def make_workspace_ijv(self):
module = C.ConvertToImage()
shape = (14, 16)
r = np.random.RandomState()
r.seed(0)
i = r.randint(0, shape[0], size=np.prod(shape))
j = r.randint(0, shape[1], size=np.prod(shape))
v = r.randint(1, 8, size=np.prod(shape))
order = np.lexsort((i, j, v))
ijv = np.column_stack((i, j, v))
ijv = ijv[order, :]
same = np.all(ijv[:-1, :] == ijv[1:, :], 1)
ijv = ijv[~same, :]
pipeline = cpp.Pipeline()
object_set = cpo.ObjectSet()
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
workspace = cpw.Workspace(pipeline, module, image_set, object_set,
cpmeas.Measurements(), image_set_list)
objects = cpo.Objects()
objects.set_ijv(ijv, shape)
object_set.add_objects(objects, OBJECTS_NAME)
self.assertGreater(len(objects.get_labels()), 1)
module.image_name.value = IMAGE_NAME
module.object_name.value = OBJECTS_NAME
return (workspace, module, ijv)
def make_workspace(self):
module = C.ConvertToImage()
labels = np.reshape(np.arange(256), (16, 16))
pipeline = cpp.Pipeline()
object_set = cpo.ObjectSet()
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
workspace = cpw.Workspace(pipeline, module, image_set, object_set,
cpmeas.Measurements(), image_set_list)
objects = cpo.Objects()
objects.segmented = labels
object_set.add_objects(objects, OBJECTS_NAME)
module.image_name.value = IMAGE_NAME
module.object_name.value = OBJECTS_NAME
return (workspace, module)
