def get_zooms(self, tilesize: int = 256) -> Tuple[int, int]:
"""Calculate raster min/max zoom level for input TMS."""
if self.dataset.crs != self.tms.rasterio_crs:
dst_affine, w, h = calculate_default_transform(
self.dataset.crs,
self.tms.rasterio_crs,
self.dataset.width,
self.dataset.height,
*self.dataset.bounds,
)
else:
dst_affine = list(self.dataset.transform)
w = self.dataset.width
h = self.dataset.height
# The maxzoom is defined by finding the minimum difference between
# the raster resolution and the zoom level resolution
resolution = max(abs(dst_affine[0]), abs(dst_affine[4]))
maxzoom = self.tms.zoom_for_res(resolution)
# The minzoom is defined by the resolution of the maximum theoretical overview level
overview_level = get_maximum_overview_level(w, h, minsize=tilesize)
ovr_resolution = resolution * (2**overview_level)
minzoom = self.tms.zoom_for_res(ovr_resolution)
return (minzoom, maxzoom)
def get_zooms(
src_dst,
tilesize: int = 256,
tms: morecantile.TileMatrixSet = morecantile.tms.get("WebMercatorQuad"),
zoom_level_strategy: str = "auto",
) -> Tuple[int, int]:
"""Calculate raster min/max zoom level."""
if src_dst.crs != tms.crs:
aff, w, h = calculate_default_transform(
src_dst.crs,
tms.crs,
src_dst.width,
src_dst.height,
*src_dst.bounds,
)
else:
aff = list(src_dst.transform)
w = src_dst.width
h = src_dst.height
resolution = max(abs(aff[0]), abs(aff[4]))
max_zoom = tms.zoom_for_res(
resolution,
max_z=30,
zoom_level_strategy=zoom_level_strategy,
)
overview_level = get_maximum_overview_level(w, h, minsize=tilesize)
ovr_resolution = resolution * (2**overview_level)
min_zoom = tms.zoom_for_res(ovr_resolution, max_z=30)
return (min_zoom, max_zoom)
def get_zooms(
src_dst: Union[DatasetReader, DatasetWriter, WarpedVRT],
ensure_global_max_zoom: bool = False,
tilesize: int = 256,
) -> Tuple[int, int]:
"""
Calculate raster min/max mercator zoom level.
Parameters
----------
src_dst: rasterio.io.DatasetReader
Rasterio io.DatasetReader object
ensure_global_max_zoom: bool, optional
Apply latitude correction factor to ensure max_zoom equality for global
datasets covering different latitudes (default: False).
tilesize: int, optional
Mercator tile size (default: 256).
Returns
-------
min_zoom, max_zoom: Tuple
Min/Max Mercator zoom levels.
"""
bounds = transform_bounds(src_dst.crs,
constants.WGS84_CRS,
*src_dst.bounds,
densify_pts=21)
center = [(bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2]
lat = center[1] if ensure_global_max_zoom else 0
dst_affine, w, h = calculate_default_transform(
src_dst.crs,
constants.WEB_MERCATOR_CRS,
src_dst.width,
src_dst.height,
*src_dst.bounds,
)
mercator_resolution = max(abs(dst_affine[0]), abs(dst_affine[4]))
# Correction factor for web-mercator projection latitude scale change
latitude_correction_factor = math.cos(math.radians(lat))
adjusted_resolution = mercator_resolution * latitude_correction_factor
max_zoom = zoom_for_pixelsize(adjusted_resolution, tilesize=tilesize)
overview_level = get_maximum_overview_level(w, h, minsize=tilesize)
ovr_resolution = adjusted_resolution * (2**overview_level)
min_zoom = zoom_for_pixelsize(ovr_resolution, tilesize=tilesize)
return (min_zoom, max_zoom)
def get_zooms(self, tilesize: int = 256) -> Tuple[int, int]:
"""Calculate raster min/max zoom level."""
dst_affine, w, h = calculate_default_transform(
self.dataset.crs,
self.tms.crs,
self.dataset.width,
self.dataset.height,
*self.dataset.bounds,
)
resolution = max(abs(dst_affine[0]), abs(dst_affine[4]))
maxzoom = self.tms.zoom_for_res(resolution)
overview_level = get_maximum_overview_level(w, h, minsize=tilesize)
ovr_resolution = resolution * (2 ** overview_level)
minzoom = self.tms.zoom_for_res(ovr_resolution)
return minzoom, maxzoom
def get_zooms(self, tilesize: int = 256) -> Tuple[int, int]:
"""Calculate raster min/max zoom level."""
