Perform a projective transformation (homography) of a floating point image (single or double precision), using interpolation.
For each pixel, given its homogeneous coordinate $\mathbf{x} = [x, y, 1]^T$ , its target position is calculated by multiplying with the given matrix, $H$ , to give $H \mathbf{x}$ . E.g., to rotate by theta degrees clockwise, the matrix should be:
[[cos(theta) -sin(theta) 0] [sin(theta) cos(theta) 0] [0 0 1]]
or, to translate x by 10 and y by 20:
[[1 0 10] [0 1 20] [0 0 1 ]].
Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2].
Input image.
Transformation matrix H that defines the homography.
Shape of the output image generated (default None).
Order of interpolation0: Nearest-neighbor * 1: Bi-linear (default) * 2: Bi-quadratic * 3: Bi-cubic
Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad
.
Used in conjunction with mode 'C' (constant), the value outside the image boundaries.
Projective transformation (homography).
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