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| import cv2 | |
| import numpy as np | |
| from ultralytics import YOLO | |
| import os | |
| import uuid | |
| # Load YOLOv8 model (can be customized for object detection) | |
| model = YOLO("yolov8n.pt") # You may switch to yolov8s.pt or yolov8m.pt based on size/accuracy | |
| # Maintain state of detected objects for simple tracking | |
| unattended_objects_memory = {} | |
| def detect_unattended_objects(frame, frame_index, detection_interval=30, stay_threshold=90): | |
| """ | |
| Detects unattended objects such as bags that appear in the same position for too long. | |
| Returns True and the cropped object image if detected. | |
| """ | |
| detections = model.predict(source=frame, classes=[24, 26, 28, 39], verbose=False)[0] | |
| # COCO classes for suitcase (24), handbag (26), backpack (28), and cardboard box (39) | |
| h, w, _ = frame.shape | |
| detected = False | |
| cropped_output = None | |
| for result in detections.boxes: | |
| cls = int(result.cls[0]) | |
| conf = float(result.conf[0]) | |
| x1, y1, x2, y2 = map(int, result.xyxy[0]) | |
| center = ((x1 + x2) // 2, (y1 + y2) // 2) | |
| object_id = f"{cls}-{center[0]//20}-{center[1]//20}" # simple spatial bin id | |
| if object_id not in unattended_objects_memory: | |
| unattended_objects_memory[object_id] = frame_index | |
| else: | |
| duration = frame_index - unattended_objects_memory[object_id] | |
| if duration >= stay_threshold: | |
| detected = True | |
| cropped_output = frame[y1:y2, x1:x2] | |
| unattended_objects_memory[object_id] = frame_index + 9999 # suppress repeated detection | |
| # Clear memory periodically | |
| if frame_index % (stay_threshold * 2) == 0: | |
| unattended_objects_memory.clear() | |
| return detected, cropped_output | |