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    "info": {
        "author": "Fabian Meyer",
        "author_email": "fabian.meyer@ise.fraunhofer.de",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Image Recognition"
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        "description": "# Spotlob\n\nSpotlob is a package to provide a simple, yet flexible\nand fast workflow to measure properties of features in\nimages for scientific purposes.\n\nIt provides implementations for some use cases but can\nbe easily tuned and be extended towards specific purposes.\nJupyter notebook widgets can be used to quickly find a\nset of algorithms and parameters that work for a given\nimage and should also work for similar images.\n\nThe set of parameters and algorithms are stored as a\npipeline which can be restored and distributed and \nthen be applied to a possibly large set of images.\nThis way, standard routines for repetitive and comparable\nmeasurements for a defined type of images can be forged\ninto a small and portable file.\n\n## When it's helpful\n\nIt is meant to be used in a scenario where a detection method\nhas to be applied repetetively onto a large set of similar images,\nbut the exact parameters are not clear.\nIf your set of tasks can be done by a collection of opencv-function\ncalls, that need tweaking and you wish to have a GUI to do that, \nbut without to lose scripting options, spotlob is for you.\n\nIf you already have a couple of working python algorithms and\nwant to have a GUI for them to play around, use spotlob.\n\nIf you need to evaluate some images and you don't know which\nof the thousand parameters of an algorithm you found on\ngoogle work best, you might be able to find the right ones\nfaster with spotlob.\n\n![Spotlob jupyter widget](/demo.gif)\n\n### Usage example\n\n```python\nfrom spotlob.spim import Spim\nfrom spotlob.defaults import default_pipeline()\n\nmy_spim = Spim.from_file(\"image.jpg\", cached=True)\nmy_pipe = default_pipeline()\n\nresult_spim = my_pipe.apply_all_steps(my_spim)\n\nprint(result_spim.get_data())\n```\n\n## What it's not\n\nSpotlob is not a complete feature detection library and it does\nnot solve a detection problem, that has not already been solved\nelsewhere.\nIt is not an alternative to opencv or scikit-image, but\nrather builds on top of it.\nAt the moment it covers only a tiny fraction of what is possible\nwith these libraries, but it tries to make it easy for the\nreader to use these (or any other python image processing library) \nwithin the spotlob workflow.\n\nAlthough it might work with machine learning algorithms, it is\nnot tuned towards this usage and it is not designed with this\napplication in mind.\n\n## Installation\n\nInstall with pip\n\n```\npip install spotlob\n```",
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