MediaPipeで姿勢推定を行い、Tokyo2020オリンピック風のピクトグラムを表示するデモ

Overview

Tokyo2020-Pictogram-using-MediaPipe

MediaPipeで姿勢推定を行い、Tokyo2020オリンピック風のピクトグラムを表示するデモです。

Tokyo2020Pictgram02.mp4

Requirement

  • mediapipe 0.8.6 or later
  • OpenCV 3.4.2 or later

Demo

以下コマンドでデモを起動してください。
ESCキー押下でプログラム終了します。

python main.py
  • --device
    カメラデバイス番号の指定
    デフォルト:0
  • --width
    カメラキャプチャ時の横幅
    デフォルト:640
  • --height
    カメラキャプチャ時の縦幅
    デフォルト:360
  • --static_image_mode
    静止画モード
    デフォルト:指定なし
  • --model_complexity
    モデルの複雑度(0:Lite 1:Full 2:Heavy)
    ※性能差はPose Estimation Qualityを参照ください
    デフォルト:1
  • --min_detection_confidence
    検出信頼値の閾値
    デフォルト:0.5
  • --min_tracking_confidence
    トラッキング信頼値の閾値
    デフォルト:0.5
  • --rev_color
    背景色とピクトグラムの色を反転する
    デフォルト:指定なし

Using Docker

Ubuntuの場合はホストマシンにMediaPipeをインストールせず、Docker + docker-composeを使うこともできます。

まず環境に合わせてdocker-compose.ymlを編集します。
ビデオデバイスを指定する際video0を使う場合は以下のように編集します。

    # Edit here
    devices:
      # - "/dev/video0:/dev/video0"
      # - "/dev/video1:/dev/video0"
-     - "/dev/video2:/dev/video0"
+     - "/dev/video0:/dev/video0"

次にDockerイメージをビルドします。

docker-compose build

最後にDockerコンテナを起動します。

docker-compose up

Author

高橋かずひと(https://twitter.com/KzhtTkhs)

License

Tokyo2020-Pictogram-using-MediaPipe is under Apache-2.0 License.

Owner
KazuhitoTakahashi
KazuhitoTakahashi
Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Causality In Traffic Accident (Under Construction) Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020) Overview Data Prepa

Tackgeun 21 Nov 20, 2022
Python implementation of NARS (Non-Axiomatic-Reasoning-System)

Python implementation of NARS (Non-Axiomatic-Reasoning-System)

Bowen XU 11 Dec 20, 2022
Automatic 2D-to-3D Video Conversion with CNNs

Deep3D: Automatic 2D-to-3D Video Conversion with CNNs How To Run To run this code. Please install MXNet following the official document. Deep3D requir

Eric Junyuan Xie 1.2k Dec 30, 2022
Pytorch-Swin-Unet-V2 - a modified version of Swin Unet based on Swin Transfomer V2

Swin Unet V2 Swin Unet V2 is a modified version of Swin Unet arxiv based on Swin

Chenxu Peng 26 Dec 03, 2022
Scale-aware Automatic Augmentation for Object Detection (CVPR 2021)

SA-AutoAug Scale-aware Automatic Augmentation for Object Detection Yukang Chen, Yanwei Li, Tao Kong, Lu Qi, Ruihang Chu, Lei Li, Jiaya Jia [Paper] [Bi

DV Lab 182 Dec 29, 2022
Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training

SelfText Beyond Polygon: Unconstrained Text Detection with Box Supervisionand Dynamic Self-Training Introduction This is a PyTorch implementation of "

weijiawu 34 Nov 09, 2022
Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021)

Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021) This repository is for the following paper: "Investigating Attention

52 Nov 19, 2022
Code for the paper: On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations

Non-Parametric Prior Actor-Critic (N-PPAC) This repository contains the code for On Pathologies in KL-Regularized Reinforcement Learning from Expert D

Cong Lu 5 May 13, 2022
An exploration of log domain "alternative floating point" for hardware ML/AI accelerators.

This repository contains the SystemVerilog RTL, C++, HLS (Intel FPGA OpenCL to wrap RTL code) and Python needed to reproduce the numerical results in

Facebook Research 373 Dec 31, 2022
Object Detection and Multi-Object Tracking

Object Detection and Multi-Object Tracking

Bobby Chen 1.6k Jan 04, 2023
Augmentation for Single-Image-Super-Resolution

SRAugmentation Augmentation for Single-Image-Super-Resolution Implimentation CutBlur Cutout CutMix Cutup CutMixup Blend RGBPermutation Identity OneOf

Yubo 6 Jun 27, 2022
Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)"

Official PyTorch implementation of the paper "Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory (SB-FBSDE)" which introduces a new class of deep generative models that gene

Guan-Horng Liu 43 Jan 03, 2023
Experiments and examples converting Transformers to ONNX

Experiments and examples converting Transformers to ONNX This repository containes experiments and examples on converting different Transformers to ON

Philipp Schmid 4 Dec 24, 2022
Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite.

TFlite Ultra Fast Lane Detection Inference Example scripts for the detection of lanes using the ultra fast lane detection model in Tensorflow Lite. So

Ibai Gorordo 12 Aug 27, 2022
This repository contains code demonstrating the methods outlined in Path Signature Area-Based Causal Discovery in Coupled Time Series presented at Causal Analysis Workshop 2021.

signed-area-causal-inference This repository contains code demonstrating the methods outlined in Path Signature Area-Based Causal Discovery in Coupled

Will Glad 1 Mar 11, 2022
PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages

PESTO: Switching Point based Dynamic and Relative Positional Encoding for Code-Mixed Languages Abstract NLP applications for code-mixed (CM) or mix-li

Mohsin Ali, Mohammed 1 Nov 12, 2021
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
We are More than Our JOints: Predicting How 3D Bodies Move

We are More than Our JOints: Predicting How 3D Bodies Move Citation This repo contains the official implementation of our paper MOJO: @inproceedings{Z

72 Oct 20, 2022
HeartRate detector with ArduinoandPython - Use Arduino and Python create a heartrate detector.

Syllabus of Contents Syllabus of Contents Introduction Of Project Features Develop With Python code introduction Installation License Developer Contac

1 Jan 05, 2022
Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

Deep-rPPG: Camera-based pulse estimation using deep learning tools Deep learning (neural network) based remote photoplethysmography: how to extract pu

Terbe Dániel 138 Dec 17, 2022