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        "author": "Varun Agrawal",
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            "Operating System :: POSIX",
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            "Programming Language :: Python :: 3.5",
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        "description": "# bbox\n\n`bbox` a Python library that is intended to ease the use of 2D and 3D bounding boxes in areas such as Object Detection by providing a set of flexible primitives and functions that are intuitive and easy to use out of the box.\n\n[![Build Status](https://travis-ci.org/varunagrawal/bbox.svg?branch=master)](https://travis-ci.org/varunagrawal/bbox)\n[![codecov](https://codecov.io/gh/varunagrawal/bbox/branch/master/graph/badge.svg)](https://codecov.io/gh/varunagrawal/bbox)\n\n\n[![PyPI version](https://badge.fury.io/py/bbox.svg)](https://badge.fury.io/py/bbox)\n![PyPI format](https://img.shields.io/pypi/format/bbox.svg)\n![](https://img.shields.io/pypi/status/bbox.svg)\n![](https://img.shields.io/pypi/pyversions/bbox.svg)\n\n\n![](https://img.shields.io/pypi/l/bbox.svg)\n[![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/varunagrawal)\n\n\n## Features\n\n### 2D Bounding Box\n\nEasily work with bounding boxes using a simple class that abstracts and maintains various attributes.\n\n```python\nfrom bbox import BBox2D, XYXY\n\n# x, y, w, h\nbox = BBox2D([0, 0, 32, 32])\n\n# equivalently, in (x1, y1, x2, y2) (aka two point format), we can use\nbox = BBox2D([0, 0, 31, 31], mode=XYXY)\n\nprint(box.x1, box.y1)  # -> 0 0\nprint(box.x2, box.y2)  # -> 31 31\nprint(box.height, box.width)  # -> 32 32\n\n# Syntatic sugar for height and width\nprint(box.h, box.w)  # -> 32 32\n```\n### Sequence of 2D bounding boxes\n\nMost tasks involve dealing with multiple bounding boxes. This can also be handled conveniently with the `BBox2DList` class.\n\n```python\nbbl = BBox2DList(np.random.randint(10, 4),\n                 mode=XYWH)\n```\n\nThe above snippet creates a list of 10 bounding boxes neatly abstracted into a convenient object.\n\n### Non-maximum Suppression\n\nNeed to perform non-maximum suppression? It is as easy as a single function call.\n```python\nfrom bbox.utils import nms\n\n# bbl -> BBox2DList\n# scores -> list/ndarray of confidence scores\nnew_boxes = nms(bbl, scores)\n```\n\n### Intersection over Union (Jaccard Index)\n\nThe Jaccard Index or IoU is a very useful metric for finding similarities between bounding boxes. `bbox` provides native support for this.\n\n```python\nfrom bbox.metrics import jaccard_index_2d\n\nbox1 = BBox2D([0, 0, 32, 32])\nbox2 = BBox2D([10, 12, 32, 46])\n\niou = jaccard_index_2d(box1, box2)\n```\n\nWe can even use the Jaccard Index to compute a distance metric between boxes as a distance matrix:\n\n```python\nfrom bbox.metrics import multi_jaccard_index_2d\n\ndist = 1 - multi_jaccard_index_2d(bbl, bbl)\n```\n\n### 3D Bounding Box\n\n`bbox` also support 3D bounding boxes, providing convenience methods and attributes for working with them.\n\n\n",
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