def test_crop_size_larger_than_image(self):
data = np.random.random((8, 4, 64, 56))
seg = np.ones(data.shape)
d, s = random_crop(data, seg, 96, 0)
self.assertTrue(all(i == j for i, j in zip((8, 4, 96, 96), d.shape)), "data has unexpected return shape")
self.assertTrue(all(i == j for i, j in zip((8, 4, 96, 96), s.shape)), "seg has unexpected return shape")
self.assertNotEqual(np.sum(s == 0), 0, "seg was not padded properly")
def __call__(self, **data_dict):
data = data_dict.get(self.data_key)
seg = data_dict.get(self.label_key)
data, seg = random_crop(data, seg, self.crop_size, self.margins)
data_dict[self.data_key] = data
if seg is not None:
data_dict[self.label_key] = seg
return data_dict
def test_random_crop_2D_from_List(self):
data = [np.random.random((4, 64+i, 56+i)) for i in range(32)]
seg = [np.random.random((4, 64+i, 56+i)) for i in range(32)]
d, s = random_crop(data, seg, 32, 0)
self.assertTrue(all(i == j for i, j in zip((32, 4, 32, 32), d.shape)), "data has unexpected return shape")
self.assertTrue(all(i == j for i, j in zip((32, 4, 32, 32), s.shape)), "seg has unexpected return shape")
self.assertEqual(np.sum(s == 0), 0, "Zeros encountered in seg meaning that we did padding which should not have"
" happened here!")
def test_random_crop_2D(self):
data = np.random.random((32, 4, 64, 56))
seg = np.ones(data.shape)
d, s = random_crop(data, seg, 32, 0)
self.assertTrue(all(i == j for i, j in zip((32, 4, 32, 32), d.shape)), "data has unexpected return shape")
self.assertTrue(all(i == j for i, j in zip((32, 4, 32, 32), s.shape)), "seg has unexpected return shape")
self.assertEqual(np.sum(s == 0), 0, "Zeros encountered in seg meaning that we did padding which should not have"
" happened here!")
def __call__(self, **data_dict):
data = data_dict.get(self.data_key)
seg = data_dict.get(self.label_key)
data, seg = random_crop(data, seg, self.crop_size, self.margins)
data_dict[self.data_key] = data
if seg is not None:
data_dict[self.label_key] = seg
return data_dict
def __call__(self, **data_dict):
data = data_dict.get("data")
seg = data_dict.get("seg")
data, seg = random_crop(data, seg, self.crop_size, self.margins)
data_dict["data"] = data
if seg is not None:
data_dict["seg"] = seg
return data_dict
def test_randomness_2(self):
data = np.random.random((1, 1, 30, 30, 30))
crop_size = (16, 18, 7)
margin = (-4, -6, 5)
sums = [] # these should always be different
for _ in range(50):
data_cropped, _ = random_crop(data, crop_size=crop_size, margins=margin)
s = np.sum(data_cropped)
sums.append(s)
assert len(np.unique(sums)) == 50
def test_randomness_1(self):
data = np.ones((1, 2, 30, 30, 30))
crop_size = (16, 16, 16)
margin = (-4, -4, -4)
sums = [] # these should always be different
for _ in range(50):
data_cropped, _ = random_crop(data, crop_size=crop_size, margins=margin)
s = np.sum(data_cropped[0, 0, 8, 8, :])
assert 12 <= s <= 16
sums.append(s)
assert len(np.unique(sums)) != 0
def test_random_crop_with_cropsize_larger_image(self):
'''
should fall back to center crop
:return:
'''
data = [np.random.random((4, 64+i, 56+i)) for i in range(32)]
seg = [np.random.random((4, 64+i, 56+i)) for i in range(32)]
d, s = random_crop(data, seg, 32, 32)
self.assertTrue(all(i == j for i, j in zip((32, 4, 32, 32), d.shape)), "data has unexpected return shape")
self.assertTrue(all(i == j for i, j in zip((32, 4, 32, 32), s.shape)), "seg has unexpected return shape")
self.assertEqual(np.sum(s == 0), 0, "Zeros encountered in seg meaning that we did padding which should not have"
" happened here!")
