Create unattended_object.py

unattended_object.py

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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