Commit 32bbd6fa authored by katharina.loeffler's avatar katharina.loeffler
Browse files

added pyproject.toml to build project

parent d09432f7
......@@ -3,27 +3,36 @@ This repository contains the code to the publication:
Katharina Löffler, Tim Scherr, Ralf Mikut
bioRxiv 2021.03.16.435631; doi: https://doi.org/10.1101/2021.03.16.435631
The code has been tested on Windows and Linux using Python 3.8.
## Setup
### 1) create folder structure
- create local directory LOCAL_DIR
- clone code to LOCAL_DIR
- add folders data and results in LOCAL_DIR
- create a project directory LOCAL_DIR
- create two folders named data and results in LOCAL_DIR
- clone the code and install dependencies:
```
conda create --name venv_graph_tracking_kit_sch_ge_2021 python==3.8
conda activate venv_graph_tracking_kit_sch_ge_2021
git clone git@git.scc.kit.edu:KIT-Sch-GE/2021-cell-tracking.git
pip install -e ./2021-cell-tracking
```
so the final structure is
```
LOCAL_DIR
└───data (contains the ground truth data sets)
└───code
└───2021-cell-tracking (contains our tracking code)
└───results (synthetically degraded segmentation masks will be stored here)
```
### 2) create environment
- conda env create -f requirements.yml
- install gurobi 9.1.1 (see help_gurobi.txt)
### 2) Install gurobi
see help_gurobi.txt
## run tracking
- the tracking algorithm can be used with any 2D/3D image data with a segmentation which needs to be provided by the user
- it is assumed that the image data and segmentation data have a similar naming convention as used by the cell tracking challenge (http://celltrackingchallenge.net)
```
python -m run_tracking --image_path IMAGE_PATH --segmentation_path SEGMENTATION_PATH --results_path RESULTS_PATH
```
## Reproduce synthetic data sets
### 1) download data sets
......@@ -34,10 +43,13 @@ LOCAL_DIR
└───data (contains the ground truth data sets)
│ └───Fluo-N2DH-SIM+
│ └───Fluo-N3DH-SIM+
└───code
└───2021-cell-tracking
└───results (synthetically degraded segmentation masks stored will be stored here)
```
### 2) run code
- run create_synth_segm_data.py to create synthetically degraded segmentation mask images
- run create_synth_segm_data.py to create synthetically degraded segmentation mask images
```
python -m create_synth_segm_data
```
[build-system]
requires = ["setuptools >= 40.6.0", "wheel"]
build-backend = "setuptools.build_meta"
[tool.pytest.ini_options]
minversion = "6.0"
addopts = "-ra -q"
testpaths = [
"tracker",
]
\ No newline at end of file
[metadata]
name = kit_sch_ge_cell_tracking
description = a Python library for graph-based cell tracking
long_description = file: README.md
long_description_content_type = text/markdown; charset=UTF-8
url = https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking
author = Katharina Löffler, Tim Scherr
author_email = katharina.loefffler@kit.edu,
license = MIT License
version = 1.0.0
license_file = LICENSE
classifiers =
Development Status :: 5 - Production/Stable
Environment :: Console
Environment :: MacOS X
Intended Audience :: Science/Research
Natural Language :: English
Programming Language :: Python :: 3.8
Topic :: Scientific/Engineering :: Bio-Informatics
project_urls =
Segmentation = https://git.scc.kit.edu/KIT-Sch-GE/2021_segmentation
[options]
zip_safe = False
packages = find:
platforms = any
include_package_data = True
install_requires =
numpy >= 1.19.2
pandas >= 1.2.2
scipy>=1.6.1
scikit-learn >= 0.23.2
scikit-image >= 0.17.2
cvxopt >= 1.2.0
gurobipy == 9.1.1
imagecodecs == 2021.1.11
tifffile == 2021.1.14
python_requires = ==3.8.*
setup_requires =
setuptools_scm
[bdist_wheel]
universal = 1
#!/usr/bin/env python
import setuptools
if __name__ == "__main__":
setuptools.setup()
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