Mosaic
The rio-tiler-mosaic library has been moved into rio-tiler. The goal of the
rio_tiler.mosaic module is to create a mercator tile from multiple
observations. This is useful when a source image doesn't fill the entire
mercator tile of interest.
Often when creating a mercator tile from multiple assets, there will be portions
of overlap where a pixel could be chosen from multiple datasets. To handle this,
the rio-tiler.mosaic module provides pixel selection methods which define
how to handle these cases for each pixel:
- First: select value from the first non-missing asset
- Highest: loop though all the assets and return the highest value
- Lowest: loop though all the assets and return the lowest value
- Mean: compute the mean value of the whole stack
- Median: compute the median value of the whole stack
- Stdev: compute the standard deviation value of the whole stack
- LastBandHigh: Use last band (highest) as a decision factor (note: the last band will be excluded from in the output)
- LastBandLow: Use last band (lowest) as a decision factor (note: the last band will be excluded from in the output)
API¶
Image¶
rio_tiler.mosaic.mosaic_reader(
mosaic_assets: Sequence[str],
reader: Callable[..., ImageData],
*args: Any,
pixel_selection: Union[Type[MosaicMethodBase], MosaicMethodBase] = FirstMethod,
chunk_size: Optional[int] = None,
threads: int = MAX_THREADS,
allowed_exceptions: Tuple = (TileOutsideBounds,),
**kwargs,
)
- mosaic_assets : list, tuple of rio-tiler compatible assets (url or sceneid)
- reader: Callable that returns a
ImageDatainstance or a tuple ofnumpy.array - *args: arguments to be forwarded to the callable.
- pixel_selection : optional pixel selection algorithm (default: "first").
- chunk_size: optional, control the number of assets to process per loop.
- threads: optional, number of threads to use in each loop.
- allowed_exceptions: optional, allow some exceptions to be ignored.
- **kwargs: tiler specific keyword arguments.
Returns: - img, assets_used : tuple of ImageData and list of used assets to construct the output data.
Examples¶
from rio_tiler.io import Reader
from rio_tiler.mosaic import mosaic_reader
from rio_tiler.mosaic.methods import defaults
from rio_tiler.models import ImageData
def tiler(src_path: str, *args, **kwargs) -> ImageData:
with Reader(src_path) as src:
return src.tile(*args, **kwargs)
mosaic_assets = ["mytif1.tif", "mytif2.tif", "mytif3.tif"]
x = 1000
y = 1000
z = 9
# Use Default First value method
img, _ = mosaic_reader(mosaic_assets, tiler, x, y, z)
assert isinstance(img, ImageData)
assert img.data.shape == (3, 256, 256)
# Use Highest value: defaults.HighestMethod()
img, _ = mosaic_reader(
mosaic_assets,
tiler,
x,
y,
z,
pixel_selection=defaults.HighestMethod()
)
Point¶
rio_tiler.mosaic.mosaic_point_reader(
mosaic_assets: Sequence[str],
reader: Callable[..., PointData],
*args: Any,
pixel_selection: Union[Type[MosaicMethodBase], MosaicMethodBase] = FirstMethod,
chunk_size: Optional[int] = None,
threads: int = MAX_THREADS,
allowed_exceptions: Tuple = (TileOutsideBounds,),
**kwargs,
)
- mosaic_assets : list, tuple of rio-tiler compatible assets (url or sceneid)
- reader: Callable that returns a
PointDatainstance - *args: arguments to be forwarded to the callable.
- pixel_selection : optional pixel selection algorithm (default: "first").
- chunk_size: optional, control the number of assets to process per loop.
- threads: optional, number of threads to use in each loop.
- allowed_exceptions: optional, allow some exceptions to be ignored.
- **kwargs: tiler specific keyword arguments.
Returns: - point, assets_used : tuple of PointData and list of used assets to construct the output data.
