Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.

Overview

Face Detect MQTT

Face or Pose detector that emits MQTT events when a face or human body is detected and not detected.

I built this as an alternative to using PIR motion sensors to turn on the lights in my office. I found that when sitting at my computer (somewhat motionless), the PIR motion sensors stop detecting motion and turn off the lights while I am still in the room.

Instead of using motion sensors, this project is constantly monitoring a camera (attached to a raspberry pi) and looking to see if a face is present on the camera - if a face is present, the lights stay on.

My raspberry pi + camera are placed on my desk under my computer monitors. When I walk into the room and sit down at my computer my face is detected - and continue to be detected while I sit at the computer.

Lights On

Lights Off

Detection Modes

Use the DETECTION_METHOD environment variable to set which detection mode (face or pose).

Face only detects your face.

Pose detects full body poses (and seems to work fine when your body is obstructed behind a desk).

MQTT Events

Note: the mqtt client id is customisable via environment variables. The default cvzone_tracker_01 is used in the examples below

Face/Pose Detected

A face or pose has been detected

MQTT Topic: home/cvzone_tracker_01/detected
Payload: 1

Face/Pose Not Detected

A face or pose is no longer detected (a face or pose must be detected first)

MQTT Topic: home/cvzone_tracker_01/detected
Payload: 0

Connected

MQTT client has connected

MQTT Topic: home/cvzone_tracker_01/status
Payload: connected

Disconnected

MQTT client has disconnected (sent as MQTT last will message)

MQTT Topic: home/cvzone_tracker_01/status
Payload: disconnected

Raspberry Pi Pre-requisites (using the RPi Camera Module)

Required: Raspberry Pi OS 64-bit

Set the following options in raspi-config and reboot:

  • GPU Memory -> 256
  • Legacy Camera Stack -> Enabled

Install docker:

curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker pi
sudo systemctl enable docker
sudo reboot

Run with docker

docker run \
  -d \
  --restart=unless-stopped \
  --device /dev/video0 \
  -e MQTT_ADDRESS="10.1.1.100" \
  -e MQTT_PORT="1883" \
  -e MQTT_CLIENT_ID="cvzone_tracker_01" \
  -e DETECTION_METHOD="face" \
  -e MIN_FACE_SCORE="0.5" \
  -e ROTATE_IMAGE="0" \
  --name=face-detect-mqtt \ 
  selexin/face-detect-mqtt:latest

Environment Variables

  • MQTT_ADDRESS - IP Address of MQTT broker on local network
  • MQTT_PORT - Port of MQTT broker on local network
  • MQTT_CLIENT_ID - Custom MQTT client ID to use
  • DETECTION_METHOD - Either face or pose. Face only detects faces. Pose detects full body poses.
  • MIN_FACE_SCORE - Number between 0.0 and 1.0. Ignore face detections with a confidence lower than this number (only used when DETECTION_METHOD = face).
  • ROTATE_IMAGE - Set to "1" to if your camera is upside-down

Manually install and run

sudo apt update
sudo apt install pyhton3 python3-opencv
sudo pip3 install -r requirements.txt

python3 src/main.py

License

MIT - see LICENSE.md

Owner
Jacob Morris
Freelance Software Engineer
Jacob Morris
🕹️ Official Implementation of Conditional Motion In-betweening (CMIB) 🏃

Conditional Motion In-Betweening (CMIB) Official implementation of paper: Conditional Motion In-betweeening. Paper(arXiv) | Project Page | YouTube in-

Jihoon Kim 81 Dec 22, 2022
Code and models for "Rethinking Deep Image Prior for Denoising" (ICCV 2021)

DIP-denosing This is a code repo for Rethinking Deep Image Prior for Denoising (ICCV 2021). Addressing the relationship between Deep image prior and e

Computer Vision Lab. @ GIST 36 Dec 29, 2022
YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.

Adam Van Etten 145 Jan 01, 2023
CSAC - Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization

CSAC Introduction This repository contains the implementation code for paper: Co

ScottYuan 5 Jul 22, 2022
Lightweight Face Image Quality Assessment

LightQNet This is a demo code of training and testing [LightQNet] using Tensorflow. Uncertainty Losses: IDQ loss PCNet loss Uncertainty Networks: Mobi

Kaen 5 Nov 18, 2022
Breast Cancer Detection 🔬 ITI "AI_Pro" Graduation Project

BreastCancerDetection - This program is designed to predict two severity of abnormalities associated with breast cancer cells: benign and malignant. Mammograms from MIAS is preprocessed and features

6 Nov 29, 2022
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.

TradingGym TradingGym is a toolkit for training and backtesting the reinforcement learning algorithms. This was inspired by OpenAI Gym and imitated th

Yvictor 1.1k Jan 02, 2023
TCPNet - Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition

Temporal-attentive-Covariance-Pooling-Networks-for-Video-Recognition This is an implementation of TCPNet. Introduction For video recognition task, a g

Zilin Gao 21 Dec 08, 2022
A curated list of awesome resources combining Transformers with Neural Architecture Search

A curated list of awesome resources combining Transformers with Neural Architecture Search

Yash Mehta 173 Jan 03, 2023
Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

IIC2233 - Programación Avanzada Evaluación Las evaluaciones serán efectuadas por medio de actividades prácticas en clases y tareas. Se calculará la no

IIC2233 @ UC 47 Sep 06, 2022
Parameter Efficient Deep Probabilistic Forecasting

PEDPF Parameter Efficient Deep Probabilistic Forecasting (PEDPF) is a repository containing code to run experiments for several deep learning based pr

Olivier Sprangers 10 Jun 13, 2022
A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

G3NN This repo provides a pytorch implementation for the 4 instantiations of the flexible generative framework as described in the following paper: A

Jiaqi Ma 14 Oct 11, 2022
FairFuzz: AFL extension targeting rare branches

FairFuzz An AFL extension to increase code coverage by targeting rare branches. FairFuzz has a particular advantage on programs with highly nested str

Caroline Lemieux 222 Nov 16, 2022
A python implementation of Deep-Image-Analogy based on pytorch.

Deep-Image-Analogy This project is a python implementation of Deep Image Analogy.https://arxiv.org/abs/1705.01088. Some results Requirements python 3

Peng Lu 171 Dec 14, 2022
The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"

MangaLineExtraction_PyTorch The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines" Usage model_torch.py [sourc

Miaomiao Li 82 Jan 02, 2023
Differentiable Factor Graph Optimization for Learning Smoothers @ IROS 2021

Differentiable Factor Graph Optimization for Learning Smoothers Overview Status Setup Datasets Training Evaluation Acknowledgements Overview Code rele

Brent Yi 60 Nov 14, 2022
On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition

On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition With the spirit of reproducible research, this repository contains codes requ

0 Feb 24, 2022
This repository contains the source code for the paper First Order Motion Model for Image Animation

!!! Check out our new paper and framework improved for articulated objects First Order Motion Model for Image Animation This repository contains the s

13k Jan 09, 2023
《Towards High Fidelity Face Relighting with Realistic Shadows》(CVPR 2021)

Towards High Fidelity Face-Relighting with Realistic Shadows Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu. In CVPR, 2021. T

114 Dec 10, 2022
This repository provides data for the VAW dataset as described in the CVPR 2021 paper titled "Learning to Predict Visual Attributes in the Wild"

Visual Attributes in the Wild (VAW) This repository provides data for the VAW dataset as described in the CVPR 2021 Paper: Learning to Predict Visual

Adobe Research 36 Dec 30, 2022