dst_affine, w, h = calculate_default_transform(
CRS.from_epsg(self.epsg),
self.tms.crs,
self.profile["width"],
self.profile["height"],
*self.native_bounds,
)
resolution = max(abs(dst_affine[0]), abs(dst_affine[4]))
maxzoom = self.tms.zoom_for_res(resolution)
overview_level = get_maximum_overview_level(w, h, minsize=tilesize)
ovr_resolution = resolution * (2 ** overview_level)
minzoom = self.tms.zoom_for_res(ovr_resolution)
return minzoom, maxzoom
def get_zooms(src_dst, lat=0.0, tilesize=256) -> Tuple[int, int]:
"""
Calculate raster max zoom level.
Parameters
----------
src: rasterio.io.DatasetReader
Rasterio io.DatasetReader object
lat: float, optional
Center latitude of the dataset. This is only needed in case you want to
apply latitude correction factor to ensure consitent maximum zoom level
(default: 0.0).
tilesize: int, optional
Mercator tile size (default: 256).
Returns
-------
max_zoom: int
Max zoom level.
"""
dst_affine, w, h = calculate_default_transform(src_dst.crs, "epsg:3857",
src_dst.width,
src_dst.height,
*src_dst.bounds)
native_resolution = max(abs(dst_affine[0]), abs(dst_affine[4]))
# Correction factor for web-mercator projection latitude distortion
latitude_correction_factor = math.cos(math.radians(lat))
corrected_resolution = native_resolution * latitude_correction_factor
max_zoom = zoom_for_pixelsize(corrected_resolution, tilesize=tilesize)
overview_level = get_maximum_overview_level(w, h, minsize=tilesize)
ovr_resolution = corrected_resolution * (2**overview_level)
min_zoom = zoom_for_pixelsize(ovr_resolution, tilesize=tilesize)
return (min_zoom, max_zoom)
def get_zooms(
src_dst: Union[DatasetReader, DatasetWriter, WarpedVRT],
tilesize: int = 256,
tms: morecantile.TileMatrixSet = morecantile.tms.get("WebMercatorQuad"),
zoom_level_strategy: str = "auto",
) -> Tuple[int, int]:
"""Calculate raster min/max zoom level."""
# If the raster is not in the TMS CRS we calculate its projected properties (height, width, resolution)
if src_dst.crs != tms.rasterio_crs:
aff, w, h = calculate_default_transform(
src_dst.crs,
tms.rasterio_crs,
src_dst.width,
src_dst.height,
*src_dst.bounds,
)
else:
aff = list(src_dst.transform)
w = src_dst.width
h = src_dst.height
resolution = max(abs(aff[0]), abs(aff[4]))
# The maxzoom is defined by finding the minimum difference between
# the raster resolution and the zoom level resolution
max_zoom = tms.zoom_for_res(
resolution,
max_z=30,
zoom_level_strategy=zoom_level_strategy,
)
# The minzoom is defined by the resolution of the maximum theoretical overview level
max_possible_overview_level = get_maximum_overview_level(w, h, minsize=tilesize)
ovr_resolution = resolution * (2**max_possible_overview_level)
min_zoom = tms.zoom_for_res(ovr_resolution, max_z=30)
return (min_zoom, max_zoom)
def cog_translate( # noqa: C901
source: Union[str, pathlib.PurePath, DatasetReader, DatasetWriter,
WarpedVRT],
dst_path: Union[str, pathlib.PurePath],
dst_kwargs: Dict,
indexes: Optional[Sequence[int]] = None,
nodata: Optional[Union[str, int, float]] = None,
dtype: Optional[str] = None,
add_mask: bool = False,
overview_level: Optional[int] = None,
overview_resampling: str = "nearest",
web_optimized: bool = False,
tms: morecantile.TileMatrixSet = morecantile.tms.get("WebMercatorQuad"),
zoom_level_strategy: str = "auto",
aligned_levels: Optional[int] = None,
resampling: str = "nearest",
in_memory: Optional[bool] = None,
config: Optional[Dict] = None,
allow_intermediate_compression: bool = False,
forward_band_tags: bool = False,
quiet: bool = False,
temporary_compression: str = "DEFLATE",
):
"""
Create Cloud Optimized Geotiff.
