MicMac - Nouvelles pages [fr]

MicMacRoom Tutorial: 03 Pierrerue

2024-05-14T12:52:24Z

Hubrice : /* Download */

[[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> <br>. 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> <br>
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> <br>. 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: 06 Gréalou

2024-04-30T13:29:47Z

Hubrice : /* Wiki Tutorial Sheet - Gréalou */

This tutorial will be added soon !

MicMacRoom Installation

2024-04-30T12:37:45Z

Hubrice :

[[Image:picto-liste.png|25px|link=Tutorials]] [[MicMacRoom Tutorials|Tutorials index]]
== Information ==
This guide will cover the installation of MicMacRoom on WINDOWS.

Throughout this guide, MicMacRoom and Meshroom MicMac refer to the same thing; both terms are interchangeable.

== Necessary software needed for installation ==

=== MicMac Installation ===

In the current version of MicMacRoom, you will need MicMac v1.1.1.
To install it, go to [https://github.com/micmacIGN/micmac/releases the MicMac GitHub] and download the `micmac_windows.zip` under the v1.1.1 release.
Then, follow the [[Install MicMac Windows|tutorial here]]. Make sure that MicMac is not installed in the Program Files folder.

=== Meshroom Installation ===

In the current version of MicMacRoom, you will need Meshroom 2023.3.0.
To install it, go to [https://github.com/alicevision/Meshroom/releases the Meshroom GitHub] and download `Meshroom-2023.3.0` for Windows under Meshroom 2023.3.0.
Unzip it, and that's it. You can launch it simply by clicking on the `.exe` file.

== MicMacRoom Installation ==

Now that you have the required software, you can install MicMacRoom itself. Go to the [https://github.com/alicevision/MeshroomMicMac MicMacRoom GitHub] and then, either do a git clone of the repository or simply download it as a ZIP by clicking the green "<> Code" button and then "Download ZIP". Unzip it in the folder of your choice, preferably not Program Files nor the folder containing either MicMac or Meshroom.

Then, access the environment variables (on Windows 10, right-click on the Windows logo on the bottom of your screen -> then System -> then, on the right "Advanced system settings" -> then at the bottom "Environment Variables"). If you have installed MicMac, you should be familiar with this screen.
We need to add two new variables, to do so click on New and then add:

* Variable Name: `MESHROOM_NODES_PATH`, Variable Value = full file path to the MicMacRoom folder, for example `C:\MeshroomMicMac`.
* Variable Name: `MESHROOM_PIPELINE_TEMPLATES_PATH`, Variable Value = full file path to the MicMacRoom folder/pipelines, for example `C:\Meshroom\pipelines`.

== Verification ==
Close all settings windows, go to your Meshroom installation folder, and open Meshroom by double-clicking on the `.exe`. If you want to check whether the installation has worked, then go to the "Graph Editor" at the bottom of the Meshroom window, and right-click. If there is a MicMac category in the menu that appears, then you should be good.

MicMacRoom Troubleshooting

2024-04-29T14:33:29Z

Hubrice :

[[Image:picto-liste.png|25px|link=Tutorials]] [[MicMacRoom Tutorials|Tutorials index]]
== Description ==

This page will cover some basic tips to help run the various pipelines you can create by following the tutorial or otherwise. This list is not exhaustive.

== Useful Tools ==

=== Node Colors ===
Under the name of each node will be a line that can be of different colors:

- Grey: the node has not been executed yet.
- Light blue: the node is pending execution, generally happens when the previous operations are not yet done.
- Orange: the node is currently being executed.
- Green: the node has finished its execution (without problem).
- Red: An error has occurred.

It is important to know that MicMacRoom keeps all intermediary data and, as such, will never recalculate nodes that are green, no matter how many times you press start. If you wish to redo all calculations, select all nodes, right-click, and choose "Delete data". This action can also be done on individual nodes.

=== The Log Tab ===

When you click on a node, you can see on the bottom right that there are multiple tabs, notably the log tab. Clicking on it will show you every operation the node is performing in the background. This can be used in real-time as the node is running.

If a node turns red and you want information on what went wrong, check the log tab. Error messages typically appear at the bottom of the log.

=== The Command Prompt ===

You might have noticed that before the proper interface of Meshroom appears, Meshroom will first open a command prompt. Do not close it as it will close the software. This prompt is used to launch the actual MicMac commands that the node will execute. If a node fails to launch, it is possible that the error will appear here. Although it is important to mention that if an error appears here, it is most likely not fixable by changing node settings or inputs and outputs and requires a modification of the code.

=== Relaunching a Node ===

If a node stops midway through its execution, it is recommended to delete its data. To do that, right-click on it and choose "Delete data". The color line will turn grey, and then you can fix the error and press start.

=== Last Resort ===

Have you tried turning it off and on again?

MicMacRoom Tutorial: 01 Gravillions

2024-04-24T14:17:13Z

Hubrice : Hubrice a déplacé la page MicMacRoom Tutorial: 01 Gravillions vers MicMacRoom Tutorial: 01 Gravillons

[[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 overlaps with MicMacRoom. This dataset is light by design (4 images), in order to focus on the MicMacRoom tools.
This tutorial is designed especially for MicMacRoom beginners with a light photogrammetry background.

==Download==
You can find this dataset at <code>https://micmac.ensg.eu/data/gravillons_dataset.zip</code> <br>
Once you have downloaded it, you have to extract 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.
Files present in the directory are:
*4 images : 1.JPG, 2.JPG, 3.JPG, 4.JPG.
[[Image:01_gravillons_caroussel.png]]
*GCP coordinates: Dico-Appuis.xml.
*Measures of GCPs in images: Mesure-Appuis.xml.
*1 Mask: 1_Masq.tif/1_Masq.xml
*2 command scripts: gravillons.sh (Linux) and gravillons.bat (Windows)

==Tutorial==

===Project Creation===
The usage of MicMacRoom requires a few steps to create your project. First, select all nodes (in the Graph Editor) and delete them. Then, save your project.
The first real step is to set up two nodes (to create a node, right click on the Graph Editor space and search for the node):
#The CameraInit node
#The MicMacProject node

You don't need to change any parameter on these, you just need to link the SfMData Output of CameraInit to the SfMData Input of MicMacProject.
This step allows Meshroom and MicMac to work together properly, they are needed in all project where you wish to use the MicMac commands in Meshroom.
You can then import all your images.

