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Remove dlib (#725)

Browse Source 
* Sort to vgg_face. Remove Face Recognition requirements

* Remove dlib components from setup.py

* Remove dlib from Windows Installer script

* Remove dlib and cmake from requirements.txt

* Update INSTALL.md to remove all dlib requirements

* Remove dlib and cmake from dockerfiles

* Windows installer. Output cli to details Window. Setup.py bugfix

* Skip FaceFilter if invalid paths passed in
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torzdf 2019-05-12 23:24:25 +01:00 committed by GitHub
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9 changed files with 214 additions and 390 deletions
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87
.install/windows/install.nsi
View File

@ -27,15 +27,6 @@ InstallDir $PROFILE\faceswap
!define flagsRepo "--depth 1 --no-single-branch ${wwwRepo}"
!define flagsEnv "-y python=3.6"
# Dlib Wheel prefix
!define prefixDlib "dlib-19.16.99-cp36-cp36m-win_amd64"
!define dlibFinalName "dlib-19.16.99-cp36-cp36m-win_amd64.whl" # Dlib Wheel MUST have this name before installing
!define cudaDlib "_cuda90"
!define avxDlib "_avx"
!define sseDlib "_sse4"
!define noneDlib "_none"
# Folders
Var ProgramData
Var dirTemp
@ -48,7 +39,6 @@ Var dirConda
# Items to Install
Var InstallGit
Var InstallConda
Var dlibWhl
# Misc
Var gitInf
@ -78,6 +68,7 @@ Var envName
Page custom pgPrereqCreate pgPrereqLeave
# Install Faceswap Page
!define MUI_PAGE_CUSTOMFUNCTION_SHOW InstFilesShow
!define MUI_PAGE_HEADER_SUBTEXT "Installing Faceswap..."
!insertmacro MUI_PAGE_INSTFILES
@ -99,11 +90,16 @@ Function .onInit
StrCpy $gitInf "$dirTemp\git_install.inf"
StrCpy $envName "faceswap"
SetOutPath "$dirTemp"
File *.whl
File git_install.inf
Call CheckPrerequisites
FunctionEnd
# Enable the cancel button during installation
Function InstFilesShow
GetDlgItem $0 $HWNDPARENT 2
EnableWindow $0 1
FunctionEnd
Function VerifyInstallDir
# Check install folder does not already exist
IfFileExists $INSTDIR 0 +3
@ -278,7 +274,6 @@ Section Install
Call InstallPrerequisites
Call CloneRepo
Call SetEnvironment
Call InstallDlib
Call SetupFaceSwap
Call DesktopShortcut
SectionEnd
@ -292,7 +287,10 @@ Function InstallPrerequisites
${If} $0 == "OK"
DetailPrint "Installing Git..."
SetDetailsPrint listonly
ExecWait "$\"$dirTemp\git_installer.exe$\" ${flagsGit} /LOADINF=$\"$gitInf$\"" $0
ExecDos::exec /NOUNLOAD /ASYNC /DETAILED "$\"$dirTemp\git_installer.exe$\" ${flagsGit} /LOADINF=$\"$gitInf$\""
pop $0
ExecDos::wait $0
pop $0
SetDetailsPrint both
${If} $0 != 0
DetailPrint "Error Installing Git"
@ -312,7 +310,10 @@ Function InstallPrerequisites
${If} $0 == "OK"
DetailPrint "Installing Miniconda3. This will take a few minutes..."
SetDetailsPrint listonly
ExecWait "$\"$dirTemp\Miniconda3.exe$\" ${flagsConda}" $0
ExecDos::exec /NOUNLOAD /ASYNC /DETAILED "$\"$dirTemp\Miniconda3.exe$\" ${flagsConda}"
pop $0
ExecDos::wait $0
pop $0
StrCpy $dirConda "$dirMiniconda"
SetDetailsPrint both
${If} $0 != 0
@ -340,7 +341,10 @@ Function CloneRepo
${Else}
StrCpy $9 "git"
${EndIf}
ExecWait "$9 clone ${flagsRepo} $\"$INSTDIR$\"" $0
