def __call__(self, data):
d = dict(data)
box_start, box_end = generate_spatial_bounding_box(
d[self.source_key], self.select_fn, self.channel_indices, self.margin
)
center = list(np.mean([box_start, box_end], axis=0).astype(int))
current_size = list(np.subtract(box_end, box_start).astype(int))
if np.all(np.less(current_size, self.spatial_size)):
cropper = SpatialCrop(roi_center=center, roi_size=self.spatial_size)
box_start = cropper.roi_start
box_end = cropper.roi_end
else:
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
for key in self.key_iterator(d):
meta_key = f"{key}_{self.meta_key_postfix}"
d[meta_key][self.start_coord_key] = box_start
d[meta_key][self.end_coord_key] = box_end
d[meta_key][self.original_shape_key] = d[key].shape
image = cropper(d[key])
d[meta_key][self.cropped_shape_key] = image.shape
d[key] = image
return d
def __call__(self, data):
d = dict(data)
box_start, box_end = generate_spatial_bounding_box(
d[self.source_key], self.select_fn, self.channel_indices,
self.margin, self.allow_smaller)
center = list(
np.mean([box_start, box_end], axis=0).astype(int, copy=False))
current_size = list(
np.subtract(box_end, box_start).astype(int, copy=False))
if np.all(np.less(current_size, self.spatial_size)):
cropper = SpatialCrop(roi_center=center,
roi_size=self.spatial_size)
box_start = np.array([s.start for s in cropper.slices])
box_end = np.array([s.stop for s in cropper.slices])
else:
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
for key, meta_key, meta_key_postfix in self.key_iterator(
d, self.meta_keys, self.meta_key_postfix):
meta_key = meta_key or f"{key}_{meta_key_postfix}"
d[meta_key][self.start_coord_key] = box_start
d[meta_key][self.end_coord_key] = box_end
d[meta_key][self.original_shape_key] = d[key].shape
image = cropper(d[key])
d[meta_key][self.cropped_shape_key] = image.shape
d[key] = image
return d
def __call__(self, data):
d = dict(data)
box_start, box_end = \
generate_spatial_bounding_box(data[self.source_key], self.select_fn, self.channel_indexes, self.margin)
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
for key in self.keys:
d[key] = cropper(d[key])
return d
def __call__(self, img: np.ndarray) -> np.ndarray:
"""
Apply the transform to `img`, assuming `img` is channel-first and
slicing doesn't change the channel dim.
"""
box_start, box_end = generate_spatial_bounding_box(
img, self.select_fn, self.channel_indices, self.margin)
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
return cropper(img)
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
d = dict(data)
box_start, box_end = generate_spatial_bounding_box(
d[self.source_key], self.select_fn, self.channel_indices, self.margin
)
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
for key in self.keys:
d[key] = cropper(d[key])
return d
def __call__(self, img: np.ndarray) -> np.ndarray:
"""
See also: :py:class:`monai.transforms.utils.generate_spatial_bounding_box`.
"""
bbox = []
for channel in range(img.shape[0]):
start_, end_ = generate_spatial_bounding_box(img, select_fn=self.select_fn, channel_indices=channel)
bbox.append([i for k in zip(start_, end_) for i in k])
return np.stack(bbox, axis=0)
def get_center_pos(self, mask_data, z_axis):
if self.center_mode == "center":
starts, ends = generate_spatial_bounding_box(
mask_data, lambda x: x > 0)
return tuple((st + ed) // 2 for st, ed in zip(starts, ends))
elif self.center_mode == "maximum":
axes = np.delete(np.arange(3), z_axis)
mask_data_ = mask_data.squeeze()
z_index = np.argmax(np.count_nonzero(mask_data_, axis=tuple(axes)))
if z_index == 0 and self.crop_mode == "parallel":
z_index = (self.n_slices - 1) // 2
elif (z_index == mask_data_.shape[z_axis] - 1
and self.crop_mode == "parallel"):
z_index -= (self.n_slices - 1) // 2
starts, ends = generate_spatial_bounding_box(
np.take(mask_data_, z_index, z_axis)[np.newaxis, ...],
lambda x: x > 0)
centers = [(st + ed) // 2 for st, ed in zip(starts, ends)]
centers.insert(z_axis, z_index)
return tuple(centers)
def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
d = dict(data)
box_start, box_end = generate_spatial_bounding_box(
d[self.source_key], self.select_fn, self.channel_indices, self.margin
)
d[self.start_coord_key] = np.asarray(box_start)
d[self.end_coord_key] = np.asarray(box_end)
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
for key in self.key_iterator(d):
self.push_transform(d, key, extra_info={"box_start": box_start, "box_end": box_end})
d[key] = cropper(d[key])
return d
def __call__(self, data):
# load data
d = dict(data)
image = d["image"]
image_spacings = d["image_meta_dict"]["pixdim"][1:4].tolist()
if "label" in self.keys:
label = d["label"]
label[label < 0] = 0
if self.training:
# only task 04 does not be impacted
cropped_data = self.crop_foreg({"image": image, "label": label})
image, label = cropped_data["image"], cropped_data["label"]
