Face Recognizing and Creating EC2 instance and EBS in AWS, Sending Whatsapp Message using ML model and python.
When it recognizes your face then -
👉 It sends mail to your mail-id by writing this is the face of your_name.
👉 Second, it sends a WhatsApp message to your friend, it can be anything.
📌 When it recognizes the second face, it can be your friend or family member's face.
👉 Create EC2 instance in the AWS using CLI.
👉 Create 5 GB EBS volume and attach it to the instance.
- To recognize the face I have used a Pre-built model i.e. Haarcascade_frontalface which is an OpenCV algorithm model used to detect a face. And it can detect one face at a time so if your requirement is to detect multiple objects then we can use CNN, multi-classification.
Let's jump to the code:
- I have commented everything in my code so if you don't understand the code please check those comments.
Step 1 - Create Training Data
In [1]:
import cv2
import smtplib
import pywhatkit
import os
from twilio.rest import Client
from email import message
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
file_name_path = './faces/user/' + 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()
print("Collecting Samples Complete")
Step 2 - Train Model
In [2]:
cap.release()
In [3]:
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 = './faces/user/'
onlyfiles = [f for f in listdir(data_path) if isfile(join(data_path, f))]
# Create arrays for training data and labels
Training_Data, Labels = [], []
# Open training images in our datapath
# Create a numpy array for training data
for i, files in enumerate(onlyfiles):
image_path = data_path + onlyfiles[i]
images = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
Training_Data.append(np.asarray(images, dtype=np.uint8))
Labels.append(i)
# Create a numpy array for both training data and labels
Labels = np.asarray(Labels, dtype=np.int32)
# Initialize facial recognizer
# model = cv2.face.createLBPHFaceRecognizer()
# NOTE: For OpenCV 3.0 use cv2.face.createLBPHFaceRecognizer()
# pip install opencv-contrib-python
# model = cv2.createLBPHFaceRecognizer()
#change moodel name as per your name
ranjit_model = cv2.face_LBPHFaceRecognizer.create()
# Let's train our model
ranjit_model.train(np.asarray(Training_Data), np.asarray(Labels))
print("Model trained sucessefully")
Step 3 - Run Our Facial Recognition
In [9]:
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)
xyz=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 = ranjit_model.predict(face)
# harry_model.predict(face)
if results[1] < 500:
confidence = int( 100 * (1 - (results[1])/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 >=90:
cv2.putText(image, "Hey Ranjit", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow('Face Recognition', image )
gmail_user= '' # Sender mailID
gmail_password= '' # Sender Mail Password
mail_from =gmail_user
mail_to = '' # Receiver MailID
mail_subject= 'hello'
mail_message_body ='Your Face recognized!'
mail_message= '''\
From:%s
To:%s
subject:%s
%s
'''%(mail_from,mail_to,mail_subject,mail_message_body)
server= smtplib.SMTP_SSL('smtp.gmail.com',465)
server.login(gmail_user,gmail_password)
server.sendmail(mail_from,mail_to,mail_message)
server.close()
xyz=999
break
elif confidence <=80 || confidence <=90:
os.system("terraform init")
os.system("terraform apply ec2EBS.tf -auto-approve")
print("AWS Infra Creation Completed!")
break
else:
cv2.putText(image, "I dont know, how r u", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
cv2.imshow('Face Recognition', image )
except:
cv2.putText(image, "No Face Found", (220, 120) , cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
cv2.putText(image, "looking 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()
if xyz==999:
pywhatkit.sendwhatmsg("+917xxxxxxxxx", "Hii This Is Ranjit !", 18,41)
- Let's see the terraform code.
- You must have an AWS profile configured and to configure you have to provide a Private key and public key. the command is: "aws configure --profile Your_name"
provider "aws" {
region = "ap-south-1"
profile = "ranjit"
}
resource "tls_private_key" "generated_key" {
algorithm = "RSA"
rsa_bits = 2048
}
resource "local_file" "the_key" {
content = tls_private_key.generated_key.private_key_pem
filename = "mykey.pem"
file_permission = 0400
}
resource "aws_key_pair" "key" {
//key_name = "my_private_key"
public_key = tls_private_key.generated_key.public_key_openssh
}
resource "aws_default_vpc" "default" {
tags = {
Name = "Default VPC"
}
}
resource "aws_security_group" "security_groups_created" {
//name = "allow_ssh_http"
description = "Allow ssh inbound traffic"
vpc_id = aws_default_vpc.default.id
ingress {
description = "http"
from_port = 80
to_port = 80
protocol = "tcp"
cidr_blocks = ["0.0.0.0/0"]
}
ingress {
description = "ssh"
from_port = 22
to_port = 22
protocol = "tcp"
cidr_blocks = ["0.0.0.0/0"]
}
egress {
from_port = 0
to_port = 0
protocol = "-1"
cidr_blocks = ["0.0.0.0/0"]
}
tags = {
Name = "allowing http ssh"
}
}
//output "security"{
//value=aws_security_group.allow_tls
//}
resource "aws_instance" "web" {
ami = "ami-0447a12f28fddb066"
instance_type = "t2.micro"
key_name = aws_key_pair.key.key_name
security_groups = [ aws_security_group.security_groups_created.name ]
connection {
type = "ssh"
user = "ec2-user"
private_key = tls_private_key.generated_key.private_key_pem
host = aws_instance.web.public_ip
}
provisioner "remote-exec" {
inline = [
"sudo yum install git httpd php -y",
"sudo systemctl restart httpd",
"sudo systemctl enable httpd"
]
}
tags = {
Name = "ranjit AWS EC2 Instance"
}
}
resource "aws_ebs_volume" "ebs1" {
availability_zone = aws_instance.web.availability_zone
size = 1
tags = {
Name = "EBS volume"
}
}
resource "aws_volume_attachment" "ebs_att" {
device_name = "/dev/sdh"
volume_id = aws_ebs_volume.ebs1.id
instance_id = aws_instance.web.id
force_detach = true
}
resource "null_resource" "null1"{
depends_on=[
aws_volume_attachment.ebs_att
]
connection {
type = "ssh"
user = "ec2-user"
private_key = tls_private_key.generated_key.private_key_pem
host = aws_instance.web.public_ip
}
provisioner "remote-exec" {
inline = [
"sudo mkfs.ext4 /dev/xvdh",
"sudo mount /dev/xvdh /var/www/html",
"sudo rm -rf /var/www/html/*",
"sudo git clone https://github.com/ranjitben10/cloudtask.git /var/www/html"
]
}
}
- Here I am coping some images and installing required packages to make OS a server that will show some images in the web app. We are mounting the /var/www/html with /dev/xvdh so if OS goes down we won't lose our data.
Task Completed...
Thank You.