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(img, obj, mask)
returns landmarks_on_leftside (left), landmarks_on_right (right), landmarks_at_center_along_the_horizontal_axis (center_h)
- img - A copy of the original image (RGB or grayscale) generated using np.copy
- 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.
- Used to identify a set of sixty equidistant landmarks on the vertical axis. Once scaled these can be used for shape analysis.
- Output data stored: Data ('left_lmk', 'right_lmk', 'center_h_lmk') automatically gets stored to the
Outputsclass 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
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(img, obj, mask) # Access data stored out from y_axis_pseudolandmarks left_landmarks = pcv.outputs.observations['left_lmk']['value']
Image of points selected