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scipy.ndimage.

# geometric_transform

scipy.ndimage.geometric_transform(input, mapping, output_shape=None, output=None, order=3, mode='constant', cval=0.0, prefilter=True, extra_arguments=(), extra_keywords=None)

  Apply an arbitrary geometric transform.

  The given mapping function is used to find, for each point in the output, the corresponding coordinates in the input. The value of the input at those coordinates is determined by spline interpolation of the requested order.

  Parameters:
    input array_like

      The input array.

    mapping{callable, scipy.LowLevelCallable}

      A callable object that accepts a tuple of length equal to the output array rank, and returns the corresponding input coordinates as a tuple of length equal to the input array rank.

    output_shape tuple of ints, optional

      Shape tuple.

    output array or dtype, optional

      The array in which to place the output, or the dtype of the returned array. By default an array of the same dtype as input will be created.

    order int, optional

      The order of the spline interpolation, default is 3. The order has to be in the range 0-5.

    mode{‘reflect’, ‘grid-mirror’, ‘constant’, ‘grid-constant’, ‘nearest’, ‘mirror’, ‘grid-wrap’, ‘wrap’}, optional

      The mode parameter determines how the input array is extended beyond its boundaries. Default is ‘constant’. Behavior for each valid value is as follows (see additional plots and details on boundary modes):

      ‘reflect’ (d c b a | a b c d | d c b a)

        The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

      ‘grid-mirror’

        This is a synonym for ‘reflect’.

      ‘constant’ (k k k k | a b c d | k k k k)

        The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter. No interpolation is performed beyond the edges of the input.

      ‘grid-constant’ (k k k k | a b c d | k k k k)

        The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter. Interpolation occurs for samples outside the input’s extent as well.

      ‘nearest’ (a a a a | a b c d | d d d d)

        The input is extended by replicating the last pixel.

      ‘mirror’ (d c b | a b c d | c b a)

        The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

      ‘grid-wrap’ (a b c d | a b c d | a b c d)

        The input is extended by wrapping around to the opposite edge.

      ‘wrap’ (d b c d | a b c d | b c a b)

        The input is extended by wrapping around to the opposite edge, but in a way such that the last point and initial point exactly overlap. In this case it is not well defined which sample will be chosen at the point of overlap.

    cval scalar, optional

      Value to fill past edges of input if mode is ‘constant’. Default is 0.0.

    prefilter bool, optional

      Determines if the input array is prefiltered with `spline_filter` before interpolation. The default is True, which will create a temporary float64 array of filtered values if `order > 1`. If setting this to False, the output will be slightly blurred if `order > 1`, unless the input is prefiltered, i.e. it is the result of calling `spline_filter` on the original input.

    extra_arguments tuple, optional

      Extra arguments passed to mapping.

    extra_keywords dict, optional

      Extra keywords passed to mapping.

  Returns:
    output ndarray

      The filtered input.

  See also

  `map_coordinates`, `affine_transform`, `spline_filter1d`

  Notes

  This function also accepts low-level callback functions with one the following signatures and wrapped in `scipy.LowLevelCallable`:

      int mapping(npy_intp *output_coordinates, double *input_coordinates,
                  int output_rank, int input_rank, void *user_data)
      int mapping(intptr_t *output_coordinates, double *input_coordinates,
                  int output_rank, int input_rank, void *user_data)

  The calling function iterates over the elements of the output array, calling the callback function at each element. The coordinates of the current output element are passed through `output_coordinates`. The callback function must return the coordinates at which the input must be interpolated in `input_coordinates`. The rank of the input and output arrays are given by `input_rank` and `output_rank` respectively. `user_data` is the data pointer provided to `scipy.LowLevelCallable` as-is.

  The callback function must return an integer error status that is zero if something went wrong and one otherwise. If an error occurs, you should normally set the Python error status with an informative message before returning, otherwise a default error message is set by the calling function.

  In addition, some other low-level function pointer specifications are accepted, but these are for backward compatibility only and should not be used in new code.

  For complex-valued input, this function transforms the real and imaginary components independently.

  Added in version 1.6.0: Complex-valued support added.

  Array API Standard Support

  `geometric_transform` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable `SCIPY_ARRAY_API=1` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

  Library | CPU | GPU
  --- | --- | ---
  NumPy | ✅ | n/a
  CuPy | n/a | ⛔
  PyTorch | ✅ | ⛔
  JAX | ⚠️ no JIT | ⛔
  Dask | ⚠️ computes graph | n/a

  > See Support for the array API standard for more information.

  Examples

      >>> import numpy as np
      >>> from scipy.ndimage import geometric_transform
      >>> a = np.arange(12.).reshape((4, 3))
      >>> def shift_func(output_coords):
      ...     return (output_coords[0] - 0.5, output_coords[1] - 0.5)
      ...
      >>> geometric_transform(a, shift_func)
      array([[ 0.   ,  0.   ,  0.   ],
             [ 0.   ,  1.362,  2.738],
             [ 0.   ,  4.812,  6.187],
             [ 0.   ,  8.263,  9.637]])

      >>> b = [1, 2, 3, 4, 5]
      >>> def shift_func(output_coords):
      ...     return (output_coords[0] - 3,)
      ...
      >>> geometric_transform(b, shift_func, mode='constant')
      array([0, 0, 0, 1, 2])
      >>> geometric_transform(b, shift_func, mode='nearest')
      array([1, 1, 1, 1, 2])
      >>> geometric_transform(b, shift_func, mode='reflect')
      array([3, 2, 1, 1, 2])
      >>> geometric_transform(b, shift_func, mode='wrap')
      array([2, 3, 4, 1, 2])