clst.py

import cv2

n_imgs = 600

all_imgs = []

for i in range(n_imgs):
    img = plt.imread(image_paths[i])
    img = cv2.resize(img, (100, 100), cv2.INTER_LINEAR).astype('float')
#    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY).astype('float')
    img = cv2.normalize(img, None, 0.0, 1.0, cv2.NORM_MINMAX)
    img = img.reshape(1, -1)
    all_imgs.append(img)

img_mat = np.vstack(all_imgs)
img_mat.shape

from scipy.spatial.distance import pdist, squareform

sq_dists = squareform(pdist(img_mat))
print(sq_dists.shape)
sns.clustermap(
    sq_dists,
    figsize=(12,12),
    cmap=plt.get_cmap('viridis')
)