1. computer
2. package
3. install
4. use

ubuntu 14.04
nvidia-1050TI

CUDA 8.0
CUDNN 6.0
python 3.5
ipython
opencv 3.3
tensorflow-gpu 1.4
pathlib==1.0.1
scandir==1.6
h5py==2.7.1
Keras==2.1.2
opencv-python==3.3.0.10
scikit-image
dlib
face_recognition
tqdm
matplotlib

download GPU driver run.file http://www.geforce.cn/drivers my GPU use runflie: NVIDIA-Linux-x86_64-384.111.run

sudo chmod a+x NVIDIA-Linux-x86_64-384.111.run
sudo gedit /etc/modprobe.d/blacklist.conf 

append backlist nouveauin the end

sudo apt-get --purge remove xserver-xorg-video-nouveau
sudo reboot
sudo apt-get --purge remove nvidia-*
sudo init3
sudo /etc/init.d/lightdm stop

crtl+alt+F3 Enter username and password

sudo su
cd /home/usrname/
./NVIDIA-Linux-x86_64-384.111.run -no-x-check -no-nouveau-check -no-opengl-files

than perform operations accept>continue install>Yes>ok.....

sudo /etc/init.d/lightdm start
sudo apt-get install mesa-utills

Check whether your gpus can be installed with CUDA.

lspci | grep -i nvidia

Check whether your gpus can install CUDA.

 uname -m && cat /etc/*release

Verify whether the operating system installed the GCC

gcc -v

Install the CUDA Toolkit(deb local) download cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64.deb from https://developer.nvidia.com/cuda-downloads

sudo dpkg -i cuda-repo-ubuntu1404-8-0-local-ga2_8.0.61-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

After the installation is complete, declare the environment variable and write it to the end of ~/.bashrc. export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

source ~/.bashrc

Then set the environment variable and the dynamic link library in the command line input

sudo gedit /etc/profile

Add at the end of the open file

export PATH=/usr/local/cuda/bin:$PATH

After saving, create the link file.

sudo gedit /etc/ld.so.conf.d/cuda.conf

Add at the end of the open file

/usr/local/cuda/lib64

sudo ldconfig

test suda Samples

cd /usr/local/cuda-7.5/samples/1_Utilities/deviceQuery
make
sudo ./deviceQuery

download  cudnn 6.0 (cudnn-8.0-linux-x64-v6.0.tgz) in https://developer.nvidia.com/rdp/cudnn-download extract the tgz file entry the include file in the extacted file

sudo cp cudnn.h /usr/local/cuda/include/ 

entry the lib64 file in the extacted file

sudo cp lib* /usr/local/cuda/lib64/ 

then

cd /usr/local/cuda/lib64/
sudo rm -rf libcudnn.so libcudnn.so.6           
sudo ln -s libcudnn.so.6.0.21 libcudnn.so.6     
sudo ln -s libcudnn.so.6 libcudnn.so            

check the cudnn version

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

note: never remove the python3 or python2 which already in your system

sudo add-apt-repository ppa:fkrull/deadsnakes  
sudo apt-get update  
sudo apt-get install python3.5 

Cancel the original Python 3.4 and link Python3 to the latest 3.5.

sudo mv /usr/bin/python3 /usr/bin/python3-old  
sudo ln -s /usr/bin/python3.5 /usr/bin/python3 

install newest pip3 and ipython

wget https://bootstrap.pypa.io/get-pip.py  
sudo python3 get-pip.py  
sudo pip3 install setuptools --upgrade  
sudo pip3 install ipython[all] 

optional: switch back to the link file

sudo rm /usr/bin/python3  
sudo mv /usr/bin/python3-old /usr/bin/python3  

download opencv 3.3.0 in https://opencv.org/releases.html unzip opencv-3.3.0.zip file

cd opencv-3.3.0 
sudo apt-get install build-essential libgtk2.0-dev libvtk5-dev libjpeg-dev libtiff4-dev libjasper-dev libopenexr-dev libtbb-dev
mkdir build && cd build 
cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_TBB=ON  -D WITH_V4L=ON -D CMAKE_INSTALL_PREFIX=/usr/local/opencv330 .. 

or

cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local/opencv330 PYTHON3_EXECUTABLE = /usr/bin/python3 PYTHON_INCLUDE_DIR = /usr/include/python3.5m PYTHON_INCLUDE_DIR2 = /usr/include/x86_64-linux-gnu/python3.5m PYTHON_LIBRARY = /usr/lib/x86_64-linux-gnu/libpython3.5m.so PYTHON3_NUMPY_INCLUDE_DIRS = /usr/local/lib/python3.5/dist-packages/numpy/core/include/ ..
make 
sudo make install  
sudo gedit ~/.bashrc 

