Extracts essential Mediapipe face landmarks and arranges them in a sequenced order.

Overview

simplified_mediapipe_face_landmarks

Extracts essential Mediapipe face landmarks and arranges them in a sequenced order.

The default 478 Mediapipe face landmarks are scattered randomly all over the place and makes it difficult to isolate specific parts of the face. This mpFaceSimplified.py library returns 138 landmarks of left eyebrow → right eyebrow → left eye → right eye → inner lip → outer lip → face boundary, in a sequence, making it easier to isolate these parts.

Original Landmarks from Mediapipe face_mesh

  • Left Eyebrow = [70,63,105,66,107,55,65,52,53,46]
  • Right Eyebrow = [300,293,334,296,336,285,295,282,283,276]
  • Left Eye = [33,246,161,160,159,158,157,173,133,155,154,153,145,144,163,7]
  • Right Eye = [263,466,388,387,386,385,384,398,362,382,381,380,374,373,390,249]
  • Inner Lip = [78,191,80,81,82,13,312,311,310,415,308,324,318,402,317,14,87,178,88,95]
  • Outer Lip = [61,185,40,39,37,0,267,269,270,409,291,375,321,405,314,17,84,181,91,146]
  • Face Boundary = [10,338,297,332,284,251,389,356,454,323,361,288,397,365,379,378,400,377,152,148,176,149,150,136,172,58,132,93,234,127,162,21,54,103,67,109]
  • Left iris = [468,469,470,471,472]
  • Right iris = [473,474,475,476,477]


    originalLandmarks

Simplified Landmarks after sequencing

  • Left Eyebrow = [0->9]
  • right Eyebrow = [10->19]
  • Left Eye = [20->35]
  • Right Eye = [36->51]
  • Iner Lip = [52->71]
  • outer Lip = [72->91]
  • Face Boundary = [92->127]
  • Left iris = [128->132]
  • Right iris = [133->137]


    simplifiedLandmarks

Keep 'mpFaceSimplified.py' and 'exampleProgram.py' in the same folder and then run 'exampleProgram.py' to try it out.

Owner
Irfan
Irfan
Code and results accompanying our paper titled Mixture Proportion Estimation and PU Learning: A Modern Approach at Neurips 2021 (Spotlight)

Mixture Proportion Estimation and PU Learning: A Modern Approach This repository is the official implementation of Mixture Proportion Estimation and P

Approximately Correct Machine Intelligence (ACMI) Lab 23 Dec 28, 2022
Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM)

Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM) Introduction The average lifetime of the $D^{0}$ me

Son Gyo Jung 1 Dec 17, 2021
PyTorch implementation for STIN

STIN This repository contains PyTorch implementation for STIN. Abstract: In single-photon LiDAR, photon-efficient imaging captures the 3D structure of

Yiweins 2 Nov 22, 2022
A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

Aboleth A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation [1] with stochastic gradient variational Bayes

Gradient Institute 127 Dec 12, 2022
Relative Positional Encoding for Transformers with Linear Complexity

Stochastic Positional Encoding (SPE) This is the source code repository for the ICML 2021 paper Relative Positional Encoding for Transformers with Lin

Antoine Liutkus 48 Nov 16, 2022
GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms Trying to publish a new machine learning model and can't write a decent title for your pa

264 Nov 08, 2022
This is a clean and robust Pytorch implementation of DQN and Double DQN.

DQN/DDQN-Pytorch This is a clean and robust Pytorch implementation of DQN and Double DQN. Here is the training curve: All the experiments are trained

XinJingHao 15 Dec 27, 2022
Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

Equinox Callable PyTrees and filtered JIT/grad transformations = neural networks in JAX Equinox brings more power to your model building in JAX. Repr

Patrick Kidger 909 Dec 30, 2022
PyContinual (An Easy and Extendible Framework for Continual Learning)

PyContinual (An Easy and Extendible Framework for Continual Learning) Easy to Use You can sumply change the baseline, backbone and task, and then read

Zixuan Ke 176 Jan 05, 2023
TextBPN Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection

TextBPN Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection; Accepted by ICCV2021. Note: The complete code (including training and t

S.X.Zhang 84 Dec 13, 2022
This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling

deSpeckNet-TF-GEE This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling publi

Adugna Mullissa 16 Sep 07, 2022
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urba

Yu Tian 115 Dec 29, 2022
Boston House Prediction Valuation Tool

Boston-House-Prediction-Valuation-Tool From Below Anlaysis The Valuation Tool is Designed Correlation Matrix Regrssion Analysis Between Target Vs Pred

0 Sep 09, 2022
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images This repository contains the implementation of our paper MetaAvatar: Learni

sfwang 96 Dec 13, 2022
[ACM MM2021] MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification

Introduction This project is developed based on FastReID, which is an ongoing ReID project. Projects BUC In projects/BUC, we implement AAAI 2019 paper

WuYiming 7 Apr 13, 2022
State-Relabeling Adversarial Active Learning

State-Relabeling Adversarial Active Learning Code for SRAAL [2020 CVPR Oral] Requirements torch = 1.6.0 numpy = 1.19.1 tqdm = 4.31.1 AL Results The

10 Jul 14, 2022
Code for paper: Towards Tokenized Human Dynamics Representation

Video Tokneization Codebase for video tokenization, based on our paper Towards Tokenized Human Dynamics Representation. Prerequisites (tested under Py

Kenneth Li 20 May 31, 2022
unofficial pytorch implementation of RefineGAN

RefineGAN unofficial pytorch implementation of RefineGAN (https://arxiv.org/abs/1709.00753) for CSMRI reconstruction, the official code using tensorpa

xinby17 5 Jul 21, 2022
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing da

MIT CSAIL Computer Vision 4.5k Jan 08, 2023
Linear Variational State Space Filters

Linear Variational State Space Filters To set up the environment, use the provided scripts in the docker/ folder to build and run the codebase inside

0 Dec 13, 2021