vton-imaterialist

A python package for Binary Segmentation DataSet ( vton_plus and imaterialist [topwear])

📚 PyPi Project Documentation 📚

Download dataset

Note - This step can be performed after installing package as well.

Download dataset from following drive and unzip it.

gdrive

Install package

Installation with pypi

pip3 install python-vtonimat

Installation from source

git clone https://github.com/aarti-b/vton-imaterialist
python3 setup.py install

Set path to use package outside directory

export PYTHONPATH="$PYTHONPATH:/path_to_github-clone-package/package/package/"

Usage Guide

There are two datasets this package focuses on

• vton
• imaterialist

vton dataset

default option for dataset is vton. Follow the following commands to load data. assign path value to the folder where data is downloaded and unzipped.

Load whole data

from vtonimat import SegData
images, labels = SegData(path='path_to_datafiles').load_training()

Load batchwise dataset

Load by batches. Following command returns list of batches. Batch size is input parameter in method load_training_in_batches.

Load whole data

from vton import SegData
images, labels = SegData().load_training_in_batches(1000)

imaterialist'19 topwear dataset

from vtonimat import SegData
images, labels = SegData(path='path_to_datafiles', dataset='imat19').load_training()

Load batchwise dataset

Load by batches. Following command returns list of batches. Batch size is input parameter in method load_training_in_batches.

from vton import SegData
images, labels = SegData().load_training_in_batches(1000)

There is a python file convert.py to convert dataset to ubyte format the dataset you downloaded from google drive link. This file converts 3D images and 2D labels images to ubyte format.

Usage to convert data

python3 convert.py train 0    #0 is ratio, which means whole data is converted to train. you can add proportions.
python3 convert.py test 0

This package is still in progress. If you find any issue please feel free to contact or create a new issue. You are welcome to contribute in this project.