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:
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
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]
areas
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. 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?
======= Side question
Is there anyway to change my final labeled image which has only top 2 areas back to the style of the first image (black and white version)