videoTester.py

import os
import cv2
import numpy as np
import faceRecognition as fr


confidence_level = 50
# This module captures images via webcam and performs face recognition
# face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer = cv2.face.LBPHFaceRecognizer.create()
face_recognizer.read('trainingData.yml')    # Load saved training data

# people to be recognized
name = {0: "Dhiraj", 1: "Om", 2: "Tanushree"}


cap = cv2.VideoCapture(0)

while True:
    ret, test_img = cap.read()  # captures frame and returns boolean value and captured image
    faces_detected, gray_img = fr.faceDetection(test_img)

    # for (x, y, w, h) in faces_detected:
    #     cv2.rectangle(test_img, (x, y), (x + w, y + h), (255, 0, 0), thickness=7)
    #
    # resized_img = cv2.resize(test_img, (800, 800))
    # cv2.imshow('face detection Tutorial ', resized_img)
    # cv2.waitKey(10)

    for face in faces_detected:
        (x, y, w, h) = face
        roi_gray = gray_img[y:y+w, x:x+h]
        label, confidence = face_recognizer.predict(roi_gray)   # predicting the label of given image
        print("confidence:", confidence)
        print("label:", label)
        fr.draw_rect(test_img, face)
        predicted_name = name[label]
        # If confidence greater than 37 then don't print predicted face text on screen means 62%
        if confidence < confidence_level:
            fr.put_text(test_img, predicted_name, x, y)
        else:
            continue

    resized_img = cv2.resize(test_img, (800, 800))
    cv2.imshow('face recognition tutorial ', resized_img)
    if cv2.waitKey(10) == ord('q'):     # wait until 'q' key is pressed
        break

cap.release()
cv2.destroyAllWindows