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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

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

import smtplib

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

电子邮件发送功能

from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText


def send_email(to_email, from_email, object_detected=1):
    """Sends an email notification indicating the number of objects detected; defaults to 1 object."""
    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

from time import time

import cv2
import torch

from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors


class ObjectDetection:
    def __init__(self, capture_index):
        """Initializes an ObjectDetection instance with a given camera index."""
        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):
        """Run prediction using a YOLO model for the input image `im0`."""
        results = self.model(im0)
        return results

    def display_fps(self, im0):
        """Displays the FPS on an image `im0` by calculating and overlaying as white text on a black rectangle."""
        self.end_time = time()
        fps = 1 / 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):
        """Plots bounding boxes on an image given detection results; returns annotated image and class IDs."""
        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):
        """Run object detection on video frames from a camera stream, plotting and showing the results."""
        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



Created 2023-12-02, Updated 2024-06-19
Authors: ambitious-octopus (1), glenn-jocher (6), IvorZhu331 (1), RizwanMunawar (1)

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