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fef5934 11aea5a fef5934 11aea5a fef5934 11aea5a fef5934 11aea5a fef5934 | 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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | import streamlit as st
import cv2
import numpy as np
import os
import tempfile
from PIL import Image
from ultralytics import YOLO
import re
# ----------- Page config ----------------
st.set_page_config(
page_title='Fire Detection',
page_icon='',
layout='wide'
)
st.title("Fire Detection")
st.write('Upload an image or video to detect Fire')
# -------------- load model detection -----------
@st.cache_resource
def load_model():
return YOLO('best.pt')
model = load_model()
# switching tabs
x, y = st.tabs(['Image Detection', 'Video Detection'])
# ==================================================
# Image Detection
# ==================================================
with x:
st.header('Image Detection')
img_path = st.file_uploader('Please upload an image')
if img_path is not None:
image = Image.open(img_path)
image_np = np.array(image)
# YOLO inference
result = model(image_np, conf=0.4)
annot_img = result[0].plot()
# Convert BGR to RGB
annot_img = cv2.cvtColor(annot_img, cv2.COLOR_BGR2RGB)
annot_img = cv2.cvtColor(annot_img, cv2.COLOR_BGR2RGB)
# Display side by side
ori_img, pre_img = st.columns(2)
with ori_img:
st.markdown('#### ***Original Image***')
st.image(image, width=400)
with pre_img:
st.markdown('#### ***Detected Image***')
st.image(annot_img, width=400)
# ==================================================
# Video Detection
# ==================================================
with y:
st.header("Video Fire Detection")
video_file = st.file_uploader(
"Upload a Video",
type=["mp4", "avi", "mov"]
)
if video_file is not None:
temp_video = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
temp_video.write(video_file.read())
temp_video.close()
cap = cv2.VideoCapture(temp_video.name)
col1, col2 = st.columns(2)
with col1:
st.markdown("#### **Original Video**")
orig_frame = st.empty()
with col2:
st.markdown("#### **Detected Video**")
pred_frame = st.empty()
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model(frame, conf=0.4)
annotated_frame = results[0].plot()
orig_frame.image(frame, channels="BGR", width=400)
pred_frame.image(annotated_frame, channels="BGR", width=400)
cap.release()
os.remove(temp_video.name)
st.success("Video processing completed")
# ---------------- Sample Test Images & Videos ----------------
st.markdown("---")
st.subheader("Try with Sample Images / Videos")
st.write("Don't have files? Use the samples below to test the model.")
SAMPLE_IMAGES = {
"Fire Image 1": "pcb1.jpg",
"Fire Image 2": "pcb4.jpg",
"Fire Image 3": "pcb5.jpg"
}
SAMPLE_VIDEOS = {
"Fire Video 1": "v1.mp4",
"Fire Video 2": "v2.mp4",
"Fire Video 3": "v3.mp4",
"Fire Video 4": "v4.mp4"
}
col1, col2 = st.columns(2)
# -------- Sample Images --------
with col1:
st.markdown("### Sample Images")
selected_img = st.selectbox(
"Choose a sample image",
["None"] + list(SAMPLE_IMAGES.keys())
)
if selected_img != "None":
img_path = SAMPLE_IMAGES[selected_img]
image = Image.open(img_path)
st.image(image, caption=selected_img, use_container_width=True)
if st.button("Detect Fire in Image"):
results = model(image)
annotated_img = results[0].plot()
st.image(annotated_img, caption="Detection Result", use_container_width=True)
# -------- Sample Videos --------
with col2:
st.markdown("### 🎥 Sample Videos")
selected_vid = st.selectbox(
"Choose a sample video",
["None"] + list(SAMPLE_VIDEOS.keys())
)
if selected_vid != "None":
video_path = SAMPLE_VIDEOS[selected_vid]
st.video(video_path)
if st.button("Detect Fire in Video"):
cap = cv2.VideoCapture(video_path)
stframe = st.empty()
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = model(frame)
annotated_frame = results[0].plot()
stframe.image(annotated_frame, channels="BGR", use_container_width=True)
cap.release()
# ---------------- Footer ----------------
st.markdown("""
<br>
<div style='text-align:center; padding:12px; background-color:#111111; border-radius:10px;'>
<span style='color:#AAAAAA; font-size:16px;'>
Designed & Developed by <b style='color:#CCCCCC;'>Yedeedya Injeti</b><br>
Under <b style='color:#B8860B;'>Innomatics Research Labs</b>
</span>
</div>
<br>
""", unsafe_allow_html=True)
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