My goal with this project was an end-to-end machine learning project, and although I admit when I found out about Colormind I almost gave up on this, they're using deep GANs, I was gonna use K-Means, but I decided to go through with it anyway even though it's not deep learning so not cool by definition, my simple app lives on.
A deep Generative Adverserial Network is on my list of things to try next.
The app is deployed on Streamlit. You can try it here.
NOTE: for some reason, the caching system for streamlit causes the app to crash with certain images, I've pinpointed the error to unhashable dataframes and will fix soon.
I used Intel® Extension for Scikit-learn to optimize my app since it trains a model for each picture the user uploads, I'm satisfied with the results, the app is fast and responsive.
Still from Chungking Express (1994) by Wong Kar-Wai
Palette Sorted by Luminance
k = 8
Still from Fallen Angels (1995) by Wong Kar-Wai
k = 12
Still from Drive (2011) by Nicolas Winding Refn
Randomly Sampled Colors from Larger Number of Clusters
Stills from Parasite (2019) by Bong Joon-ho
k = 36
k = 36, 8 random colors are selected for palette
k = 36, full palette
k = 1, but image was segmented into 36 regions to generate palette
Effect of Scale on Palette
K-Means Clustering (Ensemble of K-Means and MiniBatchKMeans)
Stills from Parasite (2019) by Bong Joon-ho
k = 8
Scaling Factor = 0.5
Scaling Factor = 0.6
Scaling Factor = 0.7
Scaling Factor = 0.8
Scaling Factor = 0.9
The range of colors is limited and sorted by dominance as opposed to prominence in image. Take this image for example, the palette should include the color of the pupils, but it doesn't even when I use 16 clusters.
For reference, the palette generated by Colormind is shown below, although it took a couple of random generates, it did detect the pupil color, which is what I'd like my model to achieve.
Still from Dune (2021) by Denis Villeneuve
