ehewes

ehewes / pyframe

Public

PyFrame splits GIFs into equal time windows and picks the frame with the highest motion delta from each one. This way you get good scene coverage and catch peak frames without sending every frame to AWS Rekognition cuts costs by ~93% with minimal accuracy loss.

ellishewes.comblogsPyFrame aws aws-image-moderation aws-rek huggingface huggingface-transformers
15
0
100% credibility
Found Feb 09, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

PyFrame is a tool for moderating GIFs and images by extracting key frames to detect inappropriate content using either cloud services or local AI models.

How It Works

1
🔍 Discover PyFrame

You learn about a smart tool that checks GIFs and images for inappropriate content by focusing only on the key changing moments to save time and money.

2
📁 Prepare Your Files

You organize a folder with your GIFs or images and get the tool ready on your computer.

3
Choose Checking Method
☁️
Cloud Scanner

Use a super-accurate online service that's quick but has a small fee per check.

🏠
Local Scanner

Run a free checker entirely on your own computer with no extra charges.

4
🖼️ Select Your Picture

Pick the GIF or image you want to review.

5
⚡ Run the Smart Check

The tool grabs the most important frames and scans them quickly to spot any issues.

✅ See Safety Results

You get a simple report telling you if everything is safe or needs attention, making moderation easy and affordable.

Sign up to see the full architecture

4 more

Sign Up Free

Star Growth

See how this repo grew from 11 to 15 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is pyframe?

PyFrame is a Python library that extracts key frames from GIFs by dividing them into equal time windows and picking the one with the highest motion delta in each, ensuring full scene coverage while catching peak action frames. It pipes these frames to AWS Rekognition for content moderation or runs them through local HuggingFace models like vit-base-nsfw-detector, cutting AWS costs by 93% with minimal accuracy loss. Users get simple pipelines to process single GIFs, images, or batches, plus video-to-GIF conversion, returning moderation labels above a confidence threshold.

Why is it gaining traction?

It stands out by smartly reducing frames analyzed from hundreds to a handful, slashing AWS bills without sacrificing detection on dynamic GIFs where alternatives process every frame. The dual AWS/local modes let devs start free locally then scale to cloud accuracy, and options like merging frames into grids further cut costs. Developers hook on the 10x efficiency for high-volume moderation.

Who should use this?

Backend engineers building media upload pipelines for social apps or forums needing NSFW filtering on GIFs. DevOps teams optimizing AWS Rekognition spend for user-generated content at scale. Indie hackers prototyping cheap content moderation before adding paid services.

Verdict

Grab it for AWS cost wins on GIF moderation if you're prototyping—solid README and usage examples make it dead simple to test. At 13 stars and 1.0% credibility, it's immature with no tests, so validate accuracy on your data before production.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.