SieveNet

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This is the unofficial implementation of SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On

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# SieveNet #

![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

This is the unofficial implementation of 'SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On' 
Paper can be found from [here](https://arxiv.org/pdf/2001.06265.pdf)

# Dataset downloading and processing #
Dataset download instructions and link of dataset can be found from official repo of [CP-VTON](https://github.com/sergeywong/cp-vton) and [VITON](https://github.com/xthan/VITON) 
Put dataset in `data` folder

# Usage #
Clone the repo and install requirements through ```pip install -r requirements.txt```

# Traning #
#### Coarse-to-Fine Warping module ####
&nbsp;&nbsp;&nbsp;&nbsp; In `config.py` set ```self.datamode='train'``` and ```self.stage='GMM'```
 &nbsp;&nbsp;&nbsp;&nbsp; then run ```python train.py```
 You can observe results while traning in tensorboard as below

![SS from tensorboard while training gmm](https://github.com/levindabhi/SieveNet/blob/master/images/train_gmm.png)

#### Conditional Segmentation Mask generation module ####
&nbsp;&nbsp;&nbsp;&nbsp; In `config.py` set ```self.datamode='Train'``` and ```self.stage='SEG'```
 &nbsp;&nbsp;&nbsp;&nbsp; then run ```python train.py```

![SS from tensorboard while training segm](https://github.com/levindabhi/SieveNet/blob/master/images/train_seg.jpeg)

#### Segmentation Assisted Texture Translation module ####
&nbsp;&nbsp;&nbsp;&nbsp; In `config.py` set ```self.datamode='Train'``` and ```self.stage='TOM'```
 &nbsp;&nbsp;&nbsp;&nbsp; then run ```python train.py```

![SS from tensorboard while training tom](https://github.com/levindabhi/SieveNet/blob/master/images/train_tom.png)

# Testing on dataset #
Please download checkpoint of all three modules from [google drive](https://drive.google.com/drive/folders/17aPw4vzY4v0Rzayrp71ygzdo6zlqzmjM?usp=sharing) and put them in `checkpoints` folder

For testing, in `config.py` set ```self.datamode='test'```
 For Testing of Coarse-to-Fine Warping module, Conditional Segmentation Mask generation module, and Segmentation Assisted Texture Translation module set ```self.stage='GMM'```, ```self.stage='SEG'```, and ```self.stage='TOM'``` respectively.

Here is testing result. For Coarse-to-Fine Warping module,

![SS from tensorboard while testing gmm](https://github.com/levindabhi/SieveNet/blob/master/images/test_gmm.jpg)
For Segmentation Assisted Texture Translation module, 

![SS from tensorboard while testing gmm](https://github.com/levindabhi/SieveNet/blob/master/images/test_tom.png)

# Testing on custom image #
1. Please download checkpoint of all three modules from [google drive](https://drive.google.com/drive/folders/17aPw4vzY4v0Rzayrp71ygzdo6zlqzmjM?usp=sharing) and put them in `checkpoints` folder.
2. Please download caffe-model from [here](http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel) and put the model in `pose` folder. 
3. Generate human parsing from [Self-Correction-Human-Parsing](https://github.com/PeikeLi/Self-Correction-Human-Parsing) repo or from this [colab demo](https://colab.research.google.com/drive/1JOwOPaChoc9GzyBi5FUEYTSaP2qxJl10?usp=sharing). Select `LIP` dataset while generating human parsing.
4. Set input-image's, cloth-image's, and output of human parsing image's path in config file.
5. Then run ```python inference.py```
Output will be saved in `outputs` folder.

## Update: Inference using colab
Please find inference code of Sievenet in 2nd part of this notebook

[colab-badge]: <https://colab.research.google.com/assets/colab-badge.svg>
[![Open In Colab][colab-badge]](https://colab.research.google.com/github/levindabhi/ACGPN/blob/master/ACGPN_and_SieveNet.ipynb)

# Acknowledgements #
Some modules of this implementation is based on this [repo](https://github.com/sergeywong/cp-vton)
For generating pose keypoints, I have used [learnopencv](https://github.com/spmallick/learnopencv/tree/master/OpenPose-Multi-Person) implementation of OpenPose

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