bboxtools-2 0.0.1

Converting bounding box annotations to popular formats like a breeze.

Bboxtools

Installation

git clone https://github.com/ODAncona/bbox-tools.git
cd bbox-tools
python3 setup.py install

or

pip install bboxtools

See the installation guide for more information.

Introduction

What is a bounding box?

Bounding boxes are a crucial component of object detection algorithms, which are used to identify and classify objects within an image or video. A bounding box is a rectangle that surrounds an object of interest in the image, and is typically represented by a set of coordinates that define the box's position and size. These boxes can be used to locate and extract objects from an image, and can also provide important information about the size, shape, and orientation of the objects

Various types and format

When you work with bounding box you have severals things to consider.

First, the bounding box could be stored in different types like:

• Top-Left Bottom-Right (TLBR), (x_min, y_min, x_max, y_max)
• Top-Left Width Height (TLWH), (x_min, y_min, width, height)
• Center Width Height (CWH), (x_center, y_center, width, height)

Which are popular among different formats like :

• COCO (Common Objects in Context)
• Pascal_voc (Visual Object Classes)
• YOLO (You Only Look Once)

Furthermore, the bounding box could be stored in different file formats like:

• csv
• xml
• json
• manifest
• parquet
• pickle

Goal

The goal of this library is to seamlessly convert bounding box format using easy syntax.

It should be a breeze like...

import bboxtools as bt

# Input file path
input_path1 = './examples/example1.csv'

# Output file path
output_path2 = './examples/output/test2.json'

# Mapping between the input file and the bboxtools format
bbox_map = dict(
    class_name='class',
    file_path='name',
    x_min='top_left_x',
    y_min='top_left_y',
    width='w',
    height='h',
    image_width='img_size_x',
    image_height='img_size_y',
)

# Read the input file
parser = bt.read_csv(input_path1, mapping=bbox_map)

# Export the file to the desired format
parser.export(output_path=output_path2, format='coco')

Documentation

API Reference

• BBox

How to guide

• Installation
• Parse bbox
• Export bbox

Tutorials

• Prepare dataset

Explanation

• Object detection and bbox

Contributing

Contributions are welcome!

License

This project is licensed under the GPLV3 License - see the LICENSE file for details.

Acknowledgments

• Pascal VOC
• COCO
• YOLO
• Albumentation