1 #!/usr/bin/env python 2 3 import cv2 4 import numpy as np 5 import sys 6 7 8 def shift_dft(src, dst=None): 9 ''' 10 Rearrange the quadrants of Fourier image so that the origin is at 11 the image center. Swaps quadrant 1 with 3, and 2 with 4. 12 13 src and dst arrays must be equal size & type 14 ''' 15 16 if dst is None: 17 dst = np.empty(src.shape, src.dtype) 18 elif src.shape != dst.shape: 19 raise ValueError("src and dst must have equal sizes") 20 elif src.dtype != dst.dtype: 21 raise TypeError("src and dst must have equal types") 22 23 if src is dst: 24 ret = np.empty(src.shape, src.dtype) 25 else: 26 ret = dst 27 28 h, w = src.shape[:2] 29 30 cx1 = cx2 = w/2 31 cy1 = cy2 = h/2 32 33 # if the size is odd, then adjust the bottom/right quadrants 34 if w % 2 != 0: 35 cx2 += 1 36 if h % 2 != 0: 37 cy2 += 1 38 39 # swap quadrants 40 41 # swap q1 and q3 42 ret[h-cy1:, w-cx1:] = src[0:cy1 , 0:cx1 ] # q1 -> q3 43 ret[0:cy2 , 0:cx2 ] = src[h-cy2:, w-cx2:] # q3 -> q1 44 45 # swap q2 and q4 46 ret[0:cy2 , w-cx2:] = src[h-cy2:, 0:cx2 ] # q2 -> q4 47 ret[h-cy1:, 0:cx1 ] = src[0:cy1 , w-cx1:] # q4 -> q2 48 49 if src is dst: 50 dst[:,:] = ret 51 52 return dst 53 54 if __name__ == "__main__": 55 56 if len(sys.argv)>1: 57 im = cv2.imread(sys.argv[1]) 58 else : 59 im = cv2.imread('../data/baboon.jpg') 60 print "usage : python dft.py <image_file>" 61 62 # convert to grayscale 63 im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) 64 h, w = im.shape[:2] 65 66 realInput = im.astype(np.float64) 67 68 # perform an optimally sized dft 69 dft_M = cv2.getOptimalDFTSize(w) 70 dft_N = cv2.getOptimalDFTSize(h) 71 72 # copy A to dft_A and pad dft_A with zeros 73 dft_A = np.zeros((dft_N, dft_M, 2), dtype=np.float64) 74 dft_A[:h, :w, 0] = realInput 75 76 # no need to pad bottom part of dft_A with zeros because of 77 # use of nonzeroRows parameter in cv2.dft() 78 cv2.dft(dft_A, dst=dft_A, nonzeroRows=h) 79 80 cv2.imshow("win", im) 81 82 # Split fourier into real and imaginary parts 83 image_Re, image_Im = cv2.split(dft_A) 84 85 # Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2) 86 magnitude = cv2.sqrt(image_Re**2.0 + image_Im**2.0) 87 88 # Compute log(1 + Mag) 89 log_spectrum = cv2.log(1.0 + magnitude) 90 91 # Rearrange the quadrants of Fourier image so that the origin is at 92 # the image center 93 shift_dft(log_spectrum, log_spectrum) 94 95 # normalize and display the results as rgb 96 cv2.normalize(log_spectrum, log_spectrum, 0.0, 1.0, cv2.NORM_MINMAX) 97 cv2.imshow("magnitude", log_spectrum) 98 99 cv2.waitKey(0) 100 cv2.destroyAllWindows() 101
