Implement Deep Learning based Rppg Model & PPG 2 ABP using pytorch

model list (TODO : UPDATE)

Facial Image Based ppg measurement algorithm
DeepPhys : DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks
MTTS :Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
- need to verification
DeepPhys + LSTM
3D physNet : Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks
2D phsyNet + LSTM
PPG Based Blood Pressure estimation algorithm
PP-Net: A Deep Learning Framework for PPG based Blood Pressure and Heart Rate Estimation

TODO

Additional info

* How to test (Assessment of ROI selection for facial video based rPPG)

before test modify sample2.cfg(./pyVHR/analysis/sample2.cfg)

[DEFAULT]
'''
methods         = ['POS','CHROM','ICA','SSR','LGI','PBV','GREEN'] # Change Method
'''
[VIDEO]
dataset     = LGI_PPGI # change dataset
videodataDIR= /media/hdd1/LGGI/ # change dataset path
BVPdataDIR  = /media/hdd1/LGGI/
;videoIdx    = all
videoIdx    = [1,2,5,6] # change test video idx
detector    = media-pipe # use media-pipe, it's proposed ROI option

before test, modify test suit file(./pyVHR/analysis/testsuite.py), all regions one-hot mapping.

   '''
   test for all region
    '''
    # tmp = bin(test)
    # binary = ''
    # for i in range(mask_num-len(tmp[2:])):
    #     binary += '0'
    # binary += tmp[2:]
    '''
    test for top-5 & bot -5
    '''
    if test_case == 0 :
        binary = '0011000000000000000100000001001'
    else :
        binary = '0000000001100001011000000000000'