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Meta-rPPG

(★ 186)

Implementation of "Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner"

파일 탐색기

최종 버전 다운로드 (.zip)

- __init__.py
- data_utils.py
- dataload.py
- pre_dataload.py
- - __init__.cpython-35.pyc
  - __init__.cpython-36.pyc
  - loss.cpython-36.pyc
  - main_model.cpython-36.pyc
  - main_models.cpython-36.pyc
  - main_models2.cpython-36.pyc
  - pretrained_models.cpython-35.pyc
  - pretrained_models.cpython-36.pyc
  - sub3D_models.cpython-36.pyc
  - sub3D_models2.cpython-36.pyc
  - sub_model.cpython-36.pyc
  - sub_models.cpython-36.pyc
- __init__.py
- loss.py
- main_model.py
- sub_model.py
demo1.gif
demo2.gif
demo3.gif
LICENSE
README.md
requirements.txt
rppg-overview.png
settings.py
train.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/eugenelet/Meta-rPPG

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd Meta-rPPG

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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[![License CC BY-NC-SA 4.0](https://img.shields.io/badge/license-MIT-blue)](https://github.com/eugenelet/NeuralScale-Private/blob/master/LICENSE)
![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg)

# Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner

This repository is the official implementation of *Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner* that has been accepted to ECCV 2020.

<img src="rppg-overview.png" width="600">

## Heatmap Visualization

Left to right:

1. Cropped input image
2. End-to-end trained model (baseline)
3. Meta-rPPG (transducive inference)
4. Top to down: rPPG signal, Power Spectral Density (PSD), Predicted and ground truth heart rate

<img src="demo1.gif" width="600">

<img src="demo2.gif" width="600">

<img src="demo3.gif" width="600">

## Requirements

To install requirements:

```setup
pip install -r requirements.txt
```

All experiments can be run on a single NVIDIA GTX1080Ti GPU.

The code was tested with python3.6 the following software versions:

| Software        | version | 
| ------------- |-------------| 
| cuDNN         | 7.6.5 |
| Pytorch      | 1.5.0  |
| CUDA | 10.2    |

## Training

### Training Data Preparation

Download training data ([example.pth](https://drive.google.com/file/d/1Z4GWiYjoQSXMYBhxBRZK9gUa1mYP0JsN/view?usp=sharing)) from Google Drive. Due to privacy issue (face images), provided data contains only a subset of the entire training data, i.e. contains faces of the authors of this paper.

Move `example.pth` to `data/` directory:
```
mv example.pth data/
```

### Begin Training

To begin training, run:

```
python3 train.py
```

## Validation Data

Validation data can be requested from:

[MAHNOB-HCI](https://mahnob-db.eu/hci-tagging/)

[UBFC-rPPG](https://sites.google.com/view/ybenezeth/ubfcrppg)

## Contributing

If you find this work useful, consider citing our work using the following bibTex:
```
@inproceedings{lee2020meta,
  title={Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner},
  author={Lee, Eugene and Chen, Evan and Lee, Chen-Yi},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2020}
}
```