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""" |
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Resize functions (equivalent to scipy's zoom, pytorch's interpolate) |
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based on grid_pull. |
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""" |
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__all__ = ['resize'] |
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from .api import grid_pull |
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from .utils import make_list, meshgrid_ij |
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from . import backend, jitfields |
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import torch |
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def resize(image, factor=None, shape=None, anchor='c', |
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interpolation=1, prefilter=True, **kwargs): |
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"""Resize an image by a factor or to a specific shape. |
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Notes |
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----- |
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.. A least one of `factor` and `shape` must be specified |
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.. If `anchor in ('centers', 'edges')`, exactly one of `factor` or |
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`shape must be specified. |
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.. If `anchor in ('first', 'last')`, `factor` must be provided even |
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if `shape` is specified. |
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.. Because of rounding, it is in general not assured that |
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`resize(resize(x, f), 1/f)` returns a tensor with the same shape as x. |
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edges centers first last |
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e - + - + - e + - + - + - + + - + - + - + + - + - + - + |
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| . | . | . | | c | . | c | | f | . | . | | . | . | . | |
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+ _ + _ + _ + + _ + _ + _ + + _ + _ + _ + + _ + _ + _ + |
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| . | . | . | | . | . | . | | . | . | . | | . | . | . | |
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+ _ + _ + _ + + _ + _ + _ + + _ + _ + _ + + _ + _ + _ + |
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| . | . | . | | c | . | c | | . | . | . | | . | . | l | |
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e _ + _ + _ e + _ + _ + _ + + _ + _ + _ + + _ + _ + _ + |
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Parameters |
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---------- |
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image : (batch, channel, *inshape) tensor |
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Image to resize |
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factor : float or list[float], optional |
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Resizing factor |
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* > 1 : larger image <-> smaller voxels |
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* < 1 : smaller image <-> larger voxels |
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shape : (ndim,) list[int], optional |
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Output shape |
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anchor : {'centers', 'edges', 'first', 'last'} or list, default='centers' |
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* In cases 'c' and 'e', the volume shape is multiplied by the |
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zoom factor (and eventually truncated), and two anchor points |
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are used to determine the voxel size. |
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* In cases 'f' and 'l', a single anchor point is used so that |
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the voxel size is exactly divided by the zoom factor. |
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This case with an integer factor corresponds to subslicing |
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the volume (e.g., `vol[::f, ::f, ::f]`). |
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* A list of anchors (one per dimension) can also be provided. |
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interpolation : int or sequence[int], default=1 |
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Interpolation order. |
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prefilter : bool, default=True |
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Apply spline pre-filter (= interpolates the input) |
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Returns |
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------- |
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resized : (batch, channel, *shape) tensor |
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Resized image |
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""" |
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if backend.jitfields and jitfields.available: |
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return jitfields.resize(image, factor, shape, anchor, |
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interpolation, prefilter, **kwargs) |
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factor = make_list(factor) if factor else [] |
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shape = make_list(shape) if shape else [] |
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anchor = make_list(anchor) |
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nb_dim = max(len(factor), len(shape), len(anchor)) or (image.dim() - 2) |
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anchor = [a[0].lower() for a in make_list(anchor, nb_dim)] |
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bck = dict(dtype=image.dtype, device=image.device) |
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inshape = image.shape[-nb_dim:] |
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if factor: |
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factor = make_list(factor, nb_dim) |
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elif not shape: |
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raise ValueError('One of `factor` or `shape` must be provided') |
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if shape: |
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shape = make_list(shape, nb_dim) |
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else: |
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shape = [int(i*f) for i, f in zip(inshape, factor)] |
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if not factor: |
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factor = [o/i for o, i in zip(shape, inshape)] |
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lin = [] |
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for anch, f, inshp, outshp in zip(anchor, factor, inshape, shape): |
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if anch == 'c': |
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lin.append(torch.linspace(0, inshp - 1, outshp, **bck)) |
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elif anch == 'e': |
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scale = inshp / outshp |
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shift = 0.5 * (scale - 1) |
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lin.append(torch.arange(0., outshp, **bck) * scale + shift) |
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elif anch == 'f': |
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lin.append(torch.arange(0., outshp, **bck) / f) |
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elif anch == 'l': |
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shift = (inshp - 1) - (outshp - 1) / f |
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lin.append(torch.arange(0., outshp, **bck) / f + shift) |
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else: |
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raise ValueError('Unknown anchor {}'.format(anch)) |
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kwargs.setdefault('bound', 'nearest') |
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kwargs.setdefault('extrapolate', True) |
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kwargs.setdefault('interpolation', interpolation) |
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kwargs.setdefault('prefilter', prefilter) |
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grid = torch.stack(meshgrid_ij(*lin), dim=-1) |
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resized = grid_pull(image, grid, **kwargs) |
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return resized |
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