OTB processing in Python


In the otbApplication module, two main classes can be manipulated :

  • Registry, which provides access to the list of available applications, and can create applications.
  • Application, the base class for all applications. This allows one to interact with an application instance created by the Registry.

Here is one example of how to use Python to run the Smoothing application, changing the algorithm at each iteration.

#  Example on the use of the Smoothing application

# We will use sys.argv to retrieve arguments from the command line.
# Here, the script will accept an image file as first argument,
# and the basename of the output files, without extension.
from sys import argv

# The python module providing access to OTB applications is otbApplication
import otbApplication

# otbApplication.Registry can tell you what application are available
print('Available applications: ')
print (str( otbApplication.Registry.GetAvailableApplications()))

# Let's create the application  "Smoothing"
app = otbApplication.Registry.CreateApplication("Smoothing")

# We print the keys of all its parameters
print (app.GetParametersKeys())

# First, we set the input image filename
app.SetParameterString("in", argv[1])

# The smoothing algorithm can be set with the "type" parameter key
# and can take 3 values: 'mean', 'gaussian', 'anidif'
for type in ['mean', 'gaussian', 'anidif']:

  print('Running with ' + type + ' smoothing type')

  # Now we configure the smoothing algorithm
  app.SetParameterString("type", type)

  # Set the output filename, using the algorithm type to differentiate the outputs
  app.SetParameterString("out", argv[2] + type + ".tif")

  # This will execute the application and save the output to argv[2]

If you want to handle the parameters from a Python dictionary, you can use the functions SetParameters() and GetParameters().

params = {"in":"myInput.tif", "type.mean.radius":4}
params2 = app.GetParameters()

Numpy array processing

Input and output images to any OTB application in the form of NumPy array is now possible in OTB Python wrapping. The Python wrapping only exposes OTB Application engine module (called ApplicationEngine) which allows one to access existing C++ applications. Due to blissful nature of ApplicationEngine’s loading mechanism no specific wrapping is required for each application.

NumPy extension to Python wrapping allows data exchange to application as an array rather than a disk file. Of course, it is possible to load an image from file and then convert it to NumPy array or just provide a file as explained in the previous section via Application.SetParameterString(...).

The bridge between NumPy and OTB makes it easy to plug OTB into any image processing chain via Python code that uses GIS/Image processing tools such as GDAL, GRASS GIS, OSSIM that can deal with NumPy.

Below code reads an input image using Python Pillow library (fork of PIL) and convert it to NumPy array. The NumPy array is used as an input to the application via SetImageFromNumpyArray(...) method. The application used in this example is ExtractROI. After extracting a small area the output image is taken as NumPy array with GetImageFromNumpyArray(...) method thus avoid writing output to a temporary file.

import sys
import os
import numpy as np
import otbApplication
from PIL import Image as PILImage

pilimage = PILImage.open('poupees.jpg')
npimage = np.asarray(pilimage)

ExtractROI = otbApplication.Registry.CreateApplication('ExtractROI')
ExtractROI.SetImageFromNumpyArray('in', npimage)
ExtractROI.SetParameterInt('startx', 140)
ExtractROI.SetParameterInt('starty', 120)
ExtractROI.SetParameterInt('sizex', 150)
ExtractROI.SetParameterInt('sizey', 150)

ExtractOutput = ExtractROI.GetImageAsNumpyArray('out')
output_pil_image = PILImage.fromarray(np.uint8(ExtractOutput))

In-memory connection

Applications are often used as part of larger processing workflows. Chaining applications currently requires to write/read back images between applications, resulting in heavy I/O operations and a significant amount of time dedicated to writing temporary files.

Since OTB 5.8, it is possible to connect an output image parameter from one application to the input image parameter of the next parameter. This results in the wiring of the internal ITK/OTB pipelines together, permitting image streaming between the applications. Consequently, this removes the need of writing temporary images and improves performance. Only the last application of the processing chain is responsible for writing the final result images.