Parameters
----------
source : str, PathLike object or rasterio.io.DatasetReader
A dataset path, URL or rasterio.io.DatasetReader object.
Will be opened in "r" mode.
dst_path : str or PathLike object
An output dataset path or or PathLike object.
Will be opened in "w" mode.
dst_kwargs: dict
Output dataset creation options.
indexes : tuple or int, optional
Raster band indexes to copy.
nodata, int, optional
Overwrite nodata masking values for input dataset.
dtype: str, optional
Overwrite output data type. Default will be the input data type.
add_mask, bool, optional
Force output dataset creation with a mask.
overview_level : int, optional (default: None)
COGEO overview (decimation) level. By default, inferred from data size.
overview_resampling : str, optional (default: "nearest")
Resampling algorithm for overviews
web_optimized: bool, optional (default: False)
Create web-optimized cogeo.
tms: morecantile.TileMatrixSet, optional (default: "WebMercatorQuad")
TileMatrixSet to use for reprojection, resolution and alignment.
zoom_level_strategy: str, optional (default: auto)
Strategy to determine zoom level (same as in GDAL 3.2).
LOWER will select the zoom level immediately below the theoretical computed non-integral zoom level, leading to subsampling.
On the contrary, UPPER will select the immediately above zoom level, leading to oversampling.
Defaults to AUTO which selects the closest zoom level.
ref: https://gdal.org/drivers/raster/cog.html#raster-cog
aligned_levels: int, optional.
Number of overview levels for which GeoTIFF tile and tiles defined in the tiling scheme match.
Default is to use the maximum overview levels.
resampling : str, optional (default: "nearest")
Resampling algorithm.
in_memory: bool, optional
Force processing raster in memory (default: process in memory if small)
config : dict
Rasterio Env options.
allow_intermediate_compression: bool, optional (default: False)
Allow intermediate file compression to reduce memory/disk footprint.
Note: This could reduce the speed of the process.
Ref: https://github.com/cogeotiff/rio-cogeo/issues/103
forward_band_tags: bool, optional
Forward band tags to output bands.
Ref: https://github.com/cogeotiff/rio-cogeo/issues/19
quiet: bool, optional (default: False)
Mask processing steps.
temporary_compression: str, optional
Compression used for the intermediate file, default is deflate.
"""
if isinstance(indexes, int):
indexes = (indexes, )
config = config or {}
with rasterio.Env(**config):
with ExitStack() as ctx:
if isinstance(source, (DatasetReader, DatasetWriter, WarpedVRT)):
src_dst = source
else:
src_dst = ctx.enter_context(rasterio.open(source))
meta = src_dst.meta
indexes = indexes if indexes else src_dst.indexes
nodata = nodata if nodata is not None else src_dst.nodata
dtype = dtype if dtype else src_dst.dtypes[0]
alpha = utils.has_alpha_band(src_dst)
mask = utils.has_mask_band(src_dst)
if not add_mask and (
(nodata is not None or alpha)
and dst_kwargs.get("compress") in ["JPEG", "jpeg"]):
warnings.warn(
"Using lossy compression with Nodata or Alpha band "
"can results in unwanted artefacts.",
LossyCompression,
)
tilesize = min(int(dst_kwargs["blockxsize"]),
int(dst_kwargs["blockysize"]))
if src_dst.width < tilesize or src_dst.height < tilesize:
tilesize = 2**int(
math.log(min(src_dst.width, src_dst.height), 2))
if tilesize < 64:
warnings.warn(
"Raster has dimension < 64px. Output COG cannot be tiled"
" and overviews cannot be added.",
IncompatibleBlockRasterSize,
)
dst_kwargs.pop("blockxsize", None)
dst_kwargs.pop("blockysize", None)
dst_kwargs.pop("tiled")
overview_level = 0
else:
warnings.warn(
"Block Size are bigger than raster sizes. "
"Setting blocksize to {}".format(tilesize),
