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README.md 0 → 100644
# pyotb: a pythonic extension of OTB
## Installation
```bash
python setup.py install
```
## Running an OTB app as a onliner
pyotb has been written so that it is more convenient to run an application in Python.
For example, let's consider one wants to undersample a raster. Using OTB, the code would be like :
```python
import otbApplication
input_path = 'my_image.tif'
resampled = otbApplication.Registry.CreateApplication('RigidTransformResample')
resampled.SetParameterString('in', input_path)
resampled.SetParameterString('interpolator', 'linear')
resampled.SetParameterFloat('transform.type.id.scalex', 0.5)
resampled.SetParameterFloat('transform.type.id.scaley', 0.5)
resampled.SetParameterString('out', 'output.tif')
resampled.ExecuteAndWriteOutput()
```
Instead, using pyotb :
```python
import pyotb
input_path = 'my_image.tif'
pyotb.RigidTransformResample({'in': input_path, 'interpolator': 'linear', 'out': 'output.tif',
'transform.type.id.scaley': 0.5, 'transform.type.id.scalex': 0.5})
```
## In-memory connections
The big asset of pyotb is the ease of in-memory connections between apps.
Let's start from our previous example. Consider the case where one wants to apply binary morphological dilatation
following the undersampling. Using OTB :
```python
import otbApplication
input_path = 'my_image.tif'
resampled = otbApplication.Registry.CreateApplication('RigidTransformResample')
resampled.SetParameterString('in', input_path)
resampled.SetParameterString('interpolator', 'linear')
resampled.SetParameterFloat('transform.type.id.scalex', 0.5)
resampled.SetParameterFloat('transform.type.id.scaley', 0.5)
resampled.Execute()
dilated = otbApplication.Registry.CreateApplication('BinaryMorphologicalOperation')
dilated.ConnectImage('in', resampled, 'out')
dilated.SetParameterString("filter", 'dilatation')
dilated.SetParameterString("structype", 'ball')
dilated.SetParameterInt("xradius", 3)
dilated.SetParameterInt("yradius", 3)
dilated.SetParameterString('out', 'output.tif')
dilated.ExecuteAndWriteOutput()
```
Using pyotb :
```python
import pyotb
input_path = 'my_image.tif'
resampled = pyotb.RigidTransformResample({'in': input_path, 'interpolator': 'linear',
'transform.type.id.scaley': 0.5, 'transform.type.id.scalex': 0.5})
pyotb.BinaryMorphologicalOperation(resampled, out='output.tif', filter='dilatation',
structype='ball', xradius=3, yradius=3)
```
## Arithmetic operations
Every pyotb object supports arithmetic operations, such as addition, subtraction, comparison...
Consider an example where we want to perform the arithmetic operation `image1 * image2 - 2*image3`
Using OTB, the following code works for 3-bands images :
```python
import otbApplication
bmx = otbApplication.Registry.CreateApplication('BandMathX')
bmx.SetParameterStringList('il', ['image1.tif', 'image2.tif', 'image3.tif']) # all images are 3-bands
exp = 'im1b1*im2b1 - 2*im3b1; im1b2*im2b2 - 2*im3b2; im1b3*im2b3 - 2*im3b3'
bmx.SetParameterString('exp', exp)
bmx.SetParameterString('out', 'output.tif')
bmx.SetParameterOutputImagePixelType('out', otbApplication.ImagePixelType_uint8)
bmx.ExecuteAndWriteOutput()
```
With pyotb, the following works with images of any number of bands :
```python
import pyotb
# transforming filepaths to pyotb objects
input1, input2, input3 = pyotb.Input('image1.tif'), pyotb.Input('image2.tif') , pyotb.Input('image3.tif')
res = input1 * input2 - 2 * input2
res.write('output.tif', pixel_type='uint8')
```
## Slicing
pyotb objects support slicing in a Python fashion :
```python
import pyotb
# transforming filepath to pyotb object
input = pyotb.Input('my_image.tif')
input[:, :, :3] # selecting first 3 bands
input[:, :, [0, 1, 4]] # selecting bands 1, 2 & 5
input[:1000, :1000] # selecting 1000x1000 subset
```
Using OTB only, this would be more laborious :
```python
import otbApplication
# selecting first 3 bands
extracted = otbApplication.Registry.CreateApplication('ExtractROI')
extracted.SetParameterString('in', 'my_image.tif')
extracted.SetParameterStringList('cl', ['Channel1', 'Channel2', 'Channel3'])
extracted.Execute()
# selecting 1000x1000 subset
extracted = otbApplication.Registry.CreateApplication('ExtractROI')
extracted.SetParameterString('in', 'my_image.tif')
extracted.SetParameterString('mode', 'extent')
extracted.SetParameterString('mode.extent.unit', 'pxl')
extracted.SetParameterFloat('mode.extent.ulx', 0)
extracted.SetParameterFloat('mode.extent.uly', 0)
extracted.SetParameterFloat('mode.extent.lrx', 999)
extracted.SetParameterFloat('mode.extent.lry', 999)
extracted.Execute()
```
## pyotb.where function
The `pyotb.where` function has been written to mimic the behavior of `numpy.where`.
