Difference between revisions of "CNF Example Meshes"

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(→‎Synthetic Gaussian Data)
(→‎Synthetic Gaussian Data)
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docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/Gaussian2.vtk  --output 2D_CASES/output/Gaussian_me_10_wl_1e-1.vtk --weight-limit=0.05 --min-edge=10
 
docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/Gaussian2.vtk  --output 2D_CASES/output/Gaussian_me_10_wl_1e-1.vtk --weight-limit=0.05 --min-edge=10
 
</pre>
 
</pre>
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== GPDGK16Numerical_140519 ==
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* Input distribution size : 100,000 cells
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* Uniform case: 7587 triangles
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* Adapted (min_edge = 2)  case:  623  triangles
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* Adapted (min_edge = 0.5) case:  1,409 triangles
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<gallery mode="packed" heights=300px>
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File:GPDGK16Numerical 140519 X50 me2 uniform.png
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File:GPDGK16Numerical 140519 X50 me2 wl 1e-1.png
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File:GPDGK16Numerical 140519 X50 me0.5 wl 1e-1.png
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</gallery>
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Commands to generate meshes :
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'''Uniform :'''
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<pre>
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docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output 2D_CASES/output/GPDGK16Numerical_140519_me_2_uniform.vtk --min-edge=2 --uniform
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</pre>
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'''Adaptive :'''(min_edge = 2)
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<pre>
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docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output 2D_CASES/output/GPDGK16Numerical_140519_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1
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</pre>
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 +
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'''Adaptive :'''(min_edge = 0.5)
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<pre>
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docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output 2D_CASES/output/GPDGK16Numerical_140519_me_0.5_wl_1e-1.vtk --min-edge=0.5 --weight-limit=0.1
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</pre>
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'''Note : ''' By using `min-edge` less than 1 we are essentially generating triangles with an edge smaller that the input pixels. Using  values much smaller than 1 is not expected to help the discitization since we are esetially packing more element into a pixel which has constant value.
  
 
=3D Example Meshes=
 
=3D Example Meshes=

Revision as of 20:38, 22 November 2019

2D Example Meshes

Synthetic Gaussian Data

  • Input distribution size : 1,000,000 cells
  • Uniform case: 30,949 triangles
  • Adapted case: 3,788 triangles
  • Gaussian me 10 uniform.png
  • Gaussian me 10 wl 1e-1 adapted.png

Commands to generate meshes :

Uniform : 

docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/Gaussian2.vtk  --output 2D_CASES/output/Gaussian_me_10_uniform.vtk --uniform --min-edge=10

Adaptive : 

docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/Gaussian2.vtk  --output 2D_CASES/output/Gaussian_me_10_wl_1e-1.vtk --weight-limit=0.05 --min-edge=10


GPDGK16Numerical_140519

  • Input distribution size : 100,000 cells
  • Uniform case: 7587 triangles
  • Adapted (min_edge = 2) case: 623 triangles
  • Adapted (min_edge = 0.5) case: 1,409 triangles


  • GPDGK16Numerical 140519 X50 me2 uniform.png
  • GPDGK16Numerical 140519 X50 me2 wl 1e-1.png
  • GPDGK16Numerical 140519 X50 me0.5 wl 1e-1.png

Commands to generate meshes :

Uniform : 

docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output 2D_CASES/output/GPDGK16Numerical_140519_me_2_uniform.vtk --min-edge=2 --uniform

Adaptive :(min_edge = 2) 

docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output 2D_CASES/output/GPDGK16Numerical_140519_me_2_wl_1e-1.vtk --min-edge=2 --weight-limit=0.1


Adaptive :(min_edge = 0.5) 

docker run -v $(pwd):/data/ cnf_tools tessellate2d  --input 2D_CASES/input/GPDGK16Numerical_140519_X50.vtk --output 2D_CASES/output/GPDGK16Numerical_140519_me_0.5_wl_1e-1.vtk --min-edge=0.5 --weight-limit=0.1

Note : By using `min-edge` less than 1 we are essentially generating triangles with an edge smaller that the input pixels. Using values much smaller than 1 is not expected to help the discitization since we are esetially packing more element into a pixel which has constant value.

3D Example Meshes

CFF_14052019

GPDGK16Numerical_140519: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDGK16Numerical_140519.nrrd  --weight-limit 0.1 --min-edge 1 --output ./GPDGK16Numerical_140519,d=5,wl=0.1,me=1.vtk

GPDGK16Numerical_140519,d=5,wl=0.1,me=1

GPDMMS13_140519: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDMMS13_140519.nrrd  --weight-limit 0.05 --min-edge 1 --output ./GPDMMS13_140519,d=5,wl=0.05,me=1.vtk

GPDMMS13_140519,d=5,wl=0.05,me=1

GPDVGG99_140519: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/GPDVGG99_140519.nrrd  --weight-limit 0.05 --min-edge 1 --output ./GPDVGG99_140519,d=5,wl=0.05,me=1.vtk

