Voir la source de 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.