Task Description 📄
6 min readSep 7, 2021
❄️ Create a program that perform below mentioned task upon recognizing a particular face.
📌 When it recognize your face then —
👉 It send mail to your mail id by writing this is face of your_name.
👉 Second it send whatsapp message to your friend With Attached Image With Face Labelled With Name.
👉 Third It sends SMS to Your Phone Number With Attached Document Of Recognized Face.
📌 When it recognize second face, it can be your friend or family members face.
👉 Create EC2 instance in the AWS using CLI.
👉 Create 5 GB EBS volume and attach it to the instance
Mail and Whatapps
import cv2
import time
import numpy as np
from os import listdir
from os.path import isfile, join
import pywhatkit
import smtplib
import getpassname = input("Enter Your Name: ")
email = input('Enter E-mail:')
pwd = getpass.getpass(prompt='Enter your Password:')
phn_no = input("Enter your Friend's phone Number: ")
1. Creating DataSet
# Load HAAR face classifier
Classifier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")#Initialize Internal Webcam
cap = cv2.VideoCapture(0)#Detection of the face
#(images passed in this function one by one and then it returns the cropped face)
def face_detection(img):
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = Classifier.detectMultiScale(photo)
if len(faces)==0:
return None
#else part which crop the input image
for (x,y,w,h) in faces:
cropped_face = img[y:y+h, x:x+w]
return cropped_face#Collecting 100 samples of your face from webcam input (Required data)
count = 0
while True:
#Read single image of the face
ret, photo = cap.read()
if face_detection(photo) is not None:
# face found count increase
count +=1
# resizing every image
face = cv2.resize(face_detection(photo), (200, 200))
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
#save the data file in the specified directory
#image saved as count_number.jpg in particular directory path
file_path = './faces/user/'+ str(count)+'.jpg'
cv2.imwrite(file_path, face)
#putting the count text on image to display the live count
cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow('Face Cropper', face)
else:
pass
if cv2.waitKey(1) == 13 or count == 100:
break
cap.release()
cv2.destroyAllWindows()
print("Collecting Samples Complete")
print("+-----------------------------+")
print("Dataset Successfully Created!")
2. Train the model
#Get the data to train from the specified directory
data_path = './faces/user/'#list of images
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_LBPHFaceRecognizer.create()
# Let's train our model
model.train(np.asarray(Training_Data), np.asarray(Labels))
print("Model trained successefully!!")
3. Adding Functionalities
def sendMail():
try:
From=email
To = 'rupeshj845@gmail.com'
server = smtplib.SMTP_SSL("smtp.gmail.com",465)
server.login(email,pwd)
server.sendmail(From,To,'This is the face of '+ name)
print('Mail Sent!')
except Exception as error:
print('Username or Password is Incorrect')
def sendWhatsApp():
msg = input("Enter your Message: ")
pywhatkit.sendwhatmsg_instantly(phn_no,msg,wait_time=4)
print("Message Sent!")4. Run the Model
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 == ():
return img, []
for (x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)
roi = img[y:y+h, x:x+w]
roi = cv2.resize(roi, (200, 200))
return img, roiimport os
# Open Internal Webcam
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
image, face = face_detector(frame)
try:
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
# "results" comprises of a tuple containing the label and the confidence value
results = model.predict(face) #passing face for prediction
if results[1] < 500:
confidence = int( 100 * (1 - (results[1])/400) )
display_string = str(confidence) + '% Correct'
cv2.putText(image, display_string, (185, 120), cv2.FONT_ITALIC, 1, (255,40,150), 2)
if confidence >= 90:
cv2.putText(image,"Face Found!",(190, 80) , cv2.FONT_ITALIC,1, (0,0,255), 2)
cv2.putText(image, "Hello!," + name, (175,440), cv2.FONT_ITALIC, 1, (0,255,0), 2)
cv2.imshow('Detecting Face', image )
isConfident = True
else:
cv2.putText(image, "User Not Found!", (175,440), cv2.FONT_ITALIC, 1, (0,0,255), 2)
cv2.imshow('Detecting Face', image )
except:
cv2.putText(image, "No Face Found!", (185, 100) , cv2.FONT_ITALIC,1, (0,0,255), 2)
cv2.putText(image, "Looking for face..", (175, 440), cv2.FONT_ITALIC, 1, (0,0,255), 2)
cv2.imshow('Detecting Face', image )
pass
if cv2.waitKey(1) == 13: #13 is the Enter Key
break
cap.release()
cv2.destroyAllWindows()
if isConfident == True:
print("Face Detected!")
print("-----------------------")
print("Sending Mail...")
sendMail()
print("-----------------------")
print("Sending WhatsApp...")
