def test_convert_to_coco(self):
DatasetCatalog.register("test_dataset",
lambda: TestConvertCOCO.generate_data())
MetadataCatalog.get("test_dataset").set(thing_classes=["test_label"])
convert_to_coco_dict("test_dataset")
DatasetCatalog.pop("test_dataset")
MetadataCatalog.pop("test_dataset")
def test(self):
# Make a dummy dataset.
mask = make_mask()
DatasetCatalog.register("test_dataset",
lambda: make_dataset_dicts(mask))
MetadataCatalog.get("test_dataset").set(thing_classes=["test_label"])
# Dump to json.
json_dict = convert_to_coco_dict("test_dataset")
with tempfile.TemporaryDirectory() as tmpdir:
json_file_name = os.path.join(tmpdir, "test.json")
with open(json_file_name, "w") as f:
json.dump(json_dict, f)
# Load from json.
dicts = load_coco_json(json_file_name, "")
# Check the loaded mask matches the original.
anno = dicts[0]["annotations"][0]
loaded_mask = mask_util.decode(anno["segmentation"])
self.assertTrue(np.array_equal(loaded_mask, mask))
DatasetCatalog.pop("test_dataset")
MetadataCatalog.pop("test_dataset")
def cleanup(self):
# remove temporarily registered dataset and json file
DatasetCatalog.pop(self.new_ds_name, None)
MetadataCatalog.pop(self.new_ds_name, None)
if hasattr(self, "tmp_dir"):
shutil.rmtree(self.tmp_dir)
