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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 | |
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"<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=1100, height=700) | |
st.markdown( | |
""" | |
## Explanation of The Ship Detection Result | |
<p align="center"> | |
<img src="https://huggingface.co/spaces/billsar1912/YOLOv5x6-marine-vessels-detection/resolve/main/apps/image/result.png" alt="Example of the result"/> | |
</p> | |
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" | |
) |