def profile(input, tile, tilesize, zoom, add_kernels, add_stdout, config):
"""Profile COGReader Mercator Tile read."""
if not tile:
with COGReader(input) as cog:
if zoom is None:
zoom = randint(cog.minzoom, cog.maxzoom)
extrema = tile_extrema(cog.bounds, zoom)
tile_x = sample(range(extrema["x"]["min"], extrema["x"]["max"]), 1)[0]
tile_y = sample(range(extrema["y"]["min"], extrema["y"]["max"]), 1)[0]
tile_z = zoom
log.debug(f"reading tile: {tile_z}-{tile_x}-{tile_y}")
else:
tile_z, tile_x, tile_y = list(map(int, tile.split("-")))
@profiler(
kernels=add_kernels,
quiet=True,
add_to_return=True,
raw=add_stdout,
config=config,
)
def _read_tile(src_path: str, x: int, y: int, z: int, tilesize: int = 256):
with COGReader(src_path) as cog:
return cog.tile(x, y, z, tilesize=tilesize)
(_, _), stats = _read_tile(input, tile_x, tile_y, tile_z, tilesize)
click.echo(json.dumps(stats))
def get_web_optimized_params(
src_dst,
tilesize=256,
latitude_adjustment: bool = True,
warp_resampling: str = "nearest",
grid_crs=CRS.from_epsg(3857),
) -> Dict:
"""Return VRT parameters for a WebOptimized COG."""
bounds = list(
transform_bounds(src_dst.crs,
CRS.from_epsg(4326),
*src_dst.bounds,
densify_pts=21))
center = [(bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2]
lat = 0 if latitude_adjustment else center[1]
_, max_zoom = get_zooms(src_dst, lat=lat, tilesize=tilesize)
extrema = tile_extrema(bounds, max_zoom)
left, _, _, top = mercantile.xy_bounds(extrema["x"]["min"],
extrema["y"]["min"], max_zoom)
vrt_res = _meters_per_pixel(max_zoom, 0, tilesize=tilesize)
vrt_transform = Affine(vrt_res, 0, left, 0, -vrt_res, top)
vrt_width = (extrema["x"]["max"] - extrema["x"]["min"]) * tilesize
vrt_height = (extrema["y"]["max"] - extrema["y"]["min"]) * tilesize
return dict(
crs=grid_crs,
transform=vrt_transform,
width=vrt_width,
height=vrt_height,
resampling=ResamplingEnums[warp_resampling],
)
def random(input, zoom):
"""Get random tile."""
with COGReader(input) as cog:
if zoom is None:
zoom = randint(cog.minzoom, cog.maxzoom)
extrema = tile_extrema(cog.bounds, zoom)
x = sample(range(extrema["x"]["min"], extrema["x"]["max"]), 1)[0]
y = sample(range(extrema["y"]["min"], extrema["y"]["max"]), 1)[0]
click.echo(f"{zoom}-{x}-{y}")
def overview(bbox, endpoint, out_path, max_cloud, retina, start_date, end_date,
pixel_selection):
"""Create overview landsat images
"""
bounds = list(map(float, bbox.split(',')))
if start_date is not None:
start_date = dateparse(start_date)
if end_date is not None:
end_date = dateparse(end_date)
results = construct_mosaic(endpoint=endpoint,
bounds=bounds,
minzoom=9,
max_cloud=max_cloud,
retina=retina,
start_date=start_date,
end_date=end_date,
seasons=['spring', 'summer'],
tile_format='tif')
tilescale = 2 if retina is True else 1
tilesize = 256 * tilescale
zoom = results["maxzoom"] - (tilescale - 1)
# Mercator tiles covering bounds
extrema = tile_extrema(bounds, zoom)
tiles = mercantile.tiles(*bounds, zooms=zoom)
query_params = {
# True Color RGB
'bands': "4,3,2",
# Looks nice
'color_ops': "gamma RGB 3.5, saturation 1.7, sigmoidal RGB 15 0.35",
'pixel_selection': pixel_selection,
}
tiles_url = results["tiles"][0] + urllib.parse.urlencode(query_params)
assemble_tif(out_path=out_path,
zoom=zoom,
tiles=tiles,
tiles_url=tiles_url,
extrema=extrema,
tilesize=tilesize)
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 cog_translate(
source,
dst_path,
dst_kwargs,
indexes=None,
nodata=None,
dtype=None,
add_mask=None,
overview_level=None,
overview_resampling="nearest",
web_optimized=False,
latitude_adjustment=True,
resampling="nearest",
in_memory=None,
config=None,
allow_intermediate_compression=False,
forward_band_tags=False,
quiet=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 Path-like 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: 6)
COGEO overview (decimation) level
overview_resampling : str, optional (default: "nearest")
Resampling algorithm for overviews
web_optimized: bool, option (default: False)
Create web-optimized cogeo.
latitude_adjustment: bool, option (default: True)
Use mercator meters for zoom calculation or ensure max zoom equality.
