A GAN created to generate faces based on records of criminals
- Linux or Mac
- Python 3.6
- Pip
- imagemagick
In this case we are going to use the following datasets:
NIST Special Database 18
NIST Mugshot Identification Database (MID)
https://www.nist.gov/srd/nist-special-database-18
AT&T Laboratories Cambridge hosted in conjunction with Cambridge University Computer Laboratory
The Database of Faces
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
BioID Face Database - FaceDB
https://www.bioid.com/About/BioID-Face-Database
FGnet - IST-2000-26434 Face and Gesture Recognition Working group
Head Pose Image Database
http://www-prima.inrialpes.fr/FGnet/html/home.html
Clone this project and run the following
Setting up the virtual env
pip install virtualenv
virtualenv venv_cgan
source venv_cgan/bin/activateInstalling dependencies
pip install -r requirements.txtUse the following script or follow all the commands below
cd util
sh install_dataset.shDownload the datasets
cd data/
wget https://s3.amazonaws.com/nist-srd/SD18/sd18.zip
wget http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.zip
wget ftp://ftp.uni-erlangen.de/pub/facedb/BioID-FaceDatabase-V1.2.zip
wget http://www-prima.inrialpes.fr/perso/Gourier/Faces/HeadPoseImageDatabase.tar.gzCreate a folder to contain all the dataset
mkdir all_imagesNIST Special Database 18
unzip sd18.zip
mv sd18/*/*/*_F.png all_images/
rm -rf sd18BioID Face Database - FaceDB
unzip BioID-FaceDatabase-V1.2.zip -d bioid/
convert bioid/*.pgm all_images/bioid_%03d.png
rm -rf bioid/AT&T - The Database of Faces
unzip att_faces.zip -d att/
convert att/*/*.pgm all_images/att_%03d.png
rm -rf att/FGnet - Head Pose Image Database
tar xvz -f HeadPoseImageDatabase.tar.gz Front/
convert Front/*.jpg all_images/att_%03d.png
rm -rf Front/mkdir resized/
mogrify -resize 64x64! -quality 100 -path resized/ all_images/* -type Grayscale
rm -rf all_images/$ python gan.py
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Created by Davidson Mizael
# Starting...
# Loading data...
# Starting generator and descriminator...
# Starting epochs (15)...
# Progress: [0/15][38/38] Loss_D: 1.5640 Loss_G: 0.0004
# Progress: [1/15][38/38] Loss_D: 1.4570 Loss_G: 0.0004
...
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