Detecting Grid blocks using Computer vision in realtime (Open CV, Python)

Question

I want to detect the centroid of individual blocks in the following grids for path planning. The idea is that a central navigation system like overhead camera will detect the blocks of grids along with the bot and help in navigation. Till now I have tried Hough lines Probabilistic and Harris corner detection but both of them either detect extra points or fail in real world scenario. I want to detect the blocks in real time and number them. Those numbering should not change or the whole path planning will be messed up.

Is there any solution to this problem that I missed.

thanks in advance

Answer 1

You need to learn how to eliminate noise. This is not a complete answer. The more time you spend and learn, the better your results will be.

import cv2
import numpy as np
import sys

# Load source as grayscale
im = cv2.imread(sys.path[0]+'/im.jpg', cv2.IMREAD_GRAYSCALE)
H, W = im.shape[:2]

# Convert im to black and white
im = cv2.adaptiveThreshold(
    im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 2)

# Remove noise
im = cv2.medianBlur(im, 11)
im = cv2.erode(im, np.ones((15, 15)))

# Fill the area around the shape
im = ~im
mask = np.zeros((H+2, W+2), np.uint8)
cv2.floodFill(im, mask, (0, 0), 255)
cv2.floodFill(im, mask, (W-1, 0), 255)
cv2.floodFill(im, mask, (0, H-1), 255)
cv2.floodFill(im, mask, (W-1, H-1), 255)

# Remove noise again
im = cv2.dilate(im, np.ones((15, 15)))

# Find the final blocks
cnts, _ = cv2.findContours(~im, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
    x, y, w, h = cv2.boundingRect(c)
    cv2.circle(im, (x+w//2, y+h//2), max(w, h)//2, 127, 5)
print("Found any: ", len(cnts) > 0)

# Save the output
cv2.imwrite(sys.path[0]+'/im_.jpg', im)