Pandora plugin to optimize the cost volume with the LibSGM library
Project description
Plugin LibSgm
Pandora plugin to optimize the cost volume with the LigSGM library
Installation
Non-developper mode
This procedure allows you to install the plugin_libsgm, pandora and libsgm, without prior cloning them. Note that sources will not be accessible with this procedure.
To install it, follow the steps:
u@m $ python -m venv myEnv
u@m $ source myEnv/bin/activate
(myEnv) u@m $ pip install --upgrade pip
(myEnv) u@m $ pip install numpy
(myEnv) u@m $ pip install pandora_plugin_libsgm
Developper mode
This procedure allows you to install the plugin_libsgm, pandora, libsgm and have access to the sources.
To install it, follow the steps:
- Initializing the environment
u@m $ python -m venv myEnv
u@m $ source myEnv/bin/activate
(myEnv) u@m $ pip install --upgrade pip
(myEnv) u@m $ pip install numpy
- Pandora installation
(myEnv) u@m $ git clone https://github.com/CNES/Pandora_pandora.git
(myEnv) u@m $ cd Pandora_pandora
(myEnv) u@m $ pip install .
- LibSGM installation
(myEnv) u@m $ git clone https://github.com/CNES/Pandora_libsgm.git
(myEnv) u@m $ cd Pandora_libsgm
(myEnv) u@m $ pip install .
- Plugin installation
(myEnv) u@m $ git clone https://github.com/CNES/Pandora_plugin_libsgm.git
(myEnv) u@m $ cd Pandora_libsgm
(myEnv) u@m $ pip install .
Documentation
Build documentation Make sure latex and dvipng is already installed
pip install sphinx-rtd-theme
python setup.py build_sphinx
Documentation is built in plugin_libsgm/doc/build/html
Documentation is available from the pandora and libsgm repositories.
How to find P2 penalty parameter: For Census measure, the P2 range determined is [15, 120]. For a window_size of 5x5, its is Cmax=25.
p2_min_census, p2_max_census and cmax_census are used to determined the P2 range of other measures thanks to P2_census / Cmax_census ratio.
Thus to determine P2 range of a new measure: p2_min_measure = cmax_measure * (p2_min_census / cmax_census) p2_max_measure = cmax_measure * (p2_max_census / cmax_census)
Usage
Non-developper mode
Run pandora :
pandora config.json output_dir
with the config.json file :
{
"input" : {
"img_ref" : "PATH/TO/img_ref.tif",
"img_sec" : "PATH/TO/img_sec.tif",
"disp_min" : -100,
"disp_max" : 100,
"ref_mask" : "PATH/TO/ref_mask.tif",
"sec_mask" : "PATH/TO/sec_mask.tif"
},
"stereo" : {
"stereo_method": "census",
"window_size": 5,
"subpix": 1
},
"optimization" : {
"optimization_method": "sgm",
"P1": 8,
"P2": 32,
"penalty_method": "sgm_penalty"
},
"refinement": {
"refinement_method": "vfit"
},
"filter" : {
"filter_method": "median",
"filter_size": 3
},
"validation" : {
"validation_method": "cross_checking",
"cross_checking_threshold": 1.,
"right_left_mode": "accurate"
}
}
Developper mode
Run pandora, with the configuration file of the plugin_libsgm:
pandora plugin_libsgm/conf/sgm.json output_dir
Notes
For tests, we use images coming from 2003 Middleburry dataset (D. Scharstein and R. Szeliski. High-accuracy stereo depth maps using structured light. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), volume 1, pages 195-202, Madison, WI, June 2003.)
References
If you use this CNES software, please cite the following paper:
Cournet, M., Sarrazin, E., Dumas, L., Michel, J., Guinet, J., Youssefi, D., Defonte, V., Fardet, Q., 2020. Ground-truth generation and disparity estimation for optical satellite imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
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