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#/usr/bin/env python |
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import cv2 |
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import numpy as np |
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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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if __name__ == "__main__": |
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if len(sys.argv)>1: |
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im = cv2.imread(sys.argv[1]) |
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else : |
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im = cv2.imread('../c/baboon.jpg') |
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print "usage : python dft.py <image_file>" |
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# convert to grayscale |
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im = cv2.cvtColor(im, cv2.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 = cv2.getOptimalDFTSize(w) |
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dft_N = cv2.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 cv2.dft() |
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cv2.dft(dft_A, dst=dft_A, nonzeroRows=h) |
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cv2.imshow("win", im) |
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# Split fourier into real and imaginary parts |
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image_Re, image_Im = cv2.split(dft_A) |
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# Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2) |
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magnitude = cv2.sqrt(image_Re**2.0 + image_Im**2.0) |
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# Compute log(1 + Mag) |
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log_spectrum = cv2.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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cv2.normalize(log_spectrum, log_spectrum, 0.0, 1.0, cv2.cv.CV_MINMAX) |
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cv2.imshow("magnitude", log_spectrum) |
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cv2.waitKey(0) |
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cv2.destroyAllWindows() |
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