def test_width_patch():
# width and height of the patch should be less than the image
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
with pytest.raises(ValueError):
extract_patches_2d(x, (4, 1))
with pytest.raises(ValueError):
extract_patches_2d(x, (1, 4))
def test_extract_patches_all_color():
face = orange_face
i_h, i_w = face.shape[:2]
p_h, p_w = 16, 16
expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
patches = extract_patches_2d(face, (p_h, p_w))
assert patches.shape == (expected_n_patches, p_h, p_w, 3)
def test_reconstruct_patches_perfect_color():
face = orange_face
p_h, p_w = 16, 16
patches = extract_patches_2d(face, (p_h, p_w))
face_reconstructed = reconstruct_from_patches_2d(patches, face.shape)
np.testing.assert_array_almost_equal(face, face_reconstructed)
def test_extract_patches_all():
face = downsampled_face
i_h, i_w = face.shape
p_h, p_w = 16, 16
expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
patches = extract_patches_2d(face, (p_h, p_w))
assert patches.shape == (expected_n_patches, p_h, p_w)
def test_extract_patches_less_than_max_patches():
face = downsampled_face
i_h, i_w = face.shape
p_h, p_w = 3 * i_h // 4, 3 * i_w // 4
# this is 3185
expected_n_patches = (i_h - p_h + 1) * (i_w - p_w + 1)
patches = extract_patches_2d(face, (p_h, p_w), max_patches=4000)
assert patches.shape == (expected_n_patches, p_h, p_w)
def test_extract_patches_max_patches():
face = downsampled_face
i_h, i_w = face.shape
p_h, p_w = 16, 16
patches = extract_patches_2d(face, (p_h, p_w), max_patches=100)
assert patches.shape == (100, p_h, p_w)
expected_n_patches = int(0.5 * (i_h - p_h + 1) * (i_w - p_w + 1))
patches = extract_patches_2d(face, (p_h, p_w), max_patches=0.5)
assert patches.shape == (expected_n_patches, p_h, p_w)
with pytest.raises(ValueError):
extract_patches_2d(face, (p_h, p_w), max_patches=2.0)
with pytest.raises(ValueError):
extract_patches_2d(face, (p_h, p_w), max_patches=-1.0)
# 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)
def test_extract_patch_same_size_image():
face = downsampled_face
# Request patches of the same size as image
# Should return just the single patch a.k.a. the image
patches = extract_patches_2d(face, face.shape, max_patches=2)
assert patches.shape[0] == 1
# Learn the dictionary of images
print('Learning the dictionary... ')
rng = np.random.RandomState(0)
kmeans = MiniBatchKMeans(n_clusters=81, random_state=rng, verbose=True)
patch_size = (20, 20)
buffer = []
t0 = time.time()
# The online learning part: cycle over the whole dataset 6 times
index = 0
for _ in range(6):
for img in faces.images:
data = extract_patches_2d(img,
patch_size,
max_patches=50,
random_state=rng)
data = np.reshape(data, (len(data), -1))
buffer.append(data)
index += 1
if index % 10 == 0:
data = np.concatenate(buffer, axis=0)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)
kmeans.partial_fit(data)
buffer = []
if index % 100 == 0:
print('Partial fit of %4i out of %i' %
(index, 6 * len(faces.images)))
dt = time.time() - t0