 
# CSF
Airborne LiDAR filtering method based on Cloth Simulation.
This is the code for the article:
W. Zhang, J. Qi*, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan, “An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation,” Remote Sens., vol. 8, no. 6, p. 501, 2016.
(http://www.mdpi.com/2072-4292/8/6/501/htm)
**This is a modified version of the original Code for 3DFin purposes**
For now this is only intended to be used inside [3DFin](https://github.com/3DFin/3Dfin)/[dendromatics](https://github.com/3DFin/dendromatics).
But this repository also serve as a playground to bootstrap a planned full rewrite of CSF.
List of changes:
- Better handling of numpy arrays in the python bindings (avoid expensive copies)
- Bug fixes and improvement (mostly backport from CloudCompare version of CSF and original ones)
- Better (but yet to be improved) documentation and code structure.
**New feature has been implemented:**
Now, We has wrapped a Python interface for CSF with swig. It is simpler to use now. This new feature can make CSF easier to be embeded into a large project. For example, it can work with Laspy (https://github.com/laspy/laspy). What you do is just read a point cloud into a python 2D list, and pass it to CSF.
The following example shows how to use it with laspy.
```python
# coding: utf-8
import laspy
import CSF
import numpy as np
inFile = laspy.read(r"in.las") # read a las file
points = inFile.points
xyz = np.vstack((inFile.x, inFile.y, inFile.z)).transpose() # extract x, y, z and put into a list
csf = CSF.CSF()
# prameter settings
csf.params.bSloopSmooth = False
csf.params.cloth_resolution = 0.5
# more details about parameter: http://ramm.bnu.edu.cn/projects/CSF/download/
csf.setPointCloud(xyz)
ground = CSF.VecInt() # a list to indicate the index of ground points after calculation
non_ground = CSF.VecInt() # a list to indicate the index of non-ground points after calculation
csf.do_filtering(ground, non_ground) # do actual filtering.
outFile = laspy.LasData(inFile.header)
outFile.points = points[np.array(ground)] # extract ground points, and save it to a las file.
out_file.write(r"out.las")
```
**Reading data from txt file:**
If the lidar data is stored in txt file (x y z for each line), it can also be imported directly.
```python
import CSF
csf = CSF.CSF()
csf.readPointsFromFile('samp52.txt')
csf.params.bSloopSmooth = False
csf.params.cloth_resolution = 0.5
ground = CSF.VecInt() # a list to indicate the index of ground points after calculation
non_ground = CSF.VecInt() # a list to indicate the index of non-ground points after calculation
csf.do_filtering(ground, non_ground) # do actual filtering.
csf.savePoints(ground,"ground.txt")
```
### How to use CSF in Python
Thanks to [@rjanvier](https://github.com/rjanvier)'s contribution. Now we can install CSF from pip as:
```python
pip install cloth-simulation-filter
```
### How to use CSF in Matlab
see more details from file `demo_mex.m` under matlab folder.
### How to use CSF in R
Thanks to the nice work of @Jean-Romain, through the collaboration, the CSF has been made as a R package, the details can be found in the [RCSF repository](https://github.com/Jean-Romain/RCSF). This package can be used easily with the [lidR package](https://github.com/Jean-Romain/lidR):
```r
library(lidR)
las <- readLAS("file.las")
las <- lasground(las, csf())
```
### How to use CSF in C++
Now, CSF is built by CMake, it produces a static library, which can be used by other c++ programs.
#### linux
To build the library, run:
```bash
mkdir build #or other name
cd build
cmake ..
make
sudo make install
```
or if you want to build the library and the demo executable `csfdemo`
```bash
mkdir build #or other name
cd build
cmake -DBUILD_DEMO=ON ..
make
sudo make install
```
#### Windows
You can use CMake GUI to generate visual studio solution file.
### Binary Version
For binary release version, it can be downloaded at: http://ramm.bnu.edu.cn/projects/CSF/download/
Note: This code has been changed a lot since the publication of the corresponding paper. A lot of optimizations have been made. We are still working on it, and wish it could be better.
### Cloudcompare Pulgin
At last, if you are interested in Cloudcompare, there is a good news. our method has been implemented as a Cloudcompare plugin, you can refer to : https://github.com/cloudcompare/trunk
### Related project
A tool named `CSFTools` has been recently released, it is based on CSF, and provides dem/chm generation, normalization. Please refer to: https://github.com/jianboqi/CSFTools
### License
CSF is maintained and developed by Jianbo QI. It is now released under Apache 2.0.
