faces_train.py

import os
import cv2
import numpy as np
import pickle
from PIL import Image
BASE_DIR=os.path.dirname(os.path.abspath(__file__))
image_dir=os.path.join(BASE_DIR,"images")
recognizer = cv2.face.LBPHFaceRecognizer_create()
face_cascade=cv2.CascadeClassifier("cascades/data/haarcascade_frontalface_default.xml")

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))
            print(label,path)
            if not label in label_ids:
                label_ids[label]=current_id
                current_id+=1
            id_=label_ids[label]
            print(label_ids)

            pil_image=Image.open(path).convert("L")
            image_array=np.array(pil_image,"uint8")
            print(image_array)
            faces=face_cascade.detectMultiScale(image_array,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_)


with open("labels.pickle","wb") as f:
    pickle.dump(label_ids, f)

recognizer.train(x_train, np.array(y_labels))
recognizer.save("trainner.yml")