poseconnect 0.4.0

Tools for constructing 3D pose tracks from multi-camera 2D poses

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)
• 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()