/usr/lib/python3/dist-packages/rasterio/rio/shapes.py is in python3-rasterio 0.36.0-2build5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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import os
import click
import cligj
from .helpers import coords, write_features
from . import options
import rasterio
from rasterio.transform import Affine
from rasterio.crs import CRS
logger = logging.getLogger('rio')
# Common options used below
# Unlike the version in cligj, this one doesn't require values.
files_inout_arg = click.argument(
'files',
nargs=-1,
type=click.Path(resolve_path=True),
metavar="INPUTS... OUTPUT")
all_touched_opt = click.option(
'-a', '--all', '--all_touched', 'all_touched',
is_flag=True,
default=False,
help='Use all pixels touched by features, otherwise (default) use only '
'pixels whose center is within the polygon or that are selected by '
'Bresenhams line algorithm')
@click.command(short_help="Write shapes extracted from bands or masks.")
@options.file_in_arg
@options.output_opt
@cligj.precision_opt
@cligj.indent_opt
@cligj.compact_opt
@cligj.projection_geographic_opt
@cligj.projection_projected_opt
@cligj.sequence_opt
@cligj.use_rs_opt
@cligj.geojson_type_feature_opt(True)
@cligj.geojson_type_bbox_opt(False)
@click.option('--band/--mask', default=True,
help="Choose to extract from a band (the default) or a mask.")
@click.option('--bidx', 'bandidx', type=int, default=None,
help="Index of the band or mask that is the source of shapes.")
@click.option('--sampling', type=int, default=1,
help="Inverse of the sampling fraction; "
"a value of 10 decimates.")
@click.option('--with-nodata/--without-nodata', default=False,
help="Include or do not include (the default) nodata regions.")
@click.option('--as-mask/--not-as-mask', default=False,
help="Interpret a band as a mask and output only one class of "
"valid data shapes.")
@click.pass_context
def shapes(
ctx, input, output, precision, indent, compact, projection, sequence,
use_rs, geojson_type, band, bandidx, sampling, with_nodata, as_mask):
"""Extracts shapes from one band or mask of a dataset and writes
them out as GeoJSON. Unless otherwise specified, the shapes will be
transformed to WGS 84 coordinates.
The default action of this command is to extract shapes from the
first band of the input dataset. The shapes are polygons bounding
contiguous regions (or features) of the same raster value. This
command performs poorly for int16 or float type datasets.
Bands other than the first can be specified using the `--bidx`
option:
$ rio shapes --bidx 3 tests/data/RGB.byte.tif
The valid data footprint of a dataset's i-th band can be extracted
by using the `--mask` and `--bidx` options:
$ rio shapes --mask --bidx 1 tests/data/RGB.byte.tif
Omitting the `--bidx` option results in a footprint extracted from
the conjunction of all band masks. This is generally smaller than
any individual band's footprint.
A dataset band may be analyzed as though it were a binary mask with
the `--as-mask` option:
$ rio shapes --as-mask --bidx 1 tests/data/RGB.byte.tif
"""
# These import numpy, which we don't want to do unless it's needed.
import numpy as np
import rasterio.features
import rasterio.warp
verbosity = ctx.obj['verbosity'] if ctx.obj else 1
logger = logging.getLogger('rio')
dump_kwds = {'sort_keys': True}
if indent:
dump_kwds['indent'] = indent
if compact:
dump_kwds['separators'] = (',', ':')
stdout = click.open_file(
output, 'w') if output else click.get_text_stream('stdout')
bidx = 1 if bandidx is None and band else bandidx
# This is the generator for (feature, bbox) pairs.
class Collection(object):
def __init__(self, env):
self._xs = []
self._ys = []
self.env = env
@property
def bbox(self):
return min(self._xs), min(self._ys), max(self._xs), max(self._ys)
def __call__(self):
with rasterio.open(input) as src:
if bidx is not None and bidx > src.count:
raise ValueError('bidx is out of range for raster')
img = None
msk = None
# Adjust transforms.
transform = src.affine
if sampling > 1:
# Decimation of the raster produces a georeferencing
# shift that we correct with a translation.
transform *= Affine.translation(
src.width % sampling, src.height % sampling)
# And follow by scaling.
transform *= Affine.scale(float(sampling))
# Most of the time, we'll use the valid data mask.
# We skip reading it if we're extracting every possible
# feature (even invalid data features) from a band.
if not band or (band and not as_mask and not with_nodata):
if sampling == 1:
msk = src.read_masks(bidx)
else:
msk_shape = (
src.height // sampling, src.width // sampling)
if bidx is None:
msk = np.zeros(
(src.count,) + msk_shape, 'uint8')
else:
msk = np.zeros(msk_shape, 'uint8')
msk = src.read_masks(bidx, msk)
if bidx is None:
msk = np.logical_or.reduce(msk).astype('uint8')
# Possibly overridden below.
img = msk
# Read the band data unless the --mask option is given.
if band:
if sampling == 1:
img = src.read(bidx, masked=False)
else:
img = np.zeros(
(src.height // sampling, src.width // sampling),
dtype=src.dtypes[src.indexes.index(bidx)])
img = src.read(bidx, img, masked=False)
# If --as-mask option was given, convert the image
# to a binary image. This reduces the number of shape
# categories to 2 and likely reduces the number of
# shapes.
if as_mask:
tmp = np.ones_like(img, 'uint8') * 255
tmp[img == 0] = 0
img = tmp
if not with_nodata:
msk = tmp
# Transform the raster bounds.
bounds = src.bounds
xs = [bounds[0], bounds[2]]
ys = [bounds[1], bounds[3]]
if projection == 'geographic':
xs, ys = rasterio.warp.transform(
src.crs, CRS({'init': 'epsg:4326'}), xs, ys)
if precision >= 0:
xs = [round(v, precision) for v in xs]
ys = [round(v, precision) for v in ys]
self._xs = xs
self._ys = ys
# Prepare keyword arguments for shapes().
kwargs = {'transform': transform}
if not with_nodata:
kwargs['mask'] = msk
src_basename = os.path.basename(src.name)
# Yield GeoJSON features.
for i, (g, val) in enumerate(
rasterio.features.shapes(img, **kwargs)):
if projection == 'geographic':
g = rasterio.warp.transform_geom(
src.crs, 'EPSG:4326', g,
antimeridian_cutting=True, precision=precision)
xs, ys = zip(*coords(g))
yield {
'type': 'Feature',
'id': "{0}:{1}".format(src_basename, i),
'properties': {
'val': val, 'filename': src_basename
},
'bbox': [min(xs), min(ys), max(xs), max(ys)],
'geometry': g
}
if not sequence:
geojson_type = 'collection'
try:
with rasterio.Env(CPL_DEBUG=(verbosity > 2)) as env:
write_features(
stdout, Collection(env), sequence=sequence,
geojson_type=geojson_type, use_rs=use_rs,
**dump_kwds)
except Exception:
logger.exception("Exception caught during processing")
raise click.Abort()
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