Metadata-Version: 2.1
Name: python-vtonimat
Version: 0.12
Summary: Simple VTON and Imaterialist data parser written in pure Python
Home-page: https://github.com/aarti-b/vton-imaterialist
Author: Arti.B
Author-email:  
License: BSD
Description: # vton-imaterialist
        A python package for Binary Segmentation DataSet ( vton_plus and imaterialist [topwear])
        
        [📚 PyPi Project Documentation 📚](https://pypi.org/project/python-vtonimat/#description)
        # Download dataset 
        
        `Note - This step can be performed after installing package as well.`
        
        ### Download dataset from following drive and unzip it.
        [gdrive](https://drive.google.com/drive/folders/1cGp0-s5p8n4oNnZr5AM_AaYCVzlJbkCo?usp=sharing)
        
        
        # 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.
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
