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figures.py
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figures.py
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#import laplacian
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
import fista
import preprocess
import single
import scales
def figures(Phi=None):
IMAGES = laplacian.laplacian(image_dim=32*32, images='vanhateran', scales=3, normalize=True, num_patches=10000)
(base_image_dim, num_images) = IMAGES[0].shape
if Phi == None:
Phi = laplacian.learn(IMAGES=IMAGES, iterations=1000, patch_dim=8*8, alpha=100, save=True, batch=100)
# Example reconstruction
fig1, ax = plt.subplots(2)
reconstruction = laplacian.reconstruct(Phi,IMAGES[0],base_image_dim)
ax[0].imshow(laplacian.laplacian_reconstruct(IMAGES[0]))
ax[1].imshow(reconstruction)
fig2, ax = plt.subplots(1)
def snr():
# Function that plots reconstruction error by sparsity
sparsity = [0.001, 0.01, 0.1, 1]
for s in sparsity:
print(s)
def mse_vs_sparsity(batch=100, image_dim=32*32, patch_dim=9*9):
patch_side = int(np.sqrt(patch_dim))
image_side = int(np.sqrt(image_dim))
pad = (patch_side-1)/2
I = preprocess.extract_images(images='vanhateran', num_images=batch, image_dim=image_dim)
I = I.T
I = np.pad(I.reshape(image_side, image_side, batch), ((pad, pad), (pad, pad), (0, 0)), mode='constant')
I = I.reshape((image_side+2*pad)*(image_side+2*pad), batch)
"""
Phi_conv_oc2_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian/dictionaries/conv-oc-2-i40-p9-t1900-2014-02-04 09:39:29.570434.npy').item()
Phi_conv_oc4_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian/dictionaries/conv-oc-4-i40-p9-t400-2014-02-04 15:47:39.352604.npy').item()
Phi_oc_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian/dictionaries/oc1-i40-p9-t1000-2014-02-03 22:54:32.030729.npy').item()
Phi_oc2_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian/dictionaries/oc2-i40-p9-t1000-2014-02-04 00:51:06.116929.npy').item()
Phi_oc4_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian/dictionaries/oc4-i40-p9-t1800-2014-02-04 16:00:06.398794.npy').item()
=======
Phi_conv_oc2_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian2/dictionaries/conv-oc-2-i40-p9-t1900-2014-02-04 09:39:29.570434.npy').item()
Phi_conv_oc4_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian2/dictionaries/conv-oc-4-i40-p9-t400-2014-02-04 15:47:39.352604.npy').item()
Phi_oc_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian2/dictionaries/oc1-i40-p9-t1000-2014-02-03 22:54:32.030729.npy').item()
Phi_oc2_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian2/dictionaries/oc2-i40-p9-t1000-2014-02-04 00:51:06.116929.npy').item()
Phi_oc4_l005 = np.load('/Users/zayd/Dropbox/Code/multiscale/laplacian2/dictionaries/oc4-i40-p9-t1800-2014-02-04 16:00:06.398794.npy').item()
>>>>>>> 6764613a3f3ef85b2fb61beb9079b57bba057ece
A_conv_oc2_l005 = fista.fista(I, Phi_conv_oc2_l005, lambdav=0.05, max_iterations=100)
A_conv_oc4_l005 = fista.fista(I, Phi_conv_oc4_l005, lambdav=0.05, max_iterations=100)
A_oc2_l005 = fista.fista(I, Phi_oc2_l005, lambdav=0.05, max_iterations=100)
A_oc4_l005 = fista.fista(I, Phi_oc4_l005, lambdav=0.05, max_iterations=100)
R_conv_oc2_l005 = single.reconstruct(Phi_conv_oc2_l005, A_conv_oc2_l005)
R_conv_oc4_l005 = single.reconstruct(Phi_conv_oc4_l005, A_conv_oc4_l005)
R_oc2_l005 = single.reconstruct(Phi_oc2_l005, A_oc2_l005)
R_oc4_l005 = single.reconstruct(Phi_oc4_l005, A_oc4_l005)
mse_conv_oc2_l005 = np.sum((I-R_conv_oc2_l005)**2)/float(batch)
mse_conv_oc4_l005 = np.sum((I-R_conv_oc4_l005)**2)/float(batch)
mse_oc2_l005 = np.sum((I-R_oc2_l005)**2)/float(batch)
mse_oc4_l005 = np.sum((I-R_oc4_l005)**2)/float(batch)
l1_conv_oc2_l005 = np.sum(np.abs(A_conv_oc2_l005))/float(batch)
l1_conv_oc4_l005 = np.sum(np.abs(A_conv_oc4_l005))/float(batch)
l1_oc2_l005 = np.sum(np.abs(A_oc2_l005))/float(batch)
l1_oc4_l005 = np.sum(np.abs(A_oc4_l005))/float(batch)
x = [l1_conv_oc2_l005, l1_conv_oc4_l005, l1_oc2_l005, l1_oc4_l005]
y = [mse_conv_oc2_l005, mse_conv_oc4_l005, mse_oc2_l005, mse_oc4_l005]
y = [s/float(image_dim) for s in y]
"""
plt.scatter(x,y)
plt.xlabel('Sparsity (l1 Norm)')
plt.ylabel('MSE (per pixel)')
plt.show()
return (x,y)
def mse_vs_sparsity(file_names, batch=100, image_dim=32*32):
dictionaries = []
for f in file_names:
dictionaries.append(np.load(f).item())
for d in dictionaries:
a.append(scales.inference())
plt.scatter(x,y)
plt.show()