def resample(arr_in, out_shape):
"""Resample a 3-dimensional array to the desired shape.
Inputs
----------
arr_in: ndarray, shape = [height, width, depth]
Input array
out_shape: tuple of int
Desired output shape after resampling.
Format = [new_height, new_width, new_depth]
Returns
-------
arr_out: ndarray, shape = `out_shape`
Resampled input array of shape `out_shape`.
"""
assert arr_in.ndim == 3
assert len(out_shape) == arr_in.ndim
h_in, w_in, d_in = arr_in.shape
h_out, w_out, d_out = out_shape
narr = np.ascontiguousarray(arr_in.copy(), dtype='f')
arr_out = np.empty(out_shape, dtype=narr.dtype)
_resample_float32(narr, arr_out)
return arr_out
def resample(arr_in, out_shape, order=0, intp2d=False):
"""Resample a 3-dimensional array to the desired shape.
Parameters
----------
arr_in: ndarray, shape = [height, width, depth]
Input array
out_shape: tuple of int
Desired output shape after resampling.
Format = [new_height, new_width, new_depth]
order: int, optional
Interpolation order. 0=nearest, 1=linear, 3=cubic, etc.
Values between 0 and 5 are possible. Default is 0.
intp2d: bool, optional
If True, indicates that we only want to interpolate in 2d
within every "slice" along the third dimension (depth).
Default is False.
Returns
-------
arr_out: ndarray, shape = `out_shape`
Resampled input array of shape `out_shape`.
Example
-------
XXX: more here
>>> import numpy as np
>>> from skimage.shape import resample
>>> X = np.random.randn(10, 10, 4)
>>> X.shape
>>> (10, 10, 4)
>>> Y = resample(X, (5, 5, 4), intp2d=True)
>>> Y.shape
>>> (5, 5, 4)
"""
# -- list of interpolation methods
possible_orders = range(5 + 1)
# -------------------------------------------------------------------------
# -- Check arguments
# -------------------------------------------------------------------------
assert arr_in.ndim == 3
assert order in possible_orders
assert type(intp2d) == bool
assert len(out_shape) == arr_in.ndim
if intp2d and arr_in.shape[2] != out_shape[2]:
raise ValueError(
"intp2d cannot be True if the arguments don't have the same depth "
"(i.e. arr_in.shape[2] must be equal to out_shape[2])"
)
h_in, w_in, d_in = arr_in.shape
h_out, w_out, d_out = out_shape
# -------------------------------------------------------------------------
# -- Special case where order=0 (i.e. interpolation 'nearest')
# -------------------------------------------------------------------------
# We use Cython for faster processing. Only float32 is supported for now.
# XXX: (WIP) template cython code to be compatible with all dtypes
if arr_in.dtype == np.float32 and order == 0:
# prepare output array
arr_out = np.empty(out_shape, dtype=arr_in.dtype)
_resample_float32(arr_in, arr_out)
return arr_out
# -------------------------------------------------------------------------
# -- 2D interpolation(s)
# -------------------------------------------------------------------------
if intp2d:
# -- initialize output array
arr_out = np.empty(out_shape, dtype=arr_in.dtype)
# -- output grid in the first two dimensions
h_out_grid, w_out_grid = np.mgrid[:h_out, :w_out]
# -- rescaling of the grids to input array range
h_out_grid = (1. * h_out_grid / h_out_grid.max()) * (h_in - 1.)
w_out_grid = (1. * w_out_grid / w_out_grid.max()) * (w_in - 1.)
# -- coordinates of the output array pixels
coordinates = np.array([h_out_grid, w_out_grid])
# -- loop over the third dimension (2D interpolation)
for d in xrange(int(d_out)):
slice2D = ndi.map_coordinates(
arr_in[:, :, d], coordinates, order=order)
arr_out[:, :, d] = slice2D
# -- return output array
return arr_out
# -------------------------------------------------------------------------
# -- 3D interpolation
# -------------------------------------------------------------------------
# -- output grid
h_out_grid, w_out_grid, d_out_grid = np.mgrid[:h_out, :w_out, :d_out]
# -- rescaling of the grids to input array range
h_out_grid = (1. * h_out_grid / h_out_grid.max()) * (h_in - 1.)
w_out_grid = (1. * w_out_grid / w_out_grid.max()) * (w_in - 1.)
d_out_grid = (1. * d_out_grid / d_out_grid.max()) * (d_in - 1.)
