121 lines
2.8 KiB
Python
121 lines
2.8 KiB
Python
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#!/usr/bin/env python
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'''
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sample for disctrete fourier transform (dft)
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USAGE:
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dft.py <image_file>
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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import sys
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def shift_dft(src, dst=None):
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'''
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Rearrange the quadrants of Fourier image so that the origin is at
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the image center. Swaps quadrant 1 with 3, and 2 with 4.
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src and dst arrays must be equal size & type
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'''
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if dst is None:
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dst = np.empty(src.shape, src.dtype)
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elif src.shape != dst.shape:
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raise ValueError("src and dst must have equal sizes")
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elif src.dtype != dst.dtype:
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raise TypeError("src and dst must have equal types")
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if src is dst:
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ret = np.empty(src.shape, src.dtype)
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else:
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ret = dst
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h, w = src.shape[:2]
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cx1 = cx2 = w // 2
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cy1 = cy2 = h // 2
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# if the size is odd, then adjust the bottom/right quadrants
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if w % 2 != 0:
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cx2 += 1
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if h % 2 != 0:
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cy2 += 1
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# swap quadrants
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# swap q1 and q3
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ret[h-cy1:, w-cx1:] = src[0:cy1 , 0:cx1 ] # q1 -> q3
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ret[0:cy2 , 0:cx2 ] = src[h-cy2:, w-cx2:] # q3 -> q1
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# swap q2 and q4
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ret[0:cy2 , w-cx2:] = src[h-cy2:, 0:cx2 ] # q2 -> q4
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ret[h-cy1:, 0:cx1 ] = src[0:cy1 , w-cx1:] # q4 -> q2
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if src is dst:
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dst[:,:] = ret
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return dst
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def main():
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if len(sys.argv) > 1:
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fname = sys.argv[1]
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else:
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fname = 'baboon.jpg'
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print("usage : python dft.py <image_file>")
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im = cv.imread(cv.samples.findFile(fname))
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# convert to grayscale
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im = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
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h, w = im.shape[:2]
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realInput = im.astype(np.float64)
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# perform an optimally sized dft
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dft_M = cv.getOptimalDFTSize(w)
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dft_N = cv.getOptimalDFTSize(h)
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# copy A to dft_A and pad dft_A with zeros
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dft_A = np.zeros((dft_N, dft_M, 2), dtype=np.float64)
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dft_A[:h, :w, 0] = realInput
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# no need to pad bottom part of dft_A with zeros because of
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# use of nonzeroRows parameter in cv.dft()
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cv.dft(dft_A, dst=dft_A, nonzeroRows=h)
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cv.imshow("win", im)
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# Split fourier into real and imaginary parts
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image_Re, image_Im = cv.split(dft_A)
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# Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
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magnitude = cv.sqrt(image_Re**2.0 + image_Im**2.0)
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# Compute log(1 + Mag)
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log_spectrum = cv.log(1.0 + magnitude)
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# Rearrange the quadrants of Fourier image so that the origin is at
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# the image center
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shift_dft(log_spectrum, log_spectrum)
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# normalize and display the results as rgb
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cv.normalize(log_spectrum, log_spectrum, 0.0, 1.0, cv.NORM_MINMAX)
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cv.imshow("magnitude", log_spectrum)
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cv.waitKey(0)
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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