# pyotb: Orfeo ToolBox for Python
[](https://gitlab.orfeo-toolbox.org/nicolasnn/pyotb/-/releases)
[](https://gitlab.orfeo-toolbox.org/nicolasnn/pyotb/-/commits/develop)
[](https://gitlab.orfeo-toolbox.org/nicolasnn/pyotb/-/commits/develop)
[](https://pyotb.readthedocs.io/en/master/)
**pyotb** wraps the [Orfeo Toolbox](https://www.orfeo-toolbox.org/) in a pythonic, developer friendly
fashion.
## Key features
- Easy use of Orfeo ToolBox (OTB) applications from python
- Simplify common sophisticated I/O features of OTB
- Lazy execution of operations thanks to OTB streaming mechanism
- Interoperable with popular python libraries ([numpy](https://numpy.org/) and
[rasterio](https://rasterio.readthedocs.io/))
- Extensible
Documentation hosted at [pyotb.readthedocs.io](https://pyotb.readthedocs.io/).
## Example
Building a simple pipeline with OTB applications
```py
import pyotb
# RigidTransformResample, with input parameters as dict
resampled = pyotb.RigidTransformResample({
"in": "https://myserver.ia/input.tif", # Note: no /vsicurl/
"interpolator": "linear",
"transform.type.id.scaley": 0.5,
"transform.type.id.scalex": 0.5
})
# OpticalCalibration, with input parameters as args
calib = pyotb.OpticalCalibration(resampled)
# BandMath, with input parameters as kwargs
ndvi = pyotb.BandMath(calib, exp="ndvi(im1b1, im1b4)")
# Pythonic slicing
roi = ndvi[20:586, 9:572]
# Pipeline execution. The actual computation happens here!
roi.write("output.tif", "float")
```
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"description": "# pyotb: Orfeo ToolBox for Python\n\n[](https://gitlab.orfeo-toolbox.org/nicolasnn/pyotb/-/releases)\n[](https://gitlab.orfeo-toolbox.org/nicolasnn/pyotb/-/commits/develop)\n[](https://gitlab.orfeo-toolbox.org/nicolasnn/pyotb/-/commits/develop)\n[](https://pyotb.readthedocs.io/en/master/)\n\n**pyotb** wraps the [Orfeo Toolbox](https://www.orfeo-toolbox.org/) in a pythonic, developer friendly \nfashion. \n\n## Key features\n\n- Easy use of Orfeo ToolBox (OTB) applications from python\n- Simplify common sophisticated I/O features of OTB\n- Lazy execution of operations thanks to OTB streaming mechanism\n- Interoperable with popular python libraries ([numpy](https://numpy.org/) and \n[rasterio](https://rasterio.readthedocs.io/))\n- Extensible\n\nDocumentation hosted at [pyotb.readthedocs.io](https://pyotb.readthedocs.io/).\n\n## Example\n\nBuilding a simple pipeline with OTB applications\n\n```py\nimport pyotb\n\n# RigidTransformResample, with input parameters as dict\nresampled = pyotb.RigidTransformResample({\n \"in\": \"https://myserver.ia/input.tif\", # Note: no /vsicurl/\n \"interpolator\": \"linear\", \n \"transform.type.id.scaley\": 0.5,\n \"transform.type.id.scalex\": 0.5\n})\n\n# OpticalCalibration, with input parameters as args\ncalib = pyotb.OpticalCalibration(resampled)\n\n# BandMath, with input parameters as kwargs\nndvi = pyotb.BandMath(calib, exp=\"ndvi(im1b1, im1b4)\")\n\n# Pythonic slicing\nroi = ndvi[20:586, 9:572]\n\n# Pipeline execution. The actual computation happens here!\nroi.write(\"output.tif\", \"float\")\n```\n",
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