{
    "info": {
        "author": "Ashwin Nair",
        "author_email": "ash1995@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n# COCO-Assistant \n\n![CircleCI](https://circleci.com/gh/ashnair1/COCO-Assistant/tree/master.svg?style=shield&circle-token=553c83e37198fe02a71743d42ee427c292336743) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\nHelper for dealing with MS-COCO annotations. <img src=\"rep_stuff/coco.png\" height=\"40\">\n\n\n## Overview\nThe MS COCO annotation format along with the pycocotools library is quite popular among the computer vision community. Yet I for one found it difficult to play around with the annotations. Deleting a specific category, combining multiple mini datasets to generate a larger dataset, viewing distribution of classes in the annotation file are things I would like to do without writing a separate script for each. The COCO Assistant is designed (or being designed) to assist with this problem. Any contributions and/or suggestions are welcome.\n\n### Requirements\nYour data directory should look as follows:\n\n```\nExample:\n.\n\u251c\u2500\u2500 images\n\u2502   \u251c\u2500\u2500 train\n\u2502   \u251c\u2500\u2500 val\n|   \u251c\u2500\u2500 test\n|   \n\u251c\u2500\u2500 annotations\n\u2502   \u251c\u2500\u2500 train.json\n\u2502   \u251c\u2500\u2500 val.json\n\u2502   \u251c\u2500\u2500 test.json\n\n``` \n\n## Installation\n\n```\n# Clone the repository\ngit clone https://github.com/ashnair1/COCO-Assistant.git\n# Build and install the library\nmake\n```\n\n## Usage\n\nUsage is similar to how you would use `pycocotools`\n\n```\nfrom coco_assistant import COCO_Assistant\n\n# Specify image and annotation directories\nimg_dir = os.path.join(os.getcwd(), 'images')\nann_dir = os.path.join(os.getcwd(), 'annotations')\n\n# Create COCO_Assistant object\ncas = COCO_Assistant(img_dir, ann_dir)\n```\n## So what can this package do?:\n\n#### Merge datasets\n\nThe `combine` function allows you to merge  multiple datasets.\n\n```\ncas = COCO_Assistant(img_dir, ann_dir)                                                                                                                                                              \nloading annotations into memory...\nDone (t=0.09s)\ncreating index...\nindex created!\nloading annotations into memory...\nDone (t=0.06s)\ncreating index...\nindex created!\n\ncas.combine()                                                                                                                                                                                       \nMerging image dirs\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:00<00:00, 18.33it/s]\nMerging annotations\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:00<00:00, 14.72it/s]\n\n```\nThe merged dataset (images and annotation) can be found in `./results/combination`\n\n\n#### Remove_cat\n\nRemoves a specific category from an annotation file.\n\n```\ncas = COCO_Assistant(img_dir, ann_dir)                                                                                                                                                              \nloading annotations into memory...\nDone (t=0.09s)\ncreating index...\nindex created!\nloading annotations into memory...\nDone (t=0.06s)\ncreating index...\nindex created!\n\ncas.remove_cat()\n['tiny.json', 'tiny2.json']\nWho needs a cat removal?\ntiny.json\n\nCategories present:\n['building', 'vehicles]\n\nEnter categories you wish to remove:\nbuilding\n['building']\nPress n if you're done entering categories, else continue\nn\nRemoving specified categories...\n```\nThe modified annotation can be found in `./results/removal`\n\n\n#### Generate annotation statistics\n\n1. Generate countplot of instances per category that occur in the annotation files.\n`cas.ann_stats(stat=\"area\",arearng=[10,144,512,1e5],save=False)`\n2. Generate pie-chart that shows distribution of objects according to their size (as specified in areaRng).\n`cas.ann_stats(stat=\"cat\", show_count=False, save=False)`\n\n#### Visualise annotations\n\nCouldn't `pycocotools` visualise annotations (via [showAnns](https://github.com/cocodataset/cocoapi/blob/636becdc73d54283b3aac6d4ec363cffbb6f9b20/PythonAPI/pycocotools/coco.py#L233)) as well? Sure it could, but I required a way to freely view all the annotations of a particular dataset so here we are.\n\n```\ncas.visualise()\nChoose directory:\n['tiny', 'tiny2']\n```\n\n![](./rep_stuff/visualiser.gif)\n\n\n### Todo: \n1. Converter for converting COCO annotations to YOLO format\n2. Write tests for stats, converters and visualiser.\n\n\n",
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