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Case Study

AR Smart Mirror

Real-time interactive AR virtual try-on system designed for retail fashion.

The Problem

In-store clothing fitting is slow, creates long queues in fitting rooms, and increases garment wear. Retailers need an instant, hygienic, and engaging digital mirror that lets shoppers visualize outfits without undressing.

My Role

Built solo with AI-assisted development — computer vision pipeline design, MediaPipe pose detection integration, Canvas AR overlay engine, FastAPI backend services, Docker multi-container topology, and Cloudflare Zero Trust ingress.

Highlights

  • Sub-30ms real-time body tracking and landmark extraction using MediaPipe Pose
  • Dynamic Canvas AR garment warping and affine transformation for realistic clothing drape
  • FastAPI backend with PostgreSQL catalog and Redis session pub/sub synchronization
  • Mobile controller pairing allowing customers to browse catalogs and trigger try-ons from their smartphones
  • Production-grade Docker Compose architecture with Nginx reverse proxy and Cloudflare Tunnel

Stack

Python 3.11FastAPIMediaPipeHTML5 CanvasPostgreSQL 15Redis 7DockerNginx

Constraints

  • Hard real-time latency target (<33ms per frame / 30 FPS) to avoid motion sickness and visual lag.
  • Zero customer biometric persistence — video feeds must process in memory without saving raw footage to disk.
  • Compatibility with standard consumer hardware (e.g. RTX 3060) without requiring specialized depth cameras.

System Architecture

Capture & Client
Webcam Feed
MediaPipe Pose (Client A)
Mobile Controller (Client B)
Edge & Ingress
Cloudflare Tunnel
Nginx Reverse Proxy
SSL Termination
Application Services
FastAPI Gateway (:8000)
Garment Overlay Service
WebSocket Pub/Sub
Data & Storage
PostgreSQL 15 (Catalog/Users)
Redis 7 (Session Sync)
Local Volume Storage
Telemetry
Prometheus
Grafana Monitoring (:3000)

Key Trade-offs

The decisions worth defending — what I chose, what I turned down, and why.

Pose tracking execution model

Chose

Client-side MediaPipe Pose on Canvas API with GPU acceleration

Rejected

Server-side video streaming and frame-by-frame inference

Server inference introduces network jitter, high bandwidth overhead, and privacy concerns streaming raw camera feeds over the network.

Garment deformation approach

Chose

Real-time 2D affine mesh deformation keyed to skeletal anchor landmarks

Rejected

Full 3D physics-based volumetric cloth simulation

3D cloth simulation cannot sustain 30+ FPS on edge kiosk hardware; 2D mesh triangulation provides fluid response with low computational cost.

Remote store interconnect

Chose

Cloudflare Tunnel over outbound HTTPS

Rejected

Direct port forwarding and dynamic DNS on store routers

Retail store Wi-Fi and ISP firewalls prevent inbound port forwarding; Cloudflare Tunnels punch through NAT securely without public IPs.

What I'd Do Differently

An honest retrospective — the stuff I'd change with more time, more users, or a second pass.

  1. 1Implement WebGPU shaders for cloth lighting and shadow blending to improve realism under dynamic store lighting.
  2. 2Add an offline fallback queue for store analytics so telemetry buffers locally during internet interruptions.
  3. 3Optimize landmark smoothing filters (OneEuro filter) to eliminate micro-jitter when shoppers stand at edge distances.

Computer Vision & Pose Tracking Shell

Benchmark the real-time AR try-on pipeline (status, camera init, pose detect, mesh align -shirt, latency bench).

icelegends-interactive-shell ~ [armirror]
History: ↑ / ↓
cv-engine@ar-mirror:~/pipeline$

Technical Deep-Dive

Architecture, specifications, and implementation details.

15 — Disaster Recovery Playbook

#Severity Levels

LevelDescriptionResponse Time
P0Mirror completely down in-storeImmediate
P1Camera/pose not working<15 minutes
P2Degraded performance<1 hour
P3Analytics/monitoring issue<24 hours

#Scenario 1: Full System Down (P0)

Symptoms: Browser shows error, Docker containers not running

# Step 1: Check Docker
docker compose ps

# Step 2: If containers stopped — restart
docker compose up -d

# Step 3: If Docker Desktop crashed
# Restart Docker Desktop from taskbar icon
# Then: docker compose up -d

# Step 4: If still failing — check logs
docker compose logs ar-api
docker compose logs ar-db

#Scenario 2: Database Corruption (P0)

# Step 1: Stop API
docker compose stop ar-api

# Step 2: Restore latest backup
docker exec -i ar-db psql -U postgres armirror < backups/latest.sql

# Step 3: Restart
docker compose start ar-api

# Verify: docker compose logs -f ar-api

#Scenario 3: Pose Detection Broken (P1)

Symptoms: Camera works but no body detected

  • Verify webcam is connected and shows in browser
  • Clear browser cache: Ctrl+Shift+Delete → All time → Cached files
  • Try different browser (Chrome recommended)
  • Check webcam driver: Device Manager → Cameras

#Scenario 4: Cloudflare Tunnel Down (P1)

Symptoms: Remote access broken, local still works

# Check tunnel status
cloudflared tunnel info ar-mirror

# Restart tunnel
cloudflared tunnel run ar-mirror

# If credentials expired
cloudflared tunnel login --force
cloudflared tunnel run ar-mirror

#Scenario 5: Out of Disk Space (P2)

# Check disk
df -h

# Clean Docker unused images
docker system prune -a

# Clean old logs
docker compose logs --no-color > /dev/null
truncate -s 0 $(docker inspect --format='{{.LogPath}}' ar-api)

#Emergency Contact

  • Developer: Popa Matei
  • Phone: +40 730 155 075
  • Email: alexmatei2330@gmail.com
  • Response: Within 1 hour during business hours
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