Snippet of my Seam Carving Report from my Msc Computer Science Georgia Tech’s Computational Photography module
Besides removing of streams, we can also add streams. We identify k streams for removal and duplicate by averaging the left and right neighbours. The computation of these averages is done by convolving the following matrix with the images’ colour channels.
kernel = np.array([[0, 0, 0],
[0.5, 0, 0.5],
[0, 0, 0]])
In the implementation of my scaling_up algorithm, I first remove k streams (depending on ratio set by user) and recorded the coordinates and cumulative energy values of the original picture in each removal.
Then I reverse the whole process by adding the stream back together with the averaged values of neighbours
I implemented this scaling_up algorithn for the dolphin pictures.
- 8(a) is the original picture
- 8© Enlarged picture with added streams: python main.py fig8 u c 1.5 y
- 8(d) Enalrged picture without added streams: python main.py fig8 u c 1.5 n
- 8(f) Enlarged picture with scaling up algorithm implemented twice: python main.py fig8_processed u c 1.5 n
Figure 8(a), 8©, 8(d), (f)