IncompatibleBlockRasterSize,
)
dst_kwargs["blockxsize"] = tilesize
dst_kwargs["blockysize"] = tilesize
vrt_params = {
"add_alpha": True,
"dtype": dtype,
"width": src_dst.width,
"height": src_dst.height,
}
if nodata is not None:
vrt_params.update(
dict(nodata=nodata, add_alpha=False, src_nodata=nodata))
if alpha:
vrt_params.update(dict(add_alpha=False))
if web_optimized:
params = utils.get_web_optimized_params(
src_dst,
tilesize=tilesize,
warp_resampling=resampling,
zoom_level_strategy=zoom_level_strategy,
aligned_levels=aligned_levels,
tms=tms,
)
vrt_params.update(**params)
with WarpedVRT(src_dst, **vrt_params) as vrt_dst:
meta = vrt_dst.meta
meta["count"] = len(indexes)
if add_mask:
meta.pop("nodata", None)
meta.pop("alpha", None)
if (dst_kwargs.get("photometric", "").upper() == "YCBCR"
and meta["count"] == 1):
warnings.warn(
"PHOTOMETRIC=YCBCR not supported on a 1-band raster"
" and has been set to 'MINISBLACK'")
dst_kwargs["photometric"] = "MINISBLACK"
meta.update(**dst_kwargs)
meta.pop("compress", None)
meta.pop("photometric", None)
if allow_intermediate_compression:
meta["compress"] = temporary_compression
if in_memory is None:
in_memory = vrt_dst.width * vrt_dst.height < IN_MEMORY_THRESHOLD
if in_memory:
tmpfile = ctx.enter_context(MemoryFile())
tmp_dst = ctx.enter_context(tmpfile.open(**meta))
else:
tmpfile = ctx.enter_context(TemporaryRasterFile(dst_path))
tmp_dst = ctx.enter_context(
rasterio.open(tmpfile.name, "w", **meta))
# Transfer color interpolation
if len(indexes) == 1 and (vrt_dst.colorinterp[indexes[0] - 1]
is not ColorInterp.palette):
tmp_dst.colorinterp = [ColorInterp.gray]
else:
tmp_dst.colorinterp = [
vrt_dst.colorinterp[b - 1] for b in indexes
]
if tmp_dst.colorinterp[0] is ColorInterp.palette:
try:
tmp_dst.write_colormap(1, vrt_dst.colormap(1))
except ValueError:
warnings.warn(
"Dataset has `Palette` color interpretation"
" but is missing colormap information")
wind = list(tmp_dst.block_windows(1))
if not quiet:
click.echo("Reading input: {}".format(source), err=True)
fout = os.devnull if quiet else sys.stderr
with click.progressbar(
wind, file=fout,
show_percent=True) as windows: # type: ignore
for _, w in windows:
matrix = vrt_dst.read(window=w, indexes=indexes)
tmp_dst.write(matrix, window=w)
if add_mask or mask:
# Cast mask to uint8 to fix rasterio 1.1.2 error (ref #115)
mask_value = vrt_dst.dataset_mask(
window=w).astype("uint8")
tmp_dst.write_mask(mask_value, window=w)
if overview_level is None:
overview_level = get_maximum_overview_level(
vrt_dst.width, vrt_dst.height, minsize=tilesize)
if not quiet and overview_level:
click.echo("Adding overviews...", err=True)
overviews = [2**j for j in range(1, overview_level + 1)]
tmp_dst.build_overviews(overviews,
ResamplingEnums[overview_resampling])
if not quiet:
click.echo("Updating dataset tags...", err=True)
for i, b in enumerate(indexes):
tmp_dst.set_band_description(i + 1,
src_dst.descriptions[b - 1])
if forward_band_tags:
tmp_dst.update_tags(i + 1, **src_dst.tags(b))
tags = src_dst.tags()
tags.update(
dict(
OVR_RESAMPLING_ALG=ResamplingEnums[overview_resampling]
.name.upper()))
tmp_dst.update_tags(**tags)
tmp_dst._set_all_scales(
[vrt_dst.scales[b - 1] for b in indexes])
tmp_dst._set_all_offsets(
[vrt_dst.offsets[b - 1] for b in indexes])
if not quiet:
click.echo("Writing output to: {}".format(dst_path),
err=True)
copy(tmp_dst, dst_path, copy_src_overviews=True, **dst_kwargs)
def create_overview_cogs(
mosaic_path: str,
output_profile: Dict,
prefix: str = "mosaic_ovr",
max_overview_level: int = 6,
method: str = "first",
config: Dict = None,
threads=1,
in_memory: bool = True,
) -> None:
"""
Create Low resolution mosaic image from a mosaicJSON.
