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Face recognition Based home alarm system
The Face Recognition process in this tutorial is divided into three steps.
#Prepare training data: In this step we will read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs to.
#Train Face Recognizer: In this step we will train OpenCV's LBPH face recognizer by feeding it the data we prepared in step 1.
#Testing: In this step we will pass some test images to face recognizer and see if it predicts them correctly.
Requirements
You need Python 3.6 64bit to run facerecognition Dlib libraries as these are not available in version latest then 3.6.
Installations Quick start
Import Required Modules #import OpenCV module import cv2 #import os module for reading training data directories and paths import os #import numpy to convert python lists to numpy arrays as #it is needed by OpenCV face recognizers import numpy as np
#matplotlib for display our images
import matplotlib.pyplot as plt
%matplotlib inline py -m pip install numpy py -m pip install opencv-python py -m pip install matplotib py -m pip install pytesseract
For Alarm use
pip install py-notifier
For example
from pynotifier import Notification
Notification( title='Notification Title', description='Notification Description', icon_path='path/to/image/file/icon.png', # On Windows .ico is required, on Linux - .png duration=5, # Duration in seconds urgency=Notification.URGENCY_CRITICAL ).send()
#Detector
Loading Recognizer Lets start the main loop and do the following basic steps Starts capturing frames from the camera object Convert it to Gray Scale Detect and extract faces from the images Use the recognizer to recognize the Id of the user Put predicted Id/Name and Rectangle on detected face.
#function to detect face using OpenCV
def detect_face(img): #convert the test image to gray image as opencv face detector expects gray images gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#return only the face part of the image return gray[y:y+w, x:x+h], faces[0]#Main Code for continuous face recognition
