MicMac - Contributions de l’utilisateur [fr]

MicMacRoom Tutorial: 03 Pierrerue

2024-05-15T16:53:02Z

Lilacandolle :

[[Image:picto-liste.png|25px|link=Tutorials]] [[MicMacRoom Tutorials|Tutorials index]]
==Description==
In this tutorial, we will approach general concepts, basics tools, and how to process an image dataset with MicMacRoom in order to obtain a georeferenced orthophoto. The integration of MicMac in the Meshroom graphic interface can be downloaded following the tutorial at <code>https://github.com/alicevision/MeshroomMicMac</code> . This tutorial is based on the content of the [[Pierrerue tutorial]].
==Download==
You can find this dataset at <code>https://micmac.ensg.eu/data/pierrerue_dataset.zip</code> 
Once you have downloaded it, you have to unzip the ".zip" archive.
==Tutorial==
===From the set up of the images to the visualization of relative orientation===
With this pipeline,
Open the 'File' tab in MicMac and launch a new pipeline with the template "pierrerue_part1". Check if the pipeline is empty. If not, you can erase the old dataset with "Remove All Images". Don't forget to save your new project: MicMac will give to your project a default name that you cannot change. Then drop all your images and dataset on the file of the project, and drop at least one image on the interface.
Then, press on the start button, you should obtain a .ply file in the Apericloud folder. It is possible to open this file with a software like CloudCompare to visualize the relative orientation.
===Set up the images into the coordinates system of the support points===
This step cannot be completed with MicMac Meshroom, you have to follow instructions at <code>https://micmac.ensg.eu/index.php/Pierrerue_tutorial#Measurement_process</code> . Then, launch a new pipeline with the template "pierrerue_part2". It will first run the computation of 3D similarity and open a window where you have to validate the points left, following the same method as for SaisieAppuisInitQT. When this step is completed, you can launch the end of the pipeline, which will compute absolute orientation and final adjustment.
The third pipeline, namesd "pierrerue_part3" allows to create a 3D mask and launch 3D reconstruction.

MicMacRoom Tutorial: 03 Pierrerue

2024-05-15T16:27:02Z

Lilacandolle :

MicMacRoom Tutorial: 03 Pierrerue

2024-05-14T12:52:24Z

Lilacandolle : Page créée avec « link=Tutorials Tutorials index ==Description== In this tutorial, we will approach general concepts, basics tools, a... »

RepLocBascule

2024-04-24T11:46:27Z

Lilacandolle : /* Syntax */

[[Image:picto-liste.png|25px]] [[Command|List of commands]]
==Description==
RepLocBascule is a tool useful to define a local repair without changing the orientation.

===Syntax===
The global syntax for RepLocBascule is 
<pre>mm3d RepLocBascule FullName InputOrientation XMLMeasuresFile OutputXMLFile NamedArgs</pre>

===Help===
You can access to the help by typing : 
<pre>mm3d RepLocBascule -help</pre> 

Mandatory unnamed args :
*string :: {Full name (Dir+Pat)}
*string :: {Input orientation}
*string :: {Image measures xml file, set "HOR" if horizontal line is wanted, "NONE" if unused }
*string :: {Output xml file}

Named args :
*[Name=ExpTxt] bool :: {Are tie points in ascii mode ? (Def=false)}
*[Name=PostPlan] string :: {Postfix for plane name, (Def=_Masq)}
*[Name=OrthoCyl] bool :: {Is the coordinate system in ortho-cylindric mode?}

===Example===
In the data set of Mur Saint Martin, we can type : 
<pre>RepLocBascule "IMGP41((6[7-9])|([7-8][0-9])).JPG" Ground MesureBasc.xml RepCorr.xml PostPlan=_MasqPlan</pre>

Pierrerue tutorial

2024-04-03T11:49:00Z

Lilacandolle :

[[Image:picto-liste.png|25px|link=Tutorials]] [[Tutorials|Tutorials index]]
==Description==
This dataset allow you to process a georeferenced orthophoto from a front. This dataset was acquired by student of ENSG during their summer internship in Forcalquier. They have use targets surveyed with total station.

==Download==
You can find this dataset at <code>https://micmac.ensg.eu/data/pierrerue_dataset.zip</code> 
Once you have downloaded it, you have to unzip the ".zip" archive.

==Presentation==
[[Image:Pierrerue1.png|thumb|180px||alt=Pierrerue|Pierrerue Chapel]]
*31 JPG images
*1 file containing the support points (Pierrerue.xml)
*We will use the folder 001_Elements-de-georeferencement

==About the data==
The shooting contain 31 images, taken with the Sony alpha850 with a 244mm lens.
Check that the folder contains images recovering :
*facade n°1
*facade n°2
* the corner between these 2 façade (images link).
Support points are available and allows to georeference the readings (see the folder 001_Elements-de-georeferencement).

