Openface: Instalasi Deep Learning di Ubuntu 16.04 Server
Sumber: http://allskyee.blogspot.co.id/2017/03/face-detection-and-recognition-using.html
Disini menggunakan dlib, yang katanya lebih baik daripada Haar-cascade based classifier OpenCV.
Instalasi Paket Pendukung
sudo su
apt -y install git \
libopenblas-dev libopencv-dev libboost-dev \
libboost-python-dev python-dev \
build-essential gcc g++ cmake
apt -y install software-properties-common \
libgraphicsmagick1-dev libfftw3-dev sox libsox-dev \
libsox-fmt-all python-software-properties \
build-essential gcc g++ curl \
cmake libreadline-dev git-core libqt4-dev libjpeg-dev \
libpng-dev ncurses-dev imagemagick libzmq3-dev gfortran \
unzip gnuplot gnuplot-x11 ipython \
gcc-4.9 libgfortran-4.9-dev g++-4.9
Instalasi dlib face landmark detection
sudo su
apt -y install build-essential cmake libgtk-3-dev \
python-pip libboost-all-dev libboost-dev
apt -y install libboost-python-dev
pip install numpy
pip install scipy
pip install scikit-image
pip install dlib
pip install opencv-python # opencv tampaknya masih dibutuhkan
Instalasi Torch
sudo su cd /usr/local/src git clone https://github.com/torch/distro.git torch --recursive cd torch; bash install-deps; ./install.sh # /usr/local/src/torch/install/bin/torch-activate source ~/.bashrc
apt install luarocks # update common package ke versi terakhir luarocks install torch luarocks install nn luarocks install graph luarocks install cunn luarocks install cutorch luarocks install torchnet luarocks install optnet luarocks install iterm
Instalasi Openface
cd /usr/local/src git clone https://github.com/cmusatyalab/openface.git openface cd openface
python setup.py install ./models/get-models.sh pip install -r requirements.txt luarocks install csvigo luarocks install dpnn
Ambil Muka / Face yang sudah di label
cd /usr/local/src/openface/ wget http://vis-www.cs.umass.edu/lfw/lfw.tgz tar -zxvf lfw.tgz
Pilih data yang tidak lebih dari 10 muka, simpan di big_db.
cd /usr/local/src/openface/
find lfw/ -mindepth 1 -maxdepth 2 -type d -exec bash -c "echo -ne '{} '; ls '{}' | wc -l" \; | awk '$NF>10{print $1}' > big_db
Ambil 10 orang (random) dari daftar big_db, copy ke training_images
cd /usr/local/src/openface/ mkdir -p training-images cat big_db | shuf -n 10 | xargs cp -avt training-images/
Jika anda ingin wajah anda di recognize, tambahkan folder di training-images. Pastikan memasukan beberapa foto anda yang berisi muka anda sendirian.
Selanjutnya kita akan menjalankan face landmark detection untuk setiap foto yang akan
- Mendeteksi muka yang terbesar
- Mendeteksi tanda2 di muka (outer eye, hidung dan bibir bawah)
- Warp affine ke canonical face
- Simpan output (96x96) ke file dalam format yang mudah di akses.
Perintah yang di jalankan,
cd /usr/local/src/openface/ ./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96
Next, run feature extraction on each of the images.
./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/
As a final step, train a classifier from generated representations.
./demos/classifier.py train ./generated-embeddings/
This will create a file called classifier.pkl in generated-embeddings folder.
Operasikan
5. Run To test if everything is working properly before working with the trained model from Section 4, let's try this on a pre-trained model. There is a pre-trained model on celebrities in folder. To test using this image, run
./demos/classifier.py infer models/openface/celeb-classifier.nn4.small2.v1.pkl images/examples/adams.jpg
Artifacts of sections 1~4 done on RPI are on this link. Be sure to extract it from /home/pi/ to produce /home/pi/fd_fr as the internal scripts are hardcoded to that location. I've made a couple of changes to the base as this is run on a 32bit environment. Running "git diff" from /home/pi/fd_fr/openface will reveal the changes.
This should correctly predict the sample image as Amy Adams which predicts with 81% certainty but on RPI is 34% which I find odd...
To complete the final leg of our journey, I recommend testing on yourself. I saved a photo of myself that I didn't include in the training set as sky_chon.jpg. To test the trained model on me, I run
./demos/classifier.py infer generated-embeddings/classifier.pkl sky_chon.jpg
To run using the webcam (dev/video0) on VGA.
./demos/classifier_webcam.py --width 640 --height 480 --captureDevice 0 generated-embeddings/classifier.pkl
Works like a charm!!
References
https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.lugw83dgc https://cmusatyalab.github.io/openface/ http://dlib.net/ http://blog.dlib.net/2014/02/dlib-186-released-make-your-own-object.html http://bamos.github.io/2016/01/19/openface-0.2.0/ https://github.com/davisking/dlib http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 https://github.com/torch/torch7/issues/966 https://wiki.debian.org/RaspberryPi/qemu-user-static https://hblok.net/blog/posts/2014/02/06/chroot-to-arm/ https://lukeplant.me.uk/blog/posts/sharing-internet-connection-to-chroot/ https://hblok.net/blog/posts/2014/02/06/chroot-to-arm/ https://github.com/cmusatyalab/openface/issues/42