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
Instalasi dlib face landmark detection
Checkout versi terakhir (misalnya 19.3)
cd /usr/local/src git clone https://github.com/davisking/dlib.git dlib cd dlib git checkout -b v19.3
The build process is done with cmake. If you have a relatively new CPU, it should have the AVX (Advanced Vector Extensions) which greatly enhances the performance. You can find either or not your CPU supports this functionality by executing "cat /proc/cpuinfo | grep avx". If you get a non-empty text, then your CPU supports it. If it does, include the "-DUSE_AVX..." statement below.
mkdir build; cd build cmake ../tools/python -DUSE_AVX_INSTRUCTIONS=1 cmake --build . --config Release
Copy generated dlib.so file to Python's lib/ path and test it by importing it from Python.
cp dlib.so ${VENV_ROOT}/lib/python2.7/dist-packages/
python -c "import dlib"
If this second statement returns an error, you have done something wrong.
(optional) To run Dlib with a face and landmark detector on a webcam feed, first download the model for the latter from here and unzip it. I have written a short script on my gist page which requires the webcam class. You need to download the latter to run the former as well as the model.
Instalasi Torch
sudo su
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
cd /usr/local/src git clone https://github.com/torch/distro.git torch --recursive cd torch
./install.sh source install/bin/torch-activate
Caveat 1.
The necessary paths in "install/bin/torch-activate" are hard-coded absolute paths so if you move the installed directory, be sure to change these as well.
Caveat 2. Older versions of CUDA will not work. If you don't feel like updating, just install without it by adding "path_to_nvcc=" to line 82 in install.sh file.
Caveat 2-1. For old or Atom based systems that do not have AVX, the library is automatically going to use SSE which has a bug on randperm function. Rest assured, it was fixed recently so be sure to pull that patch if running on Atom.
Instalasi Openface
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
wget http://vis-www.cs.umass.edu/lfw/lfw.tgz tar -zxvf lfw.tgz
I'm going to select from this a list that only has an excess of 10 faces and save it into a file called big_db.
find lfw/ -mindepth 1 -maxdepth 2 -type d -exec bash -c "echo -ne '{} '; ls '{}' | wc -l" \; | awk '$NF>10{print $1}' > big_db
Next, I'm going to select 10 random people out of this list and copy them to another folder called training-images.
mkdir -p training-images cat big_db | shuf -n 10 | xargs cp -avt training-images/
If you'd like to recognize yourself, add a folder in training-images. Be sure to include many photos in which you're the single identifiable human face.
We're now doing to run face landmark detection on each photo which will
- Detect the biggest face
- Detect the facial landmarks (outer eyes, nose and lower lip)
- Warp affine to a canonical face
- Save output (96x96) to a file in an easy to access format
To do this, run
./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