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README.md

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[![Pylint](https://github.com/TimoK93/ctc-metrics/actions/workflows/pylint.yml/badge.svg)](https://github.com/TimoK93/ctc-metrics/actions/workflows/pylint.yml)
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[![Python package](https://github.com/TimoK93/ctc-metrics/actions/workflows/python-package.yml/badge.svg)](https://github.com/TimoK93/ctc-metrics/actions/workflows/python-package.yml)
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# CTC-Metrics
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A python implementation of the metrics used in the
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[Cell-Tracking-Challenge](https://celltrackingchallenge.net/). Additionally,
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[CHOTA](...),
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and the metrics of the
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[MOTChallenge](https://motchallenge.net/) are implemented.
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# Py-CTCMetrics
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A python implementation of the metrics used in the paper
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[CHOTA: A Higher Order Accuracy Metric for Cell Tracking](...) by
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*Kaiser et al.*. The code is
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designed to evaluate tracking results in the format of the
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[Cell-Tracking-Challenge](https://celltrackingchallenge.net/) but can also be used
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for custom results.
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The repository contains the metrics of the
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[Cell-Tracking-Challenge](https://celltrackingchallenge.net/),
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the [MOTChallenge](https://motchallenge.net/), and the
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[CHOTA](...) metric.
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Detailed descriptions of the metrics can be found in the [paper](...).
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---
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# Features at a Glance
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- **Validation**: Check if the tracking results are correctly formatted.
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- **Evaluation**: Evaluate the tracking results with respect to the ground truth using CTC, MotChallenge, and CHOTA metrics.
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- **Visualization**: Visualize the tracking results.
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- **Video Creation**: Create a video of the visualized tracking results for presentation purposes.
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- **Noise Evaluation**: Evaluate the impact of specific errors on the evaluation metrics.
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---
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### Requirements
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## Requirements
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We tested the code with **Python 3.10**. Additional packages that will be
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installed automatically are listed in the [requirements.txt](requirements.txt).
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---
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## Installation
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The package can be installed via pip:
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```bash
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pip install git+https://github.com/TimoK93/ctc-metrics.git
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pip install git+https://github.com/CellTrackingChallenge/py-ctcmetrics.git
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```
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or from the source code:
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```bash
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git clone https://github.com/TimoK93/ctc-metrics
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cd ctc-metrics
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git clone https://github.com/CellTrackingChallenge/py-ctcmetrics
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cd py-ctcmetrics
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pip install .
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```
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---
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## Usage
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recursively evaluates all sequences in the directory.
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Per default, the code is executed using multiple processes with one process per
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available CPU core. Multiprocessing increases the execution time but also
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available CPU core. Multiprocessing decreases the execution time but also
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increases
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the memory consumption. If you need to set the maximal number of processes,
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the number of processes can be specified with the argument
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```--num-threads``` or ```-n```:
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| -- save-after | Save the intermediate results after a specific number of runs. | 100 |
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---
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## Notes
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- If you only have segmentation results and want to evaluate the *SEG* metric,
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it is important that you pass the *--seg* flag to the evaluation command.
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Otherwise, the code could flag your input as invalid, because the
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*res_track.txt* file is missing or inconsistent.
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## Contributing
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Contributions are welcome! For bug reports or requests please
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pre-commit run --all-files
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```
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## Acknowledgement and Citations
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The code was developed by Timo Kaiser on behalf of the [Institute of Information
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Processing](https://www.tnt.uni-hannover.de/) at the Leibniz University Hanover in conjunction with the
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organizers of the [Cell-Tracking-Challenge](https://celltrackingchallenge.net/).
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If you use this code or the [CHOTA]() metric in your research, please cite the following paper:
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If you use this code or the CHOTA metric in your research, please cite the following paper:
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```bibtex
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@inproceedings{kaiser2024chota,
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author = {Kaiser, Timo and Ulman, Vladimír and Rosenhahn, Bodo},
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}
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```
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## License
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This project is licensed under the BSD 2-Clause License - see the
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setup.py

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from setuptools import setup, find_packages
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setup(
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name="ctc-metrics",
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name="py-ctcmetrics",
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version="1.0.0",
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packages=find_packages(),
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install_requires=[

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