How to measure average thickness of labeled segmented image

I have an image and I've done some pre-processing on the that image. Below I showed my preprocessing:

median=cv2.medianBlur(img,13)
ret, th = cv2.threshold(median, 0 , 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel=np.ones((3,15),np.uint8)
closing1 = cv2.morphologyEx(th, cv2.MORPH_CLOSE, kernel, iterations=2)
kernel=np.ones((1,31),np.uint8)
closing2 = cv2.morphologyEx(closing1, cv2.MORPH_CLOSE, kernel)

kernel=np.ones((1,13),np.uint8)
opening1= cv2.morphologyEx(closing2, cv2.MORPH_OPEN, kernel,  iterations=2)

So, basically I used "Threshold filtering" , "closing" and "opening" and the result looks like this:

Please note that when I used type(opening1), I got numpy.ndarray. So the image at this step is numpy array with 1021 x 1024 size.

Then I labeled my image:

label_image=measure.label(opening1, connectivity=opening1.ndim)
props= measure.regionprops_table (label_image, properties=['label', "area", "coords"])

and the result looks like this

Please note that when I used type(label_image), I got numpy.ndarray. So the image at this step is numpy array with 1021 x 1024 size.

As you can see, currently the image has 6 labels. Some of these labels are short and small pieces, so I tried to keep top 2 label based on area

slc=label_image
rps=regionprops(slc)
areas=[r.area for r in rps]

id=np.argsort(props["area"])[::-1]
new_slc=np.zeros_like(slc)

for i in id[:2]:
    new_slc[tuple(rps[i].coords.T)]=i+1

Now the result looks like this:

It looks like I was successful in keeping 2 top regions (please note that by changing id[:2] you can select thickest white layer or thin layer). Now:

What I want to do: I want to find the average thickness of these two regions

Also, please note that I know each of my pixels is 314 nm

Can anyone here advise how I can do this task?

from How to measure average thickness of labeled segmented image