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2_Facial_Recognition_Dataset_Training.py
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2_Facial_Recognition_Dataset_Training.py
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import cv2
import numpy as np
from PIL import Image
import os
import pyttsx3
def text_to_speech(user_text):
engine = pyttsx3.init()
engine.say(user_text)
engine.runAndWait()
text_to_speech('Welcome. Here we will train the machine for the samples taken from the previous program.')
# Path for face image database
path = 'dataset'
recognizer = cv2.face.LBPHFaceRecognizer_create()
detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml");
# function to get the images and label data
def getImagesAndLabels(path):
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
faceSamples = []
ids = []
for imagePath in imagePaths:
PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
img_numpy = np.array(PIL_img, 'uint8')
id = int(os.path.split(imagePath)[-1].split(".")[1])
faces = detector.detectMultiScale(img_numpy)
for (x, y, w, h) in faces:
faceSamples.append(img_numpy[y:y + h, x:x + w])
ids.append(id)
return faceSamples, ids
text_to_speech('Training model for the face samples. It will take a few seconds. Wait ...')
faces, ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))
# Save the model into trainer/trainer.yml
recognizer.write('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi
# Print the number of faces trained and end program
# print("\n {0} faces trained. Exiting Program".format(len(np.unique(ids))))
text_to_speech('Model Trained. Go back to the Welcome Interface.')
print('\n Either proceed to the next file or \n')
print('\n Go back to the Welcome Interface.')