✂️ EyeLipCropper is a Python tool to crop eyes and mouth ROIs of the given video.

Overview

EyeLipCropper

EyeLipCropper is a Python tool to crop eyes and mouth ROIs of the given video. The whole process consists of three parts: frame extraction, face alignment, and eye/mouth cropping. The cropped eye/mouth image size can be customized.

vis

Usage

Prerequisites

>>> pip install -r requirements.txt

1. Extract frames of a given video

>>> python frame_extract.py -h
usage: frame_extract.py [-h] [--video-path VIDEO_PATH] [--images-path IMAGES_PATH]

extract frames with opencv

optional arguments:
  -h, --help            show this help message and exit
  --video-path VIDEO_PATH
                        the input video path
  --images-path IMAGES_PATH
                        the output frames path
 
# default for test: this will generate frames of the video in `./test/images`
>>> python frame_extract.py

2. Align faces of the frames, with library face-alignment

>>> python face_align.py -h
usage: face_align.py [-h] [--images-path IMAGES_PATH] [--landmarks-path LANDMARKS_PATH] [--boxes-path BOXES_PATH] [--device DEVICE] [--log-path LOG_PATH]

align faces with `https://github.com/1adrianb/face-alignment`

optional arguments:
  -h, --help            show this help message and exit
  --images-path IMAGES_PATH
                        the input frames path
  --landmarks-path LANDMARKS_PATH
                        the output 68 landmarks path
  --boxes-path BOXES_PATH
                        the output bounding boxes path
  --device DEVICE       cpu or gpu cuda device
  --log-path LOG_PATH   logging when there are no faces detected
  
# default for test: this will generate landmarks and bounding boxes in
# `./test/landmarks` and `./test/boxes`
>>> python face_align.py

3. Crop left eye, right eye, mouth ROIs, with code modified from processing tools of [Eye] RT-GENE and [Mouth] LipForensics

>>> python eye_mouth_crop.py -h
usage: eye_mouth_crop.py [-h] [--images-path IMAGES_PATH] [--landmarks-path LANDMARKS_PATH] [--boxes-path BOXES_PATH] [--eye-width EYE_WIDTH] [--eye-height EYE_HEIGHT]
                         [--face-roi-width FACE_ROI_WIDTH] [--face-roi-height FACE_ROI_HEIGHT] [--left-eye-path LEFT_EYE_PATH] [--right-eye-path RIGHT_EYE_PATH]
                         [--mean-face MEAN_FACE] [--mouth-width MOUTH_WIDTH] [--mouth-height MOUTH_HEIGHT] [--start-idx START_IDX] [--stop-idx STOP_IDX]
                         [--window-margin WINDOW_MARGIN] [--mouth-path MOUTH_PATH]

crop eye and mouth regions

optional arguments:
  -h, --help            show this help message and exit
  --images-path IMAGES_PATH
                        [COMMON] the input frames path
  --landmarks-path LANDMARKS_PATH
                        [COMMON] the input 68 landmarks path
  --boxes-path BOXES_PATH
                        [EYE] the input bounding boxes path
  --eye-width EYE_WIDTH
                        [EYE] width of cropped eye ROIs
  --eye-height EYE_HEIGHT
                        [EYE] height of cropped eye ROIs
  --face-roi-width FACE_ROI_WIDTH
                        [EYE] maximize this argument until there is a warning message
  --face-roi-height FACE_ROI_HEIGHT
                        [EYE] maximize this argument until there is a warning message
  --left-eye-path LEFT_EYE_PATH
                        [EYE] the output left eye images path
  --right-eye-path RIGHT_EYE_PATH
                        [EYE] the output right eye images path
  --mean-face MEAN_FACE
                        [MOUTH] mean face pathname
  --mouth-width MOUTH_WIDTH
                        [MOUTH] width of cropped mouth ROIs
  --mouth-height MOUTH_HEIGHT
                        [MOUTH] height of cropped mouth ROIs
  --start-idx START_IDX
                        [MOUTH] start of landmark index for mouth
  --stop-idx STOP_IDX   [MOUTH] end of landmark index for mouth
  --window-margin WINDOW_MARGIN
                        [MOUTH] window margin for smoothed_landmarks
  --mouth-path MOUTH_PATH
                        [MOUTH] the output mouth images path

# default for test: this will generate the final cropped left eye,
# right eye, and mouth images in `./test/left_eye`, `./test/right_eye`
# , and `./test/mouth`
>>> python eye_mouth_crop.py
  • Note that the argument --face-roi-width and --face-roi-height should be maximized until there is a printed warning.