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.
"""
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 = has_alpha_band(src_dst)
mask = 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 = dict(add_alpha=True, dtype=dtype)
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:
bounds = list(
transform_bounds(*[src_dst.crs, "epsg:4326"] +
list(src_dst.bounds),
densify_pts=21))
center = [(bounds[0] + bounds[2]) / 2,
(bounds[1] + bounds[3]) / 2]
lat = 0 if latitude_adjustment else center[1]
max_zoom = get_max_zoom(src_dst, lat=lat, tilesize=tilesize)
extrema = tile_extrema(bounds, max_zoom)
w, n = mercantile.xy(*mercantile.ul(
extrema["x"]["min"], extrema["y"]["min"], max_zoom))
vrt_res = _meters_per_pixel(max_zoom, 0, tilesize=tilesize)
vrt_transform = Affine(vrt_res, 0, w, 0, -vrt_res, n)
vrt_width = (extrema["x"]["max"] -
extrema["x"]["min"]) * tilesize
vrt_height = (extrema["y"]["max"] -
extrema["y"]["min"]) * tilesize
vrt_params.update(
dict(
crs="epsg:3857",
transform=vrt_transform,
width=vrt_width,
height=vrt_height,
resampling=ResamplingEnums[resampling],
))
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)
if not allow_intermediate_compression:
meta.pop("compress", None)
meta.pop("photometric", None)
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,
length=len(wind),
file=fout,
show_percent=True) as windows:
for ij, 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, 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 cog_translate(
src_path,
dst_path,
dst_kwargs,
indexes=None,
nodata=None,
add_mask=None,
overview_level=None,
overview_resampling="nearest",
web_optimized=False,
latitude_adjustment=True,
resampling="nearest",
in_memory=None,
config=None,
quiet=False,
):
"""
Create Cloud Optimized Geotiff.
Parameters
----------
src_path : str or PathLike object
A dataset path or URL. Will be opened in "r" mode.
dst_path : str or Path-like object
An output dataset path or or PathLike object.
Will be opened in "w" mode.
dst_kwargs: dict
Output dataset creation options.
indexes : tuple, int, optional
Raster band indexes to copy.
nodata, int, optional
Overwrite nodata masking values for input dataset.
add_mask, bool, optional
Force output dataset creation with a mask.
overview_level : int, optional (default: 6)
COGEO overview (decimation) level
overview_resampling : str, optional (default: "nearest")
Resampling algorithm for overviews
web_optimized: bool, option (default: False)
Create web-optimized cogeo.
latitude_adjustment: bool, option (default: True)
Use mercator meters for zoom calculation or ensure max zoom equality.
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.
quiet: bool, optional (default: False)
Mask processing steps.
"""
config = config or {}
with rasterio.Env(**config):
with rasterio.open(src_path) as src_dst:
meta = src_dst.meta
indexes = indexes if indexes else src_dst.indexes
nodata = nodata if nodata is not None else src_dst.nodata
alpha = has_alpha_band(src_dst)
mask = 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 = dict(add_alpha=True)
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:
bounds = list(
transform_bounds(*[src_dst.crs, "epsg:4326"] +
list(src_dst.bounds),
densify_pts=21))
center = [(bounds[0] + bounds[2]) / 2,
(bounds[1] + bounds[3]) / 2]
lat = 0 if latitude_adjustment else center[1]
max_zoom = get_max_zoom(src_dst, lat=lat, tilesize=tilesize)
extrema = tile_extrema(bounds, max_zoom)
w, n = mercantile.xy(*mercantile.ul(
extrema["x"]["min"], extrema["y"]["min"], max_zoom))
vrt_res = _meters_per_pixel(max_zoom, 0, tilesize=tilesize)
vrt_transform = Affine(vrt_res, 0, w, 0, -vrt_res, n)
vrt_width = (extrema["x"]["max"] -
extrema["x"]["min"]) * tilesize
vrt_height = (extrema["y"]["max"] -
extrema["y"]["min"]) * tilesize
vrt_params.update(
dict(
crs="epsg:3857",
transform=vrt_transform,
width=vrt_width,
height=vrt_height,
resampling=ResamplingEnums[resampling],
))
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)
meta.update(**dst_kwargs)
meta.pop("compress", None)
meta.pop("photometric", None)
if in_memory is None:
in_memory = vrt_dst.width * vrt_dst.height < IN_MEMORY_THRESHOLD
with ExitStack() as ctx:
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))
wind = list(tmp_dst.block_windows(1))
if not quiet:
click.echo("Reading input: {}".format(src_path),
err=True)
fout = os.devnull if quiet else sys.stderr
with click.progressbar(wind,
length=len(wind),
file=fout,
show_percent=True) as windows:
for ij, w in windows:
matrix = vrt_dst.read(window=w, indexes=indexes)
tmp_dst.write(matrix, window=w)
if add_mask or mask:
mask_value = vrt_dst.dataset_mask(window=w)
tmp_dst.write_mask(mask_value, window=w)
if overview_level is None:
overview_level = get_maximum_overview_level(
vrt_dst, 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])
tags = src_dst.tags()
tags.update(
dict(OVR_RESAMPLING_ALG=ResamplingEnums[
overview_resampling].name.upper()))
tmp_dst.update_tags(**tags)
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_low_level_cogs(
mosaic_path: str,
output_profile: Dict,
prefix: str = "mosaic_ovr",
max_overview_level: int = 6,
config: Dict = None,
threads=1,
) -> None:
"""
Create WebOptimized Overview COG from a mosaic definition file.