def generate_train_batch(self):
if not self.was_initialized:
self.initialize()
if self._current_position >= len(self._data):
self._reset()
self._current_epoch += 1
if 0 < self.epochs <= self._current_epoch:
raise StopIteration
data_batch = []
vector_batch = None if self.vector_generator is None else []
label_batch = []
loaded_cts = {}
for i in range(self.batch_size):
index = self._current_position + i
if index < len(self._data):
label, data, segmentation_file = self._data[index]
data_file = data if self.volumetric else data[0]
slice = None if self.volumetric else data[1]
if data_file in loaded_cts:
data_np = loaded_cts[data_file]
else:
data_sitk = sitk.ReadImage(data_file)
data_np = np.expand_dims(sitk.GetArrayFromImage(data_sitk).transpose(), axis=0)
if self.normalization_range is not None:
data_np = normalize(data_np, self.normalization_range, [0, 1])
loaded_cts[data_file] = data_np
if slice is not None:
data_np = data_np[:, :, :, slice]
visualize_data(data_np)
vector_gen_args = {"input_shape_pre": data_np.shape[-3:]}
if self.mode == DataLoader.Mode.RESIZE:
data_np = augment_resize(data_np, None, self.input_shape)[0].squeeze(0)
data_np = np.expand_dims(data_np, 0)
vector_gen_args["input_shape_post"] = self.input_shape
elif self.mode == DataLoader.Mode.SAMPLE:
data_np = np.expand_dims(data_np, 0)
with RNGContext(self.seed):
data_np = random_crop(data_np, crop_size=self.input_shape)[0].squeeze(0)
vector_gen_args["input_shape_post"] = self.input_shape
if self.vector_generator is not None:
vector_batch.append(self.vector_generator(**vector_gen_args))
data_batch.append(data_np)
label_batch.append(label)
batch = {"data": np.stack(data_batch),
"vector": None if vector_batch is None else np.stack(vector_batch),
"label": np.stack(label_batch)}
self._current_position += self.number_of_threads_in_multithreaded * self.batch_size
return batch
def random_crop_generator(generator, crop_size=128, margins=(0, 0, 0)):
warn("using deprecated generator random_crop_generator", Warning)
'''
yields a random crop of size crop_size, crop_size may be a tuple with one entry for each dimension of your data (2D/3D)
:param margins: allows to give cropping margins measured symmetrically from the image boundaries, which
restrict the 'box' from which to randomly crop
'''
for data_dict in generator:
assert "data" in list(
data_dict.keys()), "your data generator needs to return a python dictionary with at least a 'data' key value pair"
data = data_dict["data"]
seg = None
if "seg" in list(data_dict.keys()):
seg = data_dict["seg"]
data, seg = random_crop(data, seg, crop_size, margins)
data_dict["data"] = data
if seg is not None:
data_dict["seg"] = seg
yield data_dict
def random_crop_generator(generator, crop_size=128, margins=(0, 0, 0)):
warn("using deprecated generator random_crop_generator", Warning)
'''
yields a random crop of size crop_size, crop_size may be a tuple with one entry for each dimension of your data (2D/3D)
:param margins: allows to give cropping margins measured symmetrically from the image boundaries, which
restrict the 'box' from which to randomly crop
'''
for data_dict in generator:
assert "data" in list(
data_dict.keys()
), "your data generator needs to return a python dictionary with at least a 'data' key value pair"
data = data_dict["data"]
seg = None
if "seg" in list(data_dict.keys()):
seg = data_dict["seg"]
data, seg = random_crop(data, seg, crop_size, margins)
data_dict["data"] = data
if seg is not None:
data_dict["seg"] = seg
yield data_dict
