{ "info": { "author": "Davis King", "author_email": "davis@dlib.net", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Operating System :: MacOS :: MacOS X", "Operating System :: Microsoft", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX", "Operating System :: POSIX :: Linux", "Programming Language :: C++", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Image Recognition", "Topic :: Software Development" ], "description": "# dlib C++ library [![Travis Status](https://travis-ci.org/davisking/dlib.svg?branch=master)](https://travis-ci.org/davisking/dlib)\n\nDlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See [http://dlib.net](http://dlib.net) for the main project documentation and API reference.\n\n\n\n## Compiling dlib C++ example programs\n\nGo into the examples folder and type:\n\n```bash\nmkdir build; cd build; cmake .. ; cmake --build .\n```\n\nThat will build all the examples.\nIf you have a CPU that supports AVX instructions then turn them on like this:\n\n```bash\nmkdir build; cd build; cmake .. -DUSE_AVX_INSTRUCTIONS=1; cmake --build .\n```\n\nDoing so will make some things run faster.\n\n## Compiling your own C++ programs that use dlib\n\nThe examples folder has a [CMake tutorial](https://github.com/davisking/dlib/blob/master/examples/CMakeLists.txt) that tells you what to do. There are also additional instructions on the [dlib web site](http://dlib.net/compile.html).\n\n## Compiling dlib Python API\n\nBefore you can run the Python example programs you must compile dlib. Type:\n\n```bash\npython setup.py install\n```\n\nor type\n\n```bash\npython setup.py install --yes USE_AVX_INSTRUCTIONS\n```\n\nif you have a CPU that supports AVX instructions, since this makes some things run faster. Note that you need to have boost-python installed to compile the Python API.\n\n\n\n## Running the unit test suite\n\nType the following to compile and run the dlib unit test suite:\n\n```bash\ncd dlib/test\nmkdir build\ncd build\ncmake ..\ncmake --build . --config Release\n./dtest --runall\n```\n\nNote that on windows your compiler might put the test executable in a subfolder called `Release`. If that's the case then you have to go to that folder before running the test.\n\nThis library is licensed under the Boost Software License, which can be found in [dlib/LICENSE.txt](https://github.com/davisking/dlib/blob/master/dlib/LICENSE.txt). The long and short of the license is that you can use dlib however you like, even in closed source commercial software.\n\n## dlib sponsors\n\nThis research is based in part upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under contract number 2014-14071600010. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government.\n\nVersion: 19.7\nDate: Sun Sep 17 08:28:45 EDT 2017\nMercurial Revision ID: 6ee27f33d90c\n\n\n", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/davisking/dlib", "keywords": "dlib,Computer Vision,Machine Learning", "license": "Boost Software License", "maintainer": "", "maintainer_email": "", "name": "friday-dlib", "package_url": "https://pypi.org/project/friday-dlib/", "platform": "", "project_url": "https://pypi.org/project/friday-dlib/", "project_urls": { "Homepage": "https://github.com/davisking/dlib" }, "release_url": "https://pypi.org/project/friday-dlib/19.7.0/", "requires_dist": null, "requires_python": "", "summary": "A toolkit for making real world machine learning and data analysis applications", "version": "19.7.0" }, "last_serial": 3209404, "releases": { "19.7.0": [ { "comment_text": "", "digests": { "md5": "28b10e98d9408702c4c9eed2c24b3683", "sha256": "70e610b14e418a911f4ca2de03282a2cf7868beb76f95480050b9f7d18696c13" }, "downloads": -1, "filename": "friday_dlib-19.7.0-cp36-cp36m-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "28b10e98d9408702c4c9eed2c24b3683", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 3379911, "upload_time": "2017-09-28T08:20:09", "url": "https://files.pythonhosted.org/packages/1b/56/bf1a0bfa80c9a7ccf668f859602014bcb63df9bb7dc18d5f9fb6e732d1e7/friday_dlib-19.7.0-cp36-cp36m-manylinux1_x86_64.whl" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "28b10e98d9408702c4c9eed2c24b3683", "sha256": "70e610b14e418a911f4ca2de03282a2cf7868beb76f95480050b9f7d18696c13" }, "downloads": -1, "filename": "friday_dlib-19.7.0-cp36-cp36m-manylinux1_x86_64.whl", "has_sig": false, "md5_digest": "28b10e98d9408702c4c9eed2c24b3683", "packagetype": "bdist_wheel", "python_version": "cp36", "requires_python": null, "size": 3379911, "upload_time": "2017-09-28T08:20:09", "url": "https://files.pythonhosted.org/packages/1b/56/bf1a0bfa80c9a7ccf668f859602014bcb63df9bb7dc18d5f9fb6e732d1e7/friday_dlib-19.7.0-cp36-cp36m-manylinux1_x86_64.whl" } ] }