Spaces:
Sleeping
Sleeping
| import os | |
| import gradio as gr | |
| import numpy as np | |
| import io | |
| import random | |
| from PIL import Image | |
| from dotenv import load_dotenv | |
| import pystac_client | |
| from datetime import datetime | |
| from src.auth.auth import S3Connector | |
| from src.utils.utils import extract_s3_path_from_url | |
| from src.utils.stac_client import ProductDownloader | |
| # Load environment variables | |
| load_dotenv() | |
| # Get credentials from environment variables | |
| ACCESS_KEY_ID = os.environ.get("ACCESS_KEY_ID") | |
| SECRET_ACCESS_KEY = os.environ.get("SECRET_ACCESS_KEY") | |
| ENDPOINT_URL = 'https://eodata.dataspace.copernicus.eu' | |
| # Initialize the connector | |
| s3_connector = S3Connector( | |
| endpoint_url=ENDPOINT_URL, | |
| access_key_id=ACCESS_KEY_ID, | |
| secret_access_key=SECRET_ACCESS_KEY | |
| ) | |
| # Connect to S3 | |
| s3_connector.connect() | |
| s3_client = s3_connector.get_s3_client() | |
| ENDPOINT_STAC = "https://stac.dataspace.copernicus.eu/v1/" | |
| catalog = pystac_client.Client.open(ENDPOINT_STAC) | |
| def fetch_sentinel_image(longitude, latitude, date_from, date_to, cloud_cover): | |
| """Fetch a Sentinel image based on criteria.""" | |
| try: | |
| # Use the coordinates from inputs | |
| LON, LAT = float(longitude), float(latitude) | |
| # Use the date range from inputs | |
| date_range = f"{date_from}/{date_to}" | |
| cloud_query = f"eo:cloud_cover<{cloud_cover}" | |
| items_txt = catalog.search( | |
| collections=['sentinel-2-l2a'], | |
| intersects=dict(type="Point", coordinates=[LON, LAT]), | |
| datetime=date_range, | |
| query=[cloud_query] | |
| ).item_collection() | |
| if len(items_txt) == 0: | |
| return None, f"No images found for the specified criteria at coordinates ({LON}, {LAT}) with cloud cover < {cloud_cover}%." | |
| # Randomly select an image from the available items | |
| selected_item = random.choice(items_txt) | |
| # Format datetime for readability | |
| datetime_str = selected_item.properties.get('datetime', 'N/A') | |
| try: | |
| dt = datetime.fromisoformat(datetime_str.replace('Z', '+00:00')) | |
| formatted_date = dt.strftime('%Y-%m-%d %H:%M:%S UTC') | |
| except: | |
| formatted_date = datetime_str | |
| # Extract metadata for display | |
| metadata = f""" | |
| ## Product Information | |
| - **Location**: {LAT}°N, {LON}°E | |
| - **Date**: {formatted_date} | |
| - **Cloud Cover**: {selected_item.properties.get('eo:cloud_cover', 'N/A')}% | |
| - **Cloud Cover Threshold**: < {cloud_cover}% | |
| - **Satellite**: {selected_item.properties.get('platform', 'N/A')} | |
| - **Product ID**: {selected_item.id} | |
| - **Items Found**: {len(items_txt)} matching products | |
| """ | |
| # Get the TCI_60m asset from the randomly selected item | |
| product_url = extract_s3_path_from_url(selected_item.assets['TCI_60m'].href) | |
| print(f"Selected product URL: {product_url}") | |
| # Initialize the handler with the S3 connector | |
| handler = ProductDownloader(s3_client=s3_client, bucket_name='eodata') | |
| # Get the image content as bytes | |
| product_bytes, filename = handler.get_product_content(product_url) | |
| print(f"Downloaded {filename}, content size: {len(product_bytes)} bytes") | |
| # Convert to PIL Image | |
| img = Image.open(io.BytesIO(product_bytes)) | |
