coco-assistant 0.1.0

Helper for dealing with MS-COCO annotations

COCO-Assistant

Helper for dealing with MS-COCO annotations.

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) 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']

Todo:

1. Converter for converting COCO annotations to YOLO format
2. Write tests for stats, converters and visualiser.