from sklearn.feature_extraction.image import extract_patches_2d
from sklearn.feature_extraction.image import reconstruct_from_patches_2d
###############################################################################
# Load Lena image and extract patches
lena = lena() / 256.0
# downsample for higher speed
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
lena /= 4.0
height, width = lena.shape
# Distort the right half of the image
print('Distorting image...')
distorted = lena.copy()
distorted[:, height // 2:] += 0.075 * np.random.randn(width, height // 2)
# Extract all reference patches from the left half of the image
print('Extracting reference patches...')
t0 = time()
patch_size = (7, 7)
data = extract_patches_2d(distorted[:, :height // 2], patch_size)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
print('done in %.2fs.' % (time() - t0))
###############################################################################
# Learn the dictionary from reference patches
from sklearn.feature_extraction.image import extract_patches_2d
from sklearn.feature_extraction.image import reconstruct_from_patches_2d
###############################################################################
# Load Lena image and extract patches
lena = lena() / 256.0
# downsample for higher speed
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
lena /= 4.0
height, width = lena.shape
# Distort the right half of the image
print('Distorting image...')
distorted = lena.copy()
distorted[:, height // 2:] += 0.075 * np.random.randn(width, height // 2)
# Extract all reference patches from the left half of the image
print('Extracting reference patches...')
t0 = time()
patch_size = (7, 7)
data = extract_patches_2d(distorted[:, :height // 2], patch_size)
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
print('done in %.2fs.' % (time() - t0))
###############################################################################
# Learn the dictionary from reference patches
###############################################################################
# Extract noisy patches and reconstruct them using the dictionary
transform_algorithms = [
('Least-angle regression\n5 atoms', 'lars',
{'transform_n_nonzero_coefs': 5}),
('Orthogonal Matching Pursuit\n1 atom', 'omp',
{'transform_n_nonzero_coefs': 1}),
('Orthogonal Matching Pursuit\n2 atoms', 'omp',
{'transform_n_nonzero_coefs': 2}),
('Thresholding\n alpha=0.1', 'threshold', {'transform_alpha': .1})]
reconstructions = {}
for title, transform_algorithm, kwargs in transform_algorithms:
print(title + '...')
reconstructions[title] = lena.copy()
t0 = time()
dico.set_params(transform_algorithm=transform_algorithm, **kwargs)
code = dico.transform(data)
highDictionary=dualDictLearn.l2ls_learn_basis_dual(data.T,code.T,1)
highDictionary=highDictionary.T
print 'Distance: %f'% np.sum(np.abs(V-highDictionary))
pl.figure(figsize=(4.2, 4))
for i, comp in enumerate(highDictionary[:100]):
pl.subplot(10, 10, i + 1)
pl.imshow(comp.reshape(patch_size), cmap=pl.cm.gray_r,interpolation='nearest')
pl.xticks(())
pl.yticks(())
pl.suptitle('Dictionary learned from Lena patches\n' +'Train time %.1fs on %d patches' % (dt, len(data)), fontsize=16)
pl.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
def imageDenoisingTest01():
from time import time
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import lena
from sklearn.decomposition import MiniBatchDictionaryLearning
from sklearn.feature_extraction.image import extract_patches_2d
from sklearn.feature_extraction.image import reconstruct_from_patches_2d
#Load image and extract patches
lena = lena() / 256.0
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
lena /= 4.0
height, width = lena.shape
#Distort the right half of the image
print "distorting image"
distorted = lena.copy()
distorted[:, height//2:] += 0.075 * np.random.randn(width, height // 2)
#plt.imshow(distorted[:, :height//2], cmap = plt.cm.gray, interpolation = "nearest")
#plt.show()
print "Extacting reference patches"
#这里是从distorted的左半边抽取patches
t0 = time()
patch_size = (7, 7)
data = extract_patches_2d(distorted[:, :height//2], patch_size)
