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Script the pipeline
===================
All gaze analysis pipeline objects are accessible from Python script.
This could be particularly useful for realtime gaze interaction applications.
## Load ArFrame configuration from dictionary
First of all, [ArFrame](../../../argaze.md/#argaze.ArFeatures.ArFrame) configuration can be loaded from a python dictionary.
```python
from argaze import ArFeatures
# Edit a dict with ArFrame configuration
configuration = {
"name": "My FullHD screen",
"size": (1920, 1080),
...
"gaze_movement_identifier": {
...
},
"scan_path": {
...
},
"scan_path_analyzers": {
...
},
"heatmap": {
...
},
"layers": {
"MyLayer": {
...
},
...
},
"image_parameters": {
...
}
}
# Load ArFrame
ar_frame = ArFeatures.ArFrame.from_dict(configuration)
# Do something with ArFrame
...
```
## Access to ArFrame and ArLayers attributes
Then, once the configuration is loaded, it is possible to access to its attributes: [read ArFrame code reference](../../../argaze.md/#argaze.ArFeatures.ArFrame) to get a complete list of what is available.
Thus, the [ArFrame.layers](../../../argaze.md/#argaze.ArFeatures.ArFrame) attribute allows to access each loaded layer and so, access to their attributes: [read ArLayer code reference](../../../argaze.md/#argaze.ArFeatures.ArLayer) to get a complete list of what is available.
```python
from argaze import ArFeatures
# Assuming the ArFrame is loaded
...
# Iterate over each ArFrame layers
for name, ar_layer in ar_frame.layers.items():
...
```
## Pipeline execution outputs
[ArFrame.look](../../../argaze.md/#argaze.ArFeatures.ArFrame.look) method returns many data about pipeline execution.
```python
# Assuming that timestamped gaze positions are available
...
# Look ArFrame at a timestamped gaze position
gaze_movement, scan_path_analysis, layers_analysis, execution_times, exception = ar_frame.look(timestamp, gaze_position)
# Check if a gaze movement has been identified
if gaze_movement.valid and gaze_movement.finished:
# Do something with identified fixation
if GazeFeatures.is_fixation(gaze_movement):
...
# Do something with identified saccade
elif GazeFeatures.is_saccade(gaze_movement):
...
# Do something with scan path analysis
for module, analysis in scan_path_analysis.items():
for data, value in analysis.items():
...
# Do something with each layer AOI scan path analysis
for layer_name, layer_aoi_scan_path_analysis in layers_analysis.items():
for module, analysis in layer_aoi_scan_path_analysis.items():
for data, value in analysis.items():
...
# Do something with pipeline execution times
...
# Do something with pipeline exception
if exception:
...
```
Let's understand the meaning of each returned data.
### *gaze_movement*
A [GazeMovement](../../../argaze.md/#argaze.GazeFeatures.GazeMovement) once it have been identified by [ArFrame.gaze_movement_identifier](../../../argaze.md/#argaze.ArFeatures.ArFrame) object from incoming consecutive timestamped gaze positions. If no gaze movement have been identified, it returns an [UnvalidGazeMovement](../../../argaze.md/#argaze.GazeFeatures.UnvalidGazeMovement).
This could also be the current gaze movement if [ArFrame.filter_in_progress_identification](../../../argaze.md/#argaze.ArFeatures.ArFrame) attribute is false.
In that case, the returned gaze movement *finished* flag is false.
Then, the returned gaze movement type can be tested thanks to [GazeFeatures.is_fixation](../../../argaze.md/#argaze.GazeFeatures.is_fixation) and [GazeFeatures.is_saccade](../../../argaze.md/#argaze.GazeFeatures.is_saccade) functions.
### *scan_path_analysis*
A dictionary with all last scan path analysis if new scan step have been added to the [ArFrame.scan_path](../../../argaze.md/#argaze.ArFeatures.ArFrame) object.
### *layers_analysis*
A dictionary with all layers AOI scan path analysis if new AOI scan step have been added to an [ArLayer.aoi_scan_path](../../../argaze.md/#argaze.ArFeatures.ArLayer) object.
### *execution_times*
A dictionary with each pipeline step execution time.
### *exception*
A [python Exception](https://docs.python.org/3/tutorial/errors.html#exceptions) object raised during pipeline execution.
## Setup ArFrame image parameters
[ArFrame.image](../../../argaze.md/#argaze.ArFeatures.ArFrame.image) method parameters can be configured thanks to a python dictionary.
```python
# Assuming ArFrame is loaded
...
# Edit a dict with ArFrame image parameters
image_parameters = {
"draw_scan_path": {
...
},
"draw_layers": {
"MyLayer": {
...
}
},
...
}
# Pass image parameters to ArFrame
ar_frame_image = ar_frame.image(**image_parameters)
# Do something with ArFrame image
...
```
|
