How to improve the detection of the defect on the surface?

Question

First of all this is my original image which I try to detect the defects (parallel lines) on a brushed aluminium surface.

Here is the steps I take:

1. Gaussian Blur
2. Dilate the image
3. Converting the image to grayscale
4. Morph Close Operation
5. Dilate again
6. Difference of the image
7. Canny Edge Detection
8. Finding the contours
9. Drawing a green line around the contours

Here is my code:

import numpy as np
import cv2
from matplotlib import pyplot as plt
import imutils
path = ''
path_output = ''

img_bgr = cv2.imread(path)
plt.imshow(img_bgr)

# bgr to rgb
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)

# Converting to grayscale
img_just_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)

# Displaying the grayscale image
plt.imshow(img_just_gray, cmap='gray')

# Gaussian Blur
ksize_w = 13
ksize_h = 13

img_first_gb = cv2.GaussianBlur(img_rgb, (ksize_w,ksize_h), 0, 0, cv2.BORDER_REPLICATE);
plt.imshow(img_first_gb)

# Dilate the image

dilated_img = cv2.dilate(img_first_gb, np.ones((11,11), np.uint8))
plt.imshow(dilated_img)

# Converting to grayscale
img_gray_operated = cv2.cvtColor(dilated_img, cv2.COLOR_BGR2GRAY)

# Displaying the grayscale image
plt.imshow(img_gray_operated, cmap='gray')

# closing:
kernel_closing = np.ones((7,7),np.uint8)
img_closing = cv2.morphologyEx(img_gray_operated, cv2.MORPH_CLOSE, kernel_closing)
plt.imshow(img_closing, cmap='gray')

# dilation:
# add pixels to the boundaries of objects in an image
kernel_dilation = np.ones((3,3),np.uint8)
img_dilation2 = cv2.dilate(img_closing, kernel_dilation, iterations = 1)
plt.imshow(img_dilation2, cmap='gray')

diff_img = 255 - cv2.absdiff(img_just_gray, img_dilation2)
plt.imshow(diff_img, cmap='gray')

# canny
edgesToFindImage = img_dilation2

v = np.median(img_just_gray)
#print(v)
sigma = 0.33
lower_thresh = int(max(0,(1.0-sigma)*v))
higher_thresh = int(min(255,(1.0+sigma)*v))

img_edges =  cv2.Canny(edgesToFindImage, lower_thresh, higher_thresh)
plt.imshow(img_edges, cmap='gray')

kernel_dilation2 = np.ones((2,2),np.uint8)
img_dilation2 = cv2.dilate(img_edges, kernel_dilation, iterations = 2)
plt.imshow(img_dilation2, cmap='gray')

# find contours
contoursToFindImage = img_dilation2

(_, cnts, _) = cv2.findContours(contoursToFindImage.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)
print(type(cnts))
print(len(cnts))

# -1 for all
cntsWhichOne = -1

# -1 for infill
# >0 for edge thickness
cntsInfillOrEdgeThickness = 3

img_drawing_contours_on_rgb_image = cv2.drawContours(img_rgb.copy(), cnts, cntsWhichOne, (0, 255, 0), cntsInfillOrEdgeThickness)
plt.imshow(img_drawing_contours_on_rgb_image)

and this is the result.

How can I improve this detection? Is there a more effective method to detect lines?

Answer 1

Here is one way in Python OpenCV. You are close, you should use adaptive thresholding, morphology to clean up the small regions and skip the canny edges.

Input:

import cv2
import numpy as np

# load image
img = cv2.imread('scratches.jpg')

# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# adaptive threshold 
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, -35)

# apply morphology
kernel = np.ones((3,30),np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
kernel = np.ones((3,35),np.uint8)
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)

# get hough line segments
threshold = 25
minLineLength = 10
maxLineGap = 20
lines = cv2.HoughLinesP(morph, 1, 30*np.pi/360, threshold, minLineLength, maxLineGap)

# draw lines
linear1 = np.zeros_like(thresh)
linear2 = img.copy()
for [line] in lines:
    x1 = line[0]
    y1 = line[1]
    x2 = line[2]
    y2 = line[3]
    cv2.line(linear1, (x1,y1), (x2,y2), 255, 1)
    cv2.line(linear2, (x1,y1), (x2,y2), (0,0,255), 1)

print('number of lines:',len(lines))

# save resulting masked image
cv2.imwrite('scratches_thresh.jpg', thresh)
cv2.imwrite('scratches_morph.jpg', morph)
cv2.imwrite('scratches_lines1.jpg', linear1)
cv2.imwrite('scratches_lines2.jpg', linear2)

# display result
cv2.imshow("thresh", thresh)
cv2.imshow("morph", morph)
cv2.imshow("lines1", linear1)
cv2.imshow("lines2", linear2)
cv2.waitKey(0)
cv2.destroyAllWindows()

Threshold image:

Morphology cleaned image:

Lines on original image:

Lines on black background: