This is a filtering method used to identify and highlight fine edges based on the 2nd derivative.
laplace_filter(img, k, scale, device, debug=None)
returns device, filtered image
- img - binary image object. This image will be returned after filling.
- k - apertures size used to calculate 2nd derivative filter, specifies the size of the kernel (must be an odd integer: 1,3,5...)
- scale - scaling factor applied (multiplied) to computed Laplacian values (scale = 1 is unscaled)
- device - Counter for image processing steps
- debug- None, "print", or "plot". Print = save to file, Plot = print to screen. Default = None
- Used to define edges around objects
- Example use:
Input grayscale image
from plantcv import plantcv as pcv # Apply to a grayscale image # Filtered image will highlight areas of rapid pixel intensity change device, lp_img = pcv.laplace_filter(img, 1, 1, device, debug="print")
Image after Laplace filter