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colorization / docs /pipeline.md

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Study Pipeline

Overview

A one-page web app that runs a perceptual discrimination study. Participants see images one at a time and judge whether colorization artifacts are present or absent. No login, no score reveal — pure signal detection data collection.

Participant Flow

Welcome / Colorblindness Screen
  └─ 3 Ishihara plates (demo → red-green → blue-yellow)
  └─ Expertise dropdown (novice / hobbyist / professional / researcher)
        │
        ▼
Tutorial (3 steps)
  Step 1 — artifact reference: what artifacts look like (static explainer image)
  Step 2 — practice real: participant sees a ground-truth image, makes a judgment, gets feedback
  Step 3 — practice fake: participant sees a colorized image, makes a judgment, gets feedback
        │
        ▼
50 Trial Loop
  ├─ Image displayed (fixed-size container, objectFit: contain)
  ├─ Participant responds: ABSENT (←) or PRESENT (→)
  │     • Swipe right / → key / green button = PRESENT (artifacts detected)
  │     • Swipe left  / ← key / red button   = ABSENT  (no artifacts)
  ├─ Response + timing logged to backend (fire-and-forget)
  └─ Repeat until trial 50
        │
        ▼
Done Screen
  └─ Thank-you message + share link

Image Sampling (per session)

10 ground-truth (real) images sampled randomly from 15 available
8 colorized (fake) images per method × 5 methods = 40
Total: 50 trials, shuffled
Balance enforced: within each method, 1 image is drawn from each of the 6 (variant × dataset) groups (6 groups × 1 = 6; 2 extra drawn randomly from remaining pool)
GT varies session-to-session; same 15 base scenes, different 10 selected each time

Methods: bigcolor, ddcolor, disco, unicolor, mixed
Variants: ortho, standard
Datasets: coco, imagenet, instance

Response Encoding

User action	Button	Color	`response` value stored	Meaning
Swipe right / →	PRESENT	Green	`fake`	Participant detected artifacts
Swipe left / ←	ABSENT	Red	`real`	Participant saw no artifacts

label field encodes ground truth: fake (colorized) or real (ground-truth photo).
correct = 1 when response == label.

Tech Stack

Layer	Tech
Frontend	React 18 + Vite + MUI v5 dark theme
Backend	FastAPI + aiosqlite
Database	SQLite (local: `./responses.db`, Fly.io: `/app/data/responses.db`)
Deployment	Fly.io — single Docker container, persistent volume
Session state	`localStorage` (resume on reload, UUID per participant)

Key Files

colorization-webapp/
├── backend/
│   ├── main.py            — FastAPI app, static mounts
│   ├── db.py              — SQLite init, connection
│   └── routes/
│       ├── session.py     — /api/session/start, /respond, /complete + sampling logic
│       └── results.py     — /api/results/csv, /summary (key-protected)
├── frontend/src/
│   ├── pages/
│   │   ├── Welcome.jsx    — colorblindness plates + expertise
│   │   ├── Tutorial.jsx   — 3-step practice
│   │   ├── Trial.jsx      — main 50-trial loop
│   │   └── Done.jsx       — thank-you + share
│   └── components/
│       ├── SwipeCard.jsx  — touch/drag swipe handler
│       └── ProgressBar.jsx
├── image_samples/         — 165 images (served at /images/...)
├── tutorial/              — tutorial assets + Ishihara plates
├── manifest.json          — image metadata index
├── Dockerfile
└── fly.toml

Admin Endpoints

Both require ?key=colorturingtest2025.

Endpoint	Description
`GET /api/results/csv?key=colorturingtest2025`	Download full response CSV
`GET /api/results/summary?key=colorturingtest2025`	JSON summary: per-method detection rates, overall accuracy