import argparse import base64 import time from concurrent.futures import ThreadPoolExecutor, as_completed from pathlib import Path import numpy as np import rasterio from PIL import Image from rasterio import Affine from tqdm import tqdm from pkg.image import random_file_width from pkg.spatial import deg2num, lonlat_to_meters from pkg.thread import download_tile if __name__ == '__main__': parser = argparse.ArgumentParser(description='Exfiltrate data from WMS servers.') parser.add_argument('base_url', help='The base URL for the WMS server. Example: https://wmts.nlsc.gov.tw/wmts/nURBAN/default/EPSG:3857/') parser.add_argument('--zoom', type=int, required=True, help='The zoom level to use.') parser.add_argument('--threads', type=int, default=10, help='Number of download threads to use.') parser.add_argument('--referer', help='The content of the Referer header to send.') parser.add_argument('--output', default='wmts-output', help='Output directory path.') parser.add_argument('--proxy', action='store_true', help='Enable using a proxy.') parser.add_argument('--output-tiff', help='Path for output GeoTIFF. Default: wmts-output/output.tiff') parser.add_argument('--bbox', required=True, type=str, metavar='Bounding Box', nargs='+', default=(None, None, None, None), help='Bounding Box of the area to download. Separate each value with a space. (top left lat, top left lon, bottom right lat, bottom right lon)') args = parser.parse_args() args.base_url = args.base_url.strip('/') + f'/{args.zoom}/' base_output = Path(args.output).resolve().absolute().expanduser() url_hash = base64.b64encode(args.base_url.encode()).decode('utf-8').strip('==') tiles_output = base_output / url_hash / str(args.zoom) tiles_output.mkdir(parents=True, exist_ok=True) top_left_lat, top_left_lon, bottom_right_lat, bottom_right_lon = map(float, args.bbox) min_col, min_row = deg2num(top_left_lat, top_left_lon, args.zoom) max_col, max_row = deg2num(bottom_right_lat, bottom_right_lon, args.zoom) if args.output_tiff: output_tiff = Path(args.output_tiff) else: output_tiff = base_output / f'output-z{args.zoom}-{top_left_lat}x{top_left_lon}-{bottom_right_lat}x{bottom_right_lon}.tiff' r_headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36', 'Accept': 'image/avif,image/webp,*/*', 'Accept-Language': 'en-US,en;q=0.5' } if args.referer: r_headers['Referer'] = args.referer tiles = [] retries = [] total_downloaded = 0 row_i = min_row row_iter = range(min_row, max_row + 1) row_bar = tqdm(total=len(row_iter), desc=f'Row {row_i}', postfix={'new_files': total_downloaded, 'failures': len(retries)}) for row in row_iter: row_i = row col_bar = tqdm(total=len(range(min_col, max_col + 1)), leave=False, disable=True) with ThreadPoolExecutor(args.threads) as executor: futures = [executor.submit(download_tile, (row, col, args.base_url, r_headers, tiles_output, args.proxy)) for col in range(min_col, max_col + 1)] for future in as_completed(futures): result = future.result() if result: result_row, result_col, new_image = result if new_image == 'success': col_bar.disable = False # Only show the col_bar if we've downloaded new tiles. This prevents updating at the speed of light when checking existing files. total_downloaded += 1 tiles.append((result_row, result_col)) elif new_image == 'exist': tiles.append((result_row, result_col)) elif new_image == 'failure': retries.append((result_row, result_col)) row_bar.set_postfix({'new_files': total_downloaded, 'failures': len(retries)}) col_bar.update() row_bar.refresh() col_bar.close() row_bar.set_postfix({'new_files': total_downloaded, 'failures': len(retries)}) row_bar.update() row_bar.close() col_bar = tqdm(total=len(retries), desc=f'Tile Retries') with ThreadPoolExecutor(args.threads) as executor: futures = [executor.submit(download_tile, (row, col, args.base_url, r_headers, tiles_output, args.proxy)) for row, col in retries] for future in as_completed(futures): result = future.result() if result: result_row, result_col, new_image = result tiles.append((result_row, result_col)) if new_image == 'success': total_downloaded += 1 elif new_image == 'failure': col_bar.write(f'{(result_row, result_col)} failed!') col_bar.update() col_bar.close() print(f'Downloaded {total_downloaded} images.') tile_size = random_file_width(tiles_output) # Define the number of rows and columns based on the bounding box num_rows = max_row - min_row + 1 num_cols = max_col - min_col + 1 # Create an empty array to store the image data image_data = np.empty((num_rows * tile_size, num_cols * tile_size, 3), dtype=np.uint8) def build_tiff_data(task): row, col = task tile_file = tiles_output / f"{row}_{col}.png" if not tile_file.is_file(): raise Exception(f'Tile does not exist: {tile_file}') with Image.open(tile_file) as img: tile_data = np.array(img) # Remove the alpha channel tile_data = tile_data[:, :, :3] # Replace white pixels with NODATA tile_data[np.all(tile_data == [255, 255, 255], axis=-1)] = [0, 0, 0] # ArcGIS does not like pixels that have zeros in them, eg. (255, 0, 0). We need to convert the zeros to ones, eg. (255, 1, 1). mask = np.any(tile_data == 0, axis=-1) & np.any(tile_data != 0, axis=-1) # Identify pixels where not all bands are zero and at least one band is zero. for i in range(3): # Iterate over each band. # For these pixels, set zero bands to one. tile_data[mask & (tile_data[:, :, i] == 0), i] = 0.1 # Calculate the position of the tile in the image data array. row_pos = (row - min_row) * tile_size col_pos = (col - min_col) * tile_size # Insert the tile data into the image data array at the correct spot. image_data[row_pos:row_pos + tile_size, col_pos:col_pos + tile_size] = tile_data with ThreadPoolExecutor() as executor: futures = {executor.submit(build_tiff_data, task) for task in tiles} for future in tqdm(as_completed(futures), total=len(futures), desc='Building TIFF'): pass # Transpose the image data array to the format (bands, rows, cols). image_data = np.transpose(image_data, (2, 0, 1)) # Convert geographic coordinates to Web Mercator coordinates. Not 100% sure this is nessesary. top_left_mx, top_left_my = lonlat_to_meters(top_left_lon, top_left_lat) bottom_right_mx, bottom_right_my = lonlat_to_meters(bottom_right_lon, bottom_right_lat) # Define the transformation from pixel coordinates to geographic coordinates, which is an Affine transformation that # maps pixel coordinates in the image to geographic coordinates on the Earth's surface. transform = (Affine.translation(top_left_lon, top_left_lat) # Create a translation transformation that shifts the image and set the origin of the image to the top-left corner of the bounding box. # Create a scaling transformation that scales the image in the x and y directions to convert the pixel coordinates of the image to the geographic coordinates of the bounding box. * Affine.scale((bottom_right_lon - top_left_lon) / image_data.shape[2], (bottom_right_lat - top_left_lat) / image_data.shape[1])) # Write the image data to a GeoTIFF file print('Saving to:', output_tiff) start = time.time() with rasterio.open(output_tiff, "w", driver="GTiff", height=num_rows * tile_size, width=num_cols * tile_size, count=3, dtype=str(image_data.dtype), crs='EPSG:4326', transform=transform, compress="DEFLATE", nodata=0) as dst: dst.write(image_data, indexes=[1, 2, 3]) print(f'Saved in {int(time.time() - start)} seconds.')