SeetaPsych Hertz

Heart rate estimation modules for SeetaPsych

Usage

This project is already included in the seetapsych-lib default configuration. Download and use it via seetapsych-manager download.

For usage, refer to SeetaPsych.

You can additionally add this algorithm module using the following methods.

Heart rate estimation requires processing video or real-time video streams to extract heart rate information.

WebUI

Run seetapsych-webui with the --files argument to use it.

seetapsych-webui --files seetapsych_hertz/modules/seeta.yml

Programmatic Usage

Add the following code in your program to use this algorithm module.

from seetapsych_lib.runtime.factory import Factory
from seetapsych_lib.runtime.pipeline import Pipeline

factory = Factory()
factory.load_file_modules("seetapsych_hertz/modules/seeta.yml")

pipeline = Pipeline(factory, ...)

pipeline.add_attributes("face/heart_rate")

Introduction

SeetaHeartRateDetector (Signal-based)

Traditional signal-processing-based heart rate estimation. Extracts ROI from facial dense landmarks, then applies chrominance analysis with cubic spline interpolation and FFT frequency-domain peak detection to estimate heart rate from BGR channel signals.

Module config: seeta.yml. Provide Attributes: face/heart_rate.

Requires: face/dense_landmarks.

Parameters:

  • min_seconds (float, default 1): Minimum signal duration in seconds before estimation starts.
  • min_frames (int, default 10): Minimum number of frames before estimation starts.
  • max_frames (int, default 300): Maximum frames kept in the sliding signal window.

AdaChrom (Adaptive Chrominance)

Adaptive chrominance-based heart rate estimation using frame-wise sliding window analysis. Extracts photoplethysmographic signals from an adaptive forehead skin ROI, processes each frame with chrominance methods to produce real-time BPM output without FFT fusion.

Module config: ada-chrom.yml. Provide Attributes: face/heart_rate.

Requires: face/dense_landmarks.

Parameters:

  • window_samples (int, default 300): Number of frames in the sliding estimation window.

TinyHR (ONNX-based)

RhythmFormer ONNX heart rate estimation engine. Converts face-frame sequences into BVP signals using a deep neural network, then applies long-window BVP fusion and Welch spectral analysis for stable heart-rate output.

Module config: tiny-hr.yml. Provide Attributes: face/heart_rate.

Requires: face/detection.

Available model: seeta-hertz-tinyhr.onnx.

Parameters:

  • fps (float, default 30): Expected camera/video FPS.
  • interval (float, default 1): Interval in seconds between heart rate estimations.

Project links

Data verified by PyPI on Aug 30, 2026
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  • Released:
Newer release available (0.0.3.post1)

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GitHub Statistics

Data verified by PyPI on Aug 30, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Copyright (c) 2026, Visual Information Processing and Learning (VIPL) group,

Requires

Python >=3.10

Provides Extra

all dev

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