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test_histogramequalization.py
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import cellprofiler.image
import numpy
import numpy.testing
import skimage.exposure
import histogramequalization
instance = histogramequalization.HistogramEqualization
def test_run(image, image_set, module, workspace):
module.x_name.value = "example"
module.y_name.value = "HistogramEqualization"
module.nbins.value = 256
module.mask.value = "Leave blank"
module.local.value = False
module.run(workspace)
actual = image_set.get_image("HistogramEqualization")
data = image.pixel_data
expected_data = skimage.exposure.equalize_hist(data)
expected = cellprofiler.image.Image(
image=expected_data,
parent_image=image,
dimensions=image.dimensions
)
numpy.testing.assert_array_equal(expected.pixel_data, actual.pixel_data)
def test_run_local(image, image_set, module, workspace):
module.x_name.value = "example"
module.y_name.value = "HistogramEqualization"
module.nbins.value = 256
module.local.value = True
module.run(workspace)
actual = image_set.get_image("HistogramEqualization")
data = image.pixel_data
if image.volumetric:
expected_data = numpy.zeros_like(data)
for index, plane in enumerate(data):
expected_data[index] = skimage.exposure.equalize_adapthist(plane, kernel_size=256)
else:
expected_data = skimage.exposure.equalize_adapthist(data, kernel_size=256)
expected = cellprofiler.image.Image(
image=expected_data,
parent_image=image,
dimensions=image.dimensions
)
numpy.testing.assert_array_equal(expected.pixel_data, actual.pixel_data)
def test_run_nbins(image, image_set, module, workspace):
module.x_name.value = "example"
module.y_name.value = "HistogramEqualization"
module.nbins.value = 128
module.local.value = False
module.mask.value = "Leave blank"
module.run(workspace)
actual = image_set.get_image("HistogramEqualization")
data = image.pixel_data
expected_data = skimage.exposure.equalize_hist(data, nbins=128)
expected = cellprofiler.image.Image(
image=expected_data,
parent_image=image,
dimensions=image.dimensions
)
numpy.testing.assert_array_equal(expected.pixel_data, actual.pixel_data)
def test_run_mask(image, image_set, module, workspace):
data = image.pixel_data
mask_data = numpy.zeros_like(data, dtype="bool")
if image.multichannel:
mask_data[5:-5, 5:-5, :] = True
elif image.dimensions == 3:
mask_data[:, 5:-5, 5:-5] = True
else:
mask_data[5:-5, 5:-5] = True
mask = cellprofiler.image.Image(
image=mask_data,
dimensions=image.dimensions
)
image_set.add("Mask", mask)
module.x_name.value = "example"
module.y_name.value = "HistogramEqualization"
module.nbins.value = 256
module.local.value = False
module.mask.value = "Mask"
module.run(workspace)
actual = image_set.get_image("HistogramEqualization")
expected_data = skimage.exposure.equalize_hist(data, mask=mask_data)
expected = cellprofiler.image.Image(
image=expected_data,
parent_image=image,
dimensions=image.dimensions
)
numpy.testing.assert_array_equal(expected.pixel_data, actual.pixel_data)