# Histogram On Curve This function block analyzes pixel values along a detected curved line across a grayscale or binary image. It detects line points, fits a smooth curve, evaluates pixel values along that curve against a threshold, and returns visualizations and numeric results to help you inspect line quality and detect gaps or peaks. ## đŸ“Ĩ Inputs `Input Image (Binary Image)` Grayscale or binary image to analyze. Preferred when foreground/background contrast is strong. `Line Location (pixel)` Number of sample lines to use when detecting the curve (the block samples multiple straight lines and derives curve points). `Line detection threshold` Pixel intensity threshold used during line pixel detection (valid range 0–255). `Pixel Value Threshold` Pixel intensity threshold used when evaluating pixel values along the detected curve (valid range 0–255). ## 📤 Outputs `Output Image` Annotated source image showing the sampled lines/curve and detected points. `Plot Image` Plot visualization showing pixel profiles and gradient information used during detection. `Curve Image` Visualization of the detected curve and pixel values along it. `Line Angle` Estimated angle of the fitted curve (useful for geometry checks). `Is Line Valid?` Boolean flag indicating whether the detected curve passes internal validity checks (useful to gate further processing). ## 🕹ī¸ Controls `Relation` Dropdown to select line sampling relation (vertical vs horizontal orientation). This changes how the block samples image lines during curve detection. ## ✨ Features * Detects curve points by sampling several straight lines across the image and fitting a smooth polynomial through these points. * Produces both image annotations and plotted diagnostics to help verify detection quality. * Validity check that flags unreliable detections so downstream blocks can handle failures. * Adjustable thresholds and line sampling count via inputs for fine control over sensitivity. ## 📝 Usage Instructions 1. Provide a clear grayscale or binary image to `Input Image (Binary Image)`. 2. Set `Line Location (pixel)` to the number of sampling lines (higher numbers increase robustness but are slower). 3. Tune `Line detection threshold` to reliably identify line pixels on sampled lines. 4. Tune `Pixel Value Threshold` to mark significant pixel regions along the fitted curve. 5. Inspect `Output Image`, `Plot Image`, and `Curve Image` to validate detection. Use `Is Line Valid?` to decide whether to continue processing. ## 📊 Evaluation When evaluated, the block: * Samples multiple straight lines across the image (orientation chosen via `Relation`), * Detects line pixels and computes curve points, * Fits a smooth curve and computes angle and pixel statistics, * Returns annotated images, plots, and validation flags for downstream use. ## 💡 Tips and Tricks * Preprocess noisy images with `Blur` to reduce spurious peaks before curve detection. * Use `Image Threshold` to create a binary image if the curve contrast is low — this often improves line detection reliability. * Crop the area of interest with `Image ROI Select` so the block focuses on the relevant region and avoids unrelated edges. * Send visual outputs to `Show Image` to inspect `Output Image` / `Plot Image` / `Curve Image` interactively while tuning parameters. * If you want to save analysis results for later review, use `Image Logger` or `Image Write` with the images returned from this block. (hint: combine preprocessing blocks such as `Blur` and `Image Threshold` upstream, then feed into this block; visualize results with `Show Image` and optionally archive them with `Image Logger` or `Image Write`.) ## 🛠ī¸ Troubleshooting * If you get an error about invalid line detection, try increasing the number of sampled lines (`Line Location (pixel)`) or preprocess with `Blur` and `Image Threshold`. * If no peaks appear in plots, reduce thresholds or improve contrast of the `Input Image (Binary Image)`. * If the detected curve is shifted or noisy, crop the image with `Image ROI Select` to remove distracting regions and try again. --- # 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/histogram-on-curve.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.