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        "author": "Peter Abeles",
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        "description": "PyBoof is [Python](http://www.python.org) wrapper for the computer vision library [BoofCV](http://boofcv.org). Since this is a Java library you will need to have java and javac installed.  The former is the Java compiler.  In the future the requirement for javac will be removed since a pre-compiled version of the Java code will be made available and automatically downloaded.  Installing the Java JDK is platform specific, so a quick search online should tell you how to do it.\n\nTo start using the library simply install the latest stable version using pip\n```bash\nsudo pip install pyboof\n```\n\n# Installing From Source\nOne advantage to checkout the source code and installing from source is that you also get all the example code and the example datasets.\n```bash\ngit clone --recursive https://github.com/lessthanoptimal/PyBoof.git\n```\n\nIf you forgot --recursive then you can checkout the data directory with the following command.\n\n```bash\ngit submodule update --init --recursive\n```\n\nAfter you have the source code on your local machine you can install it and its dependencies with the following commands:\n\n1. cd PyBoof\n2. ./setup.py build\n3. sudo ./setup.py install\n\nYes you do need to do the build first.  This will automatically build the Java jar and put it into the correct place.\n\n# Supported Platforms\n\nThe code has been developed and tested on Ubuntu Linux 16.04.  Should work on any other Linux variant.  Might work on Mac OS and a slim chance of working on Windows.\n\n# Examples\n\nExamples are included with the source code.  You can obtain them by either checkout the source code, as described above, or browsing \n[github here](https://github.com/lessthanoptimal/PyBoof/tree/master/examples).  If you don't check out the source code you won't have example data and not\nall of the examples will work.\n\nTo run any of the examples simply invoke python on the script\n\n1. cd PyBoof/examples\n2. python example_blur_image.py\n\nCode for applying a Gaussian and mean spatial filter to an image and displays the results.\n```Python\nimport numpy as np\nimport pyboof as pb\n\noriginal = pb.load_single_band('../data/example/outdoors01.jpg', np.uint8)\n\ngaussian = original.createSameShape() # useful function which creates a new image of the\nmean = original.createSameShape()     # same type and shape as the original\n\n# Apply different types of blur to the image\npb.blur_gaussian(original, gaussian,radius=3)\npb.blur_mean(original, mean, radius=3)\n\n# display the results in a single window as a list\nimage_list = [(original, \"original\"), (gaussian, \"gaussian\"), (mean, \"mean\")]\npb.swing.show_list(image_list, title=\"Outputs\")\n\ninput(\"Press any key to exit\")\n\n```\n\n# Dependencies\n\nPyBoof depends on the following python packages.  They should be automatically installed\n\n* py4j\n* numpy",
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