Upload updated_app.py

updated_app.py

@@ -0,0 +1,196 @@
+import os
+import ee
+import geemap
+import json
+import geopandas as gpd
+import streamlit as st
+import pandas as pd
+from fastkml import kml
+import geojson
+
+ee_credentials = os.environ.get("EE")
+os.makedirs(os.path.expanduser("~/.config/earthengine/"), exist_ok=True)
+with open(os.path.expanduser("~/.config/earthengine/credentials"), "w") as f:
+    f.write(ee_credentials)
+
+ee.Initialize()
+
+def convert_3d_to_2d(geometry):
+    """
+    Recursively convert any 3D coordinates in a geometry to 2D.
+    """
+    if geometry.is_empty:
+        return geometry
+
+    if geometry.geom_type == 'Polygon':
+        return geojson.Polygon([[(x, y) for x, y, *_ in ring] for ring in geometry.coordinates])
+
+    elif geometry.geom_type == 'MultiPolygon':
+        return geojson.MultiPolygon([
+            [[(x, y) for x, y, *_ in ring] for ring in poly]
+            for poly in geometry.coordinates
+        ])
+
+    elif geometry.geom_type == 'LineString':
+        return geojson.LineString([(x, y) for x, y, *_ in geometry.coordinates])
+
+    elif geometry.geom_type == 'MultiLineString':
+        return geojson.MultiLineString([
+            [(x, y) for x, y, *_ in line]
+            for line in geometry.coordinates
+        ])
+
+    elif geometry.geom_type == 'Point':
+        x, y, *_ = geometry.coordinates
+        return geojson.Point((x, y))
+
+    elif geometry.geom_type == 'MultiPoint':
+        return geojson.MultiPoint([(x, y) for x, y, *_ in geometry.coordinates])
+
+    return geometry  # Return unchanged if not a supported geometry type
+
+def convert_to_2d_geometry(geom): #Handles Polygon Only
+  if geom is None:
+    return None
+  elif geom.has_z:
+    # Extract exterior coordinates and convert to 2D
+    exterior_coords = geom.exterior.coords[:]  # Get all coordinates of the exterior ring
+    exterior_coords_2d = [(x, y) for x, y, *_ in exterior_coords] # Keep only the x and y coordinates, ignoring z
+
+    # Handle interior rings (holes) if any
+    interior_coords_2d = []
+    for interior in geom.interiors:
+        interior_coords = interior.coords[:]
+        interior_coords_2d.append([(x, y) for x, y, *_ in interior_coords])
+
+    # Create a new Polygon with 2D coordinates
+    return type(geom)(exterior_coords_2d, interior_coords_2d)
+  else:
+    return geom
+  
+def kml_to_gdf(kml_file):
+  try:
+    gdf = gpd.read_file(kml_file)
+    for i in range(len(gdf)):
+      geom = gdf.iloc[i].geometry
+      new_geom = convert_to_2d_geometry(geom)
+      gdf.loc[i, 'geometry'] = new_geom
+      print(gdf.iloc[i].geometry)
+    print(f"KML file '{kml_file}' successfully read")
+  except Exception as e:
+    print(f"Error: {e}")
+  return gdf
+
+def kml_to_geojson(kml_string):
+    k = kml.KML()
+    k.from_string(kml_string.encode('utf-8'))  # Convert the string to bytes
+    features = list(k.features())
+    
+    geojson_features = []
+    for feature in features:
+        geometry_2d = convert_3d_to_2d(feature.geometry)
+        geojson_features.append(geojson.Feature(geometry=geometry_2d))
+    
+    geojson_data = geojson.FeatureCollection(geojson_features)
+    return geojson_data
+
+def geojson_to_ee(geojson_data):
+  ee_object = ee.FeatureCollection(geojson_data)
+  return ee_object
+
+def kml_to_gdf(kml_file):
+  try:
+    gdf = gpd.read_file(kml_file)
+    for i in range(len(gdf)):
+      geom = gdf.iloc[i].geometry
+      new_geom = convert_to_2d_geometry(geom)
+      gdf.loc[i, 'geometry'] = new_geom
+      print(gdf.iloc[i].geometry)
+    print(f"KML file '{kml_file}' successfully read")
+  except Exception as e:
+    print(f"Error: {e}")
+  return gdf
+
+# put title in center
+st.markdown("""
+<style>
+h1 {
+    text-align: center;
+}
+</style>
+""", unsafe_allow_html=True)
+
+st.title("Mean NDVI Calculator")
+
+# get the start and end date from the user
+col = st.columns(2)
+start_date = col[0].date_input("Start Date", value=pd.to_datetime('2021-01-01'))
+end_date = col[1].date_input("End Date", value=pd.to_datetime('2021-01-30'))
+start_date = start_date.strftime("%Y-%m-%d")
+end_date = end_date.strftime("%Y-%m-%d")
+
+max_cloud_cover = st.number_input("Max Cloud Cover", value=20)
+
+# Get the geojson file from the user
+uploaded_file = st.file_uploader("Upload KML/GeoJSON file", type=["geojson", "kml"])
+
+# Read the KML file
+if uploaded_file is None:
+    file_name = "Bhankhara_Df_11_he_5_2020-21.geojson"
+    st.write(f"Using default file: {file_name}")
+    data = gpd.read_file(file_name)
+    with open(file_name) as f:
+      str_data = f.read()
+else:
+    st.write(f"Using uploaded file: {uploaded_file.name}")
+    file_name = uploaded_file.name
+    bytes_data = uploaded_file.getvalue()
+    str_data = bytes_data.decode("utf-8")
+
+
+if file_name.endswith(".geojson"):
+  geojson_data = json.loads(str_data)
+elif file_name.endswith(".kml"):
+  geojson_data = json.loads(kml_to_gdf(str_data).to_json())
+  print(geojson_data)
+
+# Read Geojson File
+ee_object = geojson_to_ee(geojson_data)
+
+# Filter data based on the date, bounds, cloud coverage and select NIR and Red Band
+collection = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filterBounds(ee_object).filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', max_cloud_cover)).filter(ee.Filter.date(start_date, end_date)).select(['B4', 'B8'])
+
+# Print Number of Images in collection
+# print("Number of images", collection.size().getInfo())
+st.write(f"Number of images: {collection.size().getInfo()}")
+
+# Calculate NDVI as Normalized Index
+def calculate_ndvi(image):
+  ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
+  return image.addBands(ndvi)
+
+collection = collection.map(calculate_ndvi)
+
+# Write Zonalstats into csv file
+# out_dir = os.path.join("Output")
+# out_NDVI_stats = os.path.join(out_dir, "tmp.csv")
+
+# if not os.path.exists(out_dir):
+#     os.makedirs(out_dir)
+
+geemap.zonal_stats(collection.select(["NDVI"]), ee_object, "tmp.csv", stat_type="mean", scale=10)
+
+# Show the table
+df = pd.read_csv("tmp.csv")
+df = df.T
+df = df.reset_index()
+df = df.iloc[:-2]
+df['index'] = pd.to_datetime(df['index'].apply(lambda x: x.split('_')[1].split('T')[0])).dt.strftime('%Y-%m-%d')
+df.rename(columns={'index': 'Date', 0: 'Mean NDVI'}, inplace=True)
+st.write(df)
+
+# plot the time series
+st.write("Time Series Plot")
+st.line_chart(df.set_index('Date'))
+
+st.write(f"Overall Mean NDVI: {df['Mean NDVI'].mean():.2f}")