def test_invert_binary_invert():
#
# Regression for issue #1329
#
r = numpy.random.RandomState()
r.seed(1102)
m = cellprofiler.measurement.Measurements()
pixel_data = r.uniform(size=(20, 20)) > 0.5
m.add("inputimage", cellprofiler.image.Image(pixel_data))
module = cellprofiler.modules.imagemath.ImageMath()
module.images[0].image_name.value = "inputimage"
module.output_image_name.value = "intermediateimage"
module.operation.value = cellprofiler.modules.imagemath.O_INVERT
module.set_module_num(1)
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.add_module(module)
module = cellprofiler.modules.imagemath.ImageMath()
module.images[0].image_name.value = "intermediateimage"
module.output_image_name.value = "outputimage"
module.operation.value = cellprofiler.modules.imagemath.O_INVERT
module.set_module_num(2)
pipeline = cellprofiler.pipeline.Pipeline()
workspace = cellprofiler.workspace.Workspace(pipeline, module, m, None, m,
None)
for module in pipeline.modules():
module.run(workspace)
numpy.testing.assert_array_equal(
pixel_data,
m.get_image("inputimage").pixel_data > 0.5)
def make_workspace(labels,
operation,
iterations=1,
wants_outlines=False,
wants_fill_holes=False):
object_set = cellprofiler.object.ObjectSet()
objects = cellprofiler.object.Objects()
objects.segmented = labels
object_set.add_objects(objects, INPUT_NAME)
module = cellprofiler.modules.expandorshrinkobjects.ExpandOrShrink()
module.object_name.value = INPUT_NAME
module.output_object_name.value = OUTPUT_NAME
module.operation.value = operation
module.iterations.value = iterations
module.wants_fill_holes.value = wants_fill_holes
module.set_module_num(1)
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.add_module(module)
image_set_list = cellprofiler.image.ImageSetList()
workspace = cellprofiler.workspace.Workspace(
pipeline,
module,
image_set_list.get_image_set(0),
object_set,
cellprofiler.measurement.Measurements(),
image_set_list,
)
return workspace, module
def make_workspace(self, parents, children, fake_measurement=False):
"""Make a workspace for testing Relate"""
pipeline = cellprofiler.pipeline.Pipeline()
if fake_measurement:
class FakeModule(cellprofiler.module.Module):
def get_measurement_columns(self, pipeline):
return [
(
CHILD_OBJECTS,
MEASUREMENT,
cellprofiler.measurement.COLTYPE_FLOAT,
),
(
CHILD_OBJECTS,
IGNORED_MEASUREMENT,
cellprofiler.measurement.COLTYPE_INTEGER,
),
]
module = FakeModule()
module.set_module_num(1)
pipeline.add_module(module)
module = cellprofiler.modules.relateobjects.Relate()
module.x_name.value = PARENT_OBJECTS
module.y_name.value = CHILD_OBJECTS
module.find_parent_child_distances.value = (
cellprofiler.modules.relateobjects.D_NONE)
module.wants_child_objects_saved.value = False
new_module_num = 2 if fake_measurement else 1
module.set_module_num(new_module_num)
pipeline.add_module(module)
object_set = cellprofiler.object.ObjectSet()
image_set_list = cellprofiler.image.ImageSetList()
image_set = image_set_list.get_image_set(0)
m = cellprofiler.measurement.Measurements()
m.add_image_measurement(cellprofiler.measurement.GROUP_NUMBER, 1)
m.add_image_measurement(cellprofiler.measurement.GROUP_INDEX, 1)
workspace = cellprofiler.workspace.Workspace(pipeline, module,
image_set, object_set, m,
image_set_list)
o = cellprofiler.object.Objects()
if parents.shape[1] == 3:
# IJV format
o.ijv = parents
else:
o.segmented = parents
object_set.add_objects(o, PARENT_OBJECTS)
o = cellprofiler.object.Objects()
if children.shape[1] == 3:
o.ijv = children
else:
o.segmented = children
object_set.add_objects(o, CHILD_OBJECTS)
return workspace, module
def make_obj_workspace(ground_truth_obj, id_obj, ground_truth, id):
"""make a workspace to test comparing objects"""
""" ground truth object and ID object are dictionaires w/ the following keys"""
"""i - i component of pixel coordinates
j - j component of pixel coordinates
l - label """
module = cellprofiler.modules.measureobjectoverlap.MeasureObjectOverlap()
module.set_module_num(1)
module.object_name_GT.value = GROUND_TRUTH_OBJ
module.object_name_ID.value = ID_OBJ
module.wants_emd.value = True
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.RunExceptionEvent)
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cellprofiler.image.ImageSetList()
image_set = image_set_list.get_image_set(0)
for name, d in (
(GROUND_TRUTH_OBJ_IMAGE_NAME, ground_truth),
(ID_OBJ_IMAGE_NAME, id),
):
image = cellprofiler.image.Image(d["image"],
mask=d.get("mask"),
crop_mask=d.get("crop_mask"))
image_set.add(name, image)
object_set = cellprofiler.object.ObjectSet()
for name, d in ((GROUND_TRUTH_OBJ, ground_truth_obj), (ID_OBJ, id_obj)):
object = cellprofiler.object.Objects()
if d.shape[1] == 3:
object.ijv = d
else:
object.segmented = d
object_set.add_objects(object, name)
workspace = cellprofiler.workspace.Workspace(
pipeline,
module,
image_set,
object_set,
cellprofiler.measurement.Measurements(),
image_set_list,
)
return workspace, module
def make_workspace(ground_truth, test, dimensions=2):
"""Make a workspace with a ground-truth image and a test image
ground_truth and test are dictionaries with the following keys:
image - the pixel data
mask - (optional) the mask data
crop_mask - (optional) a cropping mask
returns a workspace and module
"""
module = cellprofiler.modules.measureimageoverlap.MeasureImageOverlap()
module.set_module_num(1)
module.ground_truth.value = GROUND_TRUTH_IMAGE_NAME
module.test_img.value = TEST_IMAGE_NAME
module.wants_emd.value = True
pipeline = cellprofiler.pipeline.Pipeline()
def callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.RunExceptionEvent)
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cellprofiler.image.ImageSetList()
image_set = image_set_list.get_image_set(0)
for name, d in ((GROUND_TRUTH_IMAGE_NAME, ground_truth), (TEST_IMAGE_NAME,
test)):
image = cellprofiler.image.Image(
d["image"],
mask=d.get("mask"),
crop_mask=d.get("crop_mask"),
dimensions=dimensions,
)
image_set.add(name, image)
workspace = cellprofiler.workspace.Workspace(
pipeline,
module,
image_set,
cellprofiler.object.ObjectSet(),
cellprofiler.measurement.Measurements(),
image_set_list,
)
return workspace, module
def run_imagemath(images, modify_module_fn=None, measurement=None):
"""Run the ImageMath module, returning the image created
images - a list of dictionaries. The dictionary has keys:
pixel_data - image pixel data
mask - mask for image
cropping - cropping mask for image
modify_module_fn - a function of the signature, fn(module)
that allows the test to modify the module.
