def bettiCurve_pipe1(img_file):
"""
Pipeline 1: Binarizer --> Height Filtration --> Cubical Persistance --> Betti Curve
"""
img = cv2.imread(img_file)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# blur the image to reduce noise
figure_size = 9 # the dimension of the x and y axis of the kernal.
img = cv2.blur(img, (figure_size, figure_size))
shape = img.shape
images = np.zeros((1, *shape))
images[0] = img
bz = Binarizer(threshold=40 / 255)
binned = bz.fit_transform(images)
p = make_pipeline(HeightFiltration(direction=np.array([1, 1])),
CubicalPersistence(), BettiCurve(n_bins=50))
return p.fit_transform(binned)
def pipeline1(images):
"""
Binarizer --> Height Filtration, Erosion Filtration, Dilation Filtration --> Cubical Persistance --> Amp, PE
return: Array of pipelines
"""
# Pipeline parameters
bin_thresholds = [np.percentile(images[0], 93) / np.max(images[0])]
directions = [
np.array([np.cos(t), np.sin(t)])
for t in np.linspace(0, 2 * np.pi, 8)[:-1]
]
n_iterations = np.linspace(1, 21, 5).astype(int).tolist()
features = [('bottleneck', Amplitude(metric='bottleneck', n_jobs=-1)),
('PE', PersistenceEntropy(n_jobs=-1))]
# Make filtrations
binned_steps = [('binarizer_{}'.format(t), Binarizer(threshold=t,
n_jobs=-1))
for t in bin_thresholds]
filtrations = [('height_{}'.format(d),
HeightFiltration(direction=d, n_jobs=-1))
for d in directions]
filtrations += [('erosion_{}'.format(i),
ErosionFiltration(n_iterations=i, n_jobs=-1))
for i in n_iterations]
filtrations += [('dilation_{}'.format(i),
DilationFiltration(n_iterations=i, n_jobs=-1))
for i in n_iterations]
# Make pipelines
cubical_lower = ('cubical', CubicalPersistence(n_jobs=-1))
partial_pipeline_steps = []
partial_pipeline_steps.append([cubical_lower])
partial_pipeline_steps.append([('inverter', Inverter(n_jobs=-1)),
cubical_lower])
for b, f in itertools.product(binned_steps, filtrations):
partial_pipeline_steps.append(
[b, f, ('cubical', CubicalPersistence(n_jobs=-1))])
feature_pipelines = []
for s, f in itertools.product(partial_pipeline_steps, features):
feature_pipelines.append(Pipeline(s + [f]))
return feature_pipelines
def test_binarizer_fit_transform_plot():
Binarizer().fit_transform_plot(images_2D, sample=0)
def test_binarizer_transform(threshold, expected):
binarizer = Binarizer(threshold=threshold)
assert_almost_equal(binarizer.fit_transform(expected), expected)
def test_binarizer_errors():
threshold = 'a'
binarizer = Binarizer(threshold=threshold)
with pytest.raises(TypeError):
binarizer.fit(images_2D)
def test_binarizer_not_fitted():
binarizer = Binarizer()
with pytest.raises(NotFittedError):
binarizer.transform(images_2D)
np.zeros((7, 8, 4))
],
axis=0)
images_3D_float = np.stack([
2.5 * np.ones((7, 8, 4)),
3. * np.concatenate([np.ones(
(7, 4, 4)), np.zeros((7, 4, 4))], axis=1),
np.zeros((7, 8, 4))
],
axis=0)
@pytest.mark.parametrize(
"transformer",
[Binarizer(), Inverter(),
Padder(), ImageToPointCloud()])
def test_invalid_input_shape(transformer):
X = np.ones((1, 1, 1, 1, 1))
with pytest.raises(ValueError, match="Input of `fit`"):
transformer.fit(X)
def test_binarizer_not_fitted():
binarizer = Binarizer()
with pytest.raises(NotFittedError):
binarizer.transform(images_2D)
def test_binarizer_errors():
threshold = 'a'
random_state=42)
# IMAGE FUNCTION
# st.sidebar.selectbox('Select Digit', options = ('0','1','2','3','4','5','6','7','8','9','10'))
# Bianizer
im_idx = np.flatnonzero(y_train == str(
st.sidebar.selectbox('Select Digit',
options=('0', '1', '2', '3', '4', '5', '6', '7', '8',
'9', '10'))))[0]
im = X_train[im_idx][None, :, :]
binarizer = Binarizer(threshold=0.4)
im_binarized = binarizer.fit_transform(im)
binplot = binarizer.plot(im_binarized)
binplot.update_layout(template='plotly_dark')
# Radial Filtration
radial_filtration = RadialFiltration(center=np.array([20, 6]))
im_filtration = radial_filtration.fit_transform(im_binarized)
radplot = radial_filtration.plot(im_filtration, colorscale="jet")