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")

face_cascade = cv2.CascadeClassifier("cascade/data/haarcascade_frontalface_alt2.xml")
recognizer = cv2.face.LBPHFaceRecognizer_create()

current_id = 0
labels_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()
            #print(label, path)

            if not label in labels_ids:
                labels_ids[label] = current_id
                current_id+=1
            
            id_ = labels_ids[label]
            #print(labels_ids)

            pil_image = Image.open(path).convert("L") #Escala grises
            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(labels_ids,f)

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