import numpy as np
import os
import matplotlib.pyplot as plt
from supreme.io import imread
from supreme.noise import dwt_denoise
import demo_data
X = demo_data.chelsea()
noise = np.random.normal(loc=0, scale=30, size=X.shape)
X_ = X + noise
X_ = np.clip(X_, 0, 255)
X_ /= X_.max()
Y = np.clip(dwt_denoise(X_, wavelet='db16', alpha=0.02, levels=4), 0, 255)
Y /= Y.max()
print "MSE:", np.mean((X - Y)**2)/np.prod(X.shape)
plt.subplot(2, 2, 1)
plt.imshow(X)
plt.title('Input Image')
plt.subplot(2, 2, 2)
plt.imshow(X_)
plt.title('Severe Noise Added')
plt.subplot(2, 2, 3)
import numpy as np
from demo_data import chelsea
from supreme.register.parzen import joint_hist, mutual_info
from supreme.transform import homography
import matplotlib.pyplot as plt
h1 = chelsea(grey=True).astype(np.uint8)
plt.suptitle('Parzen-Window Joint PDF Estimator')
for n, t in enumerate([0, 0.01, 0.1]):
plt.subplot(1, 3, n + 1)
h2 = homography(h1, [[np.cos(t), -np.sin(t), 0],
[np.sin(t), np.cos(t), 0],
[0, 0, 1]])
if n == 0:
plt.ylabel('Grey levels in A')
plt.xlabel('Grey levels in B')
H = joint_hist(h1, h2, win_size=5, std=1)
S = mutual_info(H)
plt.title('Rotation $%.2f^\\circ$\nS=%.5f' % ((t / np.pi * 180), S))
plt.imshow(np.log(H + 10), interpolation='nearest')
plt.show()
import numpy as np
from demo_data import chelsea
from supreme.register.parzen import joint_hist, mutual_info
from supreme.transform import homography
import supreme.register as register
import sys
if len(sys.argv) == 3:
from supreme.io import imread
A = imread(sys.argv[1], flatten=True).astype(np.uint8)
Ac = imread(sys.argv[1], flatten=False).astype(np.uint8)
B = imread(sys.argv[2], flatten=True).astype(np.uint8)
Bc = imread(sys.argv[2], flatten=False).astype(np.uint8)
else:
A = chelsea(grey=True).astype(np.uint8)
Ac = A
t = 20 / 180. * np.pi
x = 3
y = 6
B = homography(
A, [[np.cos(t), -np.sin(t), x], [np.sin(t), np.cos(t), y], [0, 0, 1]])
Bc = B
M, S = register.dense_MI(A, B, levels=3, std=5, win_size=9)
print "Mutual information: ", S
if S < 1.5:
print "Warning: registration probably failed."
print "Transformation matrix:"
print np.array2string(M, separator=', ')