FacialRecognition-Demo/application/face_rec.py

import face_recognition
import os
import cv2
from 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


# Returns (R, G, B) from name
def name_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)*8 for c in name[:3]]
    return color

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 = []
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.lname).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)


print('Processing unknown faces...')
# Now let's loop over a folder of faces we want to label

image = face_recognition.load_image_file('C:/Users/ofjok/Desktop/1.png')

# 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 people

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 = 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("1", image)
    cv2.waitKey(0)
    cv2.destroyWindow("1")