Seeing Images

Seeing Images

Compensatory vision tools based on blindspots measured by vision diagnostic v1-v4 on 2026-03-25. Which model it ran on is not recorded here and it has not been re-run since, so read the thresholds as measured-then: they say a tool exists for each failure, not that the current model fails at exactly that number. Re-run the diagnostic before relying on a specific threshold.

When to Use

Activate this skill when:

• Describing an uploaded image in detail
• Reproducing an image as SVG (use BEFORE drawing to establish ground truth)
• Comparing two images or regions for differences
• Reading text in degraded/compressed/low-contrast images
• Identifying subtle features (gradients, faint overlays, reflections)
• Any image task where accuracy matters more than speed

Known Blindspots (from diagnostics)

Measured, not guessed:

Workflow

Setup (one line, every time)

import sys; sys.path.insert(0, '/mnt/skills/user/seeing-images/scripts')
from see import grid, sample, enhance, edges, histogram, isolate, palette, compare, count_elements, gradient_map, denoise, crop

Quick Analysis (2-3 tool calls)

grid(path, rows=2, cols=2)   # → view the output
sample(path, [(x1,y1), ...]) # → verify colors at points of interest

Deep Analysis (for SVG reproduction, spot-the-difference, etc.)

grid(path, rows=3, cols=3)                    # 1. Overview
palette(path, n=10)                           # 2. Dominant colors
edges(path, threshold=30)                     # 3. Shape boundaries
sample(path, [(x1,y1), (x2,y2), ...])        # 4. Exact RGB at points
enhance(path, region=(x,y,w,h), mode='auto')  # 5. Reveal low-contrast areas
isolate(path, region=(x,y,w,h))              # 6. Remove context bias

Tool Reference

All functions in scripts/see.py. Every function that produces an image saves to /home/claude/see_*.png and returns the path. Use view tool on the returned path.

grid(path, rows=3, cols=3, labels=True)

Splits image into labeled cells for systematic inspection. Call it first: it reduces attentional competition.

sample(path, points, radius=3)

Returns exact RGB values at specified pixel coordinates. Use to verify what you think you see. Averages over a small radius to handle noise.

histogram(path, region=None)

Color histogram showing value distribution. Reveals bimodal distributions (hidden gradients), dominant colors, and contrast range. With region=(x,y,w,h), analyzes only that area.

enhance(path, region=None, factor=2.0, mode='contrast')

Boosts contrast in the image or a region. Modes: 'contrast', 'brightness', 'color', 'sharpness'. Use factor=3-5 for near-threshold features.

edges(path, threshold=50)

Sobel edge detection revealing shape boundaries invisible at low contrast. Lower threshold = more edges (noisier). Output is a white-on-black edge map.

gradient_map(path, region=None)

Computes local gradient magnitude across the image. Bright = high gradient, dark = flat. Reveals gradients below the 30-step detection threshold.

isolate(path, region, padding=20, bg=(128,128,128))

Extracts a region and places it on a neutral gray background. Removes surrounding context that causes simultaneous contrast and Dress-type illusions. The bg parameter defaults to mid-gray to minimize context bias.

compare(path, r1, r2)

Side-by-side comparison of two regions with diff overlay. Highlights pixel-level differences with amplification. Use for spot-the-difference tasks.

count_elements(path, region=None, color_range=None, min_size=3)

Programmatic element counting using connected component analysis. Specify approximate color_range as ((r_min,g_min,b_min), (r_max,g_max,b_max)) to count specific colored elements.

denoise(path, region=None, strength=3)

Median filter to reduce photographic noise, revealing subtle features hidden in the noise floor (like steam, faint reflections).

palette(path, n=8)

Extracts the n most dominant colors using k-means clustering. Returns RGB values and their proportions. Essential for SVG reproduction.

Accuracy Notes

Call grid() first on a complex image. Verify colors near context boundaries with sample() or isolate(), counts above 15 with count_elements(), gradients with gradient_map(), and faint features with enhance() before describing them. Each of these is a row of the blindspot table, so the tool call is the evidence — perception alone is not.