else:
d["original_shape"] = np.array(image.shape[1:])
box_start, box_end = generate_spatial_bounding_box(image)
image = SpatialCrop(roi_start=box_start, roi_end=box_end)(image)
d["bbox"] = np.vstack([box_start, box_end])
d["crop_shape"] = np.array(image.shape[1:])
original_shape = image.shape[1:]
# calculate shape
resample_flag = False
anisotrophy_flag = False
if self.target_spacing != image_spacings:
# resample
resample_flag = True
resample_shape = self.calculate_new_shape(image_spacings,
original_shape)
anisotrophy_flag = self.check_anisotrophy(image_spacings)
image = resample_image(image, resample_shape, anisotrophy_flag)
if self.training:
label = resample_label(label, resample_shape, anisotrophy_flag)
d["resample_flag"] = resample_flag
d["anisotrophy_flag"] = anisotrophy_flag
# clip image for CT dataset
if self.low != 0 or self.high != 0:
image = np.clip(image, self.low, self.high)
image = (image - self.mean) / self.std
else:
image = self.normalize_intensity(image.copy())
d["image"] = image
if "label" in self.keys:
d["label"] = label
return d
def get_center_pos_(self, mask_data):
axes = np.delete(np.arange(3), self.z_axis)
starts, ends = generate_spatial_bounding_box(mask_data,
lambda x: x > 0)
z_start, z_end = (
starts[self.z_axis] + (self.n_slices - 1) // 2,
ends[self.z_axis] - (self.n_slices - 1) // 2,
)
centers = []
for z in np.arange(z_start, z_end):
center = [(st + ed) // 2 for st, ed in zip(
np.array(starts)[axes],
np.array(ends)[axes])]
center.insert(self.z_axis, z)
centers.append(tuple(center))
return centers
def compute_bounding_box(self, img: np.ndarray):
"""
Compute the start points and end points of bounding box to crop.
And adjust bounding box coords to be divisible by `k`.
"""
box_start, box_end = generate_spatial_bounding_box(
img, self.select_fn, self.channel_indices, self.margin)
box_start_ = np.asarray(box_start, dtype=np.int16)
box_end_ = np.asarray(box_end, dtype=np.int16)
orig_spatial_size = box_end_ - box_start_
# make the spatial size divisible by `k`
spatial_size = np.asarray(
compute_divisible_spatial_size(spatial_shape=orig_spatial_size,
k=self.k_divisible))
# update box_start and box_end
box_start_ = box_start_ - np.floor_divide(
np.asarray(spatial_size) - orig_spatial_size, 2)
box_end_ = box_start_ + spatial_size
return box_start_, box_end_
def get_mask_edges(
seg_pred: Union[np.ndarray, torch.Tensor],
seg_gt: Union[np.ndarray, torch.Tensor],
label_idx: int = 1,
crop: bool = True,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Do binary erosion and use XOR for input to get the edges. This
function is helpful to further calculate metrics such as Average Surface
Distance and Hausdorff Distance.
The input images can be binary or labelfield images. If labelfield images
are supplied, they are converted to binary images using `label_idx`.
`scipy`'s binary erosion is used to calculate the edges of the binary
labelfield.
In order to improve the computing efficiency, before getting the edges,
the images can be cropped and only keep the foreground if not specifies
``crop = False``.
We require that images are the same size, and assume that they occupy the
same space (spacing, orientation, etc.).
Args:
seg_pred: the predicted binary or labelfield image.
seg_gt: the actual binary or labelfield image.
label_idx: for labelfield images, convert to binary with
`seg_pred = seg_pred == label_idx`.
crop: crop input images and only keep the foregrounds. In order to
maintain two inputs' shapes, here the bounding box is achieved
by ``(seg_pred | seg_gt)`` which represents the union set of two
images. Defaults to ``True``.
"""
# Get both labelfields as np arrays
if isinstance(seg_pred, torch.Tensor):
seg_pred = seg_pred.detach().cpu().numpy()
if isinstance(seg_gt, torch.Tensor):
seg_gt = seg_gt.detach().cpu().numpy()
if seg_pred.shape != seg_gt.shape:
raise ValueError(
f"seg_pred and seg_gt should have same shapes, got {seg_pred.shape} and {seg_gt.shape}."
)
# If not binary images, convert them
if seg_pred.dtype != bool:
seg_pred = seg_pred == label_idx
if seg_gt.dtype != bool:
seg_gt = seg_gt == label_idx
if crop:
if not np.any(seg_pred | seg_gt):
return np.zeros_like(seg_pred), np.zeros_like(seg_gt)
seg_pred, seg_gt = np.expand_dims(seg_pred,
0), np.expand_dims(seg_gt, 0)
box_start, box_end = generate_spatial_bounding_box(
np.asarray(seg_pred | seg_gt))
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
seg_pred, seg_gt = np.squeeze(cropper(seg_pred)), np.squeeze(
cropper(seg_gt))
# Do binary erosion and use XOR to get edges
edges_pred = binary_erosion(seg_pred) ^ seg_pred
edges_gt = binary_erosion(seg_gt) ^ seg_gt
return edges_pred, edges_gt
def __call__(self, img):
box_start, box_end = generate_spatial_bounding_box(img, self.select_fn, self.channel_indexes, self.margin)
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
return cropper(img)