def dense_patch_slices(
image_size: Sequence[int],
patch_size: Sequence[int],
scan_interval: Sequence[int],
) -> List[Tuple[slice, ...]]:
"""
Enumerate all slices defining ND patches of size `patch_size` from an `image_size` input image.
Args:
image_size: dimensions of image to iterate over
patch_size: size of patches to generate slices
scan_interval: dense patch sampling interval
Returns:
a list of slice objects defining each patch
"""
num_spatial_dims = len(image_size)
patch_size = get_valid_patch_size(image_size, patch_size)
scan_interval = ensure_tuple_size(scan_interval, num_spatial_dims)
scan_num = []
for i in range(num_spatial_dims):
if scan_interval[i] == 0:
scan_num.append(1)
else:
num = int(math.ceil(float(image_size[i]) / scan_interval[i]))
scan_dim = first(d for d in range(num) if d * scan_interval[i] + patch_size[i] >= image_size[i])
scan_num.append(scan_dim + 1 if scan_dim is not None else 1)
starts = []
for dim in range(num_spatial_dims):
dim_starts = []
for idx in range(scan_num[dim]):
start_idx = idx * scan_interval[dim]
start_idx -= max(start_idx + patch_size[dim] - image_size[dim], 0)
dim_starts.append(start_idx)
starts.append(dim_starts)
out = np.asarray([x.flatten() for x in np.meshgrid(*starts, indexing="ij")]).T
return [tuple(slice(s, s + patch_size[d]) for d, s in enumerate(x)) for x in out]
def iter_patch(arr, patch_size, start_pos=(), copy_back=True, pad_mode="wrap", **pad_opts):
"""
Yield successive patches from `arr` of size `patch_size`. The iteration can start from position `start_pos` in `arr`
but drawing from a padded array extended by the `patch_size` in each dimension (so these coordinates can be negative
to start in the padded region). If `copy_back` is True the values from each patch are written back to `arr`.
Args:
arr (np.ndarray): array to iterate over
patch_size (tuple of int or None): size of patches to generate slices for, 0 or None selects whole dimension
start_pos (tuple of it, optional): starting position in the array, default is 0 for each dimension
copy_back (bool): if True data from the yielded patches is copied back to `arr` once the generator completes
pad_mode (str, optional): padding mode, see `numpy.pad`
pad_opts (dict, optional): padding options, see `numpy.pad`
Yields:
Patches of array data from `arr` which are views into a padded array which can be modified, if `copy_back` is
True these changes will be reflected in `arr` once the iteration completes.
"""
# ensure patchSize and startPos are the right length
patch_size = get_valid_patch_size(arr.shape, patch_size)
start_pos = ensure_tuple_size(start_pos, arr.ndim)
# pad image by maximum values needed to ensure patches are taken from inside an image
arrpad = np.pad(arr, tuple((p, p) for p in patch_size), pad_mode, **pad_opts)
# choose a start position in the padded image
start_pos_padded = tuple(s + p for s, p in zip(start_pos, patch_size))
# choose a size to iterate over which is smaller than the actual padded image to prevent producing
# patches which are only in the padded regions
iter_size = tuple(s + p for s, p in zip(arr.shape, patch_size))
for slices in iter_patch_slices(iter_size, patch_size, start_pos_padded):
yield arrpad[slices]
# copy back data from the padded image if required
if copy_back:
slices = tuple(slice(p, p + s) for p, s in zip(patch_size, arr.shape))
arr[...] = arrpad[slices]
def iter_patch(
arr: np.ndarray,
patch_size: Union[Sequence[int], int] = 0,
start_pos: Sequence[int] = (),
copy_back: bool = True,
mode: Union[NumpyPadMode, str] = NumpyPadMode.WRAP,
**pad_opts: Dict,
):
"""
Yield successive patches from `arr` of size `patch_size`. The iteration can start from position `start_pos` in `arr`
but drawing from a padded array extended by the `patch_size` in each dimension (so these coordinates can be negative
to start in the padded region). If `copy_back` is True the values from each patch are written back to `arr`.
Args:
arr: array to iterate over
patch_size: size of patches to generate slices for, 0 or None selects whole dimension
start_pos: starting position in the array, default is 0 for each dimension
copy_back: if True data from the yielded patches is copied back to `arr` once the generator completes
mode: {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``,
``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
One of the listed string values or a user supplied function. Defaults to ``"wrap"``.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
pad_opts: padding options, see `numpy.pad`
Yields:
Patches of array data from `arr` which are views into a padded array which can be modified, if `copy_back` is
True these changes will be reflected in `arr` once the iteration completes.
