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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

Cell Labelling Web UI for microscopy images using SAM2.1 (Segment Anything Model). A 2-stage approach where labeled data from this tool will train a segmentation model later.

Architecture

  • Backend: FastAPI (Python) - REST API for image serving, SAM inference, and annotation management
  • Frontend: React + Vite (JavaScript) - Interactive canvas for click-to-segment labelling
  • Model: SAM2.1 for real-time segmentation from user clicks
  • Data: 3D cell images from data/raw/rec_8bit_Paganin.zip, processed as independent 2D slices

Development Commands

Backend

cd backend
uv sync
uv run uvicorn app.main:app --reload

Frontend

cd frontend
npm install
npm run dev

Testing (Backend)

cd backend
uv run pytest                        # Run all tests
uv run pytest tests/test_foo.py      # Run single file
uv run pytest -k "test_name"         # Run by name pattern

Linting & Type Checking

cd backend
uv run ruff check .          # Lint
uv run ruff check --fix .    # Lint with auto-fix
uv run basedpyright          # Type check

Pre-commit

pre-commit install           # Set up hooks (once)
pre-commit run --all-files   # Run manually

Access Points

Backend Development

Use TDD (Test-Driven Development): write tests first, then implement the code to make them pass.

Key Design Decisions

  • Multi-point prompting: Collect several positive/negative clicks before confirming segmentation (not instant on each click)
  • Separate encode step: Cache image embeddings on backend for faster repeated inference
  • COCO format export: Standard format for training downstream segmentation models
  • Keyboard-driven workflow: Extensive shortcuts for efficient labelling (Enter to save, Esc to clear, 1-9 for class selection)