Perhaps in a later post I'll get into what the code looks like and a dialog of what each part does but for now I'll just put up some of the early results and learning points that I've encountered. Ultimately, I'll get the code up on github and follow along with that here.
I'm using two images for this exercise: one that contains a rainbow of colors to test and another to represent the effect on a more likely picture.
Initially, I looked at just 'dragging' each color down the image while increasing the normalization scale along the vertical to give a wash out effect further down the image. This represents how the water would continue to wash color away as it 'pulled' higher colors down the hanging image. This worked well for the rainbow version of the image:
That result has a good 'bleed' in that stronger colors last longer down the vertical. However, with the colors in the house image the washout was too aggressive and wiped out much of the original content.
If you ignore the washout, though, it is clear that the draw of colors down the image is visible (especially around the windows) meaning that reducing the washout might still produce a more realistic effect if enough of the original image could be preserved.
With that in mind, I added the ability to preserve a percentage of the original image while applying the effect. This allows a user to tune the washout per image to get just the right look. For example, too much reuse (20%) on the house and the washout effect has little impact:
But taking that down to 7.5% gets closer to what I am looking for:
There is still plenty for me to do such as handling the edges and water line better and further manipulating the washed image. For example, adding additional transforms to the result to get more seep/bleed on the washed colors. The image below shows a simple kernel convolution to add blur to the pencils, for example.
