k-mean clustering calculate distance between all points and the initial centroids

I want to do image segmention with k-means clustering, and i want to:

1. Calculate distances between all points and the initial centroids.
2. Assign all points to their closest centroid.

Here is my code:

def init(ds, k, random_state=42):
np.random.seed(random_state)
centroids = [ds[0]]

for _ in range(1, k):
    dist_sq = np.array([min([np.inner(c-x,c-x) for c in centroids]) for x in ds])
    probs = dist_sq/dist_sq.sum()
    cumulative_probs = probs.cumsum()
    r = np.random.rand()
    
    for j, p in enumerate(cumulative_probs):
        if r < p:
            i = j
            break
    
    centroids.append(ds[i])

return np.array(centroids)

k = 4
centroids = init(pixels, k, random_state=42)
print(centroids)

# First centroid
centroids[0]

#Calculate distances between all points and the initial centroids.


# Assign all points to their closest centroid.