In this blog we are going to create an alert system and AWS automation program.
Alert System : In alert system created a program that will detect the face in the image and as soon as it detects an image it will capture it and send that image to the admins email address and sends whatsapp message to admin.
AWS automation : In AWS automation, created a program that will recognize the users face and in the basis of accuracy of face recognition it will run the Terraform code that will create instance in AWS along with 5GB of volume and attach that volume to the instance.
let's start with the code:
- Libraries included are :
import cv2 from PIL import Image from email.message import EmailMessage import smtplib, ssl from email.mime.text import MIMEText from email.mime.base import MIMEBase from email.mime.multipart import MIMEMultipart from email import encoders import imghdr import os import pywhatkit # for whatsapp
Here pywhatkit library is used for whatsapp, it is used in program for sending the messages.
imghdr library is used to find the types of an image.
PIL stands for Python Image Library which is used for image processing.
email and smtlib libraries are used for using the mail service and helps to send the mail.
- load the model
Here haarcascade model is used to detect the face in image.
- Now let's discuss the code for image processing
while True: cap = cv2.VideoCapture(0) ret, photo = cap.read() faces = model.detectMultiScale(photo) if len(faces) == 0: pass else: x1=faces y1=faces x2=x1+faces y2=y1+faces aphoto = cv2.rectangle(photo, (x1,y1), (x2,y2), [0,255,0], 5) cv2.imshow("Image Capturing", aphoto) if cv2.waitKey(5)==13: #13 is the code for Enter Key break cv2.destroyAllWindows() cap.release()
when the following code in run then webcam will open and it will detect the face.
image = Image.fromarray(aphoto) image.save('Alert.png') image_show=Image.open(r"Alert.png") image_crop = image_show.crop((x1,y1,x2,y2)) image_crop.show() image_crop.save('Alert_face_detected.png') print("Image Captured")
In the following code image is cropped and saved in the system.
- Now let's code for Email Alert
email_id = os.environ['my_email'] email_receiver = os.environ['receiver_email'] password = os.environ['my_password'] #Sender, Reciever, Body of Email sender = email_id receivers = email_receiver body_of_email = 'Alert intrucder has been detected' #added sender and reciver email addresses msg = MIMEMultipart() msg['Subject'] = 'Alert Intruder detected' msg['From'] = sender msg['To'] = receivers part = MIMEBase('application', 'octet-stream') part.set_payload(open('Alert.png', 'rb').read())#Image attached encoders.encode_base64(part) part.add_header('Content-Disposition', 'attachment; filename ="Alert.png"') msg.attach(part) #Connecting to Gmail SMTP Server s = smtplib.SMTP_SSL(host = 'smtp.gmail.com', port = 465) s.login(user = sender, password = password) s.sendmail(sender, receivers, msg.as_string())
In the following code the image that was saved is mailed to other user using smtp protocol, which is used by the gmail.
- Now let's code for WhatsApp alert
number = os.environ['phone_number'] import pywhatkit pywhatkit.sendwhatmsg(number, 'Alert Intruder Detected ',2,29)
In the following code sendwhatmsg is a function from pywhatkit library that is use to send the whatsapp message.
Here Alert Intruder Detected is message and 2,29 is a time when to send the message.
After code is run, whatsaap will open in browser and message will be send.
So that's how as soon as face is detected code will run that will capture image and send it to admin's email address along with the whatsapp alert message.
AWS automation using Terraform with the help of Face Recognition
To create a Face Recognition program, first we need to create Dataset and then using the dataset we need to train the model that will help us in Face recognition.
- Create Dataset
To create dataset use the code below, following code will capture the user image 100 times and store it in a directory that will be used by the model.
import cv2 import numpy as np # Load HAAR face classifier face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # Load functions def face_extractor(img): # Function detects faces and returns the cropped face # If no face detected, it returns the input image gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) faces = face_classifier.detectMultiScale(gray, 1.3, 5) if faces is (): return None # Crop all faces found for (x,y,w,h) in faces: cropped_face = img[y:y+h, x:x+w] return cropped_face # Initialize Webcam cap = cv2.VideoCapture(0) count = 0 # Collect 100 samples of your face from webcam input while True: ret, frame = cap.read() if face_extractor(frame) is not None: count += 1 face = cv2.resize(face_extractor(frame), (200, 200)) face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY) # Save file in specified directory with unique name #path file_name_path = 'Path_of_dir/' + str(count) + '.jpg' cv2.imwrite(file_name_path, face) # Put count on images and display live count cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2) cv2.imshow('Face Cropper', face) else: print("Face not found") pass if cv2.waitKey(1) == 13 or count == 100: #13 is the Enter Key break cap.release() cv2.destroyAllWindows()
Here you can see *Count == 100 * that means image will be capture 100 times and above part image is croped so that only the face part is captured.
like this 100 images will captured and stored in a directory.
- Train the Model
To train the model use the code below and provide the directory of image dataset in the code which will help in model training.
import cv2 import numpy as np from os import listdir from os.path import isfile, join # Get the training data we previously made data_path_1 = 'path_of_image_dataset/' onlyfiles_1 = [f for f in listdir(data_path_1) if isfile(join(data_path_1, f))] # Create arrays for training data and labels Training_Data_1, Labels_1 = ,  # Create arrays for training data and labels # Create a numpy array for training dataset 1 for i, files in enumerate(onlyfiles_1): image_path = data_path_1 + onlyfiles_1[i] images = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) Training_Data_1.append(np.asarray(images, dtype=np.uint8)) Labels_1.append(i) Labels_1 = np.asarray(Labels_1, dtype=np.int32) Nitesh_model = cv2.face_LBPHFaceRecognizer.create() Nitesh_model.train(np.asarray(Training_Data_1), np.asarray(Labels_1)) print("Model trained sucessefully")
Now our model is trained .
- Create Face Recognition program
To create the Face Recognition program use the code below, now as it will recognize the face and bases on the accuracy of the face recognition terraform code will be executed which will create an instance in AWS along with 5GB of ebs volume and attach it to the instance.
import cv2 import numpy as np import os face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') def face_detector(img, size=0.5): # Convert image to grayscale gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) faces = face_classifier.detectMultiScale(gray, 1.3, 5) if faces is (): return img,  for (x,y,w,h) in faces: cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,255),2) roi = img[y:y+h, x:x+w] roi = cv2.resize(roi, (200, 200)) return img, roi # Open Webcam cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() image, face = face_detector(frame) try: face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY) # Pass face to prediction model # "results" comprises of a tuple containing the label and the confidence value results = Nitesh_model.predict(face) if results < 500: confidence = int( 100 * (1 - (results)/400) ) display_string = str(confidence) + '% Confident it is User' cv2.putText(image, display_string, (100, 120), cv2.FONT_HERSHEY_COMPLEX, 1, (255,120,150), 2) if confidence > 70: cv2.putText(image, "Hello Nitesh", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2) cv2.imshow('Face Recognitioned', image ) if cv2.waitKey(1)==13: cap.release() cv2.destroyAllWindows() !terraform init !terraform apply --auto-approve else: cv2.putText(image, "Unrecognised Face", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2) cv2.imshow('Face Recognition', image ) except: cv2.putText(image, "Face Not Found", (220, 120) , cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2) cv2.putText(image, "Searching for Face....", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2) cv2.imshow('Face Recognition', image ) pass if cv2.waitKey(1) == 13: #13 is the Enter Key break cap.release() cv2.destroyAllWindows()
Here you can see 92% is an accuracy which is more than the accuracy mentioned in a program therefore it will execute the terraform code.
To watch the Demo 👇
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