Fontaine tutorial - Historique des versions

Mdaakir : /* Download */

2022-07-14T18:17:05Z

‎Download

AmstrongMF : /* 6. Automatic method */

2017-08-01T18:53:02Z

‎6. Automatic method

Dias : /* Download */

2016-12-09T16:44:55Z

‎Download

Dias : /* Download */

2016-12-09T16:42:53Z

‎Download

Dias le 9 décembre 2016 à 16:41

2016-12-09T16:41:48Z

Dias le 19 août 2016 à 09:06

2016-08-19T09:06:30Z

XavierR : Fix typo

2016-06-17T13:50:12Z

Fix typo

Dias : /* Presentation */

2016-06-01T13:13:49Z

‎Presentation

Dias le 1 juin 2016 à 13:13

2016-06-01T13:13:12Z

XavierR : Fix typo

2016-05-18T07:45:01Z

Fix typo

@@ Ligne 6 : / Ligne 6 : @@
 ==Download==
-You can find this dataset at <code>http://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
+You can find this dataset at <code>https://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
 Once you have downloaded it, you have to unzip the ".zip" archive.

@@ Ligne 77 : / Ligne 77 : @@
 ===6. Automatic method===
 [[Image:SaisieMasqQT_ply_Fontaine.png|thumb|180px|SaisieMasqQT]]
-We will work here with the same dataset of 30 images, obtained with the Canon 70D on a 18mm lens. We don't make any difference between each points of view for this exercise. The setting up computation is the same that the previous exercise : we can get as an output, the results from the Ori-MEP/, from the command [[Tapas]]. We can obtain aswell the AperiCloud_MEP.ply file, as a result of the [[AperiCloud]] command we did before.
+We will work here with the same dataset of 30 images, obtained with the Canon 70D on a 18mm lens. We don't make any difference between each points of view for this exercise. The setting up computation is the same that the previous exercise : we can get as an output, the results from the Ori-Fontaine/, from the command [[Tapas]]. We can obtain aswell the AperiCloud_Fontaine.ply file, as a result of the [[AperiCloud]] command we did before.
-the calculation mode that we will use, based on the geometry pattern. MicMac will choose itself master images and associated images, from a 3D mask that we will draw on points cloud : AperiCloud_MEP.ply.
+the calculation mode that we will use, based on the geometry pattern. MicMac will choose itself master images and associated images, from a 3D mask that we will draw on points cloud : AperiCloud_Fontaine.ply.
-We will start to draw a 3D mask, from the AperiCloud_MEP.ply file.<br>
+We will start to draw a 3D mask, from the AperiCloud_Fontaine.ply file.<br>
-<pre>mm3d SaisieMasqQT AperiCloud_MEP.ply</pre>
+<pre>mm3d SaisieMasqQT AperiCloud_Fontaine.ply</pre>
 Then, we can run the 3D reconstruction with the C3DC command :
 <pre>mm3d C3DC BigMac "(A|B|C|D).*JPG" Fontaine Masq3D=AperiCloud_Fontaine_selectionInfo.xml</pre>

@@ Ligne 6 : / Ligne 6 : @@
 ==Download==
-You can find this dataset at :<code>http://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
+You can find this dataset at <code>http://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
 Once you have downloaded it, you have to unzip the ".zip" archive.

@@ Ligne 6 : / Ligne 6 : @@
 ==Download==
-You can download this dataset at :<code>http://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
+You can find this dataset at :<code>http://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
 Once you have downloaded it, you have to unzip the ".zip" archive.

@@ Ligne 6 : / Ligne 6 : @@
 ==Download==
-*There is a direct download link to download this dataset at :<code>http://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
+You can download this dataset at :<code>http://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
 Once you have downloaded it, you have to unzip the ".zip" archive.

@@ Ligne 1 : / Ligne 1 : @@
-[[Image:picto-liste.png|25px]] [[Tutorial|Tutorial index]]
+[[Image:picto-liste.png|25px|link=Tutorials]] [[Tutorials|Tutorials index]]
 ==Description==
 In this tutorial we go further in command details, process, results, products etc...

