def make_workspace(self, classes):
module = C.ClassifyPixels()
module.module_num = 1
module.image_name.value = INPUT_IMAGE_NAME
path, filename = os.path.split(self.classifier_file)
module.h5_directory.dir_choice = C.ABSOLUTE_FOLDER_NAME
module.h5_directory.custom_path = path
module.classifier_file_name.value = filename
module.probability_maps[
0].output_image.value = get_output_image_name(0)
module.probability_maps[0].class_sel.value = classes[0]
for i, class_sel in enumerate(classes):
module.add_probability_map()
module.probability_maps[
i + 1].output_image.value = get_output_image_name(i + 1)
module.probability_maps[i + 1].class_sel.value = class_sel
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
r = np.random.RandomState()
r.seed(0)
pixels = r.uniform(size=(64, 72))
image_set.add(INPUT_IMAGE_NAME, cpi.Image(pixels, scale=255))
workspace = cpw.Workspace(pipeline, module, image_set,
cpo.ObjectSet(), cpmeas.Measurements(),
image_set_list)
return workspace, module
def test_03_01_prepare_to_create_batch(self):
module = C.ClassifyPixels()
module.h5_directory.dir_choice = C.ABSOLUTE_FOLDER_NAME
module.h5_directory.custom_path = "/foo"
def fn_alter_path(path, *args, **kwargs):
self.assertEqual(path, "/foo")
return "/bar"
module.prepare_to_create_batch(None, fn_alter_path)
self.assertEqual(module.h5_directory.custom_path, "/bar")