In-memory connection between applications is available both at the C++ API level and using the Python bindings.

Here is a Python code sample which connects several applications together:

import otbApplication as otb

app1 = otb.Registry.CreateApplication("Smoothing")
app2 = otb.Registry.CreateApplication("Smoothing")
app3 = otb.Registry.CreateApplication("Smoothing")
app4 = otb.Registry.CreateApplication("ConcatenateImages")

app1.IN = argv[1]

# Connection between app1.out and app2.in

# Execute call is mandatory to wire the pipeline and expose the
# application output. It does not write image

app3.IN = argv[1]

# Execute call is mandatory to wire the pipeline and expose the
# application output. It does not write image

# Connection between app2.out, app3.out and app4.il using images list

app4.OUT = argv[2]

# Call to ExecuteAndWriteOutput() both wires the pipeline and
# actually writes the output, only necessary for last application of
# the chain.

Note: Streaming will only work properly if the application internal implementation does not break it, for instance by using an internal writer to write intermediate data. In this case, execution should still be correct, but some intermediate data will be read or written.

Interactions with OTB pipeline

[Since OTB 6.6]

The application framework has been extended in order to provide ways to interact with the pipelines inside each application. It applies only to applications that use input or output images. Let’s check which functions are available in the Application class. There are lots of getter functions:

Function name return value
GetImageOrigin(...) origin of the image (physical position of the first pixel center)
GetImageSpacing(...) signed spacing of the image
GetImageSize(...) size of the LargestPossibleRegion
GetImageNbBands(...) number of components per pixel
GetImageProjection(...) Projection WKT string
GetImageKeywordlist(...) Ossim keywordlist (sensor model)
GetImageMetaData(...) the entire MetaDataDictionary
GetImageRequestedRegion(...) requested region
GetImageBasePixelType(...) pixel type of the underlying Image/VectorImage.

All these getters functions use the following arguments:

  • key: a string containing the key of the image parameter
  • idx: an optional index (default is 0) that can be used to access ImageList parameters transparently

There is also a function to send orders to the pipeline:

PropagateRequestedRegion(key, region, idx=0): sets a given RequestedRegion on the image and propagate it, returns the memory print estimation. This function can be used to measure the requested portion of input images necessary to produce an extract of the full output.

Note: a requested region (like other regions in the C++ API of otb::Image) consists of an image index and a size, which defines a rectangular extract of the full image.

This set of functions has been used to enhance the bridge between OTB images and Numpy arrays. There are now import and export functions available in Python that preserve the metadata of the image during conversions to Numpy arrays:

  • ExportImage(self, key): exports an output image parameter into a Python dictionary.
  • ImportImage(self, key, dict, index=0): imports the image from a Python dictionary into an image parameter (as a monoband image).
  • ImportVectorImage(self, key, dict, index=0): imports the image from a Python dictionary into an image parameter (as a multiband image).

The Python dictionary used has the following entries:

  • 'array': the Numpy array containing the pixel buffer
  • 'origin': origin of the image
  • 'spacing': signed spacing of the image
  • 'size': full size of the image
  • 'region': region of the image present in the buffer
  • 'metadata': metadata dictionary (contains projection, sensor model,...)

Now some basic Q&A about this interface:

Q: What portion of the image is exported to Numpy array? A: By default, the whole image is exported. If you had a non-empty requested region (the result of calling PropagateRequestedRegion()), then this region is exported.

Q: What is the difference between ImportImage and ImportVectorImage? A: The first one is here for Applications that expect a monoband otb::Image. In most cases, you will use the second one: ImportVectorImage.

Q: What kind of objects are there in this dictionary export? A: The array is a numpy.ndarray. The other fields are wrapped objects from the OTB library but you can interact with them in a Python way: they support len() and str() operator, as well as bracket operator []. Some of them also have a keys() function just like dictionaries.

This interface allows you to export OTB images (or extracts) to Numpy array, process them by other means, and re-import them with preserved metadata. Please note that this is different from an in-memory connection.