The output will be a web optimized COG with bounds matching the mosaicJSON bounds and
with its resolution matching the mosaic MinZoom - 1.
Attributes
----------
mosaic_path : str, required
Mosaic definition path.
output_profile : dict, required
prefix : str
max_overview_level : int
method: str, optional
pixel_selection method name (default is 'first').
config : dict
Rasterio Env options.
threads: int, optional
maximum number of threads to use (default is 1).
in_memory: bool, optional
Force COG creation in memory (default is True).
"""
pixel_method = PIXSEL_METHODS[method]
with MosaicBackend(mosaic_path) as mosaic:
base_zoom = mosaic.metadata["minzoom"] - 1
mosaic_quadkey_zoom = mosaic.quadkey_zoom
bounds = mosaic.metadata["bounds"]
mosaic_quadkeys = set(mosaic._quadkeys)
# Select a random quakey/asset and get dataset info
tile = mercantile.quadkey_to_tile(random.sample(mosaic_quadkeys, 1)[0])
assets = mosaic.assets_for_tile(*tile)
info = _get_info(assets[0])
extrema = tile_extrema(bounds, base_zoom)
tilesize = 256
resolution = _meters_per_pixel(base_zoom, 0, tilesize=tilesize)
# Create multiples files if coverage is too big
extremas = _split_extrema(extrema, max_ovr=max_overview_level)
for ix, extrema in enumerate(extremas):
click.echo(f"Part {1 + ix}/{len(extremas)}", err=True)
output_path = f"{prefix}_{ix}.tif"
blocks = list(_get_blocks(extrema, tilesize))
random.shuffle(blocks)
width = (extrema["x"]["max"] - extrema["x"]["min"]) * tilesize
height = (extrema["y"]["max"] - extrema["y"]["min"]) * tilesize
w, n = mercantile.xy(*mercantile.ul(
extrema["x"]["min"], extrema["y"]["min"], base_zoom))
params = dict(
driver="GTiff",
dtype=info["dtype"],
count=len(info["band_descriptions"]),
width=width,
height=height,
crs="epsg:3857",
transform=Affine(resolution, 0, w, 0, -resolution, n),
nodata=info["nodata_value"],
)
params.update(**output_profile)
config = config or {}
with rasterio.Env(**config):
with ExitStack() as ctx:
if in_memory:
tmpfile = ctx.enter_context(MemoryFile())
tmp_dst = ctx.enter_context(tmpfile.open(**params))
else:
tmpfile = ctx.enter_context(
TemporaryRasterFile(output_path))
tmp_dst = ctx.enter_context(
rasterio.open(tmpfile.name, "w", **params))
def _get_tile(wind):
idx, window = wind
x = extrema["x"]["min"] + idx[1]
y = extrema["y"]["min"] + idx[0]
t = mercantile.Tile(x, y, base_zoom)
kds = set(find_quadkeys(t, mosaic_quadkey_zoom))
if not mosaic_quadkeys.intersection(kds):
return window, None, None
try:
(tile, mask), _ = mosaic.tile(
t.x,
t.y,
t.z,
tilesize=tilesize,
pixel_selection=pixel_method(),
)
except NoAssetFoundError:
return window, None, None
return window, tile, mask
with futures.ThreadPoolExecutor(
max_workers=threads) as executor:
future_work = [
executor.submit(_get_tile, item) for item in blocks
]
with click.progressbar(
futures.as_completed(future_work),
length=len(future_work),
show_percent=True,
label="Loading tiles",
) as future:
for res in future:
pass
for f in _filter_futures(future_work):
window, tile, mask = f
if tile is None:
continue
tmp_dst.write(tile, window=window)
if info["nodata_type"] == "Mask":
tmp_dst.write_mask(mask.astype("uint8"),
window=window)
min_tile_size = tilesize = min(
int(output_profile["blockxsize"]),
int(output_profile["blockysize"]),
)
overview_level = get_maximum_overview_level(
tmp_dst.width, tmp_dst.height, minsize=min_tile_size)
overviews = [2**j for j in range(1, overview_level + 1)]
tmp_dst.build_overviews(overviews)
copy(tmp_dst,
output_path,
copy_src_overviews=True,
**params)
def test_max_overview(width, height, minsize, expected):
overview_level = get_maximum_overview_level(width, height, minsize)
assert overview_level == expected
def cog_translate( # noqa: C901
source: Union[str, pathlib.PurePath, DatasetReader, DatasetWriter,
WarpedVRT],
dst_path: Union[str, pathlib.PurePath],
dst_kwargs: Dict,
indexes: Optional[Sequence[int]] = None,
nodata: Optional[Union[str, int, float]] = None,
dtype: Optional[str] = None,
add_mask: bool = False,
overview_level: Optional[int] = None,
overview_resampling: str = "nearest",
web_optimized: bool = False,
tms: Optional[morecantile.TileMatrixSet] = None,
zoom_level_strategy: str = "auto",
zoom_level: Optional[int] = None,
aligned_levels: Optional[int] = None,
resampling: str = "nearest",
in_memory: Optional[bool] = None,
config: Optional[Dict] = None,
allow_intermediate_compression: bool = False,
forward_band_tags: bool = False,
quiet: bool = False,
temporary_compression: str = "DEFLATE",
colormap: Optional[Dict] = None,
additional_cog_metadata: Optional[Dict] = None,
use_cog_driver: bool = False,
):
"""
Create Cloud Optimized Geotiff.
