NOTE:

This repository has been upgraded, a new deep learning model has been provided(works only on full body and top wear clothes) has been released. Algorithm migration has been carried for the ease segmentation in version1.1

Version 1.1

Requirements

Tensorflow 2.0-alpha

OpenCV

Python3.6

Inference

download the pretrained model and directly run

python run.py mydress.jpg

download_pretrained

Snippet to integrate anywhere

api    = fashion_tools(f,saved)
image_ = api.get_dress()
cv2.imwrite("out.png",image_)

Version 1.0

A New Approach by using the Blend of Image-Processing Technique and Deep-Learning Algorithm to Segment any Fashion and e-commerce Retail Images. The code can be used for any industry on any images and the core algortithm is 'grab-cut algorithm" with the blend of Deep-Learning Convolutional Neural Networks. The Repo is designed in a preview way and its limited for fashion Images with auto-segmenting Top-wear clothes(Example: Tshirt, shirts) and Full-body clothes(salwar,gowns, shirt-pants-shoes)*

Image-Processing Resource

https://en.wikipedia.org/wiki/GrabCut

Deep Learning

https://en.wikipedia.org/wiki/Deep_learning

Package Requirements

1.Python

2.OpenCV 3.1.0

3.Keras with tensorflow backend

4.Pandas

5.NumPy

Demo v1.0

Note: Demo Annotation shouldn't be replaced, adding new will not enable the code adaptation to new classes of images.(The demo phase classes : Fashion full-body, Top-wear)

1.*clone* the Repo to your local pc ensuring that all the package requirements satisfied.<enter>
  
2.Run the code from the terminal **python fashion.py image1.jpg /Users/demo/save** <enter>
  
3.argument1 -- *image_name -- image1.jpg*, argument2 -- *save_directory -- /Users/demo/*

4.Visualize the results in your save_directory.