Python cv2 Image Pyramids

Question

Trying to implement the famous Orange/Apple pyramids blending (cv2 Image Pyramids).

Note: Both images shape is 307x307.

However, since the result image is blurred due to clipping values in cv2.subtract and cv2.add (as stated in cv2 vs numpy Matrix Arithmetics), I have used numpy arithmetics instead as suggested in StackOverflow: Reconstructed Image after Laplacian Pyramid Not the same as original image.

I have tested this by performing pyramids on one image and the result image constructed back using pyramids has the same Max,Min,Average pixels values as opposed to using cv2 arithmetics.

However, on pyramids level 7, the result image gets a 'noise' of a red dot and on level 9 the result image gets a lot of green pixels noises. Images of levels 6, 7, 9 - Imgur Album.

Any ideas why would this happen? The pyramid level 9 green noise I would say happened because the image went below 1x1 shape. But what about the red dot on 7 level pyramid?

• EDIT : Code Added

  numberOfPyramids = 9
  
  # generate Gaussian pyramids for A and B Images
  GA = A.copy()
  GB = B.copy()
  gpA = [GA]
  gpB = [GB]
  
  for i in xrange(numberOfPyramids):
      GA = cv2.pyrDown(GA)
      GB = cv2.pyrDown(GB)
      gpA.append(GA)
      gpB.append(GB)
  
  # generate Laplacian Pyramids for A and B Images
  lpA = [gpA[numberOfPyramids - 1]]
  lpB = [gpB[numberOfPyramids - 1]]
  
  for i in xrange(numberOfPyramids - 1, 0, -1):
      geA = cv2.pyrUp(gpA[i], dstsize = np.shape(gpA[i-1])[:2]) 
      geB = cv2.pyrUp(gpB[i], dstsize = np.shape(gpB[i-1])[:2])
  
      laplacianA = gpA[i - 1] - geA if i != 1 else cv2.subtract(gpA[i-1], geA)
      laplacianB = gpB[i - 1] - geB if i != 1 else cv2.subtract(gpB[i-1], geB)
  
      lpA.append(laplacianA)
      lpB.append(laplacianB)
  
  # Now add left and right halves of images in each level
  LS = []
  for la, lb in zip(lpA, lpB):
      _, cols, _ = la.shape
      ls = np.hstack((la[:, : cols / 2], lb[:, cols / 2 :]))
      LS.append(ls)
  
  # now reconstruct
  ls_ = LS[0]
  for i in xrange(1, numberOfPyramids):
      ls_ = cv2.pyrUp(ls_, dstsize = np.shape(LS[i])[:2])
      ls_ = ls_ + LS[i] if i != numberOfPyramids - 1 else cv2.add(ls_, LS[i])
  
  cv2.imshow(namedWindowName, ls_)
  cv2.waitKey()
  

Answer 1

After read the original article about laplacian pyramid, I find I misunderstood this method, we can fully reconstruct the original image without blur, because we use of additional pix information. And It is true that clipping value lead to blurred. Well now we come back to the beginning again :)

So the code you post is still clipping value, I advise you use int16 to save the laplacian pyramid, and not use cv2.subtract. Hope it works.