Attributes
----------
mosaic_path : str, required
Mosaic definition path.
output_profile : dict, required
prefix : str
max_overview_level : int
config : dict
Rasterio Env options.
"""
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.tile(*tile)
info = _get_info(assets[0])
extrema = tile_extrema(bounds, base_zoom)
tilesize = 256
res = _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,
tilesize=tilesize)
for ix, extrema in enumerate(extremas):
blocks = list(_get_blocks(extrema))
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[1],
count=len(info[6]),
width=width,
height=height,
crs="epsg:3857",
transform=Affine(res, 0, w, 0, -res, n),
nodata=info[4],
tiled=True,
blockxsize=256,
blockysize=256,
)
config = config or {}
with rasterio.Env(**config):
with MemoryFile() as memfile:
with memfile.open(**params) as mem:
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
assets = mosaic.tile(*t)
if not assets:
raise Exception(
f"No asset for tile {x}-{y}-{base_zoom}")
if assets:
tile, mask = mosaic_tiler(
assets,
x,
y,
base_zoom,
cogeo.tile,
indexes=info[6],
tilesize=tilesize,
pixel_selection=defaults.FirstMethod(),
)
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,
) as future:
for res in future:
pass
for f in _filter_futures(future_work):
window, tile, mask = f
if tile is None:
continue
mem.write(tile, window=window)
if info[5]:
mem.write_mask(mask.astype("uint8"),
window=window)
cog_translate(
mem,
f"{prefix}_{ix}.tif",
output_profile,
config=config,
in_memory=True,
)
def create_low_level_cogs(
mosaic_definition: Dict,
output_profile: Dict,
prefix: str = "mosaic_ovr",
max_overview_level: int = 6,
config: Dict = None,
threads=1,
) -> None:
"""
Create WebOptimized Overview COG from a mosaic definition file.
Attributes
----------
mosaic_definition : dict, required
Mosaic definition.
prefix : str
add_mask, bool, optional
Force output dataset creation with a mask.
max_overview_level : int
config : dict
Rasterio Env options.
"""
tilesize = 256
base_zoom = mosaic_definition["minzoom"] - 1
bounds = mosaic_definition["bounds"]
asset = _get_asset_example(mosaic_definition)
info = _get_info(asset)
extrema = tile_extrema(bounds, base_zoom)
res = _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,
tilesize=tilesize)
for ix, extrema in enumerate(extremas):
blocks = list(_get_blocks(extrema))
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[1],
count=info[0],
width=width,
height=height,
crs="epsg:3857",
transform=Affine(res, 0, w, 0, -res, n),
nodata=info[4],
tiled=True,
blockxsize=256,
blockysize=256,
)
config = config or {}
with rasterio.Env(**config):
with MemoryFile() as memfile:
with memfile.open(**params) as mem:
def _get_tile(wind):
idx, window = wind
x = extrema["x"]["min"] + idx[1]
y = extrema["y"]["min"] + idx[0]
assets = list(
set(get_assets(mosaic_definition, x, y,
base_zoom)))
if assets:
tile, mask = mosaic_tiler(
assets,
x,
y,
base_zoom,
cogeoTiler,
tilesize=tilesize,
pixel_selection=defaults.FirstMethod(),
)
if tile is None:
raise Exception("Empty")
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,
) as future:
for res in future:
pass
for f in _filter_futures(future_work):
window, tile, mask = f
mem.write(tile, window=window)
if info[5]:
mem.write_mask(mask.astype("uint8"), window=window)
cog_translate(
mem,
f"{prefix}_{ix}.tif",
output_profile,
config=config,
in_memory=True,
)