ExecDos::exec /NOUNLOAD /ASYNC /DETAILED "$9 clone ${flagsRepo} $\"$INSTDIR$\""
pop $0
ExecDos::wait $0
pop $0
SetDetailsPrint both
${If} $0 != 0
DetailPrint "Error Downloading Faceswap"
@ -352,14 +356,20 @@ Function SetEnvironment
# Updating Conda breaks setup.py. Commented out in case this issue gets resolved in future
# DetailPrint "Initializing Conda..."
# SetDetailsPrint listonly
# ExecWait "$dirConda\scripts\activate.bat && conda update -y -n base -c defaults conda && conda deactivate" $0
# ExecDos::exec /NOUNLOAD /ASYNC /DETAILED "$dirConda\scripts\activate.bat && conda update -y -n base -c defaults conda && conda deactivate"
# pop $0
# ExecDos::wait $0
# pop $0
# SetDetailsPrint both
DetailPrint "Creating Conda Virtual Environment..."
IfFileExists "$dirConda\envs\$envName" DeleteEnv CreateEnv
DeleteEnv:
SetDetailsPrint listonly
ExecWait "$\"$dirConda\scripts\activate.bat$\" && conda env remove -y -n $\"$envName$\" && conda deactivate" $0
ExecDos::exec /NOUNLOAD /ASYNC /DETAILED "$\"$dirConda\scripts\activate.bat$\" && conda env remove -y -n $\"$envName$\" && conda deactivate"
pop $0
ExecDos::wait $0
pop $0
SetDetailsPrint both
${If} $0 != 0
DetailPrint "Error deleting Conda Virtual Environment"
@ -368,7 +378,10 @@ Function SetEnvironment
CreateEnv:
SetDetailsPrint listonly
ExecWait "$\"$dirConda\scripts\activate.bat$\" && conda create ${flagsEnv} -n $\"$envName$\" && conda deactivate" $0
ExecDos::exec /NOUNLOAD /ASYNC /DETAILED "$\"$dirConda\scripts\activate.bat$\" && conda create ${flagsEnv} -n $\"$envName$\" && conda deactivate"
pop $0
ExecDos::wait $0
pop $0
SetDetailsPrint both
${If} $0 != 0
DetailPrint "Error Creating Conda Virtual Environment"
@ -376,46 +389,18 @@ Function SetEnvironment
${EndIf}
FunctionEnd
Function InstallDlib
DetailPrint "Installing Dlib..."
SetDetailsPrint listonly
StrCpy $dlibWhl ${prefixDlib}
${If} $noNvidia != 1
StrCpy $dlibWhl "$dlibWhl${cudaDlib}"
${EndIf}
${If} $hasAVX == 1
StrCpy $dlibWhl "$dlibWhl${avxDlib}"
${ElseIf} $hasSSE4 == 1
StrCpy $dlibWhl "$dlibWhl${sseDlib}"
${Else}
StrCpy $dlibWhl "$dlibWhl${noneDlib}"
${EndIf}
StrCpy $dlibWhl "$dlibWhl.whl"
DetailPrint "Renaming $dlibWhl to ${dlibFinalName}"
Rename "$dirTemp\$dlibWhl" "$dirTemp\${dlibFinalName}"
ExecWait "$\"$dirConda\scripts\activate.bat$\" && conda activate $\"$envName$\" && pip install $\"$dirTemp\${dlibFinalName}$\" && conda deactivate" $0
SetDetailsPrint both
${If} $0 != 0
DetailPrint "Error Installing Dlib"
Call Abort
${EndIf}
FunctionEnd
Function SetupFaceSwap
DetailPrint "Setting up FaceSwap Environment"
DetailPrint "Setting up FaceSwap Environment... This may take a while"
StrCpy $0 "${flagsSetup}"
${If} $noNvidia != 1
StrCpy $0 "$0 --gpu"
${EndIf}
SetDetailsPrint listonly
ExecWait "$\"$dirConda\scripts\activate.bat$\" && conda activate $\"$envName$\" && python $\"$INSTDIR\setup.py$\" $0 && conda deactivate" $0
ExecDos::exec /NOUNLOAD /ASYNC /DETAILED "$\"$dirConda\scripts\activate.bat$\" && conda activate $\"$envName$\" && python $\"$INSTDIR\setup.py$\" $0 && conda deactivate"
pop $0
ExecDos::wait $0
pop $0
SetDetailsPrint both
${If} $0 != 0
DetailPrint "Error Setting up Faceswap"

