def resample_and_clip(
cls,
data_array: NdarrayOrTensor,
output_spatial_shape: Optional[Sequence[int]] = None,
mode: str = InterpolateMode.BICUBIC,
):
"""
Resample ``data_array`` to ``output_spatial_shape`` if needed.
Args:
data_array: input data array. This method assumes the 'channel-last' format.
output_spatial_shape: output spatial shape.
mode: interpolation mode, defautl is ``InterpolateMode.BICUBIC``.
"""
data: np.ndarray = convert_data_type(data_array, np.ndarray)[0]
if output_spatial_shape is not None:
output_spatial_shape_ = ensure_tuple_rep(output_spatial_shape, 2)
mode = look_up_option(mode, InterpolateMode)
align_corners = None if mode in (InterpolateMode.NEAREST, InterpolateMode.AREA) else False
xform = Resize(spatial_size=output_spatial_shape_, mode=mode, align_corners=align_corners)
_min, _max = np.min(data), np.max(data)
if len(data.shape) == 3:
data = np.moveaxis(data, -1, 0) # to channel first
data = convert_data_type(xform(data), np.ndarray)[0] # type: ignore
data = np.moveaxis(data, 0, -1)
else: # (H, W)
data = np.expand_dims(data, 0) # make a channel
data = convert_data_type(xform(data), np.ndarray)[0][0] # type: ignore
if mode != InterpolateMode.NEAREST:
data = np.clip(data, _min, _max)
return data
def __init__(self,
keys: KeysCollection,
spatial_size,
interp_order: str = "area",
align_corners: Optional[bool] = None):
super().__init__(keys)
self.interp_order = ensure_tuple_rep(interp_order, len(self.keys))
self.resizer = Resize(spatial_size=spatial_size,
align_corners=align_corners)
def __init__(
self,
keys: KeysCollection,
spatial_size: Union[Sequence[int], int],
mode: InterpolateModeSequence = InterpolateMode.AREA,
align_corners: Union[Sequence[Optional[bool]], Optional[bool]] = None,
) -> None:
super().__init__(keys)
self.mode = ensure_tuple_rep(mode, len(self.keys))
self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))
self.resizer = Resize(spatial_size=spatial_size)
def __init__(self,
keys,
spatial_size,
order=1,
mode='reflect',
cval=0,
clip=True,
preserve_range=True,
anti_aliasing=True,
anti_aliasing_sigma=None):
super().__init__(keys)
self.resizer = Resize(spatial_size, order, mode, cval, clip,
preserve_range, anti_aliasing,
anti_aliasing_sigma)
def __init__(
self,
keys,
spatial_size,
order=1,
mode="reflect",
cval=0,
clip=True,
preserve_range=True,
anti_aliasing=True,
):
super().__init__(keys)
self.order = ensure_tuple_rep(order, len(self.keys))
self.mode = ensure_tuple_rep(mode, len(self.keys))
self.cval = ensure_tuple_rep(cval, len(self.keys))
self.clip = ensure_tuple_rep(clip, len(self.keys))
self.preserve_range = ensure_tuple_rep(preserve_range, len(self.keys))
self.anti_aliasing = ensure_tuple_rep(anti_aliasing, len(self.keys))
self.resizer = Resize(spatial_size=spatial_size)
def write_png(
data: np.ndarray,
file_name: str,
output_spatial_shape: Optional[Sequence[int]] = None,
mode: Union[InterpolateMode, str] = InterpolateMode.BICUBIC,
scale: Optional[int] = None,
) -> None:
"""
Write numpy data into png files to disk.
Spatially it supports HW for 2D.(H,W) or (H,W,3) or (H,W,4).
If `scale` is None, expect the input data in `np.uint8` or `np.uint16` type.
It's based on the Image module in PIL library:
https://pillow.readthedocs.io/en/stable/reference/Image.html
Args:
data: input data to write to file.
file_name: expected file name that saved on disk.
output_spatial_shape: spatial shape of the output image.
mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``, ``"area"``}
The interpolation mode. Defaults to ``"bicubic"``.
See also: https://pytorch.org/docs/stable/nn.functional.html#interpolate
scale: {``255``, ``65535``} postprocess data by clipping to [0, 1] and scaling to
[0, 255] (uint8) or [0, 65535] (uint16). Default is None to disable scaling.
Raises:
ValueError: When ``scale`` is not one of [255, 65535].
"""
if not isinstance(data, np.ndarray):
raise ValueError("input data must be numpy array.")
if len(
data.shape
) == 3 and data.shape[2] == 1: # PIL Image can't save image with 1 channel
data = data.squeeze(2)
if output_spatial_shape is not None:
output_spatial_shape_ = ensure_tuple_rep(output_spatial_shape, 2)
mode = look_up_option(mode, InterpolateMode)
align_corners = None if mode in (InterpolateMode.NEAREST,
InterpolateMode.AREA) else False
xform = Resize(spatial_size=output_spatial_shape_,
mode=mode,
align_corners=align_corners)
_min, _max = np.min(data), np.max(data)
if len(data.shape) == 3:
data = np.moveaxis(data, -1, 0) # to channel first
data = xform(data) # type: ignore
data = np.moveaxis(data, 0, -1)
else: # (H, W)
data = np.expand_dims(data, 0) # make a channel
data = xform(data)[0] # type: ignore
if mode != InterpolateMode.NEAREST:
data = np.clip(data, _min, _max) # type: ignore
if scale is not None:
data = np.clip(
data, 0.0, 1.0
) # type: ignore # png writer only can scale data in range [0, 1]
if scale == np.iinfo(np.uint8).max:
data = (scale * data).astype(np.uint8)
elif scale == np.iinfo(np.uint16).max:
data = (scale * data).astype(np.uint16)
else:
raise ValueError(
f"Unsupported scale: {scale}, available options are [255, 65535]"
)
# PNG data must be int number
if data.dtype not in (np.uint8, np.uint16): # type: ignore
data = data.astype(np.uint8)
data = np.moveaxis(data, 0, 1)
img = Image.fromarray(data)
img.save(file_name, "PNG")
return
