laplace_filter.md

Laplace Filter

This is a filtering method used to identify and highlight fine edges based on the 2nd derivative.

plantcv.laplace_filter(gray_img, ksize, scale, roi=None)

returns filtered image

• Parameters:

  • gray_img - Grayscale image data
  • ksize - Kernel specified as a binary numpy.ndarray for arbitrary shapes, shape tuple for a rectangular kernel, or integer for a square kernel. Kernel inputs will be coerced to int and specify the size of the kernel (must be an odd integer: 1,3,5...) using the first element of a tuple or size of first dimension.
  • scale - scaling factor applied (multiplied) to computed Laplacian values (scale = 1 is unscaled)
  • 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 define edges around objects

• Example use:

  • Below

Input grayscale 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"

# Apply to a grayscale image
# Filtered image will highlight areas of rapid pixel intensity change
lp_img = pcv.laplace_filter(gray_img=gray_img, ksize=1, scale=1)

Image after Laplace filter

Source Code: Here