Parameters
----------
source : str, PathLike object or rasterio.io.DatasetReader
A dataset path, URL or rasterio.io.DatasetReader object.
Will be opened in "r" mode.
dst_path : str or PathLike object
An output dataset path or or PathLike object.
Will be opened in "w" mode.
dst_kwargs: dict
Output dataset creation options.
indexes : tuple or int, optional
Raster band indexes to copy.
nodata, int, optional
Overwrite nodata masking values for input dataset.
dtype: str, optional
Overwrite output data type. Default will be the input data type.
add_mask, bool, optional
Force output dataset creation with a mask.
overview_level : int, optional (default: None)
COGEO overview (decimation) level. By default, inferred from data size.
overview_resampling : str, optional (default: "nearest")
Resampling algorithm for overviews
web_optimized: bool, optional (default: False)
Create web-optimized cogeo.
tms: morecantile.TileMatrixSet, optional (default: "WebMercatorQuad")
TileMatrixSet to use for reprojection, resolution and alignment.
zoom_level_strategy: str, optional (default: auto)
Strategy to determine zoom level (same as in GDAL 3.2).
LOWER will select the zoom level immediately below the theoretical computed non-integral zoom level, leading to subsampling.
On the contrary, UPPER will select the immediately above zoom level, leading to oversampling.
Defaults to AUTO which selects the closest zoom level.
ref: https://gdal.org/drivers/raster/cog.html#raster-cog
zoom_level: int, optional.
Zoom level number (starting at 0 for coarsest zoom level). If this option is specified, `--zoom-level-strategy` is ignored.
aligned_levels: int, optional.
Number of overview levels for which GeoTIFF tile and tiles defined in the tiling scheme match.
Default is to use the maximum overview levels. Note: GDAL use number of resolution levels instead of overview levels.
resampling : str, optional (default: "nearest")
Resampling algorithm.
in_memory: bool, optional
Force processing raster in memory (default: process in memory if small)
config : dict
Rasterio Env options.
allow_intermediate_compression: bool, optional (default: False)
Allow intermediate file compression to reduce memory/disk footprint.
Note: This could reduce the speed of the process.
Ref: https://github.com/cogeotiff/rio-cogeo/issues/103
forward_band_tags: bool, optional
Forward band tags to output bands.
Ref: https://github.com/cogeotiff/rio-cogeo/issues/19
quiet: bool, optional (default: False)
Mask processing steps.
temporary_compression: str, optional
Compression used for the intermediate file, default is deflate.
colormap: dict, optional
Overwrite or add a colormap to the output COG.
additional_cog_metadata: dict, optional
Additional dataset metadata to add to the COG.
use_cog_driver: bool, optional (default: False)
Use GDAL COG driver if set to True. COG driver is available starting with GDAL 3.1.