Add at the end of the file

 export PKG_CONFIG_PATH=/usr/local/opencv330/lib/pkgconfig  
 export LD_LIBRARY_PATH=/usr/local/opencv330/lib  

save and

source ~/.bashrc 
pkg-config --modversion opencv 

use in python3

sudo pip3 install opencv-python==3.3.0.10

pip3 install --upgrade tensorflow==1.4

test

python3
>>import tensorflow as tf

without any error

download dlib package in http://dlib.net/ ,any version can.

cd /dlib-19.10/examples
mkdir build
cd build
cmake ..
cmake --build . --config Release
cd ../../
wget https://bootstrap.pypa.io/ez_setup.py -O - | sudo python
sduo python3 setup.py install

sudo pip3 install pathlib==1.0.1
sudo pip3 install scandir==1.6
sudo pip3 install h5py==2.7.1
sudo pip3 install Keras==2.1.2
sudo pip3 install scikit-image
sudo pip3 install face_recognition
sudo pip3 install tqdm
sudo pip3 install matplotlib

FFmpeg is very useful to make more input photo to train.(nearly more than 400 photos). It turns video into a lot of images. We need to download install package from http://www.ffmpeg.org/download.html

sudo apt-get install yasm 
sudo apt-get install libx264-dev
sudo apt-get install libfaac-dev 
sudo apt-get install libmp3lame-dev 
sudo apt-get install libtheora-dev 
sudo apt-get install libvorbis-dev 
sudo apt-get install libxvidcore-dev 
sudo apt-get install libxext-dev 
sudo apt-get install libxfixes-dev 
cd ffmpeg-4.0
sudo -s
source configure
make
make install

test

ffmpeg -i "video.mp4" -r 5 -q:v 2 -f image2 image-3%d.jpeg

cd faceswap-huang
sudo python3 setup.py
python3 faceswap.py -h

So here's a problem. We have a ton of pictures of both our subjects, but they're just pictures of them doing stuff or in an environment with other people. Their bodies are on there, they're on there with other people... It's a mess. We can only train our bot if the data we have is consistent and focusses on the subject we want to swap. This is where faceswap first comes in.

# To convert trump:
python faceswap.py extract -i ~/faceswap/photo/trump -o ~/faceswap/data/trump
# To convert cage:
python faceswap.py extract -i ~/faceswap/photo/cage -o ~/faceswap/data/cage

We specify our photo input directory and the output folder where our training data will be saved. The script will then try its best to recognize face landmarks, crop the image to that size, and save it to the output folder. Note: this script will make grabbing test data much easier, but it is not perfect. It will (incorrectly) detect multiple faces in some photos and does not recognize if the face is the person who we want to swap. Therefore: Always check your training data before you start training. The training data will influence how good your model will be at swapping. You can see the full list of arguments for extracting via help flag. i.e.

python faceswap.py extract -h

The training process will take the longest, especially on CPU. We specify the folders where the two faces are, and where we will save our training model. It will start hammering the training data once you run the command. I personally really like to go by the preview and quit the processing once I'm happy with the results.

python faceswap.py train -A ~/faceswap/data/trump -B ~/faceswap/data/cage -m ~/faceswap/models/
# or -p to show a preview
python faceswap.py train -A ~/faceswap/data/trump -B ~/faceswap/data/cage -m ~/faceswap/models/ -p 

If you use the preview feature, select the preview window and press ENTER to save your processed data and quit gracefully. Without the preview enabled, you might have to forcefully quit by hitting Ctrl+C to cancel the command. Note that it will save the model once it's gone through about 100 iterations, which can take quite a while. So make sure you save before stopping the process. You can see the full list of arguments for training via help flag. i.e.

python faceswap.py train -h

Now that we're happy with our trained model, we can convert our video. How does it work? Similarly to the extraction script, actually! The conversion script basically detects a face in a picture using the same algorithm, quickly crops the image to the right size, runs our bot on this cropped image of the face it has found, and then (crudely) pastes the processed face back into the picture. Remember those initial pictures we had of Trump? Let's try swapping a face there. We will use that directory as our input directory, create a new folder where the output will be saved, and tell them which model to use.

python faceswap.py convert -i ~/faceswap/photo/trump/ -o ~/faceswap/output/ -m ~/faceswap/models/

It should now start swapping faces of all these pictures. You can see the full list of arguments available for converting via help flag. i.e.

python faceswap.py convert -h

All of the above commands and options can be run from the GUI. This is launched with:

python faceswap.py gui

A video is just a series of pictures in the form of frames. Therefore you can gather the raw images from them for your dataset or combine your results into a video.

You can perform various video processes with the built in effmpeg tool. You can see the full list of arguments available by running:

python tools.py effmpeg -h

Alternatively you can split a video into seperate frames using ffmpeg for instance. Below is an example command to process a video to seperate frames.

ffmpeg -i /path/to/my/video.mp4 /path/to/output/video-frame-%d.png

If you split a video, using ffmpeg for example, and used them as a target for swapping faces onto you can combine these frames again. The command below stitches the png frames back into a single video again.

ffmpeg -i video-frame-%0d.png -c:v libx264 -vf "fps=25,format=yuv420p" out.mp4