diff --git a/Unix-installation.md b/Unix-installation.md deleted file mode 100644 index 8cb573b..0000000 --- a/Unix-installation.md +++ /dev/null @@ -1,222 +0,0 @@ -This installation was tested on the following configuration: -* OS: Ubuntu 16.04 -* CUDA 9.0 -* CUDNN 7.1 -* OpenCV 3.4 -* Caffe 1.0 -* OpenFace 2.0 -* Boost 1.5 - -**If you have Ubuntu 18.04, please refer to the [configuration information](https://github.molgen.mpg.de/perceptual/opengaze_old/wiki/Unix-Installation#ubuntu-1804).** - -## Dependency installation - -OpenGaze requires cmake, OpenCV 3.1.0 (or newer), Caffe, OpenFace, and boost. OpenFace relies on tbb, OpenBlas and dlib. Caffe relies on protobuf, glog, gflags, hdf5 and cuDNN. - -Since we use convolutional neural networks as our model, the speed performance can be optimized with a powerful Nvidia GPU. Here we will show you how to install the driver and GPU library. - -### Install GPU driver and library -1. Install GPU driver:
- Check if you already have the Nvidia driver with `nvidia-smi`, which should give you the GPU information. Note that we tested on driver version 384.130.
- If you do not have the driver, then you can install the driver with `sudo ubuntu-drivers autoinstall`
- Then reboot your computer
- You now should test the Nvidia driver with `nvidia-smi`
- -2. Install CUDA
- CUDA is a parallel computing platform and programming model invented by NVIDIA. You can check with `nvcc --version` to see if you already have the GPU or not. We tested on version 9.0.176. You can install CUDA with the following steps:
- -``` - wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda_9.0.176_384.81_linux-run - chmod +x cuda_9.0.176_384.81_linux-run - sudo ./cuda_9.0.176_384.81_linux-run --override -``` - Answer these questions as follows while installation begins: - - Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? n - - Install the CUDA 9.0 Toolkit? y - - Do you want to install a symbolic link at /usr/local/cuda? y - - Install the CUDA 10.0 Samples? n - - Set up your paths:
-``` - echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc - echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc - echo 'export CPATH=/usr/local/cuda/include:$CPATH' >> ~/.bashrc - source ~/.bashrc -``` - You can now test your CUDA installation with `nvcc --version`, which should show your nvcc version.
- -3. Install cuDNN
- cuDNN is a GPU-accelerated library from Nvidia.
- Go to [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive) to download "cuDNN v7.1.4 (May 16, 2018), for CUDA 9.0", and then install it with:
-``` - tar -xzvf cudnn-9.0-linux-x64-v7.1.tgz - sudo cp cuda/include/cudnn.h /usr/local/cuda/include - sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 - sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* -``` - -## Install OpenGaze with pre-compiled binary -You can easily install OpenGaze with our pre-compiled binary file. -1. Install other dependencies with `./install.sh`. This will install all the dependencies (tbb, boost, OpenBlas, dlib, protobuf, glog, gflags and hdf5) except OpenFace and Caffe. -2. Install OpenGaze with `sudo dpkg -i opengaze.deb`. This will install the OpenGaze library itself and also the OpenFace and Caffe library. -4. Compile the test example: -``` -cd exe/ -mkdir build -cd build -cmake .. -make -``` -4. Run the test [Run the test](https://github.molgen.mpg.de/perceptual/opengaze_old/wiki/Unix-Installation#test-opengaze)
- -## Compile OpenGaze from source -If the pre-compiled file does not work for you, or you want to make changes to the OpenGaze source code, then you can compile it from source.
- -### Install OpenFace v2.0
-1. Install -``` -git clone https://github.com/TadasBaltrusaitis/OpenFace.git -cd OpenFace -``` -Open the file 'install.sh', and change the "BUILD_SHARED_LIBS=OFF" to "BUILD_SHARED_LIBS=ON" for OpenCV. Then run -``` -./install.sh -``` -**Warning:** It requires typing a "Y" in the middle of the installation. Do not leave it unattended.
- -2. Download the necessary models: -Open the file "download_models.sh", set your OpenGaze root directory, which defualt value is "~/OpenGaze".
-``` -chmod +x download_models.sh -./download_models.sh -``` -3. Test it: -``` -cd build/ -cp ../lib/local/LandmarkDetector/model/patch_experts/cen_patches_0.25_of.dat ./bin/model/patch_experts/ -cp ../lib/local/LandmarkDetector/model/patch_experts/cen_patches_0.35_of.dat ./bin/model/patch_experts/ -cp ../lib/local/LandmarkDetector/model/patch_experts/cen_patches_0.50_of.dat ./bin/model/patch_experts/ -cp ../lib/local/LandmarkDetector/model/patch_experts/cen_patches_1.00_of.dat ./bin/model/patch_experts/ -./bin/FaceLandmarkVid -f "../samples/changeLighting.wmv" -f "../samples/2015-10-15-15-14.avi" -``` -For more details, please visit the OpenFace installation [guidelines](https://github.com/TadasBaltrusaitis/OpenFace/wiki/Unix-Installation).