"""
tms = tms or morecantile.tms.get("WebMercatorQuad")
dst_kwargs = dst_kwargs.copy()
if isinstance(indexes, int):
indexes = (indexes, )
config = config or {}
with rasterio.Env(**config):
with ExitStack() as ctx:
if isinstance(source, (DatasetReader, DatasetWriter, WarpedVRT)):
src_dst = source
else:
src_dst = ctx.enter_context(rasterio.open(source))
meta = src_dst.meta
indexes = indexes if indexes else src_dst.indexes
nodata = nodata if nodata is not None else src_dst.nodata
dtype = dtype if dtype else src_dst.dtypes[0]
alpha = utils.has_alpha_band(src_dst)
mask = utils.has_mask_band(src_dst)
if colormap and len(indexes) > 1:
raise IncompatibleOptions(
"Cannot add a colormap for multiple bands data.")
if not add_mask and (
(nodata is not None or alpha)
and dst_kwargs.get("compress", "").lower() == "jpeg"):
warnings.warn(
"Nodata/Alpha band will be translated to an internal mask band.",
)
add_mask = True
indexes = (utils.non_alpha_indexes(src_dst)
if len(indexes) not in [1, 3] else indexes)
tilesize = min(int(dst_kwargs["blockxsize"]),
int(dst_kwargs["blockysize"]))
if src_dst.width < tilesize or src_dst.height < tilesize:
tilesize = 2**int(
math.log(min(src_dst.width, src_dst.height), 2))
if tilesize < 64:
warnings.warn(
"Raster has dimension < 64px. Output COG cannot be tiled"
" and overviews cannot be added.",
IncompatibleBlockRasterSize,
)
dst_kwargs.pop("blockxsize", None)
dst_kwargs.pop("blockysize", None)
dst_kwargs.pop("tiled")
overview_level = 0
else:
warnings.warn(
"Block Size are bigger than raster sizes. "
"Setting blocksize to {}".format(tilesize),
IncompatibleBlockRasterSize,
)
dst_kwargs["blockxsize"] = tilesize
dst_kwargs["blockysize"] = tilesize
vrt_params = {
"add_alpha": True,
"dtype": dtype,
"width": src_dst.width,
"height": src_dst.height,
"resampling": ResamplingEnums[resampling],
}
if nodata is not None:
vrt_params.update(
dict(nodata=nodata, add_alpha=False, src_nodata=nodata))
if alpha:
vrt_params.update(dict(add_alpha=False))
if web_optimized and not use_cog_driver:
params = utils.get_web_optimized_params(
src_dst,
zoom_level_strategy=zoom_level_strategy,
zoom_level=zoom_level,
aligned_levels=aligned_levels,
tms=tms,
)
vrt_params.update(**params)
with WarpedVRT(src_dst, **vrt_params) as vrt_dst:
meta = vrt_dst.meta
meta["count"] = len(indexes)
if add_mask:
meta.pop("nodata", None)
meta.pop("alpha", None)
if (dst_kwargs.get("photometric", "").upper() == "YCBCR"
and meta["count"] == 1):
warnings.warn(
"PHOTOMETRIC=YCBCR not supported on a 1-band raster"
" and has been set to 'MINISBLACK'")
dst_kwargs["photometric"] = "MINISBLACK"
meta.update(**dst_kwargs)
meta.pop("compress", None)
meta.pop("photometric", None)
if allow_intermediate_compression:
meta["compress"] = temporary_compression
if in_memory is None:
in_memory = vrt_dst.width * vrt_dst.height < IN_MEMORY_THRESHOLD
if in_memory:
tmpfile = ctx.enter_context(MemoryFile())
tmp_dst = ctx.enter_context(tmpfile.open(**meta))
else:
tmpfile = ctx.enter_context(TemporaryRasterFile(dst_path))
tmp_dst = ctx.enter_context(
rasterio.open(tmpfile.name, "w", **meta))
# Transfer color interpolation
if len(indexes) == 1 and (vrt_dst.colorinterp[indexes[0] - 1]
is not ColorInterp.palette):
tmp_dst.colorinterp = [ColorInterp.gray]
else:
tmp_dst.colorinterp = [
vrt_dst.colorinterp[b - 1] for b in indexes
]
if colormap:
if tmp_dst.colorinterp[0] is not ColorInterp.palette:
tmp_dst.colorinterp = [ColorInterp.palette]
warnings.warn(
"Dataset color interpretation was set to `Palette`"