@@ Ligne 3 : / Ligne 3 : @@
 In this tutorial we go further in command details, process, results, products etc...
 Here you will learn how to process a dataset of a circular point of view around an object.
-It's also a good way to compare the old [[Malt]] and the new pipeline [[Pims]]specially for geometry based on images.
+It's also a good way to compare the old [[Malt]] and the new pipeline [[Pims]], specially for geometry based on images.
 ==Download==

@@ Ligne 11 : / Ligne 11 : @@
 ==Presentation==
 The folder contain 30 JPG files. In this dataset there isn't any data about the camera which was used. The shooting set contains 30 images, all taken with the Canon 70D camera with an 18mm lens. The camera saves metadata for all the pictures (exif data). If you are looking in the property data of each pictures, the 18mm lens is mentioned and the various chosen settings while the production of this dataset was realized (opening, break, ...). <br>
 There are 5 parts in this dataset :
 *4 parts includes 4 differents points of view of the fountain. Each part was created with a cross points of view. This means there is one master image at the center and 4 images around it (top, bottom, left and right from the master image).

@@ Ligne 2 : / Ligne 2 : @@
 ==Download==
 *There is a direct download link to download this dataset at :<code>http://micmac.ensg.eu/data/fontaine_dataset.zip</code> <br>
-Once you have download it, you have to UnZip the ".zip" archive.
+Once you have downloaded it, you have to unzip the ".zip" archive.
 ==Description==
-The folder contain 30 JPG files. In this dataset there isn't any data about the camera which was used. The shooting set contains 30 images, all taken with the Canon 70D camera with an 18mm lens. The camera saves metadata for all the pictures (exif data). If you are looking in the property data of each pictures, the 18mm lens is mentionned and the various chosen settings while the production of this dataset was realized (opening, break, ...). <br>
+The folder contain 30 JPG files. In this dataset there isn't any data about the camera which was used. The shooting set contains 30 images, all taken with the Canon 70D camera with an 18mm lens. The camera saves metadata for all the pictures (exif data). If you are looking in the property data of each pictures, the 18mm lens is mentioned and the various chosen settings while the production of this dataset was realized (opening, break, ...). <br>
 <br>
 There are 5 parts in this dataset :
@@ Ligne 15 : / Ligne 15 : @@
 ==Tutorial==
 ===1. Tie-points search===
-The tie points search for all the images should be computed simultaneously so the 4 parts are linked with each others. The orientation will be in an arbitrary system, but the same system will be kept for all the images. The 4 depth maps generated (as well as the 4 clouds) will therefore be in the same cordinate system.
+The tie points search for all the images should be computed simultaneously so the 4 parts are linked with each others. The orientation will be in an arbitrary system, but the same system will be kept for all the images. The 4 depth maps generated (as well as the 4 clouds) will therefore be in the same coordinate system.
 First, we need to run the tie-points search :
@@ Ligne 44 : / Ligne 44 : @@
 We choose the image pattern that contains our master image aswell as the secondary images and we define the master image thanks to the Master setting. The ZoomF setting allows to define the last stage of the image pyramid which will be used. For this computation, we don't do it on full resolution images. The [[Malt]] pattern will begin to compute first on sub-sampled images and then increase the image sizes until sub-sampled images from a level 2.
-Within the process, you can verify if the correlation is working by analysing the files MM-Malt-Img-AIMG_2470/Correl_STD-MALT_Num_#.tif each files matches to a stage of the images pyramid. These files contains correlation scores : white means there is a very good matching score. More the grey is dark, less the matching process went well.
+Within the process, you can verify if the correlation is working by analyzing the files MM-Malt-Img-AIMG_2470/Correl_STD-MALT_Num_#.tif each files matches to a stage of the images pyramid. These files contains correlation scores : white means there is a very good matching score. More the gray is dark, less the matching process went well.
 ===5. Visualize 3D products results===
@@ Ligne 84 : / Ligne 84 : @@
 ===7. 3D Reconstruction===
-NB : The tools we will use now aren't completly finished yet, so please remind developpers if errors occurred.<br>
+NB : The tools we will use now aren't completely finished yet, so please remind developers if errors occurred.<br>
 When you get a 3D point cloud, you can compute a 3D textured model. First, you have to create a mesh. The tool to create mesh is [[TiPunch]] :
 <div class="toccolours mw-collapsible mw-collapsed" style="background-color: Lavender">