{
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        "author": "Nicolas Cedilnik",
        "author_email": "nicolas.cedilnik@inria.fr",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: CEA CNRS Inria Logiciel Libre License, version 2.1 (CeCILL-2.1)",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Image Recognition"
        ],
        "description": "SPADE: Small PArticle DEtection\n===============================\n\n.. image:: http://www-sop.inria.fr/morpheme/images/logo2.png\n   :width: 200 px\n   :target: http://www-sop.inria.fr/morpheme/team.html\n\nAn algorithm primarily design to detect objects whose sizes aren't larger a\nfew pixels (particles) on fluorescence microscopy images.\n\nIt is an simplified version of marked point process.\n\n\nRequirements\n============\nSPADE has only been tested with Python 3.5 but should work with older versions.\nThe only strict requirement is the package `numpy`.\nIn order to use SPADE to its full extent, we recommend installing the\n`scikit-image` package and the full `scipy` ecosystem.\n\n\nUsage\n=====\n\nIn a python script\n------------------\n\nYou can install the package using PIP. The main function, `spade2d`, is in the\n`spade.detection_2d` module. It takes an image, a shapes library and a\nthreshold as input. A typical usage would look like like\n\n.. code:: python\n\n    from skimage.io import imread, imshow\n    from skimage.filters import threshold_otsu\n    from skimage.color import label2rgb\n    from numpy import percentile\n    import matplotlib.pyplot as plt\n\n    from spade.detection_2d import spade2d\n    from spade.shapes.examples import potatoids5x5_smallest4px\n\n\n    # Load the example image.\n    image = imread(\"example_2d.png\")\n\n    # Separate cell image from background, using by Otsu's thresholding method.\n    cell = image > threshold_otsu(image)\n\n    # Focus on brightest pixels only\n    potential_centers = image > percentile(image[cell], 99)\n\n    # Detect particles.\n    particles = spade2d(image=image,\n                        shapes_library=potatoids5x5_smallest4px,\n                        threshold=20,\n                        potential_centers=potential_centers,\n                        mask=cell)\n\n    # Show detected particles as overlay on our original image.\n    imshow(label2rgb(particles, image, bg_label=0))\n    plt.show()\n\n\nSee the examples in the `example` folder of the source dist for more\ninformation. Note that `matplotlib` and `skimage` are not required for SPADE\nto work.\n\nFrom the command line\n---------------------\n\nAlso provided is a command line interface, that can be used as following:\n\n.. code:: bash\n\n    python -m spade.cli INPUT_IMAGE THRESHOLD -o /path/to/output_image.png\n\nLaunch this command with `-h` for more information on its usage.\n\n\n\nAuthors & Acknowledgments\n=========================\n\n\nSPADE was developed by `Nicolas Cedilnik\n<mailto:nicoco@nicoco.fr>`_, `\u00c9ric Debreuve\n<http://www.i3s.unice.fr/~debreuve/>`_ and `Xavier Descombes\n<http://www-sop.inria.fr/members/Xavier.Descombes/>`_ at the `MORPHEME team\n<http://www-sop.inria.fr/morpheme/team.html>`_.\n\nIf you use SPADE for your scientific work, please include this `bibtex` (or\nequivalent) entry:\n\n.. code:: latex\n\n    @misc{cedilnik2016spade,\n      title={SPADE: Small particle detection}\n      author={Cedilnik, Nicolas and Debreuve, \u00c9ric and Descombes, Xavier}\n      url={https://pypi.python.org/pypi/small-particle-detection}\n      year={2016}\n    }\n\nLicense\n=======\nSPADE is released under the `CeCILL-2.1 licence\n<http://www.cecill.info/licences/Licence_CeCILL_V2.1-en.txt>`_.\n",
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        "name": "small-particle-detection",
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