summit/backend/venv/lib/python3.12/site-packages/rasterio/mask.py

200 lines
7.4 KiB
Python

"""Mask the area outside of the input shapes with no data."""
import logging
import warnings
import numpy as np
from rasterio.errors import WindowError
from rasterio.features import geometry_mask, geometry_window
logger = logging.getLogger(__name__)
def raster_geometry_mask(dataset, shapes, all_touched=False, invert=False,
crop=False, pad=False, pad_width=0.5):
"""Create a mask from shapes, transform, and optional window within original
raster.
By default, mask is intended for use as a numpy mask, where pixels that
overlap shapes are False.
If shapes do not overlap the raster and crop=True, a ValueError is
raised. Otherwise, a warning is raised, and a completely True mask
is returned (if invert is False).
Parameters
----------
dataset : a dataset object opened in 'r' mode
Raster for which the mask will be created.
shapes : iterable object
The values must be a GeoJSON-like dict or an object that implements
the Python geo interface protocol (such as a Shapely Polygon).
all_touched : bool (opt)
Include a pixel in the mask if it touches any of the shapes.
If False (default), include a pixel only if its center is within one of
the shapes, or if it is selected by Bresenham's line algorithm.
invert : bool (opt)
Determines whether to mask pixels outside or inside the shapes.
The default (False) is to mask pixels outside shapes.
When invert is used with crop, the area outside the cropping window is
considered selected and should be processed accordingly by the user.
crop : bool (opt)
Crop the dataset to the extent of the shapes. Useful for
processing rasters where shapes cover only a relatively small area.
Defaults to False.
pad : bool (opt)
If True, the features will be padded in each direction by
one half of a pixel prior to cropping dataset. Defaults to False.
pad_width : float (opt)
If pad is set (to maintain back-compatibility), then this will be the
pixel-size width of the padding around the mask.
Returns
-------
tuple
Three elements:
mask : np.ndarray of type 'bool'
Mask suitable for use in a MaskedArray where valid pixels are
marked `False` and invalid pixels are marked `True`.
out_transform : affine.Affine()
Information for mapping pixel coordinates in `masked` to another
coordinate system.
window: rasterio.windows.Window instance
Window within original raster covered by shapes. None if crop
is False.
"""
if crop and pad:
pad_x = pad_width
pad_y = pad_width
else:
pad_x = 0
pad_y = 0
try:
window = geometry_window(dataset, shapes, pad_x=pad_x, pad_y=pad_y)
except WindowError:
# If shapes do not overlap raster, raise Exception or UserWarning
# depending on value of crop
if crop:
raise ValueError('Input shapes do not overlap raster.')
else:
warnings.warn('shapes are outside bounds of raster. '
'Are they in different coordinate reference systems?')
# Return an entirely True mask (if invert is False)
mask = np.ones(shape=dataset.shape[-2:], dtype="bool")
if invert:
mask = ~mask
return mask, dataset.transform, None
if crop:
transform = dataset.window_transform(window)
out_shape = (int(window.height), int(window.width))
else:
window = None
transform = dataset.transform
out_shape = (int(dataset.height), int(dataset.width))
mask = geometry_mask(shapes, transform=transform, invert=invert,
out_shape=out_shape, all_touched=all_touched)
return mask, transform, window
def mask(dataset, shapes, all_touched=False, invert=False, nodata=None,
filled=True, crop=False, pad=False, pad_width=0.5, indexes=None):
"""Creates a masked or filled array using input shapes.
Pixels are masked or set to nodata outside the input shapes, unless
`invert` is `True`.
If shapes do not overlap the raster and crop=True, a ValueError is
raised. Otherwise, a warning is raised.
Parameters
----------
dataset : a dataset object opened in 'r' mode
Raster to which the mask will be applied.
shapes : iterable object
The values must be a GeoJSON-like dict or an object that implements
the Python geo interface protocol (such as a Shapely Polygon).
all_touched : bool (opt)
Include a pixel in the mask if it touches any of the shapes.
If False (default), include a pixel only if its center is within one of
the shapes, or if it is selected by Bresenham's line algorithm.
invert : bool (opt)
If False (default) pixels outside shapes will be masked. If True,
pixels inside shape will be masked.
nodata : int or float (opt)
Value representing nodata within each raster band. If not set,
defaults to the nodata value for the input raster. If there is no
set nodata value for the raster, it defaults to 0.
filled : bool (opt)
If True, the pixels outside the features will be set to nodata.
If False, the output array will contain the original pixel data,
and only the mask will be based on shapes. Defaults to True.
crop : bool (opt)
Whether to crop the raster to the extent of the shapes. Defaults to
False.
pad : bool (opt)
If True, the features will be padded in each direction by
one half of a pixel prior to cropping raster. Defaults to False.
indexes : list of ints or a single int (opt)
If `indexes` is a list, the result is a 3D array, but is
a 2D array if it is a band index number.
Returns
-------
tuple
Two elements:
masked : numpy.ndarray or numpy.ma.MaskedArray
Data contained in the raster after applying the mask. If
`filled` is `True` and `invert` is `False`, the return will be
an array where pixels outside shapes are set to the nodata value
(or nodata inside shapes if `invert` is `True`).
If `filled` is `False`, the return will be a MaskedArray in
which pixels outside shapes are `True` (or `False` if `invert`
is `True`).
out_transform : affine.Affine()
Information for mapping pixel coordinates in `masked` to another
coordinate system.
"""
if nodata is None:
if dataset.nodata is not None:
nodata = dataset.nodata
else:
nodata = 0
shape_mask, transform, window = raster_geometry_mask(
dataset, shapes, all_touched=all_touched, invert=invert, crop=crop,
pad=pad, pad_width=pad_width)
if indexes is None:
out_shape = (dataset.count, ) + shape_mask.shape
elif isinstance(indexes, int):
out_shape = shape_mask.shape
else:
out_shape = (len(indexes), ) + shape_mask.shape
out_image = dataset.read(
window=window, out_shape=out_shape, masked=True, indexes=indexes)
out_image.mask = out_image.mask | shape_mask
if filled:
out_image = out_image.filled(nodata)
return out_image, transform