OTB processing in Python

Basics

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 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 with codename "Smoothing"
app = otbApplication.Registry.CreateApplication("Smoothing")

# We print the keys of all its parameter
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'

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

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

  # This will execute the application and save the output file
  app.ExecuteAndWriteOutput()

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 ApplicationEngine module which allow 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. Ofcourse, it is possible to load an image from file and then convert to numpy array or just provide a file as earlier via Application.SetParameterString(...).

This bridge that completes 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 (PIL) and convert it to numpy array. This numpy array is used 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 wiriting 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)
inshow(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)
ExtractROI.Execute()

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

In-memory connection

Applications are often use as parts of larger processing chains. 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, allowing to perform image streaming between the applications. There is therefore no more writing of temporary images. 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 connecting 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]
app1.Execute()

# Connection between app1.out and app2.in
app2.SetParameterInputImage("in",app1.GetParameterOutputImage("out"))

# Execute call is mandatory to wire the pipeline and expose the
# application output. It does not write image
app2.Execute()

app3.IN = argv[1]

# Execute call is mandatory to wire the pipeline and expose the
# application output. It does not write image
app3.Execute()

# Connection between app2.out, app3.out and app4.il using images list
app4.AddImageToParameterInputImageList("il",app2.GetParameterOutputImage("out"));
app4.AddImageToParameterInputImageList("il",app3.GetParameterOutputImage("out"));

app4.OUT = argv[2]

# Call to ExecuteAndWriteOutput() both wires the pipeline and
# actually writes the output, only necessary for last application of
# the chain.
app4.ExecuteAndWriteOutput()

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.

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 by future developments. 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("vec","../../src/OTB-Data/Input/Classification/variousVectors.sqlite")
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.UpdateParameters()
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, projection WKT. It means that if you want to import back a Numpy array into OTB, the image won’t have any of these metadata. It can be a problem for applications doing geometry, projections, and also calibration.

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

Setting of boolean parameters

Most of the parameters are set using functions SetParameterXXX(). The boolean parameters are handled differently (also called Empty parameter). Let’s take an example with the application ReadImageInfo:

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

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

app.IsParameterEnabled("keywordlist")

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

app.EnableParameter("keywordlist")
app.DisableParameter("keywordlist")

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

app.SetParameterInt("keywordlist", 0)
app.IsParameterEnabled("keywordlist")

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