Homology: Acute¶

Identify landmark positions within a contour for morphometric analysis

plantcv.homology.acute(img, mask, win, threshold)

returns

homolog_pts = pseudo-landmarks selected from each landmark cluster

start_pts = pseudo-landmark island starting position; useful in parsing homolog_pts in downstream analyses

stop_pts = pseudo-landmark island end position ; useful in parsing homolog_pts in downstream analyses

ptvals = average values of pixel intensity from the mask used to generate cont; useful in parsing homolog_pts in downstream analyses

chain = raw angle scores for entire contour, used to visualize landmark clusters

max_dist = supplemental list which stores coordinates, distance from landmark cluster edges, and angle score for entire contour. Used in troubleshooting.

• Parameters:
  • img - The original image, used for plotting purposes
  • mask - Binary mask used to generate contour array (necessary for ptvals)
  • win - The maximum cumulative pixel distance window for calculating angle score; 1 cm in pixels often works well
  • thresh - Angle score threshold to be applied for mapping out landmark coordinate clusters within each contour
• Context:
  • Used to identify pseudo-landmark positions along the contour of a plant for morphometric analysis
  • Use pcv.params.verbose = False to quiet print statements
• Example use:
  • Use In Homology Tutorial
• Output data stored: Data ('num_acute_pts') automatically gets stored to the Outputs class when this function is ran. These data can always get accessed during a workflow (example below). For more detail about data output see Summary of Output Observations

Original image

Image mask

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"

# Given an image, mask, and object contours, identify pseudo-landmarks with acute
homolog_pts, start_pts, stop_pts, ptvals, chain, max_dist = pcv.homology.acute(img=img, mask=mask, win=25, threshold=90)

# Access data stored in outputs
num_pts = pcv.outputs.observations['plant']['num_acute_pts']['value']

Pseudo-landmark points

Source Code: Here