CSF: Ground Filtering based on Cloth Simulation
Project description
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)
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.
# 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.
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
Download the source code. under python folder:
python setup.py build
python setup.py install
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. This package can be used easily with the lidR package:
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:
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
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.
Project details
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MD5 | eb59fa784b6f60d878a0e5e9f8dae662 |
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BLAKE2b-256 | c85d8ccd25af2f654c93346e30f3caae6ec6fb48339e0699709db6413658370b |
Hashes for cloth_simulation_filter-1.1.4-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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SHA256 | 2ed8acf4d92297d9a8401edcd738143fd1178e3fdd79a2f1df66b9cb6c9f7619 |
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MD5 | 228dd6170913a9f9db9e0fd46df5a77b |
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BLAKE2b-256 | b1532d291893b31f8311132f0d71b87fc4bd1f6531f282fe513bff71499ef3df |
Hashes for cloth_simulation_filter-1.1.4-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 98fd5656843ac7e00bd7e5e9e85ce22ecfa4f41f00db939455127f2d2bebe616 |
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MD5 | ac9888d40008f49e520ecd1b14bb913c |
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BLAKE2b-256 | dd4c2081d47fce39658dd19f71237d2fc769845d0ec313924fb00677eaa828c1 |
Hashes for cloth_simulation_filter-1.1.4-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8e34d0df3ad661d9baa1152f272e9374d85222f1f3c78d9dd70c938d23cd6706 |
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MD5 | cbd0b178af66212d98ced342c6a4cd8a |
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BLAKE2b-256 | f6cb8fa23b1658f04dc068726bf254ce5952363bf91d11d0cb497822c0f1c79c |
Hashes for cloth_simulation_filter-1.1.4-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 273553f096b58f3b770fb00bc0f8145e2785297cc022751fc027a2e0f774f66f |
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MD5 | 5f0f62eb308572c3b91cb5bba290064a |
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BLAKE2b-256 | 8a29ae42d7c3001326dec4fa9c3aaf64398dc00a17158658ff0dbfed561fdc6e |
Hashes for cloth_simulation_filter-1.1.4-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3261b078153b105a964f1ec92df6dd00cabeb7c7b20a9817b7202e93c1c9792d |
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MD5 | e7fefabed5c43e4835c49f2f00f633b7 |
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BLAKE2b-256 | c66481247ed51e667b005b4bcbbb2995dbda87bcc4c6b4e770a75daf22aea994 |
Hashes for cloth_simulation_filter-1.1.4-cp37-cp37m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6df231565114fd11de53f777298d01d52339784fb6964aca56dc095d4d479830 |
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MD5 | ef55cbecf776bf4fded3df11d7a8cd45 |
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BLAKE2b-256 | 5146207ded4b43894af6c6b806aa0610875fd87f73ea5bc1d2b78e438fa16a22 |
Hashes for cloth_simulation_filter-1.1.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e6cfaef58c75e3ef87af5d7bbabd82f979965155528327d3e64d4b3e482feffe |
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MD5 | 3c85ab0e0e6c269202572a195c778262 |
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BLAKE2b-256 | 63098046078028e2d18510892efed0463c60ac99c4148c716a3dd39fba9ce708 |
Hashes for cloth_simulation_filter-1.1.4-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1452d6b989dcbf0b38f7edb8b36d1f21ad1d5a4fdd9c0f8ce489a2dbc0864876 |
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MD5 | 2c645995f61c38ee3ee728f72a593d78 |
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BLAKE2b-256 | 88f85af187fc311a629345772998c0bf24a47b86c2d84bec3fd6398430c7955c |
Hashes for cloth_simulation_filter-1.1.4-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 243a365bfa6c5443485324b469e8b6b81eeff640233986c649df6ba3a8599a29 |
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MD5 | 2f338baeaeb75cca008479c39870a52f |
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BLAKE2b-256 | ec8eb282d97587109b4798f23a548c183a42f9bf6e22dcc67b16e8c6e90ef596 |
Hashes for cloth_simulation_filter-1.1.4-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f8952c89079f722a8d2c316691b433b3b2d47d562b9ff4fc30915a8a7843cede |
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MD5 | 191e99b37e72da2d9e211931dce05e83 |
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BLAKE2b-256 | f0335e9eeb776a706b3dc4dc4eabefb8c415227ee8984d7d46bc712f456b3040 |
Hashes for cloth_simulation_filter-1.1.4-cp36-cp36m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3eda39ff52fd53f2658e6d86681a2e98b2a78c8c7a606da3802b38c73c5fe350 |
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MD5 | 165982eeed3b9b0fda352020d147cd43 |
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BLAKE2b-256 | f190ddd3fd3405f4d397fbdc2fe484538a5431883673df4cb7ccf023766d1883 |
Hashes for cloth_simulation_filter-1.1.4-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7834459c27e0ad1bd082282c08d4fd6e85889d2ddc2298e9b2e1497dc2bb91f5 |
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MD5 | 4b7fd6b18cb42773839bec8e15f3a451 |
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BLAKE2b-256 | e41575582138600c4f43bb634fe0acb369044effb965a7681be2e5d59267ce63 |
Hashes for cloth_simulation_filter-1.1.4-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7dcdbb389d6b66830c2e80cce596748742a383a0d9e4677468836b7fb43d23df |
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MD5 | e1735d562512c8951208375d955b2389 |
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BLAKE2b-256 | 90774b41ab4dc1676727edbca26d6729506a59c8190a1930424bde0346a6222b |
Hashes for cloth_simulation_filter-1.1.4-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d13f72a2697768b721f433379ba8ace539226b8ac9cee100bbb7be8a1c20468b |
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MD5 | c6e271b13a4f6463dd0bbfebffe64e4c |
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BLAKE2b-256 | 099d6b199620db95c080ea8c926fd2f8d3d7c4b8f5f06823edc8170e8fb2b715 |
Hashes for cloth_simulation_filter-1.1.4-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b7d4c9bdcfb6ef7ed705bf0b4fa4452a36571341c843d7fb8f2dc9538647abf8 |
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MD5 | 846300acef8b88820b43d27f604ea68c |
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BLAKE2b-256 | 863ddbe02f11dc502397d1b28834d1a0c86d6b849f0562cdb94abe95c2e7bee7 |
Hashes for cloth_simulation_filter-1.1.4-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7d0fd167d33bdaebe02e9a56455705f3145cd91f1e6dd6dcfd82a9ee064de396 |
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MD5 | 11679a3ec655089302bee86f864c33b8 |
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BLAKE2b-256 | b15b1a3c25bc3ddd71133b7d7eb7b7dfe43a06f161d86f04177454a957e76bf3 |