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# Import required libraries
import PIL
import cv2
import streamlit as st
from ultralytics import YOLO
import tempfile
# Replace the relative path to your weight file
model_path = 'https://huggingface.co/spaces/ankitkupadhyay/fire_and_smoke/blob/main/best.pt'
# Setting page layout
st.set_page_config(
page_title="Object Detection using YOLOv8", # Setting page title
page_icon="🤖", # Setting page icon
layout="wide", # Setting layout to wide
initial_sidebar_state="expanded" # Expanding sidebar by default
)
# Creating sidebar
with st.sidebar:
st.header("Image/Video Config") # Adding header to sidebar
# Adding file uploader to sidebar for selecting images and videos
source_file = st.file_uploader(
"Choose an image or video...", type=("jpg", "jpeg", "png", 'bmp', 'webp', 'mp4'))
# Model Options
confidence = float(st.slider(
"Select Model Confidence", 25, 100, 40)) / 100
# Creating main page heading
st.title("Object Detection using YOLOv8")
# Creating two columns on the main page
col1, col2 = st.columns(2)
# Adding image to the first column if image is uploaded
with col1:
if source_file:
# Check if the file is an image
if source_file.type.split('/')[0] == 'image':
# Opening the uploaded image
uploaded_image = PIL.Image.open(source_file)
# Adding the uploaded image to the page with a caption
st.image(source_file,
caption="Uploaded Image",
use_column_width=True
)
else:
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(source_file.read())
vidcap = cv2.VideoCapture(tfile.name)
try:
model = YOLO(model_path)
except Exception as ex:
st.error(
f"Unable to load model. Check the specified path: {model_path}")
st.error(ex)
if st.sidebar.button('Detect Objects'):
if source_file.type.split('/')[0] == 'image':
res = model.predict(uploaded_image,
conf=confidence
)
boxes = res[0].boxes
res_plotted = res[0].plot()[:, :, ::-1]
with col2:
st.image(res_plotted,
caption='Detected Image',
use_column_width=True
)
try:
with st.expander("Detection Results"):
for box in boxes:
st.write(box.xywh)
except Exception as ex:
st.write("No image is uploaded yet!")
else:
# Open the video file
success, image = vidcap.read()
while success:
res = model.predict(image,
conf=confidence
)
boxes = res[0].boxes
res_plotted = res[0].plot()[:, :, ::-1]
with col2:
st.image(res_plotted,
caption='Detected Frame',
use_column_width=True
)
try:
with st.expander("Detection Results"):
for box in boxes:
st.write(box.xywh)
except Exception as ex:
st.write("No video is uploaded yet!")
success, image = vidcap.read()
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