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import time | |
from turtle import width | |
import torch | |
import folium | |
import numpy as np | |
import pandas as pd | |
import streamlit as st | |
from folium.plugins import MarkerCluster | |
from streamlit_folium import folium_static | |
st.set_page_config( | |
page_title="Ship Detection using YOLOv5 Medium Model", | |
page_icon=":ship:", | |
layout="wide" | |
) | |
st.write("# Welcome to Ship Detection Application! :satellite:") | |
st.markdown( | |
""" | |
This application is build based on YOLOv5 with extral large model. User just | |
upload an image, and press the 'Predict' button to make a prediction base on | |
a training model before. | |
### For more information, please visit: | |
- Check out [my github](https://github.com/bills1912) | |
- Jump into YOLOv5 [documentation](https://docs.ultralytics.com/) | |
""" | |
) | |
st.write("## Ship Imagery Prediction") | |
map_col1, map_col2, map_col3 = st.columns(3) | |
ais = pd.read_csv("https://raw.githubusercontent.com/bills1912/marin-vessels-detection/main/data/MarineTraffic_VesselExport_2022-11-25.csv") | |
ais_jakarta = ais[ais['Destination Port'] == 'JAKARTA'] | |
ais_list = ais_jakarta.values.tolist() | |
f = folium.Figure(width=1000, height=500) | |
jakarta_vessels = folium.Map(location=[-5.626954250925966, 106.70735731868719], zoom_start=8).add_to(f) | |
ais_data = folium.FeatureGroup(name="marine_vessels") | |
mCluster = MarkerCluster(name="Marine Vessels") | |
for i in ais_list: | |
html = f"<h3>{i[1]}</h3> Vessel Type: {i[8]} </br> Destination Port: {i[2]} </br> Reported Destination: {i[4]} </br> Current Port: {i[6]}\ | |
</br> Latitude: {i[10]} </br> Longitude: {i[11]}" | |
iframe = folium.IFrame(html) | |
popup = folium.Popup(iframe, min_width=250, max_width=300) | |
ais_data.add_child(mCluster.add_child(folium.Marker(location=[i[10], i[11]], popup=popup, icon=folium.Icon(color="black", icon="ship", prefix="fa")))) | |
jakarta_vessels.add_child(ais_data) | |
folium_static(jakarta_vessels, width=1370, height=700) | |
st.write("### Model evaluation:") | |
eval_col1, eval_col2, eval_col3, eval_col4 = st.columns(spec=4) | |
eval_col1.metric("Precision", "89.52%") | |
eval_col2.metric("Recall", "83.54%") | |
eval_col3.metric("mAP 0.5", "85.39%") | |
eval_col4.metric("mAP 0.5:0.95", "62.63%") | |
uploaded_file = st.file_uploader("Choose a ship imagery") | |
if uploaded_file is not None: | |
st.image(uploaded_file, caption='Image to predict') | |
folder_path = st.text_input("Image path", | |
help="This field the image path field that the model will predict the object inside the image that we have uploaded", | |
placeholder="Copy the path of image to this field") | |
prediction = st.button("Predict") | |
if prediction: | |
ship_model = torch.hub.load('ultralytics/yolov5', 'custom', path="best.pt", force_reload=True) | |
results = ship_model(f"{folder_path}") | |
with st.spinner("Loading..."): | |
time.sleep(3.5) | |
st.success("Done!") | |
st.image(np.squeeze(results.render())) | |
results.print() | |