# -- interpolation
coordinates = np.array([h_out_grid, w_out_grid, d_out_grid])
arr_out = ndi.map_coordinates(arr_in, coordinates, order=order)
# -- return output array
return arr_out
def resample(arr_in, out_shape, order=0, intp2d=False):
"""Resample a 3-dimensional array to the desired shape.
Parameters
----------
arr_in: ndarray, shape = [height, width, depth]
Input array
out_shape: tuple of int
Desired output shape after resampling.
Format = [new_height, new_width, new_depth]
order: int, optional
Interpolation order. 0=nearest, 1=linear, 3=cubic, etc.
Values between 0 and 5 are possible. Default is 0.
intp2d: bool, optional
If True, indicates that we only want to interpolate in 2d
within every "slice" along the third dimension (depth).
Default is False.
Returns
-------
arr_out: ndarray, shape = `out_shape`
Resampled input array of shape `out_shape`.
Example
-------
XXX: more here
>>> import numpy as np
>>> from skimage.shape import resample
>>> X = np.random.randn(10, 10, 4)
>>> X.shape
>>> (10, 10, 4)
>>> Y = resample(X, (5, 5, 4), intp2d=True)
>>> Y.shape
>>> (5, 5, 4)
"""
# -- list of interpolation methods
possible_orders = range(5 + 1)
# -------------------------------------------------------------------------
# -- Check arguments
# -------------------------------------------------------------------------
assert arr_in.ndim == 3
assert order in possible_orders
assert type(intp2d) == bool
assert len(out_shape) == arr_in.ndim
if intp2d and arr_in.shape[2] != out_shape[2]:
raise ValueError(
"intp2d cannot be True if the arguments don't have the same depth "
"(i.e. arr_in.shape[2] must be equal to out_shape[2])")
h_in, w_in, d_in = arr_in.shape
h_out, w_out, d_out = out_shape
# -------------------------------------------------------------------------
# -- Special case where order=0 (i.e. interpolation 'nearest')
# -------------------------------------------------------------------------
# We use Cython for faster processing. Only float32 is supported for now.
# XXX: (WIP) template cython code to be compatible with all dtypes
if arr_in.dtype == np.float32 and order == 0:
# prepare output array
arr_out = np.empty(out_shape, dtype=arr_in.dtype)
_resample_float32(arr_in, arr_out)
return arr_out
# -------------------------------------------------------------------------
# -- 2D interpolation(s)
# -------------------------------------------------------------------------
if intp2d:
# -- initialize output array
arr_out = np.empty(out_shape, dtype=arr_in.dtype)
# -- output grid in the first two dimensions
h_out_grid, w_out_grid = np.mgrid[:h_out, :w_out]
# -- rescaling of the grids to input array range
h_out_grid = (1. * h_out_grid / h_out_grid.max()) * (h_in - 1.)
w_out_grid = (1. * w_out_grid / w_out_grid.max()) * (w_in - 1.)
# -- coordinates of the output array pixels
coordinates = np.array([h_out_grid, w_out_grid])
# -- loop over the third dimension (2D interpolation)
for d in xrange(int(d_out)):
slice2D = ndi.map_coordinates(arr_in[:, :, d],
coordinates,
order=order)
arr_out[:, :, d] = slice2D
# -- return output array
return arr_out
# -------------------------------------------------------------------------
# -- 3D interpolation
# -------------------------------------------------------------------------
# -- output grid
h_out_grid, w_out_grid, d_out_grid = np.mgrid[:h_out, :w_out, :d_out]
# -- rescaling of the grids to input array range
h_out_grid = (1. * h_out_grid / h_out_grid.max()) * (h_in - 1.)
w_out_grid = (1. * w_out_grid / w_out_grid.max()) * (w_in - 1.)
d_out_grid = (1. * d_out_grid / d_out_grid.max()) * (d_in - 1.)
# -- interpolation
coordinates = np.array([h_out_grid, w_out_grid, d_out_grid])
arr_out = ndi.map_coordinates(arr_in, coordinates, order=order)
# -- return output array
return arr_out