FaceChecker.py

import numpy as np
import cv2
import pickle

face_cascade = cv2.CascadeClassifier('venv/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("trainner.yml")
labels = {"person_name": 1}

with open("labels.pickle", "rb") as f:
    og_labels = pickle.load(f)
    labels = {v: k for k, v in og_labels.items()}

cap = cv2.VideoCapture(0)

while(True):
    # Capture frame-by-frame
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)
    for(x, y, w, h) in faces:
        #print(x, y, w, h)
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = frame[y:y+h, x:x+w]

        id_, conf = recognizer.predict(roi_gray)
        if conf >=45 and conf <=85:
            print(labels[id_])
            font = cv2.FONT_HERSHEY_SIMPLEX
            name = labels[id_]
            color = (255, 255, 255)
            stroke = 2
            cv2.putText(frame, name, (x, y), font, 1, color, stroke,cv2.LINE_AA)


        img_item_gray = "my-imagegray.png"
        img_item_colour = "my-imagecolour.png"
        cv2.imwrite(img_item_colour, roi_color)
        cv2.imwrite(img_item_gray, roi_gray)

        color = (255, 0, 0)
        stroke = 2
        width = x + w
        height = y + h
        cv2.rectangle(frame, (x, y), (width, height), color, stroke)



    # Our operations on the frame come here
    # Display the resulting frame
    cv2.imshow('frame', frame)
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()