affine_transform(input, matrix, offset=0.0, output_shape=None, output=None, order=3, mode='constant', cval=0.0, prefilter=True)
Given an output image pixel index vector o, the pixel value is determined from the input image at position np.dot(matrix, o) + offset.
This does 'pull' (or 'backward') resampling, transforming the output space to the input to locate data. Affine transformations are often described in the 'push' (or 'forward') direction, transforming input to output. If you have a matrix for the 'push' transformation, use its inverse (numpy.linalg.inv) in this function.
The given matrix and offset are used to find for each point in the output the corresponding coordinates in the input by an affine transformation. The value of the input at those coordinates is determined by spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode.
For complex-valued input, this function transforms the real and imaginary components independently.
The input array.
The inverse coordinate transformation matrix, mapping output coordinates to input coordinates. If ndim is the number of dimensions of input, the given matrix must have one of the following shapes:
(ndim, ndim): the linear transformation matrix for each output coordinate.(ndim,): assume that the 2-D transformation matrix is diagonal, with the diagonal specified by the given value. A more efficient algorithm is then used that exploits the separability of the problem.(ndim + 1, ndim + 1): assume that the transformation is specified using homogeneous coordinates . In this case, any value passed tooffsetis ignored.(ndim, ndim + 1): as above, but the bottom row of a homogeneous transformation matrix is always[0, 0, ..., 1], and may be omitted.
Shape tuple.
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.
The order of the spline interpolation, default is 3. The order has to be in the range 0-5.
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 <ndimage-interpolation-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.
Value to fill past edges of input if mode is 'constant'. Default is 0.0.
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.
The transformed input.
Apply an affine transformation.
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.ndimage._interpolation:geometric_transform
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