face_detection.py

import scipy as sp
#import grayscale_conversion 
import os 
import cv2 as cv
import numpy as np
DIR=r'Faces'
people=[]

for i in os.listdir(DIR):
   people.append(i)

print(people)
haar_cascade = cv.CascadeClassifier('haar_face.xml')

features=[]
labels=[]
def create_train():
   for person in people:
      
      path=os.path.join(DIR,person)
      
      print(path)
      label = people.index(person)

      
      for img in os.listdir(path):
         img_path=os.path.join(path,img)
         img_array=cv.imread(img_path)
         gray = cv.cvtColor(img_array,cv.COLOR_BGR2GRAY)
                  
         faces_rect = haar_cascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=4)

         for(x,y,w,h) in faces_rect:
            #face region of interest
            faces_roi=gray[y:y+h,x:x+w]
            features.append(faces_roi)
            labels.append(label)

create_train()
print("Training Done")

features = np.array(features,dtype='object')
labels=np.array(labels)

face_recognizer=cv.face.LBPHFaceRecognizer_create()

face_recognizer.train(features,labels)

face_recognizer.save('face_trained.yml')
np.save("features.npy",features)
np.save("labels.npy",labels)