OpenCV - Computing distance between two edges in an image - image-processing

Question

I am trying to compute distance (in # of pixels) between two edges in an image. I have corrected for image perspective using cv2.warpPerspective method and have converted the resulting image into grayscale followed by filtering using gaussian blur. I have tried various thresholding methods and found out that cv2.ADAPTIVE_THRESH_GAUSSIAN works best. Other methods are too noisy or miss the second edge in the left side of the object as seen in result of adaptive gaussian thresholding.
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Load the image
imgRoadvR10 = cv2.imread('sampleimage.jpg') # image is already corrected for perspective warp using cv2.warpPerspective
# convert to grayscale
imgRoadvR10_GrayPersp = cv2.cvtColor(imgRoadvR10, cv2.COLOR_BGR2GRAY)
# gaussian blur
a10lvR10_gblur = cv2.GaussianBlur(imgRoadvR10_GrayPersp,(5,5),0)
# Try different thresholding methods
ret,a10lvR10_th1 = cv2.threshold(a10lvR10_gblur,127,255,cv2.THRESH_BINARY)
a10lvR10_th2 = cv2.adaptiveThreshold(a10lvR10_gblur,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY,11,2)
a10lvR10_th3 = cv2.adaptiveThreshold(a10lvR10_gblur,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY_INV,11,2)
# Otsu's thresholding
ret2,a10lvR10_th4 = cv2.threshold(a10lvR10_gblur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
print(ret2)
# Plot results
plt.figure()
titles = ['Original Image', 'Global Thresholding (v = 127)',
'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding','OTSU Thresholding']
images = [a10lvR10_gblur, a10lvR10_th1, a10lvR10_th2, a10lvR10_th3, a10lvR10_th4]
for i in range(5):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
plt.show()
Closer look at result of adaptive gaussian thresholding:
I want to find the width of this rectangular object. The width is measured from the second edge on the left side to the edge on the right side (see image below):
How can I measure the width? I have been reading upon morphological operations and edge detection, But not sure how to proceed next. Any suggestions will be appreciated

Answer 1

This is not the best idea and I think a more logical and simple solution can be obtained. However, this idea may help you.
import cv2
import numpy as np
#load image
im = cv2.imread("test3.jpg", 1)
#Convert to gray
mask = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
#convert to black and white
mask = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
#try to remove noise
#you can just use median blur or any other method
mask = cv2.erode(mask, np.ones((8, 0), "uint8"))
mask = cv2.dilate(mask, np.ones((32, 0), "uint8"))
mask = cv2.medianBlur(mask, 9)
#save cleaned image
cv2.imwrite("out1.jpg", mask)
A cleaner version of your output image:
out1:
Next we can get the coordinates of the lines. I got the coordinates of the first line from the left. I think you have to change the code a bit to get the coordinates of the sidebar.
h = len(mask) - 1
def count(row):
counter = 0
for i in range(0, len(row)):
if row[i] == 255:
break
counter += 1
return counter
def line(im, pt1, pt2, color, thickness):
im = cv2.line(
img=im,
pt1=pt1,
pt2=pt2,
color=color,
thickness=thickness,
lineType=cv2.LINE_AA,
)
return im
def center(x1, y1, x2, y2):
return (int((x1 + x2) / 2), int((y1 + y2) / 2))
topLeft = count(mask[0])
bottomLeft = count(mask[h])
# to shadow and hide the old left line
mask = line(mask, (topLeft, 0), (bottomLeft, h), (0, 0, 0), 80)
topRight = count(mask[0])
bottomRight = count(mask[h])
# to shadow and hide the old right line
mask = line(mask, (topRight, 0), (bottomRight, h), (0, 0, 0), 80)
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# to draw new clean left line
mask = line(mask, (topLeft, 0), (bottomLeft, h), (128, 0, 255), 25)
# to draw new clean right line
mask = line(mask, (topRight, 0), (bottomRight, h), (128, 0, 255), 25)
a = center(topLeft, 0, bottomLeft, h)
b = center(topRight, 0, bottomRight, h)
mask = line(mask, a, b, (128, 0, 255), 25)
cv2.imwrite("out2.jpg", mask)
out2:
Now you can calculate the distance between "a" and "b".