Note:
coordinate format is:
[1st_dim_start, 1st_dim_end,
2nd_dim_start, 2nd_dim_end,
...,
Nth_dim_start, Nth_dim_end]]
"""
# ensure patchSize and startPos are the right length
patch_size_ = get_valid_patch_size(arr.shape, patch_size)
start_pos = ensure_tuple_size(start_pos, arr.ndim)
# pad image by maximum values needed to ensure patches are taken from inside an image
arrpad = np.pad(arr, tuple((p, p) for p in patch_size_),
look_up_option(mode, NumpyPadMode).value, **pad_opts)
# choose a start position in the padded image
start_pos_padded = tuple(s + p for s, p in zip(start_pos, patch_size_))
# choose a size to iterate over which is smaller than the actual padded image to prevent producing
# patches which are only in the padded regions
iter_size = tuple(s + p for s, p in zip(arr.shape, patch_size_))
for slices in iter_patch_slices(iter_size, patch_size_, start_pos_padded):
# compensate original image padding
coords_no_pad = tuple((coord.start - p, coord.stop - p)
for coord, p in zip(slices, patch_size_))
yield arrpad[slices], np.asarray(
coords_no_pad
) # data and coords (in numpy; works with torch loader)
# copy back data from the padded image if required
if copy_back:
slices = tuple(slice(p, p + s) for p, s in zip(patch_size_, arr.shape))
arr[...] = arrpad[slices]
def __call__(self, img: np.ndarray) -> np.ndarray:
self.randomize()
if not self._do_transform:
return img
sigma = ensure_tuple_size(tup=(self.x, self.y, self.z), dim=img.ndim - 1)
return GaussianSmooth(sigma=sigma, approx=self.approx)(img)
def dense_patch_slices(
image_size: Sequence[int],
patch_size: Sequence[int],
scan_interval: Sequence[int],
) -> List[Tuple[slice, ...]]:
"""
Enumerate all slices defining 2D/3D patches of size `patch_size` from an `image_size` input image.
Args:
image_size: dimensions of image to iterate over
patch_size: size of patches to generate slices
scan_interval: dense patch sampling interval
Raises:
ValueError: When ``image_size`` length is not one of [2, 3].
Returns:
a list of slice objects defining each patch
"""
num_spatial_dims = len(image_size)
if num_spatial_dims not in (2, 3):
raise ValueError(
f"Unsupported image_size length: {len(image_size)}, available options are [2, 3]"
)
patch_size = get_valid_patch_size(image_size, patch_size)
scan_interval = ensure_tuple_size(scan_interval, num_spatial_dims)
scan_num = list()
for i in range(num_spatial_dims):
if scan_interval[i] == 0:
scan_num.append(1)
else:
num = int(math.ceil(float(image_size[i]) / scan_interval[i]))
scan_dim = first(
d for d in range(num)
if d * scan_interval[i] + patch_size[i] >= image_size[i])
scan_num.append(scan_dim + 1)
slices: List[Tuple[slice, ...]] = []
if num_spatial_dims == 3:
for i in range(scan_num[0]):
start_i = i * scan_interval[0]
start_i -= max(start_i + patch_size[0] - image_size[0], 0)
slice_i = slice(start_i, start_i + patch_size[0])
for j in range(scan_num[1]):
start_j = j * scan_interval[1]
start_j -= max(start_j + patch_size[1] - image_size[1], 0)
slice_j = slice(start_j, start_j + patch_size[1])
for k in range(0, scan_num[2]):
start_k = k * scan_interval[2]
start_k -= max(start_k + patch_size[2] - image_size[2], 0)
slice_k = slice(start_k, start_k + patch_size[2])
slices.append((slice_i, slice_j, slice_k))
else:
for i in range(scan_num[0]):
start_i = i * scan_interval[0]
start_i -= max(start_i + patch_size[0] - image_size[0], 0)
slice_i = slice(start_i, start_i + patch_size[0])
for j in range(scan_num[1]):
start_j = j * scan_interval[1]
start_j -= max(start_j + patch_size[1] - image_size[1], 0)
slice_j = slice(start_j, start_j + patch_size[1])
slices.append((slice_i, slice_j))
return slices