===Tie-Points search===
The second step is to look for tie points (points that are seen in more than one image), this step is called image matching and performed by the command [[Tapioca]], however, you will see that there are multiple Tapiocas available in MicMacRoom, this is to differentiate beetween certain important options that changes the inputs and outputs of the command.
In this case, we will use TapiocaAll (The All option is used here because we know that all the images are going to have tie points with each other (they all depict the same area)).
In this node, set the ImageSize to 1500 (ot to -1 if you want to process the tie-points at full resolution).
Then, connect ProjectDirectory from MicMacProject to the TapiocaAll node. This is something that you will need to do a lot, as all ProjectDirectory will be linked between all nodes. This is important as it tells MicMacRoom the position of the input and ouput files.

===Internal Orientation+Relative Orientation===
Photogrammetry is composed of three steps :
*Internal Orientation : to determine camera parameters (focal length, PPA, PPS, distortion center, or distortion parameters).
*Relative Orientation : to determine the relative position of each camera in an arbitrary coordinate system.
*Absolute Orientation : to map the relative orientations to a scaled and oriented coordinate system (typically WGS84)
In digital photogrammetry, the two first steps are generally processed at the same time. In MicMac, the tools which perform internal and relative orientation is called [[Tapas]], in MicMacRoom, the node is also called Tapas, you can add it after the TapiocaAll node and link ProjectDirectory, ImagePattern, and HomolDirectory

Then in the Tapas node, put the Calibration Model to FraserBasic
This tool uses a compensation by least squares to determine camera parameters and relative orientations. The option "FraserBasic", correspond to a model of distortion for our camera.

===Visualize Relative Orientation===
MicMacRoom include a tools which create a sparse point clouds (TPs) for visualization. This tool is based on the MicMac command [[AperiCloud]] and is also named AperiCloud.
To use it, add an ApriCloud node and link ProjectDirectory, Image Pattern, Homol Directory, and OrientationDirectory with the Tapas node.
At this point, you can start the process if you wish to see the Point Cloud. To find this file, go to the place where you saved your project then Meshroom Cache -> MicMacProject -> Then only folder that should be here is one with a long string as a name -> project and in there you should find all the inputs, intermediary, and output files, including the AperiCloud.ply file which can be viewed in Softwares such as CloudCompare or Meshlab (see [[Install|Useful softwares for MicMac]]).

===Absolute Orientation===

====GCPBascule ====
For this datasets, Ground Control Points, are already measured in images (file "Mesure-Appuis.xml"). With 3 points (X,Y,Z) we can determine the 3D transformation between the arbitrary system (Relative Orientation) and the georeferenced system, this operation is call "Bascule" and can be performed with the [[GCPBascule]] MicMac commmand, and with the GCPBascule node as well.
To do this: add a GCPBascule node after the AperiCloud. Link the Project Directory and Image Pattern from AperiCloud. Also, link the Orientation Directory from AperiCloud to the Input Orientation of GCPBascule.

It is important at this point to have started the process once, as this is the only way to create the project directory which you will need to access. If you have done it after the previous step to check the Point Cloud then there is no need to start it again. Do not worry about losing time, Meshroom stores every intermediary steps and so when you add more nodes, it will not recaculate the previous ones (expect if you tell it to do so).

Once the process if finished, go to the place where you saved your project then Meshroom Cache -> MicMacProject -> Then only folder that should be here is one with a long string as a name -> project, in this folder, put the Dico-Appuis.xml and Mesure-Appuis.xml that were in the data given at the beginning of this tutorial.
Then, in the GCPBascule node, put Dico-Appuis.xml in GCP 3D Coordinate File and Mesure-Appuis.xml in GCP Image coordinates File.

====Campari====
This tool process a first Bascule only with the GCPs (Directory Ori-Ground_Init), we will now calculate a second Bascule with GCPs and TPs. To do that, we use the equivalent of the MicMac command [[Campari]], which is the Campari node.

Add a Campari node, link Project Directory and Image Pattern from GCPBascule. Also, link Output Orientation from GCPBascule to Orientation Directory. The last step is to link Homol Directory from """AperiCloud""" to Homol Directory.

====AperiCloud2====
We can visualize the new orientation calculated with Campari with AperiCloud.

Add an AperiCloud node. Link Project Directory, Image Pattern, and Homol Directory from Campari. Also link the topmost Orientation Directory from Campari (the one that is only an output) to Orientation Directory on the latest AperiCloud.

On this AperiCloud node, go to the bottom of the settings and change the name of PointCloud to something like AperiCloud2.ply, this will make it so the result of the two AperiCloud do not Override each other.

===Dense Point Cloud===
The last step is to densify the PointCloud, to do that, we will use the C3DC node that is the equivalent of the [[C3DC]] MicMac command.

Add a C3DC node. Link Project Directory, Image Pattern, Homol Directory, and Orientation Directory from AperiCloud2.
Now you just need to press start!

This is the expected Pipeline after following this tutorial (Whithout the green lines under the names of the nodes as this indicates that the pipeline has been executed):
[[Image:Gravillions.png|center|thumb|1000px]]

===Result===
To find your results: go to the place where you saved your project then Meshroom Cache -> MicMacProject -> Then only folder that should be here is one with a long string as a name -> project

===Conclusion===
With this tutorial, you went through a complete photogrammetric process with MicMacRoom. This first tutorial was willingly easy and on a minimal dataset to help you kickstart your MicMacRoom skills. To go further, try the next tutorials.

MicMacRoom Tutorial: 04 Zhenjue

2024-04-24T11:28:34Z

Hubrice :

[[Image:picto-liste.png|25px|link=Tutorials]] [[MicMacRoom Tutorials|Tutorials index]]
== Tutorial Zhenjue ==

=== Summary ===
# [[#Description|Description]]
# [[#Download|Download]]
# [[#Tutorial|Tutorial]]
## [[#project-creation|Project creation and Image Importation]]
## [[#base-element|Base Element]]
## [[#Relative Orientation|Relative Orientation]]

=== Description ===
This tutorial focuses on independently reconstructing statues using images of different focal lengths (24mm and 100mm). to reconstruct object in 3D by image geometry (PIMs).

