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
The offcial repository for 'CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos', SIGIR2022

CharacterBERT-DR The offcial repository for CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos, Sh

ielab 11 Nov 15, 2022
A Genetic Programming platform for Python with TensorFlow for wicked-fast CPU and GPU support.

Karoo GP Karoo GP is an evolutionary algorithm, a genetic programming application suite written in Python which supports both symbolic regression and

Kai Staats 149 Jan 09, 2023
Implements the training, testing and editing tools for "Pluralistic Image Completion"

Pluralistic Image Completion ArXiv | Project Page | Online Demo | Video(demo) This repository implements the training, testing and editing tools for "

Chuanxia Zheng 615 Dec 08, 2022
X-modaler is a versatile and high-performance codebase for cross-modal analytics.

X-modaler X-modaler is a versatile and high-performance codebase for cross-modal analytics. This codebase unifies comprehensive high-quality modules i

910 Dec 28, 2022
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

FairEdit Relevent Publication FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

5 Feb 04, 2022
Streamlit tool to explore coco datasets

What is this This tool given a COCO annotations file and COCO predictions file will let you explore your dataset, visualize results and calculate impo

Jakub Cieslik 75 Dec 16, 2022
Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

ARM-Net Dependencies Python 3.6 Pytorch 1.7 Results Train Data preprocessing cd data_scripts python extract_subimages_test.py python data_augmentation

Bohong Chen 55 Nov 24, 2022
iNAS: Integral NAS for Device-Aware Salient Object Detection

iNAS: Integral NAS for Device-Aware Salient Object Detection Introduction Integral search design (jointly consider backbone/head structures, design/de

顾宇超 77 Dec 02, 2022
https://sites.google.com/cornell.edu/recsys2021tutorial

Counterfactual Learning and Evaluation for Recommender Systems (RecSys'21 Tutorial) Materials for "Counterfactual Learning and Evaluation for Recommen

yuta-saito 45 Nov 10, 2022
Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters.

Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters. Overview This project is a Torch implementation for our CVPR 2016 paper

Jianwei Yang 278 Dec 25, 2022
[ICLR 2022] Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators

AMOS This repository contains the scripts for fine-tuning AMOS pretrained models on GLUE and SQuAD 2.0 benchmarks. Paper: Pretraining Text Encoders wi

Microsoft 22 Sep 15, 2022
Sparse-dense operators implementation for Paddle

Sparse-dense operators implementation for Paddle This module implements coo, csc and csr matrix formats and their inter-ops with dense matrices. Feel

北海若 3 Dec 17, 2022
Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN", accepted to ACM MM 2021 BNI Track.

RecycleD Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN

Yunan Zhu 23 Nov 05, 2022
[CVPR 2021] Pytorch implementation of Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs

Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs In this work, we propose a framework HijackGAN, which enables non-linear latent space travers

Hui-Po Wang 46 Sep 05, 2022
Arabic Car License Recognition. A solution to the kaggle competition Machathon 3.0.

Transformers Arabic licence plate recognition 🚗 Solution to the kaggle competition Machathon 3.0. Ranked in the top 6️⃣ at the final evaluation phase

Noran Hany 17 Dec 04, 2022
The datasets and code of ACL 2021 paper "Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions".

Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction This repo contains the data sets and source code of our paper: Aspect-Category-Opinion-S

NUSTM 144 Jan 02, 2023
Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification

S-multi-SNE Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification A repository containing the code to reproduce the findings

Theodoulos Rodosthenous 3 Apr 15, 2022
Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch

Semantic Segmentation Easy to use and customizable SOTA Semantic Segmentation models with abundant datasets in PyTorch Features Applicable to followin

sithu3 530 Jan 05, 2023
RAANet: Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Density Level Estimation

RAANet: Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Density Level Estimation Anonymous submission Abstract 3D obj

30 Sep 16, 2022