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

face_cascade = cv2.CascadeClassifier('/home/nitish/Desktop/project/s_w/opencv-4.1.2/data/haarcascades/haarcascade_frontalface_alt2.xml')

recognizer = cv2.face.LBPHFaceRecognizer_create()

BASE_DIR = os.path.dirname(os.path.abspath(__file__))

image_dir = os.path.join(BASE_DIR, "/home/nitish/Desktop/project/faces/")

current_id = 0
label_ids = {}
y_labels = []
x_train = []

count = 0

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).replace(" ", "-").lower()
            # print(label,path)
            if not label in label_ids:
                label_ids[label] = current_id
                current_id += 1
            id_ = label_ids[label]
            pil_image = Image.open(path).convert("L")
            size = (80, 80)
            final_image = pil_image.resize(size, Image.ANTIALIAS)
            image_array = np.array(final_image, "uint8")
            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]
                count += 1
                x_train.append(roi)
                # print(id_)
                y_labels.append(id_)

# print(np.array(x_train[0]))
# print(np.array(y_labels))
print(count)
with open("lables.pickle", 'wb') as f:
    pickle.dump(label_ids, f)

recognizer.train(x_train, np.array(y_labels))
recognizer.save('trainer.yml')
print ("Model Training Complete ^-^")