def get_area(
address, bbox, max_img_size=512, bbox_crs="epsg:4326", out_crs="epsg:3857", nodata=0
):
"""Read image part."""
bounds = transform_bounds(bbox_crs, out_crs, *bbox, densify_pts=21)
vrt_params = dict(add_alpha=True, crs=out_crs, resampling=Resampling.bilinear)
if nodata is not None:
vrt_params.update(
dict(
nodata=nodata,
add_alpha=False,
src_nodata=nodata,
init_dest_nodata=False,
)
)
with rasterio.open(address) as src:
vrt_transform, vrt_width, vrt_height = get_vrt_transform(src, bounds)
vrt_width = round(vrt_width) if vrt_width < max_img_size else max_img_size
vrt_height = round(vrt_height) if vrt_height < max_img_size else max_img_size
vrt_params.update(
dict(transform=vrt_transform, width=vrt_width, height=vrt_height)
)
with WarpedVRT(src, **vrt_params) as vrt:
data = vrt.read(
out_shape=(1, vrt_height, vrt_width),
resampling=Resampling.bilinear,
indexes=[1],
)
return data
def get_area_stats(
src,
bounds,
max_img_size=512,
indexes=None,
nodata=None,
resampling_method="bilinear",
bbox_crs="epsg:4326",
histogram_bins=20,
histogram_range=None,
):
"""
Read data and mask.
Attributes
----------
srd_dst : rasterio.io.DatasetReader
rasterio.io.DatasetReader object
bounds : list
bounds (left, bottom, right, top)
tilesize : int
Output image size
indexes : list of ints or a single int, optional, (defaults: None)
If `indexes` is a list, the result is a 3D array, but is
a 2D array if it is a band index number.
nodata: int or float, optional (defaults: None)
resampling_method : str, optional (default: "bilinear")
Resampling algorithm
histogram_bins: int, optional
Defines the number of equal-width histogram bins (default: 10).
histogram_range: str, optional
The lower and upper range of the bins. If not provided, range is simply
the min and max of the array.
Returns
-------
out : array, int
returns pixel value.
"""
if isinstance(indexes, int):
indexes = [indexes]
elif isinstance(indexes, tuple):
indexes = list(indexes)
with rasterio.open(src) as src_dst:
bounds = transform_bounds(bbox_crs,
src_dst.crs,
*bounds,
densify_pts=21)
vrt_params = dict(add_alpha=True,
resampling=Resampling[resampling_method])
indexes = indexes if indexes is not None else src_dst.indexes
nodata = nodata if nodata is not None else src_dst.nodata
def _get_descr(ix):
"""Return band description."""
name = src_dst.descriptions[ix - 1]
if not name:
name = "band{}".format(ix)
return name
band_descriptions = [(ix, _get_descr(ix)) for ix in indexes]
vrt_transform, vrt_width, vrt_height = get_vrt_transform(
src_dst, bounds, bounds_crs=src_dst.crs)
vrt_params.update(
dict(transform=vrt_transform, width=vrt_width, height=vrt_height))
width = round(vrt_width) if vrt_width < max_img_size else max_img_size
height = round(
vrt_height) if vrt_height < max_img_size else max_img_size
out_shape = (len(indexes), width, height)
if nodata is not None:
vrt_params.update(
dict(nodata=nodata, add_alpha=False, src_nodata=nodata))
if has_alpha_band(src_dst):
vrt_params.update(dict(add_alpha=False))
with WarpedVRT(src_dst, **vrt_params) as vrt:
arr = vrt.read(out_shape=out_shape, indexes=indexes, masked=True)
if not arr.any():
return None, band_descriptions
params = {}
if histogram_bins:
params.update(dict(bins=histogram_bins))
if histogram_range:
params.update(dict(range=histogram_range))
stats = {
indexes[b]: _stats(arr[b], **params)
for b in range(arr.shape[0])
if vrt.colorinterp[b] != ColorInterp.alpha
}
return stats, band_descriptions
def read_cog_tile(src,
bounds,
tile_size,
indexes=None,
nodata=None,
resampling_method="bilinear",
tile_edge_padding=2):
"""
Read cloud-optimized geotiff tile.
Notes
-----
Modified from `rio-tiler <https://github.com/cogeotiff/rio-tiler>`_.
License included below per terms of use.
BSD 3-Clause License
(c) 2017 Mapbox
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Arguments
---------
src : rasterio.io.DatasetReader
rasterio.io.DatasetReader object
bounds : list
Tile bounds (left, bottom, right, top)
tile_size : list
Output image size
indexes : list of ints or a single int, optional, (defaults: None)
If `indexes` is a list, the result is a 3D array, but is
a 2D array if it is a band index number.
nodata: int or float, optional (defaults: None)
resampling_method : str, optional (default: "bilinear")
Resampling algorithm
tile_edge_padding : int, optional (default: 2)
Padding to apply to each edge of the tile when retrieving data
to assist in reducing resampling artefacts along edges.
Returns
-------
out : array, int
returns pixel value.
"""
if isinstance(indexes, int):
indexes = [indexes]
elif isinstance(indexes, tuple):
indexes = list(indexes)
vrt_params = dict(
add_alpha=True, crs='epsg:' + str(src.crs.to_epsg()),
resampling=Resampling[resampling_method]
)
vrt_transform, vrt_width, vrt_height = get_vrt_transform(
src, bounds, bounds_crs='epsg:' + str(src.crs.to_epsg()))
out_window = Window(col_off=0, row_off=0,
width=vrt_width, height=vrt_height)
if tile_edge_padding > 0 and not \
_requested_tile_aligned_with_internal_tile(src, bounds, tile_size):
vrt_transform = vrt_transform * Affine.translation(
-tile_edge_padding, -tile_edge_padding
)
orig__vrt_height = vrt_height
orig_vrt_width = vrt_width
vrt_height = vrt_height + 2 * tile_edge_padding
vrt_width = vrt_width + 2 * tile_edge_padding
out_window = Window(
col_off=tile_edge_padding,
row_off=tile_edge_padding,
width=orig_vrt_width,
height=orig__vrt_height,
)
vrt_params.update(dict(transform=vrt_transform,
width=vrt_width,
height=vrt_height))
indexes = indexes if indexes is not None else src.indexes
out_shape = (len(indexes), tile_size[1], tile_size[0])
nodata = nodata if nodata is not None else src.nodata
if nodata is not None:
vrt_params.update(dict(nodata=nodata,
add_alpha=False,
src_nodata=nodata))
if has_alpha_band(src):
vrt_params.update(dict(add_alpha=False))
with WarpedVRT(src, **vrt_params) as vrt:
data = vrt.read(
out_shape=out_shape,
indexes=indexes,
window=out_window,
resampling=Resampling[resampling_method],
)
mask = vrt.dataset_mask(out_shape=(tile_size[1], tile_size[0]),
window=out_window)
return data, mask, out_window, vrt_transform
