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
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
OpenDILab RL Kubernetes Custom Resource and Operator Lib

DI Orchestrator DI Orchestrator is designed to manage DI (Decision Intelligence) jobs using Kubernetes Custom Resource and Operator. Prerequisites A w

OpenDILab 205 Dec 29, 2022
Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

Stanford Intelligent Systems Laboratory 9 Jun 06, 2022
Minimal implementation and experiments of "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging".

No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging Minimal implementation and experiments of "No-Transaction Band N

19 Jan 03, 2023
This is implementation of AlexNet(2012) with 3D Convolution on TensorFlow (AlexNet 3D).

AlexNet_3dConv TensorFlow implementation of AlexNet(2012) by Alex Krizhevsky, with 3D convolutiional layers. 3D AlexNet Network with a standart AlexNe

Denis Timonin 41 Jan 16, 2022
3D dataset of humans Manipulating Objects in-the-Wild (MOW)

MOW dataset [Website] This repository maintains our 3D dataset of humans Manipulating Objects in-the-Wild (MOW). The dataset contains 512 images in th

Zhe Cao 28 Nov 06, 2022
Implementation of Memory-Compressed Attention, from the paper "Generating Wikipedia By Summarizing Long Sequences"

Memory Compressed Attention Implementation of the Self-Attention layer of the proposed Memory-Compressed Attention, in Pytorch. This repository offers

Phil Wang 47 Dec 23, 2022
Code for pre-training CharacterBERT models (as well as BERT models).

Pre-training CharacterBERT (and BERT) This is a repository for pre-training BERT and CharacterBERT. DISCLAIMER: The code was largely adapted from an o

Hicham EL BOUKKOURI 31 Dec 05, 2022
Fairness Metrics: All you need to know

Fairness Metrics: All you need to know Testing machine learning software for ethical bias has become a pressing current concern. Recent research has p

Anonymous2020 1 Jan 17, 2022
A curated list of awesome Active Learning

Awesome Active Learning 🤩 A curated list of awesome Active Learning ! 🤩 Background (image source: Settles, Burr) What is Active Learning? Active lea

BAI Fan 431 Jan 03, 2023
LTR_CrossEncoder: Legal Text Retrieval Zalo AI Challenge 2021

LTR_CrossEncoder: Legal Text Retrieval Zalo AI Challenge 2021 We propose a cross encoder model (LTR_CrossEncoder) for information retrieval, re-retrie

Xuan Hieu Duong 7 Jan 12, 2022
A smart Chat bot that can help to know about corona virus and Make prediction of corona using X-ray.

TRINIT_Hum_kuchh_nahi_karenge_ML01 Document Link https://github.com/Jatin-Goyal-552/TRINIT_Hum_kuchh_nahi_karenge_ML01/blob/main/hum_kuchh_nahi_kareng

JatinGoyal 1 Feb 03, 2022
Haze Removal can remove slight to extreme cases of haze affecting an image

Haze Removal can remove slight to extreme cases of haze affecting an image. Its most typical use is for landscape photography where the haze causes low contrast and low saturation, but it can also be

Grace Ugochi Nneji 3 Feb 15, 2022
Implementation of " SESS: Self-Ensembling Semi-Supervised 3D Object Detection" (CVPR2020 Oral)

SESS: Self-Ensembling Semi-Supervised 3D Object Detection Created by Na Zhao from National University of Singapore Introduction This repository contai

125 Dec 23, 2022
A complete, self-contained example for training ImageNet at state-of-the-art speed with FFCV

ffcv ImageNet Training A minimal, single-file PyTorch ImageNet training script designed for hackability. Run train_imagenet.py to get... ...high accur

FFCV 92 Dec 31, 2022
End-To-End Optimization of LiDAR Beam Configuration

End-To-End Optimization of LiDAR Beam Configuration arXiv | IEEE Xplore This repository is the official implementation of the paper: End-To-End Optimi

Niclas 30 Nov 28, 2022
Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation"

Keyword2Text This repository contains the code of the paper: "A Plug-and-Play Method for Controlled Text Generation", if you find this useful and use

57 Dec 27, 2022
Experiments on continual learning from a stream of pretrained models.

Ex-model CL Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them

Antonio Carta 6 Dec 04, 2022
Source code for paper: Knowledge Inheritance for Pre-trained Language Models

Knowledge-Inheritance Source code paper: Knowledge Inheritance for Pre-trained Language Models (preprint). The trained model parameters (in Fairseq fo

THUNLP 31 Nov 19, 2022