Trying to Plot NDVI Images with Polygon Boundary

[Muhammad-Waqas5012]

I want to plot Mean NDVI Images for each GeoJSON's feature.

Get the Mean NDVI image for each feature and also this feature's boundary.
Convert the EE Image to a NumPy array.
Plot the Mean NDVI array and feature's boundary using Matplotlib or any other Library.
Save the plot as a PNG file.

# Libraries to import
import geopandas as gpd
import earthpy.plot as ep
import ee
import geemap
import os
import glob
import datetime
from shapely.geometry import Polygon, MultiPolygon
import json
import matplotlib.pyplot as plt
import numpy as np

# Authenticate Earth Engine (Note: if already not) or Authentication is expired, Remove "#" to run the next line.
# ee.Authenticate()

# Initialize Earth Engine
ee.Initialize()

# Specify the folder path containing GeoJSON files
folder_path = 'D:/R1'

# Get a list of all GeoJSON files in the folder
geojson_files = glob.glob(folder_path + '/*.geojson')

# Iterate over each GeoJSON file
for geojson_file in geojson_files:
    with open(geojson_file, 'r') as f:
        data = json.load(f)

    # Access the features in the GeoJSON file
    features = [feature for feature_list in data['features'] for feature in feature_list]

    # Iterate over each feature in the GeoJSON file
    for feature in features:
        # Extract properties and geometry
        properties = feature['properties']
        coordinates = feature['geometry']['coordinates']
        geometry = MultiPolygon([Polygon([(float(coord[0]), float(coord[1])) for coord in coordinates])])

        # Extract feature ID
        feature_id = properties.get('field_id')
        farmer_id = properties['farmer_id']
        field_crop_id = properties['field_crop_id']
        if feature_id is None:
            raise ValueError("Field ID is missing in the GeoJSON file.")

        # Create a GeoDataFrame for the current feature
        feature_gdf = gpd.GeoDataFrame.from_features([{'geometry': geometry, 'properties': properties}])
        feature_gdf = feature_gdf.set_crs('EPSG:4326')

        # Load the GeoJSON string into Earth Engine
        feature_geojson = geemap.gdf_to_ee(feature_gdf, geodesic=False)

        # Calculate Mean NDVI for the feature
        start_date = ee.Date(datetime.datetime.now()).advance(-5, 'day')
        end_date = ee.Date(datetime.datetime.now())
        
        # Load Sentinel-2 image collection
        s2_sr_collection = (ee.ImageCollection('COPERNICUS/S2_SR')
                            .filterBounds(feature_geojson)
                            .filterDate(start_date, end_date))
        
        # Check if the collection is empty
        image_count = s2_sr_collection.size().getInfo()
        if image_count == 0:
            # No images available for this feature
            print(f"No images available for Feature ID {feature_id}. Skipping...")
            continue

        # Batch processing of images
        s2_list = s2_sr_collection.toList(image_count)

        # Iterate over each image in the collection
        for i in range(image_count):
            # Get the ith image
            image = ee.Image(s2_list.get(i))

            # Compute NDVI
            # Clip the image with the feature boundary
            clipped_image = image.clip(feature_geojson)

            # Compute NDVI for the clipped image
            ndvi_image = clipped_image.normalizedDifference(['B8', 'B4']).rename('NDVI')

            # Get the image date
            date = ee.Date(image.get('system:time_start')).format('YYYY-MM-dd').getInfo()
            
            # Convert the EE image to an Earthpy numpy array
            ndvi_array = geemap.ee_to_numpy(ndvi_image, region=feature_geojson.geometry())
            
# Plot the boundary of the GeoDataFrame
feature_gdf.boundary.plot(ax=ax, color='black')
            fig, ax = plt.subplots(1, 1, figsize=(5.932,5.932))
            fig.tight_layout()
            # Plot NDVI array
            ax.imshow(ndvi_array, cmap='RdYlGn')

[giswqs]

Again, provide minimal script without absolute paths. A simple script without looping and complicated stuff.