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

watershed_segmentation(device, img, mask, distance=10, filename=False, debug=None)**

returns device, watershed_header,watershed_data, analysis_images

• Parameters:
  • device - Counter for image processing steps
  • img - img object
  • 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
  • debug - None, "print", or "plot". Print = save to file, Plot = print to screen. Default = None
• Context:
  • Used to segment image into parts

Original image

from plantcv import plantcv as pcv

# Segment image with watershed function
device, watershed_header, watershed_data,analysis_images=pcv.watershed_segmentation(device, crop_img,thresh,10,'./examples',debug='print')

print(watershed_header)
print(watershed_data)

Watershed Segmentation

('HEADER_WATERSHED', 'estimated_object_count')
('WATERSHED_DATA', 10)