src/experiments/algorithms/fft.py

@@ -1,68 +0,0 @@
-from __future__ import print_function
-import sys
-import cv2 as cv
-import matplotlib.pyplot as plt
-import numpy as np
-def print_help():
-    print('''
-    This program demonstrated the use of the discrete Fourier transform (DFT).
-    The dft of an image is taken and it's power spectrum is displayed.
-    Usage:
-    discrete_fourier_transform.py [image_name -- default lena.jpg]''')
-
-def main(argv):
-    print_help()
-    filename = argv[0] if len(argv) > 0 else 'lena.jpg'
-    I = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)
-    if I is None:
-        print('Error opening image')
-        return -1
-    I = cv.resize(I, (0,0), None, fx=1, fy=1)
-
-    rows, cols = I.shape
-    m = cv.getOptimalDFTSize( rows )
-    n = cv.getOptimalDFTSize( cols )
-    padded = cv.copyMakeBorder(I, 0, m - rows, 0, n - cols, cv.BORDER_CONSTANT, value=[0, 0, 0])
-    
-    planes = [np.float32(padded), np.zeros(padded.shape, np.float32)]
-    complexI = cv.merge(planes)         # Add to the expanded another plane with zeros
-    
-    cv.dft(complexI, complexI)         # this way the result may fit in the source matrix
-    
-    cv.split(complexI, planes)                   # planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
-    cv.magnitude(planes[0], planes[1], planes[0])# planes[0] = magnitude
-    magI = planes[0]
-    
-    matOfOnes = np.ones(magI.shape, dtype=magI.dtype)
-    cv.add(matOfOnes, magI, magI) #  switch to logarithmic scale
-    cv.log(magI, magI)
-    
-    magI_rows, magI_cols = magI.shape
-    # crop the spectrum, if it has an odd number of rows or columns
-    magI = magI[0:(magI_rows & -2), 0:(magI_cols & -2)]
-    cx = int(magI_rows/2)
-    cy = int(magI_cols/2)
-    q0 = magI[0:cx, 0:cy]         # Top-Left - Create a ROI per quadrant
-    q1 = magI[cx:cx+cx, 0:cy]     # Top-Right
-    q2 = magI[0:cx, cy:cy+cy]     # Bottom-Left
-    q3 = magI[cx:cx+cx, cy:cy+cy] # Bottom-Right
-    tmp = np.copy(q0)               # swap quadrants (Top-Left with Bottom-Right)
-    magI[0:cx, 0:cy] = q3
-    magI[cx:cx + cx, cy:cy + cy] = tmp
-    tmp = np.copy(q1)               # swap quadrant (Top-Right with Bottom-Left)
-    magI[cx:cx + cx, 0:cy] = q2
-    magI[0:cx, cy:cy + cy] = tmp
-    
-    cv.normalize(magI, magI, 0, 1, cv.NORM_MINMAX) # Transform the matrix with float values into a
-    
-    cv.imshow("Input Image"       , I   )    # Show the result
-    cv.imshow("spectrum magnitude", magI)
-    cv.imwrite("src\\experiments\\algorithms\\image\\fft.jpg", magI)
-    cv.waitKey()
-
-    x = cv.calcHist([I], [0], None, [256], [0, 256])
-    plt.plot(x)
-    plt.show()
-
-if __name__ == "__main__":
-    main(sys.argv[1:])