o i(@s.ddlZddlZddlZddlmZzddlZWney,edede Ynwde_ e Z e ZeeZeZeeZeZeeZeZeeZddZdd Zd d Zd d Zd'ddZ d(ddZ!d(ddZ"d)ddZ#d)ddZ$d*ddZ%d*dd Z&d+d"d#Z'd,d%d&Z(dS)-N) ImageFilez\Please install `face_recognition_models` with this command before using `face_recognition`: zCpip install git+https://github.com/ageitgey/face_recognition_modelsTcCs||||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 N)toprightbottomleft)rectrZC:\Users\psant\Downloads\PAF backend\school\venv\Lib\site-packages\face_recognition/api.py _rect_to_css sr cCs t|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 rN)dlibZ rectangle)cssrrr _css_to_rect*s rcCs<t|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 rr r r N)maxmin)rZ image_shaperrr _trim_css_to_bounds4s<rcCs*t|dkr tdStjj||ddS)a Given a list of face encodings, compare them to a known face encoding and get a euclidean distance for each comparison face. 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"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 cnnN)cnn_face_detector face_detectorimgnumber_of_times_to_upsamplemodelrrr _raw_face_locations\s  r)cs<|dkrfddt|dDSfddt||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 r"csg|] }tt|jjqSrrr rshape.0facer&rr wz"face_locations..csg|] }tt|jqSr)rr r+r,r/rr r0ysN)r)r%rr/r face_locationsls r2cCst|||dS)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_sizeN)r#)imagesr'r4rrr _raw_face_locations_batched|sr6cs&fdd}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 csfdd|DS)Ncs"g|] }tt|jdjqS)rr*r,r5rr r0s"zObatch_face_locations..convert_cnn_detections_to_css..r)Z detectionsr7rr convert_cnn_detections_to_csssz;batch_face_locations..convert_cnn_detections_to_cssN)r6listmap)r5r'r4r8Zraw_detections_batchedrr7r batch_face_locationss r;largecsD|dur t}ndd|D}t|dkrtfdd|DS)NcSsg|]}t|qSr)rr-Z face_locationrrr r0sz'_raw_face_landmarks..smallcsg|]}|qSrrr= face_imageZpose_predictorrr r0s)r)pose_predictor_68_pointpose_predictor_5_point)r@r2r(rr?r _raw_face_landmarkss rCcCsNt|||}dd|D}|dkrdd|DS|dkr#dd|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. 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"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 s g|] }tt|qSr)rr face_encoderZcompute_face_descriptor)r-Zraw_landmark_setr@ num_jittersrr r0s z"face_encodings..N)rC)r@Zknown_face_locationsrbr(Z raw_landmarksrrar rs r333333?cCstt|||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 N)r9r)Zknown_face_encodingsZface_encoding_to_checkZ tolerancerrr compare_facess rd)r)r r!)r r3)Nr<)Nr r>)rc))Z PIL.ImagerrZnumpyrrZface_recognition_models ExceptionprintquitZLOAD_TRUNCATED_IMAGESZget_frontal_face_detectorr$Zpose_predictor_model_locationZpredictor_68_point_modelZshape_predictorrAZ(pose_predictor_five_point_model_locationZpredictor_5_point_modelrBZ cnn_face_detector_model_locationZcnn_face_detection_modelZcnn_face_detection_model_v1r#Zface_recognition_model_locationZface_recognition_modelZface_recognition_model_v1r`r rrrr r)r2r6r;rCr_rrdrrrr sD                 #