=== Download ===
You can find this dataset at [https://micmac.ensg.eu/data/zhenjue_dataset.zip](https://micmac.ensg.eu/data/zhenjue_dataset.zip) Once you have downloaded it, you have to unzip the ".zip" archive.

=== Tutorial ===

==== 1) Project creation and Image Importation ====
First and foremost, open MicMacRoom and save your project, you can then use the File > Import Images to import the images from the dataset. Then select all of the nodes in the graph editor and delete them all.

==== 2) Base Element ====
To allow MicMacRoom to work we need to specific nodes at the beginning of the pipeline. These are: CameraInit which will recover the image information from the camera and MicMacProject which will create the directory.
To add a node, place your cursor in the graph editor, right-click and select the node you want, either by searching the list of by doing of search by keyword.
CameraInit is placed first and MicMacProject second. You can then take the SfmData output of CameraInit (Output are located on the right of a node) and link it with the SfmData input of MicMacProject (Input are located on the left of a node), if you see a line between the two nodes, then you have succeeded. You can also check the MicMacProject node and left-click on it to see that the SfmData parameter now has an address.
If you have created a wrong link, you can delete it by pressing right-click and remove on it.

==== 3) Relative Orientation ====

'''TapiocaAll'''

To search for attachment points, use the TapiocaAll node (use Tapioca with All commands authorized) to access them by right-clicking in the "Graph Editor" window > micmac > tapiocaAll
(or right click in the "Graph Editor" window and type the node name in the search bar at the top) you get a block. Then link the MicMacProject Project Directory to TapiocaAll. You change the Image Size settings which define the resolution of the image (how high we want our resolution to be) and the Homol directory to set the output name of the file. This block allows you to calculate the connection points for all images in a given resolution.

[[Image:TapiocaAll.PNG|center|thumb|1000px]]

'''tapas'''

For this dataset, the images have different focal length, so we first need to calculate an orientation for the 24mm focal length images. to click to access it right in the "Graph Editor" window > micmac > Tapas you obtain a block. Then link the Project Directory, Image Patern, Homol Directory of TapiocaALL to Tapas you modify the Calibration Model parameters on RadialStd and you must go to the avencer parameters (node -tapas at the top right click on the 3 small dots > Filter Attributes > check Advanced) then choose in Ec Init max 30 and min 20

[[Image:Advanced.PNG|center|thumb|1500px]]
[[Image:Tapas.PNG|center|thumb|1000px]]

'''tapas_2'''

Check residual and number of points used per images.
We use the 24mm orientation as an entry for our command in order to indicate to MicMac there is different focal length: to perform this action we will take the tapas node to access it right click in the "Graph Editor" window > micmac > Tapas you get a block. Then link the Project Directory, Image Patern, Homol Directory and Orientation Directory put it in In Orientation Directory from Tapas to Tapas_2
you change the Calibration Model settings on RadialStd

image

'''AperiCloud'''

We will now generate a sparse cloud to visualize the relative orientation.
To carry out this action we will take the AperiCloud node to access it right click in the "Graph Editor" window > micmac > AperiCloud you obtain a block. Then link the Project Directory, Image Patern, Homol Directory and Orientation Directory of Tapas_2 to AperiCloud no changes to be made to the parameters

[[Image:AperiCloud.PNG|center|thumb|1000px]]

'''SaisieMasqQT '''

To set a mask for dense correlation, use CommandMasqQT. Here we establish a 3D mask:
To perform this task, navigate to the SeizureMasqQT node. Right-click in the "Graph Editor" window > micmac > SaisieMasqQT to generate a block. Then connect the Sparse point cloud to the file path and both directory projects, from AperiCloud to SaisieMasqQT, with no settings changes needed.

[[Image:SaisieMasqQT.PNG|center|thumb|1000px]]

'''C3DC'''

The new C3DC tool works without requiring a master image for 3D reconstruction. To complete this task, follow these steps: First, navigate to the C3DC node by right-clicking in the Graph Editor window > micmac > C3DC, which creates a block. Then, establish the connections as follows: Output file to Masq 3D and the two projects directory from SaisieMasqQT to C3DC. Next, adjust the settings in MicMac: in Image Pattern, set "DSC_313[2-9].JPG", go to advanced settings, check Custom ZoomF and set Zoom F to 4. Finally, in Output Name, set the output file wish. name.

[[Image:C3DC_micmac.PNG|center|thumb|1000px]]

[[Image:Zhenjue_Template.PNG|center|thumb|1500px]]

MicMacRoom Tutorial: 05 GrandLeez

2024-04-24T09:41:42Z

Hubrice : /* Wiki Tutorial Sheet - GrandLeez */

[[Image:picto-liste.png|25px|link=Tutorials]] [[MicMacRoom Tutorials|Tutorials index]]
== Wiki Tutorial Sheet - GrandLeez ==

'''Dataset Type:''' Tutorial to illustrate processing on aerial images (drone, airplane, etc.). This pipeline enables the generation of different types of point clouds: one point cloud with relative orientation with respect to the camera of capture and two point clouds with absolute position and orientation for a given reference system.

'''Data Retrieval:'''
The GrandLeez tutorial data is available on the MicMac wiki ensg. Make sure to download all the necessary data for the processing chain.

[[Image:PipelineGL.png|thumb|1300px|Processing Pipeline]]

==='''Creation of the project'''===
Open the 'File' tab in MicMac and launch a new pipeline with the tamplate "Mic Mac Aero TapiocaFile". 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.

[[Image:Project directory.png]]

Here in red you can see the hold pattern of your project created by Micmac (defaulf name that you cannot change)

==='''1) Calculation of Homologous Points: TapiocaFile '''===
Calculation of homologous points in all images. Here, we use the TapiocaFile command to calculate homologous points: the use of this command is made possible by the existence in the dataset of an XML file (FileImagesNeighbour.xml) listing all pairs of overlapping images. With this file, the Tapioca command directly searches for homologous points between these pairs of images, rather than processing all images together (processing time gain). A new pipeline under implementation will allow the use of another variant of tapioca for this tutorial if you do not have an XML file for your dataset.

