Metadata-Version: 2.1
Name: coco-assistant
Version: 0.1.0
Summary: Helper for dealing with MS-COCO annotations
Home-page: https://github.com/ashnair1/COCO-Assistant
Author: Ashwin Nair
Author-email: ash1995@gmail.com
License: MIT
Download-URL: https://github.com/ashnair1/COCO-Assistant/archive/v0.1.0.tar.gz
Description: 
        # COCO-Assistant 
        
        ![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)
        
        Helper for dealing with MS-COCO annotations. <img src="rep_stuff/coco.png" height="40">
        
        
        ## Overview
        The 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.
        
        ### Requirements
        Your data directory should look as follows:
        
        ```
        Example:
        .
        ├── images
        │   ├── train
        │   ├── val
        |   ├── test
        |   
        ├── annotations
        │   ├── train.json
        │   ├── val.json
        │   ├── test.json
        
        ``` 
        
        ## Installation
        
        ```
        # Clone the repository
        git clone https://github.com/ashnair1/COCO-Assistant.git
        # Build and install the library
        make
        ```
        
        ## Usage
        
        Usage is similar to how you would use `pycocotools`
        
        ```
        from coco_assistant import COCO_Assistant
        
        # Specify image and annotation directories
        img_dir = os.path.join(os.getcwd(), 'images')
        ann_dir = os.path.join(os.getcwd(), 'annotations')
        
        # Create COCO_Assistant object
        cas = COCO_Assistant(img_dir, ann_dir)
        ```
        ## So what can this package do?:
        
        #### Merge datasets
        
        The `combine` function allows you to merge  multiple datasets.
        
        ```
        cas = COCO_Assistant(img_dir, ann_dir)                                                                                                                                                              
        loading annotations into memory...
        Done (t=0.09s)
        creating index...
        index created!
        loading annotations into memory...
        Done (t=0.06s)
        creating index...
        index created!
        
        cas.combine()                                                                                                                                                                                       
        Merging image dirs
        100%|█████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 18.33it/s]
        Merging annotations
        100%|█████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 14.72it/s]
        
        ```
        The merged dataset (images and annotation) can be found in `./results/combination`
        
        
        #### Remove_cat
        
        Removes a specific category from an annotation file.
        
        ```
        cas = COCO_Assistant(img_dir, ann_dir)                                                                                                                                                              
        loading annotations into memory...
        Done (t=0.09s)
        creating index...
        index created!
        loading annotations into memory...
        Done (t=0.06s)
        creating index...
        index created!
        
        cas.remove_cat()
        ['tiny.json', 'tiny2.json']
        Who needs a cat removal?
        tiny.json
        
        Categories present:
        ['building', 'vehicles]
        
        Enter categories you wish to remove:
        building
        ['building']
        Press n if you're done entering categories, else continue
        n
        Removing specified categories...
        ```
        The modified annotation can be found in `./results/removal`
        
        
        #### Generate annotation statistics
        
        1. Generate countplot of instances per category that occur in the annotation files.
        `cas.ann_stats(stat="area",arearng=[10,144,512,1e5],save=False)`
        2. Generate pie-chart that shows distribution of objects according to their size (as specified in areaRng).
        `cas.ann_stats(stat="cat", show_count=False, save=False)`
        
        #### Visualise annotations
        
        Couldn'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.
        
        ```
        cas.visualise()
        Choose directory:
        ['tiny', 'tiny2']
        ```
        
        ![](./rep_stuff/visualiser.gif)
        
        
        ### Todo: 
        1. Converter for converting COCO annotations to YOLO format
        2. Write tests for stats, converters and visualiser.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