2
Dockerfile.cpu
View File

@ -7,8 +7,6 @@ RUN apt-get update -qq -y \
COPY requirements.txt /opt/
RUN pip3 install --upgrade pip
RUN pip3 install cmake
RUN pip3 install dlib
RUN pip3 --no-cache-dir install -r /opt/requirements.txt && rm /opt/requirements.txt
WORKDIR "/srv"

2
Dockerfile.gpu
View File

@ -7,8 +7,6 @@ RUN apt-get update -qq -y \
COPY requirements.txt /opt/
RUN pip3 install --upgrade pip
RUN pip3 install cmake
RUN pip3 install dlib
RUN pip3 --no-cache-dir install -r /opt/requirements.txt && rm /opt/requirements.txt
RUN pip3 install jupyter matplotlib
RUN pip3 install jupyter_http_over_ws

262
INSTALL.md
View File

@ -4,24 +4,13 @@
- [Hardware Requirements](#hardware-requirements)
- [Supported operating systems](#supported-operating-systems)
- [Important before you proceed](#important-before-you-proceed)
- [General Install Guide](#general-install-guide)
- [Installing dependencies](#installing-dependencies)
- [Getting the faceswap code](#getting-the-faceswap-code)
- [Setup](#setup)
- [About some of the options](#about-some-of-the-options)
- [Run the project](#run-the-project)
- [Notes](#notes)
- [Windows Install Guide](#windows-install-guide)
- [Installer](#installer)
- [Manual Install](#Manual-install)
- [Manual Install](#manual-install)
- [Prerequisites](#prerequisites-1)
- [Microsoft Visual Studio 2015](#microsoft-visual-studio-2015)
- [Cuda](#cuda)
- [cuDNN](#cudnn)
- [CMake](#cmake)
- [Anaconda](#anaconda)
- [Git](#git)
- [Setup](#setup-1)
- [Setup](#setup)
- [Anaconda](#anaconda-1)
- [Set up a virtual environment](#set-up-a-virtual-environment)
- [Entering your virtual environment](#entering-your-virtual-environment)
@ -31,9 +20,13 @@
- [Running Faceswap](#running-faceswap)
- [Create a desktop shortcut](#create-a-desktop-shortcut)
- [Updating faceswap](#updating-faceswap)
- [Dlib](#dlib)
- [Build Latest Dlib with GPU Support](#build-latest-dlib-with-gpu-support)
- [Easy install of Dlib without GPU Support](#easy-install-of-dlib-without-gpu-support)
- [General Install Guide](#general-install-guide)
- [Installing dependencies](#installing-dependencies)
- [Getting the faceswap code](#getting-the-faceswap-code)
- [Setup](#setup-1)
- [About some of the options](#about-some-of-the-options)
- [Run the project](#run-the-project)
- [Notes](#notes)
# Prerequisites
Machine learning essentially involves a ton of trial and error. You're letting a program try millions of different settings to land on an algorithm that sort of does what you want it to do. This process is really really slow unless you have the hardware required to speed this up.
@ -69,6 +62,84 @@ Alternatively, there is a docker image that is based on Debian.
The developers are also not responsible for any damage you might cause to your own computer.
# Windows Install Guide
## Installer
Windows now has an installer which installs everything for you and creates a desktop shortcut to launch straight into the GUI. You can download the installer from https://github.com/deepfakes/faceswap/releases.
If you have issues with the installer then read on for the more manual way to install Faceswap on Windows.
## Manual Install
Setting up Faceswap can seem a little intimidating to new users, but it isn't that complicated, although a little time consuming. It is recommended to use Linux where possible as Windows will hog about 20% of your GPU Memory, making Faceswap run a little slower, however using Windows is perfectly fine and 100% supported.
## Prerequisites
### Anaconda
Download and install the latest Python 3 Anaconda from: https://www.anaconda.com/download/. Unless you know what you are doing, you can leave all the options at default.
### Git
Download and install Git for Windows: https://git-scm.com/download/win. Unless you know what you are doing, you can leave all the options at default.
## Setup
Reboot your PC, so that everything you have just installed gets registered.
### Anaconda
#### Set up a virtual environment
- Open up Anaconda Navigator
- Select "Environments" on the left hand side
- Select "Create" at the bottom
- In the pop up:
- Give it the name: faceswap
- **IMPORTANT**: Select python version 3.6
- Hit "Create" (NB: This may take a while as it will need to download Python 3.6)
![Anaconda virtual env setup](https://i.imgur.com/Tl5tyVq.png)
#### Entering your virtual environment
To enter the virtual environment:
- Open up Anaconda Navigator
- Select "Environments" on the left hand side
- Hit the ">" arrow next to your faceswap environment and select "Open Terminal"
![Anaconda enter virtual env](https://i.imgur.com/rKSq2Pd.png)
### Faceswap
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Get the Faceswap repo by typing: `git clone --depth 1 https://github.com/deepfakes/faceswap.git`
- Enter the faceswap folder: `cd faceswap`
#### Easy install
- Enter the command `python setup.py` and follow the prompts:
- If you have issues/errors follow the Manual install steps below.
#### Manual install
- Install tkinter (required for the GUI) by typing: `conda install tk`
- Install requirements: `pip install -r requirements.txt`
- Install Tensorflow (either GPU or CPU version depending on your setup):
- GPU Version: `pip install tensorflow-gpu`
- Non GPU Version: `pip install tensorflow`
