Comments (3)
To boost performance, you would either want a faster system or someone would need to optimize the code. Although it is written in C++, I did not pay very much attention to that, so I am sure it could be improved.
from heartbeat.
Thanks @nalinmittal. It is true that a higher FPS allows the pulse estimate to be more accurate. The way to get a constant FPS is to pre-record a video, and then feed it into the algorithm using the -i
argument (pulse estimation is then not live).
from heartbeat.
That's helpful to know! But are there any other ways to run 30 FPS on live estimation.
Also how to performance boost because if I pre-record a 30 FPS video it does not seem to run realtime fast.
from heartbeat.
Related Issues (20)
- Does it work on Windows 10? HOT 3
- compile error on Debian9.8 with opencv & opencv_contrib 3.1.0 HOT 1
- Did you ever published an article about this work? HOT 1
- Sharing executable
- OSC output HOT 1
- get trigger at peak HOT 5
- use IR videos HOT 2
- Confusion on rescanning HOT 1
- getFps function and fps variable confusion HOT 1
- detrend algorithm? HOT 2
- How about only using keypoints and no masking? HOT 1
- The link in readme is broken HOT 1
- Does this repo work on windows too? HOT 1
- Measure high bpm after workout. HOT 2
- HEART
- Liveness Detection HOT 1
- Code performance and porting to Python HOT 1
- Poor performance with video files recorded using auto settings on DSLR camera HOT 1
- Is there a respiratory rate implementation HOT 1
- update Repo HOT 2
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from heartbeat.