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Library to enable easy use of the Orfeo ToolBox (OTB) in Python

Project description

pyotb: a pythonic extension of Orfeo Toolbox

latest release pipeline status read the docs status

pyotb wraps the Orfeo Toolbox (OTB) python bindings to make it more developer friendly.

Key features

  • Easy use of OTB applications from python
  • Simplify common sophisticated I/O features of OTB
  • Lazy execution of in-memory pipelines with OTB streaming mechanism
  • Interoperable with popular python libraries (numpy, rasterio)
  • Extensible

Documentation hosted at


Building a simple pipeline with OTB applications

import pyotb

# RigidTransformResample application, with input parameters as dict
resampled = pyotb.RigidTransformResample({
    "in": "",  # Note: no /vsicurl/...
    "interpolator": "linear", 
    "": 0.5,
    "": 0.5

# OpticalCalibration, with automatic input parameters resolution
calib = pyotb.OpticalCalibration(resampled)

# BandMath, with input parameters passed as kwargs
ndvi = pyotb.BandMath(calib, exp="ndvi(im1b1, im1b4)")

# Pythonic slicing using lazy computation (no memory used)
roi = ndvi[20:586, 9:572]

# Pipeline execution
# The actual computation happens here !
roi.write("output.tif", pixel_type="float")

pyotb's objects also enable easy interoperability with numpy and rasterio:

# Numpy and RasterIO style attributes
print(roi.shape, roi.dtype, roi.transform)

# Other useful information

array = roi.to_numpy()
array, profile = roi.to_rasterio()


Contributions are welcome on Github or the source repository hosted on the Orfeo ToolBox GitLab.

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