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Record gaze analysis
=================
[ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) and [ArLayer](../../argaze.md/#argaze.ArFeatures.ArLayer) analyses can be recorded by registering observers for their **look** method.
## Export gaze analysis to CSV file
[ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) and [ArLayer](../../argaze.md/#argaze.ArFeatures.ArLayer) have an **observers** attribute to enable pipeline execution recording.
Here is an extract from the JSON ArFrame configuration file where recording is enabled for the [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) and for one [ArLayer](../../argaze.md/#argaze.ArFeatures.ArLayer) by loading classes from *my_recorders.py* Python file:
```json
{
"argaze.ArFeatures.ArFrame": {
"name": "My FullHD screen",
"size": [1920, 1080],
...
"observers": {
"my_recorders.ScanPathAnalysisRecorder": {
"path": "scan_path_metrics.csv"
}
},
"layers": {
"MyLayer": {
...
"observers": {
"my_recorders.AOIScanPathAnalysisRecorder": {
"path": "aoi_scan_path_metrics.csv"
}
}
}
}
}
}
```
!!! note
[ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) and its [ArLayers](../../argaze.md/#argaze.ArFeatures.ArLayer) automatically notify **look** method observers after each call.
!!! note
The *scan_path_metrics.csv* and *aoi_scan_path_metrics.csv* are created relatively to the place where the Python script is executed.
Here is *my_recorders.py* file:
```python
from argaze.utils import UtilsFeatures
class ScanPathAnalysisRecorder(UtilsFeatures.FileWriter):
def __init__(self, **kwargs):
# Init FileWriter
super().__init__(**kwargs)
# Edit header line
self.header = "Timestamp (ms)", "Duration (ms)", "Steps number"
def on_look(self, timestamp, ar_frame, exception):
"""Record scan path metrics"""
if ar_frame.is_analysis_available():
analysis = ar_frame.analysis()
data = (
timestamp,
analysis['argaze.GazeAnalysis.Basic.ScanPathAnalyzer'].path_duration,
analysis['argaze.GazeAnalysis.Basic.ScanPathAnalyzer'].steps_number
)
# Write to file
self.write(data)
class AOIScanPathAnalysisRecorder(UtilsFeatures.FileWriter):
def __init__(self, **kwargs):
# Init FileWriter
super().__init__(**kwargs)
# Edit header line
self.header = "Timestamp (ms)", "NGram counts"
def on_look(self, timestamp, ar_layer, exception):
"""Record aoi scan path metrics."""
if ar_layer.is_analysis_available():
data = (
timestamp,
ar_layer.analysis['argaze.GazeAnalysis.NGram.AOIScanPathAnalyzer'].ngrams_count
)
# Write to file
self.write(data)
```
!!! note
*my_recorders.py* file have to be in the same folder than the JSON ArFrame configuration file.
Assuming that [ArGaze.GazeAnalysis.Basic](../../argaze.md/#argaze.GazeAnalysis.Basic) scan path analysis module is enabled for 'My FullHD screen' ArFrame, a ***scan_path_metrics.csv*** file would be created:
|Timestamp (ms)|Duration (ms)|Steps number|
|:-------------|:------------|:-----------|
|3460 |1750 |2 |
|4291 |2623 |3 |
|4769 |3107 |4 |
|6077 |4411 |5 |
|6433 |4760 |6 |
|7719 |6050 |7 |
|... |... |... |
Assuming that [ArGaze.GazeAnalysis.NGram](../../argaze.md/#argaze.GazeAnalysis.NGram) AOI scan path analysis module is enabled for 'MyLayer' ArLayer, a ***aoi_scan_path_metrics.csv*** file would be created:
|Timestamp (ms)|NGram counts|
|:-------------|:-----------|
|5687 |"{3: {}, 4: {}, 5: {}}"|
|6208 |"{3: {('LeftPanel', 'GeoSector', 'CircularWidget'): 1}, 4: {}, 5: {}}"|
|... |... |
!!! note ""
Learn to [script the pipeline](./advanced_topics/scripting.md) to know more about [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) and [ArLayers](../../argaze.md/#argaze.ArFeatures.ArLayer) attributes.
### Export gaze analysis to video file
As explained in [pipeline steps visualization chapter](visualization.md), it is possible to get [ArFrame.image](../../argaze.md/#argaze.ArFeatures.ArFrame.image) once timestamped gaze positions have been processed by [ArFrame.look](../../argaze.md/#argaze.ArFeatures.ArFrame.look) method.
Here is the JSON ArFrame configuration file where [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) observers are extended with a new my_recorders.FrameImageRecorder instance:
```json
{
"name": "My FullHD screen",
"size": [1920, 1080],
"observers": {
...
"my_recorders.FrameImageRecorder": {
"path": "video.mp4",
"width": 1920,
"height": 1080,
"fps": 15
}
...
}
```
!!! note
The *video.mp4* is created relatively to the place where the Python script is executed.
Here is *my_recorders.py* file extended with a new FrameImageRecorder class:
```python
...
class FrameImageRecorder(UtilsFeatures.VideoWriter):
def __init__(self, **kwargs):
# Init VideoWriter
super().__init__(**kwargs)
def on_look(self, timestamp, ar_frame, exception):
"""Record frame image into video file."""
self.write(ar_frame.image())
```
Assuming that [ArFrame.image_parameters](../../argaze.md/#argaze.ArFeatures.ArFrame.image_parameters) are provided, ***video.mp4*** file would be created.
|
