Renaming folder.

3 files changed, 0 insertions, 283 deletions

diff --git a/docs/user_guide/augmented_reality_pipeline/aruco_scene_creation.md b/docs/user_guide/augmented_reality_pipeline/aruco_scene_creation.md
deleted file mode 100644
index d9a7be5..0000000
--- a/docs/user_guide/augmented_reality_pipeline/aruco_scene_creation.md
+++ /dev/null
@@ -1,131 +0,0 @@
-Setup ArUco markers scene
-=========================
-
-The OpenCV library provides a module to detect fiducial markers into a picture and estimate its pose (cf [OpenCV ArUco tutorial page](https://docs.opencv.org/4.x/d5/dae/tutorial_aruco_detection.html)).
-
-![OpenCV ArUco markers](https://pyimagesearch.com/wp-content/uploads/2020/12/aruco_generate_tags_header.png)
-
-The ArGaze [ArUcoMarkers submodule](../../argaze.md/#argaze.ArUcoMarkers) eases markers creation and description of their expected place for further camera pose estimation.
-
-## Print ArUco markers from a ArUco dictionary
-
-ArUco markers always belongs to a set of markers called ArUco markers dictionary.
-
-![ArUco dictionaries](../../img/aruco_dictionaries.png)
-
-Many ArUco dictionaries exist with properties concerning the format, the number of markers or the difference between each markers to avoid error in tracking.
-
-Here is the documention [about ArUco markers dictionaries](https://docs.opencv.org/3.4/d9/d6a/group__aruco.html#gac84398a9ed9dd01306592dd616c2c975).
-
-The creation of [ArUcoMarkers](../../argaze.md/#argaze.ArUcoMarkers.ArUcoMarker) pictures from a dictionary is illustrated in the code below:
- 
-``` python
-from argaze.ArUcoMarkers import ArUcoMarkersDictionary
-
-# Create a dictionary of specific April tags
-aruco_dictionary = ArUcoMarkersDictionary.ArUcoMarkersDictionary('DICT_APRILTAG_16h5')
-
-# Export marker n°5 as 3.5 cm picture with 300 dpi resolution
-aruco_dictionary.create_marker(5, 3.5).save('./markers/', 300)
-
-# Export all dictionary markers as 3.5 cm pictures with 300 dpi resolution
-aruco_dictionary.save('./markers/', 3.5, 300)
-```
-
-Let's print some of them before to go further.
-
-!!! warning
-	Print markers with a blank zone around them to help in their detection.
-
-## Describe expected ArUco markers place
-
-Once [ArUcoMarkers](../../argaze.md/#argaze.ArUcoMarkers.ArUcoMarker) pictures are placed into a scene it is possible to describe their expected 3D place into a file.
-
-![ArUco scene](../../img/aruco_scene.png)
-
-Where ever the origin point is, all expected markers places need to be described in a [right-handed 3D axis](https://robotacademy.net.au/lesson/right-handed-3d-coordinate-frame/) where:
-
-* +X is pointing to the right,
-* +Y is pointing to the top,
-* +Z is pointing to the backward.
-
-!!! warning
-	All ArUco markers spatial values must be given in **centimeters**.
-
-### Edit OBJ file description
-
-OBJ file format could be exported from most 3D editors.
-
-``` obj
-o DICT_APRILTAG_16h5#0_Marker
-v -5.000000 14.960000 0.000000
-v 0.000000 14.960000 0.000000
-v -5.000000 19.959999 0.000000
-v 0.000000 19.959999 0.000000
-vn 0.0000 0.0000 1.0000
-s off
-f 1//1 2//1 4//1 3//1
-o DICT_APRILTAG_16h5#1_Marker
-v 25.000000 14.960000 0.000000
-v 30.000000 14.960000 0.000000
-v 25.000000 19.959999 0.000000
-v 30.000000 19.959999 0.000000
-vn 0.0000 0.0000 1.0000
-s off
-f 5//2 6//2 8//2 7//2
-o DICT_APRILTAG_16h5#2_Marker
-v -5.000000 -5.000000 0.000000
-v 0.000000 -5.000000 0.000000
-v -5.000000 0.000000 0.000000
-v 0.000000 0.000000 0.000000
-vn 0.0000 0.0000 1.0000
-s off
-f 9//3 10//3 12//3 11//3
-o DICT_APRILTAG_16h5#3_Marker
-v 25.000000 -5.000000 0.000000
-v 30.000000 -5.000000 0.000000
-v 25.000000 0.000000 0.000000
-v 30.000000 0.000000 0.000000
-vn 0.0000 0.0000 1.0000
-s off
-f 13//4 14//4 16//4 15//4
-```
-
-Here are common OBJ file features needed to describe ArUco markers place:  
-
-* Object lines (line starting with *o* key) indicate markers dictionary and id by following a format: **DICTIONARY**#**ID**\_Marker.
