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https://github.com/heyarne/earth-observation-for-journalism.git
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71 lines
No EOL
2.4 KiB
Python
71 lines
No EOL
2.4 KiB
Python
import glob
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import urllib.parse
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from pathlib import Path
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import zipfile
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import geopandas as gp
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import rasterio as r
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from rasterio.warp import calculate_default_transform, reproject, Resampling
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def band_paths(p, bands, resolution=None):
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'''
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Given a zip file or folder at `p`, returns the paths inside p to the raster files containing
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information for the given bands. Because some bands are available in more than one
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resolution, this can be filtered by prodiding a third parameter (e.g. resolution='10m').
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The returned paths are formatted in the zip scheme as per Apache Commons VFS if necessary
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and can be directly opened by rasterio.
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'''
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if p.endswith('.zip'):
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with zipfile.ZipFile(p) as f:
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files = f.namelist()
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rasters = [f for f in files if f.endswith('.jp2')]
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else:
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rasters = glob.glob(Path(p) / '**/*.jp2')
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rasters = [r for r in rasters for b in bands if b in r]
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if resolution:
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rasters = [r for r in rasters if resolution in r]
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rasters = ['zip+file://' + p + '!/' + r for r in rasters]
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return rasters
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def rgb_paths(zip_file, resolution='10m'):
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return band_paths(zip_file, ['B02', 'B03', 'B04'], resolution)
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def search_osm(place):
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'''
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Returns a GeoDataFrame with results from OpenStreetMap Nominatim for the given search string.
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This allows us to fetch detailed geometries for virtually any place on earth.
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'''
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urlescaped_place = urllib.parse.quote(place)
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search_url = 'https://nominatim.openstreetmap.org/search/?q={}&format=geojson&polygon_geojson=1'.format(urlescaped_place)
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return gp.read_file(search_url)
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def reproject_raster_image(src, dst, target_crs):
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'''
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Reprojects `src` into `dst`, given a coordinate reference system `target_crs`.
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'''
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transform, width, height = calculate_default_transform(
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src.crs, target_crs, src.width, src.height, *src.bounds)
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kwargs = src.meta.copy()
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kwargs.update({
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'crs': target_crs,
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'transform': transform,
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'width': width,
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'height': height
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})
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for i in range(1, src.count + 1):
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reproject(
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source=r.band(src, i),
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destination=r.band(dst, i),
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src_transform=src.transform,
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src_crs=src.crs,
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dst_transform=transform,
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dst_crs=target_crs,
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resampling=Resampling.nearest) |