File size: 1,356 Bytes
eae54a4 26663dd eae54a4 26663dd eae54a4 26663dd 45b9fd5 26663dd eae54a4 45b9fd5 eae54a4 45b9fd5 eae54a4 26663dd eae54a4 26663dd eae54a4 45b9fd5 26663dd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
import cv2
import numpy as np
import torch
import streamlit as st
from ultralytics import YOLO
from camera_input_live import camera_input_live
# Load YOLO fire detection model
model_path = "last.pt"
if not torch.cuda.is_available():
device = "cpu"
else:
device = "cuda"
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()
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, device=device)
# 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)
c = int(box.cls[0])
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)
# Display the annotated image
st.image(cv2_img, channels="BGR", caption="Detected Fire", use_container_width=True)
|