Here is a small example of what can be done:

import otbApplication as otb

# Create a smoothing application
app = otb.Registry.CreateApplication("Smoothing")

# only call Execute() to setup the pipeline, not ExecuteAndWriteOutput() which would
# run it and write the output image

# Setup a special requested region
myRegion = otb.itkRegion()
myRegion['size'][0] = 20
myRegion['size'][1] = 25
ram = app.PropagateRequestedRegion("out",myRegion)

# Check the requested region on the input image

# Create a ReadImageInfo application
app2 = otb.Registry.CreateApplication("ReadImageInfo")

# export "out" from Smoothing and import it as "in" in ReadImageInfo
ex = app.ExportImage("out")
app2.ImportVectorImage("in", ex)

# Check the result of ReadImageInfo
someKeys = ['sizex', 'sizey', 'spacingx', 'spacingy', 'sensor', 'projectionref']
for key in someKeys:
  print(key + ' : ' + str(app2.GetParameterValue(key)))

# Only a portion of "out" was exported but ReadImageInfo is still able to detect the
# correct full size of the image

Corner cases

There are a few corner cases to be aware of when using Python wrappers. They are often limitations, that one day may be solved in future versions. If it happens, this documentation will report the OTB version that fixes the issue.

Calling UpdateParameters()

These wrappers are made as a mirror of the C++ API, so there is a function UpdateParameters(). Its role is to update parameters that depend on others. It is called at least once at the beginning of Execute().

In command line and GUI launchers, this functions gets called each time a parameter of the application is modified. In Python, this mechanism is not automated: there are cases where you may have to call it yourself.

Let’s take an example with the application PolygonClassStatictics. In this application, the choices available in the parameter field depend on the list of fields actually present in the vector file vec. If you try to set the parameters vec and field, you will get an error:

import otbApplication as otb
app = otb.Registry.CreateApplication("PolygonClassStatistics")
app.SetParameterString("field", "label")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/gpasero/Projet_OTB/build/OTB/lib/otb/python/otbApplication.py", line 897, in SetParameterString
    def SetParameterString(self, *args): return _otbApplication.Application_SetParameterString(self, *args)
RuntimeError: Exception thrown in otbApplication Application_SetParameterString: /home/gpasero/Projet_OTB/src/OTB/Modules/Wrappers/ApplicationEngine/src/otbWrapperListViewParameter.cxx:141:
itk::ERROR: ListViewParameter(0x149da10): Cannot find label

The error says that the choice label is not recognized, because UpdateParameters() was not called after setting the vector file. The solution is to call it before setting the field parameter:

app.SetParameterString("field", "label")

No metadata in NumPy arrays

With the NumPy module, it is possible to convert images between OTB and NumPy arrays. For instance, when converting from OTB to NumPy array:

  • An Update() of the underlying otb::VectorImage is requested. Be aware that the full image is generated.
  • The pixel buffer is copied into a numpy.array

As you can see, there is no export of the metadata, such as origin, spacing, geographic projection. It means that if you want to re-import a NumPy array back into OTB, the image won’t have any of these metadata. This can pose problems for applications that relate to geometry, projections, and also calibration.

Future developments will probably offer a more adapted structure to import and export images between OTB and the Python world.

Setting of EmptyParameter

Most of the parameters are set using functions SetParameterXXX(), except for one type of parameter: the EmptyParameter. This class was the first implementation of a boolean. It is now deprecated, you should use BoolParameter instead.

Let’s take an example with the application ReadImageInfo when it was still using an EmptyParameter for parameter keywordlist:

import otbApplication as otb
app = otb.Registry.CreateApplication("ReadImageInfo")

If you want the get the state of parameter keywordlist, a boolean, use:


To set this parameter ON/OFF, use the functions:


Don’t try to use other functions to set the state of a boolean. For instance, try the following commands:

app.SetParameterInt("keywordlist", 0)

You will get a state True even if you asked the opposite.