Required Arguments for TapiocaFile command:
* Project Directory: Absolute path of your project, which provides the path to your dataset folder. Note, your files must be located exactly in this folder, otherwise the command will not be able to find them.
* XML File Path: Path of the XML file of your image pairs. You can fill this argument with the filename if it is located in the project folder entered in the previous argument.
* Resolution: Integer specifying the resolution quality of images in the search for homologous points. The higher the resolution of the images, the longer the processing time for homologous points. Other arguments are optional. If you want to access advanced arguments, check the "advanced" option in "filter attributes". The output files of homologous points will be listed in the HomolTapioca folder.

[[Image:Tapioca File.png]]

Here, the resolution parameter is very low because the command takes a long time to process, but you can select a higher value.

==='''2) Image Coordinate Transformation: OriConvert'''===
This step converts the embedded GPS coordinates into a format suitable for MicMac (OriTxtInFile), changes the coordinates of the images into a local system, and calculates the relative speed of the camera (for GPS delay determination). Additionally, you can select a set of central images in the acquisition for camera calibration (this step will be repeated during the various Tapas).

'''Required Arguments:'''
* Format Specification: Output specification format (usually OriTxtInFile) of GPS coordinates in the MicMac's own format.
* Orientation File: Orientation file (e.g., in CSV format) that provides for each image in the dataset its coordinates given by the embedded GPS during the acquisition. These are the data that we are trying to transform into the requested specification format.
* Targeted Orientation: Output orientation folder name.
* Change System File: Coordinate transformation file. This string must be of the form: strgSyst1@fileSyst2. "strgSyst1" indicates the original coordinate system of the dataset, and "fileSyst2" indicates the coordinate transformation file into the desired system. The @ symbol indicates the transition from one system to another.

'''Other Parameters:'''
* MTD1=1 and CalcV=1 are used to determine the relative speed of the camera (for GPS delay determination).
* Image Center Nb Adjacent Images: Path of the central image of the acquisition and the number of images to take around this central image to calculate the internal calibration of the camera.
* Delay: GPS delay if determined beforehand.

[[Image:OriConvert.png]]

To set the advanced parameters of this command, you can select the 'Advanced Parameters' option and check the boxes to access the parameters.

==='''3) Camera Calibration: Tapas_1'''===
Command to obtain the device calibration. To do this, we can use only a portion of the dataset to reduce calculation times (usually central image block in the acquisition).

'''Required Arguments:'''
* Calibration Model: Camera calibration model.
* Image Pattern: Folder or set of images to be used for calibration.
* Homol Directory: homologous points file that is the output of the command Tapioca. This argument is already pre-filled with the correct output file from the command TapiocaFile.
* Output Name: Output calibration folder name.

[[Image:Tapas1.png]]

==='''4) Relative Orientation of Images: Tapas_2:'''===
Relative positioning of images.

'''Required Arguments:'''
* Calibration Model: Camera calibration model (same that Tapas_1).
* Image Pattern: Set of images to be positioned
* Homol Directory: homologous points file that is the output of the command Tapioca. This argument is already pre-filled with the correct output file from the command TapiocaFile.
* Output Name: Output calibration folder name.

'''Other Parameters:'''
*In Calibration Directory: device calibration file. You don't need to modify this parameter, because the calibration file is already connected with the output of the first Tapas. Tapas_2 directly take the output of the first Tapas command.

[[Image:Tapas2.png]]

==='''5) First visualisation with point clouds: AperiCloud_1'''===
This command create a point clouds file (.ply) that you can visualize in software like CloudCompare. In this step, you can visualize the points cloud of your dataset, but the position of the 3D-model is not correct, because for the moment we only have the relative orientation of the images.

'''Required Arguments:'''
*Homol Directory: homologous points file that is the output of the command Tapioca. This argument is already pre-filled with the correct output file from the command TapiocaFile.
*Orientation Direction: orientation folder of all image orientation.

[[Image:AperiCloud1.png]]

==='''6) Absolute Orientation of Images: CenterBascule :'''===

'''Required Arguments:'''
* Image Pattern: folder/set of images to be oriented
* Input Orientation: folder of all relative orientations output by the Tapas_2 command (already pre-filled)
* Location on centers : folder of coordinate change between absolute and relative system, transformation performed by the OriConvert command; for each image, conversion file to switch from absolute to relative orientation (already pre-filled with the output of the command OriConvert)
* Output: output folder name

[[Image:CenterBascule.png]]

==='''7) Second visualisation with point clouds: AperiCloud_2'''===
This command create a point clouds file (.ply) that you can visualize in software like CloudCompare. In this step, you can visualize the points cloud of your dataset, with the absolute orientation of your model, positioning with the system given in the command OriConvert.

'''Required Arguments:'''
*Homol Directory: homologous points file that is the output of the command Tapioca. This argument is already pre-filled with the correct output file from the command TapiocaFile.
*Orientation Direction: orientation folder of all image orientation.

[[Image:AperiCloud2.png]]

==='''8) Change of image orientation system : ChgSysCo (optional)'''===
This command allow you changing the system of coordinate of your images, giving a changing system file.

'''Required Arguments:'''
*Input Orientation: initial orientation file (already pre-filled in the pipeline)
*ChgSysFile: file that indicate the new system of orientation
*Output: name of the final orientation file

[[Image:ChgSysCo.png]]

==='''9) Third visualisation with point clouds: AperiCloud_3 (optional)'''===
This command create a point clouds file (.ply) that you can visualize in software like CloudCompare. In this step, you can visualize the points cloud of your dataset, with the absolute orientation of your model, positioning with the system of your choice given in the command ChgSysCo.

'''Required Arguments:'''
*Homol Directory: homologous points file that is the output of the command Tapioca. This argument is already pre-filled with the correct output file from the command TapiocaFile.
*Orientation Direction: orientation folder of all image orientation.

If you have any problem with this tutorial, you can check the pipeline "modele_aeroGPS_GrandLeez.mg", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 02 Fontaine

2024-04-03T14:07:10Z

Hubrice :

[[Image:picto-liste.png|25px|link=Tutorials]] [[MicMacRoom Tutorials|Tutorials index]]
== Fontaine Tutorial ==

=== Description ===
In this tutorial, we go further into command details, process, results, products, etc. Here you will learn how to process a dataset of a circular point of view around an object. To achieve this, we are going to create a dense point cloud of a 3D object.

