131 lines
4.2 KiB
Python
131 lines
4.2 KiB
Python
"""$ rio shapes"""
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import logging
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import click
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import cligj
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import rasterio
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from rasterio.rio import options
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from rasterio.features import dataset_features
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from rasterio.rio.helpers import write_features
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logger = logging.getLogger(__name__)
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@click.command(short_help="Write shapes extracted from bands or masks.")
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@options.file_in_arg
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@options.output_opt
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@cligj.precision_opt
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@cligj.indent_opt
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@cligj.compact_opt
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@cligj.projection_geographic_opt
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@cligj.projection_projected_opt
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@options.sequence_opt
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@cligj.use_rs_opt
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@cligj.geojson_type_feature_opt(True)
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@cligj.geojson_type_bbox_opt(False)
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@click.option('--band/--mask', default=True,
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help="Choose to extract from a band (the default) or a mask.")
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@click.option('--bidx', 'bandidx', type=int, default=None,
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help="Index of the band or mask that is the source of shapes.")
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@click.option('--sampling', type=int, default=1,
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help="Inverse of the sampling fraction; "
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"a value of 10 decimates.")
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@click.option('--with-nodata/--without-nodata', default=False,
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help="Include or do not include (the default) nodata regions.")
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@click.option('--as-mask/--not-as-mask', default=False,
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help="Interpret a band as a mask and output only one class of "
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"valid data shapes.")
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@click.pass_context
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def shapes(
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ctx, input, output, precision, indent, compact, projection, sequence,
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use_rs, geojson_type, band, bandidx, sampling, with_nodata, as_mask):
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"""Extracts shapes from one band or mask of a dataset and writes
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them out as GeoJSON. Unless otherwise specified, the shapes will be
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transformed to WGS 84 coordinates.
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The default action of this command is to extract shapes from the
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first band of the input dataset. The shapes are polygons bounding
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contiguous regions (or features) of the same raster value. This
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command performs poorly for int16 or float type datasets.
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Bands other than the first can be specified using the `--bidx`
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option:
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$ rio shapes --bidx 3 tests/data/RGB.byte.tif
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The valid data footprint of a dataset's i-th band can be extracted
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by using the `--mask` and `--bidx` options:
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$ rio shapes --mask --bidx 1 tests/data/RGB.byte.tif
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Omitting the `--bidx` option results in a footprint extracted from
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the conjunction of all band masks. This is generally smaller than
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any individual band's footprint.
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A dataset band may be analyzed as though it were a binary mask with
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the `--as-mask` option:
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$ rio shapes --as-mask --bidx 1 tests/data/RGB.byte.tif
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"""
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# These import numpy, which we don't want to do unless it's needed.
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dump_kwds = {'sort_keys': True}
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if indent:
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dump_kwds['indent'] = indent
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if compact:
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dump_kwds['separators'] = (',', ':')
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stdout = click.open_file(
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output, 'w') if output else click.get_text_stream('stdout')
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bidx = 1 if bandidx is None and band else bandidx
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if not sequence:
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geojson_type = 'collection'
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geographic = True if projection == 'geographic' else False
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with ctx.obj["env"] as env:
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with rasterio.open(input) as src:
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write_features(
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stdout,
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feature_gen(
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src,
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env,
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bidx,
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sampling=sampling,
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band=band,
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as_mask=as_mask,
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with_nodata=with_nodata,
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geographic=geographic,
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precision=precision,
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),
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sequence=sequence,
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geojson_type=geojson_type,
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use_rs=use_rs,
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**dump_kwds
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)
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def feature_gen(src, env, *args, **kwargs):
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class Collection:
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def __init__(self, env):
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self.bboxes = []
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self.env = env
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@property
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def bbox(self):
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minxs, minys, maxxs, maxys = zip(*self.bboxes)
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return min(minxs), min(minys), max(maxxs), max(maxys)
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def __call__(self):
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for f in dataset_features(src, *args, **kwargs):
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self.bboxes.append(f['bbox'])
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yield f
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return Collection(env)
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