# Match Shapes This function block compares two shapes (contours) and returns a resemblance score between 0 and 1. It is useful when you want a single numeric measure of how similar two contours are — where values closer to 1 mean the shapes are more similar. ## đŸ“Ĩ Inputs `Contour` — First contour to compare (reference). `Contour` — Second contour to compare (query). ## 📤 Outputs `Resemblence` — A number between 0 and 1 expressing how similar the two contours are (higher = more similar). ## 🕹ī¸ Controls `Resemblence` — A read-only label that displays the current resemblance value for quick feedback in the UI. ## ⚙ī¸ Running mechanism When the block runs it accepts two contour inputs, converts them into comparable representations, and computes a single resemblance score. The score is presented both as the output value and on the block's `Resemblence` label so you can see results immediately. ## đŸŽ¯ Features * Quick numeric similarity score for two contours. * UI feedback via a live resemblance label. * Works with contours produced by other shape analysis and detection blocks. ## 📝 How to use 1. Provide two contours to the inputs. Contours can come from detector or contour-processing blocks. 2. Run the flow; the block returns `Resemblence` and updates the label for instant inspection. 3. Use the numeric output for decisions, logging, or further processing. ## 💡 Tips and Tricks * If you only have an image, extract contours first using the `Find Contour` block, then feed the resulting contours into this block. * To simplify complex contours (reduce noise or number of points) try `Approximate Contour` before matching; simpler contours often yield more stable resemblance values. * If you need the contour cropped into an image for visual comparison, use `Contour to Image` and preview the result with `Show Image`. * To compare multiple candidate contours against a reference, combine this block with `Most Similar Shape` to automatically pick the best match. * When matching shapes in specific regions, crop the image first using `Image ROI Select` so contours come from the expected area. * If contours originate from images with different scales, normalize sizes with `Image Resize` or convert contours to cropped images of the same dimensions using `Contour to Image`. ## 🛠ī¸ Troubleshooting * If the resemblance seems unexpectedly low: ensure both contours are similarly scaled and oriented. Try simplifying the contours with `Approximate Contour` or normalizing their image sizes. * If the output is unstable across frames, check the contour extraction step (e.g., `Find Contour`) for noisy detections and consider preprocessing with `Blur` or `Image Threshold`. * No output or invalid contour input: verify the contour-producing block actually returns contour data (use `Debug Input` or preview intermediate images with `Show Image`). --- # 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/blocks-reference/detections-shapes/detectors/match-shapes.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.