measurement - an image measurement value
"""
image_set_list = cellprofiler.image.ImageSetList()
image_set = image_set_list.get_image_set(0)
module = cellprofiler.modules.imagemath.ImageMath()
module.set_module_num(1)
for i, image in enumerate(images):
pixel_data = image["pixel_data"]
mask = image.get("mask", None)
cropping = image.get("cropping", None)
if i >= 2:
module.add_image()
name = "inputimage%s" % i
module.images[i].image_name.value = name
img = cellprofiler.image.Image(pixel_data,
mask=mask,
crop_mask=cropping)
image_set.add(name, img)
module.output_image_name.value = "outputimage"
if modify_module_fn is not None:
modify_module_fn(module)
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.add_module(module)
measurements = cellprofiler.measurement.Measurements()
if measurement is not None:
measurements.add_image_measurement(MEASUREMENT_NAME, str(measurement))
workspace = cellprofiler.workspace.Workspace(
pipeline,
module,
image_set,
cellprofiler.object.ObjectSet(),
measurements,
image_set_list,
)
module.run(workspace)
return image_set.get_image("outputimage")
def make_pipeline(csv_text, name=None):
if name is None:
handle, name = tempfile.mkstemp(".csv")
fd = os.fdopen(handle, "w")
else:
fd = open(name, "w")
fd.write(csv_text)
fd.close()
csv_path, csv_file = os.path.split(name)
module = cellprofiler.modules.loaddata.LoadText()
module.csv_directory.dir_choice = cellprofiler.setting.ABSOLUTE_FOLDER_NAME
module.csv_directory.custom_path = csv_path
module.csv_file_name.value = csv_file
module.set_module_num(1)
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.add_module(module)
def error_callback(caller, event):
assert not isinstance(event, cellprofiler.pipeline.RunExceptionEvent)
pipeline.add_listener(error_callback)
return pipeline, module, name
def test_add_and_do_nothing():
#
# Regression for issue #1333 - add one, do nothing, input image
# is changed
#
r = numpy.random.RandomState()
r.seed(1101)
m = cellprofiler.measurement.Measurements()
pixel_data = r.uniform(size=(20, 20))
m.add("inputimage", cellprofiler.image.Image(pixel_data))
module = cellprofiler.modules.imagemath.ImageMath()
module.images[0].image_name.value = "inputimage"
module.output_image_name.value = "outputimage"
module.operation.value = cellprofiler.modules.imagemath.O_NONE
module.addend.value = 0.5
module.set_module_num(1)
pipeline = cellprofiler.pipeline.Pipeline()
pipeline.add_module(module)
workspace = cellprofiler.workspace.Workspace(pipeline, module, m, None, m,
None)
module.run(workspace)
numpy.testing.assert_array_almost_equal(
pixel_data,
m.get_image("inputimage").pixel_data)
def test_module_must_be_last():
"""Make sure that the pipeline is invalid if CreateBatchFiles is not last"""
#
# First, make sure that a naked CPModule tests valid
#
pipeline = cellprofiler.pipeline.Pipeline()
module = cellprofiler.module.Module()
module.set_module_num(len(pipeline.modules()) + 1)
pipeline.add_module(module)
pipeline.test_valid()
#
# Make sure that CreateBatchFiles on its own tests valid
#
pipeline = cellprofiler.pipeline.Pipeline()
module = cellprofiler.modules.createbatchfiles.CreateBatchFiles()
module.set_module_num(len(pipeline.modules()) + 1)
pipeline.add_module(module)
pipeline.test_valid()
module = cellprofiler.module.Module()
module.set_module_num(len(pipeline.modules()) + 1)
pipeline.add_module(module)
with pytest.raises(cellprofiler.setting.ValidationError):
pipeline.test_valid()
def loader(module_num, mn=mn):
module = cellprofiler.modules.instantiate_module(mn)
module.set_module_num(module_num)
return module
def loader(module_num, mn=mn):
module = cellprofiler.modules.instantiate_module(mn)
module.set_module_num(module_num)
return module