/
test_thresholding.py
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/
test_thresholding.py
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# -*- coding: utf-8 -*-
import numpy
import numpy.testing
import pytest
import skimage.data
import skimage.exposure
import skimage.filters
import skimage.morphology
import cellprofiler.image
import cellprofiler.modules.thresholding
instance = cellprofiler.modules.thresholding.Thresholding()
@pytest.fixture(
scope="module",
params=[
(skimage.data.camera()[0:128, 0:128], 2),
(numpy.tile(skimage.data.camera()[0:32, 0:32], (2, 1)).reshape(2, 32, 32), 3)
],
ids=[
"grayscale_image",
"grayscale_volume"
]
)
def image(request):
data, dimensions = request.param
return cellprofiler.image.Image(image=data, dimensions=dimensions)
def test_run_global_iterative_selection(image, module, workspace):
module.x_name.value = "example"
module.local.value = False
module.global_operation.value = u"Iterative selection thresholding"
module.bins.value = 256
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
threshold = skimage.filters.threshold_isodata(
image=data,
nbins=256
)
expected = data >= threshold
numpy.testing.assert_array_equal(expected, actual)
def test_run_manual(image, module, workspace):
module.x_name.value = "example"
module.local.value = False
module.global_operation.value = u"Manual"
module.minimum.value = 0.2
module.maximum.value = 0.8
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_float(image.pixel_data)
data = skimage.exposure.rescale_intensity(data)
expected = numpy.zeros_like(data, dtype=numpy.bool)
expected[data > 0.2] = True
expected[data < 0.8] = False
numpy.testing.assert_array_equal(expected, actual)
def test_run_global_minimum_cross_entropy(image, module, workspace):
module.x_name.value = "example"
module.local.value = False
module.global_operation.value = u"Minimum cross entropy thresholding"
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
threshold = skimage.filters.threshold_li(data)
expected = data >= threshold
numpy.testing.assert_array_equal(expected, actual)
def test_run_global_otsu(image, module, workspace):
module.x_name.value = "example"
module.local.value = False
module.global_operation.value = u"Otsu’s method"
module.bins.value = 256
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
threshold = skimage.filters.threshold_otsu(
image=data,
nbins=256
)
expected = data >= threshold
numpy.testing.assert_array_equal(expected, actual)
def test_run_global_yen(image, module, workspace):
module.x_name.value = "example"
module.local.value = False
module.global_operation.value = u"Yen’s method"
module.bins.value = 256
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
threshold = skimage.filters.threshold_yen(
image=data,
nbins=256
)
expected = data >= threshold
numpy.testing.assert_array_equal(expected, actual)
def test_run_local_adaptive_gaussian(image, module, workspace):
module.x_name.value = "example"
module.local.value = True
module.local_operation.value = u"Adaptive"
module.adaptive_method.value = u"Gaussian"
module.sigma.value = 2
module.block_size.value = 7
module.offset.value = 0
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
if image.volumetric:
expected = numpy.zeros_like(data, dtype=numpy.bool)
for index, plane in enumerate(data):
expected[index] = skimage.filters.threshold_adaptive(
image=plane,
block_size=7,
method="gaussian",
offset=0,
param=2
)
else:
expected = skimage.filters.threshold_adaptive(
image=data,
block_size=7,
method="gaussian",
offset=0,
param=2
)
numpy.testing.assert_array_equal(expected, actual)
def test_run_local_adaptive_mean(image, module, workspace):
module.x_name.value = "example"
module.local.value = True
module.local_operation.value = u"Adaptive"
module.adaptive_method.value = u"Mean"
module.block_size.value = 7
module.offset.value = 0
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
if image.volumetric:
expected = numpy.zeros_like(data, dtype=numpy.bool)
for index, plane in enumerate(data):
expected[index] = skimage.filters.threshold_adaptive(
image=plane,
block_size=7,
method="mean",
offset=0
)
else:
expected = skimage.filters.threshold_adaptive(
image=data,
block_size=7,
method="mean",
offset=0
)
numpy.testing.assert_array_equal(expected, actual)
def test_run_local_adaptive_median(image, module, workspace):
module.x_name.value = "example"
module.local.value = True
module.local_operation.value = u"Adaptive"
module.adaptive_method.value = u"Median"
module.block_size.value = 7
module.offset.value = 0
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
if image.volumetric:
expected = numpy.zeros_like(data, dtype=numpy.bool)
for index, plane in enumerate(data):
expected[index] = skimage.filters.threshold_adaptive(
image=plane,
block_size=7,
method="median",
offset=0
)
else:
expected = skimage.filters.threshold_adaptive(
image=data,
block_size=7,
method="median",
offset=0
)
numpy.testing.assert_array_equal(expected, actual)
def test_run_local_otsu(image, module, workspace):
module.x_name.value = "example"
module.local.value = True
module.local_operation.value = u"Otsu’s method"
module.radius.value = 8
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
disk = skimage.morphology.disk(8)
if image.volumetric:
expected = numpy.zeros_like(data)
for index, plane in enumerate(data):
expected[index] = skimage.filters.rank.otsu(plane, disk)
else:
expected = skimage.filters.rank.otsu(data, disk)
expected = data >= expected
numpy.testing.assert_array_equal(expected, actual)
def test_run_local_percentile(image, module, workspace):
module.x_name.value = "example"
module.local.value = True
module.local_operation.value = u"Percentile"
module.radius.value = 8
module.run(workspace)
output = workspace.image_set.get_image("Thresholding")
actual = output.pixel_data
data = skimage.img_as_ubyte(image.pixel_data)
disk = skimage.morphology.disk(8)
if image.volumetric:
expected = numpy.zeros_like(data)
for index, plane in enumerate(data):
expected[index] = skimage.filters.rank.percentile(plane, disk)
else:
expected = skimage.filters.rank.percentile(data, disk)
expected = data >= expected
numpy.testing.assert_array_equal(expected, actual)