Metadata-Version: 2.1
Name: poseconnect
Version: 0.4.0
Summary: Tools for constructing 3D pose tracks from multi-camera 2D poses
Home-page: https://github.com/WildflowerSchools/poseconnect
Author: Theodore Quinn
Author-email: ted.quinn@wildflowerschools.org
License: UNKNOWN
Description: # poseconnect
        
        Tools for constructing 3D pose tracks from multi-camera 2D poses
        
        ## Task list
        
        * Remove duplicates from sample sensor data
        * Add ability to set command line defaults using environment variables
        * Add ability to set library defaults using environment variables
        * Add ability to specify environment variables using `dotenv`
        * Provide better command line UI for which `None` value has specific meaning
        * Regularize use of progress bars (everywhere or nowhere)
        * Consider removing pose pair score distance method options
        * Consider removing pose pair score summary method options
        * Add documentation for command line interface
        * Add documentation for library interface
        * Add documentation for installation
        * Add documentation for sample/demo usage
        * Add documentation for help functionality
        * Add simple video overlay capability
        * Add basic batch processing capabilities
        * Add basic multiprocessing capabilities
        * Separate Wildflower-specific and non-Wildflower-specific portions of `colmap` helper library
        * Separate Wildflower-specific and non-Wildflower-specific portions of `smc_kalman` library
        * Design and implement better 3D pose smoothing method than simple interpolation
        * Consider moving core of reconstruction algorithm to `numpy`
        * Consider moving all of pose pair portion of reconstruction algorithm to `networkx`
        * Diagnose bottlenecks in reconstruction algorithms
        * Set up defaults for visualization functions
        * 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
        * 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()`
        
Keywords: pose reconstruction,pose tracking
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
