Colour-Vision-Deficiency Simulation + Collision Check

Status UIs lean on colour — a green “ok” vs a red “error” dot, a colour-coded chart legend. For the ~8% of men with a colour-vision deficiency (CVD) those can be indistinguishable, and nothing in the framework could check it. cvd_simulate adds the two primitives an accessibility / design check needs.

  • simulate_cvd() — map an (r, g, b) colour through a dichromat simulation matrix (protanopia / deuteranopia / tritanopia) at a given severity (0 = unaffected, 1 = full dichromacy).

  • colors_collide() — simulate two colours under a CVD type and report whether they become too similar to tell apart (a perceptual redmean distance below threshold).

  • color_distance() — the underlying redmean colour-difference metric.

Pure standard library — no numpy / OpenCV — operating on plain RGB tuples, so it is fully testable. Imports no PySide6.

Headless API

from je_auto_control import simulate_cvd, colors_collide

# How does the "error red" look to a deuteranope?
simulate_cvd((220, 40, 40), "deuteranopia")        # -> (r, g, b)

# Are my ok-green and error-red distinguishable for them?
report = colors_collide((60, 200, 60), (220, 60, 60), kind="deuteranopia")
report["collide"]    # True if the two are confusable
report["distance"]   # the perceptual distance after simulation

simulate_cvd accepts friendly aliases (protan / deutan / tritan, or red / green / blue). severity interpolates between the original colour and the full dichromat simulation, for the milder anomalous trichromacies. colors_collide returns {collide, distance, kind, severity, simulated_left, simulated_right}.

Executor commands

AC_simulate_cvd (rgb [r, g, b] + kind / severity → {rgb}) and AC_colors_collide (left / right [r, g, b] + kind / severity / threshold → the report). RGB inputs accept a JSON list. They are the matching read-only ac_* MCP tools and Script Builder commands under Image.