sampled_X_on_sphere = put_on_sphere(sampled_X)
# print(sampled_X_on_sphere[0])
# Find centroid of data #
final_p = sphere_centroid_finder_vecs(sampled_X_on_sphere, sampled_X.shape[1],
0.05, 0.01)
# print(final_p)
final_p_img = final_p.reshape(m, n)
plt.imshow(final_p_img, cmap=plt.get_cmap('gray'))
plt.show()
h = choose_h_gaussian(sampled_X_on_sphere, final_p,
85) # needs to be very high!
radius = choose_h_binary(sampled_X_on_sphere, final_p, 40)
upper, curve, lower = principal_boundary(sampled_X_on_sphere, sampled_X.shape[1], 0.02, h, radius, \
start_point=final_p, kernel_type="gaussian", max_iter=40)
print("upper")
for j in range(5):
for i in range(9):
plt.subplot(330 + 1 + i)
plt.imshow(upper[i + 9 * j].reshape(m, n), cmap=plt.get_cmap('gray'))
plt.show()
print("curve")
for j in range(5):
for i in range(9):
plt.subplot(330 + 1 + i)
plt.imshow(curve[i + 9 * j].reshape(m, n), cmap=plt.get_cmap('gray'))
# Sample from train_X #
train_samples = np.random.choice(train_X.shape[0], size=SAMPLES)
sampled_X = train_X[train_samples]
sampled_X_on_sphere = put_on_sphere(sampled_X)
# print(sampled_X_on_sphere[0])
# Find centroid of data #
final_p = sphere_centroid_finder_vecs(sampled_X_on_sphere, sampled_X.shape[1], 0.05, 0.01, max_iter=100)
# print(final_p)
final_p_img = final_p.reshape(m, n)
plt.imshow(final_p_img, cmap=plt.get_cmap('gray'))
plt.show()
# Find principal flow and display first 27 images of flow obtained #
h = choose_h_binary(sampled_X_on_sphere, final_p, 10) # needs to be very high!
radius = choose_h_binary(sampled_X_on_sphere, final_p, 30)
curve = principal_flow(sampled_X_on_sphere, sampled_X.shape[1], 0.01, h, \
start_point=final_p, kernel_type="binary", max_iter=30)
# idea: upsample images
print("curve")
for j in range(6):
for i in range(9):
plt.subplot(330 + 1 + i)
plt.imshow(curve[i + 9*j].reshape(m, n), cmap=plt.get_cmap('gray'))
plt.show()
np.save(os.path.abspath(os.curdir)+'\\data\\cartoon_grayscale_30_flow.npy', curve)
'''