## Running Faceswap
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Enter the faceswap folder: `cd faceswap`
- Enter the following to see the list of commands: `python faceswap.py -h` or enter `python faceswap.py gui` to launch the GUI
## Create a desktop shortcut
A desktop shortcut can be added to easily launch straight into the faceswap GUI:
- Open Notepad
- Paste the following:
```
%USERPROFILE%\Anaconda3\envs\faceswap\python.exe %USERPROFILE%/faceswap/faceswap.py gui
```
- Save the file to your desktop as "faceswap.bat"
## Updating faceswap
It's good to keep faceswap up to date as new features are added and bugs are fixed. To do so:
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Enter the faceswap folder: `cd faceswap`
- Enter the following `git pull --all`
- Once the latest version has downloaded, make sure your requirements are up to date: `pip install --upgrade -r requirements.txt`
# General Install Guide
## Installing dependencies
- Python >= 3.2-3.6 64-bit (cannot be 3.7.x as Tensorflow has not been updated to provide support)
@ -78,10 +149,6 @@ The developers are also not responsible for any damage you might cause to your o
- [virtualenv](https://github.com/pypa/virtualenv) and [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io) may help when you are not using docker.
- If you are using an Nvidia graphics card You should install CUDA (https://developer.nvidia.com/cuda-zone) and CUDNN (https://developer.nvidia.com/cudnn). If you do not plan to build Tensorflow yourself, make sure you install no higher than version 10.0 of CUDA and 7.4.x of CUDNN
- dlib is required for face recognition and is compiled as part of the setup process. You will need the following applications for your os to successfully install dlib (nb: list may be incomplete. Please raise an issue if another prerequisite is required for your OS):
- Windows: Visual Studio 2015, CMake v3.8.2
- Linux: build-essential, cmake
- macOS: xquartz
## Getting the faceswap code
Simply download the code from http://github.com/deepfakes/faceswap - For development, it is recommended to use git instead of downloading the code and extracting it.
@ -193,157 +260,4 @@ Proceed to [../blob/master/USAGE.md](USAGE.md)
## Notes
This guide is far from complete. Functionality may change over time, and new dependencies are added and removed as time goes on.
If you are experiencing issues, please raise them in the [faceswap-playground](https://github.com/deepfakes/faceswap-playground) repository instead of the main repo.
# Windows Install Guide
## Installer
Windows now has an installer which installs everything for you and creates a desktop shortcut to launch straight into the GUI. You can download the installer from https://github.com/deepfakes/faceswap/releases.
If you have issues with the installer then read on for the more manual way to install Faceswap on Windows.
## Manual Install
Setting up Faceswap can seem a little intimidating to new users, but it isn't that complicated, although a little time consuming. It is recommended to use Linux where possible as Windows will hog about 20% of your GPU Memory, making Faceswap run a little slower, however using Windows is perfectly fine and 100% supported.
## Prerequisites
### Microsoft Visual Studio 2015
**Important** Make sure to download the 2015 version of Microsoft Visual Studio
Download and install Microsoft Visual Studio 2015 from: https://go.microsoft.com/fwlink/?LinkId=532606&clcid=0x409
On the install screen:
- Select "Custom" then click "Next"\
![MSVS Custom](https://i.imgur.com/Bx8fjzT.png)
- Uncheck all previously checked options
- Expand "Programming Languages" and select "Visual C++"\
![MSVS C++](https://i.imgur.com/c8k1IYD.png)
- Select "Next" and "Install"
### Cuda
**GPU Only** If you do not have an Nvidia GPU you can skip this step.
At the time of writing Tensorflow (version 1.13.1) only supports Cuda up to version 10.0, but check https://www.tensorflow.org/install/gpu for the latest supported version. It is crucial that you download the correct version of Cuda.
Download and install the correct version of the Cuda Toolkit from: https://developer.nvidia.com/cuda-toolkit-archive
NB: Make a note of the install folder as you'll need to access it in the next step.
### cuDNN
**GPU Only** If you do not have an Nvidia GPU you can skip this step.
As with Cuda you will need to install the correct version of cuDNN that the latest Tensorflow supports. At the time of writing this is Tensorflow v1.13.1 which supports cuDNN version 7.4, but check https://www.tensorflow.org/install/gpu for the latest supported version.
Download cuDNN from https://developer.nvidia.com/cudnn. You will need to create an account with Nvidia.
At the bottom of the list of latest cuDNN release will be a link to "Archived cuDNN Releases":
![cuDNN Archive](https://i.imgur.com/dHiAsxg.png)
Select this and choose the latest version of cuDNN that supports the version of Cuda you installed and has a minor version greater than or equal to the latest version that Tensorflow supports. (Eg Tensorflow 1.12 supports Cuda 9.0 and cuDNN 7.2. There is not an archived version of cuDNN 7.2 for Cuda 9.0, so select cuDNN version 7.3)
- Open the zip file
