tracking_down.py

mask = np.zeros_like(sq_dists, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

# upper triangle of matrix set to np.nan
sq_dists[np.triu_indices_from(mask)] = np.nan
sq_dists[0, 0] = np.nan

fig = plt.figure(figsize=(12,8))
# maximally dissimilar image
ax = fig.add_subplot(1,2,1)
maximally_dissimilar_image_idx = np.nanargmax(np.nanmean(sq_dists, axis=1))
plt.imshow(plt.imread(image_paths[maximally_dissimilar_image_idx]))
plt.title('maximally dissimilar')

# maximally similar image
ax = fig.add_subplot(1,2,2)
maximally_similar_image_idx = np.nanargmin(np.nanmean(sq_dists, axis=1))
plt.imshow(plt.imread(image_paths[maximally_similar_image_idx]))
plt.title('maximally similar')

# # now compute the mean image
#ax = fig.add_subplot(1,3,3)
#mean_img = gray_imgs_mat.mean(axis=0).reshape(rescaled_dim, rescaled_dim, 3)
#plt.imshow(cv2.normalize(mean_img, None, 0.0, 1.0, cv2.NORM_MINMAX))
#plt.title('mean image')
/opt/conda/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:703: RuntimeWarning:

Mean of empty slice

/opt/conda/lib/python3.6/site-packages/numpy/lib/nanfunctions.py:703: RuntimeWarning:

Mean of empty slice