def test_threshold_denoise(image):
denoised_thresholded = pre.threshold(image,
sigma=5,
radius=15,
smooth_method='tv')
thresholded = pre.threshold(image, sigma=0, radius=15)
assert (_total_variation(thresholded) >
_total_variation(denoised_thresholded))
def test_threshold2d_sauvola(image):
res = image.meta['Scan']['PixelHeight'] * 1e9 # nm/pixel
radius = int(50 / res) # radius of 50nm in pixels
sigma = 0.1 * radius
thresholded0 = pre.threshold(
image, sigma=sigma, radius=radius, method='sauvola', offset=0.2
)
assert thresholded0.dtype == bool
assert thresholded0.shape == image.shape
thresholded1 = pre.threshold(
image, sigma=sigma, radius=radius, method='sauvola', offset=0.075
)
assert np.all(thresholded1 <= thresholded0)
def test_threshold_2d_median(image):
sigma = 2
radius = 5
thresholded = pre.threshold(
image[:100, :100], sigma=sigma, radius=radius, method='median'
)
assert thresholded.shape == (100, 100)
assert thresholded.dtype == bool
def test_threshold_2d_niblack(image):
res = image.meta['Scan']['PixelHeight'] * 1e9 # nm/pixel
radius = int(50 / res)
sigma = 0.1 * radius
thresholded0 = pre.threshold(
image, sigma=sigma, radius=radius, method='niblack', offset=0.075
)
assert thresholded0.shape == image.shape
def test_threshold_no_method(image):
with pytest.raises(ValueError):
pre.threshold(image, radius=1, method='no method')
def test_threshold_2d_otsu(image):
thresholded_otsu = pre.threshold(image)
assert thresholded_otsu.shape == image.shape
def test_thresholded(test_image):
thresholded = pre.threshold(test_image, sigma=2, radius=31, offset=0.075)
return thresholded
# IPython log file
import os
os.chdir('/Users/jni/Dropbox/data1/malaria/adam-oli-schizont-30sec')
import imageio as iio
im = iio.imread('Shchizont4_UninfRBC10_02.tif', format='fei')
scale = im.meta['Scan']['PixelHeight'] * 1e9 # convert m to nm
sigma = 5/scale # nm / (nm/pixel) ==> pixel
import matplotlib.pyplot as plt
plt.imshow(im, cmap='gray')
from skan import pre
from skimage import morphology
binary = pre.threshold(im, sigma=sigma, radius=round(10*sigma))
skeleton_image = morphology.skeletonize(binary)
plt.imshow(skeleton_image)
shape = feature.shape_index(im, sigma=5)
plt.imshow(shape, cmap='RdBu')
shape_skeleton = skeleton_image * shape
from skan import summarise
data = summarise(shape_skeleton)
data.head()
from skan import draw
draw.overlay_skeleton_2d(im, shape_skeleton, dilate=1)