import numpy as np
import cv2 as cv

haar_cascade = cv.CascadeClassifier('haar_face.xml')

people = ['Ben Afflek', 'Elton John', 'Jerry Seinfield', 'Madonna', 'Mindy Kaling']  # can manually type it
# features = np.load('features.npy', allow_pickle = True)
# labels = np.load('labels.npy')

face_recognizer = cv.face.LBPHFaceRecognizer_create()
face_recognizer.read('face_trained.yml')   # trained data

img = cv.imread(r'C:\Users\user\projects\git\opencv-course\Resources\Faces\val\ben_afflek\5.jpg')  # validation

gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cv.imshow('Person', gray) 

# Detect the face in the image
faces_rect = haar_cascade.detectMultiScale(gray, 1.1, 4)
for (x,y,w,h) in faces_rect:
    faces_roi = gray[y:y+h, x:x+h]

    label, confidence = face_recognizer.predict(faces_roi)
    print(f'Label = {people[label]} with a confidence of {confidence}')

    cv.putText(img, str(people[label]), (20,20), cv.FONT_HERSHEY_COMPLEX, 1.0, (0, 255, 0), thickness = 2)
    cv.rectangle (img, (x,y), (x+w,y+h), (0,255,0), thickness = 2)

cv.imshow('Detected Face', img)
cv.waitKey(0)