128 lines
5.6 KiB
Python
128 lines
5.6 KiB
Python
import argparse
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import base64
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import time
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from pathlib import Path
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import numpy as np
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import rasterio
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from PIL import Image
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from rasterio import Affine
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from tqdm import tqdm
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from pkg.image import random_file_width
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from pkg.spatial import deg2num
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from pkg.thread import download_tile
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# TODO: support reusing TIFFs
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Exfiltrate data from WMS servers.')
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parser.add_argument('base_url', help='The base URL for the WMS server. Example: https://wmts.nlsc.gov.tw/wmts/nURBAN/default/EPSG:3857/')
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parser.add_argument('--zoom', type=int, required=True, help='The zoom level to use.')
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parser.add_argument('--threads', type=int, default=10, help='Number of download threads to use.')
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parser.add_argument('--referer', help='The content of the Referer header to send.')
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parser.add_argument('--output', default='wmts-output', help='Output directory path.')
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parser.add_argument('--output-tiff', help='Path for output GeoTIFF. Default: wmts-output/output.tiff')
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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)')
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args = parser.parse_args()
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args.base_url = args.base_url.strip('/') + f'/{args.zoom}/'
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base_output = Path(args.output).resolve().absolute().expanduser()
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url_hash = base64.b64encode(args.base_url.encode()).decode('utf-8').strip('==')
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tiles_output = base_output / url_hash / str(args.zoom)
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tiles_output.mkdir(parents=True, exist_ok=True)
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top_left_lat, top_left_lon, bottom_right_lat, bottom_right_lon = map(float, args.bbox)
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min_col, min_row = deg2num(top_left_lat, top_left_lon, args.zoom)
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max_col, max_row = deg2num(bottom_right_lat, bottom_right_lon, args.zoom)
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if args.output_tiff:
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output_tiff = Path(args.output_tiff)
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else:
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output_tiff = base_output / f'output-z{args.zoom}-{top_left_lat}x{top_left_lon}-{bottom_right_lat}x{bottom_right_lon}.tiff'
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r_headers = {
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'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',
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'Accept': 'image/avif,image/webp,*/*',
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'Accept-Language': 'en-US,en;q=0.5'
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}
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if args.referer:
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r_headers['Referer'] = args.referer
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tiles = []
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total_downloaded = 0
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row_i = min_row
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for row in tqdm(range(min_row, max_row + 1), desc=f'Row {row_i}'):
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row_i = row
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bar = tqdm(total=len(range(min_col, max_col + 1)), leave=False)
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with ThreadPoolExecutor(args.threads) as executor:
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futures = [executor.submit(download_tile, (row, col, args.base_url, r_headers, tiles_output)) for col in range(min_col, max_col + 1)]
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for future in as_completed(futures):
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result = future.result()
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if result:
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result_row, result_col, new_image = result
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tiles.append((result_row, result_col))
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if new_image:
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total_downloaded += 1
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bar.update()
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bar.close()
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print(f'Downloaded {total_downloaded} images.')
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tile_size = random_file_width(tiles_output)
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# Define the number of rows and columns based on the bounding box
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num_rows = max_row - min_row + 1
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num_cols = max_col - min_col + 1
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# Create an empty array to store the image data
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image_data = np.empty((num_rows * tile_size, num_cols * tile_size, 3), dtype=np.uint8)
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def build_tiff_data(task):
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row, col = task
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# try:
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# row, col = map(int, file.name[:-4].split("_"))
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# except:
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# print('Bad file:', file)
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# return
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tile_file = tiles_output / f"{row}_{col}.png"
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if not tile_file.is_file():
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raise Exception(f'Tile does not exist: {tile_file}')
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with Image.open(tile_file) as img:
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tile_data = np.array(img)
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# Remove the alpha channel
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tile_data = tile_data[:, :, :3]
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# Calculate the position of the tile in the image data array
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row_pos = (row - min_row) * tile_size
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col_pos = (col - min_col) * tile_size
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# Insert the tile data into the image data array
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image_data[row_pos:row_pos + tile_size, col_pos:col_pos + tile_size] = tile_data
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with ThreadPoolExecutor() as executor:
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futures = {executor.submit(build_tiff_data, task) for task in tiles}
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for future in tqdm(as_completed(futures), total=len(futures), desc='Building TIFF'):
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pass
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# Transpose the image data array to the format (bands, rows, cols)
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image_data = np.transpose(image_data, (2, 0, 1))
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# Define the transformation from pixel coordinates to CRS coordinates
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transform = Affine.translation(top_left_lon, top_left_lat) * Affine.scale((bottom_right_lon - top_left_lon) / (num_cols * tile_size), (bottom_right_lat - top_left_lat) / (num_rows * tile_size))
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# Define the CRS
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crs = rasterio.crs.CRS.from_string("EPSG:3857")
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# Write the image data to a GeoTIFF file
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print('Saving to:', output_tiff)
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start = time.time()
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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=crs, transform=transform, compress="DEFLATE") as dst:
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dst.write(image_data, indexes=[1, 2, 3])
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print(f'Saved in {int(time.time() - start)} seconds.')
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