- Extract all of the files and folders into your Cuda folder (It is likely to be located in `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA`):\
![cuDNN to Cuda](https://i.imgur.com/X098w0N.png)
### CMake
Install the latest stable release of CMake from https://cmake.org/download/. (Scroll down the page for Latest Releases and select the relevant Binary distribution installer for your OS).
When installing CMake make sure to enable the option to CMake to the system path:
![cmake path](https://i.imgur.com/XTtacdY.png)
### Anaconda
Download and install the latest Python 3 Anaconda from: https://www.anaconda.com/download/. Unless you know what you are doing, you can leave all the options at default.
### Git
Download and install Git for Windows: https://git-scm.com/download/win. Unless you know what you are doing, you can leave all the options at default.
## Setup
Reboot your PC, so that everything you have just installed gets registered.
### Anaconda
#### Set up a virtual environment
- Open up Anaconda Navigator
- Select "Environments" on the left hand side
- Select "Create" at the bottom
- In the pop up:
- Give it the name: faceswap
- **IMPORTANT**: Select python version 3.6
- Hit "Create" (NB: This may take a while as it will need to download Python 3.6)
![Anaconda virtual env setup](https://i.imgur.com/Tl5tyVq.png)
#### Entering your virtual environment
To enter the virtual environment:
- Open up Anaconda Navigator
- Select "Environments" on the left hand side
- Hit the ">" arrow next to your faceswap environment and select "Open Terminal"
![Anaconda enter virtual env](https://i.imgur.com/rKSq2Pd.png)
### Faceswap
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Get the Faceswap repo by typing: `git clone https://github.com/deepfakes/faceswap.git`
- Enter the faceswap folder: `cd faceswap`
#### Easy install
- Enter `python setup.py` and follow the prompts.
If you have issues/errors follow the Manual install steps below.
#### Manual install
If dlib failed to install you can follow the steps to [manually install dlib](#dlib).\
Once dlib is installed follow these steps:
- Install tkinter (required for the GUI) by typing: `conda install tk`
- Install requirements: `pip install -r requirements.txt`
- Install Tensorflow (either GPU or CPU version depending on your setup):
- GPU Version: `pip install tensorflow-gpu`
- Non GPU Version: `pip install tensorflow`
## Running Faceswap
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Enter the faceswap folder: `cd faceswap`
- Enter the following to see the list of commands: `python faceswap.py -h` or enter `python faceswap.py gui` to launch the GUI
## Create a desktop shortcut
A desktop shortcut can be added to easily launch straight into the faceswap GUI:
- Open Notepad
- Paste the following:
```
%USERPROFILE%\Anaconda3\envs\faceswap\python.exe %USERPROFILE%/faceswap/faceswap.py gui
```
- Save the file to your desktop as "faceswap.bat"
## Updating faceswap
It's good to keep faceswap up to date as new features are added and bugs are fixed. To do so:
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- Enter the faceswap folder: `cd faceswap`
- Enter the following `git pull --all`
- Once the latest version has downloaded, make sure your requirements are up to date: `pip install --upgrade -r requirements.txt`
## Dlib
You should only need to follow these steps if you want the latest Dlib code or the process was unable to install Dlib for you.
For reasons outside of our control, this is the trickiest part of the process, and most of the prerequisites you installed are to support just Dlib. It is recommended to build Dlib from source for 3 main reasons:
1) To get the latest version
2) Enable GPU Support in Dlib
3) To prevent yourself from running into a whole host of issues later in the process.
If you are not bothered about having GPU support or the latest version, scroll to the end of this section for a simple one-liner to install the CPU version of Dlib.
### Build Latest Dlib with GPU Support
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- In the terminal type: `git clone https://github.com/davisking/dlib.git`
- Enter the dlib folder: `cd dlib`
- Add Visual Studio to your path by typing: `SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin`
- Enter: `python setup.py -G "Visual Studio 14 2015" install --yes USE_AVX_INSTRUCTIONS --yes DLIB_USE_CUDA --clean`
This will build and install dlib for you. It is worth backing up the generated .egg file somewhere so that you can re-install it if you ever need to rather than having to re-compile:
- From within the dlib folder copy the file named `dlib-xx.yy.zz-py3.5-win-amd64.egg` to somewhere safe
- If you ever need to install it again, then from within your virtual environment enter: `python -m easy_install <path to saved .egg>`
Once Dlib is built, you can remove Visual Studio and CMake from your PC.
### Easy install of Dlib without GPU Support
NB: Don't do this if you have already compiled Dlib with GPU support.
- If you are not already in your virtual environment follow [these steps](#entering-your-virtual-environment)
- In the terminal type: `conda install -c conda-forge dlib`
If you are experiencing issues, please raise them in the [faceswap-playground](https://github.com/deepfakes/faceswap-playground) repository instead of the main repo.

