Finding distinct center of 2 objects

Question

I am trying to find centers of 2 squares in the same image which looks as follows:

I am able to detect the lines that make up the square. My output looks as follows:

As documented here to find the center of a polygon, I used moments to find center. Here is what I did.

import cv2
import numpy as np

img = cv2.imread('images/sq.png', 0)
gray = img

kernel_size = 5
blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0)
ret,thresh = cv2.threshold(blur_gray,100,255,0)

low_threshold = 50
high_threshold = 150
edges = cv2.Canny(thresh, low_threshold, high_threshold)

rho = 1  # distance resolution in pixels of the Hough grid
theta = np.pi / 180  # angular resolution in radians of the Hough grid
threshold = 3  # minimum number of votes (intersections in Hough grid cell)
min_line_length = 50  # minimum number of pixels making up a line
max_line_gap = 20 # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0  # creating a blank to draw lines on

# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
                    min_line_length, max_line_gap)

for line in lines:
    for x1,y1,x2,y2 in line:
        cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),2)
        print("x1 {} y1 {} x2 {} y2 {}".format(x1,y1,x2,y2))

lines_edges = cv2.addWeighted(img, 0.5, line_image, 1, 0)

line_image_gray = cv2.cvtColor(line_image, cv2.COLOR_RGB2GRAY)

M = cv2.moments(line_image_gray)

cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])

cv2.circle(lines_edges, (cx, cy), 5, (0, 0, 255), 1)

cv2.imshow("res", lines_edges)
cv2.imshow("line_image", line_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

But this finds the center between 2 detected squares. How could I find the centers of each square while only using Hough methods?

Answer 1

You could use this method combined with the following code to find which lines are part of the same square:

How can I check if two segments intersect?

Where 'lines' is a list of the recognized lines, and intersects(line1, line2) is a function using the process in the above link

squares = [[lines(1)]]    

for line1 in lines:
    for square in squares:
        for line2 in square:
            if line1 != line2:
                if intersects(line1, line2):
                    square.append(line1)
                else:
                    squares.append([line1])

This gives you 'squares' that contain the lines that are a part of it. You could then use the moment function on each individually.

Answer 2

Given that you have a requirement to use the Hough transform, I suggest you prepare the image better for it. The Canny edge detector will detect the inner and outer edges of the black line here, leading to two pairs of lines detected by Hough.

Instead, follow a procedure like this:

• Find all black (or nearly-black) pixels. For example pixels where all three RGB components are below 50. This will return the squares by themselves.

• Apply a morphological thinning (or a skeleton) to turn this into a 1-pixel thick outline of the squares.

• Apply the Hough transform on the result, and detect line segments.

Proper pre-processing makes the Hough transform easier to set up, as there will be a larger range of parameters that yields the correct results.

Next, find segments that start or end at the same pixel, with a little bit of tolerance (i.e. start or end points are within a few pixels of each other), to determine which of the lines belong together in the same shape.