sketch2im using Conditional GAN (pix2pix)

Image to Image Translation Using Generative Adversarial Networks

sketch2im using Conditional GAN (pix2pix)

This script shows how to reconstruct face images from their sketch-like image using pix2pix that is a kind of conditional GAN.

This code is created based on https://github.com/matlab-deep-learning/pix2pix.

Preparation

Fisrt of all, please download CelebAMask-HQ dataset. CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset.

https://github.com/switchablenorms/CelebAMask-HQ

To download the dataset, the following sites are available;

• Google Drive: downloading link
• Baidu Drive: downloading link

After downloading CelebA-HQ-img, put the file in the current path like below.

After the installation,please push RUN button as shown below.

Installation

Run the function install.m to ensure that all required files are added to the MATLAB path.

clear;clc;close all
install();

Create sketch-like images

Run the function img2sketch.m to convert the face images into skech-like ones. Then, the folder "CelebA_Line" should be created now.

img2sketch()

Training the model

To train a model you need pairs of images of "before" and "after", which correspond to CelebA-HQ-img and CelebA_Line, respectively now.

labelFolder='CelebA_Line';
targetFolder='CelebA-HQ-img';

We can tune the training parameters as below.

options = p2p.trainingOptions('MaxEpochs',1,'MiniBatchSize',8,'VerboseFrequency',30);

Note training the model will take several hours on a GPU and requires around 6GB of GPU memory.

p2pModel = p2p.train(labelFolder, targetFolder, options);

Generating images

Once the model is trained we can use the generator to make generate a new image.

exampleInput = imread('./CelebA_Line/1355.jpg');
exampleInput = imresize(exampleInput, [256, 256]);

We can then use the p2p.translate function to convert the input image using trained model.

exampleOutput = p2p.translate(p2pModel, exampleInput);
figure;imshowpair(exampleInput, gather(exampleOutput), "montage");

This is the modified code formed from https://github.com/matlab-deep-learning/pix2pix by Kenta Itakura