image-comparison-tool

You compare two images using SSIM by loading them with Pillow, processing the pixel arrays with NumPy, and calculating the Structural Similarity Index via scikit-image to generate a similarity score and a visual difference map.

SSIM measures the structural similarity between two images to quantify perceptual changes, capturing structural degradation rather than absolute pixel differences to provide a more accurate visual difference detection score.

Yes, you can use OpenCV and scikit-image for automated quality assurance in image processing. They provide the necessary functions to load graphical assets, calculate SSIM scores, and detect visual regressions in automated pipelines.

To highlight differing pixels in a side-by-side visualization, you calculate the difference mask using scikit-image, apply it to the original images with OpenCV, and render the combined output plot using Matplotlib.

Yes, you need NumPy and Matplotlib to generate an image difference map. NumPy handles the array operations for difference detection, while Matplotlib creates the side-by-side visualization highlighting the visual regression.

You should use SSIM over basic pixel comparison for difference detection when you need to measure perceived structural changes rather than absolute pixel value differences, making it better suited for visual regression testing.