=== Download ===
You can find this dataset at [https://micmac.ensg.eu/data/fontaine_dataset.zip](https://micmac.ensg.eu/data/fontaine_dataset.zip). Once you have downloaded it, you have to unzip the ".zip" archive.

=== Presentation ===
The folder contains 30 JPG files. In this dataset, there isn't any data about the camera which was used. The shooting set contains 30 images, all taken with the Canon 70D camera with an 18mm lens. The camera saves metadata for all the pictures (exif data). If you are looking in the property data of each picture, the 18mm lens is mentioned and the various chosen settings while the production of this dataset was realized (opening, break, ...).
There are 5 parts in this dataset:
* 4 parts include 4 different points of view of the fountain. Each part was created with a cross points of view. This means there is one master image at the center and 4 images around it (top, bottom, left, and right from the master image).
* 1 part contains the other images (IMG.*JPG). These images are images link, this means that they will allow to link the 4 other parts together. They won't be used to generate the dense clouds points.

=== Tutorial ===

====0 Project creation and Image Importation ====
First and foremost, open MicMacRoom and save your project, you can then use the File > Import Images to import the images from the dataset. Then select all of the nodes in the graph editor and delete them all.

====1 Base Element ====
To allow MicMacRoom to work we need to specific nodes at the beginning of the pipeline. These are: CameraInit which will recover the image information from the camera and MicMacProject which will create the directory.
To add a node, place your cursor in the graph editor, right-click and select the node you want, either by searching the list of by doing of search by keyword.
CameraInit is placed first and MicMacProject second. You can then take the SfmData output of CameraInit (Output are located on the right of a node) and link it with the SfmData input of MicMacProject (Input are located on the left of a node), if you see a line between the two nodes, then you have succeeded. You can also check the MicMacProject node and left-click on it to see that the SfmData parameter now has an address.
If you have created a wrong link, you can delete it by pressing right-click and remove on it.

====2 Tie-points search ====
To perform the tie-points search, we use the TapiocaMulScale node (Important: In MicMac, Mulscale is a parameter of Tapioca however in MicMacRoom, there are different Tapioca modules for certain parameters, so it is important to choose TapiocaMulScale)
Then link the project directory from MicMacProject to TapiocaMulScale and, in this node, set Image Size low to 500 and Image Size high to 2500.
The MulScale pattern allows making a search for similar points firstly on sub-sampled images. On this dataset, it will be on 500pixels on the biggest side instead of 5472 on the originals images. This allows knowing which images have some tie points between them and to only run the tie-points search at a bigger resolution (here 2500) on the optimal sets of images.

====3 Internal Orientation and Relative Orientation ====
We are looking now to know the position of the camera, in relative to each other, but also to know the calibration of the camera used.
For this, we use the Tapas node. As usual, first, we must link the different modules. From TapiocaMulScale, link the Homol Directory, Project Directory, and Image pattern to Tapas.
Then in Tapas, set Calibration Model to RadialStd (RadialStd is the pattern generally used for classic cameras).

====4 Visualize Relative Orientation ====
The AperiCloud command allows generating a 3D clouds points, containing all the tie-points obtained with TapiocaMulScale, and the position of the camera used as a Tapas input.
Create an AperiCloud node and link Project directory, Image Pattern, Homol Directory, and Orientation Directory from Tapas.
The result of this command can be seen, for example, with the Meshlab software (Visualization in Meshroom is not available at the moment due to differences between the MicMac output format: .ply and the Meshroom preferred format: .abc). So we can see that the 4 parts containing 5 images around the fountain are connected between them thanks to the images link.

====5 Masq creation ====
We will now draw a 3D mask, from the AperiCloud_Fontaine.ply file.
Create a SaisieMasqQT and link the project directory. Then, link Sparse Point Cloud from AperiCloud into File Path of SaisieMasqQT. This node will open a new window where we will be able to draw the mask on the point cloud. The resulting file will have a name like AperiCloud_Fontaine_polygo3d.xml. (The name of this file depends on the output name parameter of the Tapas)
Be aware that since the SaisieMasqQT node technically doesn’t output anything, Meshroom will not wait for you to draw the mask to start the next node. This will lead to an incorrect result, but we will be able to fix this issue later.

====6 Densification ====
We can now run the 3D reconstruction with the C3DC command.
First, create a C3DC node. Connect the project Directory from either AperiCloud or SaisieMasqQT. You must also link Image Pattern, Homol Directory, and Orientation Directory from AperiCloud. Then, set the C3DC mode to BigMac and in the Masq 3D parameter, put the full name of your masq file (something like: AperiCloud_Fontaine_polygo3d.xml).

This is the expected Pipeline after following this tutorial:
[[Image:Pipeline_fontaine.PNG|center|thumb|1000px]]

=== Running the pipeline ===
# Press start
# Once the program reaches the SaisieMasqQT, create and save your masq in the new window.
# Press stop
# Right-click on C3DC then delete data.
# Check if the C3DC parameters are still correct (especially the Masq 3D)
# Then press start again
# Once the C3DC node becomes green, you can check the results.

MicMacRoom Tutorials

2024-04-03T13:41:52Z

Hubrice :

==Basic Tutorials ==

Here are the five MicMac Tutorials recreated in MicMacRoom:

* [[MicMacRoom Tutorial: 01 Gravillions|01 Gravillons]] (Base tutorial with 4 images)
* [[MicMacRoom Tutorial: 02 Fontaine|02 Fontaine]]
* [[MicMacRoom Tutorial: 03 Pierrerue|03 Pierrerue]]
* [[MicMacRoom Tutorial: 04 Zhenjue|04 Zhenjue]]
* [[MicMacRoom Tutorial: 05 GrandLeez|05 GrandLeez]] (Fixed wing drone with embedded GNSS)

==Specific Tutorials==

Here are some more tutorials created specifically for MicMacRoom

* [[MicMacRoom Tutorial: 06 Gréalou|06 Gréalou]] (Drone acquisition with GCPs)

== Other Ressources ==

* [[MicMacRoom Installation|Installation Guide]]
* [[MicMacRoom Troubleshooting|Troubleshooting help]]

MicMacRoom

2024-04-03T13:12:50Z

Hubrice :

== MicMacRoom ==
'''MicMacRoom''' is a project that aims to provide the MicMac photogrammetry software with a Graphic User Interface (GUI) to make it more easily accessible.

