Face recognisation Program
Note:Here in this program you need to use your own login credentials
1)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, it can be anything.
2)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.
Program:
# Step 1 — Create Training Data
import cv2
import numpy as np
import pywhatkit as pw
import time
import smtplib
import os
# 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 = ‘/home/gunasekhar/Desktop/ws/i1/’ + 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 of face 1 Completed”)
cap.release()
cap = cv2.VideoCapture(0)
count1 = 0
while True:
ret1, frame1 = cap.read()
if face_extractor(frame1) is not None:
count1 += 1
face1 = cv2.resize(face_extractor(frame1), (200, 200))
face1 = cv2.cvtColor(face1, cv2.COLOR_BGR2GRAY)
# Save file in specified directory with unique name
file_name_path1 = ‘/home/gunasekhar/Desktop/ws/i2/’ + str(count1) + ‘.jpg’
cv2.imwrite(file_name_path1, face1)
# Put count on images and display live count
cv2.putText(face1, str(count1), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow(‘Face Cropper’, face1)
else:
print(“Face not found”)
pass
if cv2.waitKey(1) == 13 or count1 == 100: #13 is the Enter Key
break
cap.release()
cv2.destroyAllWindows()
print(“Collecting Samples face 2 is Completed”)
cap.release()
#Step 2 — Train 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 = ‘/home/gunasekhar/Desktop/ws/i1/’
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()
gunasekhar_model = cv2.face_LBPHFaceRecognizer.create()
# Let’s train
gunasekhar_model.train(np.asarray(Training_Data), np.asarray(Labels))
print(“Model trained sucessefully”)
# Get the training data we previously made
data_path1 = ‘/home/gunasekhar/Desktop/ws/i2/’
onlyfiles1 = [g for g in listdir(data_path1) if isfile(join(data_path1, g))]
# Create arrays for training data and labels
Training_Data1, Labels1 = [], []
# Open training images in our datapath
# Create a numpy array for training data
for k, files1 in enumerate(onlyfiles1):
image_path1 = data_path1 + onlyfiles1[k]
images1 = cv2.imread(image_path1, cv2.IMREAD_GRAYSCALE)
Training_Data1.append(np.asarray(images1, dtype=np.uint8))
Labels1.append(k)
# Create a numpy array for both training data and labels
Labels1 = np.asarray(Labels1, 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()
other_model= cv2.face_LBPHFaceRecognizer.create()
# Let’s train
other_model.train(np.asarray(Training_Data1), np.asarray(Labels1))
print(“Other Model trained sucessefully”)
#Step 3 — Run Our Facial Recognition
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 = gunasekhar_model.predict(face)
results1= other_model.predict(face)
# harry_model.predict(face)
if results1[1]<500:
confidence1 = int( 100 * (1 — (results1[1])/400) )
display_string = str(confidence1) + ‘% Confident it is User’
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 > 80:
cv2.putText(image, “Hey Guna Sekhar”, (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow(‘Face Recognition’, image )
t=time.localtime()
pw.sendwhatmsg(‘+91 phone number’,’Guna Sekhar face detected’,t[3],t[4]+2)
import smtplib
# creates SMTP session
s = smtplib.SMTP(‘smtp.gmail.com’, 587)
# start TLS for security
s.starttls()
# Authentication
s.login(“senderemailid”, “password”)
# message to be sent
message = “This is face of Guna Sekhar”
# sending the mail
s.sendmail(“senderemailid”,”receiveremailid”, message)
# terminating the session
s.quit()
break
else:
if confidence1 > 80:
cv2.putText(image, “Hey Alex”, (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow(‘Face Recognition’, image )
os.system(‘terraform init’)
os.system(‘terraform apply — auto-approve’)
break
else:
cv2.putText(image, “Model not trained on your face”, (220, 120) , cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
cv2.imshow(‘Face Recognition’, image )
pass
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()
Output:
When it recognized my face
2)When it recognized others face(Alex)
Ec2.tf file
provider “aws”{
region=”ap-south-1"
profile=”default”
}
resource “aws_instance” “os1”{
ami=”ami-0ad704c126371a549"
instance_type=”t2.micro”
}
resource “aws_ebs_volume” “storage”{
availability_zone=aws_instance.os1.availability_zone
size=5
}
resource “aws_volume_attachment” “storage_attach”{
device_name=”/dev/sdh”
volume_id=aws_ebs_volume.storage.id
instance_id=aws_instance.os1.id
}