I'm not sure this is what you want... but found one problem:
You initialize the MediaPipe model (with mp_holistic.Holistic) and load the TF model (model = load_model()) in recv(), but this is not a good design,
because recv() is called on every frames so model loadings happen everytime a new frame arrives. It is not efficient.
You should do such computationally expensive but cachable processing only once, practically at __init__() method.
For example, this object detection demo does so as this
This app can be another example which uses MediaPipe for pose estimation.
It also initializes the model at __init__() like this.
Note that this version is using multiprocessing because MediaPipe running in a single process had a problem with Streamlit in a multi-user situation. If your case is not so, you can refer to this previous version.
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
I'm not sure this is what you want... but found one problem:
You initialize the MediaPipe model (
with mp_holistic.Holistic) and load the TF model (model = load_model()) inrecv(), but this is not a good design,because
recv()is called on every frames so model loadings happen everytime a new frame arrives. It is not efficient.You should do such computationally expensive but cachable processing only once, practically at
__init__()method.For example, this object detection demo does so as this
This app can be another example which uses MediaPipe for pose estimation.
It also initializes the model at
__init__()like this.Note that this version is using
multiprocessingbecause MediaPipe running in a single process had a problem with Streamlit in a multi-user situation. If your case is not so, you can refer to this previous version.