For this, a joint development with the [https://alicevision.org/#meshroom Meshroom] software developed by AliceVision is being undertaken. Meshroom is also a photogrammetry software that already has a GUI.

The objective is to combine the features of both software into an easily usable photogrammetry software for new users.

One of the aims of the project is also the fact that it is open-source, and the code, as well as the steps needed for installation, can be found [[MicMacRoom Installation|here]]

For this, the first five [https://micmac.ensg.eu/index.php/Tutorials tutorials] of the MicMac wiki have been recreated in MicMacRoom and can be found [[:MicMacRoom_Tutorials|here]].

TestLib

2021-11-02T07:33:29Z

Harlock : Ajout de la commande SeamlineFeathering

== SeamlineFeathering ==

./mm3d TestLib SeamlineFeathering -help

Mandatory unnamed args :
* string :: {ortho pattern}
Named args :
* [Name=Out] string :: {Name of resulting map}
* [Name=Dist] INT :: {Distance for seamline feathering blending, in chamfer 32 distance : 0-244, default 50. Processing time increases considerably with large values}
* [Name=Lambda] REAL :: {lambda value for gaussian distance weighting, def 0.4.}
* [Name=Label] string :: {label/index map (tif) previously computed by Tawny. If not provided, generate a new label map based on incidence value}
* [Name=ComputeRE] bool :: {Compute Radiometric Equalization models with TestLib EROS. def false}
* [Name=ApplyRE] bool :: {Apply Radiometric Equalization? require to dertermine models of radiometric equalization beforehand (mm3d TestLib EROS or ComputeRE==1). def false}
* [Name=FileRE] string :: {Name of xml file with models of Radiometric Equalization, def 'RadiomEgalModels.xml'. }
* [Name=SzBox] Pt2di :: {Size [pix] of mosaic tile for multiprocess computation, def=[10000,10000].}
* [Name=SzTile] Pt2di :: {Size [pix] of mosaic tile for writing result, def=[25000,25000].}
* [Name=Buffer] INT :: {Buffer [pix] to apply for each tile in order to avoid edge effect, def=300.}
* [Name=Debug] bool :: {Write intermediate results for debug purpose.}
* [Name=TmpDir] string :: {Directory for intermediate results generated in debug mode.}

Install MicMac WSL

2021-05-07T12:45:10Z

Scpsc : /* Use MicMac graphical user interfaces */

You can also use MicMac on Windows 10 through the Windows Subsystem for Linux (WSL). WSL allows you to run a Linux distribution (e.g. Ubuntu) directly on Windows, unmodified, without the overhead of a traditional virtual machine or dualboot setup. Then you can install MicMac by following the [[Install_MicMac_Ubuntu|Ubuntu installation guide]].

== Install WSL ==

The following steps are adapted from the [https://docs.microsoft.com/en-us/windows/wsl/install-win10 Windows Subsystem for Linux Installation Guide for Windows 10]. Only the manual procedure is described. For the automatic procedure (or for more details/troubleshooting), please refer to the external link.

=== Enable the "Windows Subsystem for Linux" optional feature ===

Open Windows PowerShell as Administrator (you can hit the '''Windows logo key''' and type '''powershell'''), then run:
<pre>dism.exe /online /enable-feature /featurename:Microsoft-Windows-Subsystem-Linux /all /norestart</pre>

It is recommended to update to WSL 2, but if you wish to only install WSL 1, you can now '''restart''' your machine and move on to the [[#Install your Linux distribution of choice]] step. To update to WSL 2, '''wait to restart''' your machine and move on to the next step.

=== Check requirements for running WSL 2 ===

To update to WSL 2, you must be running Windows 10:
* For x64 systems: '''Version 1903''' or higher, with '''Build 18362''' or higher.
* For ARM64 systems: '''Version 2004''' or higher, with '''Build 19041''' or higher.
* Builds lower than 18362 do not support WSL 2.

To check your version and build number, hit '''Windows logo key''' + '''R''', type '''winver''', select '''OK'''.

=== Enable Virtual Machine feature ===

Before installing WSL 2, you must enable the '''Virtual Machine Platform''' optional feature. Your machine will require [https://docs.microsoft.com/en-us/windows/wsl/troubleshooting#error-0x80370102-the-virtual-machine-could-not-be-started-because-a-required-feature-is-not-installed virtualization capabilities] to use this feature.

Open PowerShell as Administrator and run:
<pre>dism.exe /online /enable-feature /featurename:VirtualMachinePlatform /all /norestart</pre>

'''Restart''' your machine to complete the WSL install and update to WSL 2.

=== Download the Linux kernel update package to update to WSL 2 ===

# Download the latest package for either x64 or ARM64 systems, depending on your machine:
#* [https://wslstorestorage.blob.core.windows.net/wslblob/wsl_update_x64.msi WSL2 Linux kernel update package for x64 machines]
#* [https://wslstorestorage.blob.core.windows.net/wslblob/wsl_update_arm64.msi WSL2 Linux kernel update package for ARM64 machines]
# Run the update package downloaded in the previous step. (Double-click to run - you will be prompted for elevated permissions, select ‘yes’ to approve this installation.)

=== Set WSL 2 as your default version ===

Open PowerShell and run this command to set WSL 2 as the default version when installing a new Linux distribution:
<pre>wsl --set-default-version 2</pre>

=== Install your Linux distribution of choice ===

# Open the '''Microsoft Store''' and select your favorite Linux distribution (e.g. [https://www.microsoft.com/store/apps/9n6svws3rx71 Ubuntu 20.04]).
# From the distribution's page, select "Get".

[[File:Ubuntustore.png|600px]]

The first time you launch a newly installed Linux distribution, a console window will open and you'll be asked to wait for a minute or two for files to de-compress and be stored on your PC. All future launches should take less than a second.

