watershed

Image processingfunctionOpenCV 5.0.0

import { watershed } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Marker-based watershed →

Grow labelled regions across an image until competing regions meet at boundaries.

VISUAL WALKTHROUGHSegmentation

REFERENCE INPUTTwo touching objects

Their binary foreground is connected, but two separate seeds can guide a split.

→

RESULTKeep a boundary where regions meet

The orange meeting line separates the labels. In OpenCV, image contrast governs the expansion and meeting boundaries are written as −1.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

imgproc_hist

example: samples/cpp/snippets/watershed.cpp An example using the watershed algorithm example: samples/python/snippets/watershed.py An example using the watershed algorithm using python Performs a marker-based image segmentation using the watershed algorithm.

The function implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in [Meyer92] .

Before passing the image to the function, you have to roughly outline the desired regions in the image markers with positive (>0) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are "seeds" of the future image regions. All the other pixels in markers , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0's. In the function output, each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the regions.

Note: Any two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, they can touch each other in the initial marker image passed to the function.

See: findContours

watershed(image: Mat, markers: Mat): void;

image

Input 8-bit 3-channel image.

markers

Input/output value, modified by the native operation. Input/output 32-bit single-channel image (map) of markers. It should have the same size as image .

Upstream declaration ↗

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