Trainer.py

import os
from PIL import Image
import numpy as np
import cv2
import pickle


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')  # Reconhece rosto de frente

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") or file.endswith("JPG"):
            path = os.path.join(root, file)
            label = os.path.basename(root).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)

            pil_image = Image.open(path).convert("L")  # preto e branco
            size = (500, 500)
            final_image = pil_image.resize(size, Image.ANTIALIAS)

            image_array = np.array(pil_image, "uint8")
            #print(image_array)
            faces = face_cascade.detectMultiScale(image_array, scaleFactor=1.5, 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_)

#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("Trainer.yml")