Metadata-Version: 1.1
Name: wf-process-pose-data
Version: 2.4.0
Summary: Tools for fetching, processing, visualizing, and analyzing Wildflower human pose data
Home-page: https://github.com/WildflowerSchools/wf-process-pose-data
Author: Theodore Quinn
Author-email: ted.quinn@wildflowerschools.org
License: UNKNOWN
Description: # process_pose_data
        
        Tools for fetching, processing, visualizing, and analyzing Wildflower human pose data
        
        ## Task list
        
        * Implement pose track filtering
        * Implement pose track interpolation
        * Parallellize 3D pose overlay function
        * Enable `workers.reconstruct_poses_3d_alphapose_local_by_time_segment()` to be called as library function (as well as executable)
        * Write worker which produces interpolated pose tracks from local 3D pose data
        * Rewrite all log messages so formatting isn't called if log isn't printed
        * Rewrite `overlay.overlay_video_poses_2d()` to match functionality of `overlay.overlay_video_poses_3d()` (e.g., more flexible specification of videos, concatenation)
        * Extend ability to set output container and code to all overlay functions
        * Loosen checks on overlap between pose data and video data (go ahead as long as there is _some_ overlap)
        * Add ability to filter 3D pose tracks on track duration/size
        * Add option to overlay function to first filter on 3D pose track duration/size
        * Add ability to overlay for a time range (batch processing)
        * Add option to concatenate videos across time range for each camera
        * Dockerize pipeline
        * Set up pipeline for Airflow
        * Add function to fetch pose tracks from Honeycomb
        * Add progress bar option to `generate_pose_tracks()`
        * Make functions handle empty poses (all keypoints `NaN`) more gracefully (e.g., `score_pose_pairs()`, `draw_pose_2d()`)
        * Fix up `honeycomb_io` module to match design of `wf-video-io`
          - Allow user to supply Honeycomb client
          - Clean up default setting
          - Other?
          * Remove unused functions and methods
        * 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()`
        
Keywords: pose estimation
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
