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 def app(): 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 **(the explanation of the result from the detection and tutorial how to use the model is on the bottom of this page. Scroll down or [click here!](#explanation-of-the-ship-detection-result))**. ### For more information, please visit: - Check out [my github](https://github.com/bills1912) - Jump into YOLOv5 [documentation](https://docs.ultralytics.com/) """ ) 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"

{i[1]}

Vessel Type: {i[8]}
Destination Port: {i[2]}
Reported Destination: {i[4]}
Current Port: {i[6]}\
Latitude: {i[10]}
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=1100, height=700) st.markdown( """ ## Explanation of The Ship Detection Result

Example of the result

Here is the explanation of the result from the example on the image above:\ - **box**, indicate the object that the model can detect; - **label of the box**, indicate the name of the object that the model detect; - **number beside the label**, indicate how much the confidence of the model detect the object; ## Tutorial How to Use Ship Detection Model Here is the step by step how to use the model on this dashboard: - first, **prepare the satellite imagery image** that you want to use. If you don't have the image, you can use this sample image, by clicking the **"Download Image"** on the end of this dashboard usage explanation; - then, **choose the model** that you want to use **(on the side bar)**, **YOLOv5x6 Model** to use the YOLOv5x6 model or **Fine-Tuning Model** to use the fine-tuning model (study case: Tanjung Priok Port); - to upload your image, **click the "Browse File"** button, then upload your image; - after the image is uploaded, **right click the image** and then **copy the image address** by clicking **"Copy image address"** button; - then **paste the image address** on the box below the image; - finally, **click the "Predict"** button to start the detection of the object inside your image. Wait untill the result appear. """, unsafe_allow_html=True ) with open("apps/image/sample.jpg", "rb") as file: st.download_button( label="Download Sample Image", data=file, file_name="sample.jpg", mime="image/jpg" )