# process_pose_data
Tools for fetching, processing, visualizing, and analyzing Wildflower human pose data
## Task list
Switch parallel overlay code back to imap_unordered() (for less chunky progress bars) but sort output before concatenating
Ensure that all visual specs (colors, line widths, etc.) propagate to video overlay
Add drawing primitive to wf-cv-utils for text with background
Use new text-with-background drawing primitive for pose labels
Add timestamp to video overlays
Consider getting rid of geom_render module
Fix up progress bars so we can have task-level and segment-level progress bars without logging in notebook
Fix up progress bars so they work properly outside of notebook with multiprocessing
Add indentation/labeling on segment-level progress bars
Clean up argument ordering in reconstruct_poses_3d_alphapose_local_time_segment
Add poses_2d_json_format option to reconstruct_poses_3d entry point
Add endpoints for all processing functions
Add ability to write locally generated object IDs to Honeycomb
Create separate workers for uploading to Honeycomb 3D poses, 3D pose tracks, interpolated 3D pose tracks, 3D pose identification, 3D pose track identification
Dockerize pipeline
Set up pipeline for Airflow
Add additional machinery for checking UWB data integrity (e.g., duplicates)
Retool generate_inference_metadata_reconstruct_3d_poses_alphapose_local to exclude cameras without calibration data
Make function to delete Honeycomb inference executions
Make function to delete local inference metadata
Make function to delete local 3D pose files
Make function for deleting local 3D pose data
Rewrite all log messages so formatting isn’t called if log isn’t printed
Make functions handle empty poses (all keypoints NaN) more gracefully (e.g., score_pose_pairs(), draw_pose_2d())
Make visualization functions handle missing fields (e.g., pose_quality) more gracefully
Figure out inconsistent behavior of groupby(…).apply(…) (under what conditions does it add grouping variables to index?)
For functions that act on dataframes, make it optional to check dataframe structure (e.g., only one timestamp and camera pair)
For functions than iterate over previous functions, making naming and approach consistent (e.g., always use apply?)
Add keypoint_categories info to pose models in Honeycomb?
Be consistent about whether to convert track labels to integers (where possible)
Remove dependence on OpenCV by adding necessary functionality to cv_utils
Consider refactoring split between video_io and cv_utils
Fix up cv_utils Matplotlib drawing functions so that they accept an axis (or figure, as appropriate)
Fix up handling of background image alpha (shouldn’t assume white background)
Fix up _y_ axis inversion for images (go back to cv_utils?)
Add option of specifying Honeycomb client info for visualization functions that require Honeycomb
Reinstate sns.set() for Seaborn plots without making it spill over into non-Seaborn plots (see [here](https://stackoverflow.com/questions/26899310/python-seaborn-to-reset-back-to-the-matplotlib))
Refactor code in visualize to make it less repetitive (same pattern over and over for [verb]_by_camera)
Fix up legend on pose track timelines
Add visualization for number of poses per camera per timestamp
Replace cv.triangulatePoints() to increase speed (and hopefully accuracy)
Get pose video overlays working again (for data with track labels)
Rewrite geom rendering functions to handle the possibility of no track labels
Rewrite function which overlays geoms on videos so that user can specify a time span that it is a subset of the geoms and/or the video
Make all time inputs more permissive (in terms of type/format) and make all time outputs more consistent
Be consistent about accepting timestamp arguments in any format parseable by pd.to_datetime()
3 maintainers
Metadata
3 maintainers
Release files for wf-process-pose-data 6.3.0
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