We've all been there: The test suite passes 100%, but when you open the application, the submit button is overlapping the footer, or the new CSS deployment has completely broken the layout. This is 'Visual Blindness'—the inability of standard functional scripts (Selenium/Appium) to see the application like a user does.
The Gap in Functional Testing
Standard automation verifies that an element is present and clickable. It does not verify if it looks right. A button can be 1px wide, transparent, or white-on-white, and Selenium will happily click it and pass the test. Manual spot-checking is the usual solution, but it's slow, error-prone, and doesn't scale.
The Landscape: Commercial vs Open Source
There are already some fabulous visual testing tools in the market, like Applitools, which uses advanced AI to detect visual changes. (For more details on applitool). However, for teams with limited budgets, the cost of these premium tools can be prohibitive. This created a need for a robust, open-source alternative.
Enter Visual Guard
Visual Guard is a Python library I developed to bridge this gap. It's designed to be a lightweight, drop-in addition to your existing Pytest or Unittest suites. It follows a strict workflow: Snapshot -> Compare -> Report.
Key Features
- Smart Baseline Management: Automatically handles the creation and storage of baseline images.
- Region Masking: Define exclusion zones to ignore dynamic content like timestamps or carousels, eliminating false positives.
- Pixel-Perfect Accuracy: Catch even 1px shifts in your layout.
- Actionable Reporting: Generates side-by-side 'Baseline vs Actual' HTML reports with a diff overlay.
How It Works
Using Visual Guard is straightforward. You simply capture a screenshot and assert against a baseline.
from visual_guard import VisualTester
from selenium import webdriver
def test_homepage_layout():
driver = webdriver.Chrome()
driver.get("https://example.com")
tester = VisualTester()
# Assert visually
# If baseline exists, it compares. If not, it saves a new one.
tester.assert_visual(
driver,
screenshot_name="homepage_v1",
mask=[(100, 100, 200, 50)] # Mask out dynamic ads
)v0.2.1: Production-Grade Robustness
The latest release (v0.2.1) brings significant improvements for enterprise-scale usage:
- SSIM & pHash Algorithms: We moved beyond simple pixel matching. Structural Similarity (SSIM) allows tests to pass even with minor rendering differences (like anti-aliasing) that don't affect human perception.
- Strict Validation: Enforces strict dimension matching to catch layout regressions immediately.
Conclusion
Visual regression testing doesn't have to be expensive or complex. With Visual Guard, you can add a safety net to your UI ensuring that your application looks as good as it works. Check it on PyPI and add 'visual-guard' to your automation suite today.
