Connected Components
Photogrammetry-1
2 min readApr 20, 2024
To find objects in an image, we can use connected components. It works by utilizing 4 or 8 neighbourhood. It starts off by randomly selecting an unlabelled pixel, assigns an id to it and then continues finding unlabelled pixels in 4 or 8 neighbourhood assigning the same id to all of them. It keeps on finding new elements until all elements are found. Below is a function which performs connected component analysis using 4 or 8 neighbourhood. It takes as input a binary image and the neighbourhood while it provides as an output the number of elements in the binary image along with the connected image having all the elements labelled.
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import cv2
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
import random
def connected_components(binary_img, neighborhood):
connected_image = np.zeros(np.shape(binary_img))
S = []
K = 0
for i in range(200):
x_idx = random.choice(np.arange(np.shape(binary_img)[0]))
y_idx = random.choice(np.arange(np.shape(binary_img)[1]))
if connected_image[x_idx,y_idx] == 0 and binary_img[x_idx,y_idx] == 1:
S.append([x_idx,y_idx])
K = K + 1
connected_image[x_idx,y_idx] = K
for index in S:
x_idx = index[0]
y_idx = index[1]
if neighborhood == 4:
if x_idx-1 >= 0:
if binary_img[x_idx-1,y_idx] == 1 and connected_image[x_idx-1,y_idx] == 0:
connected_image[x_idx-1,y_idx] = K
S.append([x_idx-1,y_idx])
if x_idx+1 < np.shape(binary_img)[0]:
if binary_img[x_idx+1,y_idx] == 1 and connected_image[x_idx+1,y_idx] == 0:
connected_image[x_idx+1,y_idx] = K
S.append([x_idx+1,y_idx])
if y_idx-1 >= 0:
if binary_img[x_idx,y_idx-1] == 1 and connected_image[x_idx,y_idx-1] == 0:
connected_image[x_idx,y_idx-1] = K
S.append([x_idx,y_idx-1])
if y_idx+1 < np.shape(binary_img)[1]:
if binary_img[x_idx,y_idx+1] == 1 and connected_image[x_idx,y_idx+1] == 0:
connected_image[x_idx,y_idx+1] = K
S.append([x_idx,y_idx+1])
if neighborhood == 8:
if x_idx-1 >= 0 and y_idx+1 < np.shape(binary_img)[1]:
if binary_img[x_idx-1,y_idx+1] == 1 and connected_image[x_idx-1,y_idx+1] == 0:
connected_image[x_idx-1,y_idx+1] = K
S.append([x_idx-1,y_idx+1])
if x_idx+1 < np.shape(binary_img)[0] and y_idx+1 < np.shape(binary_img)[1]:
if binary_img[x_idx+1,y_idx+1] == 1 and connected_image[x_idx+1,y_idx+1] == 0:
connected_image[x_idx+1,y_idx+1] = K
S.append([x_idx+1,y_idx+1])
if x_idx-1 >= 0 and y_idx-1 >= 0:
if binary_img[x_idx-1,y_idx-1] == 1 and connected_image[x_idx-1,y_idx-1] == 0:
connected_image[x_idx-1,y_idx-1] = K
S.append([x_idx-1,y_idx-1])
if x_idx+1 < np.shape(binary_img)[0] and y_idx-1 >= 0:
if binary_img[x_idx+1,y_idx-1] == 1 and connected_image[x_idx+1,y_idx-1] == 0:
connected_image[x_idx+1,y_idx-1] = K
S.append([x_idx+1,y_idx-1])
if x_idx-1 >= 0:
if binary_img[x_idx-1,y_idx] == 1 and connected_image[x_idx-1,y_idx] == 0:
connected_image[x_idx-1,y_idx] = K
S.append([x_idx-1,y_idx])
if x_idx+1 < np.shape(binary_img)[0]:
if binary_img[x_idx+1,y_idx] == 1 and connected_image[x_idx+1,y_idx] == 0:
connected_image[x_idx+1,y_idx] = K
S.append([x_idx+1,y_idx])
if y_idx-1 >= 0:
if binary_img[x_idx,y_idx-1] == 1 and connected_image[x_idx,y_idx-1] == 0:
connected_image[x_idx,y_idx-1] = K
S.append([x_idx,y_idx-1])
if y_idx+1 < np.shape(binary_img)[1]:
if binary_img[x_idx,y_idx+1] == 1 and connected_image[x_idx,y_idx+1] == 0:
connected_image[x_idx,y_idx+1] = K
S.append([x_idx,y_idx+1])
return K, connected_image
That’s it for now, see you later.
