{ "info": { "author": "Robert M Ochshorn and Dafydd Harries", "author_email": "rmo@numm.org, daf@numm.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: X11 Applications", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License (GPL)", "Programming Language :: Python", "Topic :: Artistic Software", "Topic :: Multimedia", "Topic :: Scientific/Engineering" ], "description": "# numm\n\nFor programmers familiar with python or matlab who are interested in\ntoying with the sounds and images around them; for artists, designers,\nand composers seeking a deeper understanding of the numerical systems\nthat underlie modern media production.\n\nnumm is a minimalist media library that translates popular sound,\nimage, and video formats to and from numpy arrays. By coercing inputs\ninto common modes (eg. sample rate, bit depth), combining and mutating\nthese numpy arrays is greatly simplified.\n\nThe general strategy for these functions is to make any assumptions\nnecessary such that a single array of numbers provides sufficient\ninformation with which to reconstruct a media file, without any\nadditional metadata. For example, width and height are contained in\nthe dimensions of an array, so they may be different image to image,\nbut framerate, samplerate, &c is not inherent to the data, so it\nshould be made uniform unless explicitly specified.\n\nIn the transition from an image to an array, the primary\nconsiderations are resolution (wxh) and mode (RGB, RGBA, 8bit, 16bit,\n&c). We choose not to provide any special API for resampling an\nImage--this can be done with numpy if desired--and always return 8bit\nRGB buffers.\n\nimage2np(path)\nnp2image(np, path)\n\nIn the case of audio, we choose to force all input to 44100Hz stereo.\n\nsound2np(path)\nnp2sound(np, path)\n\nVideo is an array of wxhx3 arrays 30frames per second. Square pixels\nare assumed.\n\nvideo2np(path)\nnp2video(np, path)\n\nNote that in particular in the case of video, these numpy buffers will\nrequire a considerable amount of memory; preprocess accordingly (eg in\nffmpeg). We should also raise a NotEnoughMemory exception when RAM\nwill not hold our buffer.\n\nThe live interface is provided by numpy.run.run(), a function with\njust a few input-output controls, communicated through function\npassing.\n\n## Requirements\n\n - PyGst\n - PIL\n\nRecommended:\n\n - OpenCV\n\n## Licence\n\nCopyright (C) 2011, Dafydd Harries \nCopyright (C) 2011, Robert Ochshorn \n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or (at\nyour option) any later version.\n\nThis program is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\nGeneral Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. 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