FacialRecognition-Demo/application/face_rec.py

import face_recognition
import os
import cv2
from application.db import Session, Person
import base64
import numpy as np
from base64 import decodestring
import base64

from io import StringIO
from PIL import Image

KNOWN_FACES_DIR = 'known_faces'
UNKNOWN_FACES_DIR = 'unknown_faces'
TOLERANCE = 0.6
FRAME_THICKNESS = 3
FONT_THICKNESS = 2
MODEL = "hog"  # default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model


def readb64(base64_string):
    sbuf = StringIO()
    sbuf.write(base64.b64decode(base64_string))
    pimg = Image.open(sbuf)
    return cv2.cvtColor(np.array(pimg), cv2.COLOR_RGB2BGR)

print('Loading known faces...')
known_faces = []
known_names = []

def initFaceRec():
    session = Session()
    # We oranize known faces as subfolders of KNOWN_FACES_DIR
    # Each subfolder's name becomes our label (name)
    for face, name in session.query(Person.face, Person.person_id).all():
            # Load an image
            nparr = np.fromstring(base64.b64decode(face), np.uint8)
            image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
            
            # 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)

    session.close()

def identifyFace(image):
    print('Identifying Face')
    nparr = np.fromstring(base64.b64decode(image), np.uint8)
    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    #image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    locations = face_recognition.face_locations(image, model=MODEL)
    encodings = face_recognition.face_encodings(image, locations)

    res = {}

    for face_encoding, face_location in zip(encodings, locations):
        results = face_recognition.face_distance(known_faces, face_encoding)
        res = {known_names[i]: results[i] for i in range(0, len(results)) }
    
    return res

def identifyFaceVideo(url):
    print('Identifying Faces')
    video = cv2.VideoCapture(url)
    image = video.read()[1]
    ret, image = cv2.imencode(".png", image)
    
    nparr = np.fromstring(image, np.uint8)
    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

    locations = face_recognition.face_locations(image, model=MODEL)
    encodings = face_recognition.face_encodings(image, locations)

    for face_encoding, face_location in zip(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 = None
        if True in results:  # If at least one is true, get a name of first of found labels
            match = "name"
            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])
            color = [255, 0, 0]
            # 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
    image = cv2.imencode(".jpg", image)[1]
    return image

#identifyFace("")