cómo hacer un reconocimiento facial perfecto en un solo ejemplo de código opencv-python

Ejemplo: python facerecognizer

import face_recognition
import os
import cv2


KNOWN_FACES_DIR ='known_faces'
UNKNOWN_FACES_DIR ='unknown_faces'
TOLERANCE =0.6
FRAME_THICKNESS =3
FONT_THICKNESS =2
MODEL ='cnn'# default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model# Returns (R, G, B) from namedefname_to_color(name):# Take 3 first letters, tolower()# lowercased character ord() value rage is 97 to 122, substract 97, multiply by 8
    color =[(ord(c.lower())-97)*8for c in name[:3]]return color


print('Loading known faces...')
known_faces =[]
known_names =[]# We oranize known faces as subfolders of KNOWN_FACES_DIR# Each subfolder's name becomes our label (name)for name in os.listdir(KNOWN_FACES_DIR):# Next we load every file of faces of known personfor filename in os.listdir(f'KNOWN_FACES_DIR/name'):# Load an image
        image = face_recognition.load_image_file(f'KNOWN_FACES_DIR/name/filename')# Get 128-dimension face encoding# Always returns a list of found faces, for this purpose we take first face only (assuming one face per image as you can't be twice on one image)
        encoding = face_recognition.face_encodings(image)[0]# Append encodings and name
        known_faces.append(encoding)
        known_names.append(name)print('Processing unknown faces...')# Now let's loop over a folder of faces we want to labelfor filename in os.listdir(UNKNOWN_FACES_DIR):# Load imageprint(f'Filename filename', end='')
    image = face_recognition.load_image_file(f'UNKNOWN_FACES_DIR/filename')# This time we first grab face locations - we'll need them to draw boxes
    locations = face_recognition.face_locations(image, model=MODEL)# Now since we know loctions, we can pass them to face_encodings as second argument# Without that it will search for faces once again slowing down whole process
    encodings = face_recognition.face_encodings(image, locations)# We passed our image through face_locations and face_encodings, so we can modify it# First we need to convert it from RGB to BGR as we are going to work with cv2
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)# But this time we assume that there might be more faces in an image - we can find faces of dirrerent peopleprint(f', found len(encodings) face(s)')for face_encoding, face_location inzip(encodings, locations):# We use compare_faces (but might use face_distance as well)# Returns array of True/False values in order of passed known_faces
        results = face_recognition.compare_faces(known_faces, face_encoding, TOLERANCE)# Since order is being preserved, we check if any face was found then grab index# then label (name) of first matching known face withing a tolerance
        match =NoneifTruein results:# If at least one is true, get a name of first of found labels
            match = known_names[results.index(True)]print(f' - match from results')# Each location contains positions in order: top, right, bottom, left
            top_left =(face_location[3], face_location[0])
            bottom_right =(face_location[1], face_location[2])# Get color by name using our fancy function
            color = name_to_color(match)# Paint frame
            cv2.rectangle(image, top_left, bottom_right, color, FRAME_THICKNESS)# Now we need smaller, filled grame below for a name# This time we use bottom in both corners - to start from bottom and move 50 pixels down
            top_left =(face_location[3], face_location[2])
            bottom_right =(face_location[1], face_location[2]+22)# Paint frame
            cv2.rectangle(image, top_left, bottom_right, color, cv2.FILLED)# Wite a name
            cv2.putText(image, match,(face_location[3]+10, face_location[2]+15), cv2.FONT_HERSHEY_SIMPLEX,0.5,(200,200,200), FONT_THICKNESS)# Show image
    cv2.imshow(filename, image)
    cv2.waitKey(0)
    cv2.destroyWindow(filename)

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