FaceNet usage sample code. (How FaceNet Works and How to Work With FaceNet)

FaceNetInAction.py

import tensorflow as tf
import glob
from mtcnn import MTCNN
from tensorflow.keras.models import load_model
import cv2
import numpy as np
from scipy.spatial.distance import cosine

detector = MTCNN()

facenet_model = load_model("facenet_keras.h5")
encoding_dict = {}

def extract_face_and_preprocessing(image):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    faces = detector.detect_faces(image)
    x1, y1, width, height = faces[0]["box"]

    x1, y1 = abs(x1), abs(y1)
    x2, y2 = x1 + width, y1+height

    face = image[y1:y2, x1:x2]
    face = cv2.resize(face, (160, 160))
    face = np.expand_dims(face, axis = 0)
    face = (face - face.mean())/face.std()

    return face, (x1, x2, y1, y2)

def get_encode():
    for item in glob.glob("faces\\*\\*"):
        img = cv2.imread(item)
        face , _= extract_face_and_preprocessing(img)

        encode = facenet_model.predict(face)
        
        encoding_dict[item.split("\\")[-2]] = encode

get_encode()

name = "Unkown"
distance = float("inf")

test_image = cv2.imread("test\\6.jpg")

face, (x1, x2, y1, y2) = extract_face_and_preprocessing(test_image)

encode = facenet_model.predict(face)

for db_name, db_encode in encoding_dict.items():
    dist = cosine(encode, db_encode)

    if dist < 0.5 and dist < distance:
        name = db_name
        distance = dist

if name == "Unkown":
    cv2.rectangle(test_image, (x1, y1), (x2, y2), (0, 0, 255), 2)
    cv2.putText(test_image, "Unknown", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

else:
    cv2.rectangle(test_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
    cv2.putText(test_image, "{}:{:.2f}".format(name, distance), (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        
cv2.imshow("image", test_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

How FaceNet Works and How to Work With FaceNet