def test_stitcher(self):
# Stitch a test TIFF dataset
inputDataset = TiffVolume(
os.path.join(IMAGE_PATH, "inputs.tif"),
BoundingBox(Vector(0, 0, 0), Vector(1024, 512, 50)))
outputDataset = Array(
np.zeros(inputDataset.getBoundingBox().getNumpyDim()))
inputDataset.__enter__()
for data in inputDataset:
outputDataset.blend(data)
self.assertTrue(
(inputDataset[20].getArray() == outputDataset[20].getArray()).all,
"Blending output does not match input")
tif.imsave(os.path.join(IMAGE_PATH, "test_stitch.tif"),
outputDataset[100].getArray().astype(np.uint16))
def test_prediction(self):
if not os.path.isdir('./tests/checkpoints'):
os.mkdir('tests/checkpoints')
net = RSUNet()
checkpoint = './tests/checkpoints/iteration_10.ckpt'
inputs_dataset = TiffVolume(
os.path.join(IMAGE_PATH, "inputs.tif"),
BoundingBox(Vector(0, 0, 0), Vector(1024, 512, 50)))
inputs_dataset.__enter__()
predictor = Predictor(net, checkpoint, gpu_device=1)
output_volume = Array(
np.zeros(inputs_dataset.getBoundingBox().getNumpyDim()))
predictor.run(inputs_dataset, output_volume, batch_size=5)
tif.imsave(os.path.join(IMAGE_PATH, "test_prediction.tif"),
output_volume.getArray().astype(np.float32))