# Blur Detector This function block evaluates whether an image appears sharp or blurry. It provides a single boolean output that indicates if the input image is considered blurred and a simple readout showing the computed blur score to help you tune sensitivity. ## đŸ“Ĩ Inputs (sockets) `Input Image` Accepts an RGB/BGR image to be evaluated for blurriness. ## 📤 Outputs (sockets) `Is Blurred?` Boolean result indicating whether the provided image is considered blurred (true) or not (false). ## 🕹ī¸ Controls `Detection Threshold` Slider to adjust how sensitive the blur test is. Lower values make the test stricter (fewer images marked as blurry); higher values make it more permissive. `Current blur value` Read-only text that displays the most recent numeric blur score so you can compare it against the threshold. ## đŸŽ¯ Features * Quick pass/fail blur check suitable for real-time streams or batch image checks. * Visual numeric feedback via `Current blur value` to help find an appropriate `Detection Threshold`. * Simple single-socket input and single boolean output for easy integration into larger flows. ## ⚙ī¸ Running mechanism When a new image arrives, the block analyzes the image content to estimate overall sharpness and produces a numeric blur score. That score is compared with the user-set `Detection Threshold` to produce the boolean `Is Blurred?` output. The block updates the `Current blur value` display so you can adjust the threshold interactively. ## 📝 Usage Instructions * Connect an image source (camera, loader, or previous processing block) to `Input Image`. * Adjust `Detection Threshold` until the `Current blur value` separates acceptable and unacceptable images for your application. * Use the `Is Blurred?` output to gate downstream logic (for example, to discard frames, trigger re-capture, or log low-quality images). ## 💡 Tips and Tricks * For live camera checks, pair with `Camera USB` or other image input blocks to monitor incoming frames for focus issues. * If you need to visualize or save examples of blurry frames, connect the image stream to `Show Image` and/or `Image Logger` so you can review and tune settings. * Preprocessing can influence the blur score: try using `Image Resize` to normalize image size or `Denoising` to reduce noise that may affect the measurement. * To simulate blurry inputs during testing, use the `Blur` block upstream. * Combine with ROI tools like `Image ROI` or `Image ROI Select` to check sharpness only in relevant areas of the frame (for example, the region where the object of interest appears). ## 🛠ī¸ Troubleshooting * If every image is labeled blurry: raise the `Detection Threshold` or ensure your input images are not downscaled too aggressively before evaluation. * If no images are labeled blurry even when visibly out of focus: lower the `Detection Threshold` and verify the test region matches where blur is occurring (use ROI blocks to limit evaluation to the area of interest). * If the `Current blur value` display does not update, confirm a valid image is connected to `Input Image` and that the image source is producing frames (try connecting the source to a `Show Image` block to verify visually). --- # 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/detections-shapes/detectors/blur-detector.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.