MicMac - Contributions de l’utilisateur [fr]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-18T10:14:47Z

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: 03 Pierrerue

2024-05-18T10:06:51Z

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> . 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

2024-05-18T10:06:31Z

Hubrice : /* Description */

[[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: 06 Gréalou (added soon)

2024-05-15T09:45:14Z

Hubrice : /* Wiki Tutorial Sheet - Gréalou */

This tutorial will be added soon !

MicMacRoom Tutorial: 06 Gréalou (added soon)

2024-05-15T09:44:26Z

Hubrice : Hubrice a déplacé la page MicMacRoom Tutorial: 06 Gréalou vers MicMacRoom Tutorial: 06 Gréalou (added soon)

== Wiki Tutorial Sheet - Gréalou ==

'''Dataset Type:''' Tutorial to illustrate processing on drone acquisition. 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 one point cloud with absolute position and orientation with the GCPs.

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

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

==='''1) Calculation of Homologous Points: TapiocaMulScal '''===
Calculation of homologous points in all images. Here, we use the TapiocaMulScal command to calculate homologous points: this command degrades the image quality (low image size parameter) to roughly identify images that are close to each other. Then, in a second step, the command enhances the quality of these same images (high image size parameter) to compute homologous points, based on the similarities it first found across images in the initial stages

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.

==='''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.

==='''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.

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Tutorial: 06 Gréalou

2024-05-15T09:44:26Z

Hubrice : Hubrice a déplacé la page MicMacRoom Tutorial: 06 Gréalou vers MicMacRoom Tutorial: 06 Gréalou (added soon)

#REDIRECTION [[MicMacRoom Tutorial: 06 Gréalou (added soon)]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T13:26:10Z

Hubrice : /* Creation of the project */

[[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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:55:10Z

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 final". 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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:54:52Z

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|1000px|Processing Pipeline]]

==='''Creation of the project'''===
Open the 'File' tab in MicMac and launch a new pipeline with the tamplate "Mic Mac Aero TapiocaFile final". 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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:54:31Z

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|600px|Processing Pipeline]]

==='''Creation of the project'''===
Open the 'File' tab in MicMac and launch a new pipeline with the tamplate "Mic Mac Aero TapiocaFile final". 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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:54:07Z

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|500px|Processing Pipeline]]

==='''Creation of the project'''===
Open the 'File' tab in MicMac and launch a new pipeline with the tamplate "Mic Mac Aero TapiocaFile final". 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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:53:07Z

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|100px|Description de l'image]]

==='''Creation of the project'''===
Open the 'File' tab in MicMac and launch a new pipeline with the tamplate "Mic Mac Aero TapiocaFile final". 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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:51:59Z

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]]
[[Image:PieplineGL.png|alt=Description de l'image|thumb|200px|Description de l'image]]

==='''Creation of the project'''===
Open the 'File' tab in MicMac and launch a new pipeline with the tamplate "Mic Mac Aero TapiocaFile final". 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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:50:25Z

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]]

==='''Creation of the project'''===
Open the 'File' tab in MicMac and launch a new pipeline with the tamplate "Mic Mac Aero TapiocaFile final". 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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

Fichier:PipelineGL.png

2024-05-14T12:49:19Z

Hubrice :

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:47:57Z

Hubrice : /* 2) Image Coordinate Transformation: OriConvert */

[[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.

==='''Creation of the project'''===
Open the 'File' tab in MicMac and launch a new pipeline with the tamplate "Mic Mac Aero TapiocaFile final". 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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:44:13Z

Hubrice : /* 8) Change of image orientation system : ChgSysCo (optional) */

[[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.

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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:43:28Z

Hubrice : /* 6) Absolute Orientation of Images: CenterBascule : */

[[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.

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

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:41:43Z

Hubrice : /* 9) Third visualisation with point clouds: AperiCloud_3 (optional) */

[[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.

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

==='''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

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

[[Image:AperiCloud3.png]]

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:41:25Z

Hubrice : /* 7) Second visualisation with point clouds: AperiCloud_2 */

[[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.

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

==='''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

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:41:12Z

Hubrice : /* 5) First visualisation with point clouds: AperiCloud_1 */

[[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.

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

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:40:47Z

Hubrice : /* 4) Relative Orientation of Images: Tapas_2: */

[[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.