It is the equivalent of the muparser syntax `condition ? x : y` that can be used in OTB's BandMath.
```python
import pyotb
# transforming filepaths to pyotb objects
labels, image1, image2 = pyotb.Input('labels.tif'), pyotb.Input('image1.tif') , pyotb.Input('image2.tif')
# If labels = 1, returns image1. Else, returns image2
res = pyotb.where(labels == 1, image1, image2) # this would also work: pyotb.where(labels == 1, 'image1.tif', 'image2.tif')
# A more complex example
# If labels = 1, returns image1. If labels = 2, returns image2. If labels = 3, returns 3. Else 0
res = pyotb.where(labels == 1, image1,
pyotb.where(labels == 2, image2,
pyotb.where(labels == 3, 3, 0)))
```
## Work with images with differents footprints / resolutions
OrfeoToolBox provides a handy `Superimpose` application that enables the projection of an image into the geometry of another one.
In pyotb, a function has been created for even more convenience for the user.
Let's consider the case where we have 3 images with different resolutions and different footprints :
![Images](illustrations/pyotb_define_processing_area_initial.jpg)
```python
import pyotb
# transforming filepaths to pyotb objects
s2_image, vhr_image, labels = pyotb.Input('image_10m.tif'), pyotb.Input('image_60cm.tif'), pyotb.Input('land_cover_2m.tif')
print(s2_image.shape) # (286, 195, 4)
print(vhr_image.shape) # (2048, 2048, 3)
print(labels.shape) # (1528, 1360, 1)
```
Our goal is to obtain all images at the same footprint, same resolution and same shape.
Let's consider we want the intersection of all footprints and the same resolution as `labels` image.
![Goal](illustrations/pyotb_define_processing_area_process.jpg)
Here is the final result :
![Result](illustrations/pyotb_define_processing_area_result.jpg)
The piece of code to achieve this :
```python
s2_image, vhr_image, labels = pyotb.define_processing_area(s2_image, vhr_image, labels, window_rule='intersection',
pixel_size_rule='same_as_input',
reference_pixel_size_input=labels, interpolator='bco')
print(s2_image.shape) # (657, 520, 4)
print(vhr_image.shape) # (657, 520, 3)
print(labels.shape) # (657, 520, 1)
# Then we can do whichever computations with s2_image, vhr_image, labels
```
## Interaction with Numpy
pyotb objects can be transparently used in numpy functions.
```python
import pyotb
import numpy as np
input = pyotb.Input('image.tif') # this is a pyotb object
# Creating a numpy array of noise
white_noise = np.random.normal(0, 50, size=input.shape) # this is a numpy object
# Adding the noise to the image
noisy_image = np.add(input, white_noise) # magic: this is a pyotb object that has the same georeference as input
noisy_image.write('image_plus_noise.tif')
```
Limitations :
- The whole image is loaded into memory
- The georeference can not be modified. Thus, numpy operations can not change the image or pixel size
(e.g. it is not possible to use `np.pad`)
## Interaction with Tensorflow
We saw that numpy operations had some limitations. To bypass those limitations, it is possible to use some Tensorflow operations on pyotb objects. You can do it like this :
```python
import pyotb
# The decorator enables the use of pyotb objects as inputs/output of the function
@pyotb.run_tf_function
def scalar_product(x1, x2):
"""This is a function composed of tensorflow operations."""
import tensorflow as tf
return tf.reduce_sum(tf.multiply(x1, x2), axis=-1)
# Compute the scalar product
res = scalar_product('image1.tif', 'image2.tif') # magic: this is a pyotb object
res.write('scalar_product.tif')
```
Advantages :
- The process supports streaming, hence the whole image is **not** loaded into memory
- Can be integrated in an OTB pipeline
Limitations :
- It is not possible to use the tensorflow python API inside a script where OTBTF is used because of compilation issues between Tensorflow and OTBTF
- It is currently not possible to chain several `@pyotb.run_tf_function` functions
__version__ = "0.1"
from .core import *
This diff is collapsed.
[build-system]
requires = [
"setuptools>=42",
"wheel"
]
build-backend = "setuptools.build_meta"
[metadata]
description-file=README.md
setup.py 0 → 100644
import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="pyotb",
version="0.1",
author="Nicolas Narçon",
author_email="nicolas.narcon@gmail.com",
description="Library to enable easy use of the Orfeo Tool Box (OTB) in Python",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/TBD",
classifiers=[
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Topic :: Scientific/Engineering :: GIS",
"Topic :: Scientific/Engineering :: Image Processing",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
],
packages=setuptools.find_packages(),
python_requires=">=3.6",
keywords="remote sensing, otb, orfeotoolbox, orfeo toolbox",
)
#package_dir={"": "src"},
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