攀爬识别分析算法 基于多模态融合与YOLOv8实战

pip install ultralytics opencv-python torch torchvision

import cv2
import torch
from ultralytics import YOLO

# 加载预训练模型
# 实际部署建议使用针对特定场景（如围墙、铁塔）微调后的权重
model_det = YOLO('yolov8n.pt') 
# 姿态模型（此处以YOLOv8-pose为例，也可替换为RTMPose）
model_pose = YOLO('yolov8n-pose.pt') 

def analyze_climbing_behavior(frame):
    """
    核心逻辑：融合目标检测与姿态估计，识别攀爬行为
    """
    alerts = []
    # 1. 人员检测
    det_results = model_det(frame, classes=0, verbose=False) # class 0 is person
    
    for result in det_results:
        boxes = result.boxes
        for box in boxes:
            x1, y1, x2, y2 = map(int, box.xyxy[0])
            conf = float(box.conf[0])
            
            # 置信度过滤
            if conf < 0.6:
                continue
                
            # 2. 姿态估计（裁剪人体区域进行精细化分析）
            person_crop = frame[y1:y2, x1:x2]
            if person_crop.size == 0: continue
            
            pose_results = model_pose(person_crop, verbose=False)
            
            # 3. 关键点逻辑判定
            for pose in pose_results:
                keypoints = pose.keypoints
                if keypoints is not None:
                    # 获取关键点坐标 (简化示例，需根据实际关键点索引调整)
                    # 假设索引: 0-Nose, 5-L_Shoulder, 6-R_Shoulder, 11-L_Hip, 12-R_Hip
                    # 注意：这里需要还原回原图坐标系进行逻辑判断
                    kpts = keypoints[0].xy.cpu().numpy()
                    
                    # 简单的攀爬逻辑示例：手腕高于肩膀，且膝盖高于髋部
                    # 实际工程中需引入时序分析（如连续5帧满足条件）
                    left_wrist = kpts[9]
                    left_shoulder = kpts[5]
                    left_knee = kpts[13]
                    left_hip = kpts[11]
                    
                    # 判定条件：手部上举 + 腿部抬升
                    if (left_wrist[1] < left_shoulder[1]) and (left_knee[1] < left_hip[1]):
                        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
                        cv2.putText(frame, "CLIMBING DETECTED", (x1, y1-10), 
                                    cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
                        alerts.append("Climbing")
                        
    return frame, alerts

def handle_intrusion_alert(frame, alert_type):
    """
    处理报警逻辑：抓拍、存档、推送
    """
    import time
    timestamp = time.strftime("%Y%m%d-%H%M%S")
    filename = f"alert_{alert_type}_{timestamp}.jpg"
    
    # 存档违规图片
    cv2.imwrite(filename, frame)
    print(f"Alert Triggered: {alert_type}. Image saved.")
    
    # 模拟同步后台监控平台
    # sync_to_backend(image_path=filename, location="Zone_A_Wall")