Skip to main content

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](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()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for wf-process-pose-data, version 3.2.1
Filename, size File type Python version Upload date Hashes
Filename, size wf_process_pose_data-3.2.1-py3-none-any.whl (73.0 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size wf-process-pose-data-3.2.1.tar.gz (50.5 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page