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faceTrain.py
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faceTrain.py
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import cv2
import os
import pickle
import numpy as np
from PIL import Image
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
Image_dir = os.path.join(BASE_DIR, "Lib", 'Images')
face_cascade = cv2.CascadeClassifier('Lib/cascades/data/haarcascade_frontalface_alt2.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
current_id = 0;
label_ids = dict()
x_train = list()
y_labels = list()
for root, dirs, files in os.walk(Image_dir):
for file in files:
if file.endswith('jpg'):
path = os.path.join(root, file)
label = os.path.basename(root).lower()
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') # Converts to Grayscale
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=2,
minNeighbors=5,
minSize=(35, 35)
)
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("Trainer.yml")
print("Training is Done!")