{ "info": { "author": "Tim Joseph", "author_email": "", "bugtrack_url": null, "classifiers": [ "Programming Language :: Python", "Programming Language :: Python :: 3.6" ], "description": "# elaugment\nelaugment is a Python package for reproducable data-augmentations. In difference to most other libraries random parameters for transformations are drawn seperate from the transformations. This makes it very easy apply the same transformations to several images. An example where this behaviour is useful is semantic segmentation, when you need to modify the input and the mask in the same way.\n\nThis library is currently in it's early stages so interfaces may break and some operations are slow. \n\n## Installation\n1. Clone this repository\n2. Run \n``` pip install elaugment ```\n\n## Examples & Usage\nSee `/examples` for an comprehensive overview.\n\n## Contribute\nTransformations are easy to integrate into elaugment, so just create pull-requests when you feel like it!\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/Mctigger/elaugment", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "elaugment", "package_url": "https://pypi.org/project/elaugment/", "platform": "", "project_url": "https://pypi.org/project/elaugment/", "project_urls": { "Homepage": "https://github.com/Mctigger/elaugment" }, "release_url": "https://pypi.org/project/elaugment/0.1.0/", "requires_dist": [ "numpy", "scikit-image", "scipy" ], "requires_python": ">=3.6", "summary": "A lightweight data-augmentation library for machine learning", "version": "0.1.0" }, "last_serial": 5757301, "releases": { "0.1.0": [ { "comment_text": "", "digests": { "md5": "66382de339049f954c3af731c532199a", "sha256": "f3941e9ae850f35a99f3e738d6de05d4b9dd3fff7ea341ec66e14abed708403a" }, "downloads": -1, "filename": "elaugment-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "66382de339049f954c3af731c532199a", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 10234, "upload_time": "2019-08-29T22:25:04", "url": "https://files.pythonhosted.org/packages/19/8b/c7bb789ed74c86ab9887bd6c8914520e65d9e8932fc6585664f97a7c2d6a/elaugment-0.1.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "7c4e7ecfa0105a72c89f958a96daf7fe", "sha256": "73789cc783ed61a2f72885e68866615544995fa03b12afb79ee2ff916d06f53d" }, "downloads": -1, "filename": "elaugment-0.1.0.tar.gz", "has_sig": false, "md5_digest": "7c4e7ecfa0105a72c89f958a96daf7fe", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 7376, "upload_time": "2019-08-29T22:25:06", "url": "https://files.pythonhosted.org/packages/2d/e7/56ef40c3738fce40222f027030d213316e245e815c1ac0c7b6c5f9c15759/elaugment-0.1.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "66382de339049f954c3af731c532199a", "sha256": "f3941e9ae850f35a99f3e738d6de05d4b9dd3fff7ea341ec66e14abed708403a" }, "downloads": -1, "filename": "elaugment-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "66382de339049f954c3af731c532199a", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": ">=3.6", "size": 10234, "upload_time": "2019-08-29T22:25:04", "url": "https://files.pythonhosted.org/packages/19/8b/c7bb789ed74c86ab9887bd6c8914520e65d9e8932fc6585664f97a7c2d6a/elaugment-0.1.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "7c4e7ecfa0105a72c89f958a96daf7fe", "sha256": "73789cc783ed61a2f72885e68866615544995fa03b12afb79ee2ff916d06f53d" }, "downloads": -1, "filename": "elaugment-0.1.0.tar.gz", "has_sig": false, "md5_digest": "7c4e7ecfa0105a72c89f958a96daf7fe", "packagetype": "sdist", "python_version": "source", "requires_python": ">=3.6", "size": 7376, "upload_time": "2019-08-29T22:25:06", "url": "https://files.pythonhosted.org/packages/2d/e7/56ef40c3738fce40222f027030d213316e245e815c1ac0c7b6c5f9c15759/elaugment-0.1.0.tar.gz" } ] }