Face recognition using Python.py

#Importing the libaries
import face_recognition
import cv2
import os
from google. colab. patches import cv2_imshow

#Image preprocessing
def img_resize(path):
 img = cv2. imread(path)
 (h, w) = img. shape[:2]
 width = 500
 ratio = width / float(w)
 height = int(h * ratio)
  #resizing the image with custom width and height
 return cv2. resize(img, (width, height))

#list to store the face encodings
train_enc = []
#list to store the names of person
train_names = []

#Training the model
training_images = 'train'
for file in os. listdir(training_images):
 img = img_resize(training_images + '/' + file)
 img_enc = face_recognition. face_encodings(img)[0]
 train_enc. append(img_enc)
 train_names. append(file.split('.')[0])

#Testing the model
testing_images = 'test'
for file in os. listdir(testing_images):
 img = img_resize(testing_images + '/' + file)
 img_enc = face_recognition. face_encodings(img)[0]
 outputs = face_recognition. compare_faces(train_enc, img_enc)

#Displaying the results
for i in range(len(outputs)):
 if outputs[i]:
  name = train_names[i]
  (top, right, bottom, left) = face_recognition.face_locations(img)[0]
  cv2. rectangle(img, (left, top), (right, bottom), (0, 0, 255), 2)
  cv2. putText(img, name, (left+2, bottom+20), cv2. FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)
  cv2_imshow(img)