| return img, metadata | |
| except ValueError as ve: | |
| error_message = f"Invalid input: {str(ve)}. Please ensure longitude and latitude are valid numbers." | |
| print(error_message) | |
| return None, error_message | |
| except Exception as e: | |
| error_message = f"Error: {str(e)}" | |
| print(error_message) | |
| return None, error_message | |
| # Create Gradio interface | |
| with gr.Blocks(title="Sentinel Product Viewer") as demo: | |
| gr.Markdown("# Sentinel-2 Product Viewer") | |
| gr.Markdown("Browse and view Sentinel-2 satellite product") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| # Location inputs | |
| with gr.Row(): | |
| longitude = gr.Number(label="Longitude", value=15.0, minimum=-180, maximum=180) | |
| latitude = gr.Number(label="Latitude", value=50.0, minimum=-90, maximum=90) | |
| # Date range inputs | |
| with gr.Row(): | |
| date_from = gr.Textbox(label="Date From (YYYY-MM-DD)", value="2024-05-01") | |
| date_to = gr.Textbox(label="Date To (YYYY-MM-DD)", value="2025-02-01") | |
| # Cloud cover slider | |
| cloud_cover = gr.Slider( | |
| label="Max Cloud Cover (%)", | |
| minimum=0, | |
| maximum=100, | |
| value=50, | |
| step=5 | |
| ) | |
| # Diverse landscape location buttons | |
| gr.Markdown("### Diverse Locations") | |
| with gr.Row(): | |
| italy_btn = gr.Button("Italy") | |
| amazon_btn = gr.Button("Amazon Rainforest") | |
| with gr.Row(): | |
| tokyo_btn = gr.Button("Tokyo") | |
| great_barrier_btn = gr.Button("Great Barrier Reef") | |
| with gr.Row(): | |
| iceland_btn = gr.Button("Iceland Glacier") | |
| canada_btn = gr.Button("Baffin Island") | |
| fetch_btn = gr.Button("Fetch Random Image", variant="primary") | |
| with gr.Column(scale=2): | |
| image_output = gr.Image(type="pil", label="Sentinel-2 Image") | |
| metadata_output = gr.Markdown(label="Image Metadata") | |
| # Button click handlers for diverse landscapes | |
| italy_btn.click(lambda: (12.39, 42.05), outputs=[longitude, latitude]) | |
| amazon_btn.click(lambda: (-64.7, -3.42), outputs=[longitude, latitude]) | |
| tokyo_btn.click(lambda: (139.70, 35.65), outputs=[longitude, latitude]) | |
| great_barrier_btn.click(lambda: (150.97, -20.92), outputs=[longitude, latitude]) | |
| iceland_btn.click(lambda: (-18.17, 64.61), outputs=[longitude, latitude]) | |
| # rice_terraces_btn.click(lambda: (121.1, 16.9), outputs=[longitude, latitude]) | |
| canada_btn.click(lambda: (-71.56, 67.03), outputs=[longitude, latitude]) | |
| # Main search button | |
| fetch_btn.click( | |
| fn=fetch_sentinel_image, | |
| inputs=[longitude, latitude, date_from, date_to, cloud_cover], | |
| outputs=[image_output, metadata_output] | |
| ) | |
| gr.Markdown("## About") | |
| gr.Markdown(""" | |
| This application allows you to browse and view Sentinel-2 satellite imagery using the Copernicus Data Space Ecosystem. | |
| - **Location**: Enter longitude and latitude coordinates or select distinctive landscapes | |
| - **TCI Images**: The images shown are true color (RGB) composites at 60m resolution | |
| - **Date Range**: Specify the date range to search for images | |
| - **Cloud Cover**: Adjust the maximum acceptable cloud cover percentage | |
| - **Random Selection**: A random image that matches the criteria will be selected for display | |
| """) | |
| demo.launch() | |