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"description": " \n# CSF\nAirborne LiDAR filtering method based on Cloth Simulation.\nThis is the code for the article:\n\nW. Zhang, J. Qi*, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan, \u201cAn Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation,\u201d Remote Sens., vol. 8, no. 6, p. 501, 2016.\n(http://www.mdpi.com/2072-4292/8/6/501/htm)\n\n\n**This is a modified version of the original Code for 3DFin purposes**\n\nFor now this is only intended to be used inside [3DFin](https://github.com/3DFin/3Dfin)/[dendromatics](https://github.com/3DFin/dendromatics). \nBut this repository also serve as a playground to bootstrap a planned full rewrite of CSF.\n\nList of changes:\n\n- Better handling of numpy arrays in the python bindings (avoid expensive copies)\n- Bug fixes and improvement (mostly backport from CloudCompare version of CSF and original ones)\n- Better (but yet to be improved) documentation and code structure.\n\n**New feature has been implemented:**\n\nNow, We has wrapped a Python interface for CSF with swig. It is simpler to use now. This new feature can make CSF easier to be embeded into a large project. For example, it can work with Laspy (https://github.com/laspy/laspy). What you do is just read a point cloud into a python 2D list, and pass it to CSF.\nThe following example shows how to use it with laspy.\n```python\n# coding: utf-8\nimport laspy\nimport CSF\nimport numpy as np\n\ninFile = laspy.read(r\"in.las\") # read a las file\npoints = inFile.points\nxyz = np.vstack((inFile.x, inFile.y, inFile.z)).transpose() # extract x, y, z and put into a list\n\ncsf = CSF.CSF()\n\n# prameter settings\ncsf.params.bSloopSmooth = False\ncsf.params.cloth_resolution = 0.5\n# more details about parameter: http://ramm.bnu.edu.cn/projects/CSF/download/\n\ncsf.setPointCloud(xyz)\nground = CSF.VecInt() # a list to indicate the index of ground points after calculation\nnon_ground = CSF.VecInt() # a list to indicate the index of non-ground points after calculation\ncsf.do_filtering(ground, non_ground) # do actual filtering.\n\noutFile = laspy.LasData(inFile.header)\noutFile.points = points[np.array(ground)] # extract ground points, and save it to a las file.\nout_file.write(r\"out.las\")\n```\n\n**Reading data from txt file:**\n\nIf the lidar data is stored in txt file (x y z for each line), it can also be imported directly.\n\n```python\nimport CSF\n\ncsf = CSF.CSF()\ncsf.readPointsFromFile('samp52.txt')\n\ncsf.params.bSloopSmooth = False\ncsf.params.cloth_resolution = 0.5\n\nground = CSF.VecInt() # a list to indicate the index of ground points after calculation\nnon_ground = CSF.VecInt() # a list to indicate the index of non-ground points after calculation\ncsf.do_filtering(ground, non_ground) # do actual filtering.\ncsf.savePoints(ground,\"ground.txt\")\n```\n\n### How to use CSF in Python\nThanks to [@rjanvier](https://github.com/rjanvier)'s contribution. Now we can install CSF from pip as:\n```python\npip install cloth-simulation-filter\n```\n\n### How to use CSF in Matlab\nsee more details from file `demo_mex.m` under matlab folder.\n\n### How to use CSF in R\n\nThanks to the nice work of @Jean-Romain, through the collaboration, the CSF has been made as a R package, the details can be found in the [RCSF repository](https://github.com/Jean-Romain/RCSF). This package can be used easily with the [lidR package](https://github.com/Jean-Romain/lidR):\n\n```r\nlibrary(lidR)\nlas <- readLAS(\"file.las\")\nlas <- lasground(las, csf())\n```\n\n### How to use CSF in C++\nNow, CSF is built by CMake, it produces a static library, which can be used by other c++ programs.\n#### linux\nTo build the library, run:\n```bash\nmkdir build #or other name\ncd build\ncmake ..\nmake\nsudo make install\n```\nor if you want to build the library and the demo executable `csfdemo`\n\n```bash\nmkdir build #or other name\ncd build\ncmake -DBUILD_DEMO=ON ..\nmake\nsudo make install\n\n```\n\n#### Windows\nYou can use CMake GUI to generate visual studio solution file.\n\n### Binary Version\nFor binary release version, it can be downloaded at: http://ramm.bnu.edu.cn/projects/CSF/download/\n\nNote: This code has been changed a lot since the publication of the corresponding paper. A lot of optimizations have been made. We are still working on it, and wish it could be better.\n\n### Cloudcompare Pulgin\nAt last, if you are interested in Cloudcompare, there is a good news. our method has been implemented as a Cloudcompare plugin, you can refer to : https://github.com/cloudcompare/trunk\n\n### Related project\nA tool named `CSFTools` has been recently released, it is based on CSF, and provides dem/chm generation, normalization. Please refer to: https://github.com/jianboqi/CSFTools\n\n### License\nCSF is maintained and developed by Jianbo QI. It is now released under Apache 2.0.\n\n",
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