==Tutorial==
===Set up the images===
====Tie-Points search====
All the images should be set up simultaneously, in order that the georeference process will be expressed into one only cordinate system. First, we can run the tie-points search : <pre>mm3d Tapioca MulScale ".*JPG" 600 2000</pre>
====Internal orientation and relative orientation====
Then, we have to define the settings of the camera used from the images covering the corner between the 2 facades (more suitable than the others, because of the depth). <pre>mm3d Tapas RadialStd "angle.*JPG" Out=Calib24mm</pre> Into the command prompt, we can check if the residual from the images is admissible (around the half pixel). We can check also the number of connection points, aswell as the percentage of points keeped ("99.8258 of 28466" : 99.8% of the connection points kept from 38466 points calculated). We can process yet the setting up of all the images. starting from the camera we calculated before. <pre>mm3d Tapas RadialStd ".*JPG" InCal=Calib24mm Out=MEP</pre> Into the command prompt, we can control residual during the process. At the last step, we can see that the image residual are, for all the images, less than a half-pixel. We control also the number of support points, aswell that the percentage of points keeped ("99.8258 of 38466" : 99.8% of the support points keeped from the 38466 points calculated).

====Visualization of relative orientation====
The [[AperiCloud]] command allows to generate 3D clouds, containing all the support points obtained with [[Tapioca]], and the position of the cameras obtained from the [[Tapas]] output.
[[Image:Pierrerue2.png|thumb|180px||alt=Pierrerue|Meshlab visualization]]
<pre>mm3d AperiCloud ".*JPG" MEP</pre>The result of this command can be seen for example with the meshlab software.

===Set up the images into the coordinates system of the support points===
Now we have to measure the support points available to georeference the images, and so the incoming products, into the reference coordinate system.

====Explaining the process====
*1. We measure 3 support points well distributed ([[SaisieAppuisInit]] command) ;
*2. We can now create a 3D similarity (transformation from 7 settings containing one scale coefficient, one translation into space and one space rotation) between the arbitary system calculated while the setting up and the coordinate system chosen ([[GCPBascule]] command) ;
*3. We have now to measure the remaining points : the absolute orientation calculated from the previous step, allows to suggest an approximated position for each points ([[SaisieAppuisPredic]] command) ;
*4. We affine the absolute orientation ([[GCPBascule]] command) ;
*5. We start the final computation for the setting up (this offset allows to find the best position/orientation of the cameras while using the points measurements on the linking points and on the support points) ; ([[Campari]] command).

====Measurement process====
*1. Measurements of almost 3 support points on the facade n°1 (At least one point should be measured to be valid on at least two images). To set the points, you need to get to know the position of the 3 points thanks to the folder 001_Elements-de-georeferencement. <pre>mm3d SaisieAppuisInitQT "facade1DSC0589[8|9].JPG" MEP 1001 MesureFacade.xml</pre>
To validate two other support points (on two images each) : <pre>mm3d SaisieAppuisInitQT "facade1DSC0588[3|5].JPG" MEP 1002 MesureFacade.xml</pre>

<pre>mm3d SaisieAppuisInitQT "facade1DSC0589[3|6].JPG" MEP 1121 MesureFacade.xml</pre>
*2. Computation of the 3D similarity (absolute orientation) <pre>mm3d GCPBascule ".*JPG" MEP MEP-Basc Pierrerue.xml MesureFacade-S2D.xml</pre>
*3. Measurement of all available points : <pre>mm3d SaisieAppuisPredicQT "facade.*JPG" MEP-Basc Pierrerue.xml MesureFacade-Final.xml</pre> We must now validate the points left.
*4. Update computation of the absolute orientation.
This time, we need all the support points to calclulate the 3D similarity of the absolute orientation.
<pre>mm3d GCPBascule ".*JPG" MEP MEP-Basc2 Pierrerue.xml MesureFacade-Final-S2D.xml</pre>
*5. Final adjustement
<pre>mm3d Campari ".*JPG" MEP-Basc2 MEP-Terrain GCP=[Pierrerue.xml,0.02,MesureFacade-Final-S2D.xml,0.5]</pre>
This computation adjustement use to find the best position/orientation of the cameras when the shooting was made assuming that the support points have a 0.02m accuracy, and the linking points have a 0.5 pixel accuracy. This values allows to ponderate the measurements. It suits, at the end of the process, to control the residues on the support points and on the images residuals.
The residual images look like this :
<pre>| | RESIDU LIAISON MOYENS = 0.547721 pour Id_Pastis_Hom Evol, Moy=2.38308e-07 ,Max=0.00295916</pre>

===3D Reconstruction===
As we did for the Fountain exercise, we have to do the 3D reconstruction with the image geometry with the [[C3DC]] tools. First, we have to limit the reconstruction area. To process it, we have to create a mask on the cloud points ([[AperiCloud]]), that we have to recalculate in the new set up. <pre>mm3d AperiCloud ".*JPG" MEP-Terrain</pre>
To limit the computation area, we will create a 3D mask :<pre>mm3d SaisieMasqQT AperiCloud_MEP-Terrain.ply</pre>
Once the mask is created, we can launch the 3D reconstruction : <pre>mm3d C3DC MicMac "facade.*JPG" MEP-Terrain Masq3D=AperiCloud_MEP-Terrain.ply Out=C3DC_MicMac_Pierrerue.ply</pre>
The C3DC_MicMac_Pierrerue.ply file can be open with Meshlab.

