LDTP / run.py
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Update run.py
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import os
import ee
import geemap.foliumap as geemap
import streamlit as st
import pandas as pd
import numpy as np
st.set_page_config(page_title="TPL MAPPING",layout="wide")
st.markdown("""
<h1 style='text-align: center;'>Lake Distribution map of Tibet Plateau 🏔️</h1>
<h3 style='text-align: center;'>YuJunchuan [jason.yu.mail@qq.com](jason.yu.mail@qq.com)</h3>
""", unsafe_allow_html=True)
# geemap.set_proxy(33210)
row1_col1, row1_col2 = st.columns([4, 1])
Map = geemap.Map()
# 设置区域
region = ee.FeatureCollection("projects/useful-tempest-341103/assets/water/TPBoundary")
# 获取遥感影像
datastart='2021-06-01'
dataend='2021-10-15'
def rmCloudByQA(image):
qa = image.select('QA60')
cloudBitMask = 1 << 10
cirrusBitMask = 1 << 11
mask =qa.bitwiseAnd(cloudBitMask).eq(0)and(qa.bitwiseAnd(cirrusBitMask).eq(0))
return image.updateMask(mask);
S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
.filterDate(datastart,dataend)
.filterBounds(region)
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 50))
.map(rmCloudByQA)
.select('B.*')
.median()
.clip(region))
pred = ee.Image("projects/useful-tempest-341103/assets/TPlake/pred");
# 对分类数据进行mask处理
def pred_mask(pred,threshold):
mask=pred.where(pred.lt(threshold),0).where(pred.gte(threshold),1).toInt()
mask=mask.setDefaultProjection('epsg:4326',None,10)
water=mask.updateMask(mask.gt(0.5))
return water
with row1_col2:
# 选择底图模块
basemaps = ['HYBRID', 'SATELLITE', 'TERRAIN']
basemap = st.selectbox("Basemap", basemaps,index=basemaps.index('HYBRID'))
Map.add_basemap(basemap)
# 选择典型湖泊
Typicallakes = ["Typical Lakes", "Qinghai Lake", "Selincuo", "Zhaling Lake", "Eling Lake", "Zhuonai Lake", "Margai Chaka", "Kokexili Lake"]
lakeword = st.selectbox("Typical Lakes", Typicallakes)
if lakeword == "Qinghai Lake":
Map.setCenter(100.192956,36.936857, zoom=9)
elif lakeword == "Selincuo":
Map.setCenter(88.955657,31.810172, zoom=10)
elif lakeword == "Zhaling Lake":
Map.setCenter(97.294221,34.938479, zoom=11)
elif lakeword == "Eling Lake":
Map.setCenter(97.70816,34.91575, zoom=11)
elif lakeword == "Zhuonai Lake":
Map.setCenter(91.944098,35.555848, zoom=11)
elif lakeword == "Margai Chaka":
Map.setCenter(86.768704,35.133507, zoom=12)
elif lakeword == "Kokexili Lake":
Map.setCenter(91.129067,35.595563, zoom=11)
else:
Map.setCenter(87.745,33.092, zoom=6)
# 自定义设置阈值
Threshold = st.slider('Threshold', 0, 255, 128)
water=pred_mask(pred,Threshold)
# 选择是否分屏查看
split = st.checkbox("Split View")
if split:
left_layer = geemap.ee_tile_layer(water, {'min': 0, 'max':1, 'palette': '0905ff'}, name='water',opacity=0.7)
right_layer = geemap.ee_tile_layer(S2, {'min': 0, 'max':3000, 'bands': ['B4', 'B3', 'B2']},name='Image',shown=False)
Map.split_map(left_layer, right_layer)
else:
Map.addLayer(water, {'min': 0, 'max':1, 'palette': '0905ff'}, name='water',opacity=0.7)
st.sidebar.title("About")
st.sidebar.info(
"""
This web [app]() is maintained by [Junchuan Yu](https://junchuanyu.netlify.app/posts/). You can follow me on social media:
[GitHub](https://github.com/JunchuanYu) | [Zhihu](https://twitter.com/giswqs) .
Sentinel-2 is used as the data, and 8-band images are used as training data (B4, B3, B2, B8, B11, B12, MNDWI, SDWI). The model uses a multi-scale deep neural network model based on transfer learning, and is implemented using otop technology
"""
)
with row1_col1:
Map.to_streamlit(height=750)
st.markdown("<h5 style='text-align: center;'>you can follow the WeChat public account [45度科研人] and leave me a message!</h5>", unsafe_allow_html=True)
row2_col1, row2_col2,row2_col3,row2_col4 = st.columns([2,1,1,2])
with row2_col2:
st.markdown("<img src='https://dunazo.oss-cn-beijing.aliyuncs.com/blog/wechat-simple.png' style='margin-right:25px;width:200px;height:200px;'>", unsafe_allow_html=True)
with row2_col3:
st.markdown("<img src='https://dunazo.oss-cn-beijing.aliyuncs.com/blog/shoukuanma222.png' style='margin-right:25px;width:170px;height:190px;'>", unsafe_allow_html=True)