This release is a pre-release and may not be stable for production use.
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")
Module Catalog
| Module YAML Path | Package Name |
|---|---|
seetapsych_hertz/modules/ada-chrom.yml |
HeartRate-AdaChrom |
seetapsych_hertz/modules/seeta.yml |
HeartRate-Seeta |
seetapsych_hertz/modules/tiny-hr.yml |
HeartRate-TinyHR |
AdaChrom
Model-free rPPG heart rate estimation using adaptive chrominance analysis on skin ROI.
Module config: ada-chrom.yml
| Package | Provides | Requires |
|---|---|---|
| HeartRate-AdaChrom | face/heart_rate |
face/dense_landmarks |
Description
Adaptive chrominance rPPG heart rate estimator from adaptive forehead ROI, no neural model required.
Parameters
| Name | Type | Default | Description & Tuning |
|---|---|---|---|
window_samples |
integer | 300 |
Sliding window frame count for HR estimation. Larger values reduce noise but increase latency; adjust based on real-time demand. |
Models
(None)
SeetaHeartRateDetector
Traditional signal-processing heart rate estimation from forehead skin pixels via chrominance + FFT analysis.
Module config: seeta.yml
| Package | Provides | Requires |
|---|---|---|
| HeartRate-Seeta | face/heart_rate |
face/dense_landmarks |
Description
Signal-processing heart rate estimator using forehead ROI from dense landmarks with chrominance + FFT peak detection.
Parameters
| Name | Type | Default | Description & Tuning |
|---|---|---|---|
min_seconds |
number | 1 |
Minimum buffered signal duration (seconds) before producing first HR estimate. Lower for faster startup, higher for stability. |
min_frames |
integer | 10 |
Minimum frame count before HR estimation starts. Takes effect whichever is reached later between min_seconds and min_frames. |
max_frames |
integer | 300 |
Maximum frames kept in the sliding analysis window. Larger windows smooth outliers but increase response lag to true HR changes. |
Models
(None)
TinyHR
Lightweight neural network for fast heart rate estimation directly from face video frames.
Module config: tiny-hr.yml
| Package | Provides | Requires |
|---|---|---|
| HeartRate-TinyHR | face/heart_rate |
face/detection |
Description
Fast RhythmFormer heart rate estimator using buffered face crops + Welch spectral analysis.
Parameters
| Name | Type | Default | Description & Tuning |
|---|---|---|---|
fps |
number | 30 |
Expected camera/video FPS used for spectral analysis windowing. Mismatch with actual source FPS degrades HR accuracy. |
interval |
number | 1 |
Seconds between consecutive HR estimates. Smaller intervals yield more updates with higher jitter; larger intervals are smoother but slower. |
Models
| Model | Recommended |
|---|---|
seeta-hertz-tinyhr.onnx |
✓ |
1 maintainer
Metadata
1 maintainer
Release files for seetapsych-hertz 0.0.3rc1
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