faceRecognition.py

import cv2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os

def faceDetection(test_img):
    gray_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)
    face_haar_cascade = cv2.CascadeClassifier('C:/Users/vivek/Desktop/Face_recognition/haar_cascade/haarcascade_frontalface_default.xml')
    faces = face_haar_cascade.detectMultiScale(gray_img, scaleFactor=1.1, minNeighbors=3)
    
    return faces, gray_img

def labels_for_training_data(directory):
    faces = []
    faceID = []
    
    for path, subdirnames, filenames in os.walk(directory):
        for filename in filenames:
            if filename.startswith('.'):
                print('skipping system file')
                continue
            
            id = os.path.basename(path)
            img_path = os.path.join(path, filename)
            print('img path: ', img_path)
            print('id: ', id)
            test_img = cv2.imread(img_path)
            if test_img is None:
                print('image not loaded properly')
                continue
            faces_rect, gray_img = faceDetection(test_img)
            if len(faces_rect) != 1 :
                continue
            x,y,w,h = faces_rect[0]
            roi_gray = gray_img[y:y+w, x:x+h]
            faces.append(roi_gray)
            faceID.append(int(id))
    return faces, faceID

def train_classifier(faces, faceID):
    face_recognizer = cv2.face.LBPHFaceRecognizer_create()
    face_recognizer.train(faces, np.array(faceID))
    return face_recognizer

def draw_rect(test_img, face):
    x,y,w,h = face
    cv2.rectangle(test_img,(x,y),(x+w, y+h), (255,0,0), 2)
    
def put_text(test_img, text, x,y):
    cv2.putText(test_img, text, (x,y), cv2.FONT_HERSHEY_DUPLEX, 1, (255,0,0), thickness=1)