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diff --git a/docs/user_guide/gaze_analysis_pipeline/visualization.md b/docs/user_guide/gaze_analysis_pipeline/visualization.md index 6b9805c..32395c3 100644 --- a/docs/user_guide/gaze_analysis_pipeline/visualization.md +++ b/docs/user_guide/gaze_analysis_pipeline/visualization.md @@ -5,7 +5,7 @@ Visualization is not a pipeline step, but each [ArFrame](../../argaze.md/#argaze  -## Add image parameters to ArFrame JSON configuration file +## Add image parameters to ArFrame JSON configuration [ArFrame.image](../../argaze.md/#argaze.ArFeatures.ArFrame.image) method parameters can be configured thanks to a dedicated JSON entry. @@ -82,37 +82,6 @@ Here is an extract from the JSON ArFrame configuration file with a sample where Most of *image_parameters* entries work if related ArFrame/ArLayer pipeline steps are enabled. For example, a JSON *draw_scan_path* entry needs GazeMovementIdentifier and ScanPath steps to be enabled. -Then, [ArFrame.image](../../argaze.md/#argaze.ArFeatures.ArFrame.image) method can be called in various situations. - -## Live window display - -While timestamped gaze positions are processed by [ArFrame.look](../../argaze.md/#argaze.ArFeatures.ArFrame.look) method, it is possible to display the [ArFrame](../../argaze.md/#argaze.ArFeatures.ArFrame) image thanks to the [OpenCV package](https://pypi.org/project/opencv-python/). - -```python -import cv2 - -def main(): - - # Assuming ArFrame is loaded - ... - - # Create a window to display ArFrame - cv2.namedWindow(ar_frame.name, cv2.WINDOW_AUTOSIZE) - - # Assuming that timestamped gaze positions are being processed by ArFrame.look method - ... - - # Update ArFrame image display - cv2.imshow(ar_frame.name, ar_frame.image()) - - # Wait 10 ms - cv2.waitKey(10) - -if __name__ == '__main__': - - main() -``` - !!! note "Export to video file" Video exportation is detailed in [gaze analysis recording chapter](recording.md).
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