How to transform a FFT (Fast Fourier Transform) into a Polar Transformation with Python?
This is roughly solution to you problem; It was tested on one sample image, and the result looks credible.
# your code goes here... def transform_data(m): dpix, dpiy = m.shape x_c, y_c = np.unravel_index(np.argmax(m), m.shape) angles = np.linspace(0, np.pi*2, min(dpix, dpiy)) mrc = min(abs(x_c - dpix), abs(y_c - dpiy), x_c, y_c) radiuses = np.linspace(0, mrc, max(dpix, dpiy)) A, R = np.meshgrid(angles, radiuses) X = R * np.cos(A) Y = R * np.sin(A) return A, R, m[X.astype(int) + mrc - 1, Y.astype(int) + mrc - 1] angles, radiuses, m = transform_data(magnitude_spectrum) plt.contourf(angles, radiuses, m)
Finally, we can get the angle we want to turn the original image:
sample_angles = np.linspace(0, 2 * np.pi, len(c.sum(axis=0))) / np.pi*180turn_angle_in_degrees = 90 - sample_angles[np.argmax(c.sum(axis=0))]For my sample image I got:
turn_angle_in_degrees = 3.2015810276679844 degrees.Also, we can plot projected spectrum magnitude:
plt.plot(sample_angles, c.sum(axis=0))
Hope that helps...