GrandLeez : Différence entre versions
(→Camera Calibration with Tapas, using a block of 25 images) |
(→Orientation of the complete block in a relative system) |
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| Ligne 57 : | Ligne 57 : | ||
==Orientation of the complete block in a relative system== | ==Orientation of the complete block in a relative system== | ||
You can directly integrate the IOP determination in the relative orientation processing, by using : | You can directly integrate the IOP determination in the relative orientation processing, by using : | ||
| − | <pre>Tapas RadialBasic "R.*.JPG" Out=All-Rel | + | <pre>Tapas RadialBasic "R.*.JPG" Out=All-Rel InCal=Sample4Calib-Rel</pre> |
| + | This is the results of the last iteration : | ||
| + | <pre> | ||
| + | |||
| + | </pre> | ||
We will know compute a sparse cloud with image relative position and orientation, to check if the block is correctly computed : | We will know compute a sparse cloud with image relative position and orientation, to check if the block is correctly computed : | ||
<pre>AperiCloud "R.*.JPG" All-Rel</pre> | <pre>AperiCloud "R.*.JPG" All-Rel</pre> | ||
Version du 31 mai 2016 à 11:07
Sommaire
- 1 Download
- 2 Presentation
- 3 Tutorial
- 3.1 Conversion of image coordinates
- 3.2 Tie Point Generation with Tapioca
- 3.3 Camera Calibration with Tapas, using a block of 25 images
- 3.4 Orientation of the complete block in a relative system
- 3.5 Determination of the GPS delay (improvement of the georeferencing) with CenterBascule
- 3.6 OriConvert is (again) used for taking the delay into account
- 3.7 Georefenrencing the aerotriangulated model with CenterBascule
- 3.8 Change the coordinate system with ChgSysCo
- 3.9 Creation of the camera position (cloud)
- 3.10 Optionnaly, if meshlab is installed
- 3.11 Dense-matching with Malt. Here, we aren't interested in the generation of orthophoto
- 3.12 Convert the Digital surface model in 8bits
- 3.13 Export the dense point cloud and color it with Nuage2Ply
- 3.14 Optionnaly, if meshlab is installed
Download
- You can find this dataset at :
http://logiciels.ign.fr/?Telechargement,20
Datasets are available at the bottom of the page, in part test datasets. Then, UnZip the ".zip" archive.
Presentation
This dataset was created by L.Girod at the University of Oslo, Norway. This dataset was acquired to model a volcano model created by O.Galland. File present in the directory are :
- 200 images : R0040438.JPG ... R0040637.JPG taken with a RICOH GR DIGITAL 3 (800x600 px).
- Geolocation of images : GPS_WPK_Grand-Leez.csv
- File with images neighboor : FileImagesNeighbour.xml
- 2 commands scripts : UASGrandLeez.bat and UASGrandLeez.sh
- File with detailed commands : cmd_UAS_Grand-Leez.txt
- 2 file for coordinate system transform : SysCoRTL.xml and </>SysCoBL72_EPSG31370.xml</i>
During this tutorial, we will approach direct georeferencing concepts. We will apply the MicMac processing flow to process a forest Canopy Surface Model. For more details, go further in tutorials or directly in commands pages. This dataset is provided by "l’Unité Gestion des Ressources Forestières et des Milieux Naturels (GRFMN), Université de Liège". Contact: jo.lisein@ulg.ac.be
Tutorial
Conversion of image coordinates
OriConvert is used for 5 purposes:
- Conversion of the embedded GPS data into the micmac format : OriTxtInFile
- Generate the image pairs file
- Change the coordinate system (from WGS84 to a locally tangent system) with the argument : ChSys=DegreeWGS84@SysCoRTL.xml
- Compute relative speed of the camera (for GPS delay determination) : MTD1=1 CalcV=1
- Select a sample of the image block (PATC) for camera calibration : NameCple=FileImagesNeighbour.xml ImC=R0040536.JPG NbImC=25
mm3d OriConvert OriTxtInFile GPS_WPK_Grand-Leez.csv Nav-Brut-RTL ChSys=DegreeWGS84@SysCoRTL.xml MTD1=1 NameCple=FileImagesNeighbour.xml CalcV=1 ImC=R0040536.JPG NbImC=25
See OriConvert for more details on arguments and file format.
