Python Package for viewshed C++ library

Minimalistic python interface for C++ library viewshed. Makes running examples and experiments much easier.

Install

If you have viewshed library installed, preferably from PPA, the installation should be straight forward:

pip install git+https://github.com/JanCaha/viewshed-library-pybind11

The cmake and git need to be installed for the compilation to work.

Docker

Docker build is handled from viewshed library repository here.

There is also docker container with the Python bindings ready, which can be obtained using:

docker pull cahik/viewshed:latest-python

Running from Python

Viewshed

from pathlib import Path
import viewshed

# folder to store results in
result_folder = Path("path/to/folder/")

# load first band of file as DEM 
dem = viewshed.ProjectedSquareCellRaster(Path("path/to/file.ext"))

# create viewpoint at coordinates (1, 2) on DEM raster with observer offset 1.6
vp = viewshed.Point(1, 2, dem, 1.6)

# prepare all visibility indices algorithms with and set noData value from DEM as noData to results
algs = viewshed.VisibilityAlgorithms(dem.noData())

# prepare viewshed for calculation with viewpoint, dem and algorithms
v = viewshed.Viewshed(vp, dem, algs)

# optionally max usable threads for calculation can be set with, otherwise all available threads are used
# v.setMaxThreads(4) 

# run the calculation
v.calculate()

# save result rasters in folder
v.saveResults(result_folder)

InverseViewshed

from pathlib import Path
import viewshed

# folder to store results in
result_folder = Path("path/to/folder/")

# load first band of file as DEM 
dem = viewshed.ProjectedSquareCellRaster(Path("path/to/file.ext"))

# create viewpoint at coordinates (1, 2) on DEM raster with observer offset 1.6
tp = viewshed.Point(1, 2, dem, 0.0)

# prepare all visibility indices algorithms with and set noData value from DEM as noData to results
algs = viewshed.VisibilityAlgorithms(dem.noData())

# prepare viewshed for calculation with viewpoint, observer offset, dem and algorithms
iv = viewshed.InverseViewshed(tp, 1.6, dem, algs)

# optionally max usable threads for calculation can be set with, otherwise all available threads are used
# iv.setMaxThreads(4) 

# run the calculation
iv.calculate()

# save result rasters in folder
iv.saveResults(result_folder)