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Tools for fetching, processing, visualizing, and analyzing Wildflower human pose data

Project description

# 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](

  • 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()

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