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    "info": {
        "author": "Simon Brenner",
        "author_email": "sbrenner@cvl.tuwien.ac.at",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
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        "description": "# multispectral\n\nThis is a convenience package for basic operations on multispectral images that exist as a bunch of \nfiles in folders. Makes your lif easy with regular expressions.\n\n**CAUTION: very prototypy at the moment, would not recommend use.. yet :)**\n\n##current features:\n* easy data handling with \"Frames\" and regular expressions\n* deformable fine registration using [elastix](http://elastix.isi.uu.nl/)\n* decomposition/clustering using [scikit-learn](https://scikit-learn.org/)\n\n##requirements:\n* opencv-python\n* numpy\n* scipy\n* scikit-learn\n* [SimpleElastix](https://pypi.org/project/SimpleElastix/) **WARNING: THIS WILL NOT BE ENFORCED BY pip**\n\n##usage:\n\nSuppose you have your multispectral layers in a folder `'/somepath/codexX-pageY'` \n(or any of its subfolders), and the files look something like:\n`'codexX-pageY_400nm.tif', 'codexX-pageY_500nm.tif',...`\n\nThen you could go:\n```python\nfrom multispectral import Frame,Layer,Registration,Unmixing\n\n# collect images in root_dir matching regex; groups 1 and 2 of the match object \n# identify the document and the layer respectively (optional)\nframe = Frame(root_dir='/somepath/codexX-pageY',\n                regex='(.+-.+)_(\\d{3}nm).tif',\n                group_framename=1,\n                group_layername=2)\n\n# inter-register all layers (regex_ref defines the fixed 'reference image',\n# store the result in a given output folder (or by default frame.root_dir/registered_fine),\n# and return a frame containing those resulting images\nregistered = Registration.register_fine(frame=frame, regex_ref='500nm')\n\n# make unmixing object: loads images of frame and converts them to a data matrix\num = Unmixing(registered)\n# perform principal component analysis, store visualizations of first 5 components\n# in given output folder (or by default frame.root_dir/pca), return frame containing those\nprincipal_components = um.unmix(method=Unmixing.Method.PCA, n_components=5, p_keep=0.5)\n```\n\nSimple as that.\n\n\n",
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        "license": "MIT",
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        "name": "multispectral",
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        "summary": "A convenience package for basic operations on multispectral images.",
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