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)
- 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 generated using np.copy if debug is true it will be drawn on
- 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 = "print" # Identify a set of land mark points # Results in set of point values that may indicate tip points device, left, right, center_h = pcv.y_axis_pseudolandmarks(obj, mask, img)
Image of points selected