2
plugins/extract/detect/mtcnn.py
View File

@ -170,8 +170,6 @@ class Detect(Detector):
def recalculate_bounding_box(faces, landmarks):
""" Recalculate the bounding box for Face Alignment.
Face Alignment was built to expect a DLIB bounding
box and calculates center and scale based on that.
Resize the bounding box around features to present
a better box to Face Alignment. Helps its chances
on edge cases and helps remove 'jitter' """

3
requirements.txt
View File

@ -14,9 +14,6 @@ ffmpy==0.2.2
nvidia-ml-py3
h5py==2.9.0
Keras==2.2.4
cmake
dlib
face-recognition
# tensorflow is included within the docker image.
# If you are looking for dependencies for a manual install,

12
scripts/fsmedia.py
View File

@ -236,8 +236,9 @@ class PostProcess():
args = args if isinstance(args, tuple) else tuple()
kwargs = kwargs if isinstance(kwargs, dict) else dict()
task = globals()[action](*args, **kwargs)
logger.debug("Adding Postprocess action: '%s'", task)
actions.append(task)
if task.valid:
logger.debug("Adding Postprocess action: '%s'", task)
actions.append(task)
for action in actions:
action_name = camel_case_split(action.__class__.__name__)
@ -293,6 +294,7 @@ class PostProcessAction(): # pylint: disable=too-few-public-methods
def __init__(self, *args, **kwargs):
logger.debug("Initializing %s: (args: %s, kwargs: %s)",
self.__class__.__name__, args, kwargs)
self.valid = True # Set to False if invalid params passed in to disable
logger.debug("Initialized base class %s", self.__class__.__name__)
def process(self, output_item):
@ -391,6 +393,7 @@ class FaceFilter(PostProcessAction):
if not any(val for val in filter_lists.values()):
return None
facefilter = None
filter_files = [self.set_face_filter(f_type, filter_lists[f_type])
for f_type in ("filter", "nfilter")]
@ -403,6 +406,8 @@ class FaceFilter(PostProcessAction):
multiprocess,
ref_threshold)
logger.debug("Face filter: %s", facefilter)
else:
self.valid = False
return facefilter
@staticmethod
@ -414,6 +419,9 @@ class FaceFilter(PostProcessAction):
logger.info("%s: %s", f_type.title(), f_args)
filter_files = f_args if isinstance(f_args, list) else [f_args]
filter_files = list(filter(lambda fpath: Path(fpath).exists(), filter_files))
if not filter_files:
logger.warning("Face %s files were requested, but no files could be found. This "
"filter will not be applied.", f_type)
logger.debug("Face Filter files: %s", filter_files)
return filter_files

133
setup.py
View File

@ -155,7 +155,7 @@ class Environment():
def check_pip(self):
""" Check installed pip version """
try:
import pip
import pip # noqa pylint:disable=unused-import
except ImportError:
self.output.error("Import pip failed. Please Install python3-pip and try again")
exit(1)
@ -295,7 +295,7 @@ class Output():
def error(self, text):
""" Format ERROR Text """
global INSTALL_FAILED
global INSTALL_FAILED # pylint:disable=global-statement
trm = "ERROR "
if self.term_support_color:
trm = "{}ERROR {} ".format(self.red, self.default_color)
@ -336,7 +336,6 @@ class Checks():
self.env.cuda_version = input("Manually specify CUDA version: ")
self.env.update_tf_dep()
self.check_system_dependencies()
if self.env.os_version[0] == "Windows":
self.tips.pip()
@ -489,117 +488,6 @@ class Checks():
cudnn_checkfile = os.path.join(self.env.cuda_path, "include", "cudnn.h")
return [cudnn_checkfile]
def check_system_dependencies(self):
""" Check that system applications are installed """
self.output.info("Checking System Dependencies...")
self.cmake_check()
if self.env.os_version[0] == "Windows":
self.visual_studio_check()
self.check_cplus_plus()
if self.env.os_version[0] == "Linux":
self.gcc_check()
self.gpp_check()
def gcc_check(self):
""" Check installed gcc version for linux """
chk = Popen("gcc --version", shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = chk.communicate()
if stderr:
self.output.error("gcc not installed. Please install gcc for your distribution")
return
gcc = [re.sub(" +", " ", line.strip())
for line in stdout.decode(self.env.encoding).splitlines()
if line.lower().strip().startswith("gcc")][0]
version = gcc[gcc.rfind(" ") + 1:]
self.output.info("gcc version: {}".format(version))
def gpp_check(self):
""" Check installed g++ version for linux """
chk = Popen("g++ --version", shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = chk.communicate()
if stderr:
self.output.error("g++ not installed. Please install g++ for your distribution")
return
gpp = [re.sub(" +", " ", line.strip())
for line in stdout.decode(self.env.encoding).splitlines()