- -### Install Caffe:
-Install general dependencies:
- - `sudo apt-get install cmake libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libgflags-dev libgoogle-glog-dev liblmdb-dev`
- Download Caffe:
-`git clone https://github.com/BVLC/caffe.git`
- Download OpenGaze:
-`git clone https://github.molgen.mpg.de/perceptual/opengaze.git`
- Copy the customized layers from OpenGaze to caffe:
-`cp -r opengaze/caffe-layers/include caffe/` -`cp -r opengaze/caffe-layers/src caffe/`
- Compile:
-``` -cd caffe/ -mkdir build -cd build -cmake .. -DUSE_CUDNN=1 -DOPENCV_VERSION=3 -DBLAS=Open -make all -make install -``` - For details please visit the [Caffe website](http://caffe.berkeleyvision.org/installation.html). - -Now you should have successfully installed all the dependencies. - -## Compile OpenGaze - -Configure the build by modifying the **CMakeLists.txt** for your setup. - -1. **Caffe**
-Set the Caffe install path with "CAFFE_INSTALL_DIR"
- -2. **OpenFace**
-Set the OpenFace root directory with "OPENFACE_ROOT_DIR" - -3. **OpenGaze root path**
-Set the OpenGaze root path with "OPENGAZE_DIR", it will be the directory include Caffe models and camera calibration files etc. The defualt path is `/home/USER_NAME/OpenGaze` - -3. **Create an out-of-source build directory to store the compiled artifacts**:
-``` -cd OpenGaze -mkdir build -cd build -cmake .. -make -sudo make install -``` - -## Test OpenGaze -Download the pre-trained gaze estimation models by running:
-`./download_models.sh`
-Note that all the configuration and model files will be located in the "~/OpenGaze" directory.
-Go to "exe" folder, open "CMakeLists.txt" file, modify "OPENGAZE_DIR" if necessary. Then compile the test examples: -``` -cd exe/ -mkdir build -cd build/ -cmake .. -make -``` -Make sure your camera is connected to your computer, and then test it with:
-`./bin/GazeVisualization -d -t camera -i 0`
-Or test it with an existing video file:
-`./bin/GazeVisualization -d -t video -i ../exe/test.mp4`
- -### Make the .deb package file -I use [Checkinstall](https://wiki.debian.org/CheckInstall) to make the .deb file. When you reach the `make` step before "sudo make install" for OpenGaze, you can just type
-`sudo checkinstall --install=no` -to make a .deb file.
-Follow the instructions to edit the software description, version, and organization. At last, you will find the compressed .deb file.
- -## Ubuntu 18.04 -The default GCC version is 7.X with Ubuntu 18.04, which is not compatible with CUDA 9.0 (only works with GCC <= 6). This complicated situation results: GCC 7.x requires CUDA 9.2 and CUDA 9.2 requires a Nvidia driver version >= 396.
- -### Install Nvidia driver -Add the Nvidia ppa:
-``` -sudo apt update -sudo apt upgrade -sudo add-apt-repository ppa:graphics-drivers/ppa -sudo apt update -sudo apt upgrade -``` -If you try to install driver version 396 here, it will tell you there are some packages missing. However, you can install the driver from elsewhere.
-After that, go to the "Software & Update" in Ubuntu 18 system, go to "Additional Drivers", select `nvidia-driver-396`, and "Apply Changes".
-Reboot the computer, then test the driver installation with `nvidia-smi`.
- -### Install CUDA -``` - wget https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda_9.2.148_396.37_linux - chmod +x cuda_9.2.148_396.37_linux - sudo ./cuda_9.2.148_396.37_linux --override -``` - - Answer the following questions while installation begins: - - You are attempting to install on an unsupported configuration. Do you wish to continue? y - - Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? n - - Install the CUDA 9.0 Toolkit? y - - Do you want to install a symbolic link at /usr/local/cuda? y - - Install the CUDA 10.0 Samples? n - -and set up your paths:
-``` - echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc - echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc - echo 'export CPATH=/usr/local/cuda/include:$CPATH' >> ~/.bashrc - source ~/.bashrc -``` - You can now test your CUDA installation with `nvcc --version`, which should show your nvcc version.
- -### Install cuDNN -Go to the [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive) to download the "cuDNN v7.1.4 (May 16, 2018), for CUDA 9.2", and then install it with:
-``` - tar -xzvf cudnn-9.2-linux-x64-v7.1.tgz - sudo cp cuda/include/cudnn.h /usr/local/cuda/include - sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 - sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* -``` - -You now can go compile it from the source, and note that the pre-compiled file is only for Ubuntu 16.04. \ No newline at end of file