA1QS!#JAAAr )parts)r?landmarks r rAz"face_landmarks..s1\\\hAA0@0@AAA\\\r rRc g|]}|dd|dd|dd|dd|dd|dd|dd|dd|d gz|d gz|d gz|d gz|d gz|dd |dgz|d gz|dgz|dgz|dgz|d gzd S)r$*07@?>=<6CBA) chin left_eyebrow right_eyebrow nose_bridgenose_tipleft_eye right_eyetop_lip bottom_lipr;r?pointss r rAz"face_landmarks..s- - - -1R4L"2b5M#BrE]!"R%=r"u r"u 2be}r |3vbzlBfRj\QU[\^U_T``djkmdncoo B-6":,6&*EPR TX^_aXbWccgmnpgqfrrv|}wAvBB   - - -r rVcHg|]}|dg|dd|ddd S)rr)r{r|r}r;rs r rAz"face_landmarks..sP---  qs !  ---r zFInvalid landmarks model type. Supported models are ['small', 'large'].)r\ ValueError)rXrCr7 landmarkslandmarks_as_tupless r face_landmarksrs$JFFI\\R[\\\  - -, - - - - '  --, --- - abbbr rVcFt||}fd|DS)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) c lg|]0}tjt|1Sr;)rr* face_encodercompute_face_descriptor)r?raw_landmark_setrX num_jitterss r rAz"face_encodings..sJ M M MjzBH\99*FVXcdd e e M M Mr )r\)rXknown_face_locationsrr7 raw_landmarkss` ` r r!r!sU( 4H%PPM M M M M ML M M MMr 333333?cDtt|||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 )rNr#)known_face_encodingsface_encoding_to_check tolerances r compare_facesrs#  24JKKyX Y YYr )r$)rr/)rrD)NrR)NrrV)r)) PIL.Imager&rnumpyrrface_recognition_models ExceptionprintquitLOAD_TRUNCATED_IMAGESget_frontal_face_detectorr3pose_predictor_model_locationpredictor_68_point_modelshape_predictorrZ(pose_predictor_five_point_model_locationpredictor_5_point_modelr[ cnn_face_detector_model_locationcnn_face_detection_modelcnn_face_detection_model_v1r2face_recognition_model_locationface_recognition_modelface_recognition_model_v1rr rrr#r.r8rCrHrQr\rr!rr;r r rsm  """""    E ijjj E OPPPDFFFFF #' ..00 P2PRR.$./GHHZ1Z\\--.EFFS2SUU4D45MNNP0PRR-t-.DEE @@@:::ddd D D D     ? ? ? ? M M M M YYYYLLLL& [ [ [ [ c c c cF M M M M Z Z Z Z Z Zs%AA