def test_load_objects():
r = numpy.random.RandomState()
r.seed(1101)
labels = r.randint(0, 10, size=(30, 20)).astype(numpy.uint8)
handle, name = tempfile.mkstemp(".png")
bioformats.write_image(name, labels, bioformats.PT_UINT8)
os.close(handle)
png_path, png_file = os.path.split(name)
sbs_dir = os.path.join(tests.modules.example_images_directory(),
"ExampleSBSImages")
csv_text = """%s_%s,%s_%s,%s_DNA,%s_DNA
%s,%s,Channel2-01-A-01.tif,%s
""" % (
cellprofiler_core.measurement.C_OBJECTS_FILE_NAME,
OBJECTS_NAME,
cellprofiler_core.measurement.C_OBJECTS_PATH_NAME,
OBJECTS_NAME,
cellprofiler_core.measurement.C_FILE_NAME,
cellprofiler_core.measurement.C_PATH_NAME,
png_file,
png_path,
sbs_dir,
)
pipeline, module, csv_name = make_pipeline(csv_text)
assert isinstance(pipeline, cellprofiler_core.pipeline.Pipeline)
assert isinstance(module, cellprofiler.modules.loaddata.LoadData)
module.wants_images.value = True
try:
image_set_list = cellprofiler_core.image.ImageSetList()
measurements = cellprofiler_core.measurement.Measurements()
workspace = cellprofiler_core.workspace.Workspace(
pipeline, module, None, None, measurements, image_set_list)
pipeline.prepare_run(workspace)
key_names, g = pipeline.get_groupings(workspace)
assert len(g) == 1
module.prepare_group(workspace, g[0][0], g[0][1])
image_set = image_set_list.get_image_set(g[0][1][0] - 1)
object_set = cellprofiler_core.object.ObjectSet()
workspace = cellprofiler_core.workspace.Workspace(
pipeline, module, image_set, object_set, measurements,
image_set_list)
module.run(workspace)
objects = object_set.get_objects(OBJECTS_NAME)
assert numpy.all(objects.segmented == labels)
assert (measurements.get_current_image_measurement(
cellprofiler_core.measurement.FF_COUNT % OBJECTS_NAME) == 9)
for feature in (
cellprofiler_core.measurement.M_LOCATION_CENTER_X,
cellprofiler_core.measurement.M_LOCATION_CENTER_Y,
cellprofiler_core.measurement.M_NUMBER_OBJECT_NUMBER,
):
value = measurements.get_current_measurement(OBJECTS_NAME, feature)
assert len(value) == 9
finally:
bioformats.formatreader.clear_image_reader_cache()
os.remove(name)
os.remove(csv_name)
def test_divide_with_clip():
"""Test correction by division"""
numpy.random.seed(0)
image = numpy.random.uniform(size=(10, 10)).astype(numpy.float32)
illum = numpy.random.uniform(size=(10, 10)).astype(numpy.float32)
expected = image / illum
expected = numpy.where(expected < 0, 0, expected)
expected = numpy.where(expected > 1, 1, expected)
pipeline = cellprofiler_core.pipeline.Pipeline()
pipeline.add_listener(error_callback)
input_module = cellprofiler_core.modules.injectimage.InjectImage(
"InputImage", image
)
input_module.set_module_num(1)
pipeline.add_module(input_module)
illum_module = cellprofiler_core.modules.injectimage.InjectImage(
"IllumImage", illum
)
illum_module.set_module_num(2)
pipeline.add_module(illum_module)
module = cellprofiler.modules.correctilluminationapply.CorrectIlluminationApply()
module.set_module_num(3)
pipeline.add_module(module)
image = module.images[0]
image.image_name.value = "InputImage"
image.illum_correct_function_image_name.value = "IllumImage"
image.corrected_image_name.value = "OutputImage"
image.divide_or_subtract.value = (
cellprofiler.modules.correctilluminationapply.DOS_DIVIDE
)
image.rescale_option = cellprofiler.modules.correctilluminationapply.RE_NONE
image_set_list = cellprofiler_core.image.ImageSetList()
measurements = cellprofiler_core.measurement.Measurements()
workspace = cellprofiler_core.workspace.Workspace(
pipeline, None, None, None, measurements, image_set_list
)
pipeline.prepare_run(workspace)
input_module.prepare_group(workspace, {}, [1])
illum_module.prepare_group(workspace, {}, [1])
module.prepare_group(workspace, {}, [1])
image_set = image_set_list.get_image_set(0)
object_set = cellprofiler_core.object.ObjectSet()
workspace = cellprofiler_core.workspace.Workspace(
pipeline, input_module, image_set, object_set, measurements, image_set_list
)
input_module.run(workspace)
illum_module.run(workspace)
module.run(workspace)
output_image = workspace.image_set.get_image("OutputImage")
assert numpy.all(output_image.pixel_data == expected)
def test_color_by_bw():
"""Correct a color image with a black & white illumination fn"""
numpy.random.seed(0)
image = numpy.random.uniform(size=(10, 10, 3)).astype(numpy.float32)
illum = numpy.random.uniform(size=(10, 10)).astype(numpy.float32)
expected = image - illum[:, :, numpy.newaxis]
expected[expected < 0] = 0
pipeline = cellprofiler_core.pipeline.Pipeline()
pipeline.add_listener(error_callback)
input_module = cellprofiler_core.modules.injectimage.InjectImage(
"InputImage", image
)
input_module.set_module_num(1)
pipeline.add_module(input_module)
illum_module = cellprofiler_core.modules.injectimage.InjectImage(
"IllumImage", illum
)
illum_module.set_module_num(2)
pipeline.add_module(illum_module)
module = cellprofiler.modules.correctilluminationapply.CorrectIlluminationApply()
module.set_module_num(3)
pipeline.add_module(module)
image = module.images[0]
image.image_name.value = "InputImage"
image.illum_correct_function_image_name.value = "IllumImage"
image.corrected_image_name.value = "OutputImage"
image.divide_or_subtract.value = (
cellprofiler.modules.correctilluminationapply.DOS_SUBTRACT
)
image.rescale_option = cellprofiler.modules.correctilluminationapply.RE_NONE
measurements = cellprofiler_core.measurement.Measurements()
image_set_list = cellprofiler_core.image.ImageSetList()
measurements = cellprofiler_core.measurement.Measurements()
workspace = cellprofiler_core.workspace.Workspace(
pipeline, None, None, None, measurements, image_set_list
)
pipeline.prepare_run(workspace)
input_module.prepare_group(workspace, {}, [1])
illum_module.prepare_group(workspace, {}, [1])
module.prepare_group(workspace, {}, [1])
image_set = image_set_list.get_image_set(0)
object_set = cellprofiler_core.object.ObjectSet()
workspace = cellprofiler_core.workspace.Workspace(
pipeline, input_module, image_set, object_set, measurements, image_set_list
)
input_module.run(workspace)
illum_module.run(workspace)
module.run(workspace)
output_image = workspace.image_set.get_image("OutputImage")
assert numpy.all(output_image.pixel_data == expected)