GPDVGG99_140519,d=5,wl=0.05,me=1

NT_140519: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/NT_140519.nrrd  --weight-limit 0.07 --min-edge 1 --output ./NT_140519,d=5,wl=0.07,me=1.vtk

NT_140519,d=5,wl=0.07,me=1

OBS_ALU_140519: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/OBS_ALU_140519.nrrd  --weight-limit 0.13 --min-edge 1 --output ./OBS_ALU_140519,d=5,wl=0.13,me=1.vtk

OBS_ALU_140519,d=5,wl=0.13,me=1

OBS_CS_140519: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_14052019/OBS_CS_140519.nrrd  --weight-limit 0.01 --min-edge 1 --output ./OBS_CS_140519,d=5,wl=0.01,me=1.vtk

OBS_CS_140519,d=5,wl=0.01,me=1

CFF_DATA

cff_E.data_IM: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_E.data_IM.nrrd  --weight-limit 0.01 --min-edge 1 --output ./cff_E.data_IM,d=10,wl=0.01,me=1.vtk

cff_E.data_IM,d=10,wl=0.01,me=1

cff_E.data_REAL: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_E.data_REAL.nrrd  --weight-limit 0.1 --min-edge 1 --output ./cff_E.data_REAL,d=10,wl=0.1,me=1.vtk

cff_E.data_REAL,d=10,wl=0.1,me=1

cff_H.data_IM: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_H.data_IM.nrrd  --weight-limit 0.05 --min-edge 1 --output ./cff_H.data_IM,d=10,wl=0.05,me=1.vtk

cff_H.data_IM,d=10,wl=0.05,me=1

cff_H.data_REAL: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_H.data_REAL.nrrd  --weight-limit 0.1 --min-edge 1 --output ./cff_H.data_REAL,d=10,wl=0.1,me=1.vtk

cff_H.data_REAL,d=10,wl=0.1,me=1

cff_Ht.data_IM: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_Ht.data_IM.nrrd  --weight-limit 0.1 --min-edge 1 --output ./cff_Ht.data_IM,d=10,wl=0.1,me=1.vtk

cff_Ht.data_IM,d=10,wl=0.1,me=1

cff_Ht.data_REAL: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/CFF_DATA/cff_Ht.data_REAL.nrrd  --weight-limit 0.1 --min-edge 1 --output ./cff_Ht.data_REAL,d=10,wl=0.1,me=1.vtk

cff_Ht.data_REAL,d=10,wl=0.1,me=1

DATA_04252019

CFF_E_im: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_E_im.nrrd  --weight-limit 0.04 --min-edge 1 --output ./CFF_E_im,d=5,wl=0.04,me=1.vtk

CFF_E_im,d=5,wl=0.04,me=1

CFF_E_re: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_E_re.nrrd  --weight-limit 0.08 --min-edge 1 --output ./CFF_E_re,d=5,wl=0.08,me=1.vtk

CFF_E_re,d=5,wl=0.08,me=1

CFF_H_im: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_H_im.nrrd  --weight-limit 0.06 --min-edge 1 --output ./CFF_H_im,d=5,wl=0.06,me=1.vtk

CFF_H_im,d=5,wl=0.06,me=1

CFF_H_re: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/CFF_H_re.nrrd  --weight-limit 0.13 --min-edge 1 --output ./CFF_H_re,d=5,wl=0.13,me=1.vtk

CFF_H_re,d=5,wl=0.13,me=1

GPD_H_down: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/GPD_H_down.nrrd  --weight-limit 0.1 --min-edge 1 --output ./GPD_H_down,d=5,wl=0.1,me=1.vtk

GPD_H_down,d=5,wl=0.1,me=1

GPD_H_up: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/GPD_H_up.nrrd  --weight-limit 0.1 --min-edge 1 --output ./GPD_H_up,d=5,wl=0.1,me=1.vtk

GPD_H_up,d=5,wl=0.1,me=1

OBS_ALU: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/OBS_ALU.nrrd  --background-value 0 --weight-limit 0.07 --min-edge 1 --output ./OBS_ALU,d=5,bg=0,wl=0.07,me=1.vtk

OBS_ALU,d=5,bg=0,wl=0.07,me=1

OBS_ALU: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/DATA_04252019/OBS_ALU.nrrd --delta 2 --background-value 0 --uniform --output ./OBS_ALU,d=5,bg=0,uniform.vtk

OBS_ALU,d=2,bg=0,uniform

phase_space_000

phase_space_000: Input Image Output Mesh 

docker run -v $(pwd):/data/ cnf_tools tessellate3d --input ./CNF_SHARE/phase_space_000/phase_space_000.nrrd --delta 1.5 --weight-limit 0.005 --min-edge 1 --output ./phase_space_000,d=1.5,wl=0.005,me=1.vtk

phase_space_000,d=1.5,wl=0.005,me=1

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