pyVHR.analysis package

Submodules

pyVHR.analysis.pipeline module

class pyVHR.analysis.pipeline.Pipeline

Bases: object

This class runs the pyVHR pipeline on a single video or dataset

parse_cfg(configFilename)

parses the given configuration file for loading the test’s parameters.

Parameters

configFilename – configuation file (.cfg) name of path .

run_on_dataset(configFilename, verb=True)

Runs the tests as specified in the loaded config file.

Parameters

  • configFilename –

    • The path to the configuration file

  • verb –

    • False - not verbose

    • True - show the main steps

    (use also combinations)

run_on_video(videoFileName, cuda=False, roi_method='convexhull', roi_approach='hol', method='cpu_POS', bpm_type='welch', pre_filt=False, post_filt=True, verb=True)

Runs the pipeline on a specific video file.

Parameters

  • videoFileName –

    • The path to the video file to analyse

  • cuda –

    • True - Enable computations on GPU

    • False - Use CPU only

  • roi_method –

    • ‘convexhull’ - Uses MediaPipe’s lanmarks to compute the convex hull of the face and segment the skin

    • ’faceparsing’ - Uses BiseNet to parse face components and segment the skin

  • roi_approach –

    • ‘hol’ - Use the Holistic approach (one single ROI defined as the whole face skin region of the subject)

    • ’patches’ - Use multiple patches as Regions of Interest

  • method –

    • One of the rPPG methods defined in pyVHR

  • bpm_type –

    • the method for computing the BPM estimate on a time window

  • pre_filt –

    • True - Use Band pass filtering on the windowed RGB signal

    • False - No pre-filtering

  • post_filt –

    • True - Use Band pass filtering on the estimated BVP signal

    • False - No post-filtering

  • verb –

    • False - not verbose

    • True - show the main steps

class pyVHR.analysis.pipeline.TestResult(filename=None)

Bases: object

This class is used by pyVHR.analysis.newsuite.NewSuite to manage the results of a test for a given video dataset on multiple rPPG methods

addData(key, value)
addDataSerie()
newDataSerie()
saveResults(outFilename=None)

Save the test results in a HDF5 library that can be opened using pandas. You can analyze the results using pyVHR.analysis.stats.StatAnalysis

pyVHR.analysis.multi_method_suite module

class pyVHR.analysis.multi_method_suite.MultiMethodSuite(configFilename='default')

Bases: object

This class performs tests on a video dataset using multiple rPPG methods. The suite uses these methods individually to calculate BVP and estimate BPM. After this all the BVP signals of the methods are combined into a single n-estimator signal, which is used to estimate a last BPM signal. Using multiple methods together allows you to have many estimators, so it can be useful to have a more precise BPM estimate. You can customize all the parameters using a configuration file (.cfg); in the analysis module you can find an example of cfg file named “multi_method_cfg.cfg”.

parse_cfg(configFilename)

parses the given configuration file for loading the test’s parameters.

Parameters

configFilename – configuation file (.cfg) name of path .

start(verb=0)

Runs the tests as specified in the loaded config file.

Parameters

verb –

  • 0 - not verbose

  • 1 - show the main steps

(use also combinations)

class pyVHR.analysis.multi_method_suite.TestResult(filename=None)

Bases: object

This class is used by pyVHR.analysis.multi_method_suite.MultiMethodSuite to manage the results of a test for a given video dataset on multiple rPPG methods

addData(key, value)
addDataSerie()
newDataSerie()
saveResults(outFilename=None)

Save the test results in a HDF5 library that can be opened using pandas. You can analyze the results using pyVHR.analysis.stats.StatAnalysis

pyVHR.analysis.stats module

class pyVHR.analysis.stats.StatAnalysis(filepath, join_data=False, remove_ouliers=False)

Bases: object

Statistic analyses for multiple datasets and multiple rPPG methods

SignificancePlot(methods=None, metric='MAE')

Plots the results of hypotesis testing with the significance plot

boxPlot(methods, metric, Y, scale, title)

Creates the box plot

computeCD(avranks=None, numDatasets=None, alpha='0.05', display=True, approach='frequentist')

Returns critical difference for Nemenyi or Bonferroni-Dunn test according to given alpha (either alpha=”0.05” or alpha=”0.1”) for average ranks and number of tested datasets N. Test can be either “nemenyi” for for Nemenyi two tailed test or “bonferroni-dunn” for Bonferroni-Dunn test. See Orange package docs.

displayBoxPlot(methods=None, metric='MAE', scale=None, title=True)

Shows the distribution of populations with box-plots

getStatsData(methods=None, metric='MAE', printTable=True)

Computes statistics of data

plot_bayesian_res(stat_result)

Plots the results of bayesian significance testing

run_stats(methods=None, metric='CCC', approach='frequentist', print_report=True)

Runs the statistical testing procedure by automatically selecting the appropriate test for the available data.

Parameters

  • methods –

    • The rPPG methods to analyze

  • metric –

    • ‘MAE’ - Mean Absolute Error

    • ’RMSE’ - Root Mean Squared Error

    • ’PCC’ - Pearson’s Correlation Coefficient

    • ’CCC’ - Concordance Correlation Coefficient

  • approach –

    • ‘frequentist’ - (default) Use frequentist hypotesis tests for the analysis

    • ’bayesian’ - Use bayesian hypotesis tests for the analysis

  • print_report –

    • ‘True’ - (default) print a report of the hypotesis testing procedure

    • ’False’ - Doesn’t print any report

saveStatsData(methods=None, metric='MAE', outfilename='statsData.csv')

Saves statistics of data on disk

Module contents

pyVHR.BVP package pyVHR.datasets package