{"name":"tracktour","display_name":"Tracktour","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"tracktour.merge_explorer","title":"Merge Explorer...","python_name":"tracktour._napari._merge_explorer:MergeExplorer","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"tracktour.track_solver","title":"Track Solver...","python_name":"tracktour._napari._solver:TrackingSolver","short_title":null,"category":null,"icon":null,"enablement":null},{"id":"tracktour.track_editor","title":"Track Editor...","python_name":"tracktour._napari._track_editor:TrackEditor","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"tracktour.merge_explorer","display_name":"Merge Explorer","autogenerate":false},{"command":"tracktour.track_solver","display_name":"Track Solver","autogenerate":false},{"command":"tracktour.track_editor","display_name":"Track Editor","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.3","name":"tracktour","version":"0.0.3rc1","dynamic":null,"platform":null,"supported_platform":null,"summary":"Network flow based tracker with guided error correction","description":"# tracktour\n\n[![License](https://img.shields.io/pypi/l/tracktour.svg?color=green)](https://github.com/DragaDoncila/tracktour/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/tracktour.svg?color=green)](https://pypi.org/project/tracktour)\n[![Python Version](https://img.shields.io/pypi/pyversions/tracktour.svg?color=green)](https://python.org)\n[![CI](https://github.com/DragaDoncila/tracktour/actions/workflows/ci.yml/badge.svg)](https://github.com/DragaDoncila/tracktour/actions/workflows/ci.yml)\n\ntracktour is a simple object tracker based on a network flow linear model. tracktour takes a dataframe of detected objects and solves a linear program\n(currently using Gurobi, but we will soon add an open source solver interface) to produce tracking results.\n\ntracktour is rapidly changing and its API will change without deprecation warnings.\n\n## Usage\n\nThe `Tracker` object is the interface for producing tracking solutions. Below is a toy example with explicitly defined detections.\n\n```python\ncoords = [\n    (0, 50.0, 50.0),\n    (0, 40, 50),\n    (0, 30, 57),\n    (1, 50, 52),\n    (1, 38, 51),\n    (1, 29, 60),\n    (2, 52, 53),\n    (2, 37, 53),\n    (2, 28, 64),\n]\ncoords = pd.DataFrame(coords, columns=[\"t\", \"y\", \"x\"])\n\n# initialize Tracker object\ntracker = Tracker(\n    im_shape=(100, 100),    # size of the image detections come from. Affects cost of detections appearing/disappearing\n    k_neighbours=2          # number of neighbours to consider for assignment in the next frame (default=10)\n)\n# solve\ntracked = tracker.solve(coords)\n```\n\nThe `Tracked` object contains a copy of the detections, potentially reindexed, and a dataframe of edges that make up the solution.\nColumns `u` and `v` in `tracked_edges` are direct indices into `tracked_detections`.\n\n```python\nprint(tracked.tracked_detections)\nprint(tracked.tracked_edges)\n```\n\nYou may want to convert the solution into a networkx graph for easier manipulation.\n\n```python\nsolution_graph = tracked.as_nx_digraph()\n```\n\n### Extracting Detections\n\nIf you're starting from an image segmentation, you can use the `get_im_centers` or `extract_im_centers` functions.\n\nIf your segmentation is already loaded into a numpy array, use `extract_im_centers`. The returned `detections` DataFrame is ready for use with the `Tracker`.\n\n```python\ndetections, min_t, max_t, corners = extract_im_centers(segmentation)\n```\n\nIf your segmentation is in Cell Tracking Challenge format and lives in single tiffs per frame in a directory, use `get_im_centers`. This will also return\nthe segmentation as a numpy array.\n\n```python\nseg, detections, min_t, max_t, corners = get_im_centers('path/to/01_RES/')\n```\n\n### CLI Tool - Cell Tracking Challenge\n\nIf you're working with Cell Tracking Challenge formatted datasets, you can use the CLI to extract detections, run tracktour, and save output in CTC format.\n\n```sh\n$ tracktour ctc /path/to/seg/ /path/to/save/ -k 8\n```\n\n## Support\n\nPlease feel free to open issues with feature requests, bug reports, questions on usage, etc.\n","description_content_type":"text/markdown","keywords":null,"home_page":null,"download_url":null,"author":null,"author_email":"Draga Doncila Pop <ddoncila@gmail.com>","maintainer":null,"maintainer_email":null,"license":"BSD 3-Clause License","classifier":["Framework :: napari","Intended Audience :: Science/Research","License :: OSI Approved :: GNU General Public License v3 (GPLv3)","Operating System :: OS Independent","Programming Language :: Python :: 3","Programming Language :: Python :: 3.8","Programming Language :: Python :: 3.9","Programming Language :: Python :: 3.10","Topic :: Scientific/Engineering","Topic :: Scientific/Engineering :: Bio-Informatics","Topic :: Scientific/Engineering :: Image Processing","Topic :: Scientific/Engineering :: Information Analysis","Topic :: Scientific/Engineering :: Visualization"],"requires_dist":["gurobipy","imagecodecs","networkx","numpy","pandas","pydantic","scikit-image","scipy","tifffile","tqdm","typer","pre-commit; extra == 'dev'","arboretum; extra == 'napari'","napari; extra == 'napari'","napari-graph; extra == 'napari'","pytest>=6.0; extra == 'test'"],"requires_python":">=3.8","requires_external":null,"project_url":["homepage, https://github.com/DragaDoncila/tracktour","repository, https://github.com/DragaDoncila/tracktour"],"provides_extra":["dev","napari","test"],"provides_dist":null,"obsoletes_dist":null},"npe1_shim":false}