Easy to use multi-core affine transformations
This project explores how C++17 and OpenMP can be combined to write a surprisingly compact implementation of n-dimensional parallel affine transformations which are linked into Python via the affine_transform module.
While this project is still under development, the following features are supported:
- Linear and cubic (without prefiltering) interpolation
- Constant boundaries
- Compiling code for arbitrarily dimensional data
- Parallelism via OpenMP
- Arbitrary shaped output arrays, allowing e.g. to only extract a transformed slice
Short example usage
import numpy as np from affine_transform import transform from mgen import rotation_from_angle import matplotlib.pyplot as plt # Create a simple white square in an image original = np.zeros((601, 401)) original[100:300, 100:300] = 1 # Rotate by 22.5° (around the centre of the square (200,200)) # and shift +200 in x and +100 in y transformed = transform( original, rotation_from_angle(np.pi / 8), np.array([200, 100]), origin=(200, 200) )
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