License

GPL-3.0 License

Reference

[1] Bulat, Adrian, and Georgios Tzimiropoulos. "How far are we from solving the 2d & 3d face alignment problem?(and a dataset of 230,000 3d facial landmarks)." Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2017. GitHub: https://github.com/1adrianb/face-alignment

[2] Fischer, Tobias, Hyung Jin Chang, and Yiannis Demiris. "Rt-gene: Real-time eye gaze estimation in natural environments." Proceedings of the European Conference on Computer Vision (ECCV). 2018. GitHub: https://github.com/Tobias-Fischer/rt_gene

[3] Haliassos, Alexandros, et al. "Lips Don't Lie: A Generalisable and Robust Approach To Face Forgery Detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021. GitHub: https://github.com/ahaliassos/LipForensics/

Owner
Zi-Han Liu
Senior @ SJTU
Zi-Han Liu
Paper Title: Heterogeneous Knowledge Distillation for Simultaneous Infrared-Visible Image Fusion and Super-Resolution

HKDnet Paper Title: "Heterogeneous Knowledge Distillation for Simultaneous Infrared-Visible Image Fusion and Super-Resolution" Email:

wasteland 11 Nov 12, 2022
Dataset and Source code of paper 'Enhancing Keyphrase Extraction from Academic Articles with their Reference Information'.

Enhancing Keyphrase Extraction from Academic Articles with their Reference Information Overview Dataset and code for paper "Enhancing Keyphrase Extrac

15 Nov 24, 2022
Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases.

Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases. Ivy wraps the functional APIs of existing frameworks. Framework-agnostic functions, libraries an

Ivy 8.2k Jan 02, 2023
Official code for the paper "Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks".

Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks This repository contains the official code for the

Linus Ericsson 11 Dec 16, 2022
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning

Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning This is the official repository of "Camera Distortion-

Hanbyel Cho 12 Oct 06, 2022
A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squares.

W.I.P-Aim-Memory-Game A customisable game where you have to quickly click on black tiles in order of appearance while avoiding clicking on white squar

dE_soot 1 Dec 08, 2021
A quantum game modeling of pandemic (QHack 2022)

Contributors: @JongheumJung, @YoonjaeChung, @GyunghunKim Abstract In the regime of a global pandemic, leaders around the world need to consider variou

Yoonjae Chung 8 Apr 03, 2022
The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Website | ArXiv | Get Start | Video PIRenderer The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic

Ren Yurui 261 Jan 09, 2023
The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

Directed Graph Contrastive Learning The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL). In this paper, we present the first con

Tong Zekun 28 Jan 08, 2023
Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol.

Updated Updated for TTS(CE) = Also Known as TTN V3. The code requires the first server to be 'ttn' protocol. Introduction This balenaCloud (previously

Remko 1 Oct 17, 2021
EMNLP 2021 paper The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers.

Codebase for training transformers on systematic generalization datasets. The official repository for our EMNLP 2021 paper The Devil is in the Detail:

Csordás Róbert 57 Nov 21, 2022
A light weight data augmentation tool for training CNNs and Viola Jones detectors

hey-daug A light weight data augmentation tool for training CNNs and Viola Jones detectors (Haar Cascades). This tool inflates your data by up to six

Jaiyam Sharma 2 Nov 23, 2019
Code, pre-trained models and saliency results for the paper "Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images".

Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB This repository is the official implementation of the paper. Our results comming soon in

Xiaoqiang Wang 8 May 22, 2022
Pytorch codes for Feature Transfer Learning for Face Recognition with Under-Represented Data

FTLNet_Pytorch Pytorch codes for Feature Transfer Learning for Face Recognition with Under-Represented Data 1. Introduction This repo is an unofficial

1 Nov 04, 2020
Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images

Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images This repository contains the implementation of the following paper

Seonggwan Ko 9 Jul 30, 2022
Json2Xml tool will help you convert from json COCO format to VOC xml format in Object Detection Problem.

JSON 2 XML All codes assume running from root directory. Please update the sys path at the beginning of the codes before running. Over View Json2Xml t

Nguyễn Trường Lâu 6 Aug 22, 2022
Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

Accommodating supervised learning algorithms for the historical prices of the world's favorite cryptocurrency and boosting it through LightGBM.

1 Nov 27, 2021
Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021) This repository is the official P

Jingyun Liang 159 Dec 30, 2022
Quantify the difference between two arbitrary curves in space

similaritymeasures Quantify the difference between two arbitrary curves Curves in this case are: discretized by inidviudal data points ordered from a

Charles Jekel 175 Jan 08, 2023
Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis"

Beyond the Spectrum Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis" by Yang He, Ning Yu, Margret Keu

Yang He 27 Jan 07, 2023