Setup

STEP1: bash setup.sh

STEP2: conda activate rppg-toolbox

STEP3: pip install -r requirements.txt

Training on PURE and testing on UBFC with TSCAN

STEP1: Download the PURE raw data by asking the paper authors.

STEP2: Download the UBFC raw data via link

STEP3: Modify ./configs/PURE_PURE_UBFC_TSCAN_BASIC.yaml

STEP4: Run python main.py --config_file ./configs/PURE_PURE_UBFC_TSCAN_BASIC.yaml

Note1: Preprocessing requires only once, thus turn it off on the yaml file when you train the network after the first time.

Note2: The example yaml setting will allow 80% of PURE to train and 20% of PURE to valid. After training, it will use the best model(with the least validation loss) to test on UBFC.

Training on SCAMPS and testing on UBFC with DeepPhys

STEP1: Download the SCAMPS via this link and split it into train/val/test folders.

STEP2: Download the UBFC via link

STEP3: Modify ./configs/SCAMPS_SCAMPS_UBFC_DEEPPHYS_BASIC.yaml

STEP4: Run python main.py --config_file ./configs/SCAMPS_SCAMPS_UBFC_DEEPPHYS_BASIC.yaml

Note1: Preprocessing requires only once, thus turn it off on the yaml file when you train the network after the first time.

Note2: The example yaml setting will allow 80% of SCAMPS to train and 20% of SCAMPS to valid. After training, it will use the best model(with the least validation loss) to test on UBFC.

Predicting BVP signal and calculate heart rate on UBFC with POS/CHROME/ICA

STEP1: Download the UBFC via link

STEP3: Modify ./configs/UBFC_SIGNAL.yaml

STEP4: Run python main.py --config_file ./configs/UBFC_SIGNAL.yaml

Yaml File Setting

The rPPG-Toolbox uses yaml file to control all parameters for training and evaluation. You can modify the existing yaml files to meet your own training and testing requirements.

Here are some explanation of parameters:

• TOOLBOX_MODE:

  • train_and_test: train on dataset and used the newly trained model to test.
  • only_test: you need to set INFERENCE-MODEL_PATH, and it will use pre-trained model initialized with the MODEL_PATH to test.
  • signal method: use signal methods to predict rppg BVP signal and calculate heart rate.

• TRAIN / VALID / TEST / SIGNAL DATA:

  • DATA_PATH: The input path of raw data
  • CACHED_PATH: The output path to preprocessed data
  • EXP_DATA_NAME If it is "", the toolbox generates a EXP_DATA_NAME based on other defined parameters. Otherwise, it uses the user-defined EXP_DATA_NAME.
  • BEGIN" & "END: The portion of dataset used for training/validation/testing. For example, if the DATASET is PURE, BEGIN is 0.0 and END is 0.8 under the TRAIN, the first 80% PURE is used for training the network. If the DATASET is PURE, BEGIN is 0.8 and END is 1.0 under the VALID, the last 20% PURE is used as the validation set. It is worth noting that validation and training sets don't have overlapping subjects.
  • DATA_TYPE: How to preprocess the video data
  • LABEL_TYPE: How to preprocess the label data
  • DO_CHUNK: Whether to split the raw data into smaller chunks
  • CHUNK_LENGTH: The length of each chunk (number of frames)
  • CROP_FACE: Whether to perform face detection
  • DYNAMIC_DETECTION: If False, face detection is only performed at the first frame and the detected box is used to crop the video for all of the subsequent frames. If True, face detection is performed at a specific frequency which is defined by DYNAMIC_DETECTION_FREQUENCY.
  • DYNAMIC_DETECTION_FREQUENCY: The frequency of face detection (number of frames) if DYNAMIC_DETECTION is True
  • LARGE_FACE_BOX: Whether to enlarge the rectangle of the detected face region in case the detected box is not large enough for some special cases (e.g., motion videos)
  • LARGE_BOX_COEF: The coefficient of enlarging. See more details at https://github.com/ubicomplab/rPPG-Toolbox/blob/main/dataset/data_loader/BaseLoader.py#L162-L165.

