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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
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