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# -*- coding: utf-8 -*-
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"""
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Using the try/except import since the init is called in setup  to get pkg info
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before satisfying install requirements
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"""
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try:
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    from . import utilities
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    utilities
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except ImportError:
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    import traceback
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    traceback.print_exc()
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__version__ = "0.2.5"
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__author__ = "Jiri Borovec"
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__author_email__ = "jiri.borovec@fel.cvut.cz"
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__license__ = "BSD 3-clause"
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__homepage__ = "https://borda.github.io/BIRL",
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__copyright__ = "Copyright (c) 2014-2020, %s." % __author__
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__doc__ = 'BIRL: Benchmark on Image Registration methods with Landmark validation'
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__long_doc__ = "# %s" % __doc__ + """
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The project aims at automatic evaluation of state-of-the-art image registration
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 methods based on landmark annotation for given image dataset. In particular,
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 this project is the main evaluation framework for ANHIR challenge.
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## Main Features
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* **automatic** execution of image registration on a sequence of image pairs
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* integrated **evaluation** of registration performances
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* integrated **visualization** of performed registration
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* running several image registration experiment in **parallel**
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* **resuming** unfinished sequence of registration benchmark
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* handling around dataset and **creating own experiments**
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* rerun evaluation and visualisation for finished experiments
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## References
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1. Borovec, J., Munoz-Barrutia, A., & Kybic, J. (2018).
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 Benchmarking of image registration methods for differently stained histological slides.
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 In IEEE International Conference on Image Processing (ICIP) (pp. 3368-3372), Athens.
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 DOI: 10.1109/ICIP.2018.8451040
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2. Borovec, J. (2019). BIRL: Benchmark on Image Registration methods with Landmark validation.
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 arXiv preprint arXiv:1912.13452.
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3. Borovec, J., Kybic, J., et al. (2020).
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 ANHIR: Automatic Non-rigid Histological Image Registration Challenge.
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 In IEEE Transaction on Medical Imaging (TMI).
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 DOI: 10.1109/TMI.2020.2986331
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"""

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