• MODEL : Set used model (support Deepphys / TSCAN / Physnet right now) and their parameters.

• SIGNAL METHOD: Set used signal method. Example: ["ica", "pos", "chrome"]

• METRICS: Set used metrics. Example: ['MAE','RMSE','MAPE','Pearson']

Dataset

The toolbox supports four datasets, which are SCAMPS, UBFC, PURE and COHFACE. Cite corresponding papers when using. For now, we only recommend training with PURE or SCAMPS due to the level of synchronization and volume of the dataset.

• SCAMPS

  • D. McDuff, M. Wander, X. Liu, B. Hill, J. Hernandez, J. Lester, T. Baltrusaitis, "SCAMPS: Synthetics for Camera Measurement of Physiological Signals", Arxiv, 2022
  • In order to use this dataset in a deep model, you should organize the files as follows:

  ---

     data/SCAMPS/Train/
        |-- P00001.mat
        |-- P00002.mat
        |-- P00003.mat
     |...
     data/SCAMPS/Val/
        |-- P00001.mat
        |-- P00002.mat
        |-- P00003.mat
     |...
     data/SCAMPS/Test/
        |-- P00001.mat
        |-- P00002.mat
        |-- P00003.mat
     |...
  

  ---

• UBFC

  • S. Bobbia, R. Macwan, Y. Benezeth, A. Mansouri, J. Dubois, "Unsupervised skin tissue segmentation for remote photoplethysmography", Pattern Recognition Letters, 2017.
  • In order to use this dataset in a deep model, you should organize the files as follows:

  ---

     data/UBFC/
     |   |-- subject1/
     |       |-- vid.avi
     |       |-- ground_truth.txt
     |   |-- subject2/
     |       |-- vid.avi
     |       |-- ground_truth.txt
     |...
     |   |-- subjectn/
     |       |-- vid.avi
     |       |-- ground_truth.txt
  

  ---

• COHFACE

  • Guillaume Heusch, André Anjos, Sébastien Marcel, “A reproducible study on remote heart rate measurement”, arXiv, 2016.
  • In order to use this dataset in a deep model, you should organize the files as follows:

  ---

     data/COHFACE/
     |   |-- 1/
     |      |-- 0/
     |          |-- data.avi
     |          |-- data.hdf5
     |      |...
     |      |-- 3/
     |          |-- data.avi
     |          |-- data.hdf5
     |...
     |   |-- n/
     |      |-- 0/
     |          |-- data.avi
     |          |-- data.hdf5
     |      |...
     |      |-- 3/
     |          |-- data.avi
     |          |-- data.hdf5
  

  ---

• PURE

  • Stricker, R., Müller, S., Gross, H.-M.Non-contact Video-based Pulse Rate Measurement on a Mobile Service Robot in: Proc. 23st IEEE Int. Symposium on Robot and Human Interactive Communication (Ro-Man 2014), Edinburgh, Scotland, UK, pp. 1056 - 1062, IEEE 2014
  • In order to use this dataset in a deep model, you should organize the files as follows:

  ---

    data/PURE/
     |   |-- 01-01/
     |      |-- 01-01/
     |      |-- 01-01.json
     |   |-- 01-02/
     |      |-- 01-02/
     |      |-- 01-02.json
     |...
     |   |-- ii-jj/
     |      |-- ii-jj/
     |      |-- ii-jj.json
  

  ---

Add A New Dataloader

• Step1 : Create a new python file in dataset/data_loader, e.g. MyLoader.py

• Step2 : Implement the required functions, including:

  def preprocess_dataset(self, config_preprocess)

  @staticmethod
  def read_video(video_file)

  @staticmethod
  def read_wave(bvp_file):

• Step3 :[Optional] Override optional functions. In principle, all functions in BaseLoader can be override, but we do not recommend you to override __len__, __get_item__,save,load.

• Step4 :Set or add configuration parameters. To set paramteters, create new yaml files in configs/ . Adding parameters requires modifying config.py, adding new parameters' definition and initial values.