The goal is to find the line that represents the distance between the "hole" and the outer edge. Is Canny edge detection the best approach?

Question

The goal is to find the line that represents the distance between the "hole" and the outer edge (transition from black to white). I was able to successfully binarize this photo and get a very clean black and white image. The next step would be to find the (almost) vertical line on it and calculate the perpendicular distance to the midpoint of this vertical line and the hole.

original picture hole - zoomed in

ps: what I call "hole" is a shadow. I am shooting a laser into a hole. So the lines we can see is a steel and the black part without a line is a hole. The 2 white lines serve as a reference to measure the distance.

Is Canny edge detection the best approach? If so, what are good values for the A, B and C parameters? I can't tune it. I'm getting too much noise.

Answer 1

This is not complete; You have to take the time to reach the final result. But this idea might help you.

Preprocessors:

import os
import cv2
import numpy as np

Main code:

# Read original image
dir = os.path.abspath(os.path.dirname(__file__))
im = cv2.imread(dir+'/'+'im.jpg')
h, w = im.shape[:2]
print(w, h)

# Convert image to Grayscale
imGray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
cv2.imwrite(dir+'/im_1_grayscale.jpg', imGray)

# Eliminate noise and display laser light better
imHLine = imGray.copy()
imHLine = cv2.GaussianBlur(imHLine, (0, 9), 21)  # 5, 51
cv2.imwrite(dir+'/im_2_h_line.jpg', imHLine)

# Make a BW mask to find the ROI of laser array
imHLineBW = cv2.threshold(imHLine, 22, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite(dir+'/im_3_h_line_bw.jpg', imHLineBW)

# Remove noise with mask and extract just needed area
imHLineROI = imGray.copy()
imHLineROI[np.where(imHLineBW == 0)] = 0
imHLineROI = cv2.GaussianBlur(imHLineROI, (0, 3), 6)
imHLineROI = cv2.threshold(imHLineROI, 25, 255, cv2.THRESH_BINARY)[1]  # 22
cv2.imwrite(dir+'/im_4_ROI.jpg', imHLineROI)

# Found laser array and draw box around it
cnts, _ = cv2.findContours(
    imHLineROI, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts.sort(key=lambda x: cv2.boundingRect(x)[0])
pts = []
for cnt in cnts:
    x2, y2, w2, h2 = cv2.boundingRect(cnt)
    if h2 < h/10:
        cv2.rectangle(im, (x2, y2), (x2+w2, y2+h2), (0, 255, 0), 1)
        pts.append({'x': x2, 'y': y2, 'w': w2, 'h': h2})

circle = {
    'left': (pts[0]['x']+pts[0]['w'], pts[0]['y']+pts[0]['h']/2),
    'right': (pts[1]['x'], pts[1]['y']+pts[1]['h']/2),
}
circle['center'] = calculateMiddlePint(circle['left'], circle['right'])
circle['radius'] = (circle['right'][0]-circle['left'][0])//2

# Draw line and Circle inside it
im = drawLine(im, circle['left'], circle['right'], color=(27, 50, 120))
im = cv2.circle(im, circle['center'], circle['radius'], (255, 25, 25), 3)

# Remove pepper/salt noise to find metal edge
imVLine = imGray.copy()
imVLine = cv2.medianBlur(imVLine, 17)
cv2.imwrite(dir+'/im_6_v_line.jpg', imVLine)

# Remove remove the shadows to find metal edge
imVLineBW = cv2.threshold(imVLine, 50, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite(dir+'/im_7_v_bw.jpg', imVLineBW)

# Finding the right vertical edge of metal
y1, y2 = h/5, h-h/5
x1 = horizantalDistance(imVLineBW, y1)
x2 = horizantalDistance(imVLineBW, y2)
pt1, pt2 = (x1, y1), (x2, y2)
imVLineBW = drawLine(imVLineBW, pt1, pt2)
cv2.imwrite(dir+'/im_8_v_bw.jpg', imVLineBW)

# Draw lines
im = drawLine(im, pt1, pt2)
im = drawLine(im, calculateMiddlePint(pt1, pt2), circle['center'])

# Draw final image
cv2.imwrite(dir+'/im_8_output.jpg', im)

---

Extra functions:
Find the first white pixel in one line of picture:

# This function only processes on a horizontal line of the image
# Its job is to examine the pixels one by one from the right and
# report the distance of the first white pixel from the right of
# the image.
def horizantalDistance(im, y):
    y = int(y)
    h, w = im.shape[:2]
    for i in range(0, w):
        x = w-i-1
        if im[y][x] == 255:
            return x
    return -1

To draw a line in opencv:

def drawLine(im, pt1, pt2, color=(128, 0, 200), thickness=2):
    return cv2.line(
        im,
        pt1=(int(pt1[0]), int(pt1[1])),
        pt2=(int(pt2[0]), int(pt2[1])),
        color=color,
        thickness=thickness,
        lineType=cv2.LINE_AA  # Anti-Aliased
    )

To calculate middle point of two 2d points:

def calculateMiddlePint(p1, p2):
    return (int((p1[0]+p2[0])/2), int((p1[1]+p2[1])/2))

---

Output:
Original image:

Eliminate noise and process to see the laser array better:

Find the laser area to extract the hole:

Work on another copy of image to find the right side of metal object:

Remove the shadows to better see the right edge:

The final output:

---

I first defined an ROI area. I changed the code later but did not change the names of the variables. If you were asked.