cosmit

examples/decomposition/plot_img_denoising.py

@@ -36,11 +36,12 @@
 import numpy as np
 
 from sklearn.decomposition import DictionaryLearningOnline
-from sklearn.feature_extraction.image import extract_patches_2d, \
-                                             reconstruct_from_patches_2d
+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 = sp.lena() / 256.0
 
 # downsample for higher speed
@@ -56,20 +57,23 @@
 
 # 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
-print 'Learning the dictionary... ',
+
+print 'Learning the dictionary... '
 t0 = time()
 dico = DictionaryLearningOnline(n_atoms=100, alpha=1e-2, n_iter=500)
 V = dico.fit(data).components_
 dt = time() - t0
-print 'done in %.2f.' % dt
+print 'done in %.2fs.' % dt
 
 pl.figure(figsize=(4.2, 4))
 for i, comp in enumerate(V[:100]):
@@ -83,6 +87,8 @@
             fontsize=16)
 pl.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
 
+###############################################################################
+# Display the distorted image
 
 def show_with_diff(image, reference, title):
     """Helper function to display denoising"""
@@ -103,29 +109,33 @@ def show_with_diff(image, reference, title):
     pl.suptitle(title, size=16)
     pl.subplots_adjust(0.02, 0.02, 0.98, 0.79, 0.02, 0.2)
 
-###############################################################################
-# Display the distorted image
 show_with_diff(distorted, lena, 'Distorted image')
 
 ###############################################################################
 # Extract noisy patches and reconstruct them using the dictionary
+
 print 'Extracting noisy patches... '
+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': .1})]
+    ('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': .1}),
+]
 
 reconstructions = {}
 for title, transform_algorithm, kwargs in transform_algorithms:
-    print title, '... ',
+    print title, '... '
     reconstructions[title] = lena.copy()
     t0 = time()
     dico.set_params(transform_algorithm=transform_algorithm, **kwargs)
@@ -144,7 +154,7 @@ def show_with_diff(image, reference, title):
     reconstructions[title][:, height / 2:] = reconstruct_from_patches_2d(
         patches, (width, height / 2))
     dt = time() - t0
-    print 'done in %.2f.' % dt
+    print 'done in %.2fs.' % dt
     show_with_diff(reconstructions[title], lena,
                    title + ' (time: %.1fs)' % dt)