Erode

Perform morphological 'erosion' filtering. Keeps pixel in center of the kernel if conditions set in kernel are true, otherwise removes pixel.

plantcv.erode(gray_img, ksize, i, roi=None)

returns image after erosion

  • Parameters:

    • gray_img - Grayscale (usually binary) image data
    • ksize - Kernel size, an odd integer that is used to build a ksize x ksize matrix using np.ones. Must be greater than 1 to have an effect
    • i - An integer for number of iterations, i.e. the number of consecutive filtering passes
    • roi - Optional rectangular ROI as returned by pcv.roi.rectangle within which to apply this function. (default = None, which uses the entire image)

  • Context:

    • Used to perform morphological erosion filtering. Helps remove isolated noise pixels or remove boundary of objects.
  • Example use:
    • Below

Input grayscale image

Screenshot


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"

# Perform erosion filtering
# Results in removal of isolated pixels or boundary of object removal
er_img = pcv.erode(gray_img=gray_img, ksize=3, i=1)

Image after erosion

Screenshot

Source Code: Here