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

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:40:30Z

Hubrice : /* 3) Camera Calibration: Tapas_1 */

[[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.

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

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

Fichier:AperiCloud3.png

2024-05-14T12:37:18Z

Hubrice :

Fichier:ChgSysCo.png

2024-05-14T12:37:02Z

Hubrice :

Fichier:AperiCloud2.png

2024-05-14T12:36:46Z

Hubrice :

Fichier:CenterBascule.png

2024-05-14T12:35:01Z

Hubrice :

Fichier:AperiCloud1.png

2024-05-14T12:34:36Z

Hubrice :

Fichier:Tapas2.png

2024-05-14T12:30:49Z

Hubrice :

Fichier:Tapas1.png

2024-05-14T12:30:35Z

Hubrice :

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:21:29Z

Hubrice : /* 2) Image Coordinate Transformation: OriConvert */

[[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.

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

==='''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.

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Tutorial: 05 GrandLeez

2024-05-14T12:20:19Z

Hubrice : /* 2) Image Coordinate Transformation: OriConvert */

[[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.

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

[[Fichier:OriConvert.JPG]]

==='''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.

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

Fichier:OriConvert.png

2024-05-14T12:18:45Z

Hubrice :

MicMacRoom Tutorial: 05 GrandLeez

2024-05-03T13:17:06Z

Hubrice : /* 1) Calculation of Homologous Points: TapiocaFile */

[[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.

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

==='''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.

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Tutorial: 05 GrandLeez

2024-05-03T13:16:35Z

Hubrice : /* 1) Calculation of Homologous Points: TapiocaFile */

[[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.

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

==='''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.

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Tutorial: 05 GrandLeez

2024-05-03T13:14:01Z

Hubrice : /* Creation of the project */

[[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.

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

==='''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.

==='''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.

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

Fichier:Tapioca File.png

2024-05-03T13:10:48Z

Hubrice : tapioca file grandleez

tapioca file grandleez

Fichier:Project directory.png

2024-05-03T13:07:07Z

Hubrice : image project directory

image project directory

MicMacRoom Tutorial: 05 GrandLeez

2024-05-03T13:02:22Z

Hubrice : /* 1) Calculation of Homologous Points: TapiocaFile */

[[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.

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

==='''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.

==='''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.

==='''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.

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Tutorial: 01 Gravillions

2024-05-03T08:04:29Z

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

#REDIRECTION [[MicMacRoom Tutorial: 01 Gravillons]]

MicMacRoom Tutorial: 01 Gravillons

2024-05-03T08:04:28Z

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> 
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: 06 Gréalou (added soon)

2024-04-30T13:40:50Z

Hubrice : /* Wiki Tutorial Sheet - Gréalou */

MicMacRoom Tutorial: 06 Gréalou (added soon)

2024-04-30T13:29:47Z

Hubrice : Page créée avec « == Wiki Tutorial Sheet - Gréalou == '''Dataset Type:''' Tutorial to illustrate processing on aerial images (drone, airplane, etc.). This pipeline enables the generation... »

== Wiki Tutorial Sheet - Gréalou ==

'''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.

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

==='''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.

If your dataset doesn't have this xml file, you can generate it with the command : (A FAIRE)

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.

==='''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.

==='''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.

==='''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.

==='''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.

==='''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

==='''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.

==='''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

==='''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 "Mic Mac Aero TapiocaFile", which is a pipeline with all the parameters of the tutorial already filled.

MicMacRoom Installation

2024-04-30T13:10:19Z

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 Installation

2024-04-30T13:09:45Z

Hubrice :

== 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 Installation

2024-04-30T13:09:16Z

Hubrice :

== 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 Installation

2024-04-30T12:58:43Z

Hubrice :

== Information ==
This guide will cover the installation of MicMacRoom on WINDOWS.

== 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 parameters" -> then at the bottom "environment variable")

MicMacRoom Installation

2024-04-30T12:37:45Z

Hubrice : Page créée avec « == Information == This guide will cover the installation of MicMacRoom on WINDOWS. == Necessary software needed for installation == === MicMac Installation === In the c... »

MicMacRoom

2024-04-30T12:22:10Z

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]].

MicMacRoom

2024-04-30T12:21:47Z

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]].