# Watershed Algorithm This function block performs region-based segmentation using supplied foreground and background masks to separate touching or overlapping objects. It returns a visual overlay with boundaries and a labeled segmentation map for downstream processing. ## đŸ“Ĩ Inputs `Image Any` The original color or grayscale image to segment. `Fore Ground` A binary/grayscale image indicating sure-foreground regions (objects). `Back Ground` A binary/grayscale image indicating sure-background regions. ## 📤 Outputs `Image Any` The original image with detected region boundaries overlaid (visual result). `Shed Image` A labeled image (grayscale) representing segmented regions / markers. ## 🕹ī¸ Controls This function block has no adjustable widgets. Provide accurate `Fore Ground` and `Back Ground` inputs to control the segmentation result. ## ⚙ī¸ Running mechanism * The block uses the provided `Fore Ground` and `Back Ground` masks to determine which pixels are definitely object and which are definitely background. * It identifies the unknown region between these masks and assigns intermediate markers. * A segmentation pass separates regions, draws the detected boundaries on a copy of the input image, and produces a labeled `Shed Image` where each segmented region has a distinct marker value. * The overlaid image highlights boundaries for quick visual inspection while the labeled image is suitable for measurements or counting. ## đŸŽ¯ Features * Works well for separating touching objects when clear foreground/background masks are provided. * Produces both a visual overlay for inspection and a labeled output useful for further processing (measurements, counting, extraction). * Accepts color or grayscale input images. ## 📝 Usage instructions 1. Prepare a clear `Fore Ground` mask covering the interior of objects and a complementary `Back Ground` mask for the background. 2. Connect the original image to `Image Any` and the two masks to their respective inputs. 3. Run the block to obtain the segmented overlay and the labeled `Shed Image`. 4. Use the labeled image for downstream tasks such as object counting or extracting individual regions. ## 💡 Tips and Tricks * Use `Image Threshold`, `Image Adaptive Threshold`, or `HSV Filter` to create initial binary masks from the input image. * Clean up small holes and noise in masks with `Morphological Transformations` or `Denoising` before feeding them into this block. * For difficult backgrounds, try `Grab Cut Algorithm` or `Background Subtractor` to generate better foreground/background candidates. * Restrict processing to a region of interest with `Image ROI` or `Image ROI Select` to speed up segmentation and avoid irrelevant areas. * Preview masks and results with `Show Image` and save useful frames using `Image Logger` or `Image Write`. * After segmentation, use `Find Contour`, `Minimum Rectangle`, or `Measure Object Distance` to extract region properties and measurements from the labeled `Shed Image`. ## 🛠ī¸ Troubleshooting * Poor segmentation or merged objects: check that `Fore Ground` covers only object interiors and `Back Ground` covers only background; add morphological opening/closing to refine masks. * No boundaries visible: verify that the input images and masks are valid image types (not empty) and that masks are binary or clearly contrasted. * Over-segmentation (too many small regions): smooth or simplify masks using `Blur`, `Denoising`, or `Morphological Transformations` before segmentation. * Under-segmentation (objects not separated): increase the accuracy of foreground markers (use stronger object markers or manual ROI selection) to help separate touching objects. --- # 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/transformation-filters/watershed-algorithm.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.