def test_PREPROCESSOR_postprocessing_(self):
sample_list = self.data_io3D.get_indiceslist()
pp = Preprocessor(self.data_io3D,
batch_size=1,
analysis="fullimage",
data_aug=None)
batches = pp.run(sample_list[0:3], training=True, validation=False)
for i in range(0, 3):
pred_postprec = pp.postprocessing(sample_list[i], batches[i][1])
self.assertEqual(pred_postprec.shape, (16, 16, 16))
sam = self.data_io3D.sample_loader(sample_list[i], load_seg=True)
self.assertTrue(
np.array_equal(pred_postprec,
np.reshape(sam.seg_data, (16, 16, 16))))
def test_SUBFUNCTIONS_postprocessing(self):
ds = dict()
for i in range(0, 10):
img = np.random.rand(16, 16, 16) * 255
img = img.astype(int)
seg = np.random.rand(16, 16, 16) * 3
seg = seg.astype(int)
sample = (img, seg)
ds["TEST.sample_" + str(i)] = sample
io_interface = Dictionary_interface(ds, classes=3, three_dim=True)
self.tmp_dir = tempfile.TemporaryDirectory(prefix="tmp.CESP.")
tmp_batches = os.path.join(self.tmp_dir.name, "batches")
dataio = Data_IO(io_interface,
input_path="",
output_path="",
batch_path=tmp_batches,
delete_batchDir=False)
sf = [Resize((9, 9, 9)), Normalization(), Clipping(min=-1.0, max=0.0)]
pp = Preprocessor(dataio,
batch_size=1,
prepare_subfunctions=False,
analysis="patchwise-grid",
subfunctions=sf,
patch_shape=(4, 4, 4))
sample_list = dataio.get_indiceslist()
for index in sample_list:
sample = dataio.sample_loader(index)
for sf in pp.subfunctions:
sf.preprocessing(sample, training=False)
pp.cache["shape_" + str(index)] = sample.img_data.shape
sample.seg_data = np.random.rand(9, 9, 9) * 3
sample.seg_data = sample.seg_data.astype(int)
sample.seg_data = to_categorical(sample.seg_data, num_classes=3)
data_patches = pp.analysis_patchwise_grid(sample,
training=True,
data_aug=False)
seg_list = []
for i in range(0, len(data_patches)):
seg_list.append(data_patches[i][1])
seg = np.stack(seg_list, axis=0)
self.assertEqual(seg.shape, (27, 4, 4, 4, 3))
pred = pp.postprocessing(index, seg)
self.assertEqual(pred.shape, (16, 16, 16))
self.tmp_dir.cleanup()
