code/distributed_scanning/counting_regions/creation.md

# Counting Regions Dataset Generation

## Goal

Create tasks where the model counts separated regions in an image. Each
region inside the central square is filled with a distinctive
gradient + texture combination, and adjacent regions are guaranteed to
look visually different so a human can read them apart.

The image always contains a square in the central area. Inside the
square the canvas is partitioned into irregular regions; outside the
square stays plain.

## Core Task

Given an image containing one central square, answer:

- `How many separated regions are inside the square?`

The answer is a positive integer.

## Visual Structure

- Single square placed near the centre of the canvas with a thin dark
  border. Everything outside is a plain off-white background.
- Inside the square the area is partitioned into irregular regions.
- Each region is rendered with two cues at once:
  1. A linear two-colour gradient. The two endpoint colours are drawn
     from a fixed palette but each region perturbs them in HSV space
     so no two regions render identical colours globally — colour
     histograms cannot recover the count.
  2. A texture pattern (speckle, horizontal / vertical / diagonal
     stripes, dots, or perlin-like band) modulated multiplicatively on
     top of the gradient. Adjacent regions are forced to use different
     texture styles where possible.
- Adjacency constraints: for every pair of touching regions, their
  unordered colour pairs differ AND there is at least ~55° of hue
  separation at the closest pairing. This guarantees a human-readable
  contrast across every shared boundary.
- A low-amplitude global speckle is added across the whole canvas so
  Canny / Sobel edge detectors fire uniformly inside regions and not
  only at boundaries — this defeats the simple "boundary-detect →
  flood-fill" attack.
- No drawn boundary lines. Region discrimination relies on colour
  discontinuity and texture-style change, not strokes.

Recommended defaults:

- Image canvas `1024×1024`
- Painted square `820×820` centred
- Region-synthesis grid `50×50` (low resolution → upsampled with smooth
  per-region masks)
- Number of final regions in the range `6-12`

## Generation Procedure

### 1. Sample target count and partition

- Sample target `K` in `[min_regions, max_regions]`.
- Choose `K` spaced seed cells on a 50×50 lattice.
- Grow connected regions from the seeds via priority-queue expansion,
  weighted by per-region noise fields so boundaries are organic.

### 2. Upsample and clean

- Upsample the label map from 50×50 to canvas resolution by
  per-region soft-mask interpolation + argmax (smooth boundaries
  without aliasing).
- Absorb tiny connected-component slivers (< 0.2 % of canvas) into
  their dominant neighbour label.
- Relabel region ids contiguously after cleanup.
- **Reject** the sample if the smallest remaining region covers less
  than `min_region_frac` (default 2.5 %) of the canvas — this avoids
  tiny near-invisible regions slipping through.

### 3. Assign gradient + texture per region

- Build the region adjacency graph at canvas resolution.
- Greedy assignment over regions (descending degree first):
  - Pick `(colour_a, colour_b)` from the palette such that no
    neighbour shares the same unordered pair AND `pair_hue_gap` to
    every neighbour is at least `hue_gap_min` (default 55°).
  - Apply per-region HSV jitter (≈±18° hue, ±0.12 sat, ±0.10 val) to
    the two endpoints.
  - Pick a random gradient angle.
- Assign a texture style + parameters per region, preferring styles
  not used by already-assigned neighbours.

### 4. Render

- For each region, paint the linear gradient between its two jittered
  endpoint colours along the chosen angle.
- Multiply by `(1 + amp · texture)` per pixel to add the per-region
  texture modulation.
- Multiply by `(1 + 0.05 · global_speckle)` to add the canvas-wide
  high-frequency noise that drowns Canny boundary detection.
- Composite the painted square onto the off-white canvas with a thin
  dark border.

## Quality Checks

Reject or regenerate samples if:

- after cleanup the actual region count drops below 2
- any region is smaller than `min_region_frac` of the canvas
- gradient assignment fails to satisfy the adjacency constraints

The final `answer` field always reflects the post-cleanup region
count, which may differ from the sampled target `K`.

## Anti-Shortcut Notes

The previous version used dashed boundary lines on a uniform fill,
which was defeated in one step by `cv2.dilate(boundary, k)` followed
by `cv2.connectedComponents`. The new rendering blocks several attack
families simultaneously:

- **Boundary edge detection (Canny / Sobel)**: drowned by global
  speckle + per-region texture so edges fire uniformly across the
  whole image rather than only at region boundaries.
- **Colour histogram / k-means clustering**: the per-region HSV
  jitter ensures each region renders unique pixel colours even when
  two regions share the same palette anchors, so cluster counts have
  no clean correspondence to region counts.
- **LAB-space quantisation + connected components**: same — the
  jitter scatters pixels across many quantisation bins per region.
- **Smooth-then-detect (Gaussian / median / bilateral filter +
  Canny)**: smoothing strong enough to kill the texture also blurs
  region boundaries enough that small regions merge.

Empirically, the strongest CV attack tested (`median13 + Canny`) gives
MAE ~3.5 with 0/6 exact across a held-out set of v7 prototypes.

## Annotation Format

Each sample stores the partition metadata required to reproduce or
verify the answer:

```json
{
  "image": "images/counting_regions_00000.png",
  "width": 1024,
  "height": 1024,
  "grid_rows": 50,
  "grid_cols": 50,
  "square_left": 102.0,
  "square_top": 102.0,
  "square_size": 820.0,
  "num_regions": 7,
  "question": "How many separated regions are inside the square? ...",
  "answer": 7,
  "difficulty": "medium",
  "region_seed_cells": [...],
  "region_cell_counts": [...],
  "region_adjacency": [[0, 2], [0, 3], ...]
}
```

## Output Organization

```text
counting_regions/
  creation.py
  creation.md
  annotations.jsonl
  data.json
  images/
    counting_regions_00000.png
    ...
```