Performs a marker-based image segmentation using the watershed algorithm

marker = cv.watershed(image, marker)

Input

• image Input 8-bit 3-channel image.
• marker Input 32-bit single-channel image (map) of markers. It should have the same size as image.

Output

• marker Output 32-bit single-channel image (map) of markers. It has the same size as image.

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 cv.findContours and cv.drawContours. 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 pixels); for example, they can touch each other in the initial marker image passed to the function.

References

[Meyer92]:

Fernand Meyer. "Color image segmentation". In Image Processing and its Applications, 1992, International Conference on, pages 303-306. IET, 1992.