===Orthorectification===
To realize orthorectifications of the Pierrerue facade, we have to define a temporary landmark for each facade, where the Z axis will be perpendicular to the facade. In the first time, we will work on facade n°1. We processing it in 2 steps :

1. Mask creation into the facade : <pre>mm3d SaisieMasqQT facade1DSC05893.JPG Attr=Facade1</pre>
2. Computation of a local landmark where the Z axis is perpendicular and going through the support points included into the masks : <pre>mm3d RepLocBascule "facade1.*JPG" Ori-MEP-Terrain HOR Repere-Facade1.xml PostPlan=_MasqFacade1</pre>

The HOR setting show that for the Ox axis of our orthoimage, we use the horizontal of our worksite. It can be done here, because the set up MEP-Terrain was obtained from support points. The orthoimage will be calculated from new landmark. It is necessary here to reproject the 3D reconstruction process into this new landmark. Normally, a depthmap will be calculated into the orthorectification map : it's about an image applied on the object, where the pixels show the distance from map. We describe it as 2.5D (3D information isn't available only for a finite number of positions).

The [[PIMs2MNT]] command allows to create a depth map into the map of the facade n°1 (the one calculated before : ''Repere-Facade1.xml'') :
<pre>mm3d Pims2MNT MicMac DoOrtho=1 Repere=Repere-Facade1.xml Pat="facade1.*JPG"</pre>
The correlation map resulting, calculated into the facade n°1, can be found into the folder ''PIMs-TmpBasc'' with the name ''PIMs-Merged_Correl.tif''. This file contains the correlation results : white is corresponding to a very good correlation scores ; more the grey is dark, less the matching process went well.

Once the depth map processed, MicMac compute into the PIMs-ORTHO repertory the orthoimages for each image, aswell as incidence pictures, with the angle between the facade and the perspective ray (images Incid_facade1DSC###.tif) and images with hidden parts, that show in white the hidden parts into the image (imgaes PC_facade1DSC###.tif).
[[Image:Pierrerue1.png|thumb|180px||alt=Pierrerue|Pierrerue Chapel]]
After the calculation, each orthoimages should be mosaiced. The choice of the image to use for each pixel is done with these specificity : no hidden parts, best angle of attack, continuity in the choice of images :
<pre>mm3d Tawny PIMs-ORTHO/</pre>
The result is an image created into ''PIMs-ORTHO'' folder, called Orthophotomosaic.tif. Metadatas associated are available into the file Orthophotomosaic.tfw. We can see the resolution chosen for the orthoimage computation here (1.1mm).

[[Image:Pierrerue5.png|thumb|180px||alt=Pierrerue|Meshlab visualization]]
We can now create a faded image relief, this image allows to evaluate the quality of the reconstruction, especially in detecting the noise existing on the reconstructed surface.
<pre>mm3d GrShade PIMs-TmpBasc/PIMs-Merged_Prof.tif ModeOmbre=IgnE Mask=PIMs-TmpBasc/PIMs-Merged_Masq.tif Out=Facade1_Shade.tif</pre>
Another product can be created, it's an colorized image into the facade depth (each colour is corresponding to a scale of depth) :
<pre>mm3d to8Bits PIMs-TmpBasc/PIMs-Merged_Prof.tif Coul=1 Circ=1 Mask=PIMs-TmpBasc/PIMs-Merged_Masq.tif Out=Facade1_8Bits.tif</pre>
Files ''Facade1_Shade.tif'' and ''Facade1_8bits'' can be seen with any image viewer software.

Finally, it can be helpful to regenerate a 3D cloud points from the depth map, by colorizing it with the orthoimage. The advantage is to use radiometric equalization calculated on the orthoimage (during the [[Tawny]]) to have an equalized 3D cloud points. The disadvantage is that the 3D area is only 2,5D and that the perpendicular object from the facade map aren't showed on the cloud.
<pre>mm3d Nuage2Ply PIMs-TmpBasc/PIMs-Merged.xml Attr=PIMs-ORTHO/Orthophotomosaic.tif Out=Facade1.ply</pre>
The file ''Facade1.ply'' can be seen with Meshlab, and can be compared to the file ''C3DC_MicMac_Pierrerue.ply''.

Everything executed on the facade n°1 can be done now on facade n°2.

Pierrerue tutorial

2024-04-03T11:48:41Z

Lilacandolle :