Examples¶
from rio_tiler.io import Reader
from rio_tiler.mosaic import mosaic_reader
from rio_tiler.mosaic.methods import defaults
from rio_tiler.models import PointData
def point_reader(src_path: str, *args, **kwargs) -> PointData:
with Reader(src_path) as src:
return src.point(*args, **kwargs)
mosaic_assets = ["mytif1.tif", "mytif2.tif", "mytif3.tif"]
# Use Default First value method
pt, _ = mosaic_point_reader(mosaic_assets, point_reader, -40, 32)
assert isinstance(pt, PointData)
assert len(pt.data) == 3
# Use Highest value: defaults.HighestMethod()
img, _ = mosaic_point_reader(
mosaic_assets,
point_reader,
-40,
32,
pixel_selection=defaults.HighestMethod()
)
The MosaicMethod interface¶
the rio_tiler.mosaic.methods.base.MosaicMethodBase abstract base class defines an interface for all pixel selection methods allowed by rio_tiler.mosaic.mosaic_reader. its methods and properties are:
Properties¶
- is_done: returns a boolean indicating if the process is done filling the array
- data: returns the output mosaic array (numpy.masked.array)
Methods¶
- feed(array: numpy.ma.ndarray): update the tile and mask
Writing your own Pixel Selection method¶
The rules for writing your own pixel selection algorithm class are as follows:
- Must inherit from
MosaicMethodBase - Must provide concrete implementations of all the above methods.
See rio_tiler.mosaic.methods.defaults classes for examples.
Smart Multi-Threading¶
When dealing with an important number of image, you might not want to process the whole stack, especially if the pixel selection method stops when the tile is filled. To allow better optimization, rio_tiler.mosaic.mosaic_reader is fetching the tiles in parallel (threads) but to limit the number of files we also embedded the fetching in a loop (creating 2 level of processing):
mosaic_assets = ["1.tif", "2.tif", "3.tif", "4.tif", "5.tif", "6.tif"]
# 1st level loop - Creates chunks of assets
for chunks in _chunks(mosaic_assets, chunk_size):
# 2nd level loop - Uses threads for process each `chunk`
with futures.ThreadPoolExecutor(max_workers=max_threads) as executor:
future_tasks = [(executor.submit(_tiler, asset), asset) for asset in chunks]
More on threading¶
The number of threads used can be set in the function call with the threads= options. By default it will be equal to multiprocessing.cpu_count() * 5 or to the RIO_TILER_MAX_THREADS environment variable.
In some case, threading can slow down your application. You can set threads to 0 or 1 to run the tiler in a loop without using a ThreadPool (ref: #207).
Benchmark:
--------------------------------- benchmark '1images': 6 tests ---------------------------------
Name (time in ms) Min Max Mean Median
------------------------------------------------------------------------------------------------
1images-0threads 64.3108 (1.0) 66.9192 (1.0) 65.0202 (1.0) 64.9370 (1.0)
1images-4threads 69.0893 (1.07) 70.9919 (1.06) 69.6718 (1.07) 69.5102 (1.07)
1images-1threads 69.4884 (1.08) 71.8967 (1.07) 70.0853 (1.08) 69.9804 (1.08)
1images-5threads 69.5552 (1.08) 75.5498 (1.13) 71.7882 (1.10) 70.9849 (1.09)
1images-3threads 69.7684 (1.08) 74.4098 (1.11) 70.6282 (1.09) 70.2353 (1.08)
1images-2threads 69.9258 (1.09) 73.8798 (1.10) 70.8861 (1.09) 70.3682 (1.08)
------------------------------------------------------------------------------------------------
----------------------------------- benchmark '5images': 6 tests -----------------------------------
Name (time in ms) Min Max Mean Median
----------------------------------------------------------------------------------------------------
5images-5threads 104.1609 (1.0) 123.4442 (1.0) 110.4130 (1.0) 110.0683 (1.0)
5images-4threads 160.0952 (1.54) 170.7994 (1.38) 163.6062 (1.48) 161.8923 (1.47)
5images-3threads 161.2354 (1.55) 172.0363 (1.39) 165.1222 (1.50) 164.6513 (1.50)
5images-2threads 214.2413 (2.06) 220.7737 (1.79) 217.7740 (1.97) 217.9166 (1.98)
5images-0threads 228.2062 (2.19) 242.9397 (1.97) 231.9848 (2.10) 229.2843 (2.08)
5images-1threads 248.6630 (2.39) 251.8809 (2.04) 250.5195 (2.27) 251.2667 (2.28)
----------------------------------------------------------------------------------------------------