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Semantic Image Local Enhancement

This project generates local image enhancements based on given keywords. This repository contains the source code necessary to reproduce the results presented in the paper Keyword-based Image Color Re-rendering with Semantic Segmentation (ICIP 2017)

In order to generate semantic segmentation masks, please refer to Webly Supervised Semantic Segmentation

Example:

Using the keyword Lily

Installation

This project uses Python 3.5 and all the libraries can be installed using conda

conda env create -f environment.yml

Additionally, you need to download and setup the MIR Flickr 1M database from http://press.liacs.nl/mirflickr/ You can setup the directories according to src/config_paths.py or alter this script according to your needs

Code hierarchy

Source Code

The main code is in the src folder and separated into four main modules:

  • collector: Module for collecting features and calculating z-values
  • descriptors: Module for calculating different image descriptors
  • enhancer: Module for comparing images and keywords and enhancing them
  • filesystem: Module for reading all kind of files used in the project

It also some common helper files:

  • color_helpers.py: Color space transformations from RGB, LAB, LCH similar to Lindner's transformations
  • config_paths.py: Path configuration for images, masks, z-values and everything else, as well as path helpers
  • shared.py: Shared variables used across multiple modules

The matlab directory contains previous scripts used to generate global image enhancement

Useful scripts

Outside the source folder there are some python scripts that do the following:

  • copy-by-tag.py: Given a tag name, copies the images from the flickr db into a folder of their own
  • copy-masks.py: Copies the generated masks into a directory structure similiar to flickr
  • runner.py: Contains an example to calculate z-values
  • grabcut.py: Allows to do grabcut on a given image. Taken from https://github.com/opencv/opencv/blob/master/samples/python/grabcut.py

Notebooks

There are also a couple of notebooks that I used to generate the results. They assume you have the following folder hierarchy:

  • z-values:
    • keyword1-zvalue.pkl
    • keyword1-zvalue-local.pkl
    • keyword2-zvalue.pkl
    • keyword2-zvalue-local.pkl
  • images:
    • keyword1:
      • image1_id.jpg
      • image2_id.jpg
    • keyword2:
      • image3_id.jpg
  • masks:
    • keyword1:
      • mask1_id.jpg
      • mask2_id.jpg
    • keyword2:
      • mask3_id.jpg

Citation

Please cite the below when you use our work

@inproceedings{lahoud2017keyword,
  title={KEYWORD-BASED IMAGE COLOR RE-RENDERING WITH SEMANTIC SEGMENTATION},
  author={Lahoud, Fayez and Ortiz Segovia, Maria S and Jin, Bin and S{\"u}sstrunk, Sabine},
  booktitle={IEEE International Conference on Image Processing (ICIP)},
  number={EPFL-CONF-231739},
  year={2017}
}