Trainner.py

import cv2
import os
import numpy as np
import pickle

image_dir = 'D:\imagesdataset'

face_cascade = cv2.CascadeClassifier('D:\Cascades\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(root).replace(" ", "-").lower()

            if not label in label_ids:
                label_ids[label] = current_id
                current_id += 1
            id_ = label_ids[label]


            image_array = cv2.imread(path)
            gray = cv2.cvtColor(image_array, cv2.COLOR_BGR2GRAY)


            faces = face_cascade.detectMultiScale(gray)

            for (x, y, w, h) in faces:
                roi = gray[y:y + h, x:x + w]

                x_train.append(roi)
                y_labels.append(id_)
#print(y_labels)
#print(x_train)
print(label_ids)


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

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