python: 'cv2.' -> 'cv.' via 'import cv2 as cv'

samples/python/dft.py

@@ -11,7 +11,7 @@ USAGE:
 # Python 2/3 compatibility
 from __future__ import print_function
 
-import cv2
+import cv2 as cv
 import numpy as np
 import sys
 
@@ -65,47 +65,47 @@ def shift_dft(src, dst=None):
 if __name__ == "__main__":
 
     if len(sys.argv) > 1:
-        im = cv2.imread(sys.argv[1])
+        im = cv.imread(sys.argv[1])
     else:
-        im = cv2.imread('../data/baboon.jpg')
+        im = cv.imread('../data/baboon.jpg')
         print("usage : python dft.py <image_file>")
 
     # convert to grayscale
-    im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
+    im = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
     h, w = im.shape[:2]
 
     realInput = im.astype(np.float64)
 
     # perform an optimally sized dft
-    dft_M = cv2.getOptimalDFTSize(w)
-    dft_N = cv2.getOptimalDFTSize(h)
+    dft_M = cv.getOptimalDFTSize(w)
+    dft_N = cv.getOptimalDFTSize(h)
 
     # copy A to dft_A and pad dft_A with zeros
     dft_A = np.zeros((dft_N, dft_M, 2), dtype=np.float64)
     dft_A[:h, :w, 0] = realInput
 
     # no need to pad bottom part of dft_A with zeros because of
-    # use of nonzeroRows parameter in cv2.dft()
-    cv2.dft(dft_A, dst=dft_A, nonzeroRows=h)
+    # use of nonzeroRows parameter in cv.dft()
+    cv.dft(dft_A, dst=dft_A, nonzeroRows=h)
 
-    cv2.imshow("win", im)
+    cv.imshow("win", im)
 
     # Split fourier into real and imaginary parts
-    image_Re, image_Im = cv2.split(dft_A)
+    image_Re, image_Im = cv.split(dft_A)
 
     # Compute the magnitude of the spectrum Mag = sqrt(Re^2 + Im^2)
-    magnitude = cv2.sqrt(image_Re**2.0 + image_Im**2.0)
+    magnitude = cv.sqrt(image_Re**2.0 + image_Im**2.0)
 
     # Compute log(1 + Mag)
-    log_spectrum = cv2.log(1.0 + magnitude)
+    log_spectrum = cv.log(1.0 + magnitude)
 
     # Rearrange the quadrants of Fourier image so that the origin is at
     # the image center
     shift_dft(log_spectrum, log_spectrum)
 
     # normalize and display the results as rgb
-    cv2.normalize(log_spectrum, log_spectrum, 0.0, 1.0, cv2.NORM_MINMAX)
-    cv2.imshow("magnitude", log_spectrum)
+    cv.normalize(log_spectrum, log_spectrum, 0.0, 1.0, cv.NORM_MINMAX)
+    cv.imshow("magnitude", log_spectrum)
 
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
+    cv.waitKey(0)
+    cv.destroyAllWindows()