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import cv2
import numpy as np
import torch
import streamlit as st
import os
from ultralytics import YOLO
from camera_input_live import camera_input_live
# Load YOLO fire detection model
model_path = "last.pt"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = YOLO(model_path)
model.to(device)
# Streamlit app title
st.title("Live Fire Detection with Camera")
st.subheader("Hold the camera towards potential fire sources to detect in real-time.")
# Capture live camera input
image = camera_input_live()
fire_detected = False # Track fire detection state
# HTML & JS for Alarm Sound
alarm_html = """
<audio id="fireAlarm" src="alarm.mp3"></audio>
<script>
function playAlarm() {
var alarm = document.getElementById("fireAlarm");
if (alarm.paused) {
alarm.play();
}
}
function stopAlarm() {
var alarm = document.getElementById("fireAlarm");
alarm.pause();
alarm.currentTime = 0;
}
</script>
"""
if image is not None:
# Convert the image to OpenCV format
bytes_data = image.getvalue()
cv2_img = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
# Perform fire detection
results = model(cv2_img)
fire_present = False # Temporary flag for fire detection in this frame
# Draw bounding boxes for detected fires
for result in results:
boxes = result.boxes
for box in boxes:
b = box.xyxy[0].cpu().numpy().astype(int)
label = f'Fire {box.conf[0]:.2f}'
cv2.rectangle(cv2_img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 3)
cv2.putText(cv2_img, label, (b[0], b[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
fire_present = True # Fire detected
# Display the annotated image
st.image(cv2_img, channels="BGR", caption="Detected Fire", use_container_width=True)
# Display logs and trigger alarm
if fire_present:
st.error("🔥 Fire Detected! 🔥")
st.markdown(alarm_html + "<script>playAlarm();</script>", unsafe_allow_html=True)
fire_detected = True
else:
st.success("✅ No Fire Detected")
if fire_detected:
st.markdown(alarm_html + "<script>stopAlarm();</script>", unsafe_allow_html=True)
fire_detected = False
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