-* Vertice lines (lines starting with *v* key) indicate markers corners. The marker size will be automatically deducted from the geometry.
-* Plane normals (lines starting with *vn* key) need to be exported for further pose estimation.
-* Face (lines starting with *f* key) link vertices and normals indexes together.
-
-!!! warning
-	All markers must have the same size and belong to the same dictionary.
-
-### Edit JSON file description
-
-JSON file format allows to describe markers places using translation and euler angle rotation vectors.
-
-``` json
-{
-    "dictionary": "DICT_APRILTAG_16h5",
-    "marker_size": 5,
-    "places": {
-        "0": {
-            "translation": [-2.5, 17.5, 0],
-            "rotation": [0.0, 0.0, 0.0]
-        },
-        "1": {
-            "translation": [27.5, 17.5, 0],
-            "rotation": [0.0, 0.0, 0.0]
-        },
-        "2": {
-            "translation": [-2.5, -2.5, 0],
-            "rotation": [0.0, 0.0, 0.0]
-        },
-        "3": {
-            "translation": [27.5, -2.5, 0],
-            "rotation": [0.0, 0.0, 0.0]
-        }
-    }
-}
-```
diff --git a/docs/user_guide/augmented_reality_pipeline/introduction.md b/docs/user_guide/augmented_reality_pipeline/introduction.md
deleted file mode 100644
index a06b1e2..0000000
--- a/docs/user_guide/augmented_reality_pipeline/introduction.md
+++ /dev/null
@@ -1,19 +0,0 @@
-Overview
-========
-
-This section explains how to build augmented reality pipelines based on ArUco Markers technology for various use cases.  
-
-First, let's look at the schema below: it gives an overview of the main notions involved in the following chapters.
-
-![Augmented reality pipeline](../../img/augmented_reality_pipeline.png)
-
-To build your own augmented reality pipeline, you need to know:
-
-* [How to setup an ArUco markers scene](aruco_scene_creation.md),
-* [How to deal with an ArCamera instance](ar_camera_configuration_and_execution.md),
-* [How to add ArScene instance](ar_scene.md),
-* [How to visualize ArCamera and ArScenes](visualisation.md)
-
-More advanced features are also explained like:
-
-* [How to script augmented reality pipeline](advanced_topics/scripting.md)
diff --git a/docs/user_guide/augmented_reality_pipeline/optic_parameters_calibration.md b/docs/user_guide/augmented_reality_pipeline/optic_parameters_calibration.md
deleted file mode 100644
index 0561112..0000000
--- a/docs/user_guide/augmented_reality_pipeline/optic_parameters_calibration.md
+++ /dev/null
@@ -1,133 +0,0 @@
-Calibrate optic parameters
-==========================
-
-A camera device have to be calibrated to compensate its optical distorsion.
-
-![Optic parameters calibration](../../img/optic_calibration.png)
-
-## Print calibration board
-
-The first step to calibrate a camera is to create an [ArUcoBoard](../../argaze.md/#argaze.ArUcoMarkers.ArUcoBoard) like in the code below:
-
-``` python
-from argaze.ArUcoMarkers import ArUcoMarkersDictionary, ArUcoBoard
-
-# Create ArUco dictionary
-aruco_dictionary = ArUcoMarkersDictionary.ArUcoMarkersDictionary('DICT_APRILTAG_16h5')
-
-# Create an ArUco board of 7 columns and 5 rows with 5 cm squares with 3cm ArUco markers inside
-aruco_board = ArUcoBoard.ArUcoBoard(7, 5, 5, 3, aruco_dictionary)
-
-# Export ArUco board with 300 dpi resolution
-aruco_board.save('./calibration_board.png', 300)
-```
-
-!!! note
-      There is **A3_DICT_APRILTAG_16h5_3cm_35cmx25cm.pdf** file located in *./src/argaze/ArUcoMarkers/utils/* folder ready to be printed on A3 paper sheet.
