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"""
Copyright 2021, Dana-Farber Cancer Institute and Weill Cornell Medicine
License: GNU GPL 2.0
"""
import numpy as np
import pytest
from skimage.draw import ellipse
from skimage.measure import label
from pathml.graph import ColorMergedSuperpixelExtractor
from pathml.graph.preprocessing import SLICSuperpixelExtractor
def make_fake_instance_maps(num, image_size, ellipse_height, ellipse_width):
img = np.zeros(image_size)
# Draw n ellipses
for i in range(num):
# Random center for each ellipse
center_x = np.random.randint(ellipse_width, image_size[1] - ellipse_width)
center_y = np.random.randint(ellipse_height, image_size[0] - ellipse_height)
# Coordinates for the ellipse
rr, cc = ellipse(
center_y, center_x, ellipse_height, ellipse_width, shape=image_size
)
# Draw the ellipse
img[rr, cc] = 1
label_img = label(img.astype(int))
return label_img
def make_fake_image(instance_map):
image = instance_map[:, :, None]
image[image > 0] = 1
noised_image = (
np.random.rand(instance_map.shape[0], instance_map.shape[1], 3) * 0.15 + image
) * 255
return noised_image.astype("uint8")
@pytest.mark.parametrize("superpixel_size", [20, 200])
@pytest.mark.parametrize("compactness", [50, 100])
@pytest.mark.parametrize("blur_kernel_size", [0.2, 1])
@pytest.mark.parametrize("threshold", [0.1, 0.9])
@pytest.mark.parametrize("downsampling_factor", [4, 10])
@pytest.mark.parametrize(
"extractor", [ColorMergedSuperpixelExtractor, SLICSuperpixelExtractor]
)
def test_tissue_extractors(
superpixel_size,
compactness,
blur_kernel_size,
threshold,
downsampling_factor,
extractor,
):
image_size = (256, 256)
instance_map = make_fake_instance_maps(
num=30, image_size=image_size, ellipse_height=20, ellipse_width=8
)
image = make_fake_image(instance_map.copy())
tissue_detector = extractor(
superpixel_size=superpixel_size,
compactness=compactness,
blur_kernel_size=blur_kernel_size,
threshold=threshold,
downsampling_factor=downsampling_factor,
)
superpixels = tissue_detector.process(image)
if isinstance(superpixels, tuple):
superpixels = superpixels[0]
assert superpixels.shape == image_size
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