顔認識警報システムとAWS自動化

警報システム

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 

model=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

while True:
    cap = cv2.VideoCapture(0)
    ret, photo = cap.read()
    faces = model.detectMultiScale(photo)
    if len(faces) == 0:
        pass
    else:
        x1=faces[0][0]
        y1=faces[0][1]
        x2=x1+faces[0][2]
        y2=y1+faces[0][3]

        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()

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")

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())

number = os.environ['phone_number']

import pywhatkit
pywhatkit.sendwhatmsg(number, 'Alert Intruder Detected ',2,29)

顔認識を支援したTRARFORMを用いたAWS自動化

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()

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")

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[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 > 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()