Coding Train Live 184: uNet and BodyPix with ml5.js

TL;DR

Implementing ml5's pretrained unit model for image segmentation with challenges in background substitutions.

Transcript

I think you should hear me now have you don't see me but you hear me I have a new button that mutes and unmutes my microphone and in a moment I have a button it switches to the camera that I'm in and this song ends in about 14 seconds 13:05 county waitress flows six five four three two and you're on do you see me do you hear me is it Mond Read More

Key Insights

• 🈸 Image segmentation with ml5's U-Net model can be used for various applications such as background replacement.
• 🪈 Challenges may arise when manipulating the segmented image due to the order of operations with ml5.
• 🚂 The choice of dataset used for training the model impacts the effectiveness of image segmentation.
• 😷 Exploring advanced techniques like blending modes and alpha masks can enhance the visual output of the segmentation process.

Questions & Answers

Q: What machine learning model was used in the tutorial?

The tutorial focused on utilizing ml5's U-Net model for image segmentation.

Q: What challenges were faced in the tutorial?

Challenges arose when attempting background substitution due to issues with the ordering of image processing with the model.

Q: What data set was used in training the U-Net model?

The U-Net model in ml5 is trained on a face segmentation dataset which has a simulated 3D environment.

Q: How did the tutorial attempt to address background replacement?

By setting the alpha channel for black pixels in the segmented image output to zero, the tutorial aimed to create a transparent background during the background replacement process.

Summary & Key Takeaways

• Demonstrated use of ml5's pre-trained image segmentation model, U-Net.

• Attempted background substitution to replace green screen with blue, but faced challenges.

• Mentioned potential issues with the ordering of segmentation image processing.