)
tmp_dst.write_colormap(1, colormap)
elif tmp_dst.colorinterp[0] is ColorInterp.palette:
try:
tmp_dst.write_colormap(1, vrt_dst.colormap(1))
except ValueError:
warnings.warn(
"Dataset has `Palette` color interpretation"
" but is missing colormap information")
wind = list(tmp_dst.block_windows(1))
if not quiet:
click.echo("Reading input: {}".format(source), err=True)
fout = ctx.enter_context(open(os.devnull,
"w")) if quiet else sys.stderr
with click.progressbar(
wind, file=fout,
show_percent=True) as windows: # type: ignore
for _, w in windows:
matrix = vrt_dst.read(window=w, indexes=indexes)
tmp_dst.write(matrix, window=w)
if add_mask or mask:
# Cast mask to uint8 to fix rasterio 1.1.2 error (ref #115)
mask_value = vrt_dst.dataset_mask(
window=w).astype("uint8")
tmp_dst.write_mask(mask_value, window=w)
if overview_level is None:
overview_level = get_maximum_overview_level(
vrt_dst.width, vrt_dst.height, minsize=tilesize)
if not quiet and overview_level:
click.echo("Adding overviews...", err=True)
overviews = [2**j for j in range(1, overview_level + 1)]
tmp_dst.build_overviews(overviews,
ResamplingEnums[overview_resampling])
if not quiet:
click.echo("Updating dataset tags...", err=True)
for i, b in enumerate(indexes):
tmp_dst.set_band_description(i + 1,
src_dst.descriptions[b - 1])
if forward_band_tags:
tmp_dst.update_tags(i + 1, **src_dst.tags(b))
tags = src_dst.tags()
tags.update(
dict(
OVR_RESAMPLING_ALG=ResamplingEnums[overview_resampling]
.name.upper()))
if additional_cog_metadata:
tags.update(**additional_cog_metadata)
if web_optimized and not use_cog_driver:
default_zoom = tms.zoom_for_res(
max(tmp_dst.res),
max_z=30,
zoom_level_strategy=zoom_level_strategy,
)
dst_kwargs.update({
"@TILING_SCHEME_NAME":
tms.identifier,
"@TILING_SCHEME_ZOOM_LEVEL":
zoom_level if zoom_level is not None else default_zoom,
})
if aligned_levels:
dst_kwargs.update(
{"@TILING_SCHEME_ALIGNED_LEVELS": aligned_levels})
tmp_dst.update_tags(**tags)
tmp_dst._set_all_scales(
[vrt_dst.scales[b - 1] for b in indexes])
tmp_dst._set_all_offsets(
[vrt_dst.offsets[b - 1] for b in indexes])
if not quiet:
click.echo("Writing output to: {}".format(dst_path),
err=True)
if use_cog_driver:
if not GDALVersion.runtime().at_least("3.1"):
raise Exception(
"GDAL 3.1 or above required to use the COG driver."
)
dst_kwargs["driver"] = "COG"
if web_optimized:
dst_kwargs["TILING_SCHEME"] = (
"GoogleMapsCompatible" if tms.identifier
== "WebMercatorQuad" else tms.identifier)
if zoom_level is not None:
if not GDALVersion.runtime().at_least("3.5"):
warnings.warn(
"ZOOM_LEVEL option is only available with GDAL >3.5."
)
dst_kwargs["ZOOM_LEVEL"] = zoom_level
dst_kwargs["ZOOM_LEVEL_STRATEGY"] = zoom_level_strategy
if aligned_levels is not None:
# GDAL uses Number of resolution (not overviews)
# See https://github.com/OSGeo/gdal/issues/5336#issuecomment-1042946603
dst_kwargs["aligned_levels"] = aligned_levels + 1
if add_mask and dst_kwargs.get("compress", "") != "JPEG":
warnings.warn(
"With GDAL COG driver, mask band will be translated to an alpha band."
)
dst_kwargs["overview_resampling"] = overview_resampling
dst_kwargs["warp_resampling"] = resampling
dst_kwargs["blocksize"] = tilesize
dst_kwargs.pop("blockxsize", None)
dst_kwargs.pop("blockysize", None)
dst_kwargs.pop("tiled", None)
dst_kwargs.pop("interleave", None)
dst_kwargs.pop("photometric", None)
copy(tmp_dst, dst_path, **dst_kwargs)
else:
copy(tmp_dst,
dst_path,
copy_src_overviews=True,
**dst_kwargs)
