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

Latest revision as of 16:55, 22 May 2018

Sumber: https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation

Instalasi Aplikasi Pendukung

sudo su
apt update
apt -y install build-essential cmake libopenblas-dev liblapack-dev \
git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev \
python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev \
libtiff-dev libdc1394-22-dev checkinstall unzip \
llvm clang libc++-dev libc++abi-dev libboost-all-dev

Instalasi Openface

cd /usr/local/src
git clone https://github.com/TadasBaltrusaitis/OpenFace.git
cd OpenFace
chmod +x download_models.sh 
./download_models.sh
./install.sh

NOTE: libjasper perlu dibuang dari install.sh di ubuntu 18.04

Jika berhasil, hasilnya

..
..
[100%] Linking CXX executable ../../bin/FeatureExtraction
[100%] Built target FeatureExtraction
OpenFace successfully installed.

Test

for videos:

./bin/FaceLandmarkVid -f "../samples/changeLighting.wmv" -f "../samples/2015-10-15-15-14.avi"

for images:

./bin/FaceLandmarkImg -fdir "../samples/" -wild

for multiple faces in videos:

./bin/FaceLandmarkVidMulti -f ../samples/multi_face.avi

for feature extraction (facial landmarks, head pose, AUs, gaze and HOG and similarity aligned faces):

./bin/FeatureExtraction -verbose -f "../samples/default.wmv"

Referensi

https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation

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

Latest revision as of 16:55, 22 May 2018

Contents

Instalasi Aplikasi Pendukung

Instalasi Openface

Test

Referensi

Navigation menu

Search

@@ Line 1: / Line 1: @@
+Sumber: https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation
 ==Instalasi Aplikasi Pendukung==
-  sudo apt -y install virtualenv git \
+  sudo su
-  libopenblas-dev libopencv-dev libboost-dev libboost-python-dev python-dev \
+ apt update
- build-essential gcc g++ cmake
+ apt -y install build-essential cmake libopenblas-dev liblapack-dev \
+  git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev \
-Buat virtual environment untuk Python 2 di folder fd_fr
+ python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev \
+  libtiff-dev libdc1394-22-dev checkinstall unzip \
+  llvm clang libc++-dev libc++abi-dev libboost-all-dev
-Create a compartmentalized Python environment with Python 2 (has to be this version required by Openface) and copy necessary OpenCV's Python bindings onto it. Let's call the fd_fr directory VENV_ROOT.
- mkdir fd_fr; cd fd_fr
- export VENV_ROOT=$(pwd)
- virtualenv -p /usr/bin/python2.7 .
-  cp /usr/lib/python2.7/dist-packages/cv* lib/python2.7/site-packages/
- source bin/activate
-Tip when running on RPI : As RPI devices have very little memory and the storage device being an SDcard, performance can drop dramatically when run with tons of file I/O. One way to remedy is by flushing the page cache after large file I/O operations with the following command periodically during the installation process going forward.
- sync
- sudo bash -c "echo 3 > /proc/sys/vm/drop_caches"
-. Install and run Dlib face landmark detection
-Get Dlib either by cloning it from their github repository or downloading a released version. At the time of this writing, that is version v19.3 so I'm going to clone the repository and checkout that tag.
-  git clone https://github.com/davisking/dlib.git dlib
- cd dlib
- git checkout -b v19.3
-If on RPI, do not compile Dlib on the device as some files require 800+MB of memory which cause massive swap to device and CPU utilization falls below 5%. I recommend doing this on the host PC and run ARM emulation using qemu-arm-static and then copying. But if you're feeling lazy, I have done all this (and subsequent steps) that you can access via this link. More details on this in Section 5.
-Tip with qemu-arm-static internet connection : Especially if behind a proxy, be sure to share contents of /etc/resolv.conf with chroot environment either by copying or bind mount
+==Instalasi Openface==
- mount --bind /etc/resolv.conf /rpi/mount/etc/resolv.conf for behind proxy
-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.
-. Install and build Torch
-Torch is an opensource machine learning library based on Lua. Detailed instructions to get started available on their site. They recommend using a script to install some packages but I've been burned one too many times by erroneous scripts with sudo rights and so I will write out what it's doing in plain English.
-On another folder (not Dlib), clone the repository.
- git clone https://github.com/torch/distro.git torch --recursive
- cd torch
-To install necessary packages (if on an Ubuntu 16.04 system like moi), run
- # for Ubuntu 16.04
- sudo apt-get install software-properties-common \
-                 libgraphicsmagick1-dev libfftw3-dev sox libsox-dev \
-                 libsox-fmt-all
- sudo apt-get install python-software-properties
- sudo apt-get install 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
- sudo apt-get install -y gcc-4.9 libgfortran-4.9-dev g++-4.9
-For Raspbian
- # for Raspbian Jessie
- sudo apt-get install -y 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
-Now run the install.sh script (which is more like a build script) and source the environment activation file.
+ cd /usr/local/src
+ git clone https://github.com/TadasBaltrusaitis/OpenFace.git
+ cd OpenFace
+ chmod +x download_models.sh
+ ./download_models.sh
   ./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.
-. Install Openface
-Now for the final piece of the puzzle, clone the repository.
- git clone https://github.com/cmusatyalab/openface.git openface
- cd openface
-There has been a release on Feb 26, 2016 and it's been roughly a year since. So I think it's better to just use the tip rather than checking out  the last release. Next, install and download necessary artifacts.
- python setup.py install
- ./models/get-models.sh
- pip install -r requirements.txt
- luarocks install csvigo
- luarocks install dpnn
-. Get labeled faces and train on them
-It's now finally time to run Openface and see what it can do. To get a good batch of labeled faces on which to run the recognition task on, there is the LFW (labeled faces in the wild) dataset. Download then unzip which creates a directory lfw containing 5k directory of IDs.
- 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.
-. 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
+'''NOTE:''' libjasper perlu dibuang dari install.sh di ubuntu 18.04
- ./demos/classifier.py infer generated-embeddings/classifier.pkl sky_chon.jpg
+Jika berhasil, hasilnya
-To run using the webcam (dev/video0) on VGA.
+ ..
+ ..
+ [100%] Linking CXX executable ../../bin/FeatureExtraction
+ [100%] Built target FeatureExtraction
+ OpenFace successfully installed.
- ./demos/classifier_webcam.py --width 640 --height 480 --captureDevice 0 generated-embeddings/classifier.pkl
+==Test==
-Works like a charm!!
+for videos:
-References
+ ./bin/FaceLandmarkVid -f "../samples/changeLighting.wmv" -f "../samples/2015-10-15-15-14.avi"
-    https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.lugw83dgc
+for images:
-    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
+ ./bin/FaceLandmarkImg -fdir "../samples/" -wild
+for multiple faces in videos:
+ ./bin/FaceLandmarkVidMulti -f ../samples/multi_face.avi
+for feature extraction (facial landmarks, head pose, AUs, gaze and HOG and similarity aligned faces):
+ ./bin/FeatureExtraction -verbose -f "../samples/default.wmv"
 ==Referensi==
-* http://allskyee.blogspot.co.id/2017/03/face-detection-and-recognition-using.html
+* https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation