Blurify.AI.
The Streamlit app built on top of Face Blurring — upload an image, pick YOLOv8 or Haar Cascade, get the faces blurred in-browser.
Overview
The demo layer over Face Blurring
Blurify.AI's own README points back to the research repo directly: "Check the GitHub repo for building the models, evaluations, etc." Face Blurring is where the YOLOv8n model was trained from scratch and evaluated against Haar Cascade; Blurify.AI is the deployable wrapper — a Streamlit app plus a Dockerfile — with none of the training code.
Two backends, one blur step
The YOLOv8n path runs ultralytics inference and applies cv2.GaussianBlur((25,25), 100) to each detected box. The Haar Cascade path runs OpenCV's frontal-face classifier (scaleFactor 1.1, minNeighbors 3) and applies a slightly lighter cv2.GaussianBlur((25,25), 75). Both render an original-vs-blurred side-by-side result in the browser.
How it works
A thin Streamlit UI wraps the two detectors trained and evaluated in the Face Blurring research repo — pick a model, upload an image, get a blurred result back.
Install & use
$ git clone https://github.com/baselhusam/Blurify.AI.git$ pip install -r requirements.txt$ streamlit run main.py
$ docker build -t blurify .$ docker run -p 8501:8501 blurify