#data是 30500 * 7 * 7 维矩阵
#print data
#print len(data)
#print len(data[0][0])
#plt.imshow(data[0], cmap = plt.cm.gray, interpolation = "nearest")
#plt.show()
#print distorted[:, height//2:].shape #一半是256 * 128
#下面是把patch转换为一维向量, 然后再归一化
data = data.reshape(data.shape[0], -1)
data -= np.mean(data, axis = 0)
data /= np.std(data, axis = 0)
print 'done in ' + str(time() - t0)
# Learn the dictionary from reference patches
print "Learning the dictionary"
t0 = time()
#这一步是开始对patches进行学习
#new 一个model
dico = MiniBatchDictionaryLearning(n_components = 100, alpha = 1, n_iter = 5000)
print data.shape #data是30500 * 49维矩阵
V = dico.fit(data).components_
print V.shape #V是100 * 49维矩阵
dt = time() - t0
print "done in %.2fs." % dt
plt.figure(figsize = (4.2, 4))
for i, comp in enumerate(V[:100]):
plt.subplot(10, 10, i + 1)
plt.imshow(comp.reshape(patch_size), cmap = plt.cm.gray_r, interpolation = "nearest")
plt.xticks(())
plt.yticks(())
plt.suptitle("Dictionary learned from lena patches\n" + "Train time %.1fs on %d patches" % (dt, len(data)), fontsize = 16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
def show_with_diff(image, reference, title):
plt.figure(figsize = (5, 3.3))
plt.subplot(1, 2, 1)
plt.title('Image')
plt.imshow(image, vmin = 0, vmax = 1, cmap = plt.cm.gray, interpolation = "nearest")
plt.xticks(())
plt.yticks(())
plt.subplot(1,2,2)
difference = image - reference
plt.title("difference (norm: %.2f)" % np.sqrt(np.sum(difference ** 2)))
plt.imshow(difference, vmin = -0.5, vmax = 0.5, cmap = plt.cm.PuOr, interpolation = "nearest")
plt.xticks(())
plt.yticks(())
plt.suptitle(title, size = 16)
plt.subplots_adjust(0.02, 0.02, 0.98, 0.79, 0.02, 0.02)
show_with_diff(distorted, lena, "Distorted Image")
#plt.show()
#Extract noisy patches and reconstruct them using the dictionary
#从右半边抽取patches
print('Extracting noisy pathces...')
t0 = time()
data = extract_patches_2d(distorted[:, height//2:], patch_size)
data = data.reshape(data.shape[0], -1)
intercept = np.mean(data, axis = 0)
data -= intercept
print "done in %.2fs. " % (time() - t0)
transform_algorithms = [('Orthogonal Matching Pursuit\n1 atom', 'omp',
{'transform_n_nonzero_coefs': 1}),
('Orthogonal Matching Pursuit\n2 atoms', 'omp',
{'transform_n_nonzero_coefs': 2}),
('Least-angle regression\n5 atoms', 'lars',
{'transform_n_nonzero_coefs': 5}),
('Thresholding\n alpha = 0.1', 'threshold',
{'transform_alpha': 0.1})]
reconstructions = {}
for title, transform_algorithm, kwargs in transform_algorithms:
print title + "..."
reconstructions[title] = lena.copy()
t0 = time()
dico.set_params(transform_algorithm = transform_algorithm, **kwargs)
code = dico.transform(data) #利用之前训练的模型来获得代表系数 -- code
patches = np.dot(code, V)
if transform_algorithm == "threshold":
patches -= patches.min()
patches /= patches.max()
patches += intercept
patches = patches.reshape(len(data), *patch_size)
if transform_algorithm == "threshold":
patches -= patches.min()
patches /= patches.max()
reconstructions[title][:, height // 2:] = reconstruct_from_patches_2d(patches, (width, height // 2))
dt = time() - t0
print "done in %.2fs." % dt
show_with_diff(reconstructions[title], lena, title + '(time: %.1fs)' % dt)
plt.show()
from hdl.interface import SparseCoding ############################################################################### # Load Lena image and extract patches lena = lena() / 256.0 # downsample for higher speed lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] lena /= 4.0 height, width = lena.shape # Distort the right half of the image print 'Distorting image...' distorted = lena.copy() distorted[:, height / 2:] += 0.075 * np.random.randn(width, height / 2) # Extract all clean patches from the left half of the image print 'Extracting clean patches...' t0 = time() patch_size = (7, 7) data = extract_patches_2d(distorted[:, :height / 2], patch_size) data = data.reshape(data.shape[0], -1) data -= np.mean(data, axis=0) data /= np.std(data, axis=0) print 'done in %.2fs.' % (time() - t0) ############################################################################### # Learn the dictionary from clean patches (new way)