Face & Expression Detection
Employs YuNet and MediaPipe keypoints to analyze faces, detecting closed eyes, squinting, head turn angles, and overall expression quality to cull bad shots.
Pixel Cafe combines a local-first AI processing engine with robust cloud collaboration and camera preset simulations.
Our 5-stage pipeline runs on your GPU/CPU to filter duplicates, blurs, and bad expressions local-first.
Employs YuNet and MediaPipe keypoints to analyze faces, detecting closed eyes, squinting, head turn angles, and overall expression quality to cull bad shots.
Groups similar bursts automatically within a customizable time window (e.g. ≤3s) and HSV histogram correlation, allowing you to select only the best frame per series.
Evaluates sharpness using Laplacian edge-detection gradients and monitors over/underexposure via brightness histograms to reject technical mistakes in stage one.
An opt-in VLM scoring step (supporting Gemini, Claude, and ZhipuAI) that evaluates aesthetics, identifies main subjects/distractions, and scores photos from 1-10 with full reasoning.
Builds a local pairwise preference model. As you star or reject images, the system learns your stylistic preferences and re-ranks bursts accordingly.
Photographers sync projects to the cloud, allowing clients to select favorites instantly via WeChat.
Clients scan a QR code or paste a share link in WeChat on iOS or Android, launching a mobile viewer to select photos instantly without registering.
Client choices on their phones are synced instantly to the photographer's desktop workstation via secure WebSocket cloud routing, eliminating email lists.
Photographers can enforce select caps (e.g. choose 50 out of 500) and set link expiration dates to keep project timelines and post-processing work on track.
Local enhancement and camera profile mapping directly on your catalog.
A PyTorch-based UNet skin retouching model that runs entirely on-device, detecting faces and performing skin smoothing and tone leveling in batches.
Import Nikon custom picture control profiles (.ncp curves/LUTs) to accurately simulate in-camera color curves, saturation, contrast, and sharpening.
Built for robust deployments, packaging pipelines, and easy troubleshooting.
Each stage of the AI pipeline checkpoints progress to a local SQLite database, allowing you to resume long-running tasks immediately after computer restarts or app crashes.
Activate with a CLI parameter (--debug) or environment variables to open a dedicated logs panel to monitor Uvicorn, FastAPI, and model inferences in real-time.
A unified proxy policy with Force-Direct and Fallback-Direct options ensures reliable model index downloads from Cloudflare R2 and API calls through system proxies.
GitHub Actions workflows compile signed, ad-hoc macOS DMG and Windows EXE packages, optimizing application bundles to reduce installer sizes.