Watershed Segmentation¶
This function is based on code contributed by Suxing Liu, Arkansas State University. For more information see https://github.com/lsx1980/Leaf_count. This function uses the watershed algorithm to detect boundry of objects. Needs a mask file which specifies area which is object is white, and background is black. Requires cv2 version 3.0+
plantcv.watershed_segmentation(rgb_img, mask, distance=10, filename=False)**
returns watershed_header, watershed_data, analysis_images
- Parameters:
- rgb_img - RGB image data
- mask - binary image, single channel, object in white and background black
- distance - min_distance of local maximum (lower values are more sensitive, and segments more objects)
- filename - if user wants to output analysis images change filenames from false
- Context:
- Used to segment image into parts
Original image
from plantcv import plantcv as pcv
# Set global debug behavior to None (default), "print" (to file), or "plot" (Jupyter Notebooks or X11)
pcv.params.debug = "print"
# Segment image with watershed function
watershed_header, watershed_data, analysis_images = pcv.watershed_segmentation(crop_img, thresh, 10, './examples')
print(watershed_header)
print(watershed_data)
Watershed Segmentation
('HEADER_WATERSHED', 'estimated_object_count')
('WATERSHED_DATA', 10)