-
-Let's print the calibration board before to go further.
-
-## Capture board pictures 
-
-Then, the calibration process needs to make many different captures of an [ArUcoBoard](../../argaze.md/#argaze.ArUcoMarkers.ArUcoBoard) through the camera and then, pass them to an [ArUcoDetector](../../argaze.md/#argaze.ArUcoMarkers.ArUcoDetector.ArUcoDetector) instance to detect board corners and store them as calibration data into an [ArUcoOpticCalibrator](../../argaze.md/#argaze.ArUcoMarkers.ArUcoOpticCalibrator) for final calibration process.
-
-![Calibration step](../../img/optic_calibration_step.png)
-
-The sample of code below illustrates how to:
-
-* load all required ArGaze objects,
-* detect board corners into a Full HD camera video stream, 
-* store detected corners as calibration data then, 
-* once enough captures are made, process them to find optic parameters and, 
-* finally, save optic parameters into a JSON file.
-
-``` python
-from argaze.ArUcoMarkers import ArUcoMarkersDictionary, ArUcoOpticCalibrator, ArUcoBoard, ArUcoDetector
-
-# Create ArUco dictionary
-aruco_dictionary = ArUcoMarkersDictionary.ArUcoMarkersDictionary('DICT_APRILTAG_16h5')
-
-# Create ArUco optic calibrator
-aruco_optic_calibrator = ArUcoOpticCalibrator.ArUcoOpticCalibrator()
-
-# Create ArUco board of 7 columns and 5 rows with 5 cm squares with 3cm aruco markers inside
-# Note: This board is the one expected during further board tracking
-expected_aruco_board = ArUcoBoard.ArUcoBoard(7, 5, 5, 3, aruco_dictionary)
-
-# Create ArUco detector
-aruco_detector = ArUcoDetector.ArUcoDetector(dictionary=aruco_dictionary, marker_size=3)
-
-# Assuming that live Full HD (1920x1080) video stream is enabled
-...
-
-# Assuming there is a way to escape the while loop
-...
-
-    # Capture images from video stream
-    while video_stream.is_alive():
-
-        image = video_stream.read()
-
-        # Detect all board corners in image
-        aruco_detector.detect_board(image, expected_aruco_board, expected_aruco_board.markers_number)
-
-        # If all board corners are detected
-        if aruco_detector.board_corners_number == expected_aruco_board.corners_number:
-
-            # Draw board corners to show that board tracking succeeded
-            aruco_detector.draw_board(image)
-
-            # Append tracked board data for further calibration processing 
-            aruco_optic_calibrator.store_calibration_data(aruco_detector.board_corners, aruco_detector.board_corners_identifier)
-
-# Start optic calibration processing for Full HD image resolution
-print('Calibrating optic...')
-optic_parameters = aruco_optic_calibrator.calibrate(aruco_board, dimensions=(1920, 1080))
-
-if optic_parameters:
-
-    # Export optic parameters
-    optic_parameters.to_json('./optic_parameters.json')
-
-    print('Calibration succeeded: optic_parameters.json file exported.')
-
-else:
-
-    print('Calibration failed.')
-```
-
-Below, an optic_parameters JSON file example:
-
-```json
-{
-    "rms": 0.6688921504088245,
-    "dimensions": [
-        1920,
-        1080
-    ],
-    "K": [
-        [
-            1135.6524381415752,
-            0.0,
-            956.0685325355497
-        ],
-        [
-            0.0,
-            1135.9272506869524,
-            560.059099810324
-        ],
-        [
-            0.0,
-            0.0,
-            1.0
-        ]
-    ],
-    "D": [
-        0.01655492265003404,
-        0.1985524264972037,
-        0.002129965902489484,
-        -0.0019528582922179365,
-        -0.5792910353639452
-    ]
-}
-```