CODEWITHSUNDEEP
pythonopencvnumpyimage-processing
Victor Odouard
Victor Odouard

Reputation: 1455

Python, Numpy, OpenCV -- Creating a modified (and equally fast) "addWeighted" function

I am working on a program in python that makes use of a function very similar to the addWeighted function in openCV. The difference is that it doesn't actually add the numpy arrays representing the images, instead, it takes whichever pixel is brighter at any particular coordinate and uses that value.

What I have been finding, however, is that despite the fact that these functions do very similar things, the addWeighted function is much faster. So my question is, how can I modify my current solution to be equally as fast? Is there a way I can use the multiprocessing module, or something similar?

Here is the code:

image = np.zeros(image_1.shape)
for row_index, row in enumerate(image_1):
     for col_index, col in enumerate(row):
          pixel_1 = image_1[row_index, col_index]
          pixel_2 = image_2[row_index, col_index]
          sum_1 = int(pixel_1[0]) + int(pixel_1[1]) + int(pixel_1[2])
          sum_2 = int(pixel_2[0]) + int(pixel_2[1]) + int(pixel_2[2])

          if sum_2 > sum_1:
               image[row_index, col_index] = pixel_2
          else:
               image[row_index, col_index] = pixel_1

Where image_1 and image_2 are both numpy arrays representing images, both with the same shape (720, 1280, 3).

Upvotes: 2

Views: 710

Answers (1)

Divakar
Divakar

Reputation: 221664

One vectorized approach would be -

mask = image_2.astype(int).sum(-1) > image_1.astype(int).sum(-1)
out = np.where(mask[...,None], image_2, image_1)

Steps :

  • Convert to int dtypes, sum along the last axis and perform element-wise comparisons. This would give us a mask.

  • Use np.where with this mask, extended to the same no. of dims as input arrays to do the choosing. This employs the concept of NumPy broadcasting to do the choosing in a vectorized manner. So, that's worth a good look.

Note: Alternatively, we can also use keepdims=True to keep the no. of dims while summing and thus avoid extending dims in the next step.

Upvotes: 2

Related Questions