Spaces:
Sleeping
Sleeping
File size: 2,325 Bytes
25152cf d8eeba3 25152cf |
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 |
# Import required libraries
import PIL
import streamlit as st
from ultralytics import YOLO
# Replace the relative path to your weight file
model_path = 'model/weights/best.pt'
# Setting page layout
st.set_page_config(
page_title="Tribe-Level Bee Object Detection", # 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("Configuration") # Adding header to sidebar
# Adding file uploader to sidebar for selecting images
source_img = st.file_uploader(
"Upload an image...", type=("jpg", "jpeg", "png", 'bmp', 'webp'))
# Model Options
confidence = float(st.slider(
"Select Model Confidence", 25, 100, 40)) / 100
# Creating main page heading
st.title("Bee Detection")
st.caption('Upload a photo containing a bee :bee:, and we\'ll find it for you and tell you which tribe it belongs to')
st.caption('Use the slider to choose the confidence threshold, then hit the :blue[Detect Objects] button for your results!')
# 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_img:
# Opening the uploaded image
uploaded_image = PIL.Image.open(source_img)
# Adding the uploaded image to the page with a caption
st.image(source_img,
caption="Uploaded Image",
use_column_width=True
)
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'):
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!")
|