import numpy as np
import cv2
import os

def face_detection(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade_Haar=cv2.CascadeClassifier('C:\Users\nic 005\Downloads\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml')
faces = face_cascade_Haar.detectMultiScale(gray,scaleFactor=1.3,minNeighbors=5)
if(len(faces)==0):
    return None, None
(x,y,w,h)=faces[0]
return gray[y:y+w, x:x+h], faces[0]

def prep_training_data(path):
dirs=os.listdir(path)
faces=[]
labels=[]
k=0
for dir_names in dirs:
    label=k+1
    subject_dir_path= path + "/" + dir_names
    subject_images_names= os.listdir(subject_dir_path)
    for image_name in subject_images_names:
        if image_name.startswith("."):
            continue
        image=cv2.imread(subject_dir_path+ "/" + image_name)
        face, rect = face_detection(image)
        faces.append(face)
        labels.append(label)
    k=k+1
return faces,labels

def draw_rectangle(img, rect):
(x, y, w, h) = rect
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

def draw_text(img, text, x, y):
cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)

def predict(test_img):
img=test_img.copy()
face, rect=face_detection(img)
label=face_recognizer.predict(face)
label_name=subjects[labels[0]]
draw_rectangle(test_img,rect)
draw_text(test_img, label_name, rect[0], rect[1]-5)
return test_img

faces, labels = prep_training_data('C:\Users\nic 005\Desktop\face_recognition\db\training')
face_recognizer= cv2.face.LBPHFaceRecognizer_create()
face_recognizer.train(faces, np.array(labels))
test_img=cv2.imread('C:\Users\nic 005\Desktop\face_recognition\testing\anonym.20.jpg')
predicted_img=predict(test_img)
cv2.imshow("img",predicted_img)
cv2.waitKey(0)
cv2.destroyAllWindows()