if line.lower().strip().startswith("g++")][0]
version = gpp[gpp.rfind(" ") + 1:]
self.output.info("g++ version: {}".format(version))
def cmake_check(self):
""" Check CMake is installed """
chk = Popen("cmake --version", shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = chk.communicate()
stdout = stdout.decode(self.env.encoding)
if stderr and self.env.os_version[0] == "Windows":
stdout, stderr = self.cmake_check_windows()
if stderr:
self.output.error("CMake could not be found. See "
"https://github.com/deepfakes/faceswap/blob/master/INSTALL.md#cmake "
"for instructions")
return
cmake = [re.sub(" +", " ", line.strip())
for line in stdout.splitlines()
if line.lower().strip().startswith("cmake")][0]
version = cmake[cmake.rfind(" ") + 1:]
self.output.info("CMake version: {}".format(version))
def cmake_check_windows(self):
""" Additional checks for cmake on Windows """
chk = Popen("wmic product where \"name = 'cmake'\" get installlocation,version",
shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = chk.communicate()
if stderr:
return False, stderr
lines = [re.sub(" +", " ", line.strip())
for line in stdout.decode(self.env.encoding).splitlines()
if line.strip()]
stdout = lines[1]
location = stdout[:stdout.rfind(" ")] + "bin"
self.output.info("CMake not found in %PATH%. Temporarily adding: \"{}\"".format(location))
os.environ["PATH"] += ";{}".format(location)
stdout = "cmake {}".format(stdout)
return stdout, False
def visual_studio_check(self):
""" Check Visual Studio 2015 is installed for Windows
Somewhat hacky solution which checks for the existence
of the VS2015 Performance Report
"""
chk = Popen("reg query HKLM\\SOFTWARE\\Microsoft\\VisualStudio\\14.0\\VSPerf",
shell=True, stdout=PIPE, stderr=PIPE)
_, stderr = chk.communicate()
if stderr:
self.output.error("Visual Studio 2015 could not be found. See "
"https://github.com/deepfakes/faceswap/blob/master/"
"INSTALL.md#microsoft-visual-studio-2015 for instructions")
return
self.output.info("Visual Studio 2015 version: 14.0")
def check_cplus_plus(self):
""" Check Visual C++ Redistributable 2015 is instlled for Windows """
keys = (
"HKLM\\SOFTWARE\\Classes\\Installer\\Dependencies\\"
"{d992c12e-cab2-426f-bde3-fb8c53950b0d}",
"HKLM\\SOFTWARE\\WOW6432Node\\Microsoft\\VisualStudio\\14.0\\VC\\Runtimes\\x64")
for key in keys:
chk = Popen("reg query {}".format(key), shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = chk.communicate()
if stdout:
break
if stderr:
self.output.error("Visual C++ 2015 could not be found. Make sure you have selected "
"'Visual C++' in Visual Studio 2015 Configuration or download the "
"Visual C++ 2015 Redistributable from: "
"https://www.microsoft.com/en-us/download/details.aspx?id=48145")
return
vscpp = [re.sub(" +", " ", line.strip())
for line in stdout.decode(self.env.encoding).splitlines()
if line.lower().strip().startswith(("displayname", "version"))][0]
version = vscpp[vscpp.find("REG_SZ") + 7:]
self.output.info("Visual Studio C++ version: {}".format(version))
class Install():
""" Install the requirements """
@ -655,7 +543,7 @@ class Install():
def install_missing_dep(self):
""" Install missing dependencies """
# Install conda packages first as dlib will need Cuda
# Install conda packages first
if self.env.conda_missing_packages:
self.install_conda_packages()
if self.env.missing_packages:
@ -711,16 +599,7 @@ class Install():
# install as user to solve perm restriction
if not self.env.is_admin and not self.env.is_virtualenv:
pipexe.append("--user")
if package.startswith("dlib"):
if not self.env.enable_cuda:
pipexe.extend(["--install-option=--no", "--install-option=DLIB_USE_CUDA"])
if self.env.os_version[0] == "Windows":
pipexe.extend(["--global-option=-G", "--global-option=Visual Studio 14 2015"])
msg = ("Compiling {}. This will take a while...\n"
"Please ignore the following UserWarning: "
"'Disabling all use of wheels...'".format(package))
else:
msg = "Installing {}".format(package)
msg = "Installing {}".format(package)
self.output.info(msg)
pipexe.append(package)
try:
@ -816,8 +695,8 @@ class Tips():
"will handle the installation of CUDA and cuDNN for you:\n"
"https://www.anaconda.com/distribution/\n\n"
"2b. If you do not want to use Anaconda, or if you wish to compile DLIB with GPU\n"
"support you will need to manually install CUDA and cuDNN:\n"
"2b. If you do not want to use Anaconda you will need to manually install CUDA and "
"cuDNN:\n"
"CUDA: https://developer.nvidia.com/cuda-downloads"
"cuDNN: https://developer.nvidia.com/rdp/cudnn-download\n\n")

