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Configure and execute ArUcoCamera
=================================
Once [ArUco markers are placed into a scene](aruco_markers_description.md) and [areas of interest are described](aoi_description.md), everything is ready to setup an ArUco marker pipeline thanks to [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera) class.
As it inherits from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame), the [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera) class benefits from all the services described in [gaze analysis pipeline section](./user_guide/gaze_analysis_pipeline/introduction.md).
## Load JSON configuration file
The [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera) internal pipeline loads from a JSON configuration file thanks to [ArUcoCamera.from_json](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera.from_json) class method.
Here is a simple JSON [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera) configuration file example:
```json
{
"name": "My FullHD camera",
"size": [1920, 1080],
"aruco_detector": {
"dictionary": "DICT_APRILTAG_16h5",
"marker_size": 5
},
"image_parameters": {
"background_weight": 1,
...
"draw_detected_markers": {
"color": [0, 255, 0],
"draw_axes": {
"thickness": 3
}
}
}
}
```
Then, here is how to load the JSON file:
```python
from argaze.ArUcoMarkers import ArUcoCamera
# Load ArUcoCamera
aruco_camera = ArUcoCamera.ArUcoCamera.from_json('./configuration.json')
```
Now, let's understand the meaning of each JSON entry.
### Name - *inherited from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame)*
The name of the [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera) frame. Basically useful for visualisation purpose.
### Size - *inherited from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame)*
The size of the [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera) frame in pixels. Be aware that gaze positions have to be in the same range of value to be projected in.
### ArUco Detector
The first [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera) pipeline step is to detect ArUco markers inside input image and estimate their poses.
The [ArUcoDetector](../../argaze.md/#argaze.ArUcoMarkers.ArUcoDetector) is in charge to detect all markers from a specific dictionary with a given size in centimeters.
!!! warning
JSON *aruco_detector* entry is mandatory.
### Image parameters - *inherited from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame)*
The usual [ArFrame visualisation parameters](./user_guide/gaze_analysis_pipeline/visualisation.md) plus one additional *draw_detected_markers* field.
## Pipeline execution
Pass each camera image to [ArUcoCamera.watch](../../argaze.md/#argaze.ArFeatures.ArCamera.watch) method to execute the whole intanciated pipeline.
```python
# Assuming that Full HD (1920x1080) video stream or file is opened
...
# Assuming there is a way to escape the while loop
while ...:
# Capture image from video stream of file
image = video_capture.read()
# Detect ArUco markers and more...
aruco_camera.watch(image)
# Display ArUcoCamera frame image to check that ArUco markers are well detected and scene is well projected
... aruco_camera.image()
```
!!! warning
ArUco markers pose estimation algorithm can lead to errors due to geometric ambiguities as explain in [this article](https://ieeexplore.ieee.org/document/1717461). To discard such ambiguous cases, markers should **not be parallel to camera plan**.
At this point, the [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarkers.ArUcoCamera) only detects ArUco markers as no scene description is provided.
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