Divide plant object into twenty equidistant bins along the y-axis and assign pseudolandmark points based upon their actual (not scaled) position. Once this data is scaled this approach may provide some information regarding shape independent of size.

plantcv.y_axis_pseudolandmarks(obj, mask, img)

returns landmarks_on_leftside (left), landmarks_on_right (right), landmarks_at_center_along_the_horizontal_axis (center_h)

  • Parameters:
    • obj - A contour of the plant object (this should be output from the object_composition.py fxn)
    • mask - This is a binary image. The object should be white and the background should be black.
    • img - A copy of the original image (RGB or grayscale) generated using np.copy
  • Context:
    • Used to identify a set of sixty equidistant landmarks on the vertical axis. Once scaled these can be used for shape analysis.

Input object contour and 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"

# Identify a set of land mark points
# Results in set of point values that may indicate tip points
left, right, center_h  = pcv.y_axis_pseudolandmarks(obj, mask, img)

Image of points selected