Tie Point Generation with Tapioca
The file FileImagesNeighbour.xml contain for each images, his differents neighboors. If you open the file, you can see :
<Cple>R0040439.JPG R0040519.JPG</Cple>
<Cple>R0040439.JPG R0040514.JPG</Cple>
<Cple>R0040439.JPG R0040444.JPG</Cple>
<Cple>R0040439.JPG R0040517.JPG</Cple>
<Cple>R0040439.JPG R0040438.JPG</Cple>
<Cple>R0040439.JPG R0040440.JPG</Cple>
<Cple>R0040439.JPG R0040441.JPG</Cple>
<Cple>R0040439.JPG R0040516.JPG</Cple>
<Cple>R0040439.JPG R0040442.JPG</Cple>
<Cple>R0040439.JPG R0040515.JPG</Cple>
<Cple>R0040439.JPG R0040443.JPG</Cple>
It means, image R0040439.JPG is connected with all the images detailed in <Cple> tag. So you can run the tie point generation with Tapioca using this file :
Tapioca File FileImagesNeighbour.xml -1
The processing time is shorter, because micmac knows which pictures matching.
Camera Calibration with Tapas, using a block of 25 images
To run a Camera calibration, you can take an other dataset, with exactly the same camera settings, or you can use a part of the principal dataset. Here we use the same images as in OriConvert to determine Internal Orientation Parameters (IOP) :
Tapas RadialBasic "R0040536.JPG|R0040537.JPG|R0040535.JPG|R0040578.JPG|R0040498.JPG|R0040499.JPG|R0040579.JPG|R0040538.JPG|R0040577.JPG|R0040534.JPG|R0040497.JPG|R0040500.JPG|R0040580.JPG|R0040456.JPG|R0040616.JPG|R0040576.JPG|R0040496.JPG|R0040617.JPG|R004045.JPG|R0040457.JPG|R0040615.JPG|R0040539.JPG|R0040501.JPG|R0040581.JPG|R0040533.JPG" Out=Sample4Calib-Rel
This is the result of the last iteration :
| | Residual = 0.474718 ;; Evol, Moy=5.50743e-015 ,Max=3.70866e-014 | | Worst, Res 0.618139 for R0040576.JPG, Perc 99.446 for R0040496.JPG | | Cond , Aver 6.46061 Max 42.4603 Prop>100 0
Orientation of the complete block in a relative system
You can directly integrate the IOP determination in the relative orientation processing, by using :
Tapas RadialBasic "R.*.JPG" Out=All-Rel InCal=Sample4Calib-Rel
This is the results of the last iteration :
We will know compute a sparse cloud with image relative position and orientation, to check if the block is correctly computed :
AperiCloud "R.*.JPG" All-Rel
Optionnaly, if meshlab is installed, you can vizualise the sparse cloud (Only for Ubuntu):
meshlab All-Rel.ply
Determination of the GPS delay (improvement of the georeferencing) with CenterBascule
CenterBascule "R.*.JPG" All-Rel Nav-Brut-RTL tmp CalcV=1
OriConvert is (again) used for taking the delay into account
mm3d OriConvert OriTxtInFile GPS_WPK_Grand-Leez.csv Nav-adjusted-RTL ChSys=DegreeWGS84@SysCoRTL.xm MTD1=1 Delay=-0.0854304
Georefenrencing the aerotriangulated model with CenterBascule
CenterBascule "R.*.JPG" All-Rel Nav-adjusted-RTL All-RTL
Change the coordinate system with ChgSysCo
mm3d ChgSysCo "R.*JPG" All-RTL SysCoRTL.xml@SysCoBL72_EPSG31370.xml All-BL72
Creation of the camera position (cloud)
AperiCloud "R.*.JPG" All-BL72 Out=All-BL72-cam.ply WithPoints=0
Optionnaly, if meshlab is installed
meshlab All-BL72-cam.ply
Dense-matching with Malt. Here, we aren't interested in the generation of orthophoto
Malt Ortho "R.*JPG" All-BL72 DirMEC=MEC DefCor=0 AffineLast=1 Regul=0.005 HrOr=0 LrOr=0 ZoomF=1
Convert the Digital surface model in 8bits
to8Bits MEC/Z_Num8_DeZoom1_STD-MALT.tif
Export the dense point cloud and color it with Nuage2Ply
Nuage2Ply "MEC/NuageImProf_STD-MALT_Etape_8.xml" Scale=8 Attr="MEC/Z_Num8_DeZoom1_STD-MALT_8Bits.tif" Out=CanopySurfaceModel.ply
Optionnaly, if meshlab is installed
meshlab CanopySurfaceModel.ply
