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        "author": "Guilherme Polo",
        "author_email": "gp@instaprint.me",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Software Development :: Libraries"
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        "description": "## Build\n\nClone both `ccv` and `py-ccv`:\n\n```\nmkdir ccv_build\ncd ccv_build\ngit clone https://github.com/liuliu/ccv\ngit clone https://github.com/gpip/py-ccv\n```\n\nPatch `ccv` so it can be compiled as a dynamic lib, and compile it:\n\n```\ncd ccv/lib\npatch -p2 < ../../py-ccv/dynlib.patch\n./configure\nmake libccv.so\n```\n\nBuild and install the Python wrapper (`ARCHFLAGS` was used on a OSX build, adjust for your platform):\n\n```\ncd ../../py-ccv\nARCHFLAGS='-arch x86_64' INCDIR=../ccv/lib LIBDIR=../ccv/lib python setup.py install\n```\n\n## Build only this wrapper\n\nIf you already have `libccv.so` then you might want to install directly via pip:\n\n```\nLDFLAGS=\"-L$(pwd)/ccv/lib\" CFLAGS=\"-I$(pwd)/ccv/lib\" pip install ccv\n```\n\nRemember to adjust the paths according to where `libccv.so` and `ccv.h` are installed in your system.\n\n\n## Face Detection Usage\n\n(`DYLD_LIBRARY_PATH` was used on OSX, adjust it for your platform)\n\n##### Face detection using SCD\n\n```\n$ DYLD_LIBRARY_PATH=../ccv/lib python -m ccv.face_detect -c ../ccv/samples/face.sqlite3 img/lena.png\nimg/lena.png Feature(x1=229, y1=216, x2=381, y2=368, confidence=5.014610767364502)\n```\n\n##### Face detection using BBF\n\n```\n$ DYLD_LIBRARY_PATH=../ccv/lib python -m ccv.face_detect --bbf -c ../ccv/samples/face img/lena.png\nimg/lena.png Feature(x1=230, y1=211, x2=384, y2=365, confidence=0.4947386682033539)\n```\n\n##### Help\n\n```\n$ DYLD_LIBRARY_PATH=../ccv/lib python -m ccv.face_detect.py --help\nUsage: face_detect.py [options] filename...\n\nOptions:\n  -h, --help            show this help message and exit\n  --bbf                 Use BBF detector\n  --scd                 Use SCD detector\n  -c CASCADE, --cascade=CASCADE\n                        Path to cascade to read\n  --quiet\n```\n\n##### Using face_detect as a library\n\n```\nfrom ccv import face_detect\n\nnames = ['img/lena.png']\nresult = face_detect.main('scd', '../ccv/samples/face.sqlite3', False, *names)\n\n# face_detect.main is a generator which yields tuples of\n# (<filename>, [<list of face_detect.Feature>])\nfor name, rects in result:\n    print name, rects\n```\n\n\n##### Visualization\n\nThis wrapper does not include an utility to draw the resulting rectangles, so the following example uses ImageMagick:\n\n```\n$ convert img/lena.png -fill none -stroke blue -strokewidth 3 -draw \"rectangle 229,216 381,368\" result.png\n```\n\n![](http://i.imgur.com/yzcxwqk.png)\n\n\n## Using the library\n\n```\nimport sys\nfrom ccv import ccv_read, ccv_write, sobel, lib\n\n# Read file passed.\ninp = ccv_read(sys.argv[1])\n# Apply Sobel.\nres = sobel(inp, lib.CCV_8U)\n# Save the result as \"sobel.jpg\"\nccv_write(res, \"sobel.jpg\")\n```\n\nPointers returned by the higher level wrapper, `ccv`, are automatically freed.",
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