8000 DOC: Update notebook-style for example plot_image_denoising by AmarCodes-22 · Pull Request #22739 · scikit-learn/scikit-learn · GitHub
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DOC: Update notebook-style for example plot_image_denoising #22739

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Merged
merged 3 commits into from
Mar 17, 2022
Merged

DOC: Update notebook-style for example plot_image_denoising #22739

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9a17f5b
Update notebook-style for example plot_image_denoising
AmarCodes-22 Mar 9, 2022
264928e
Move imports closer to their use
AmarCodes-22 Mar 13, 2022
1dfc9d2
Rearrange order of sections
AmarCodes-22 Mar 17, 2022
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84 changes: 48 additions & 36 deletions examples/decomposition/plot_image_denoising.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,16 +32,12 @@

"""

from time import time

import matplotlib.pyplot as plt
# %%
# Generate distorted image
# ------------------------
import numpy as np
import scipy as sp

from sklearn.decomposition import MiniBatchDictionaryLearning
from sklearn.feature_extraction.image import extract_patches_2d
from sklearn.feature_extraction.image import reconstruct_from_patches_2d


try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
Expand All @@ -64,6 +60,44 @@
distorted = face.copy()
distorted[:, width // 2 :] += 0.075 * np.random.randn(height, width // 2)


# %%
# Display the distorted image
# ---------------------------
import matplotlib.pyplot as plt


def show_with_diff(image, reference, title):
"""Helper function to display denoising"""
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, 0.2)


show_with_diff(distorted, face, "Distorted image")


# %%
# Extract reference patches
# ----------------------------
from time import time

from sklearn.feature_extraction.image import extract_patches_2d

# Extract all reference patches from the left half of the image
print("Extracting reference patches...")
t0 = time()
Expand All @@ -74,8 +108,11 @@
data /= np.std(data, axis=0)
print("done in %.2fs." % (time() - t0))

# #############################################################################

# %%
# Learn the dictionary from reference patches
# -------------------------------------------
from sklearn.decomposition import MiniBatchDictionaryLearning

print("Learning the dictionary...")
t0 = time()
Expand All @@ -98,35 +135,10 @@
plt.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"""
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, 0.2)


show_with_diff(distorted, face, "Distorted image")

# #############################################################################
# %%
# Extract noisy patches and reconstruct them using the dictionary
# ---------------------------------------------------------------
from sklearn.feature_extraction.image import reconstruct_from_patches_2d

print("Extracting noisy patches... ")
t0 = time()
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