Label-Data-Helper

A Program that creates a bounding box that enables you to construct a predictable pipeline of high-quality training data that will teach your ML/DL-powered computer vision system to find and identify objects in image and video data.

Prerequisites

You need to have installed following softwares and libraries in your machine before running this project.

Python 3
Matplotlib
OpenCV: Image processing library.
Pillow(PIL)
Numpy
Pandas

Installation

$ pip3 install opencv-contrib-python

$ pip3 install matplotlib

$ pip3 install pillow

$ pip3 install numpy

$ pip3 install pandas

// install these libraries based on your environment.

What steps you have to follow??

Download main.py file
Keep your image datatset ready in which we have all the images.

$ tree --dirsfirst
├── Image_directory

│	├── File_1.jpg

│	├── File_2.jpg

│	├── File_3.jpg

...	│   ...

...	│   ...

│   	└── File_n.jpg

├── main.py

Execute main.py file using argparse commands :- python3 main.py --p=Image_directory/ --c=Class_name --d=CSV_file_path/file.csv
This will open images one by one.
After that you have to select the portion of image by the bounding box through mouse click e to confirm selection.
To redo selection press r instead of e thus that selection won't be saved.
A single image can have multiple bounding boxes. Like one below!
Press Escape Key to go to the next Image.
You can skip an image by pressing the same Escape key.
At the end of all iteartions a ".csv" file will be created containing the following classes = _image_name, class_name, down, left, right, top.
Down, up, left and right are the co-ordinates of the annotated area of each image with its location and class name tagged with it.

This is the usual annotation format of major object detection models. For example, PascalVOC, MS COCO, OID, KITTI, Retinanet, Imagenet, etc.

This program is using argparse commands

Execution Command :

python3 main.py --p=Image_directory/ --c=Class_name --d=CSV_file_path/file.csv

Authors