Face Recognition in Python and sending a message through Whatsapp once face is successfully recognized.

Dear Readers,

This article incorporates the code and clarification for the preparation model for face acknowledgement, face location and communicating something specific through Whatsapp whenever a face is perceived.

Collection of Samples

import cv2
import numpy as npface_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# Load haar classifier
# 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)
# We convert our color image to grayscale format for faster calculation
    faces = face_classifier.detectMultiScale(gray)
# Detecting face
    if faces is (): 
#Empty round brackets represent empty tuple
       return None
#If there are no faces detected return None i.e. resetting it back to original posititon here
    for (x,y,w,h) in faces:                 
# Cropping all possible captured faces
        cropped_face = img[ x:x+w , y:y+h]  
# We store the complete image inside cropped_face array
# .detectMultiScale() returns x,y,w,h where (x,y) are top-left edge of a rectangle , w is width , h is height
# faces[0][0] = x ; faces[0][1] = y ; faces[0][2] = w ; faces[0][3] = h
# x1 = x ; y1 = y ; x2 = x1 + w (as x1 + width = x2) ; y2 = y1 + h (as y1 + height = y2)
        print(type(cropped_face))
        return cropped_face
# returning cropped_face to img in face_extractor function                             cap = cv2.VideoCapture(0)                   
# start webcam
count = 0
# Collect 100 samples of your face from webcam input
while True:
    ret, frame = cap.read()
    if face_extractor(frame) is not None:   
# If there is cropped image ready and not empty, then true and get inside loop
        count += 1                          
# Count increases by 1 if image captured
        face = cv2.resize(face_extractor(frame), (200, 200))     
# Image is resized into size (200,200)
        face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)           
# Converting image into grayscale
        file_name_path = './faces/user/' + str(count) + '.jpg'   
# Save file in specified directory with unique name
        cv2.imwrite(file_name_path, face)                       
# Image is written on that path with name mentioned
# 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) 
# Show the window        
    else:
        print("Face not found")
        pass        if cv2.waitKey(1) == 13 or count == 100: 
# 13 is the Enter Key
                break
cap.release() 
# Close the camera
cv2.destroyAllWindows()
print("Collecting Samples Complete")
print(frame.shape)

Training Model

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/' #our main folder
onlyfiles = [f for f in listdir(data_path) if isfile(join(data_path, f))]# listdir(datapath) is used to list directories/files in the datapath folder/directory.
# f for f in listdir(datapath) means listing all labels in that directory.
# isfile returns either true or false
# join(datapath,f) joins the file in the datapath.
# overall this line means that onlyfiles will store files that are present in the directory mentioned# 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):                        
# Doing transactions one by one on <onlyfiles>
    image_path = data_path + onlyfiles[i]                   
# Storing Data Path combined with the image
    images = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)    
# Reading the image at the path
    Training_Data.append(np.asarray(images, dtype=np.uint8)) 
# Appending the training data and converting input to array(asarray)
    Labels.append(i)                                         
# Appending Labels array similarly
# Create a numpy array for both training data and labels
Labels = np.asarray(Labels, dtype=np.int32)                  
# Converting labels array into numpy format
# Initialize facial recognizer
# model = cv2.face.createLBPHFaceRecognizer()
# NOTE: For OpenCV 3.0 use cv2.face.createLBPHFaceRecognizer()
# pip install opencv-contrib-python
# model = cv2.createLBPHFaceRecognizer()
facemodel  = cv2.face_LBPHFaceRecognizer.create()           
# Creating the model 
facemodel.train(np.asarray(Training_Data), np.asarray(Labels)) 
# Training the model with the Training Data and corresponding label
print("Model trained sucessefully")
# Facemodel.save('face_recognition_model.h5')                   
# If you want to save the model in your directory

Face Recognition

import cv2
import numpy as np
import osface_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 ():           
# if faces returns no value, return the same image screen available
        return img, []        
# return image and empty list since we have no values of x,y,w,h2    
# creating a rectangle on the border of the face
    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           
# return image and the cropped photograph
# Open Webcam
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    image, face = face_detector(frame) 
# function called
    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 = facemodel.predict(face) 
# prediction using the cropped image
        print(results)
# harry_model.predict(face)        
        confidence = int( 100 * (1 - (results[1])/400) )                # formula to find confidence score
        display_string = str(confidence) + '% Confident it is User'     # variable declared to store confidence percentage
        print(confidence)        
        cv2.putText(image, display_string, (100, 120), cv2.FONT_HERSHEY_COMPLEX, 1, (255,120,150), 2)
        if confidence > 80:
            cv2.putText(image, "Hey Yash", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
            cv2.imshow('Face Recognition', image )
            break
# os.system("chrome https://www.google.com/)
# os.system("wmplayer   c:\<filename>.mp3")
# 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()

Whatsapp Module

# Here, I have used twilio for sending message through Whatsapp.from twilio.rest import Client 
 
            account_sid = 'Account SID' 
            auth_token = 'Authentication Token' 
            client = Client(account_sid, auth_token) 
 
            message = client.messages.create( 
                              from_='whatsapp:+1234567890',  
                              body='Message from Python',      
                              to='whatsapp:+1234567890' 
                                            )
            print(message.sid)

OR

pywhatkit.sendwhatmsg_instantly(phone_no="+919987371229", message="Hi, I am Yash this is whatsapp message sent with Python.")
print("Whatsapp Message sent Successfully!!")

Mailing Module

pywhatkit.send_mail(email_sender= "<email id>",
                    password= "<password>",
                    subject="Automated E-mail",
                    message="System generated mail using OpenCV face detection.",
                    email_receiver="<sender email id>")

AWS Module

# Creating instance in AWS
os.system("aws ec2 run-instances  --image-id ami-011c99152163a87ae --instance-type t2.micro  --subnet-id subnet-06f7036d  --count 1 --security-group-ids sg-5771be2b > ec2.txt")
    print("Instance Launched")
# Creating volume of size 5gb
os.system("aws ec2 create-volume --availability-zone ap-south-1a --size 5 --volume-type gp2 --tag-specification  ResourceType=volume,Tags=[{Key=face,Value=volume}]  > ebs.txt")
print("Volume Created of size 5 gb")
print("Initiating in 120 seconds")
time.sleep(120)
ec2_id = open("ec2.txt", 'r').read().split(',')[3].split(':')[1].split('"')[1]
ebs_id = open("ebs.txt", 'r').read().split(',')[6].split(':')[1].split('"')[1]
os.system("aws ec2 attach-volume --instance-id   " + ec2_id +"  --volume-id  " + ebs_id  +"  --device /dev/sdf")
print("Volume Successfully attached to the instance")

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