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Add option to return a rasterized cloud mask

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heyarne 2021-02-22 17:42:41 +00:00
commit 8af42f6091
1 changed files with 26 additions and 3 deletions
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29
true-color-image/sentinel_helpers.py
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@ -2,10 +2,12 @@ import urllib.parse
from pathlib import Path from pathlib import Path
import fiona import fiona
import folium
import geopandas as gpd import geopandas as gpd
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
import numpy as np import numpy as np
import rasterio as r import rasterio as r
from rasterio.features import geometry_mask
from rasterio.warp import calculate_default_transform, reproject, Resampling from rasterio.warp import calculate_default_transform, reproject, Resampling
from shapely.geometry import shape from shapely.geometry import shape
@ -109,11 +111,17 @@ def scihub_bgr_paths(product_path, resolution=None):
return scihub_band_paths(product_path, ['B02', 'B03', 'B04'], resolution) return scihub_band_paths(product_path, ['B02', 'B03', 'B04'], resolution)
def scihub_cloud_mask(product_path): def scihub_cloud_mask(product_path, **kwargs):
''' '''
Given a `product_path` pointing to a product downlaoded from the Copernicus Given a `product_path` pointing to a product downlaoded from the Copernicus
Open Access Hub, returns a shapely geometry representing the included cloud Open Access Hub, returns a shapely geometry representing the included cloud
mask. mask.
If an additional parameter, `rasterize=True` is given, the returned cloud
mask will be a rasterized numpy ndarray instead of a vector geometry. Two
additional parameters, `target_path` and `target_transform` are needed to
determine the size of this array. In this array, pixels with clouds are
`False` and pixels without clouds are `True`.
''' '''
with TemporaryDirectory() as tmp_dir: with TemporaryDirectory() as tmp_dir:
# we need the temporary directory to work around a problem with reading # we need the temporary directory to work around a problem with reading
@ -137,10 +145,25 @@ def scihub_cloud_mask(product_path):
warnings.simplefilter("ignore") warnings.simplefilter("ignore")
# this returns a warning because the iterator has to be # this returns a warning because the iterator has to be
# rewound; while this is a performance issue, we can ignore it # rewound; while this is a performance issue, we can ignore it
return unary_union([shape(f['geometry']) for f in features]) mask = unary_union([shape(f['geometry']) for f in features])
except ValueError: except ValueError:
# empty cloud mask # empty cloud mask
return Polygon([]) mask = Polygon([])
if kwargs.get('rasterize'):
# return raster version of the vector geometry we found above
target_shape = kwargs.get('target_shape')
target_transform = kwargs.get('target_transform')
if not target_transform or not target_shape:
error_msg = 'target_transform and target_shape need to be set ' + \
'to construct a rasterized cloud mask.'
raise ValueError(error_msg)
return geometry_mask(mask,
out_shape=target_shape,
transform=target_transform)
else:
return mask
def scihub_normalize_range(v): def scihub_normalize_range(v):