Fill Segments¶

Propagate the labels of a segmented skeleton to fill the mask.

plantcv.morphology.fill_segments(mask, objects, stem_objects=None, label=None)

returns filled_mask

• Parameters:
  • mask - Binary mask
  • objects - Segment objects (output from either plantcv.morphology.prune, plantcv.morphology.segment_skeleton, or plantcv.morphology.segment_sort).
  • stem_objects - Optional input for stem objects that will be combined into a single object before filling the mask.
  • label - Optional label parameter, modifies the variable name of observations recorded. (default = pcv.params.sample_label)
• Context:
  • Uses the watershed algorithm to fill the mask propagating the objects' labels.
• Output data stored: Data ('segment_area') automatically gets stored to the Outputs class when this function is ran without the stem_objects parameter. When the optional parameter is utilized then the data variable names will be ('leaf_area') and ('stem_area'). Data sample names are modified with an optional label prefix. These data can always get accessed during a workflow (example below). For more detail about data output see Summary of Output Observations

Reference Image: mask, objects drawn as labels

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 = "plot"
# Optionally, set a sample label name
pcv.params.sample_label = "plant"

filled_mask = pcv.morphology.fill_segments(mask=plant_mask, objects=obj)

# Convert labeled mask to a colorized image
filled_image = pcv.visualize.colorize_label_img(label_img=filled_mask)

# Access data stored out from fill_segments
segments_area = pcv.outputs.observations['plant']['segment_area']['value']

Filled Image

Source Code: Here