def test_connect_regions():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
for thr in (50, 150):
mask = face > thr
graph = img_to_graph(face, mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
def test_connect_regions():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
for thr in (50, 150):
mask = face > thr
graph = img_to_graph(face, mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
def test_connect_regions():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
# subsample by 4 to reduce run time
face = face[::4, ::4]
for thr in (50, 150):
mask = face > thr
graph = img_to_graph(face, mask)
assert ndimage.label(mask)[1] == connected_components(graph)[0]
def test_connect_regions_with_grid():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
mask = face > 50
graph = grid_to_graph(*face.shape, mask=mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
mask = face > 150
graph = grid_to_graph(*face.shape, mask=mask, dtype=None)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
def test_connect_regions_with_grid():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
mask = face > 50
graph = grid_to_graph(*face.shape, mask=mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
mask = face > 150
graph = grid_to_graph(*face.shape, mask=mask, dtype=None)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
def _downsampled_face():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
face = face.astype(np.float32)
face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]
face = face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2] + face[1::2, 1::2]
face = face.astype(np.float32)
face /= 16.0
return face
def _downsampled_face():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
face = face.astype(np.float32)
face = (face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2]
+ face[1::2, 1::2])
face = (face[::2, ::2] + face[1::2, ::2] + face[::2, 1::2]
+ face[1::2, 1::2])
face = face.astype(np.float32)
face /= 16.0
return face
def test_connect_regions_with_grid():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
# subsample by 4 to reduce run time
face = face[::4, ::4]
mask = face > 50
graph = grid_to_graph(*face.shape, mask=mask)
assert ndimage.label(mask)[1] == connected_components(graph)[0]
mask = face > 150
graph = grid_to_graph(*face.shape, mask=mask, dtype=None)
assert ndimage.label(mask)[1] == connected_components(graph)[0]
# License: BSD 3 clause
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.utils.testing import SkipTest
from sklearn.utils.fixes import sp_version
if sp_version < (0, 12):
raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
"thus does not include the scipy.misc.face() image.")
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
n_clusters = 5
np.random.seed(0)
X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array
k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
k_means.fit(X)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
# create an array from labels and values
import scipy as sp
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
from sklearn.utils.testing import SkipTest
from sklearn.utils.fixes import sp_version
if sp_version < (0, 12):
raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
"thus does not include the scipy.misc.face() image.")
# load the raccoon face as a numpy array
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
# Resize it to 10% of the original size to speed up the processing
face = sp.misc.imresize(face, 0.10) / 255.
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(face)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
import scipy as sp
from scipy.ndimage.filters import gaussian_filter
import matplotlib.pyplot as plt
from skimage import img_as_float
from skimage.transform import rescale
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
# load the raccoon face as a numpy array
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
orig_face = img_as_float(face(gray=True))
except ImportError:
orig_face = img_as_float(sp.face(gray=True))
# Resize it to 10% of the original size to speed up the processing
# Applying a Gaussian filter for smoothing prior to down-scaling
# reduces aliasing artifacts.
smoothened_face = gaussian_filter(orig_face, sigma=4.5)
rescaled_face = rescale(smoothened_face, 0.1, mode="reflect")
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(rescaled_face)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
def plot_image_denoising():
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
face = face(gray=True)
except ImportError:
face = sp.face(gray=True)
# Convert from uint8 representation with values between 0 and 255 to
# a floating point representation with values between 0 and 1.
face = face / 255.
# downsample for higher speed
face = face[::4, ::4] + face[1::4, ::4] + face[::4, 1::4] + face[1::4,
1::4]
face /= 4.0
height, width = face.shape
# Distort the right half of the image
print('Distorting image...')
distorted = face.copy()
distorted[:, width // 2:] += 0.075 * np.random.randn(height, width // 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[:, :width // 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
print('Learning the dictionary...')
t0 = time()
dico = MiniBatchDictionaryLearning(n_components=100, alpha=1, n_iter=500)
V = dico.fit(data).components_
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 face 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)
# #############################################################################
# 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
print('Extracting noisy patches... ')
t0 = time()
data = extract_patches_2d(distorted[:, width // 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
})]
reconstructions = {}
for title, transform_algorithm, kwargs in transform_algorithms:
print(title + '...')
reconstructions[title] = face.copy()
t0 = time()
dico.set_params(transform_algorithm=transform_algorithm, **kwargs)
code = dico.transform(data)
patches = np.dot(code, V)
patches += intercept
patches = patches.reshape(len(data), *patch_size)
if transform_algorithm == 'threshold':
patches -= patches.min()
patches /= patches.max()
reconstructions[title][:, width // 2:] = reconstruct_from_patches_2d(
patches, (height, width // 2))
dt = time() - t0
print('done in %.2fs.' % dt)
show_with_diff(reconstructions[title], face,
title + ' (time: %.1fs)' % dt)
plt.show()
import matplotlib.pyplot as plt
from skimage import img_as_float
from skimage.transform import rescale
from sklearn.feature_extraction.image import grid_to_graph
from sklearn.cluster import AgglomerativeClustering
# #############################################################################
# Generate data
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
orig_face = img_as_float(face(gray=True))
except ImportError:
orig_face = img_as_float(sp.face(gray=True))
# Resize it to 10% of the original size to speed up the processing
# Applying a Gaussian filter for smoothing prior to down-scaling
# reduces aliasing artifacts.
smoothened_face = gaussian_filter(orig_face, sigma=4.5)
rescaled_face = rescale(smoothened_face, 0.1, mode="reflect")
X = np.reshape(rescaled_face, (-1, 1))
# #############################################################################
# Define the structure A of the data. Pixels connected to their neighbors.
connectivity = grid_to_graph(*rescaled_face.shape)
# #############################################################################
# Compute clustering
# License: BSD 3 clause
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn.utils.testing import SkipTest
from sklearn.utils.fixes import sp_version
if sp_version < (0, 12):
raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
"thus does not include the scipy.misc.face() image.")
try:
face = sp.face(gray=True) #type = 'numpy.ndarray'
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
n_clusters = 5
np.random.seed(0)
X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array
k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
k_means.fit(X)
values = k_means.cluster_centers_.squeeze()
labels = k_means.labels_
assert len(gen_class_vector.get_shape()) == 2
assert gen_class_vector.dtype == tf.float32
# assert宣告變數在程式某一段必須為某值
a = 0
assert a == 1
print(1)
assert a == 0
print(0)
# 實作k-means
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
face = face(gray=True)
except ImportError:
face = sp.face(gray=True) # face是一張圖片，大小為768*1024
n_clusters = 5
np.random.seed(0)
X = face.reshape((-1, 1)) # 拉成一維向量 (n_sample, n_feature) array
k_means = cluster.KMeans(n_clusters=n_clusters,
n_init=4) # K_means分類，num_cluster=5
k_means.fit(X)
values = k_means.cluster_centers_.squeeze(
) # 使用不同 centroid seeds 運行 k-means 算法的時間
labels = k_means.labels_
face_compressed = np.choose(labels,
values) #labels範圍0~4，長度768*1024，values代表五個k_keans值
face_compressed.shape = face.shape