You will then need to [https://docs.microsoft.com/en-us/windows/wsl/user-support create a user account and password for your new Linux distribution]:

[[File:Ubuntuwslinstall.png|600px]]

You can now run Linux on Windows 10! To install MicMac on Ubuntu, follow [[Install_MicMac_Ubuntu|these steps]].

== Windows Terminal ==
Windows Terminal is a very convenient tool for working in command lines on Windows. Follow this [https://docs.microsoft.com/en-us/windows/terminal/get-started installation guide] to use it.

Many options for customizing the terminal are available through the <code>settings.json</code> file (accessible from the terminal preferences).

=== Some useful shortcuts ===
'''Ctrl''' + '''Tab''': switch between tabs

'''Ctrl''' + '''Shift''' + '''T''': open a new tab

'''Alt''' + '''Shift''' + '''+''': vertical split of the active tab

'''Alt''' + '''Shift''' + '''-''': horizontal split of the active tab

'''Alt''' + '''←↑→ or ↓''': move between tabs

'''Alt''' + '''Shift''' + '''←↑→ or ↓''': change the size of the active tab

'''Ctrl''' + '''Shift''' + '''W''': close the active tab

== Use MicMac's graphical user interfaces ==
To use MicMac's graphical user interfaces (GUIs) (e.g. SaisieMasq, SaisieAppuisInit) in WSL, you will need to install an X-server, such as [https://sourceforge.net/projects/vcxsrv/ VcXsrv].

If when you start VcXsrv the GUI does not appear, you may need to add some lines in your <code>.bashrc</code> file. In your Linux terminal, type:
<pre>nano ~/.bashrc</pre>
Add the following line at the end of the file:
<pre>export DISPLAY=localhost:0.0</pre>
If you use WSL 2, add:
<pre>export DISPLAY=$(grep -oP "(?<=nameserver ).+" /etc/resolv.conf):0</pre>
And if you use WSL 2 and Ubuntu 20.04, add also:
<pre>export LIBGL_ALWAYS_INDIRECT=1</pre>
(from [https://doc.ubuntu-fr.org/wsl#parametrages_divers Wiki ubuntu-fr])

Here is a set of parameters successfuly tested:
<pre>export DISPLAY=:0
export LIBGL_ALWAYS_INDIRECT=1</pre>

Close nano and open a new terminal. You should now be able to display the GUIs from this new terminal. If you end up with a black window, try changing its size by dragging the borders a bit and the GUI should display correctly.

ClipIm

2019-06-14T10:17:11Z

Scpsc : Page créée avec « 25px List of commands ==Description== ClipIm is used to extract a subset of an image. ===Syntax=== The global syntax is: <pre>mm3d C... »

[[Image:picto-liste.png|25px]] [[Command|List of commands]]
==Description==
ClipIm is used to extract a subset of an image.

===Syntax===
The global syntax is:
<pre>mm3d ClipIm ImageName BoxOrigin BoxSize NamedArgs</pre>

===Results===
ClipIm generates a subset of the input image. The coordinates provided for the box origin correspond to the top-left corner of the box, in pixels. First coordinate is the column, second is the line. The size is also in pixels, from the box origin. The image is not resampled in the process. You can use the "Out" option to specify the output name.

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

Mandatory unnamed args :
* string
* Pt2di :: {P0}
* Pt2di :: {SZ}
Named args :
* [Name=Out] string

===Example===

<pre>mm3d ClipIm img1.tif [1429,2842] [1000,1000] Out=img1_subset.tif</pre>

HomolFilterMasq

2018-10-11T15:08:20Z

Maximeseguin :

==Description==

This command can be used when you have the necessary spatial information to retrieve false tie points.
The command HomolFilterMasq can do some filtering on tie points. The masking process can be purerly in image geometry or can be done in some ground geometry.

===Syntax===
The global syntax for HomolFilterMasq is :
<pre>mm3d HomolFilterMasq ImagePattern</pre>

===Workflow===
HomolFilterMasq is a tool dedicated to filter tie point. So you have to use it after tie points computation by Tapioca and before orientation computation by Tapas (or Martini) and before tie point reduction by Schnaps.<br>
[[Image:Picto-previous.png|20px]] Previous Command : [[Tapioca]]<br>
[[Image:Picto-next.png|20px]] Next Command : [[Schnaps]],[[Tapas]],[[Martini]].<br>


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

Mandatory unnamed args :
* string :: {Full name (Dir+Pat)}
Named args :
* [Name=PostPlan] string :: {Post to plan, Def : toto ->toto_Masq.tif like with SaisieMasq}
* [Name=GlobalMasq] string :: {Global Masq to add to all image}
* [Name=KeyCalculMasq] string :: {For tuning masq per image}
* [Name=KeyEquivNoMasq] string :: {When given if KENM(i1)==KENM(i2), don't masq}
* [Name=Resol] REAL :: {Sub Resolution for masq storing, Def=10}
* [Name=ANM] bool :: {Accept no mask, def = true if MasqGlob and false else}
* [Name=ExpTxt] bool :: {Ascii format for in and out, def=false}
* [Name=PostIn] string :: {Post for Input dir Hom, Def=}
* [Name=PostOut] string :: {Post for Output dir Hom, Def=MasqFiltered}
* [Name=OriMasq3D] string :: {Orientation for Masq 3D}
* [Name=Masq3D] string :: {File of Masq3D, Def=AperiCloud_${OriMasq3D}.ply}
* [Name=SelecTer] Pt2dr :: {[Per,Prop] Period of tiling on ground selection, Prop=proporion of selected}
* [Name=DistId] REAL :: {Supress pair such that d(P1,P2) < DistId, def unused}
* [Name=DistH] REAL :: {Distance for filtering homologous point}

==Main options==

===PostPlan===
Set PostPlan=Filter if you have changed the default postfix used by SaisieMasq.
By default it will generate an error if a masq does not exist : set ANM=true if you know that non existing masq for some images is normal.

===GlobalMasq===
Set GlobalMasq if you have a masq common to all images (for example if you want to filter fiducial marqs).

===KeyCalculMasq===
If you want to apply a set of masks depending on the name of the pictures, you can add a computation key in MicMac-LocalChantierDescripteur.xml.

