From 5f48c76ba2fe6ff4f71e32469ae4d2362f260691 Mon Sep 17 00:00:00 2001
From: Tom Selier <tbj.selier@gmail.com>
Date: Sun, 1 Oct 2023 19:45:46 +0200
Subject: [PATCH] added FFT

---
 src/experiments/algorithms/fft.py | 68 +++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 src/experiments/algorithms/fft.py

diff --git a/src/experiments/algorithms/fft.py b/src/experiments/algorithms/fft.py
new file mode 100644
index 0000000..6b05f3b
--- /dev/null
+++ b/src/experiments/algorithms/fft.py
@@ -0,0 +1,68 @@
+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:])