poseLandmarker.detectForVideo does not work in Android 13 google chrome (GL ERROR :GL_INVALID_OPERATION : glFramebufferTexture2D: <- error from previous GL command)

[galharth]

Have I written custom code (as opposed to using a stock example script provided in MediaPipe)

None

OS Platform and Distribution

Android 13

Mobile device if the issue happens on mobile device

Pixel 6 API 33 android studio emulator

Browser and version if the issue happens on browser

Google Chrome

Programming Language and version

Javascript

MediaPipe version

0.9.10

Bazel version

No response

Solution

Pose

Android Studio, NDK, SDK versions (if issue is related to building in Android environment)

No response

Xcode & Tulsi version (if issue is related to building for iOS)

No response

Describe the actual behavior

poseLandmarkerResults = poseLandmarker.detectForVideo(videoElement, performance.now()); should return poselandmarks

Describe the expected behaviour

poseLandmarkerResults = poseLandmarker.detectForVideo(videoElement, performance.now()); does not return pose landmarks, it returns empty array []

I get the following errors in the console log:

[.WebGL-0x206a0ce00]GL ERROR :GL_INVALID_OPERATION : glFramebufferTexture2D: <- error from previous GL command

Standalone code/steps you may have used to try to get what you need

Install pose landmarks using javascript tasks vision.
Run on google chrome android 13:
poseLandmarkerResults = poseLandmarker.detectForVideo(videoElement, performance.now());

poseLandmarkerResults should include landmarks when the person is inside the video, but instead it returns empty landmarks.

Other info / Complete Logs

No response

[kuaashish]

Hi @galharth,

We are currently investigating this matter. In the meantime, could you please try using the latest available version, which is 0.10.9? It appears that you are currently using an older version.

Thank you!!

[kuaashish]

Hi @BoHellgren,

We have identified that using an emulator to run this program might not be ideal due to hardware limitations and the program's reliance on specific hardware features for optimal performance. Using a physical device is highly recommended for the best experience and functionality.

Because, Machine learning programs often require direct interaction with real-world hardware resources to function effectively. Emulators, while valuable for development and testing, may not provide the necessary hardware capabilities for this program to run smoothly. Unfortunately, due to limitations, We can not do much about this issue.

Thank you!!

[github-actions]

This issue has been marked stale because it has no recent activity since 7 days. It will be closed if no further activity occurs. Thank you.

[github-actions]

This issue was closed due to lack of activity after being marked stale for past 7 days.

[google-ml-butler]

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