Chart & Image Colorblind Simulator
Test UI mockups, scientific plots (Matplotlib/Seaborn/Geopandas), and brand designs with an interactive Before & After slider.
Before & After Comparison
Click to upload or drag & drop image/chart
PNG, JPG, WebP (100% processed locally in your browser)
How to Test Your Charts for Colorblind Accessibility
Verify your data figures and UI graphics in 3 quick steps before publishing or submitting.
Upload Any Chart or Screenshot
Drag and drop your Matplotlib, Seaborn, Tableau, or Figma design. All image processing runs 100% locally in your browser sandbox.
Drag the Before & After Slider
Move the interactive vertical handle to compare original hues with simulated Deuteranopia, Protanopia, or Tritanopia vision in real time.
Export Comparison Reports
Download a high-resolution side-by-side PNG report to attach to your research paper submission, audit presentation, or client handoff.
Color Vision Deficiencies Explained
Understand how different types of colorblindness impact data visualization readability.
Deuteranopia
~5% MalesGreen-blindness. The most common form of Daltonism. Red and green lines or chart slices shift toward yellow-brown tones.
Protanopia
~2.5% MalesRed-blindness. Lacks red photoreceptors; red appears dark gray or brown, making red lines appear dim or missing.
Tritanopia
<1% PopulationBlue-yellow blindness. Blue shifts to teal/gray, and yellow shifts to pink or light red. Rare but critical for map data.
Achromatopsia
RareTotal monochromacy. All hues collapse into pure grayscale. Tests whether your charts rely strictly on brightness contrast.
Best Practices for Accessible Data Visualization
Never rely solely on color hue. Combine line colors with distinct dashed/dotted stroke styles or data point symbols (circles, triangles, squares).
Test your charts in Monochromacy mode. If data categories fade into identical grays, adjust lightness values to ensure clear brightness contrast.
Adopt peer-reviewed palettes such as Okabe-Ito, Paul Tol, Wong (Nature), ColorBrewer, or Viridis/Cividis continuous gradients for heatmaps.
Place category text labels directly adjacent to data lines or chart slices instead of forcing readers to cross-reference a distant legend.
Who Benefits From Image Simulation?
Tailored for researchers, designers, analysts, and brand strategists.
Academic Researchers
Ensure journal figures (Nature, Science, Elsevier) pass reviewer accessibility guidelines before submission.
UI/UX Designers
Test web interfaces, mobile app mockups, and dashboard UI components against WCAG 2.1 AA/AAA compliance.
BI & Data Analysts
Audit Tableau, PowerBI, and Excel corporate reporting dashboards to prevent executive data misinterpretation.
Brand Strategists
Verify Pantone print assets and logo graphics to guarantee universal brand recognition for all audience members.
FAQ
Common questions about image & chart colorblind testing.
How does the Before & After Slider help with chart accessibility?
The interactive slider allows you to drag the divider handle left and right to compare your original chart screenshot against the simulated colorblind view. If two lines or pie chart slices look distinct on the left but collapse into the same color on the right, your chart needs higher luminance or distinct patterns.
Are uploaded images saved or sent to any server?
No, 100% private! All image processing, matrix math, and SVG filter rendering happen locally inside your browser sandbox. Your images never leave your computer.
Can I test Matplotlib, Seaborn, Geopandas, or Tableau charts here?
Yes! Simply take a screenshot of your Python Matplotlib, Seaborn, R ggplot2, Geopandas map, or Tableau dashboard, and drag it into our simulator to instantly verify its colorblind legibility.