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Edit and execute pipeline
=========================
Once [ArUco markers are placed into a scene](aruco_marker_description.md), they can be detected by the [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarker.ArUcoCamera) class.
As [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarker.ArUcoCamera) inherits from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame), the [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarker.ArUcoCamera) class also benefits from all the services described in the [gaze analysis pipeline section](../gaze_analysis_pipeline/introduction.md).
Once defined, an ArUco marker pipeline needs to embedded inside a context that will provides it both gaze positions and camera images to process.
## Edit JSON configuration
Here is a simple JSON [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarker.ArUcoCamera) configuration example:
```json
{
"argaze.ArUcoMarker.ArUcoCamera.ArUcoCamera": {
"name": "My FullHD camera",
"size": [1920, 1080],
"aruco_detector": {
"dictionary": "DICT_APRILTAG_16h5"
},
"gaze_movement_identifier": {
"argaze.GazeAnalysis.DispersionThresholdIdentification.GazeMovementIdentifier": {
"deviation_max_threshold": 25,
"duration_min_threshold": 150
}
},
"image_parameters": {
"background_weight": 1,
"draw_detected_markers": {
"color": [0, 255, 0],
"draw_axes": {
"thickness": 3
}
},
"draw_gaze_positions": {
"color": [0, 255, 255],
"size": 2
},
"draw_fixations": {
"deviation_circle_color": [255, 0, 255],
"duration_border_color": [127, 0, 127],
"duration_factor": 1e-2
},
"draw_saccades": {
"line_color": [255, 0, 255]
}
}
}
}
```
Let's understand the meaning of each JSON entry.
### argaze.ArUcoMarker.ArUcoCamera.ArUcoCamera
The class name of the object being loaded.
### *name - inherited from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame)*
The name of the [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarker.ArUcoCamera) frame. Basically, it is useful for visualization purposes.
### *size - inherited from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame)*
The size of the [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarker.ArUcoCamera) frame in pixels. Be aware that gaze positions have to be in the same range of value to be projected.
### *aruco_detector*
The first [ArUcoCamera](../../argaze.md/#argaze.ArUcoMarker.ArUcoCamera) pipeline step is to detect ArUco markers inside the input image.
The [ArUcoDetector](../../argaze.md/#argaze.ArUcoMarker.ArUcoDetector) is in charge of detecting all markers from a specific dictionary.
!!! warning "Mandatory"
JSON *aruco_detector* entry is mandatory.
### *gaze_movement_identifier - inherited from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame)*
The first [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) pipeline step is dedicated to identify fixations or saccades from consecutive timestamped gaze positions.
### *image_parameters - inherited from [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame)*
The usual [ArFrame visualization parameters](../gaze_analysis_pipeline/visualization.md) plus one additional *draw_detected_markers* field.
## Pipeline execution
A pipeline needs to be embedded into a context to be executed.
Copy the gaze analysis pipeline configuration defined above inside the following context configuration.
```json
{
"argaze.utils.contexts.OpenCV.Movie": {
"name": "Movie player",
"path": "./src/argaze/utils/demo/tobii_record/segments/1/fullstream.mp4",
"pipeline": JSON CONFIGURATION
}
}
```
Then, use the [*load* command](../utils/main_commands.md) to execute the context.
```shell
python -m argaze load CONFIGURATION
```
This command should open a GUI window with the detected markers and identified cursor fixations circles when the mouse moves over the window.
!!! note ""
At this point, the pipeline only processes gaze movement identification without any AOI support as no scene description is provided into the JSON configuration file.
Read the next chapters to learn [how to estimate scene pose](pose_estimation.md), [how to describe a 3D scene's AOI](aoi_3d_description.md) and [how to project them into the camera frame](aoi_3d_projection.md).
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