# Structural Similarity This function block compares two images and returns a similarity score (percentage) that reflects how structurally similar they are. It is designed for quick visual comparison tasks such as change detection, quality check, or validation of image processing steps. ## đŸ“Ĩ Inputs (sockets) `Image Any` First image to compare.\ `Image Any` Second image to compare. Note: Both input images must have the same resolution and channels for a valid comparison. ## 📤 Outputs (sockets) `Similarity Percentage` Numeric value between 0 and 100 representing the structural similarity of the two inputs. ## 🕹ī¸ Controls This function block has no interactive controls. It runs when inputs are available. ## đŸŽ¯ Features * Provides a single numeric similarity score that is easy to use for thresholds and decision logic. * Works directly with color or grayscale images (requires same dimensions). * Simple integration into inspection flows for automated pass/fail checks. ## ⚙ī¸ Running mechanism * When both image inputs are present and match in size, the block computes a structural similarity score and outputs it as a percentage. * If the images differ in size, the block reports an error and does not produce a valid score. * Designed to be used as a lightweight comparison step inside continuous image-processing pipelines. ## 📝 Usage 1. Provide two images to the input sockets. 2. Ensure both images have identical dimensions and channel count. 3. Read the `Similarity Percentage` output and use it for logging, decision making, or downstream logic. ## 💡 Tips and Tricks * To ensure images match in size, use `Image Resize` or `Image Resizer` before this block. * If images might be rotated or shifted, add `Auto Alignment` to align them first for more meaningful comparison. * To focus comparison on a specific area, crop with `Image ROI Select` and feed the cropped images to this block. * Normalize intensity or contrast differences with `Normalize Image` or `Contrast-Brightness-Gamma` to reduce false differences caused by lighting. * Use `Image AutoRotator` if input orientation may differ between sources. * Visualize inputs with `Show Image` to inspect what is being compared. * Log or save comparison images and results with `Image Logger` or `Image Write` for traceability. * Use `Mean Value of Image` or `Image Resolution and Channel Value` to quickly check brightness and size before comparison. ## 🛠ī¸ Troubleshooting * If you see an error about image size, confirm both inputs have the same width, height, and channel count. Consider using `Image Resize`. * Unexpectedly low similarity despite visually similar images may indicate misalignment, color-range differences, or different crop regions — try `Auto Alignment`, `Normalize Image`, or cropping with `Image ROI Select`. * If no output appears, check that both input sockets are connected and not sending `None`. Use `Show Image` or debugging blocks to verify incoming data. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.augelab.com/function-blocks/image-transformations/analysis/structural-similarity.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.