Computer Vision

Because commodity WiFi already bounces off your body, RuView uses cheap ESP32 nodes to detect presence, vital signs, and even body pose without cameras or wearables.

It wants to parse entire documents in one shot without the model getting stuck in repetitive loops.

It exists to handle the tedious wiring—annotations, dataset formats, tracking—that sits between a trained model and a useful application.

It turns images and PDFs into structured JSON and Markdown so your RAG pipeline doesn't have to squint.

Ultralytics wants to stop you from stitching together separate repos for every computer vision task by bundling detection, segmentation, tracking, and pose estimation into one YOLO-backed package.

A 55K-star Python project for swapping faces in photos and videos, wrapped in an unusually explicit ethical framework.

Upscayl gives desktop users a free way to enlarge and enhance low-resolution images using local Real-ESRGAN models and a Vulkan-compatible GPU.

Frigate performs real-time, local object detection on IP camera streams using OpenCV and TensorFlow, designed to integrate tightly with Home Assistant.

It rounds up hundreds of AI project links so you don't have to hunt them down yourself.

MAA automates the daily chores of Arknights by treating the game screen as a computer vision problem, using OpenCV and OCR to handle farming, recruitment, and base shifts without human tapping.

It turns images of text into searchable documents across more than 100 languages, offering both a command-line tool and a C++ library for builders.

It exists to let developers run customized vision, text, and audio machine learning across mobile, web, and edge hardware without cloud round-trips.

OpenCV is an open-source C++ computer vision library whose own README acts as a portal rather than a product page.

DeepFace wraps a zoo of pre-trained face models into a single Python API so you can verify identities, search databases, and analyze attributes without hand-rolling a Keras pipeline.

It exists so you can extract text from screenshots, PDFs, and barcodes without a network connection or a cloud bill.

Real-ESRGAN turns the ESRGAN research model into a practical tool for upscaling and restoring real-world images and videos using only synthetic training data.

It turns face swaps and lip-syncs into queued, retryable batch jobs instead of one-off scripts.

It renders high-quality novel views of real-world scenes at 30 fps by replacing costly neural radiance fields with optimized 3D Gaussians.

It bundles detection, recognition, alignment, and reconstruction into a single research-grade toolbox.

YOLOv5 made real-time object detection as easy as `torch.hub.load`, then exported to everything from iOS to edge chips.