| import streamlit as st
|
| from ultralytics import YOLO
|
| import numpy as np
|
| import cv2
|
| from PIL import Image
|
|
|
| st.title("🔍 Suspicious Activity Detection with YOLOv11")
|
|
|
|
|
| @st.cache_resource
|
| def load_model():
|
| return YOLO("yolo11l.pt")
|
|
|
| model = load_model()
|
|
|
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
|
|
|
| if uploaded_file:
|
| image = Image.open(uploaded_file)
|
| st.image(image, caption="Uploaded Image", use_column_width=True)
|
|
|
| if st.button("Detect Activity"):
|
| img_array = np.array(image.convert("RGB"))[..., ::-1]
|
| results = model.predict(img_array)
|
|
|
| for r in results:
|
| plotted = r.plot()
|
| st.image(plotted, caption="Detections", use_column_width=True)
|
|
|
| st.subheader("Detected Objects:")
|
| for box in r.boxes:
|
| conf = float(box.conf[0])
|
| cls = int(box.cls[0])
|
| cls_name = model.names[cls]
|
| st.write(f"- {cls_name} (Confidence: {conf:.2f})")
|
|
|
