Version du 31 mai 2016 à 10:37

Tutorial index

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:

1. Conversion of the embedded GPS data into the micmac format : OriTxtInFile
2. Generate the image pairs file
3. Change the coordinate system (from WGS84 to a locally tangent system) with the argument : ChSys=DegreeWGS84@SysCoRTL.xml
4. Compute relative speed of the camera (for GPS delay determination) : MTD1=1 CalcV=1
5. 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>R0040438.JPG R0040441.JPG</Cple>\\<Cple>R0040438.JPG R0040440.JPG</Cple>
     <Cple>R0040438.JPG R0040518.JPG</Cple>
     <Cple>R0040438.JPG R0040460.JPG</Cple>
     <Cple>R0040438.JPG R0040458.JPG</Cple>
     <Cple>R0040438.JPG R0040442.JPG</Cple>
     <Cple>R0040438.JPG R0040515.JPG</Cple>
     <Cple>R0040438.JPG R0040439.JPG</Cple>
     <Cple>R0040438.JPG R0040517.JPG</Cple>
     <Cple>R0040438.JPG R0040516.JPG</Cple>
     <Cple>R0040438.JPG R0040443.JPG</Cple>
     <Cple>R0040438.JPG R0040444.JPG</Cple>
     <Cple>R0040438.JPG R0040445.JPG</Cple>
     <Cple>R0040438.JPG R0040446.JPG</Cple>
     <Cple>R0040438.JPG R0040447.JPG</Cple>
     <Cple>R0040438.JPG R0040448.JPG</Cple>
     <Cple>R0040438.JPG R0040449.JPG</Cple>
     <Cple>R0040438.JPG R0040450.JPG</Cple>
     <Cple>R0040438.JPG R0040451.JPG</Cple>
     <Cple>R0040438.JPG R0040452.JPG</Cple>
     <Cple>R0040438.JPG R0040453.JPG</Cple>
     <Cple>R0040438.JPG R0040454.JPG</Cple>
     <Cple>R0040438.JPG R0040459.JPG</Cple>
     <Cple>R0040438.JPG R0040461.JPG</Cple>
     <Cple>R0040438.JPG R0040468.JPG</Cple>
     <Cple>R0040438.JPG R0040469.JPG</Cple>
     <Cple>R0040438.JPG R0040476.JPG</Cple>
     <Cple>R0040438.JPG R0040477.JPG</Cple>

Tapioca File FileImagesNeighbour.xml -1

1. Camera Calibration with Tapas, using a block of 25 images:

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

1. Visualization of the sub-block orientation with AperiCloud (and meshlab):

AperiCloud "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|R0040455.JPG|R0040457.JPG|R0040615.JPG|R0040539.JPG|R0040501.JPG|R0040581.JPG|R0040533.JPG" Sample4Calib-Rel Out=Sample4Calib-Rel.ply

1. Optionnaly, if meshlab is installed:

meshlab Sample4Calib-Rel.ply

1. Orientation of the complete block in a relative system

Tapas RadialBasic "R.*.JPG" Out=All-Rel Incal=Sample4Calib-Rel

1. Determination of the GPS delay (improvement of the georeferencing) with CenterBascule

CenterBascule "R.*.JPG" All-Rel Nav-Brut-RTL tmp CalcV=1

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

1. Georefenrencing the aerotriangulated model with CenterBascule

CenterBascule "R.*.JPG" All-Rel Nav-adjusted-RTL All-RTL

1. Change the coordinate system with ChgSysCo:

mm3d ChgSysCo  "R.*JPG" All-RTL   SysCoRTL.xml@SysCoBL72_EPSG31370.xml   All-BL72

1. creation of the camera position (cloud):

AperiCloud "R.*.JPG" All-BL72 Out=All-BL72-cam.ply WithPoints=0

1. Optionnaly, if meshlab is installed:

meshlab All-BL72-cam.ply

1. 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

1. Convert the Digital surface model in 8bits:

to8Bits MEC/Z_Num8_DeZoom1_STD-MALT.tif

1. 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

1. Optionnaly, if meshlab is installed:

meshlab CanopySurfaceModel.ply