sendWhatsApp()
print("Message Sent!")
print("-----------------------")
AWS- Face Recoginizing
import cv2
import time
import numpy as np
from os import listdir
from os.path import isfile, join
import boto3
import os
import subprocess
import timename = input("Enter Your Friend's Name: ")
1. Creating DataSet
# Load HAAR face classifier
Classifier = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")#Initialize Internal Webcam
cap = cv2.VideoCapture(0)#Detection of the face
#(images passed in this function one by one and then it returns the cropped face)
def face_detection(img):
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = Classifier.detectMultiScale(photo)
if len(faces)==0:
return None
#else part which crop the input image
for (x,y,w,h) in faces:
cropped_face = img[y:y+h, x:x+w]
return cropped_face#Collecting 100 samples of your face from webcam input (Required data)
count = 0
while True:
#Read single image of the face
ret, photo = cap.read()
if face_detection(photo) is not None:
# face found count increase
count +=1
# resizing every image
face = cv2.resize(face_detection(photo), (200, 200))
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
#save the data file in the specified directory
#image saved as count_number.jpg in particular directory path
file_path = './faces/user/'+ str(count)+'.jpg'
cv2.imwrite(file_path, face)
#putting the count text on image to display the live count
cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow('Face Cropper', face)
else:
pass
if cv2.waitKey(1) == 13 or count == 100:
break
cap.release()
cv2.destroyAllWindows()
print("Collecting Samples Complete")
print("+-----------------------------+")
print("Dataset Successfully Created!")
2. Train the model
#Get the data to train from the specified directory
data_path = './faces/user/'#list of images
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_LBPHFaceRecognizer.create()
# Let's train our model
model.train(np.asarray(Training_Data), np.asarray(Labels))
print("Model trained successefully!!")
3. Adding Functions
def createInstance():
os.system('aws ec2 run-instances --image-id ami-0ad704c126371a549 --count 1 --instance-type t2.micro --key-name XXXXXXXX --security-group-ids sg-0029df2a2a568ca7d')
print("ec2-instance Created!")
def createVolume():
az = subprocess.getoutput('aws ec2 describe-instances --filters Name=instance-state-name,Values=running --query "Reservations[].Instances[].Placement.AvailabilityZone" --output text')
os.system('aws ec2 create-volume --availability-zone {} --size 5'.format(az))
print("EBS of 5GiB Volume created!")
def attachVolume():
for i in range(20):
time.sleep(1)
print("Attaching...")
instance_id = subprocess.getoutput('aws ec2 describe-instances --filters Name=instance-state-name,Values=running --query "Reservations[].Instances[].InstanceId" --output text')
volume_id = subprocess.getoutput('aws ec2 describe-volumes --filters Name=size,Values=5 --query "Volumes[*].VolumeId" --output text')
os.system('aws ec2 attach-volume --device /dev/xvdc --instance-id {} --volume-id {}'.format(instance_id, volume_id))
print("Volume attached to the Instance!")4. Run the Model
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 == ():
return img, []
for (x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)
roi = img[y:y+h, x:x+w]
roi = cv2.resize(roi, (200, 200))
return img, roiimport os
# Open Internal Webcam
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
image, face = face_detector(frame)
try:
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
# "results" comprises of a tuple containing the label and the confidence value
results = model.predict(face) #passing face for prediction
if results[1] < 500:
confidence = int( 100 * (1 - (results[1])/400) )
display_string = str(confidence) + '% Correct'
cv2.putText(image, display_string, (185, 120), cv2.FONT_ITALIC, 1, (255,40,150), 2)
if confidence >= 90:
cv2.putText(image,"Face Found!",(190, 80) , cv2.FONT_ITALIC,1, (0,0,255), 2)
cv2.putText(image, name, (175,440), cv2.FONT_ITALIC, 1, (0,255,0), 2)
cv2.imshow('Detecting Face', image )
isConfident = True
else:
cv2.putText(image, "User Not Found!", (175,440), cv2.FONT_ITALIC, 1, (0,0,255), 2)
cv2.imshow('Detecting Face', image )
except:
cv2.putText(image, "No Face Found!", (185, 100) , cv2.FONT_ITALIC,1, (0,0,255), 2)
cv2.putText(image, "Looking for face..", (175, 440), cv2.FONT_ITALIC, 1, (0,0,255), 2)
cv2.imshow('Detecting Face', image )
pass
if cv2.waitKey(1) == 13: #13 is the Enter Key
break
cap.release()
cv2.destroyAllWindows()if isConfident == True:
print("Face Detected!")
print("-----------------------")
createInstance()
print("#########################")
createVolume()
print("#########################")
attachVolume()
print("#########################")
print("GREAT SUCCESS!!")