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https://github.com/centreborelli/s2p

Satellite Stereo Pipeline
https://github.com/centreborelli/s2p

3d image-processing remote-sensing satellite-imagery stereo-vision

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Satellite Stereo Pipeline

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README 

        
# S2P - Satellite Stereo Pipeline



![Build Status](https://github.com/centreborelli/s2p/actions/workflows/build.yml/badge.svg)

![Tests Status](https://github.com/centreborelli/s2p/actions/workflows/tests.yml/badge.svg)

[![PyPI version](https://img.shields.io/pypi/v/s2p)](https://pypi.org/project/s2p)



S2P is a Python library and command line tool that implements a stereo

pipeline which produces elevation models from images taken by high resolution

optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos. It

generates 3D point clouds and digital surface models from stereo pairs (two

images) or tri-stereo sets (three images) in a completely automatic fashion.



S2P was used to win the 2016 [IARPA Multi-View Stereo 3D Mapping Challenge](https://www.iarpa.gov/challenges/3dchallenge.html).



A wide variety of stereo correlation algorithms are supported, including several

flavors of semi-global matching (SGM), TV-L1 optical flow, etc.



The main language is Python, although several operations are handled by

binaries written in C.



The pipeline is implemented in the Python package `s2p`. It can be used

to produce surface models and 3D point clouds from arbitrarily large regions

of interest or from complete images. If needed, it cuts the region of interest

in several small tiles and process them in parallel.



Its main source code repository is https://github.com/centreborelli/s2p.



# Dependencies



## GDAL

The main dependency is GDAL. Version 2.1.0 or newer is required.



### On Ubuntu 18.04

`gdal` can be installed with `apt-get`.



    sudo apt update

    sudo apt install libgdal-dev



### On macOS

[Download GDAL](http://www.kyngchaos.com/files/software/frameworks/GDAL_Complete-2.4.dmg)

and install the `.dmg` file.



## Other dependencies (fftw, libtiff)



On Ubuntu:



    sudo apt install build-essential libfftw3-dev libgeotiff-dev libtiff5-dev



On macOS:



    brew install fftw libtiff



If lacking administrative privedges to run `sudo`, all these dependencies exist as conda 

packages and can be installed in a user directory. Then the path to them can be specified

in the s2p makefiles if compiling it from source. 



# Installation



    pip install s2p



Alternatively, if you want to get the latest commit or want to edit the

sources, install it in editable mode from a git clone:



    git clone https://github.com/centreborelli/s2p.git --recursive

    cd s2p

    pip install -e ".[test]"



The `--recursive` option for `git clone` allows to clone all git submodules, such

as the [iio](https://github.com/mnhrdt/iio) library.



If the `--recursive` option wasn't used when cloning, the submodules can now be

retrieved with



    git submodule update --init



All `s2p` python submodules are located in the `s2p` package. Some python

functions of these modules rely on external binaries. Most of these binaries

were written on purpose for the needs of the pipeline, and their source code is

provided here in the `c` folder. For the other binaries, the source code is

provided in the `3rdparty` folder.



All the sources (ours and 3rdparties) are compiled from the same makefile. Just

run `make all` from the `s2p` folder to compile them. This will create a `bin`

directory containing all the needed binaries. This makefile is used when

running `pip install .`



You can test if S2P is correctly working using:



    make test



If some libraries needed by `s2p` (such as `libfftw3`) are installed in a custom location,

for example `/usr/joe/local`, then the compilation and tests will fail with exit status 127

or mentioning not being able to load shared  libaries.  You can help the compiler to find

these libraries by defining the following variables:



    export CPATH=/usr/joe/local/include

    export LIBRARY_PATH=/usr/joe/local/lib

    

The following invocation can be used then on Linux:



    LD_LIBRARY_PATH=/usr/joe/local/lib make test



and the same for the `s2p` command later. One macOS one may use instead 

`DYLD_FALLBACK_LIBRARY_PATH`.



## Docker image (old)

[![Docker Status](http://dockeri.co/image/cmla/s2p)](https://hub.docker.com/r/cmla/s2p/)



A precompiled docker image is available and ready to use:



    docker pull cmla/s2p



# Usage



`s2p` is a Python library that can be imported into other applications. It also

comes with a Command Line Interface (CLI).



## From the command line



The `s2p` CLI usage instructions can be printed with the `-h` and `--help` switches.



    $ s2p -h

    usage: s2p.py [-h] config.json



    S2P: Satellite Stereo Pipeline



    positional arguments:

      config.json           path to a json file containing the paths to input and

                            output files and the algorithm parameters



    optional arguments:

      --start_from          Restart from a given step in case of an interruption or to try different parameters.

      -h, --help            show this help message and exit



To run the whole pipeline, call `s2p` with a json configuration file as unique argument:



    s2p tests/data/input_pair/config.json



All the parameters of the algorithm, paths to input and output data are stored

in the json file. See the provided `test.json` file for an example, and the

comments in the file `s2p/config.py` for some explanations about the roles

of these parameters.



Notice that each input image must have RPC coefficients, either in its GeoTIFF

tags or in a companion `.xml` or `.txt` file.



#### ROI definition



The processed Region of interest (ROI) is defined by the image coordinates (x,

y) of its top-left corner, and its dimensions (w, h) in pixels. These four

numbers must be given in the `json` configuration file, as in the `test.json`

example file. They are ignored if the parameter `'full_img'` is set to `true`.

In that case the full image will be processed.



#### File paths in json configuration files



In the json configuration files, input and output paths are relative to the json

file location, not to the current working directory.



### MicMac (optional)



If you want to use MicMac for the stereo matching step, you must install it

first and create a symlink to the micmac directory (the one containing a 'bin'

folder with a bunch of executables in it, among with 'MICMAC' and 'mm3d') in

the 'bin' folder:



    ln -s PATH_TO_YOUR_MICMAC_DIR bin/micmac



## References



If you use this software please cite the following papers:



[*An automatic and modular stereo pipeline for pushbroom

images*](http://dx.doi.org/10.5194/isprsannals-II-3-49-2014), Carlo de

Franchis, Enric Meinhardt-Llopis, Julien Michel, Jean-Michel Morel, Gabriele

Facciolo. ISPRS Annals 2014.



[*On Stereo-Rectification of Pushbroom

Images*](http://dx.doi.org/10.1109/ICIP.2014.7026102), Carlo de Franchis, Enric

Meinhardt-Llopis, Julien Michel, Jean-Michel Morel, Gabriele Facciolo.  ICIP

2014.



[*Automatic sensor orientation refinement of Pléiades stereo

images*](http://dx.doi.org/10.1109/IGARSS.2014.6946762), Carlo de Franchis,

Enric Meinhardt-Llopis, Julien Michel, Jean-Michel Morel, Gabriele Facciolo.

IGARSS 2014.