train.py

import os
from PIL import Image
import numpy as np
import cv2
import pickle




base_dir= os.path.dirname(os.path.abspath(__file__))
image_dir= os.path.join(base_dir, "images")


face_cascade = cv2.CascadeClassifier('data/haarcascade_frontalface_alt2.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()

current_id=0
label_ids={}

y_labels=[]
x_train=[]

for root,dirs, files in os.walk(image_dir):
    for file in files:
        if file.endswith("png") or file.endswith("jpg"):
                         path = os.path.join(root, file)
                         label = os.path.basename(os.path.dirname(path)).replace(" ", "-").lower()
                         #print(label, path)
                         if not label in label_ids:
                            label_ids[label]=current_id
                            current_id+= 1
                         id_=label_ids[label]
                         print(label_ids)
                         #y_labels.append(label)
                         #x_train.append(path)
                         pil_image= Image.open(path).convert("L")
                         size=(550, 550)
                         final_image=pil_image.resize(size, Image.ANTIALIAS)
                         image_array = np.array(final_image,"uint8")
                         #print(image_array)
                         faces = face_cascade.detectMultiScale(image_array, scaleFactor=1.3, minNeighbors=5)

                         for(x,y,w,h) in faces:
     
                             roi=image_array[y:y+h, x:x+w]
                             x_train.append(roi)
                             y_labels.append(id_)
#print(y_labels)
#print(x_train)

with open("labels.pickle", 'wb') as f:
    pickle.dump(label_ids, f)
    
recognizer.train(x_train, np.array(y_labels))
recognizer.save("trainner.yml")