Security.py

import torch
import numpy as np
import cv2
from time import time
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText

password = "" # generate a 15 digit code
from_email = ""  # must match the email used to generate the password
to_email = "@gmail.com"  # receiver email

server = smtplib.SMTP('smtp.gmail.com: 587')
server.starttls()
server.login(from_email, password)

def send_email(to_email, from_email, object_detected=1):
    message = MIMEMultipart()
    message['From'] = from_email
    message['To'] = to_email
    message['Subject'] = "Security Alert"
    # Add in the message body
    message_body = f'ALERT - {object_detected} objects has been detected!!'

    message.attach(MIMEText(message_body, 'plain'))
    server.sendmail(from_email, to_email, message.as_string())
    
class ObjectDetection:
    def __init__(self, capture_index):
        # default parameters
        self.capture_index = capture_index
        self.email_sent = False

        # model information
        self.model = YOLO("yolov8n.pt")

        # visual information
        self.annotator = None
        self.start_time = 0
        self.end_time = 0

        # device information
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'

    def predict(self, im0):
        results = self.model(im0)
        return results

    def display_fps(self, im0):
        self.end_time = time()
        fps = 1 / np.round(self.end_time - self.start_time, 2)
        text = f'FPS: {int(fps)}'
        text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1.0, 2)[0]
        gap = 10
        cv2.rectangle(im0, (20 - gap, 70 - text_size[1] - gap), (20 + text_size[0] + gap, 70 + gap), (255, 255, 255), -1)
        cv2.putText(im0, text, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 2)

    def plot_bboxes(self, results, im0):
        class_ids = []
        self.annotator = Annotator(im0, 3, results[0].names)
        boxes = results[0].boxes.xyxy.cpu()
        clss = results[0].boxes.cls.cpu().tolist()
        names = results[0].names
        for box, cls in zip(boxes, clss):
            class_ids.append(cls)
            self.annotator.box_label(box, label=names[int(cls)], color=colors(int(cls), True))
        return im0, class_ids

    def __call__(self):
        cap = cv2.VideoCapture(self.capture_index)
        assert cap.isOpened()
        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
        frame_count = 0
        while True:
            self.start_time = time()
            ret, im0 = cap.read()
            assert ret
            results = self.predict(im0)
            im0, class_ids = self.plot_bboxes(results, im0)

            if len(class_ids) > 0:  # Only send email If not sent before
                if not self.email_sent:
                    send_email(to_email, from_email, len(class_ids))
                    self.email_sent = True
            else:
                self.email_sent = False

            self.display_fps(im0)
            cv2.imshow('YOLOv8 Detection', im0)
            frame_count += 1
            if cv2.waitKey(5) & 0xFF == 27:
                break
        cap.release()
        cv2.destroyAllWindows()
        server.quit()

detector = ObjectDetection(capture_index=0)
detector()