yolov8 区域声光报警+计数

yolov8 区域报警+计数

1. 基础
2. 报警功能
- 2. 1声音报警代码
- 2. 2画面显示报警代码
3. 完整代码
4. 源码

1. 基础

本项目是在 yolov8 区域多类别计数的基础上实现的，具体区域计数原理可见上边文章

2. 报警功能

设置一个区域region_points，当行人这一类别进入该区域范围内，会触发报警系统，产生警报

2. 1声音报警代码

pygame.mixer.init()  # 初始化
pygame.mixer.music.load('out.wav')  # 加载报警音源
pygame.mixer.music.set_volume(1)   # 设置声音
pygame.mixer.music.play()  # 播放音乐

2. 2画面显示报警代码

text = "ALARM"
cv2.putText(annotated_frame, text, (600, 300), cv2.FONT_ITALIC, 2.0,(0, 0, 255), 3)

请添加图片描述

3. 完整代码


import cv2
import numpy as np
from ultralytics import YOLO
import pygamedef is_inside_region(point, region_points):"""判断点是否在指定区域内"""return cv2.pointPolygonTest(np.array(region_points), point, False) >= 0def detect():model = YOLO("yolov8n.pt")cv2.namedWindow('region couter', cv2.WINDOW_NORMAL)cv2.resizeWindow('region couter', 960, 540)  # 设置宽高cap = cv2.VideoCapture('ultralytics/assets/people.mp4')out_video = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'XVID'), 30, (1920, 1080))region_points = [(640, 270), (640, 540), (1280, 540), (1280, 270)]region_points_np = np.array(region_points)assert cap.isOpened(), "Error reading video file"while cap.isOpened():success, im0 = cap.read()if not success:print("Video frame is empty or video processing has been successfully completed.")breaktracks = model.track(im0, persist=True)annotated_frame = tracks[0].plot()boxes = tracks[0].boxes.datacount_class1 = 0count_class2 = 0for i, box in enumerate(boxes):x1, y1, x2, y2 = box[:4]x_center = (x1 + x2) / 2y_center = (y1 + y2) / 2center_point = (int(x_center), int(y_center))if is_inside_region(center_point, region_points):if box[-1] == 0:  # 类别1的标签.人count_class1 += 1pygame.mixer.init()pygame.mixer.music.load('out.wav')pygame.mixer.music.set_volume(1)pygame.mixer.music.play()text = "ALARM"cv2.putText(annotated_frame, text, (960, 400), cv2.FONT_ITALIC, 2.0,(0, 255, 255), 3)elif  box[-1] == 2:  # 类别2的标签，车count_class2 += 1# cv2.polylines(annotated_frame, [region_points_np], isClosed=True, color=(255, 0, 0), thickness=2)region_mask = np.zeros_like(annotated_frame)cv2.fillPoly(region_mask, [region_points_np], color=(255, 0, 0))# 使用透明度将填充后的区域与原始帧混合alpha = 0.3  # 调整透明度annotated_frame = cv2.addWeighted(annotated_frame, 1 - alpha, region_mask, alpha, 0)print("Number of objects in class 1:", count_class1)print("Number of objects in class 2:", count_class2)cv2.imshow("region couter", annotated_frame)out_video.write(annotated_frame)if cv2.waitKey(1) == ord('q'):breakout_video.release()cap.release()cv2.destroyAllWindows()
if __name__ == '__main__':detect()