Analyze the Size and Shape Characteristics of Objects¶

Size and shape analysis outputs numeric properties for individual plants, seeds, leaves, etc.

plantcv.analyze.size(img, labeled_mask, n_labels=1, label=None)

returns analysis_image

• Parameters:
  • img - RGB or grayscale image data for plotting.
  • labeled_mask - Labeled mask of objects (32-bit, output from pcv.create_labels or pcv.roi.filter).
  • n_labels - Total number expected individual objects (default = 1).
  • label - Optional label parameter, modifies the variable name of observations recorded. Can be a prefix or list (default = pcv.params.sample_label).
• Context:
  • Used to output size and shape characteristics of individual objects (labeled regions).
• Example use:
  • Use In VIS Tutorial
  • Use In PSII Tutorial
• Output data stored: Data ('area', 'convex_hull_area', 'solidity', 'perimeter', 'width', 'height', 'longest_path', 'center_of_mass, 'convex_hull_vertices', 'object_in_frame', 'ellipse_center', 'ellipse_major_axis', 'ellipse_minor_axis', 'ellipse_angle', 'ellipse_eccentricity') automatically gets stored to the Outputs class when this function is run. These data can be accessed during a workflow (example below). For more detail about data output see Summary of Output Observations

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

# Characterize object shapes
shape_image = pcv.analyze.size(img=img, labeled_mask=mask, n_labels=1)

# Save returned images with more specific naming
pcv.print_image(shape_image, '/home/malia/setaria_shape_img.png')

# Access data stored out from analyze.size
plant_solidity = pcv.outputs.observations['plant_1']['solidity']['value']

Image with identified objects

Image with shape characteristics

Source Code: Here