Difference between revisions of "Openface: Instalasi Deep Learning di Ubuntu 16.04 Server"

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File yang dihasilkan adalah classifier.pkl di folder generated-embeddings
 
File yang dihasilkan adalah classifier.pkl di folder generated-embeddings
  
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==Operasikan==
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==Run==
 +
 
 +
Contoh run menggunakan pre-trained model yang berlokasi di celebrities folder.
 +
Jalankan,
  
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5. Run
 
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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
 
  ./demos/classifier.py infer models/openface/celeb-classifier.nn4.small2.v1.pkl images/examples/adams.jpg
  
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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.
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Hasilnya kira-kira,
  
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This should correctly predict the sample image as Amy Adams which predicts with 81% certainty but on RPI is 34% which I find odd...
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Predict AmyAdams with 0.64 confidence.
  
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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
 
  
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./demos/classifier.py infer generated-embeddings/classifier.pkl sky_chon.jpg
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Beberapa perintah yang menarik
  
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To run using the webcam (dev/video0) on VGA.
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* Mencari target operasi
  
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  ./demos/classifier_webcam.py --width 640 --height 480 --captureDevice 0 generated-embeddings/classifier.pkl
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  ./demos/classifier.py infer generated-embeddings/classifier.pkl target-operasi.jpg
  
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Works like a charm!!
 
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References
 
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    https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.lugw83dgc
 
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    https://cmusatyalab.github.io/openface/
 
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    http://dlib.net/
 
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    http://blog.dlib.net/2014/02/dlib-186-released-make-your-own-object.html
 
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    http://bamos.github.io/2016/01/19/openface-0.2.0/
 
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    https://github.com/davisking/dlib
 
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    http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
 
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    https://github.com/torch/torch7/issues/966
 
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    https://wiki.debian.org/RaspberryPi/qemu-user-static
 
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    https://hblok.net/blog/posts/2014/02/06/chroot-to-arm/
 
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    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
 
  
 +
* Mengenali dari webcam (dev/video0) on VGA.
  
 +
./demos/classifier_webcam.py --width 640 --height 480 --captureDevice 0 generated-embeddings/classifier.pkl
  
  
 +
==Referensi==
  
 +
* 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
  
 
==Referensi==
 
==Referensi==
  
 
* http://allskyee.blogspot.co.id/2017/03/face-detection-and-recognition-using.html
 
* http://allskyee.blogspot.co.id/2017/03/face-detection-and-recognition-using.html

Revision as of 05:56, 23 May 2018

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

Selanjutnya extrak fitur dari masing-masing gambar, 

./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/

Langkah terakhir, train classifier untuk membuat representasi 

./demos/classifier.py train ./generated-embeddings/

File yang dihasilkan adalah classifier.pkl di folder generated-embeddings

Run

Contoh run menggunakan pre-trained model yang berlokasi di celebrities folder. Jalankan, 


./demos/classifier.py infer models/openface/celeb-classifier.nn4.small2.v1.pkl images/examples/adams.jpg

Hasilnya kira-kira, 

Predict AmyAdams with 0.64 confidence.


Beberapa perintah yang menarik

  • Mencari target operasi
./demos/classifier.py infer generated-embeddings/classifier.pkl target-operasi.jpg


  • Mengenali dari webcam (dev/video0) on VGA.
./demos/classifier_webcam.py --width 640 --height 480 --captureDevice 0 generated-embeddings/classifier.pkl


Referensi

Referensi

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