faces_train.py

import os
import numpy as np
from PIL import Image
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=1.1,minNeighbors=5)

            for(x,y,h,w) 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")