Estimate vital signs such as heart rate and respiratory rate from video.
vitallens-python
is a Python client for the VitalLens API, using the same neural net for inference as our free iOS app VitalLens.
Furthermore, it includes fast implementations of several other heart rate estimation methods from video such as G
, CHROM
, and POS
.
- Accepts as input either a video filepath or an in-memory video as
np.ndarray
- Performs fast face detection if required - you can also pass existing detections
vitallens.Method.VITALLENS
supports heart rate, respiratory rate, pulse waveform, and respiratory waveform estimation. In addition, it returns an estimation confidence for each vital. We are working to support more vital signs in the future.vitallens.Method.{G/CHROM/POS}
support faster, but less accurate heart rate and pulse waveform estimation.- While
VITALLENS
requires an API Key,G
,CHROM
, andPOS
do not. Register on our website to get a free API Key.
Estimate vitals in a few lines of code:
from vitallens import VitalLens, Method
vl = VitalLens(method=Method.VITALLENS, api_key="YOUR_API_KEY")
result = vl("video.mp4")
print(result)
vitallens-python
provides vital sign estimates for general wellness purposes only. It is not intended for medical use. Always consult with your doctor for any health concerns or for medically precise measurement.
See also our Terms of Service for the VitalLens API and our Privacy Policy.
General prerequisites are python>=3.8
and ffmpeg
installed and accessible via the $PATH
environment variable.
The easiest way to install the latest version of vitallens-python
and its Python dependencies:
pip install vitallens
Alternatively, it can be done by cloning the source:
git clone https://github.com/Rouast-Labs/vitallens-python.git
pip install ./vitallens-python
To start using vitallens-python
, first create an instance of vitallens.VitalLens
.
It can be configured using the following parameters:
Parameter | Description | Default |
---|---|---|
method | Inference method. {Method.VITALLENS , Method.POS , Method.CHROM or Method.G } |
Method.VITALLENS |
api_key | Usage key for the VitalLens API (required for Method.VITALLENS ) |
None |
detect_faces | True if faces need to be detected, otherwise False . |
True |
estimate_running_vitals | Set True to compute running vitals (e.g., running_heart_rate ). |
True |
fdet_max_faces | The maximum number of faces to detect (if necessary). | 1 |
fdet_fs | Frequency [Hz] at which faces should be scanned - otherwise linearly interpolated. | 1.0 |
export_to_json | If True , write results to a json file. |
True |
export_dir | The directory to which json files are written. | . |
Once instantiated, vitallens.VitalLens
can be called to estimate vitals.
This can also be configured using the following parameters:
Parameter | Description | Default |
---|---|---|
video | The video to analyze. Either a path to a video file or np.ndarray . More info here. |
|
faces | Face detections. Ignored unless detect_faces=False . More info here. |
None |
fps | Sampling frequency of the input video. Required if video is np.ndarray . |
None |
override_fps_target | Target frequency for inference (optional - use methods's default otherwise). | None |
export_filename | Filename for json export if applicable. | None |
The estimation results are returned as a list
. It contains a dict
for each distinct face, with the following structure:
[
{
'face': {
'coordinates': <Face coordinates for each frame as np.ndarray of shape (n_frames, 4)>,
'confidence': <Face live confidence for each frame as np.ndarray of shape (n_frames,)>,
'note': <Explanatory note>
},
'vital_signs': {
'heart_rate': {
'value': <Estimated global value as float scalar>,
'unit': <Value unit>,
'confidence': <Estimation confidence as float scalar>,
'note': <Explanatory note>
},
'respiratory_rate': {
'value': <Estimated global value as float scalar>,
'unit': <Value unit>,
'confidence': <Estimation confidence as float scalar>,
'note': <Explanatory note>
},
'ppg_waveform': {
'data': <Estimated waveform value for each frame as np.ndarray of shape (n_frames,)>,
'unit': <Data unit>,
'confidence': <Estimation confidence for each frame as np.ndarray of shape (n_frames,)>,
'note': <Explanatory note>
},
'respiratory_waveform': {
'data': <Estimated waveform value for each frame as np.ndarray of shape (n_frames,)>,
'unit': <Data unit>,
'confidence': <Estimation confidence for each frame as np.ndarray of shape (n_frames,)>,
'note': <Explanatory note>
},
},
"message": <Message about estimates>
},
{
<same structure for face 2 if present>
},
...
]
If the video is long enough and estimate_running_vitals=True
, the results additionally contain running vitals:
[
{
...
'vital_signs': {
...
'running_heart_rate': {
'data': <Estimated value for each frame as np.ndarray of shape (n_frames,)>,
'unit': <Value unit>,
'confidence': <Estimation confidence for each frame as np.ndarray of shape (n_frames,)>,
'note': <Explanatory note>
},
'running_respiratory_rate': {
'data': <Estimated value for each frame as np.ndarray of shape (n_frames,)>,
'unit': <Value unit>,
'confidence': <Estimation confidence for each frame as np.ndarray of shape (n_frames,)>,
'note': <Explanatory note>
}
}
...
},
...
]
There is an example Python script in examples/test.py
which lets you run vitals estimation and plot the predictions against ground truth labels recorded with gold-standard medical equipment.
Some options are available:
method
: Choose from [VITALLENS
,POS
,G
,CHROM
] (Default:VITALLENS
)video_path
: Path to video (Default:examples/sample_video_1.mp4
)vitals_path
: Path to gold-standard vitals (Default:examples/sample_vitals_1.csv
)api_key
: Pass your API Key. Required if usingmethod=VITALLENS
.
For example, to reproduce the results from the banner image on the VitalLens API Webpage:
python examples/test.py --method=VITALLENS --video_path=examples/sample_video_2.mp4 --vitals_path=examples/sample_vitals_2.csv --api_key=YOUR_API_KEY
This sample is kindly provided by the VitalVideos dataset.
from vitallens import VitalLens, Method
vl = VitalLens(method=Method.VITALLENS, api_key="YOUR_API_KEY")
result = vl("video.mp4")
from vitallens import VitalLens, Method
my_video_arr = ...
my_video_fps = 30
vl = VitalLens(method=Method.POS)
result = vl(my_video_arr, fps=my_video_fps)
Before running tests, please make sure that you have an environment variable VITALLENS_DEV_API_KEY
set to a valid API Key.
To lint and run tests:
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
pytest
To build:
python -m build