Sobel Filter

This is a filtering method used to identify and highlight coarse changes in pixel intensity based on the 1st derivative. Similar results to the Scharr filter function.

plantcv.sobel_filter(gray_img, dx, dy, ksize)

returns filtered image

  • Parameters:
    • gray_img - Grayscale image data
    • dx - derivative of x to analyze
    • dy - derivative of y to analyze
    • ksize - apertures size used to calculate 2nd derivative filter, specifies the size of the kernel (must be an odd integer)
  • Context:
    • Used to define edges within and around objects
    • Aperture size must be greater than the largest derivative (ksize >= dx & ksize >= dy) in order to run
  • Example use:

Original 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 = "print"

# Apply to a grayscale image
# Filtered image will highlight areas of coarse pixel intensity change based on 1st derivative
sb_x_img = pcv.sobel_filter(gray_img, 1, 0, 1)
sb_y_img = pcv.sobel_filter(gray_img, 0, 1, 1)

Sobel filtered (x-axis)

Screenshot

Sobel filtered (y-axis)

Screenshot