How to measure average thickness of labeled segmented image-CodePudding

The

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?

CodePudding user response：

Here is one way to do that in Python/OpenCV.

Read the input

Convert to gray

Threshold to binary

Get the contours and filter on area so that we have only the two primary lines

Sort by area

Select the first (smaller and thinner) contour

Draw it white filled on a black background

Get its skeleton

Get the points of the skeleton

Fit a line to the points and get the rotation angle of the skeleton

Loop over each of the two contours and draw them white filled on a black background. Then rotate to horizontal lines. Then get the vertical thickness of the lines from the average thickness along each column using np.count_nonzero() and print the value.

Save intermediate images

Input:

import cv2 import numpy as np import skimage.morphology import skimage.transform import math # read image img = cv2.imread('lines.jpg') # convert to grayscale gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # threshold thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY cv2.THRESH_OTSU)[1] # get contours new_contours = [] img2 = np.zeros_like(thresh, dtype=np.uint8) contour_img = thresh.copy() contour_img = cv2.merge([contour_img,contour_img,contour_img]) contours = cv2.findContours(thresh , cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contours = contours[0] if len(contours) == 2 else contours[1] for cntr in contours: area = cv2.contourArea(cntr) if area > 1000: cv2.drawContours(contour_img, [cntr], 0, (0,0,255), 1) cv2.drawContours(img2, [cntr], 0, (255), -1) new_contours.append(cntr) # sort contours by area cnts_sort = sorted(new_contours, key=lambda x: cv2.contourArea(x), reverse=False) # select first (smaller) sorted contour first_contour = cnts_sort[0] contour_first_img = np.zeros_like(thresh, dtype=np.uint8) cv2.drawContours(contour_first_img, [first_contour], 0, (255), -1) # thin smaller contour thresh1 = (contour_first_img/255).astype(np.float64) skeleton = skimage.morphology.skeletonize(thresh1) skeleton = (255*skeleton).clip(0,255).astype(np.uint8) # get skeleton points pts = np.column_stack(np.where(skeleton.transpose()==255)) # fit line to pts (vx,vy,x,y) = cv2.fitLine(pts, cv2.DIST_L2, 0, 0.01, 0.01) #print(vx,vy,x,y) x_axis = np.array([1, 0]) # unit vector in the same direction as the x axis line_direction = np.array([vx, vy]) # unit vector in the same direction as your line dot_product = np.dot(x_axis, line_direction) [angle_line] = (180/math.pi)*np.arccos(dot_product) print("angle:", angle_line) # loop over each sorted contour # draw contour filled on black background # rotate # get mean thickness from np.count_non-zeros black = np.zeros_like(thresh, dtype=np.uint8) i = 1 for cnt in cnts_sort: cnt_img = black.copy() cv2.drawContours(cnt_img, [cnt], 0, (255), -1) cnt_img_rot = skimage.transform.rotate(cnt_img, angle_line, resize=False) thickness = np.mean(np.count_nonzero(cnt_img_rot, axis=0)) print("line ",i,"=",thickness) i = i 1 # save resulting images cv2.imwrite('lines_thresh.jpg',thresh) cv2.imwrite('lines_filtered.jpg',img2) cv2.imwrite('lines_small_contour_skeleton.jpg',skeleton ) # show thresh and result cv2.imshow("thresh", thresh) cv2.imshow("contours", contour_img) cv2.imshow("lines_filtered", img2) cv2.imshow("first_contour", contour_first_img) cv2.imshow("skeleton", skeleton) cv2.waitKey(0) cv2.destroyAllWindows()

Threshold image:

Contour image:

Filtered contour image:

Skeleton image:

Angle and Thicknesses:

angle: 3.1869032185349733 line 1 = 8.79219512195122 line 2 = 49.51609756097561

CodePudding user response：

Use Deskew to straighten up the image.

Then, count the pixels of each column of the color of the label you want to measure then divide it by the number of columns to get the average thickness