101
tools/sort.py
View File

@ -13,13 +13,12 @@ import cv2
from tqdm import tqdm
# faceswap imports
import face_recognition
from lib.cli import FullHelpArgumentParser
from lib import Serializer
from lib.faces_detect import DetectedFace
from lib.multithreading import SpawnProcess
from lib.queue_manager import queue_manager, QueueEmpty
from lib.utils import GetModel
from plugins.plugin_loader import PluginLoader
from . import cli
@ -34,6 +33,7 @@ class Sort():
self.args = arguments
self.changes = None
self.serializer = None
self.vgg_face = VGGFace()
def process(self):
""" Main processing function of the sort tool """
@ -181,7 +181,7 @@ class Sort():
logger.info("Sorting by face similarity...")
img_list = [[img, face_recognition.face_encodings(cv2.imread(img))]
img_list = [[img, self.vgg_face.predict(cv2.imread(img))]
for img in
tqdm(self.find_images(input_dir),
desc="Loading",
@ -196,12 +196,7 @@ class Sort():
for j in range(i + 1, len(img_list)):
f1encs = img_list[i][1]
f2encs = img_list[j][1]
if f1encs and f2encs:
score = face_recognition.face_distance(f1encs[0],
f2encs)[0]
else:
score = float("inf")
score = self.vgg_face.find_cosine_similiarity(f1encs, f2encs)
if score < min_score:
min_score = score
j_min_score = j
@ -216,7 +211,7 @@ class Sort():
logger.info("Sorting by face dissimilarity...")
img_list = [[img, face_recognition.face_encodings(cv2.imread(img)), 0]
img_list = [[img, self.vgg_face.predict(cv2.imread(img)), 0]
for img in
tqdm(self.find_images(input_dir),
desc="Loading",
@ -228,13 +223,9 @@ class Sort():
for j in range(0, img_list_len):
if i == j:
continue
try:
score_total += face_recognition.face_distance(
[img_list[i][1]],
[img_list[j][1]])
except:
logger.info("except")
pass
score_total += self.vgg_face.find_cosine_similiarity(
img_list[i][1],
img_list[j][1])
img_list[i][2] = score_total
@ -429,7 +420,6 @@ class Sort():
# Comparison threshold used to decide how similar
# faces have to be to be grouped together.
min_threshold = self.args.min_threshold
img_list_len = len(img_list)
for i in tqdm(range(1, img_list_len),
@ -452,18 +442,16 @@ class Sort():
# processed, as the first value is None.
try:
score = self.get_avg_score_faces(f1encs, references)
except TypeError:
score = float("inf")
except ZeroDivisionError:
except (TypeError, ValueError, ZeroDivisionError):
score = float("inf")
if score < current_best[1]:
current_best[0], current_best[1] = key, score
if current_best[1] < min_threshold:
reference_groups[current_best[0]].append(f1encs[0])
reference_groups[current_best[0]].append(f1encs)
bins[current_best[0]].append(img_list[i][0])
else:
reference_groups[len(reference_groups)] = img_list[i][1]
reference_groups[len(reference_groups)] = [img_list[i][1]]
bins.append([img_list[i][0]])
return bins
@ -681,7 +669,7 @@ class Sort():
file=sys.stdout)]
elif group_method == 'group_face':
temp_list = [
[img, face_recognition.face_encodings(cv2.imread(img))]
[img, self.vgg_face.predict(cv2.imread(img))]
for img in tqdm(self.find_images(input_dir),
desc="Reloading",
file=sys.stdout)]
@ -867,13 +855,12 @@ class Sort():
scores.append(score)
return sum(scores) / len(scores)
@staticmethod
def get_avg_score_faces(f1encs, references):
def get_avg_score_faces(self, f1encs, references):
""" Return the average similarity score between a face and
reference image """
scores = []
for f2encs in references:
score = face_recognition.face_distance(f1encs, f2encs)[0]
score = self.vgg_face.find_cosine_similiarity(f1encs, f2encs)
scores.append(score)
return sum(scores) / len(scores)
@ -908,3 +895,63 @@ if __name__ == "__main__":
PARSER.set_defaults(func=bad_args)
ARGUMENTS = PARSER.parse_args()
ARGUMENTS.func(ARGUMENTS)
class VGGFace():
""" Temporary drop-in replacement for face_recognition.face_encodings """
# TODO: Replace with GPU bound keras-vgg-face
def __init__(self):
git_model_id = 7
model_filename = ["vgg_face_v1.caffemodel", "vgg_face_v1.prototxt"]
self.input_size = 224
self.model = self.get_model(git_model_id, model_filename)
@staticmethod
def get_model(git_model_id, model_filename):
""" Check if model is available, if not, download and unzip it """
root_path = os.path.abspath(os.path.dirname(sys.argv[0]))
cache_path = os.path.join(root_path, "plugins", "extract", ".cache")
model = GetModel(model_filename, cache_path, git_model_id).model_path
vgg_face = cv2.dnn.readNetFromCaffe(model[1], model[0]) # pylint: disable=no-member
vgg_face.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU) # pylint: disable=no-member
return vgg_face
def predict(self, face):
""" Return face encodings from vgg_face """
if face.shape[0] != self.input_size:
face = self.resize_face(face)
blob = cv2.dnn.blobFromImage(face, # pylint: disable=no-member
1.0,
(self.input_size, self.input_size),
[104, 117, 123],
False,
False)
logger.trace("vgg_face input shape: %s", blob.shape)
self.model.setInput(blob)
preds = self.model.forward()[0, :]
logger.trace("vgg_face encoding shape: %s", preds.shape)
return preds
def resize_face(self, face):
""" Resize incoming face to model_input_size """
if face.shape[0] < self.input_size:
interpolation = cv2.INTER_CUBIC # pylint:disable=no-member
else:
interpolation = cv2.INTER_AREA # pylint:disable=no-member
face = cv2.resize(face, # pylint:disable=no-member
dsize=(self.input_size, self.input_size),
interpolation=interpolation)
return face
@staticmethod
def find_cosine_similiarity(source_face, test_face):
""" Find the cosine similarity between a source face and a test face """
logger.trace("source_face shape: %s, test_face shape: %s",
source_face.shape, test_face.shape)
var_a = np.matmul(np.transpose(source_face), test_face)
var_b = np.sum(np.multiply(source_face, source_face))
var_c = np.sum(np.multiply(test_face, test_face))
retval = 1 - (var_a / (np.sqrt(var_b) * np.sqrt(var_c)))
logger.debug("Similarity: %s", retval)
return retval