Swapping face using Face Mesh with TensorFlow Lite

Overview
demo.mp4

Aiine Transform (アイン変換)

Swapping face using FaceMesh. (could be used to unveil masked faces)

00_doc/demo_00.jpg 00_doc/demo_03.jpg

Tested Environment

Computer

  • Windows 10 (x64) + Visual Studio 2019
    • Intel Core i7-6700 @ 3.4GHz
  • It's not tested, but this project should run on Linux (x64, aarch64)

Deep Learning Inference Framework

  • TensorFlow Lite with XNNPACK delegate

How to Build and Run

Requirements

  • OpenCV 4.x
  • CMake

Download

  • Get source code
    • If you use Windows, you can use Git Bash
    git clone https://github.com/iwatake2222/aiine_transform.git
    cd aiine_transform
    git submodule update --init --recursive --recommend-shallow --depth 1
    cd inference_helper/third_party/tensorflow
    chmod +x tensorflow/lite/tools/make/download_dependencies.sh
    tensorflow/lite/tools/make/download_dependencies.sh
  • Download prebuilt library

Windows (Visual Studio)

  • Configure and Generate a new project using cmake-gui for Visual Studio 2019 64-bit
    • Where is the source code : path-to-cloned-folder
    • Where to build the binaries : path-to-build (any)
  • Open main.sln
  • Set main project as a startup project, then build and run!
  • Note:
    • Running with Debug causes exception, so use Release or RelWithDebInfo if you use TensorFlow Lite
    • You may need to modify cmake setting for TensorRT for your environment

Linux

mkdir build && cd build
cmake ..
make
./main

Usage

./main [input]
 - input:
    - use the default image file set in source code (main.cpp): blank
        - ./main
     - use video file: *.mp4, *.avi, *.webm
        - ./main test.mp4
     - use image file: *.jpg, *.png, *.bmp
        - ./main test.jpg
    - use camera: number (e.g. 0, 1, 2, ...)
        - ./main 0
    - use camera via gstreamer on Jetson: jetson
        - ./main jetson

Control

  • '0' key: Change masking mode
  • '1' key: Switch main image
  • 'f' key: Capture face image
  • 'g' key: Read face image

Model Information

Details

License

  • Copyright 2021 iwatake2222
  • Licensed under the Apache License, Version 2.0

Acknowledgements

I utilized the following OSS in this project. I appreciate your great works, thank you very much.

Code, Library

Model

Special thanks

Image Files

Owner
iwatake
iwatake
Pytorch implementation of Nueral Style transfer

Nueral Style Transfer Pytorch implementation of Nueral style transfer algorithm , it is used to apply artistic styles to content images . Content is t

Abhinav 9 Oct 15, 2022
Text-Based Ideal Points

Text-Based Ideal Points Source code for the paper: Text-Based Ideal Points by Keyon Vafa, Suresh Naidu, and David Blei (ACL 2020). Update (June 29, 20

Keyon Vafa 37 Oct 09, 2022
QHack—the quantum machine learning hackathon

Official repo for QHack—the quantum machine learning hackathon

Xanadu 72 Dec 21, 2022
Largest list of models for Core ML (for iOS 11+)

Since iOS 11, Apple released Core ML framework to help developers integrate machine learning models into applications. The official documentation We'v

Kedan Li 5.6k Jan 08, 2023
Jax/Flax implementation of Variational-DiffWave.

jax-variational-diffwave Jax/Flax implementation of Variational-DiffWave. (Zhifeng Kong et al., 2020, Diederik P. Kingma et al., 2021.) DiffWave with

YoungJoong Kim 37 Dec 16, 2022
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

In-Place Activated BatchNorm In-Place Activated BatchNorm for Memory-Optimized Training of DNNs In-Place Activated BatchNorm (InPlace-ABN) is a novel

1.3k Dec 29, 2022
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, an

VITA 112 Nov 07, 2022
Tool for live presentations using manim

manim-presentation Tool for live presentations using manim Install pip install manim-presentation opencv-python Usage Use the class Slide as your sce

Federico Galatolo 146 Jan 06, 2023
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation By Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang SenseTime, Tsinghua Unive

33 Oct 14, 2022
Deeplab-resnet-101 in Pytorch with Jaccard loss

Deeplab-resnet-101 Pytorch with Lovász hinge loss Train deeplab-resnet-101 with binary Jaccard loss surrogate, the Lovász hinge, as described in http:

Maxim Berman 95 Apr 15, 2022
Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties 8.11.2021 Andrij Vasylenko I

Leverhulme Research Centre for Functional Materials Design 4 Dec 20, 2022
Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters"

Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters" Pipeline of CLIP-Adapter CLIP-Adapter is a drop-in modul

peng gao 157 Dec 26, 2022
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
This Deep Learning Model Predicts that from which disease you are suffering.

Deep-Learning-Project This Deep Learning Model Predicts that from which disease you are suffering. This Project Covers the Topics of Deep Learning Int

Jai Viral Doshi 0 Jan 20, 2022
Predicting 10 different clothing types using Xception pre-trained model.

Predicting-Clothing-Types Predicting 10 different clothing types using Xception pre-trained model from Keras library. It is reimplemented version from

AbdAssalam Ahmad 3 Dec 29, 2021
Train CNNs for the fruits360 data set in NTOU CS「Machine Vision」class.

CNNs fruits360 Train CNNs for the fruits360 data set in NTOU CS「Machine Vision」class. CNN on a pretrained model Build a CNN on a pretrained model, Res

Ricky Chuang 1 Mar 07, 2022
Code for the paper "Generative design of breakwaters usign deep convolutional neural network as a surrogate model"

Generative design of breakwaters usign deep convolutional neural network as a surrogate model This repository contains the code for the paper "Generat

2 Apr 10, 2022
Object Database for Super Mario Galaxy 1/2.

Super Mario Galaxy Object Database Welcome to the public object database for Super Mario Galaxy and Super Mario Galaxy 2. Here, we document all object

Aurum 9 Dec 04, 2022
GAN-generated image detection based on CNNs

GAN-image-detection This repository contains a GAN-generated image detector developed to distinguish real images from synthetic ones. The detector is

Image and Sound Processing Lab 17 Dec 15, 2022
PiRank: Learning to Rank via Differentiable Sorting

PiRank: Learning to Rank via Differentiable Sorting This repository provides a reference implementation for learning PiRank-based models as described

54 Dec 17, 2022