人员越界监测系统 越界检测AI识别算法

import cv2
import numpy as np
from ultralytics import YOLO

# 1. 初始化模型
# 使用YOLOv8n进行快速推理，实际生产环境建议使用针对特定场景微调的模型
model = YOLO('yolov8n.pt')

# 2. 定义警戒区域（多边形顶点坐标）
# 这里定义一个梯形区域作为示例
警戒区域 = np.array([[100, 500], [300, 200],reshape((-1, 1, 2))

def is_intrusion(bbox, polygon):
    """
    判断目标边界框底部中心点是否在多边形内
    bbox: [x1, y1, x2, y2]
    """
    x1, y1, x2, y2 = bbox
    # 取底部中心点作为判定基准
    point = (int((x1 + x2) / 2), int(y2))
    
    # 使用OpenCV进行点与多边形测试
    distance = cv2.pointPolygonTest(polygon, point, False)
    return distance >= 0

# 3. 视频流处理逻辑
cap = cv2.VideoCapture('test_video.mp4')

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break
        
    # 目标检测
    results = model(frame, classes=[0]) # 仅检测person类别
    
    for result in results:
        boxes = result.boxes.xyxy.cpu().numpy()
        for box in boxes:
            x1, y1, x2, y2 = map(int, box[:4])
            conf = box[4]
            
            # 越界判定
            if is_intrusion([x1, y1, x2, y2], 警戒区域):
                # 触发告警逻辑：绘制红色框
                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
                cv2.putText(frame, "Intrusion Alert!", (x1, y1 - 10), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
                
                # 此处可添加截图存档、推送到后台等逻辑
            else:
                # 正常显示绿色框
                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

    # 绘制警戒区域轮廓
    cv2.polylines(frame, [警戒区域], isClosed=True, color=(255, 0, 0), thickness=2)
    
    cv2.imshow("Intrusion Detection", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()