Example :
<pre>
<Global>
<ChantierDescripteur>
[...]
<KeyedNamesAssociations>
<IsParametrized> true </IsParametrized>
<Calcs>
<Arrite> 1 1 </Arrite>
<Direct>
<PatternTransform> [0-9]{4}_cam_([0-9]{3})\.tif </PatternTransform>
<CalcName> masq_$1.tif </CalcName>
</Direct>
</Calcs>
<Key> MyKeyCalculMasq </Key>
</KeyedNamesAssociations>
</ChantierDescripteur>
</Global>
</pre>
Then use KeyCalculMasq=MyKeyCalculMasq on HomolFilterMasq command line to automatically use the correct mask.

===Masq3D===
To use a file 3D masq as seized by SaisieMasqQT with a AperiCloud.ply file.
If you want to use this option, the orientation must be indicated with the option OriMasq3D.

===SelecTer===
This option can be used to decrease the number of tie-points while maintaining the proportion of multiplicity.
If SelecTer=[Per,Prop], then in each tile of size S = Per * Resol in the ground coordinate 1 the point are selected in the subtile of size S * Prop^0.5

===DistId===
This option supress the pair of point P1,P2 such that d(P1,P2) < DistId. This can be usefull when the acquistion was made using a turn table to automatically supress the points on the background.

Tips & Tricks

2018-08-03T12:41:18Z

Mgaudin : Début page, à compléter

* Vous pouvez accéder à des interfaces de saisie graphique en ajoutant un "v" devant le nom de la commande MicMac (''vTapioca'', ''vTapas'', etc.)

* Si un jour vous lancez '''SaisieMasqQT''' et que votre nuage ne s'affiche pas, c'est peut-être qu'il est loin car dans un repère absolu. Si ça vous arrive, faites un double clic au centre de l'écran et ça devrait ramener votre nuage au centre !

Unwrap Vault

2018-08-02T12:46:37Z

Luc Girod :

[[Image:picto-liste.png|25px|link=Tutorials]] [[Tutorials|Tutorials index]]
==Description==
The post by user mathias.f on the MicMac forum will be translated into forum format soon : [http://forum-micmac.forumprod.com/vault-or-dome-develop-developpe-d-une-vo-te-ou-d-un-dome-t1597.html Forum topic]

TestRegEx

2018-07-27T14:24:11Z

Mgaudin : Ajout tableau exemple RegEx

[[Image:picto-liste.png|25px]] [[Command|List of commands]]
==Description==
TestRegEx is a tool that allows you to test a regular expression.

===Regular Expressions===
Regular expressions (RegEx) are a sequence of characters that define a search pattern.

{| class="wikitable"
|+Pattern for regular expressions
!scope=col|RegEx
!scope=col|Description
|-
| <pre>[abc]</pre> || A single character of a, b or c
|-
| <pre>[^abc]</pre> || A character, except a, b or c
|-
| <pre>[a-z]</pre> || A character in the range: a-z
|-
| <pre>[^a-z]</pre> || A character not in the range: a-z
|-
| <pre>[a-zA-Z]</pre> || A character in the range: a-z or A-Z
|-
| <pre>.</pre> || Any single character
|-
| <pre>\s</pre> || Any whitespace character
|-
| <pre>\S</pre> || Any non-whitespace character
|-
| <pre>\d</pre> || Any digit
|-
| <pre>\D</pre> || Any non-digit
|-
| <pre>\w</pre> || Any word character
|-
| <pre>\W</pre> || Any non-word character
|-
| <pre>(...)</pre> || Capture everything enclosed
|-
| <pre>(a|b)</pre> || Match either a or b
|-
| <pre>a?</pre> || Zero or one of a
|-
| <pre>a*</pre> || Zero or more of a
|-
| <pre>a+</pre> || One or more of a
|-
| <pre>a{3}</pre> || Exactly 3 of a
|-
| <pre>a{3,}</pre> || 3 or more of a
|-
| <pre>a{3,6}</pre> || Between 3 and 6 of a
|-
| <pre>^</pre> || Start of string
|-
| <pre>$</pre> || End of string
|-
| <pre>\b</pre> || A word boundary
|-
| <pre>\B</pre> || Non-word boundary
|}

===Syntax===
The global syntax for TestRegEx is:
<pre>mm3d TestRegEx Pattern NamedArgs</pre>

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

<pre>
*****************************
* Help for Elise Arg main *
*****************************
Mandatory unnamed args :
* string :: {Pattern of files}
Named args :
* [Name=DispPat] bool :: {Display Pattern to use in cmd line ; Def=false}
* [Name=ExpList] string :: {Export list image in text file ; Def=false}
</pre>

===Example===
Let the following set of images :
<pre>IMG_4167.JPG
IMG_4168.JPG
IMG_4169.JPG
IMG_4170.JPG
IMG_4171.JPG
IMG_4172.JPG
IMG_4173.JPG
IMG_4174.JPG
IMG_4175.JPG
IMG_4176.JPG
IMG_4177.JPG
IMG_4178.JPG
IMG_4179.JPG
IMG_4180.JPG
</pre>

If you want to select only images IMG_4170.JPG, IMG_4171.JPG and IMG_4172.JPG, you can type: <pre>"IMG_417[0-2].JPG"</pre>

If you want to select all the images, you can type: <pre>".*JPG"</pre>

LumRas

2018-05-27T12:16:02Z

Maximeseguin :

[[Image:picto-liste.png|25px]] [[Command|List of commands]]
==Description==
The LumRas command, allows to mix a master image with raking lights images.

===Syntax===
The global syntax for LumRas is :
<pre>mm3d LumRas MasterImage RakingLightImages NamedArgs</pre>

===Workflow===

[[Image:Picto-previous.png|20px]] Previous Command : [[Tapioca]]<br>


===Help===

Mandatory unnamed args :
*string :: {Master Image}
*string :: {Raking Light Images Files}
Named args :
*[Name=Masq] string :: postfix for masq file (default=_Masq)

===Example===
For this example, we consider :
* a master image with ambiant light : IMG_0001.JPG,
* a masq on the object in the master image (result of command : SaisieMasq "IMG_0001.JPG"),
* three raking light images.

<pre>mm3d LumRas "IMG_0001.JPG" "IMG_000[2-4].JPG" Masq="_Masq"</pre>