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 test_binarizer_transform(threshold, expected):
binarizer = Binarizer(threshold=threshold)
assert_almost_equal(binarizer.fit_transform(expected), expected)
# 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")
radplot.update_layout(template='plotly_dark')