MAHMOUD ABDELNABY
Cro · CLI SCRIPT · 2

Visual Saliency Attention Heatmap Simulator for Landing Pages

Generate a local visual-saliency map from an image. It is a computational proxy, not eye-tracking data or a prediction of how real users will look at a page.

Problem it addresses

Build an open-source visual attention saliency generator in Python (visual_saliency.py): 1) Uses multi-scale luminance, color-opponent, and edge orientation gradients (Itti-Koch-Niebur model approximation) to calculate visual attention maps from landing page screenshots, 2) Overlays a smooth jet/turbo colormap heatmap onto the original image, 3) Computes a heuristic CTA prominence score (0-100) for comparative exploration, not a validated measure of conversion or gaze.

Who it is for

PRO operating in high-growth e-commerce, digital publishing, and direct-response marketing across Saudi Arabia, UAE, and Egypt.

What is inside

Key files listed in the README repository architecture:

  • docs/cognitive-load-benchmarks.md
  • docs/visual-hierarchy-guidelines.md
  • examples/input_hero.png
  • examples/output_heatmap.png
  • LICENSE
  • README.md
  • src/saliency_simulator.py
  • src/visual_saliency.py
  • tests/test_saliency.py

How to evaluate and use it

  1. Read the README first and confirm that the documented problem matches your use case.
  2. Inspect the listed files to find the data, tools, templates, and tests relevant to your task.
  3. Follow the repository’s Quick Start and check prerequisites before running commands.
  4. Compare outputs with documented examples and review limitations before using them in a decision or client workflow.

Quick Start from the README: python -m unittest discover -s tests · python src/saliency_simulator.py --help

Scope and limitations

  • Heuristic Scope: Saliency heatmaps represent algorithmic approximations of pre-attentive human vision (luminance, color contrast, edge orientation), not actual eye-tracking.
  • RTL Scanning Bias: Saliency models for Arabic interfaces must account for right-to-left scanning patterns that reverse traditional Western F-shaped gaze paths.
  • Above-the-Fold Viewport Constraints: Evaluates primary CTA visual pop against busy background imagery on mobile viewports (390px width).

This page summarizes the available documentation; it does not establish performance or business results. Verify data and prerequisites before use.

Related repositories