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使用安全警报系统项目Ultralytics YOLOv8

安全警报系统

利用Ultralytics YOLOv8 的安全警报系统项目集成了先进的计算机视觉功能,以加强安全措施。YOLOv8该项目由Ultralytics 开发,提供实时物体检测功能,使系统能够识别潜在的安全威胁并迅速做出反应。该项目具有以下几个优点:

  • 实时检测: YOLOv8 的高效性使安全警报系统能够实时检测和响应安全事件,最大限度地缩短响应时间。
  • 准确性: YOLOv8 以其物体检测的准确性著称,可减少误报,提高安全警报系统的可靠性。
  • 集成能力:该项目可与现有的安全基础设施无缝集成,提供一个升级的智能监控层。



观看: 利用Ultralytics YOLOv8 物体检测的安全警报系统项目

代码

导入图书馆

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

设置报文参数

备注

必须生成应用程序密码

  • 导航至应用程序密码生成器,指定一个应用程序名称,如 "安全项目",然后获取一个 16 位数的密码。复制该密码并按提示粘贴到指定的密码字段。
password = ""
from_email = ""  # must match the email used to generate the password
to_email = ""  # receiver email

创建和验证服务器

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

电子邮件发送功能

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())

物体检测和警报发送器

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()

调用对象检测类并运行推理

detector = ObjectDetection(capture_index=0)
detector()

就是这样!执行代码后,如果检测到任何对象,您的电子邮件就会收到一条通知。通知会立即发送,不会重复发送。不过,您也可以根据自己的项目要求定制代码。

收到的电子邮件样本

收到的电子邮件样本



创建于 2023-12-02,更新于 2024-02-03
作者:glenn-jocher(2)、RizwanMunawar(1)

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