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

From OnnoWiki
Jump to navigation Jump to search
 
(26 intermediate revisions by the same user not shown)
Line 8: Line 8:
  
 
  sudo su
 
  sudo su
 +
locale-gen id_ID.UTF-8
 +
 +
apt update
 
  apt -y install git \
 
  apt -y install git \
 
         libopenblas-dev libopencv-dev libboost-dev \
 
         libopenblas-dev libopencv-dev libboost-dev \
Line 20: Line 23:
 
         unzip gnuplot gnuplot-x11 ipython \
 
         unzip gnuplot gnuplot-x11 ipython \
 
         gcc-4.9 libgfortran-4.9-dev g++-4.9
 
         gcc-4.9 libgfortran-4.9-dev g++-4.9
 +
 +
Lakukan 2-3 kali supaya memastikan apps di install dengan benar.
  
 
==Instalasi dlib face landmark detection==
 
==Instalasi dlib face landmark detection==
−
 
−
Compile dlib membutuhkan memory besar. Memory 2GB gagal. Memory 4GB tampaknya minimal.
 
  
 
  sudo su
 
  sudo su
Line 33: Line 36:
 
  pip install scikit-image
 
  pip install scikit-image
 
  pip install dlib
 
  pip install dlib
 +
pip install opencv-python # opencv tampaknya masih dibutuhkan
 +
 +
Lakukan 2-3x supaya memastikan apps di install dengan benar.
  
 
==Instalasi Torch==
 
==Instalasi Torch==
Line 39: Line 45:
 
  cd /usr/local/src
 
  cd /usr/local/src
 
  git clone https://github.com/torch/distro.git torch --recursive
 
  git clone https://github.com/torch/distro.git torch --recursive
−
  cd torch
+
  cd torch; bash install-deps;
−
 
 
 
  ./install.sh
 
  ./install.sh
−
  source install/bin/torch-activate
+
  source ~/.bashrc
  
 +
Lakukan 2-3x untuk memastikan terinstalasi dengan baik.
  
−
Caveat 1.
+
# apt -y install luarocks
−
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.
+
# update common package ke versi terakhir
−
 
+
luarocks install torch
−
Caveat 2.
+
luarocks install nn
−
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.
+
luarocks install graph
−
 
+
luarocks install cunn
−
Caveat 2-1.
+
luarocks install cutorch
−
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.
+
luarocks install torchnet
 +
luarocks install optnet
 +
luarocks install iterm
  
 
==Instalasi Openface==
 
==Instalasi Openface==
Line 58: Line 66:
 
  cd /usr/local/src
 
  cd /usr/local/src
 
  git clone https://github.com/cmusatyalab/openface.git openface
 
  git clone https://github.com/cmusatyalab/openface.git openface
−
cd openface
 
  
 +
cd /usr/local/src/openface
 
  python setup.py install
 
  python setup.py install
 
  ./models/get-models.sh
 
  ./models/get-models.sh
Line 68: Line 76:
 
==Ambil Muka / Face yang sudah di label==
 
==Ambil Muka / Face yang sudah di label==
  
 +
Contoh2 foto muka untuk training.
 +
 +
cd /usr/local/src/openface/
 
  wget http://vis-www.cs.umass.edu/lfw/lfw.tgz
 
  wget http://vis-www.cs.umass.edu/lfw/lfw.tgz
 
  tar -zxvf 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.
+
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
 
  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.
+
Ambil 10 orang (random) dari daftar big_db, copy ke training_images
  
 +
cd /usr/local/src/openface/
 
  mkdir -p training-images
 
  mkdir -p training-images
 
  cat big_db | shuf -n 10 | xargs cp -avt 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.
+
Jika anda ingin wajah anda di recognize, tambahkan folder di training-images. Pastikan memasukan beberapa foto anda yang berisi muka anda sendirian.
−
 
 
−
We're now doing to run face landmark detection on each photo which will
 
  
−
* Detect the biggest face
+
Selanjutnya kita akan menjalankan face landmark detection untuk setiap foto yang akan
−
* 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
 
  
 +
* 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.
  
−
To do this, run
+
Perintah yang di jalankan,
  
 +
cd /usr/local/src/openface/
 
  ./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96
 
  ./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96
  
−
Next, run feature extraction on each of the images.
+
Selanjutnya extrak fitur dari masing-masing gambar,
  
 
  ./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/
 
  ./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/
  
−
As a final step, train a classifier from generated representations.
+
Langkah terakhir, train classifier untuk membuat representasi
  
 
  ./demos/classifier.py train ./generated-embeddings/
 
  ./demos/classifier.py train ./generated-embeddings/
  
−
This will create a file called classifier.pkl in generated-embeddings folder.
+
File yang dihasilkan adalah classifier.pkl di folder generated-embeddings
  
−
==Operasikan==
+
Masukan foto2 referensi ke bawah folder training-images/Nama_Orang_Tersebut.
 +
Supaya mudah proses training ada baiknya di batch sekaligus, caranya,
  
−
5. Run
+
cd /usr/local/src/openface/
−
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
+
rm -Rf aligned-images
 +
rm -Rf generated-embeddings/classifier.pkl
 +
./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96
 +
./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/
 +
./demos/classifier.py train ./generated-embeddings/
  
−
./demos/classifier.py infer models/openface/celeb-classifier.nn4.small2.v1.pkl images/examples/adams.jpg
+
==Run==
  
−
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.
+
Contoh run menggunakan pre-trained model yang berlokasi di celebrities folder.
 +
Jalankan,
  
−
This should correctly predict the sample image as Amy Adams which predicts with 81% certainty but on RPI is 34% which I find odd...
+
./demos/classifier.py infer models/openface/celeb-classifier.nn4.small2.v1.pkl images/examples/adams.jpg
  
−
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
+
Hasilnya kira-kira,
  
−
  ./demos/classifier.py infer generated-embeddings/classifier.pkl sky_chon.jpg
+
  Predict AmyAdams with 0.64 confidence.
  
−
To run using the webcam (dev/video0) on VGA.
 
  
−
./demos/classifier_webcam.py --width 640 --height 480 --captureDevice 0 generated-embeddings/classifier.pkl
+
Beberapa perintah yang menarik
  
−
Works like a charm!!
+
* Mencari target operasi
−
 
 
−
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
 
  
 +
./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==
  
 +
* 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

Latest revision as of 13:15, 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
locale-gen id_ID.UTF-8

apt update
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

Lakukan 2-3 kali supaya memastikan apps di install dengan benar.

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

Lakukan 2-3x supaya memastikan apps di install dengan benar.

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
source ~/.bashrc

Lakukan 2-3x untuk memastikan terinstalasi dengan baik. 

# apt -y 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 /usr/local/src/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

Contoh2 foto muka untuk training. 

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

Masukan foto2 referensi ke bawah folder training-images/Nama_Orang_Tersebut. Supaya mudah proses training ada baiknya di batch sekaligus, caranya, 

cd /usr/local/src/openface/
rm -Rf aligned-images
rm -Rf generated-embeddings/classifier.pkl
./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96
./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/
./demos/classifier.py train ./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

Retrieved from "https://onnocenter.or.id/wiki/index.php?title=Openface:_Instalasi_Deep_Learning_di_Ubuntu_16.04_Server&oldid=51094"